Facultad de Ciencias - Universidad de NavarraEn cumplimiento de la normativa para la presentación...

F a c u l t a d d e C i e n c i a s

INTEGRATIVE DEMOGRAPHIC INFERENCE IN IBERIAN

POND-BREEDING AMPHIBIANS

Gregorio Sánchez Montes

F a c u l t a d d e C i e n c i a s

INTEGRATIVE DEMOGRAPHIC INFERENCE IN IBERIAN POND-BREEDING AMPHIBIANS

Memoria presentada por D. Gregorio Sánchez Montes para aspirar al grado de Doctor por la Universidad de Navarra

El presente trabajo ha sido realizado bajo nuestra dirección en el Departamento de Biología Ambiental y autorizamos su presentación ante el Tribunal que lo ha de juzgar.

Pamplona, 28 de abril de 2017

Dr. Arturo H. Ariño Plana Dr. Íñigo Martínez-Solano González Profesor Titular Científico Titular

Dpto. Biología Ambiental Dpto. Biodiversidad y Biología Evolutiva

Universidad de Navarra Museo Nacional de Ciencias Naturales CSIC

‘If I have seen further, it is by

standing on the shoulders of giants’

Isaac Newton

Agradecimientos

Llegar a ver publicada esta tesis doctoral ha requerido un intenso camino académico y

personal en el que mucha gente me ha brindado su apoyo, el cual agradezco de corazón.

Agradezco, en primer lugar, a mis directores de tesis, Íñigo y Arturo, por darme

la oportunidad de iniciarme en el camino de la investigación, y por ser un ejemplo de

honestidad, esfuerzo y pasión por la búsqueda constante de la verdad. Cada momento de

los que hemos compartido trabajando juntos (y han sido muchos) han formado parte de

una gran lección en la que ni un solo día he dejado de aprender. Os admiro tanto en lo

académico como en lo humano, porque en ambos aspectos me habéis hecho crecer. Mi

gratitud también para los programas de becas para la formación del personal

investigador y de ayudas para la movilidad de la Asociación de Amigos de la

Universidad de Navarra, que financiaron los estudios de doctorado y parte de una

fructífera estancia predoctoral en el Institute of Zoology de Londres.

También agradezco a los excelentes profesores que, gracias a su ejemplo, desde

el colegio Nuestra Señora de Begoña de Bilbao (Alberto, Félix, Juanjo, Eskolunbe, Javi,

Asier, Aitor, Dori, Ana, Koldo, Ainhoa, Isabel y tantos otros) hasta las Universidades de

Navarra (María, Juanjo, Ricardo, Mª Elena, Enrique, Jordi, Mª Carmen, David, Antonio,

Nieves, Marina, Javier y muchos más), Autónoma y Complutense de Madrid (Juan,

José, Manolo, Ángel, Jesús, por citar algunos) prendieron intensamente en mí la llama

de la búsqueda del conocimiento. Gracias por esas lecciones que nunca he olvidado,

porque me las transmitisteis desde el fondo de vuestro corazón y de vuestro saber,

descubriéndome esta maravillosa comunión de mentes que es la ciencia. Este periodo

predoctoral también me ha permitido conocer a grandes investigadores como David

Galicia, José Luis Vizmanos, Jinliang Wang, Trent Garner, Javier Seoane, Isabel Rey,

Annie Machordom, Marta Barluenga, Mario García-París, David Buckley, Carmen

Díaz-Paniagua, Iván Gómez-Mestre o Ernesto Recuero, de quienes he aprendido

muchas maneras de enfocar los problemas que surgen constantemente en cualquier

camino hacia lo desconocido (porque eso es la investigación, nada menos). Gracias por

tratarme como a uno más y por compartir vuestra experiencia con un joven preguntón.

Agradezco especialmente a Rafa Miranda y Mariano Larraz por darme una oportunidad

en el ámbito de la divulgación de la ciencia y de la evolución, que tanto me apasiona.

En esta tesis también ha habido mucho trabajo de laboratorio, que no habría

podido desarrollar sin la tutela dedicada y afectuosa de los estupendos técnicos que

tanto me han ayudado en el Museo Nacional de Ciencias Naturales (gracias muy

especialmente a Piluchi, Isabel e Iván), en la Estación Biológica de Doñana (agradezco

mucho a Ana, Mónica, María, Antonio y José María por dos intensos meses de trabajo),

de la Universidad de Navarra (Ana, María y Ángel) y el equipo de Morfología del

Centro de Investigación Médica Aplicada (Laura, Paula, Carolina, Elena y Mª Paz). Las

técnicas que he aprendido gracias a vosotros son una formación impagable. Por otro

lado, la labor de docencia ha sido una de las que más satisfacciones me ha dado, y hacia

la que siento una intensa vocación, en buena parte gracias a Eva Montilla, con quien

siempre ha sido una auténtica delicia trabajar. También agradezco muy especialmente al

personal de administración y servicios de los centros que he visitado, por tratarme con

una humanidad y un afecto que nunca pasaron desapercibidos para mí (especialmente a

Carmentxu, Marisol, Naira y Martín, Ángel, Patxi, Justo, Peque, Jorge, José Luis y José

Javier, Marina, Inma, Carolina, Miriam e Irantzu, Jo, Amrit, Raquel, Rebeca y María).

Agradecimientos

Y cómo no, agradezco enormemente a los grandes compañeros que he conocido

durante estos años en las universidades y centros de investigación en los que he tenido

la suerte de ser acogido. Desde aquellos inicios en el Z-428 del Museo de Ciencias

Naturales de Madrid, con Miguel, Merel, Virginia, Luis, Cristina, Carmen, Paloma, Javi

y Alfonso hasta la Universidad de Navarra, donde un día de 2012 entramos como

becarios el gran Iván Vedia y yo. Desde entonces he aprendido mucho de los profesores

del Departamento de Biología Ambiental y también con las experiencias junto a

compañeros como Antonio, Iván, Ibon, Andrea, Ainhoa, Txiki, Javi, María, Rubén,

Nora, Xabi, Imanol y Amaia en Pamplona; Mar, Pablo B, Pablo L, Rosa, Mari, Noa,

Alazne, Antonio y Marina en Sevilla; Jorge, Tania, Michel, Pau, Melinda, Andrés, Iker,

David, Juanes, Miriam, Silvia, Étienne, Carlos, Yolanda, Guillermo, Chechu, Anna,

Violeta y Paula en Madrid y, ya en tierras británicas, en compañía de Chris, Donal,

Sandy y Will. Quisiera tener una mención especial para los grandes compañeros que me

prestaron una ayuda fundamental en largas jornadas de campo, sin importarles lo

complicadas que fueran las condiciones meteorológicas, como Miguel Peñalver y Espe

(siempre dispuestos para la batalla), Jorge, Garazi, Jose, Amaia, Celia, Miguel Rojo,

Luis, Rut, Rafa, Imanol, Nora, Iván, Antonio, Juan y Joaquín. Todos vosotros sabéis tan

bien como yo lo que cuesta conseguir datos en el campo y sin vuestra generosa ayuda

(y, muchas veces, tutela y consejo) no habría sido capaz. También agradezco el

incondicional apoyo de mis amigos (Álex Maestre, Álex Alonso, Ander, Diego, Joseba,

Juan, Iñaki, Lucía, Laura, Txas, Bea, Edu, Pablo Vicente, Carlos, Pablo Bazal,

Guillermo, Andoni, Anica, Mikel, Olaia, Jaime, Miren, María, Sol, MEG, Miri, Dorleta,

Loyola, Teresa, Carmen, Alfredo, Luis, João) y el afecto de las buenas amistades que he

hecho en el coro y orquesta de la Universidad de Navarra, con el gran maestro Ekhi

Ocaña al frente.

Para terminar, pero siempre en la mención más especial, agradezco a mi familia.

A mis padres y a mi hermana, por recordarme siempre quién soy y de dónde vengo; a

mis abuelos, tíos y primos (muy especialmente a Totó, Amama, José, Dioni, Pepe y

Tomás) y a Pilar, por compartir conmigo su compañía, su alegría y su maravillosa

forma de ver el mundo.

Esta tesis doctoral incluye una colección de manuscritos en diferentes estados de

publicación, cada uno de los cuales constituye un capítulo. Los manuscritos se

reproducen íntegros y en el idioma en el que fueron publicados o enviados para su

publicación, incluyendo siempre un resumen en castellano.

En cumplimiento de la normativa para la presentación de tesis doctorales en la Facultad

de Ciencias de la Universidad de Navarra se incluyen los siguientes apartados: (1) un

Resumen integrador del contenido de la tesis doctoral; (2) una Introducción general que

sitúa el trabajo realizado en su contexto teórico, planteando los Objetivos de la tesis

doctoral; (3) una Discusión general, y (4) un apartado de Conclusiones generales.

TABLE OF CONTENTS

GENERAL ABSTRACT ..................................................................................................... 19

CHAPTER I: GENERAL INTRODUCTION ..................................................................... 25

Contribution of genetics to demographic research ............................................... 28 The development of molecular biology ................................................................ 29 Possibilities and misuses of bioinformatics tools ................................................. 32

The theoretical framework of population genetics................................................ 33

Individual-based monitoring programs complementing genetic-based

demographic inferences: the effective/census size ratio ....................................... 39 Challenges faced by amphibians in an anthropized world .................................. 43

The study system: a multi-species, multi-scale approach .................................... 49 Epidalea calamita (Laurenti, 1768) ...................................................................... 50 Hyla molleri Bedriaga, 1889 ................................................................................. 53

Pelophylax perezi (López-Seoane, 1885) ............................................................. 55 Pelobates cultripes (Cuvier, 1829) ....................................................................... 57 A multi-scalar approach ........................................................................................ 59

References ................................................................................................................. 66

CHAPTER II: GENERAL OBJECTIVES ........................................................................ 89

CHAPTER III: SPECIES ASSIGNMENT IN THE PELOPHYLAX RIDIBUNDUS X P. PEREZI

HYBRIDOGENETIC COMPLEX BASED ON 16 NEWLY CHARACTERISED MICROSATELLITE

MARKERS Herpetological Journal (2016), 26 (2): 99-108 ........................................... 93

Abstract ..................................................................................................................... 95 Resumen .................................................................................................................... 97

Introduction .............................................................................................................. 99 Materials and methods .......................................................................................... 100

Results ..................................................................................................................... 105 Discussion ............................................................................................................... 109 References ............................................................................................................... 114

CHAPTER IV: EFFECTS OF SAMPLE SIZE AND FULL SIBS ON GENETIC DIVERSITY

CHARACTERIZATION: A CASE STUDY OF THREE SYNTOPIC IBERIAN POND-BREEDING

AMPHIBIANS Journal of Heredity (2017), esx038. doi: 10.1093/jhered/esx038 ......... 117

Abstract ................................................................................................................... 119 Resumen .................................................................................................................. 121 Introduction ............................................................................................................ 123 Materials and methods .......................................................................................... 126

Tissue sampling ................................................................................................... 126 DNA extraction and genotyping ......................................................................... 128 Characterization of genetic diversity and effect of full sibs ............................... 128

Effect of sample size ........................................................................................... 130

Results ..................................................................................................................... 131 Characterization of genetic diversity and effect of full sibs ............................... 131

Effect of sample size ........................................................................................... 132

Discussion ............................................................................................................... 134 References ............................................................................................................... 139

CHAPTER V: RELIABLE EFFECTIVE/CENSUS POPULATION SIZE RATIOS IN SEASONAL-

BREEDING SPECIES: OPPORTUNITY FOR INTEGRATIVE DEMOGRAPHIC INFERENCES

BASED ON CAPTURE-MARK-RECAPTURE DATA AND MULTILOCUS GENOTYPES

Ecology and Evolution (Accepted, pending minor review) ......................................... 143

Abstract ................................................................................................................... 145

Resumen .................................................................................................................. 147 Introduction ............................................................................................................ 149 Materials and methods .......................................................................................... 152

Study area and CMR monitoring program .......................................................... 152

CMR estimates of Na ........................................................................................... 153 Genetic estimates of Nb ....................................................................................... 156

Results ..................................................................................................................... 159 CMR estimates of Na ........................................................................................... 159

Genetic estimates of Nb ....................................................................................... 159

Discussion ............................................................................................................... 164 Extension to Ne/Nc estimation ............................................................................. 167

References ............................................................................................................... 170

CHAPTER VI: MOUNTAINS AS BARRIERS TO GENE FLOW IN AMPHIBIANS:

QUANTIFYING THE DIFFERENTIAL EFFECT OF A MAJOR MOUNTAIN RIDGE ON THE

GENETIC STRUCTURE OF FOUR SYMPATRIC SPECIES WITH DIFFERENT LIFE HISTORY

TRAITS Journal of Biogeography (Under review) ...................................................... 175

Abstract ................................................................................................................... 177 Resumen .................................................................................................................. 179

Introduction ............................................................................................................ 181

Materials and methods .......................................................................................... 185 Study area, targeted species and dataset collection ............................................. 185

Genetic analyses .................................................................................................. 189

Results ..................................................................................................................... 193 Dispersal potential ............................................................................................... 193

Genetic analyses .................................................................................................. 193

Discussion ............................................................................................................... 200 References ............................................................................................................... 205

CHAPTER VII: GENERAL DISCUSSION .................................................................... 211

References ............................................................................................................... 224

CHAPTER VIII: GENERAL CONCLUSIONS ............................................................... 227

APPENDIX 1: Characterization of the microsatellite sets of H. molleri, E. calamita

and P. perezi ................................................................................................................ 231

APPENDIX 2: Accumulation curves of allelic richness and expected heterozygosity

as a function of sample size ........................................................................................ 263

APPENDIX 3: Empirical and Chao & Jost (2015) profiles .................................... 271

APPENDIX 4: Relationship between FIS and error rate estimates ....................... 279

APPENDIX 5: Effect of sampling excessive close relatives on FIS and deviation

from HWE ................................................................................................................... 283

APPENDIX 6: R scripts for replicated analyses ..................................................... 289

APPENDIX 7: Summary tables of CMR models .................................................... 305

APPENDIX 8: Inferred sibship and parentage relationships ................................ 309

APPENDIX 9: Pairwise FST estimates and migration rates per generation ......... 315

APPENDIX 10: Results of clustering analyses ........................................................ 327

List of abbreviations

6-FAM One of the labelling dyes (blue) used in multiplex reactions for

genotyping with microsatellite markers on the ABI3730 sequencer

95% CI 95% confidence interval

ΔK A statistic based on the second order rate of change of the likelihood

function with respect to K, the number of genetic clusters, devised by

Evanno et al. (2005) and used for exploring the relative likelihood of

different K values in genetic clustering analyses

AICc Akaike Information Criterion corrected for small sample sizes

AR Allelic richness

Avge Average

bp Base pairs (a measure of genetic sequence length)

BPP Bayesian posterior probability

c. circa (Latin for ‘approximately’)

CJS Cormack-Jolly-Seber model of CMR

CMR Capture-mark-recapture

cox1 Mitochondrially encoded gene of cytochrome c oxidase subunit 1

DAPC Discriminant analysis of principal components

DNA Deoxyribonucleic acid

dNTP Deoxyribonucleotide triphosphate

doi Digital object identifier

DS Simpson’s dominance index

e.g. exempli gratia (Latin for ‘for the sake of an example’)

ESS Effective sample size

F1 First generation of hybrid offspring resulting from mating between pure

parentals of two different species

F2 Second generation of hybrid offspring resulting from self or cross mating

among F1 individuals

F-statistics A set of indices (sometimes termed fixation indices) derived by Sewall

Wright to describe the distribution of genetic diversity in a population,

including FIS, FST and FIT

Fig./Figs. Figure(s)

FIS An F-statistic that measures the HE within individuals as compared to HE

within the subpopulations to which they belong

FST An F-statistic that measures the HE within subpopulations as compared to

HE when accounting for all individuals as belonging to a single

population; also employed as a measure of pairwise genetic distance

G-statistics A set of statistics analogue to F-statistics

GST An analogue to FST proposed by Nei (1973)

HE Expected heterozygosity

HO Observed heterozygosity

HWE Hardy-Weinberg equilibrium

IBD Isolation by distance

ID Individual identifier

i.e. id est (Latin for ‘it is’)

IUCN International Union for the Conservation of Nature

K The predefined number of clusters in a clustering analysis

kl. Klepton (from the Greek kleptein, ‘to steal’), a hybrid species that acts as

a sexual parasite for one of its pure parental species by discarding the

complete genomic dotation corresponding to that host parental species

before meiosis (e.g. Pelophylax kl. grafi, see Box 4 and Chapter III)

LD Linkage disequilibrium

Ln Natural logarithm

m.a.s.l. Metres above sea level

MCMC Markov chain Monte Carlo

mtDNA Mitochondrial DNA

n Sample size

Na Number of alleles (used only in Chapter III as a synonym of AR)

Na Adult population size, i.e. the total number of potentially breeding adults

in a population

Nb Effective number of breeders (see Chapter V)

Nc Census population size, i.e. the total number of individuals in a

population

NCBI National Center for Biotechnology Information (website available at:

https://www.ncbi.nlm.nih.gov/)

ND2 Mitochondrially encoded gene of subunit 2 of NADH dehydrogenase

Ne Effective population size (see Chapter V)

NE Northeast

NED One of the labelling dyes (yellow) used in multiplex reactions for


p Probability value or p-value

PCR Polymerase chain reaction

PET One of the labelling dyes (red) used in multiplex reactions for genotyping

with microsatellite markers on the ABI3730 sequencer

PI Probability of identification

PIDB Probability of identity by descent

PISibs Probability of identification accounting for possible relatives included in

the sample

PIT Passive integrated transponder

q (order) Parameter which determines the sensitivity of different diversity indices

to the relative abundance of the different classes (e.g. alleles or species)

R A free software environment for statistical computing and graphics

R Info Index of marker informativeness for genetic relationship

S In Chapter IV: Expected number of classes (e.g. different alleles or

species) in a set of jackknifed samples, given the reference sample, as

calculated in EstimateS (Colwell & Elsensohn 2014)

In Chapter V: Annual survival parameter in CMR analyses

SD Standard deviation

SE Standard error

SF Sibship frequency (a method for estimating effective population size

from parentage and sibship reconstruction)

SNP Single-nucleotide polymorphism

STR Short tandem repeats (the type of sequences which are characteristic of

microsatellite DNA markers)

SVL Snout-to-vent length

SW Southwest

TBO To be obtained

tyr Nuclearly encoded gene of tyrosinase

UK United Kingdom

VIC One of the labelling dyes (green) used in multiplex reactions for


GENERAL ABSTRACT

General abstract

21

Many amphibian species across the world face a serious risk of extinction. The main cause of

this global crisis is the destruction and degradation of the habitats they need to forage, breed,

hide, termorregulate or hibernate, although additional factors such as direct human exploitation,

infectious diseases or the introduction of exotic invasive species are contributing to population

eradications worldwide. Different policies are being implemented to counteract amphibian

declines, mainly focused on protecting aquatic and terrestrial habitats, creating and adequating

new breeding sites, reducing pathogen load in the wild or reinforcing population recruitment

with captive breeding and release programs. However, the success and efficiency of these

measures is compromised by wide gaps in the knowledge about the biology and demographic

dynamics of most species. Recent advances in molecular and computational biology are

complementing traditional field-based approaches, opening an unparalleled opportunity for

molecular ecologists and evolutionary biologists to answer key questions about the biology,

demography and natural history of many species. This dissertation takes advantage of

molecular, theoretical and analytical developments in demographic research to explore some

aspects of population dynamics and connectivity in four Iberian pond-breeding anurans:

Epidalea calamita, Hyla molleri, Pelophylax perezi and Pelobates cultripes. An integrative

framework based on 1) genetic data from 15-18 species-specific microsatellite markers, 2) an

extensive sampling design including 13-19 populations per species across both slopes of a major

mountain range in Central Spain and 3) a seven-year monitoring program in an amphibian

assemblage based on capture-mark-recapture (CMR) techniques was implemented to infer some

key demographic parameters including the effective/census size ratio and regional patterns of

gene flow. First, I summarize the contributions and opportunities of molecular and individual-

based CMR methods in demographic research and discuss how the integration of both

approaches can be applied for conservation purposes (Chapter I). Then, I present the objectives

of this dissertation (Chapter II). Chapters III and IV describe the three sets of specific

microsatellite markers optimized for E. calamita, H. molleri and P. perezi, including a

comprehensive summary on their polymorphism, genotyping error rates and information

content, and assess their suitability for demographic research. Furthermore, I demonstrate that

seven of the markers of the P. perezi set are useful for cross amplification and species

assignment in the P. ridibundus x P. perezi hybridogenetic complex, each marker showing

several private alleles for each of the parental species (Chapter III). Also, genetic diversity

characterization in an extensive multi-population genotypic dataset revealed that FIS and tests of

Hardy-Weinberg equilibrium and Linkage Disequilibrium (but not allelic richness and observed

and expected heterozygosity) can be affected by the presence of full sibs in the sample (Chapter

IV), which sheds light into this critical yet unresolved issue in population genetics and

parentage analyses. A more comprehensive dataset obtained in a reference locality allowed

developing a new method for calculating the minimum sample size required for estimating

genetic diversity indexes with individual markers (Chapter IV). Chapters V and VI show that

the application of previously-described molecular tools to adequate sampling designs, coupled

with field-based data and CMR analyses can yield reliable estimates of the effective/census size

ratio (Chapter V) and regional gene flow (Chapter VI). I demonstrate that anuran species with

different life history traits show different local effective/census size ratios (Chapter V) and are

differentially affected by the barrier effect exerted by a major mountain range (Chapter VI).

Finally, I discuss the implications of these findings in the context of demographic and

evolutionary research including possible applications for conservation purposes (Chapter VII)

and outline the main conclusions of this dissertation (Chapter VIII).

Resumen general

23

Muchas especies de anfibios están en riesgo de extinción en el mundo. La causa principal de

esta crisis global es la destrucción y alteración de los hábitats que necesitan para alimentarse,

reproducirse, ocultarse, controlar su temperatura corporal o hibernar, aunque también se

conocen otros factores implicados en la pérdida de poblaciones, tales como la explotación

humana, las enfermedades infecciosas o la introducción de especies alóctonas invasoras. Con el

fin de frenar estas tendencias negativas se están desarrollando diferentes medidas de

conservación, principalmente dirigidas a proteger el hábitat acuático y terrestre, construir

nuevas charcas para la reproducción, reducir la carga de patógenos en el medio o reforzar el

reclutamiento poblacional mediante programas de cría en cautividad y posterior liberación en su

medio. Sin embargo, la falta de conocimiento sobre la biología y dinámicas poblacionales de la

mayoría de especies de anfibios comprometen la eficacia de estas medidas de conservación. Los

últimos avances en biología molecular y en computación están complementando las técnicas

tradicionales basadas en observaciones de campo, lo que supone una gran oportunidad para los

ecólogos moleculares y los biólogos evolutivos para responder algunas preguntas clave sobre la

biología, la demografía y la historia natural de muchas especies. La presente tesis doctoral

pretende aprovechar los avances moleculares, teóricos y analíticos en investigación demográfica

para explorar aspectos sobre las dinámicas poblacionales y la conectividad regional en cuatro

especies de anuros ibéricos que se reproducen en medios temporales: Epidalea calamita, Hyla

molleri, Pelophylax perezi y Pelobates cultripes. Para ello se integran 1) datos genéticos

obtenidos a partir de 15-18 microsatélites específicos, 2) un amplio diseño muestral que incluye

13-19 poblaciones de cada especie en un área comprendida en ambas vertientes de un macizo

montañoso del Sistema Central y 3) un programa de seguimiento durante siete años en una

comunidad de anfibios basado en métodos de captura-marcaje-recaptura (CMR) para estimar

parámetros demográficos relevantes como el cociente entre tamaño efectivo/tamaño de censo y

los patrones de flujo génico. En primer lugar, se comentan las contribuciones respectivas del

campo de la genética y de los métodos CMR basados en datos individuales para la investigación

en demografía, y se discute cómo la integración de ambas técnicas puede ser aprovechada para

planificar medidas de gestión eficaces (Capítulo I). A continuación se resumen los objetivos de

la presente tesis doctoral (Capítulo II). En los Capítulos III y IV se describen los tres conjuntos

de marcadores moleculares específicos (microsatélites) desarrollados para E. calamita, H.

molleri y P. perezi, con información exhaustiva sobre su polimorfismo, tasas de error de

genotipado y contenido informativo de cada marcador y se evalúa su utilidad para investigación

en demografía. Siete de los marcadores del set de P. perezi mostraron además su utilidad para

amplificación cruzada e identificación de especies en el complejo hibridogenético P. ridibundus

x P. perezi, con varios alelos privados por marcador (Capítulo III). Por otro lado, la

caracterización de la diversidad genética en múltiples poblaciones con estos marcadores reveló

que tanto FIS como los tests de equilibrio de Hardy-Weinberg y de desequilibrio de ligamiento

(pero no así la riqueza alélica ni la heterocigosidad esperada ni la observada) pueden verse

afectados por la presencia de hermanos en la muestra genética (Capítulo IV), lo que arroja algo

de luz en este aspecto crítico pero aún no resuelto en análisis poblacionales y de parentesco. Un

conjunto de datos genéticos aún más exhaustivo en una población de referencia permitió

también desarrollar un nuevo método para calcular el tamaño muestral mínimo necesario para

estimar dos índices de diversidad genética con cada marcador individualmente (Capítulo IV).

Los capítulos V y VI demuestran que la aplicación de las herramientas moleculares previamente

descritas en diseños muestrales adecuados, y complementados con datos de campo y análisis de

CMR pueden ofrecer estimas fiables sobre el cociente tamaño efectivo/tamaño de censo

(Capítulo V) y el flujo génico regional (Capítulo VI). En esta tesis se demuestra además que

especies de anuros con distintas características vitales muestran diferentes cocientes entre el

Resumen general

24

tamaño efectivo y el de censo (Capítulo V), y que se ven afectadas de distinta manera por el

efecto de barrera ejercido por un macizo montañoso (Capítulo VI). Finalmente se discuten las

implicaciones de estos resultados en el contexto de la investigación demográfica y evolutiva, y

las posibles aplicaciones que se pueden derivar para medidas de conservación (Capítulo VII) y

se exponen las principales conclusiones de esta tesis doctoral (Capítulo VIII).

CHAPTER I

GENERAL INTRODUCTION

General introduction

27

This dissertation addresses two main aspects of the study of animal populations, namely

their potential to maintain genetic diversity and their connectivity by means of gene

flow. Living creatures interact with other individuals of the same species during their

lifetime (Begon et al. 1990). The consequences of these interactions (competition,

mating, altruism, cannibalism, etc.) are so profound that they condition evolution itself,

with effects at different spatial and temporal scales that ultimately shape the process of

lineage diversification (Posada & Crandall 2001). To understand the evolutionary

mechanisms that are triggered by intraspecific interactions, evolutionary biologists

address the study of populations, or demography. Those individuals of the same species

sharing a geographical area and potentially interacting constitute a population (Begon et

al. 1990). Despite its simple definition, delineation of populations in nature is one of the

most complex challenges in ecology (Waples & Gaggiotti 2006). How can the ‘potential

interaction’ between individuals be measured?

Researchers normally establish an operative threshold for delimiting their target

population, sometimes termed the ‘neighbourhood’ (Wright 1943, 1946; Nunney 2016).

Defining the limits of a population is a necessary step (often just implicitly assumed)

before addressing the parametrization of its demographic features by describing its size,

its structure with regard to different individual traits (e.g. age, size, sex…), its turnover

rate, the survival and reproductive rates of individuals in different age and sex classes,

their mating system, the connectivity with other populations, etc. All these parameters

are crucial to characterize population dynamics, but they can also be complex to

estimate directly in natural populations, because comprehensive information from a

large number of individuals is often required to construct accurate ecological life tables

(Deevey 1947; Millar & Zammuto 1983). To help solving this difficulty, evolutionary

biologists are taking advantage of recent improvements in molecular, computational and

theoretical frameworks that allow indirect estimation of key parameters in demographic

research (Ekblom & Galindo 2011). As a result, integrative studies that both combine

these tools and calibrate indirect results with direct field-based information are

providing valuable insights on how populations are organized in time and space. This,

in turn, is expanding our knowledge about the evolutionary processes experienced by

populations, and how they play a role in shaping biodiversity patterns at broader scales

(Vucetich & Waite 2003).

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28

Evolutionary biologists may thus be presented with an exciting opportunity,

triggered by unprecedented advances in genetic research such as the widespread access

to next-generation sequencing techniques or specialized software for genetic analyses,

that can certainly encourage the exploration of demographic processes at different

spatial and temporal scales, and across a wide range of species (Excoffier & Heckel

2006; Ekblom & Galindo 2011). In this line, this dissertation capitalizes on recent

advances in genetic analyses, bioinformatics, and statistics and builds on extensive field

work to address the first multi-scale, integrative demographic study about Iberian pond-

breeding amphibians. I combine specifically optimized molecular tools with individual-

based data to investigate demographic, ecological, and evolutionary issues in four

sympatric amphibian species. This integrative approach contributes to fill deep

knowledge gaps in the natural history and population dynamics of amphibians. In

addition, it aims to set the basis for future genetic monitoring programs providing

accurate estimates of relevant demographic parameters, which represent invaluable

information for the design of efficient evidence-based conservation plans.

In the following four sections I introduce the main elements of this integrative

approach. The first two sections summarize recent contributions of genetics and

individual-based field data analyses to the study of demography. The third section

focuses on amphibians as a group of study with emphasis on the need for reliable

demographic inferences to help anticipate and revert population declines and minimize

the risk of local extinctions. Finally, the last section introduces the four target species

studied in this dissertation and presents the different scales of study.

Contribution of genetics to demographic research

The recent emergence of genetic tools has promoted the study of demography to explore

the foundations of lineage diversification, persistence and extinction (Rice et al. 2011;

Seehausen et al. 2014; Gascuel et al. 2015). These foundations are characterized by the

interaction of demographic processes operating at different spatial and temporal scales

and, consequently, genetic approaches have been applied to the study of different

aspects (Alexander et al. 2006; Anderson et al. 2010). On the one hand, despite

significant advances in phylogenetic reconstruction, delineation of operative

taxonomical units, like species, is sometimes problematic. Some examples include


29

groups with morphologically conservative taxa, extinct lineages with a limited fossil

record or hybridizing taxa, such as the case presented in Chapter III in this dissertation

(Bickford et al. 2007). Since direct species assignment based on morphological features

is challenging in such scenarios, complementary indirect genetic approaches are playing

an increasingly relevant role in evolutionary studies (Meyer & Zardoya 2003; Frost et

al. 2006; Gissi et al. 2006; San Mauro & Agorreta 2010; Yang & Rannala 2012;

Liedtke et al. 2016). On the other hand, population dynamics, which ultimately drive

lineage persistence and differentiation, remain poorly understood, because direct

quantification of demographic parameters in nature is difficult (Lowe et al. 2017). In

this demographic domain, genetic approaches have also become essential, allowing

invaluable inferences such as estimation of population size and connectivity (Allendorf

1983; England et al. 2010; Luikart et al. 2010; Wang et al. 2016). With the possibility

to account for cryptic diversity and address complex demographic questions, modern

evolutionary biology is greatly benefitting from improving molecular tools and

computational capabilities, as well as from the application of model-based genetic

analyses (Bickford et al. 2007). Thus, the latest blooming of demographic research in

evolutionary biology has been promoted by the parallel development experienced by

three disciplines: molecular biology, bioinformatics and population genetics.

The development of molecular biology

Studies in molecular biology have been measuring genetic variation during the last 50

years (Allendorf 2016). Pioneering studies employed allozymes to assess molecular

variation on the basis of the different size of homologous protein subunits (Prakash et

al. 1969) as an indirect method for the quantification of genetic variation in coding

regions. However, coding regions normally play an important role in cell metabolism,

so they are usually under selection. As a consequence, they often show limited

variability within species. Thus, allozymes were soon replaced in molecular biology by

DNA-based approaches, such as DNA sequences, microsatellites and genome-wide

single nucleotide polymorphisms (SNPs). These techniques have allowed researchers to

measure genetic variation in selectively neutral regions, and thus attaining increasing

levels of resolution (Hedrick 1999; Rice et al. 2011; Fahey et al. 2014; Seehausen et al.

2014). DNA sequencing has become almost universally accessible and applicable to

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30

non-model species in recent years, and more so since the emergence of next-generation

techniques (Rice et al. 2011; Seehausen et al. 2014). This has greatly promoted

comparative systematics by facilitating the creation of large databases containing

homologous DNA sequences for a huge number of species across the tree of life. These

databases are the basis for some global cooperative research, such as the DNA-

barcoding project, and greatly facilitate the reconstruction of phylogenetic relationships

by comparative analysis of homologous DNA regions (Hajibabaei et al. 2007).

Next-generation sequencing has also allowed the massive characterization of

species-specific markers, such as microsatellites and SNPs, for a wide variety of taxa.

Microsatellites are generally non-coding regions characterized by short tandem repeat

(STR) sequences and a high variability (Kelkar et al. 2010). Their mendelian

inheritance mode and high polymorphism make them excellent tools for the description

of patterns of genetic diversity and for the study of demography (Waples & Do 2010;

Habel et al. 2014). As a consequence, specific microsatellite sets are now available for

hundreds of non-model species in a wide variety of taxonomic groups, and their use has

become widespread in the last 25 years (Guichoux et al. 2011). However, appropriate

optimization of a set of microsatellite loci is not an easy task, and some steps must be

thoroughly followed to guarantee the suitability of selected markers for the research

question of interest (see Box 1). On the other hand, the main advantage of SNPs over

microsatellites relies in their wide distribution across the genome, including coding and

non-coding regions, although their overall variability is often low. This is typically

overcome by genotyping at hundreds or thousands of genome-wide SNPs, thus

achieving a very high power of resolution, even in intraspecific studies (Hauser et al.

2011; Hess et al. 2011). A potential drawback is related to the use of a high number of

markers in a limited number of chromosomes, which implies that many of the markers

are likely to be linked in close chromosomic locations. This non-independence must be

accounted for in the analyses to avoid biases (Waples et al. 2016).

The possibility to characterize neutral genetic diversity with unprecedented

accuracy and efficiency opened the gate to an exciting universe of demographic

research that started to be addressed by exploring the distribution of genetic diversity

among and within species. Since then, thousands of demographic, ecological,

phylogenetic and biogeographic studies have updated our knowledge about evolutionary

patterns and processes in a wide variety of taxa (Meyer & Zardoya 2003; Avise 2009;


31

Box 1. Ten steps in the optimization of a microsatellite marker set

A thorough design of the molecular toolkit that will be used for research in

molecular ecology is a fundamental task that will ultimately save time and

budget and ensure efficient work and reliable results (Selkoe & Toonen 2006).

In the case of microsatellite markers, I recommend ten steps for the process of

marker set configuration:

1. Prepare a genomic library enriched with STR sequences from DNA of

one or a few sample individuals (Zane et al. 2002).

2. Select a set of candidate microsatellites from the library, on the basis of

the STR type (e.g. regular tetranucleotides are often preferred because

they are usually frequent in the genome, less likely to be under selection

than codon-like trinucleotides and hardly prone to genotypic errors due to

the distance between alleles), the number of tandem repetitions and the

quality of the reads. Design primer sequences (forward and reverse) for

each selected microsatellite locus (Zane et al. 2002).

3. Visually (in an agarose gel) assess the amplification success, apparent

polymorphism and allele size range of selected markers in a few

selected DNA samples and under different PCR conditions.

4. Make a preliminary marker set list by selecting markers on the basis

of the consistency and intensity of the amplification and the

polymorphism observed in step 3.

5. Use a specialized software (e.g. Multiplex Manager, Holleley & Geerts

2009) to design the optimum number and combination of loci in

multiplex reactions.

6. Dye-label the forward primers of each of the markers in the

preliminary list configured in step 4 according to the best multiplex

configuration.

7. Optimize the PCR conditions for the multiplex reactions.

8. Genotype some samples (from individuals with different geographic

origin, stage and life traits, if possible) to check that all markers in the

preliminary list yield unambiguously identifiable allele peaks in all

samples. If allele identification in some marker(s) is problematic due to

irregular peak patterns, discard the marker(s) from the list and go back to

step 5. If allele ranges of two identically labelled markers within the

same multiplex reaction overlap, update allele size range information and

go back to step 5.

9. Genotype a larger sample of individuals from one or a few populations

(minimum 20-30 individuals per population) and optimize allele scoring

for each marker.

10. Perform a thorough assessment of genetic diversity indexes, mistyping

and error rates and tests for deviations from Hardy-Weinberg equilibrium

(HWE) and Linkage Disequilibrium (LD) for each marker at each sample

population. Identify and control (consider eliminating) markers showing

evidences of null alleles (see Chapter IV).

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32

Ekblom & Galindo 2011). Although, as stated before, new molecular tools are not free

from data quality control requirements, the production of data is no longer a limiting

factor for geneticists. The challenge resides now in the efficient processing of huge

amounts of information.

Possibilities and misuses of bioinformatics tools

In this respect, bioinformatics is expanding our capacity to manage increasing volumes

of genetic data (Coissac et al. 2012). Current software is capable of efficiently

organizing, filtering, summarizing and analyzing big datasets, thus enlarging our

potential to explore data distributions and hypothesis testing (Kelling et al. 2011;

Hampton et al. 2013). This is also possible by concurrent hardware optimization, which

allows the implementation of powerful computational algorithms at great speed.

Bioinformatics makes use of this increasing analytical power to develop specialized

software implementing model-based analyses. As a consequence, a plethora of

computer programs is now available for researchers interested in characterizing genetic

diversity and studying evolutionary processes such as local demographic dynamics or

population connectivity (Excoffier & Heckel 2006; Broquet & Petit 2009). Scientists

can further utilize programming tools to automate iterative analyses, therefore adapting

available computer routines to customize analytical designs. As a result, simulation and

empirical studies are flourishing worldwide, with broad application to demographic

research (Wang 2016, 2017).

However, inadequate use of genetic programs has sometimes led to

misconceptions and inaccurate applications (Hedrick 1999; Waples 2015). Empirical

genetic analyses usually test the adjustment of different demographic hypotheses to an

observed genetic diversity distribution, which is precisely characterized by means of

microsatellites or other molecular markers. However, the accuracy of these basic

genetic diversity estimates critically relies on the representativeness of the sample,

which is seldom assessed (Meirmans 2015). An extensive genetic dataset will not

produce robust demographic inferences if the data (e.g. some markers in a multilocus

dataset or a proportion of individuals in the sample) do not meet the assumptions of the

analytic model employed (Pompanon et al. 2005; Waples 2015). Although there are

several tests that assess the fit of genetic data to different model assumptions, they are


33

often inappropriately used, just as a ‘prerequisite’ demanded by reviewers before

applying sophisticated phylogenetic or demographic analyses (Waples 2015). The

application of genetic analyses to inadequate data has led to severely biased conclusions

in some studies (Pompanon et al. 2005; Waples 2015). Two prevalent sources of bias in

demographic research are insufficient sample size and undetected excess of close

relatives in the sample, but their effects vary among different studies, so it is difficult to

extract general guidelines (Anderson & Dunham 2008; Rodríguez-Ramilo & Wang

2012; Meirmans 2015; Peterman et al. 2016; Waples & Anderson 2017). For that

reason, marker performance and sampling design should always be thoroughly explored

in pilot studies, in order to guarantee the reliability of demographic inferences (Palstra

& Ruzzante 2008; Schwartz & McKelvey 2009). In agreement with this, I present a

method for calculating the minimum sample size required for the characterization of

genetic diversity in empirical studies in Chapter IV. Also, since the effect of excessive

full sibs in the sample has only been documented in specialized downstream analyses, I

explore the basics of this effect on genetic diversity indexes (Anderson & Dunham

2008; Goldberg & Waits 2010; Rodríguez-Ramilo et al. 2014; Waples & Anderson

2017). Thus, this dissertation shows that carefully planned analytic procedures coupled

with optimized sampling strategies are required to improve the robustness of genetic-

based demographic inferences and the progressive refinement of solid theoretical

frameworks.

The theoretical framework of population genetics

The theoretical backbone of demographic research stems from the development of the

Population Genetics Theory (Crow & Kimura 1970; Habel et al. 2015; Lowe et al.

2017). Advances in this discipline are leading to a more thorough understanding of the

four fundamental forces that drive molecular evolution: mutation, migration, selection

and drift (Crow & Kimura 1970; Charlesworth & Charlesworth 2017). In the long term,

population trends are conditioned by the interaction of these forces, and so their

accurate quantification offers an opportunity for understanding demographic patterns

and evolutionary processes (Vucetich & Waite 2003). Among the variables studied in

demographic research, the effective population size (Ne) is a very informative

parameter, because it summarizes the capacity of a population to maintain genetic

CHAPTER I

34

diversity (Crow & Kimura 1970; Hamilton 2009; Husemann et al. 2016). Similarly,

quantification of inter-population migration and genetic connectivity (gene flow) allows

understanding regional demographic processes such as metapopulation dynamics, and

identifying barriers potentially affecting the regional distribution of genetic diversity

(Vences & Wake 2007; Andreasen et al. 2012; Edelaar & Bolnick 2012; Holderegger &

Gugerli 2012; Baguette et al. 2013; Furrer & Pasinelli 2016; Komaki et al. 2016; Laikre

et al. 2016). Therefore, Ne and gene flow are two relevant parameters for characterizing

demographic dynamics, and their integrated study is the main objective of this

dissertation.

Ne measures the rate of loss of genetic diversity in a population due to

inbreeding and genetic drift (Wright 1931, 1938; Nei & Tajima 1981; Ewens 1982;

Crow & Denniston 1988; Charlesworth 2009). It depends on several factors such as the

breeding success of each sex and age class in the population, the mating system of the

species, the generation length, the inheritance pattern and population size fluctuations in

the past (Frankham 1995; Cornuet & Luikart 1996; Balloux & Lehmann 2003; Wang et

al. 2016). Consequently, estimation of Ne can offer valuable insights on the main

evolutionary processes affecting populations (Caballero et al. 2017). Also, the

possibility to assess population status in terms of its effective size and not just by its

presence/absence or census abundance represents a significant improvement for

conservation-oriented studies (Brede & Beebee 2006; Gasca-Pineda et al. 2013; Kajtoch

et al. 2014). In spite of its versatility, calculation of Ne is challenging through direct

methods, because estimation of all the required demographic features is difficult in

natural populations (Caballero 1994; Vucetich & Waite 1998; Waples et al. 2011; Wang

et al. 2016). Alternatively, indirect genetic methods based on the distribution of

genotypes in a sample taken from the population can be employed to estimate Ne

(Schwartz et al. 1998; Wang 2005; Luikart et al. 2010; Hollenbeck et al. 2016; Wang et

al. 2016). However, genetic estimation is complicated in iteroparous species with

overlapping generations because multi-cohort genetic samples are required and

individual (or averaged) trait information is necessary to account for the age and sex-

structure of the population (Wang et al. 2010; Waples et al. 2011, 2014; Grimm et al.

2016; Waples 2016). For that reason, the effective number of breeders (Nb) is usually

estimated in long-lived species (Hoehn et al. 2012; Waples et al. 2013; Waples & Antao

2014; Kamath et al. 2015). This measure accounts only for some of the information of


35

Ne but it can be calculated from a single-cohort offspring sample (Box 2). Estimates of

Nb obtained in successive seasons can then be used to approximate Ne (Whiteley et al.

2017).

In Chapter V, I address estimation of Nb by the sibship frequency (SF) method,

which relies on sibship and parentage reconstruction from a sample of genotyped

Box 2. Genetic estimation of the effective population size (Ne) and the

effective number of breeders (Nb)

The quantities Ne and Nb are two relevant demographic parameters that measure

the effective size, which relates to the capacity of the population to maintain

genetic diversity (Ferchaud et al. 2016; Wang et al. 2016). This capacity is

dependent on the number of successful breeders in the population and the

relative genetic contribution of those individuals to the next generation (Wang

2009). While Ne measures the effective size per generation, Nb measures the

effective size in a single breeding season (Waples 2005; Palstra & Fraser 2012).

Various methods have been derived to calculate both parameters from

neutral genetic information. The earliest approach was the temporal method, in

which genetic drift was calculated on the basis of observed allelic frequency

changes among two genetic samples taken from non-overlapping generations

(Waples 1989; Anderson 2005). Unfortunately, this method is difficult to apply

in vertebrates, because many species have long generation times and several

breeding cohorts usually overlap in a single breeding season (Wang et al. 2010;

Waples 2016). For that reason, most recent studies employ single-sample

genetic methods to calculate Ne and Nb (Wang 2005; Luikart et al. 2010). The

parameter Ne can be calculated from a comprehensive adult sample, in which all

adult age and sex classes of the population are represented (Waples et al. 2014).

On the other hand, Nb can be estimated from a representative single-cohort

offspring sample (Ferchaud et al. 2016). However, accounting for life history

traits and the mating system of targeted species is crucial for a correct

interpretation of Ne and Nb estimates, because results may show wide variation

depending on the species’ features and the sampling design (Waples et al. 2013;

Waples 2016).

As an example, in Figure B2.1 we depict two schematic cases of an

annual semelparous species (a) and a longer-lived iteroparous species with

overlapping generations (b). At each breeding season (vertical grey bars), Ne can

be calculated from a genetic sample taken among the adult individuals present in

the population (dark horizontal bars crossing the corresponding grey vertical

bar), while Nb can be calculated by sampling among the offspring of the year

(light horizontal bars crossing the corresponding grey vertical bar). It can be

noted that Ne measured at each breeding season in (a) corresponds exactly to Nb

measured in the previous year, because the same individuals contribute to both

parameters. In contrast, this does not occur in (b), where three different adult

breeding cohorts contribute to offspring at each breeding season.

CHAPTER I

36

individuals and estimates Nb on the basis of the relative frequency of siblings inferred in

the sample (Wang 2009). This method is implemented in software COLONY

(Jones & Wang 2010), which is a popular program for molecular ecologists and

population geneticists. One of the main advantages of the SF method is that sibship and

parentage reconstruction can be calibrated with direct pedigree information or evidences

of breeding activity. Such integration of genetic analyses and field observations sets an

unparalleled opportunity to obtain reliable inferences about Nb, as illustrated in Chapter

V. Once protocols for reliable Nb calculation have been optimized, monitoring Nb in a

network of populations through time will allow characterizing population dynamics at

unprecedented rate and accuracy. Demographic inferences obtained from such

Box 2. (Cont.)

While, in this example, Nb varies from year to year due to the different

number of adult breeders generating offspring in each season both in (a) and (b),

Ne experiences an additional interannual source of variation in (b) due to

different overlapping cohorts contributing offspring each year. Also, sampling

design for Ne estimation in (b) should be stratified to include all adult age-

classes present in the breeding season, whereas this is not necessary in (a). The

complexity of both cases in real populations further increases by individual

differences in survival rate, age of maturation and breeding behaviour (Waples

2016).

Figure B2.1. Two schematic representations of an annual semelparous species (a) and an

iteroparous species with overlapping generations (b). Vertical grey bars represent five successive annual breeding seasons. Each horizontal bar symbolizes the lifetime of one individual from its birth, to its juvenile stage (light) and the lifespan after sexual maturation (dark). All individuals in (a) live for one year, attain sexual maturity soon after birth, breed during the following breeding season (when dark horizontal bars cross vertical grey bars), and die after breeding. All individuals in (b) live for three years, reach sexual maturity during the first year and breed in the three following breeding seasons. Note that the number of offspring born in the breeding season is variable from year to year, but is equal in a) and b).


37

monitoring programs can readily be applied to address unsolved questions in

evolutionary biology and inform conservation policies (Schwartz et al. 2007; Hinkson

& Richter 2016; Mueller et al. 2016).

In a similar way, current methods for estimating population connectivity are

leading to improved characterization of the spatial and temporal distribution of genetic

variation (Manel et al. 2003; Holderegger & Wagner 2012; Dyer 2015; Greenbaum et

al. 2016). This is important for identifying isolated populations, which face higher risk

of extinction as a result of the genetic impoverishment caused by drift (Allendorf 1983),

but also to find highly diverse populations providing migrant individuals for nearby

localities, thus contributing to the maintenance of the genetic diversity of the species at

a broader scale (Broquet & Petit 2009; Marko & Hart 2011; Albert et al. 2013;

Sundqvist et al. 2016). Characterization of genetic structure patterns, in turn, is essential

for understanding microevolutionary processes such as population differentiation or

hybridization in secondary contact zones (Hewitt 1988; Barton & Hewitt 1989;

Hutchison & Templeton 1999; Anderson & Thompson 2002; Harrison & Larson 2014,

see also Chapter III in this dissertation). These processes operate at different spatial and

temporal scales, so multi-scalar approaches studying the hierarchical levels of

organization of genetic variation offer valuable insights about the foundations of lineage

perpetuation and differentiation (Angelone et al. 2011; Martin et al. 2016). At the same

time, multi-species comparative studies allow identifying features favouring or

hindering gene flow among populations, and assessing the relative effect of these

features in species with different life history traits (Bohonak 1999; Manel et al. 2003;

Richardson 2012; Baguette et al. 2013). The latter issue is addressed in Chapter VI, by

combining different genetic approaches in four species with different life history traits

to evaluate the differential role of a major topographic feature (a mountain range) as a

barrier to gene flow.

The combination of multiple analytic approaches aimed to provide insights

about genetic differentiation, migration rates, genetic structure and landscape-scale

connectivity largely improves gene flow inferences, as illustrated in Chapter VI. Among

these approaches, F-statistics are the classical indexes used for characterization of the

distribution of genetic variation (Rousset 1997). They were originally derived by Sewall

Wright on the basis of the relative amounts of genetic diversity (measured as observed

vs. expected heterozygosity) registered at the individual, population and regional levels

CHAPTER I

38

(Wright 1943, 1951). Since then, F-statistics (in particular, FST) have been applied in a

wide range of studies as an estimate of population differentiation (Wang 2012a). Also,

Bayesian models have been derived for the estimation of migration rates per generation

among populations (Wilson & Rannala 2003; Andreasen et al. 2012). In addition,

genetic clustering methods have become very popular among population geneticists for

the characterization of genetic structure (Evanno et al. 2005; Wang 2017). In genetic

clustering analyses, different algorithms can be applied to evaluate the degree of genetic

admixture of a sample of individuals among some predefined numbers of clusters (K,

Pritchard et al. 2000; Guillot et al. 2005; Jombart et al. 2010). Furthermore, genetic

clustering analyses can be applied in a hierarchical fashion thus aiding in the

identification of the relevant factors operating at different scales to shape observed

genetic structure patterns (Balkenhol et al. 2014; Meirmans 2015). Lastly, landscape

genetic analyses offer an excellent framework to test the relative role of different

landscape patches and putative barrier elements on observed genetic distances, while

also accounting for the effect of geographical distances among populations (Manel et al.

2003; Cushman et al. 2006, 2013; Wasserman et al. 2010; Manel & Holderegger 2013).

Because of their versatility and comprehensive inference possibilities, integrative

genetic studies including multiple analytic approaches can be applied to a wide variety

of taxa and molecular marker types, yielding robust insights about historical and current

connectivity.

In conclusion, combined advances in molecular and computational resources,

along with the continuous expansion of Population Genetics Theory are opening an

exciting field for evolutionary and conservation biologists. Demographic inferences in

all extant (and even some extinct!) species across the tree of life can be obtained as long

as adequate DNA sampling designs are implemented. This will improve critically our

understanding of evolutionary processes and show us how to alleviate the situation of

endangered species (England et al. 2010). Researchers will certainly take advantage of

this great opportunity to attain unprecedented knowledge about how evolution operates

at different spatial and temporal scales.


39

Individual-based monitoring programs complementing genetic-based

demographic inferences: the effective/census size ratio

Genetic methods are increasingly used for demographic inferences at the expense of

direct methods, which rely on demographic features that are difficult to estimate in

natural populations (Caballero 1994; Vucetich & Waite 1998). Nevertheless, direct

individual information, evidence of breeding behaviour and population estimates

obtained from individual-based field data are extremely useful for demographic

inferences, and they also provide invaluable information for contextualizing genetic

estimates (Clutton-Brock & Sheldon 2010; Efford & Fewster 2013; Álvarez et al. 2015;

Nunziata et al. 2015, 2017; Bernos & Fraser 2016). For instance, field monitoring

programs can provide estimates of relevant demographic parameters regarding

population structure, density, survival and breeding success (Lebreton et al. 1992;

Tavecchia et al. 2009; Sanz-Aguilar et al. 2016). In the same way, evidence of breeding

success (such as egg mass counts in amphibians or records of nesting in birds or litter

size in mammals) is crucial to calibrate genetic estimates of effective size, supervise

inferred recruitment rates and explore the mating system of species (see Chapter V in

this dissertation). Furthermore, individual-based data can provide inferences about

movement patterns and the dispersal potential of different species. These inferences

require time-consuming fieldwork and are scarce in the literature, but they are useful to

calibrate gene flow estimates and help characterizing connectivity among populations

(Cam et al. 2004; Clark et al. 2008; Luque et al. 2012). All these features play a

fundamental role on population persistence (Keller & Waller 2002; Palstra & Ruzzante

2008).

Importantly, the census (Nc) and the adult population sizes (Na, Frankham 1995)

can be accurately estimated by means of individual-based capture-mark-recapture

(CMR) methods, and both parameters are necessary to refine genetic inferences (Palstra

& Ruzzante 2008; Palstra & Fraser 2012). Capture-mark-recapture techniques are

extensively applied for the estimation of relevant demographic parameters such as

survival, recruitment, migration rates or population size, and different model

formulations have been developed in the last decades to accommodate to different data

types and research interests (Lebreton et al. 1992, 1993, 2003; Kendall et al. 1995;

Pradel 1996; Grosbois & Tavecchia 2003; Tavecchia et al. 2007, 2009; Sanz-Aguilar et

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40

al. 2016). However, as noted before for genetic analyses, CMR data should also be

structured in a fashion that is adequate to the assumptions of the selected formulation, to

guarantee the accuracy of results (Lebreton et al. 1992; Kendall & Nichols 1995).

Therefore, sampling design is essential to obtain reliable estimates in demographic

CMR studies. If this is carefully planned, multi-year CMR programs can take full

advantage of currently available formulations, such as ‘robust design’ models, to obtain

accurate estimates of parameters like Na (see Box 3), as illustrated in Chapter V.

Na estimates can be further enriched by separate calculation of the number of

adult males and females (White & Burnham 1999). These estimates of adult abundances

can in turn be compared with direct evidences of breeding success to explore the mating

system of species. For example, in Chapter V, the estimated number of females was

found to be similar to egg string counts in an explosive-breeding species (Epidalea

calamita), suggesting that the female breeding success rate in this species is close to

one. Other evidences of breeding behaviour that are typically recorded in monitoring

programs include individual records of time elapsed in the breeding sites and direct

observations of mating events. This information is used in Chapter V to assess the

reliability of family reconstruction in SF analyses, and to describe a within-year

monogamous mating system in E. calamita. Since Nb estimates obtained by SF methods

are directly dependent on accurate sibship reconstruction, independent field-based

information about breeding activity plays a crucial role in the calibration and

supervision of genetic results (Wang 2009). As a consequence, the integration of genetic

and demographic estimates and direct records of breeding activity allow joint

exploration of the demography and mating system of target species, and the assessment

of the reliability of Nb estimates, as illustrated in Chapter V.

Na estimates also complement genetic Nb estimates, by allowing the calculation

of the Nb/Na ratio (Palstra & Fraser 2012). The effective/census size ratio sensu lato (i.e.

Nb/Na or Ne/Nc) offers invaluable insight into population demography and represents the

most useful piece of information for population status assessment, as argued in Chapter

V. The Nb/Na ratio represents the portion of the adult mature population that contributes

to generate a given offspring cohort, whereas Ne/Nc represents the portion of the

population that contributes genetically to the next generation (Waples 2005; Palstra &

Fraser 2012; Whiteley et al. 2017). Low effective/census size ratios have been reported

in many species (Frankham 1995; Brede & Beebee 2006; Palstra & Ruzzante 2008;


41

Palstra & Fraser 2012) and can lead to deleterious effects caused by inbreeding and

genetic drift even in large populations (Ruzzante et al. 2016). In contrast, some small

populations seem to mitigate the effect of genetic drift by showing a high Nb/Na ratio

Box 3. Estimation of the number of adults in a population (Na) in seasonal

breeding species using the ‘robust design’ formulation

Individual-based capture-mark-recapture (CMR) monitoring programs are

greatly contributing to demographic research (Pradel 1996; Grosbois &

Tavecchia 2003; Cam et al. 2004; Tavecchia et al. 2009; Clutton-Brock &

Sheldon 2010; Sanz-Aguilar et al. 2016). A wide range of statistical models

(principally based on maximum likelihood approaches) is currently available for

the estimation of demographic parameters, such as Na (Frankham 1995; Kendall

et al. 1995). Software MARK is one of the most popular programs for the

analysis of CMR data, because it includes many formulations that can be applied

for different questions and types of data (White & Burnham 1999). In this

dissertation, we argue that ‘robust design’ models (Pollock 1982), implemented

in software MARK, represent one of the best approaches for annual Na estimation

in iteroparous species with demarcated annual breeding seasons. Nevertheless,

an adequate sampling design accounting for the life traits of the targeted species

is crucial for the reliability of results (Waples et al. 2013).

To illustrate the reasoning behind the robust design method, we represent

an example of the model and its main parameters in Fig. B3.1. The critical

assumption of the model is that Na is constant within each breeding season (or

whatever type of periodic season on which sampling is focused). Consequently,

there should be no mortality, nor migration of adult individuals in the population

throughout the breeding season (Pollock 1982; Kendall et al. 1995). This is

indeed an unrealistic assumption. However, it can be reasonably approximated

by minimizing the timespan between the first and the last CMR sessions.

Unfortunately, excessive concentration of CMR sessions may also introduce a

temporal sampling bias, resulting in unequal capture probabilities among

individuals (Crespin et al. 2008; Kidd et al. 2015). The optimum balance

between these two opposing sources of bias should be studied in each case.

Ultimately, if the within-season population closure can be reasonably

assumed (Stanley & Burnham 1999) and high recapture rates are obtained, the

power of robust design can be fully exploited (Kendall & Nichols 1995; Kendall

et al. 1997). The average probability of capture (p) is then modelled across all

CMR sessions, leading to a Na estimate for each breeding season (see Fig. B3.1).

At the same time, survival and migration rates between consecutive breeding

seasons are estimated, because the model accounts for Na variation across

different breeding seasons (Kendall et al. 1997). In Chapter V, I demonstrate

that this elegant approach is capable of yielding extremely accurate Na estimates

in some cases, and that precision of estimates is improved with cumulative years

of data. If within-season population closure cannot be assumed, alternative open

models can be employed, although at the cost of increased model complexity

(Kendall & Bjorkland 2001; Wagner et al. 2011).

CHAPTER I

42

(Palstra & Ruzzante 2008; Beebee 2009; Hinkson & Richter 2016).

There is still a lot of uncertainty about the range of variance of effective/census

size ratios both within and among species (Frankham 1995; Palstra & Fraser 2012;

Waples et al. 2013; Kamath et al. 2015; Bernos & Fraser 2016; Ferchaud et al. 2016;

Ruzzante et al. 2016). Accurately estimating effective and census size is very time-

consuming, so studies reporting this ratio are still scarce. The problem is further

complicated by impeded comparativeness among studies reporting either Nb/Na or Ne/Nc

(Palstra & Fraser 2012). These two ratios are related, but strong differences can be

found between them in long-lived iteroparous species (see Box 2). In addition, estimates

of Nb (or Ne) and Na (or Nc) obtained by different analytical methods and with different

sampling designs may apply to different time-scales, further complicating comparisons

(Waples 2005; Palstra & Ruzzante 2008). Currently available molecular and statistical

tools allow filling this important gap of knowledge, but an additional effort is required

Box 3. (Cont.)

Figure B3.1. Schematic representation of a ‘robust design’ model applied to an iteroparous species with demarcated breeding seasons (modified from Kendall et al. 1995). Vertical grey areas represent three consecutive annual breeding seasons with different time lengths. The ‘X’s symbolize the CMR sessions (in this example, three CMR sessions were performed during the breeding season of the first year, four during the breeding season of the second year, and two during the breeding season of the third year). The parameter pxy is the average probability of capture in each CMR session, where ‘x’ represents the breeding season and ‘y’, the session within the breeding season. Similarly, Na x represents the parameter Na of each breeding season x. S a

b, emi a

b and imm a

b symbolize annual survival, emigration and immigration rates,

respectively, from breeding season a to breeding season b.


43

to obtain reliable and comparable estimates of both effective and census sizes in

different populations across many taxa (Palstra & Fraser 2012). As demonstrated in

Chapter V, significant improvements can be accomplished by taking full advantage of

the integration of the molecular-based SF method for Nb estimation, CMR methods for

Na estimation, and field-based evidences of breeding activity (Kamath et al. 2015).

Finally, in addition to their usefulness for integrative demographic inferences,

individual-based data can also yield valuable information about dispersive behaviour

(Paton & Crouch III 2002; Perret et al. 2003). Direct records of individual dispersal

movements offer information about the potential of different species for regional

connectivity via migration or dispersal, and can also be used to calibrate gene flow

estimates (Baguette et al. 2013). The best evidences of habitat use and movement

patterns are provided by remote-control techniques, such as satellite-signaling or radio-

tracking devices (Frei et al. 2016; Groff et al. 2016). Unfortunately, these technologies

are still expensive and difficult to adapt to small sized animals (Leskovar & Sinsch

2005). In addition, their implantation is usually invasive, raising ethical concerns and

also potentially affecting the behaviour of marked animals. On the other hand, CMR

monitoring programs using smaller passive integrated transponder (PIT) tags, brands or

feature-based identification can also yield some valuable records of animal movements

(Gamble et al. 2007; Scoular et al. 2011; Gordon & Hellman 2015; Schoen et al. 2015;

Muñoz et al. 2016). Although long distance dispersal is difficult to detect with passive

methods, useful relative movement frequencies can be obtained in multi-species long-

term monitoring programs, as illustrated in Chapter VI.

Challenges faced by amphibians in an anthropized world

Amphibians are the one of the most threatened groups of vertebrates worldwide

(Blaustein et al. 1994; Houlahan et al. 2000; Stuart et al. 2004). Several factors have

been related to population declines, such as direct exploitation, infectious diseases,

climate change, contamination, habitat destruction and fragmentation, and introduction

of invasive species (Arano et al. 1995; Carey & Bryant 1995; Beebee & Griffiths 2005;

Cushman 2006; Hua et al. 2015). In spite of several records of massive mortalities and

complete population eradications, the ultimate causes of local declines are still poorly

understood (Stuart et al. 2004). Consequently, at this point it is practically impossible to

CHAPTER I

44

accurately forecast amphibian population trends. The problem is further complicated by

the wide fluctuations in abundance that characterize many species, coupled with

extensive gaps in the knowledge about their diversity and fundamental aspects of their

biology (Pechmann et al. 1991; Green 2003; Pittman et al. 2014; Semlitsch et al. 2017).

Therefore, more taxonomic and demographic studies are urgently required to

understand amphibian diversity and population dynamics and to build an empirical

background that can ultimately provide clues for efficient conservation plans (Paton &

Crouch III 2002; Wang 2012b; McCartney-Melstad & Shaffer 2015). Integrative

molecular and individual-based field methodologies introduced in the previous sections

represent an unprecedented opportunity to address some of the unsolved questions in

amphibian biology (Buckley 2009; Nunziata et al. 2015; Semlitsch et al. 2017).

Regarding taxonomy, amphibian phylogeny is in continuous revision. The

relationships among some major clades remain to be unambiguously resolved, and new

species are still described at a relatively fast rate (Frost et al. 2006; Arntzen et al. 2013;

Liedtke et al. 2016). Reconstructing a robust phylogeny is the first fundamental step for

reliably delineating the operative taxonomic units of study in evolutionary biology.

However, this is an especially complicated issue in contact zones where differentiated

lineages hybridize (Hewitt 1988; Barton & Hewitt 1989; Smith et al. 2013; Harrison &

Larson 2014; Dufresnes et al. 2015; Arntzen et al. 2016). An added difficulty in these

contact zones arises when both lineages contribute differently to the hybridization

process, for instance in hybridogenetic species complexes, such as those in Western

Palearctic waterfrogs (Box 4). Delineation of these hybrid zones is commonly hindered

by the morphological similarity of the species involved (e.g. Rivera et al. 2011; Ferrer

& Filella 2012). For that reason, molecular tools are necessary for characterizing contact

zones in these hybridogenetic complexes, and to perform ecological studies addressing

the demographic implications of this singular evolutionary process (Arano & Llorente

1995; Hotz et al. 2001; Dubey et al. 2014). In this line, the utility of newly developed

genetic tools for species assignment in the Pelophylax ridibundus x P. perezi

hybridogenetic complex is illustrated in Chapter III.

Demographically, very little is known about the factors driving the maintenance

of genetic diversity and regional connectivity (which ultimately determine long-term

persistence) in amphibian populations (Pechmann et al. 1991). For instance, the mating

system plays a crucial role in genetic diversity maintenance, because both Nb and Ne


45

depend on the number of successfully breeding individuals and their relative

contribution to the offspring’s genetic heritage (Nunney 1993; Balloux & Lehmann

2003; Hedgecock et al. 2007; Wang et al. 2016). Polygynous (polyandrous) mating

systems can dramatically reduce Nb in species with strong male (female) dominance,

because the number of successfully breeding males (females) can be extremely low

(Balloux & Lehmann 2003; Ficetola et al. 2010; Holman & Kokko 2013). In contrast,

promiscuous mating systems may have the opposite effect, increasing Nb by raising the

number of successful breeders of both sexes (Holman & Kokko 2013; Mangold et al.

2015). Although several sexual selection studies with anurans and urodeles have

Box 4. Hybridogenesis in Western Palearctic water frogs

Two major hybridogenetic complexes have been documented among Western

Palearctic water frogs: the Pelophylax ridibundus x P. lessonae complex with

the hybrid taxon P. klepton esculentus (Berger 1973), and the P. ridibundus x P.

perezi complex with the hybrid taxon P. kl. grafi (Crochet et al. 1995). In both

cases, hybridization between the two parental species generates hybridogenetic

individuals. These F1 individuals, in turn, frequently produce only clonal

gametes with the genetic heritage of one of the parental species (often P.

ridibundus), because they discard the complete genomic dotation of the other

parental species during gametogenesis (Uzzell et al. 1977; Mikulíček et al.

2015). Therefore, hybrids act as sexual parasites for one of the parental species

involved in each of the complexes (normally P. lessonae or P. perezi,

respectively). For this reason, the hybrid taxon is named klepton, from the Greek

kleptein, ‘to steal’. Genetic erosion experienced during the hybridization process

may compromise the persistence of the sexual host species (Arano et al. 1995;

Vorburger & Reyer 2003; Schmeller et al. 2007; Holsbeek et al. 2008;

Quilondrán et al. 2015).

Figure B4.1 represents an example of a P. ridibundus x P. lessonae

hybridogenetic lineage (modified from Hotz et al. 1992). The P. ridibundus x P.

perezi complex is expected to function in a similar way, but is much less

studied. The lineage typically begins with (1) an interspecific mating between a

female P. ridibundus and a male P. lessonae (the opposite crossing is usually

precluded by size-related behavioural causes, Berger et al. 1988). The progeny

of this interspecific mating is composed by male and female P. kl. esculentus

individuals. The usual line continues with P. kl. esculentus females backcrossing

with P. lessonae males. Since P. kl. esculentus breeders only produce clonal P.

ridibundus gametes, the hybridogenetic lineage perpetuates even in the absence

of pure P. ridibundus individuals (Berger 1988; Pagano et al. 2001; Reyer et al.

2015), by successive backcrosses of P. kl. esculentus females with P. lessonae

males (dotted arrow). This lineage produces both males and females P. kl.

esculentus, which constitutes a singular case among clonal vertebrate hybrids

(Berger et al. 1988).

CHAPTER I

46

Box 4. (Cont.)

Since males are the heterogametic sex in water frogs, alternative mating

between a male P. kl. esculentus and a female P. lessonae (2) produces only

female P. kl. esculentus offspring, because the parental P. kl. esculentus male

only produces clonal gametes with its P. ridibundus genomic dotation, which is

maternal (Berger et al. 1988). This P. kl. esculentus female offspring might in

turn perpetuate the hybridogenetic lineage by backcrossing with P. lessonae

males, as in the usual line described above (dotted arrow). Conversely, some of

these female hybrids mate with male hybrids from a different lineage (3),

thereby producing only female diploid P. ridibundus offspring. Furthermore,

this progeny would have a pure P. ridibundus nuclear genome, but P. lessonae

mitochondrial DNA, which is matrilineally inherited and irreversibly introduced

in the lineage in (2) (Hotz et al. 1992; Plötner et al. 2008). On the other hand, F1

x F1 crosses between individuals from the same lineage (4) produce inbred

offspring which is often not viable (Vorburger 2001; Guex et al. 2002;

Christiansen et al. 2005). In some cases, this system further complicates

resulting in the generation of polyploid individuals (Berger et al. 1986; Borkin

et al. 2004; Christiansen et al. 2005; Hoffmann et al. 2015; Herczeg et al. 2017).

Figure B4.1. Example of a P. ridibundus (white) x P. lessonae (black) hybridogenetic lineage. Hybrids (P. kl. esculentus) are depicted in dashed grey, while females and

males are symbolized by circles and squares, respectively.


47

searched for female-selected traits and investigated the consequences on fitness of

different mating strategies, to date very little is known about the intensity of sexual

selection in wild amphibian populations (Garner & Schmidt 2003; Hoeck & Garner

2007; Broquet et al. 2009; Rovelli et al. 2015). Similarly, the role of environmental

constraints in shaping breeding strategies remains largely unexplored. However,

integrative studies combining genetic markers and field-based demographic data

represent a promising approach to promote detailed research regarding mating systems.

In Chapter V, a monogamous mating system is described in a population of E. calamita.

The within-season monogamous mating behaviour of this species is probably caused by

its explosive breeding strategy, which allows exploitation of ephemeral aquatic sites.

Monogamy in this population represents a constraint to panmixia that results in a low

Nb/Na ratio, with potential evolutionary and conservation implications.

Low effective/census size ratios have been reported in many amphibian species

(Scribner et al. 1997; Jehle et al. 2001; Brede & Beebee 2006; Schmeller & Merilä

2007; Beebee 2009; Ficetola et al. 2010; Palstra & Fraser 2012). This suggests that

populations could experience genetic bottlenecks despite showing high apparent

abundances. For this reason, census estimates alone are not accurate enough for

assessing population status, so Nb estimation should also be addressed in monitoring

programs (Brede & Beebee 2006; Schwartz et al. 2007). Long-term monitoring

programs also allow characterization of natural demographic fluctuations (Blaustein et

al. 1994; Schwartz et al. 2007). This information is essential to distinguish natural

dynamics from declines caused by anthropogenic disturbances (Pechmann et al. 1991;

Pounds et al. 1997). Population size fluctuations in amphibians are traditionally

considered to be wide, but are also largely understudied. Among pond-breeding

amphibians, alternation of ‘good’ and ‘bad’ recruitment years could be driven by

weather conditions, which determine the annual hydroperiod of the breeding sites

(Salvador & Carrascal 1990; Cayuela et al. 2012; Delatorre et al. 2015). In rainy years

in which ponds maintain water throughout all the period required for larval

development, tadpole survival until metamorphosis can be high, and tadpoles may also

reach larger sizes that increase post-metamorphic survival possibilities. Dry years, in

turn, can result in the loss of complete offspring cohorts due to pond desiccation (see

Chapter V). Weather conditions also have potential effects on the activity patterns of

adult amphibians, because of their physiological constraints (Duellman & Trueb 1994).

CHAPTER I

48

This can lead to individuals skipping the breeding season and even to total absence of

population breeding activity in harsh years, as documented in Chapter V (see also

Muths et al. 2006, 2013, Cayuela et al. 2014, 2016). Altogether, extreme variation in

recruitment rates coupled with irregular activity patterns of adults have hindered

accurate population trend characterization, because long-term series are needed to

account for all possible sources of variation (Habel et al. 2014). As a consequence,

monitoring programs integrating molecular and individual-based CMR techniques with

standardized protocols are strongly required to obtain reliable and comparable estimates

of demographic parameters. This information will then be useful for informing

population management actions (Jehle et al. 2001; Paton & Crouch III 2002; Muñoz et

al. 2016).

Amphibians are also traditionally regarded as poorly dispersing, highly

philopatric organisms, although high dispersal capabilities and extensive gene flow have

been reported in many species (Blaustein et al. 1994; Smith & Green 2005, 2006; Wells

2007; Sinsch 2014). Since the emergence of genetic methods, several studies have

described patterns of genetic structure in a wide variety of taxa (Emel & Storfer 2012).

Strong genetic structure has been documented in some species, which is concordant

with low dispersal and reduced gene flow (Martínez-Solano et al. 2005; Gamble et al.

2007; Steele et al. 2009; Richardson 2012; Peterman et al. 2013; Whiteley et al. 2014).

In contrast, genetic homogeneity across large areas has been reported in more vagile

species, suggesting extensive gene flow among populations (Funk et al. 2005; Zamudio

& Wieczorek 2007; Purrenhage et al. 2009; Steele et al. 2009; Richardson 2012;

Whiteley et al. 2014). The existence of gene flow among a network of interconnected

populations can lead to metapopulation dynamics, characterized by local extinction

events that are counteracted by recolonization from nearby areas (Smith & Green 2005;

Albert et al. 2013). The balance of these metapopulation dynamics determines the

probability of regional persistence in many amphibian species (Marsh & Trenham 2001;

Van Buskirk 2005; Fortuna et al. 2006; Heard et al. 2015). Characterization of gene

flow is, therefore, crucial to understand regional evolutionary dynamics (Marko & Hart

2011).

Additionally, studies comparing genetic structure among sympatric species with

different life history traits can offer insights about the role of natural or artificial

features in shaping lineage differentiation (Steele et al. 2009; Goldberg & Waits 2010;


49

García-González et al. 2012; Richardson 2012; Espregueira Themudo et al. 2012;

Sotiropoulos et al. 2013). Such studies have improved our knowledge about historical

processes leading to current patterns of amphibian species richness (Veith et al. 2003).

For example, phylogeographic works have inferred the location of glacial refugia for

many species of the European batrachofauna, from which subsequent recolonization of

newly available habitats in formerly glaciated areas proceeded after glacial episodes

(Martínez-Solano et al. 2006; Gonçalves et al. 2009; Abellán & Svenning 2014;

D’Aoust-Messier & Lesbarrères 2015). At the same time, these studies have identified

topographic features that acted as corridors or barriers during range expansion events in

different species (Martínez-Solano et al. 2004; Akın et al. 2010). Optimally, putative

barrier effects should be assessed by combining different genetic approaches, as

illustrated in Chapter VI. Identifying corridors and barriers to gene flow in the present is

also paramount for our understanding of population connectivity (Funk et al. 2005;

Komaki et al. 2016). This information is necessary for implementing efficient landscape

management actions headed to ensure regional persistence of amphibian species.

In conclusion, I argue that integrative demographic studies including genetic

analyses and CMR monitoring programs in multi-scale and multi-species research

designs have great potential to provide accurate insights on amphibian population

dynamics (Schwartz et al. 2007; Bailey & Mazerolle 2010; Helfer et al. 2012). A solid

empirical background on demographic variability within and among different species,

coupled with an accurate quantification of gene flow patterns at local and regional

scales is urgently required for conservation purposes. Hopefully, optimized

management protocols resulting from this strategy will help reverse the current global

amphibian crisis.

The study system: a multi-species, multi-scale approach

This dissertation explores the potential of an integrative, multi-species approach to

address some of the main challenges in amphibian evolutionary and conservation

research. It is focused on different scales to encompass the study of taxonomic and

demographic issues. The species under study are four sympatric pond-breeding anurans:

the natterjack toad Epidalea calamita (Laurenti, 1768); the Iberian treefrog Hyla molleri

Bedriaga, 1889; Perez’s frog Pelophylax perezi (López-Seoane, 1885) and the Western

CHAPTER I

50

spadefoot toad Pelobates cultripes (Cuvier, 1829). These four species co-occur in

extensive areas in their native ranges, where they can be found forming breeding

assemblages (Álvarez & Salvador 1984; Salvador & Carrascal 1990). While previous

studies have described different aspects of their ecology, very little is known about their

local demographic dynamics and patterns of gene flow (García-París et al. 2004). Next I

summarize some aspects of the biology of each species, with a special focus on the

current knowledge about their taxonomy and demography.

Epidalea calamita (Laurenti, 1768)

Epidalea calamita (originally described as Bufo calamita, a nomenclature which was

maintained until recently) is a Palearctic species with a wide European distribution

(Beja et al. 2009b, see also Fig. I.1). The recent designation of this species as the only

member of the genus Epidalea is based on its ancient origin and strong genetic

differentiation with the monophyletic genera Bufo and Bufotes (Frost et al. 2006; Portik

& Papenfuss 2015; Carretero et al. 2016; Liedtke et al. 2016). Among the three species

for which new specific microsatellite sets are described in Chapter IV, E. calamita is the

only one for which additional specific genetic markers have already been published

elsewhere (Rowe et al. 1997, 2000; Rogell et al. 2005; Faucher et al. 2016). These tools

have been used in extensive demographic and phylogeographic research, which has

provided valuable evolutionary inferences in this species. Genetic diversity of

populations of E. calamita decreases with increasing latitude throughout its range,

suggesting that the Iberian Peninsula and some areas of France acted as glacial refugia

from which they expanded to occupy their present range during the Last Interglacial

Period (Beebee & Rowe 2000; Gomez-Mestre & Tejedo 2004; Rowe et al. 2006; Rowe

& Beebee 2007; Oromi et al. 2012). Also, geographic differences in population

connectivity have been reported, with limited gene flow within British and Danish

clusters of populations contrasting with wide connectivity in the Iberian Peninsula

(Gomez-Mestre & Tejedo 2004; Rowe & Beebee 2007; Allentoft et al. 2009; Oromi et

al. 2012). Results presented in Chapter VI also reveal high connectivity between

Central Iberian E. calamita populations, which showed the lowest inter-population

genetic distances among the four studied species. This is concordant with the high

dispersal capability of this species (Denton & Beebee 1993; Miaud et al. 2000; Stevens


51

et al. 2004; Daversa et al. 2012; Sinsch et al. 2012), which was further confirmed by

some long-distance cumulative movement records obtained during the field monitoring

program conducted for this dissertation (see Chapter VI).

Figure I.1. Distribution, habitat and illustrations of some life stages in Epidalea calamita. a) An adult male in a breeding site, b) distribution range shaded in grey (Beja et al. 2009b), c) a couple in amplexus with the female (below) laying an egg string, d) a group of tadpoles in a shallow puddle, e) a recently deposited egg string in a shallow puddle, and f) a typical breeding habitat composed by

shallow ponds and flooded meadows.

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52

Tadpoles and adults of E. calamita also show a great capacity of adaptation to

different, variable and unpredictable physicochemical and climatic conditions, which

constitutes the main strategy of this species for regional persistence (Beebee 1983,

1985; Sinsch et al. 1992; Romero & Real 1996; Gomez-Mestre & Tejedo 2003, 2004,

2005; García-París et al. 2004; Gomez-Mestre et al. 2004). Accordingly, wide

altitudinal and latitudinal variations have been observed in several life history traits

such as body size, larval tolerance to osmotic stress or traits associated to competitive

capacity against Bufo species (Gomez-Mestre & Tejedo 2002, 2003, 2004; Leskovar et

al. 2006; Marangoni 2006; Marangoni et al. 2008; Oromi et al. 2012). Tadpoles of E.

calamita are in general poor competitors, so interspecific competition usually results in

severe performance drawbacks, especially in the presence of B. bufo or B. spinosus, but

also R. temporaria (Beebee 1991; Beebee & Wong 1992; Griffiths et al. 1993; Bardsley

& Beebee 1998, 2001a, b; Gomez-Mestre & Tejedo 2002; Richter-Boix et al. 2007). To

avoid interspecific competition, E. calamita usually selects temporary or even

ephemeral aquatic sites for breeding, thus taking advantage of the fast development of

tadpoles (Beebee 1983). Nevertheless, this risky strategy usually leads to massive

tadpole mortalities caused by pond desiccation (García-París et al. 2004) resulting in a

high spatial and temporal variance of recruitment rates driven by annual weather

conditions. In this unpredictable scenario, regional persistence of E. calamita is

sustained by adult longevity (up to 10 and even 17 years, Banks et al. 1993; Leskovar et

al. 2006) and early sexual maturation (normally in the second year, Boomsma &

Arntzen 1985; Banks et al. 1993; Denton & Beebee 1993; Tejedo et al. 1997; Leskovar

et al. 2006; Sinsch et al. 2010; Oromi et al. 2012). Populations of E. calamita are

arranged in metapopulation networks driven by juvenile dispersal and female-biased

gene flow (Sinsch 1992a, b).

The particularities of the reproductive strategy of E. calamita make this species

especially interesting for comparative studies of demographic patterns in multiple

populations and exploration of the consequences of the mating system on effective

population size, as addressed in Chapter V. In addition, these studies are also necessary

for adequately responding to current threats for E. calamita persistence, such as pond

destruction, water pollution and road mortality (Lizana 1993; Fleming et al. 1996;

Carretero & Rosell 2000; García-París et al. 2004; García-Muñoz et al. 2010, 2011).

Although this species is globally listed as Least Concern (Beja et al. 2009b),


53

endangered populations exist in some areas of northern Spain and Britain, and

management actions are often required for maintaining relict breeding nuclei (Beebee

1983; Denton et al. 1997; Reques & Tejedo 2004; Garin-Barrio et al. 2007; Montori &

Franch 2010). Regional demographic studies are also needed in areas where gene flow

is compromised thus leading to population isolation (Beebee & Rowe 2000; Stevens et

al. 2006; Rowe & Beebee 2007; Allentoft et al. 2009).

Hyla molleri Bedriaga, 1889

Hyla molleri is distributed across the northern, western and central areas of the Iberian

Peninsula, and in southern France (Fig. I.2). Until the last decade, it was still considered

a subspecies of the European taxon H. arborea, due to their high morphological

similarity. However, recent molecular studies have highlighted the differentiation of the

H. molleri lineage and raised its taxonomic status to the species level (Stöck et al. 2008,

2012; Barth et al. 2011; Gvozdík et al. 2015). Interestingly, the sister species of H.

molleri is the geographically distant H. orientalis, and not H. arborea, with which H.

molleri overlaps in southwestern France (Gvozdík et al. 2010, 2015). The extent of this

range overlap is still debated because the two species hybridize (Stöck et al. 2012;

Gvozdík et al. 2015). Similarly, wide contact zones with the other Iberian tree frog

species, H. meridionalis, exist south of the Iberian Central System (Patón 1989; Oliveira

et al. 1991; Barbadillo & Lapeña 2003; Reino et al. 2017). Molecular tools, such as

those presented in Chapter IV for H. molleri, are required for delineating the

distribution ranges of each species and characterizing their contact zones. Additionally,

previous studies have reported low levels of genetic diversity and a very shallow genetic

structure throughout the distribution range of H. molleri (Barth et al. 2011; Gvozdík et

al. 2015). However, the use of the neutral hypervariable nuclear markers described in

Chapter IV in a comprehensive range-wide sample may reveal hidden patterns of

genetic structure (Sánchez-Montes & Martínez-Solano, unpublished data).

There are no published data about population abundances or other demographic

inferences in H. molleri, apart from some phenological studies (García et al. 1987;

Salvador & Carrascal 1990). Nevertheless, García et al. (1987) described the different

breeding strategies of male and female H. molleri, with potentially important

consequences for the relative breeding success rates of both sexes, as argued in Chapter

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54

V. On the other hand, extensive demographic research has been conducted on H.

arborea, revealing that populations of this species arrange in metapopulations that are

highly susceptible to habitat fragmentation (Carlson & Edenhamn 2000; Arens et al.

2006). Population isolation, in turn, has potential consequences on the fitness of

individuals (Edenhamn et al. 2000; Andersen et al. 2004; Luquet et al. 2011, 2013).

Interestingly, Broquet et al. (2009) found that delayed age of maturity in H. arborea

served as a mechanism compensating for possible additional genetic drift effects caused

by its polygamous mating system (see previous section and Chapter V for discussion

about the effects of the mating system on the effective population size).

Figure I.2. Distribution, habitat and illustrations of some life stages in Hyla molleri. a) An adult female in a breeding site; b) the Iberian distribution range of H. molleri shaded in grey, with diagonal lines showing the range area in southern France, where the distribution limits of H. molleri and H. arborea have not been fully resolved yet (Kaya et al. 2009); c) a couple in amplexus, showing variation in body colouration in this species, and d) a typical breeding pond for H. molleri, with abundant surrounding

shrub and arboreal vegetation.


55

The global conservation status of H. molleri has not been assessed yet, but it is

catalogued as Near Threatened in Spain (Márquez 2002). Similarly to H. arborea,

population connectivity is essential for the regional persistence of H. molleri, so

conservation efforts should focus on protecting both the aquatic and terrestrial habitats

of this species (Rosa 1995; Alarcos et al. 2003; Oliveira & Pargana 2010). Populations

throughout the range of H. molleri are experiencing increased isolation, demographic

declines and extinctions caused by anthropogenic disturbances, introduction of exotic

species and climate change (Galán 1997, 1999; Márquez 2002; Martínez-Solano et al.

2003; Cruz et al. 2008; Diego-Rasilla & Ortiz-Santaliestra 2009; Oliveira & Pargana

2010; Araújo et al. 2011). As a consequence, long-term monitoring programs and

integrative demographic studies like the one addressed in Chapter V, are urgently

required to understand population dynamics of H. molleri and to inform efficient

conservation policies.

Pelophylax perezi (López-Seoane, 1885)

Pelophylax perezi distributes natively across the whole Iberian Peninsula and southern

France, and it has been introduced to Britain and to the Madeira, Azores, Balearic and

Canary archipelagos (Llorente et al. 2002; Bosch et al. 2009, see Fig. I.3). It is listed as

Least Concern by the International Union for the Conservation of Nature (IUCN),

because it is common and locally abundant throughout its range, and shows wide

ecological tolerance (Llorente et al. 2002; Bosch et al. 2009). However, direct

exploitation, habitat destruction and alteration, water pollution and the introduction of

exotic species have been associated with severe damages to populations of this species

(Rico et al. 1987; Galán 1997, 1999; Martínez-Solano et al. 2003; Tejedo & Reques

2003; Pastor et al. 2004; Rodríguez et al. 2005). Additionally, P. perezi experiences

genetic erosion when it hybridizes with the European waterfrog P. ridibundus, or with

the hybridogenetic kleptons P. kl. grafi and P. kl. esculentus (Crochet et al. 1995, see

also Box 4). However, at the moment hybridization areas have only been reported in

areas north of the Ebro River (see Chapter III). A deeper understanding about

demographic dynamics and delineation of hybrid zones is thus necessary to anticipate

possible cryptic declines in P. perezi, and to understand the evolutionary consequences

of hybridogenesis. Specific markers characterized in Chapter III open a new field for

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56

demographic inferences in this species, and for the study of the P. perezi x P.

ridibundus hybridogenetic complex.

Pelophylax perezi is a short-lived species, with a maximum lifespan of 4-6

years, that reaches sexual maturity between the first and the third year (Docampo &

Milagrosa-Vega 1991; Patón et al. 1991; Esteban et al. 1996). Therefore, population

persistence might be based on high turnover rates, enabled by high recruitment and

migration rates, although this has not been ascertained yet (Egea-Serrano 2014). As a

consequence, the basic aspects of population dynamics of P. perezi, as well as its

mating system, need to be further explored empirically to unravel the demographic

strategy of this successful species. Following this line of research, the estimation of the

Nb/Na ratio in a P. perezi population is addressed for the first time in Chapter V,

Figure I.3. Distribution, habitat and illustrations of some life stages in Pelophylax perezi. a) An adult female hidden under dense aquatic vegetation; b) the distribution range shaded in grey (including areas of introduction in the Balearic Archipelago, Bosch et al. 2009), c) an egg mass with developing embryos, still attached to the aquatic vegetation, and d) a breeding pond for P. perezi in a water-filled

quarry, one in the wide variety of natural and anthropogenic aquatic habitats that this species uses for reproduction.


57

including an assessment of the reliability of results. The application of this monitoring

framework in a network of populations will offer invaluable information about possible

metapopulation processes driving the regional persistence of P. perezi. Additionally,

genetic structure estimates in Chapter VI represent the first inferences of regional

patterns of gene flow reported for this species. Individual records of dispersive

movements are also provided in Chapter VI, widely expanding previous information

about the species vagility (Sánchez-Montes & Martínez-Solano 2011).

Pelobates cultripes (Cuvier, 1829)

Pelobates cultripes expands across most of the Iberian Peninsula and the southern coast

of France, and is also locally present at the French Atlantic coast, a relict of a formerly

wider distribution (Beja et al. 2009a; Gutiérrez-Rodríguez et al. 2017a, Fig. I.4). Range-

wide phylogeographic structure is shallow (Crottini et al. 2010; Fitó et al. 2011;

Gutiérrez-Rodríguez et al. 2017a). This pattern has been interpreted as the result of

successive postglacial range expansions and contractions leading to overall genetic

homogeneity, with refugia in southern Iberia harbouring most genetic diversity

(Gutiérrez-Rodríguez et al. 2017a). Past demographic inferences could be significantly

improved by calibrating historical migration models with data about contemporary

dynamics of population connectivity. Unfortunately, dispersive behaviour and gene

flow in P. cultripes remain understudied, and direct records of dispersal from marked

individuals are extremely scarce (Valdeón & Sanuy 2016; Gutiérrez-Rodríguez et al.

2017b). In Chapter VI, integrative estimation of contemporary landscape-scale

connectivity is addressed for the first time in this species.

The strong fossorial habits of P. cultripes restrict its presence to habitats with

sandy or loosely compact soils, preferentially at low or intermediate elevations (Cejudo

1990; García-París et al. 2004; Recuero 2014). Although there are records of high local

densities (Petit & Delabie 1951; Cei & Crespo 1971; Rodríguez-Jiménez & Prados

1985), the only reported estimate of effective/census size ratio ranged between 0.25-0.3

(Gutiérrez-Rodríguez et al. 2017b). Reproductive behaviour has only been monitored in

a few populations, where it showed a staggered pattern in which breeding activity was

mainly triggered by weather conditions (Álvarez & Salvador 1984; Salvador &

Carrascal 1990; Lizana et al. 1994; Gutiérrez-Rodríguez et al. 2017b). Demographic

CHAPTER I

58

studies show that P. cultripes can live up to 12 years, while it reaches sexual maturity in

the second year (Talavera 1990; Díaz-Paniagua et al. 2005; Leclair et al. 2005).

However, the recruitment rate and relevant features of its mating system such as the

strength of sexual selection and the degree of polygamy remain unexplored. This

information is essential to predict future population trends and design conservation

programs, as argued before. Pelobates cultripes is catalogued as Near Threatened by the

IUCN (Tejedo & Reques 2002; Beja et al. 2009a), and population declines have already

been reported (Martínez-Solano 2006; Galán et al. 2010). The use of pesticides and

chemical fertilizers, changes in land use and introduction of invasive species are the

main factors potentially challenging population persistence (Beja & Alcazar 2003;

Ortiz-Santaliestra et al. 2006; Galán et al. 2010). Population isolation, which implies an

increased vulnerability to disturbances, has also been noted as a threat for the regional

persistence of this species (Recuero 2014).

Figure I.4. Distribution, habitat and illustrations of some life stages in Pelobates cultripes. a) An adult female (photograph by J. Agüera), b) the distribution range shaded in grey (Beja et al. 2009a), c) an egg string in a shallow pond, and d) a typical breeding pond for P. cultripes, temporary but deep

enough to maintain water throughout the long tadpole stage characteristic of this species.


59

A multi-scalar approach

In this dissertation I developed three new sets composed of 15-18 species-specific

microsatellite markers for E. calamita, H. molleri and P. perezi and demonstrated their

utility for demographic and taxonomic research (Chapters III and IV). Additionally, the

two main demographic parameters studied in this dissertation (effective population size

and gene flow) can be quantified at different spatial and temporal scales (Waples 2005;

Anderson et al. 2010). I focused on the landscape scale to quantify patterns of regional

connectivity in a network of populations of the four species (Chapter VI), and also

integrated genetic analyses and CMR methods to explore the Nb/Na ratio at a local scale

(Chapter V).

The three sets of newly developed markers were used to genotype samples of the

three species collected in several populations in Central Spain (total n = 547, 652 and

516 individuals of H. molleri, E. calamita and P. perezi, respectively, see Table IV.1

and Fig. IV.1) and along a transect on a latitudinal gradient encompassing the hybrid

zone of the P. ridibundus x P. perezi hybridogenetic complex in the eastern Iberian

Peninsula and southern France (total n = 30, see Table III.2 and Fig. III.1). I used

different programs to design multiplex reactions, score alleles, characterize genetic

diversity in sampled populations, quantify the quality of genotyping and the informative

content of each marker, and check for departures from theoretically expected genotypic

proportions (see Table I.1). Additionally, specialized software was used for species

identification in the Pelophylax ridibundus x P. perezi complex on the basis of

genotypic and sequence data (see Table I.1 and Chapter III). Finally, I used other

programs to implement sibship and parentage reconstruction to check for the effect of

close relatives in the sample, and to calculate accumulation curves to explore the effect

of sample size on genetic diversity characterization (see Table I.1 and Chapter IV).

Regional genetic connectivity was explored based on an extensive sampling in

Sierra de Guadarrama (see Chapter VI). This mountain ridge corresponds to the

northeastern range of the Iberian Central System (Fig. I.5). The Iberian Central System

is an important biogeographic feature, which marks the distribution limit of some

amphibian species and also represents the contact zone between differentiated lineages

in other species (Martínez-Solano et al. 2006; Arntzen & Espregueira Themudo 2008;

Gonçalves et al. 2009; Díaz-Rodríguez et al. 2015; Gutiérrez-Rodríguez et al. 2017a;

Reino et al. 2017). The orientation of the Iberian Central System along a west-east axis

CHAPTER I

60

had been hypothesized to constrain latitudinal population expansion/contraction events

in response to climatic changes during the Pleistocene, although its role as a barrier to

gene flow had not been explicitly tested before (see Chapter VI). It is, therefore, an

interesting area to explore ongoing evolutionary processes and to identify major

landscape features canalizing gene flow in species with different life history traits. Also,

it is a well preserved natural area that has been recently catalogued as a National Park

(Ministerio de Agricultura y Pesca, Alimentación y Medio Ambiente, 2017). The

regional climate is Mediterranean, with cold winters and mild dry summers, although at

higher elevations the climate is Alpine (López-Sáez et al. 2014). Average annual

rainfall in Navacerrada (see Fig. VI.2) is 1223 mm, although mean values vary

substantially among different months, from 23 mm in July to 176 mm in November

(AEMET, 2017).

Table I.1. List of computer programs and R packages used in this dissertation.

Name Use in this dissertation References

adegenet

(R package) Implement discriminant analyses of principal components (DAPC) to assess genetic structure patterns of E. calamita, H. molleri, P. perezi and P. cultripes in Sierra de Guadarrama (Chapter VI).

Jombart (2008)

BayesAss Estimate migration rates per generation (Chapter VI). Wilson & Rannala (2003)

BEAST Build sequence-based gene trees in the P. ridibundus x P. perezi hybridogenetic complex (Chapter III).

Drummond et al. (2012)

CLUMPAK Summarize graphically the clustering results from program structure (Chapter VI).

Kopelman et al. (2015)

CoDiDi Calculate the correlation between gene diversity and GST (Nei 1973) across markers for each dataset (Chapter VI).

Wang (2015)

COLONY Infer sibship and parentage relationships among genotyped individuals (Chapters IV, V and VI), calculate mistyping rates due to allelic dropout and false allele scoring (Chapter IV) and estimate Nb (Chapter V).

Jones & Wang (2010)

ecodist (R package)

Implement partial Mantel tests to perform causal modelling landscape genetics analyses on sampled populations of E. calamita, H. molleri, P. perezi and P. cultripes in Sierra de Guadarrama (Chapter VI).

Goslee & Urban (2007)

EstimateS Calculate jackknifed accumulation curves for AR and HE estimation with increasing sample size (Chapter

IV).

Colwell & Elsensohn (2014)

GENALEX Calculate allelic richness (AR) and observed and expected heterozygosity (HO and HE, Chapters III and

IV), the probabilities of identity (PI and PISibs, Chapter III), FIS (Chapter IV) and pairwise genetic (FST) and geographic distances between populations and implement tests of Isolation by distance (IBD, Chapter VI).

Peakall & Smouse (2006, 2012)


61

Table I.1 (cont.)

Name Use in this dissertation References

GENELAND Implement spatially explicit clustering analyses to assess genetic structure in populations of E. calamita, H. molleri, P. perezi and P. cultripes in Sierra de Guadarrama (Chapter VI).

Guillot et al. (2005)

GeneMapper Allele scoring (Chapters III, IV, V and VI). -

GENEPOP Tests of Hardy-Weinberg equilibrium (HWE) and linkage disequilibrium (LD, Chapters III and IV).

Raymond & Rousset (1995), Rousset (2008)

KinInfor Calculate the informative content of each microsatellite marker (Chapter IV).

Wang (2006)

MARK Implement CMR ‘robust design’ analyses for Na

estimation (Chapter V). White & Burnham (1999)

MICRO-CHECKER Test for null alleles at each microsatellite marker (Chapters III and IV).

Van Oosterhout et al. (2004)

Multiplex Manager

Design multiplex combinations of microsatellite markers (Chapters III and IV).

Holleley & Geerts (2009)

NewHybrids Species assignment in the P. ridibundus x P. perezi hybridogenetic complex based on microsatellite genotypes (Chapter III).

Anderson & Thompson (2002)

PartitionFinder Select optimal partition strategies and models of nucleotide substitution (Chapter III).

Lanfear et al. (2012)

PAST Calculate bootstrapped 95% confidence intervals for HE (Chapter IV).

Hammer et al. (2001)

POPGENREPORT (R package)

Calculate the least cost paths between all pairs of sampled populations in Sierra de Guadarrama at four predefined elevation-based resistance models and construct matrices of genetic and Euclidean distances (Chapter VI).

Adamack & Gruber (2014)

QGIS Elaborate maps of sampling localities and genetic structure in Sierra de Guadarrama (Chapters IV and VI).

Quantum GIS Development Team (2009)

R Inspect graphically accumulation curves for AR and HE estimation with increasing sample size and calculate empirical and Chao & Jost (2015) profiles (Chapter IV), and implement replicated analyses for Nb estimation with different sibship size priors, numbers of markers and sample sizes (Chapter V).

R Development Core Team (2009)

Sequencher Edit genetic sequences (Chapter III). -

structure Implement unsupervised Bayesian clustering analyses to assess genetic structure patterns of E. calamita, H. molleri, P. perezi and P. cultripes in Sierra de Guadarrama (Chapter VI).

Pritchard et al. (2000), Falush et al. (2003)

STRUCTURE

HARVESTER Explore the likelihood of different numbers of clusters (K) in unsupervised Bayesian structure analyses in Sierra de Guadarrama (Chapter VI).

Earl & vonHoldt (2012)

Tracer Check for convergence of parameter estimates and adequate Effective Sample Sizes (ESSs) in BEAST analyses (Chapter III).

Rambaut et al. (2014)

U-CARE Test for ‘transience’ and ‘trap-dependence’ effects in CMR data (Chapter V).

Choquet et al. (2009)

The regional sampling design included tadpole tissue samples from 13-19

populations of E. calamita, H. molleri, P. perezi and P. cultripes located between 850

CHAPTER I

62

and 1720 metres above sea level (m.a.s.l., see Table VI.2), representing all major

landscape types in Sierra de Guadarrama: Mediterranean open forests of Quercus ilex

subsp. ballota in the lowlands, deciduous (Q. pyrenaica) and coniferous (Pinus

sylvestris, P. nigra) forests at mid-elevations, shrubs above 1600 m.a.s.l., and alpine

grasslands and meadows at the top (López-Sáez et al. 2014). Tadpoles were genotyped

at 13-18 microsatellite loci per species, and this genetic dataset was used to explore the

genetic structure of the four species in a shared heterogeneous landscape. A

combination of computer programs implementing a variety of analytical approaches

(see Table I.1) revealed different patterns of connectivity among the four species, which

showed different levels of susceptibility to the barrier effect imposed by Sierra de

Guadarrama that can be partially explained by differences in their life history traits

(Chapter VI).

The regional connectivity study was complemented with a local-scale integrative

(genetic and CMR) monitoring program developed in the locality of Valdemanco

(Sierra de Guadarrama, Madrid, Spain, see Fig. I.5), in an amphibian assemblage

including E. calamita, H. molleri and P. perezi. In this site, seasonal CMR sessions

were performed on a yearly basis from 2010 to 2016, marking adult individuals of these

three species with passive integrated transponders (PIT) tags (the total numbers of

marked individuals during the seven-year period were 1086 E. calamita, 599 H. molleri

and 662 P. perezi, see Chapter VI). CMR sessions were planned as nocturnal visual

surveys in a temporal sampling strategy designed to encompass the breeding seasons of

the targeted species and to meet the statistical requeriments for ‘robust design’ analyses

(see Box 3). The study area of this monitoring program included five amphibian

breeding sites: a natural pond (Laguna de Valdemanco), and several artificial sites,

including a pond resulting from mining activities, a water trough, an abandoned

swimming pool and an old quarry where some ephemeral ponds are formed by seasonal

rainfall (see Fig. VI.1). Laguna de Valdemanco is the main amphibian breeding site in

the area, and it is used by the three species for breeding (see Chapter VI). It is a

temporal shallow pond (maximum depth = 1m) which occupies a large surface (12,800

m2), with an average annual hydroperiod of c. six months (from January to June

approx.), and its adjacent meadows are usually flooded in the spring (Sánchez-Montes

& Martínez-Solano 2011). These flooded meadows are strongly selected by females of

E. calamita for egg deposition. Females of E. calamita lay egg strings in shallow water,


63

Figure I.5. Multi-scale map showing the location of Sierra de Guadarrama in the Iberian Central

System (a), a 3D digital elevation model (DEM) of the regional study area encompassing both the northern and southern slopes of Sierra de Guadarrama (b, see sampling locations in Chapters IV and VI), and our local study area (c) including Laguna de Valdemanco (d) and breeding sites nearby.

CHAPTER I

64

facilitating annual counts as an approximation to breeding success (see Chapter V).

Long-term CMR monitoring based on individual marking and ‘robust design’

analyses implemented in the specialized software MARK (see Table I.1) allowed

estimation of annual Na with increasing precision through the years (Chapter V), and

also yielded information of relative frequencies of dispersal among the five breeding

sites (Chapter VI, see also Gutiérrez-Rodríguez et al. 2017b). Additionally, the annual

marking effort also provided a comprehensive adult tissue sample in Valdemanco. Part

of this extensive sample was used to obtain genotypes of adult males and females of the

three species using the molecular tools presented in Chapters III and IV. This genetic

dataset was complemented with larval cohort samples of each species to estimate Nb and

to calculate the Nb/Na ratio (see Table I.1 and Chapter V).

In summary, the multi-species and multi-scale integrative demographic approach

developed in this dissertation provides novel genetic resources and relevant

demographic information for Iberian pond-breeding amphibians. Microsatellite sets

described in this dissertation are the first STR markers designed for H. molleri and P.

perezi, and complement markers previously developed for E. calamita (Rowe et al.

1997; Rogell et al. 2005; Faucher et al. 2016). Combined multilocus polymorphism in

each set of markers was sufficient to allow individual identification and therefore the

three sets proved useful for demographic research. Seven of the markers isolated in P.

perezi were successfully applied for species assignment in the P. ridibundus x P. perezi

hybridogenetic complex and, in combination with mitonuclear sequence information,

allowed us to expand current knowledge about the geographic extent of the area of

hybridization (Chapter III). Furthermore, the comprehensive genetic datasets generated

have allowed exploration of the effect of close relatives and sample size on genetic

diversity characterization, as well as the development of a new method for minimum

sample size calculation (Chapter IV). The newly developed and optimized genetic tools

have also been applied to explore effective population sizes and patterns of gene flow in

the study species (Chapters V and VI). Local-scale CMR monitoring and evidences of

breeding activity (records of mating events and counts of egg strings) combined with

genetic analyses allowed to obtain reliable estimates of the Nb/Na ratio (Chapter V).

Finally, individual-based data offered valuable information about individual movement

patterns, which was useful to help interpret indirect gene flow inferences at a regional

scale (Chapter VI). All this information, in turn, provides invaluable insights into


65

population demography in Iberian pond-breeding amphibians, which can now be

applied for the design of efficient conservation plans.

CHAPTER I

66

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CHAPTER II

GENERAL OBJECTIVES

General Objectives

91

Recent advances in molecular, analytical and theoretical frameworks provide an

unprecedented opportunity for evolutionary demographic research. Taking advantage of

this, the general aim of this dissertation is to optimize protocols for integrative research

in Iberian pond-breeding amphibians, and to apply them in a multi-scale and multi-

species approach to obtain reliable demographic inferences. The particular objectives of

this dissertation aim to contribute to evolutionary, ecological, and conservation research

in four key aspects:

1. Definition of the units of study (Chapter III). I optimized a set of molecular

tools for demographic research in P. perezi (1a), and assessed their usefulness

for species assignment in the P. ridibundus x P. perezi hybridogenetic complex

(1b).

2. Optimum sampling design (Chapter IV). I optimized two additional sets of

molecular tools for demographic research in E. calamita and H. molleri (2a);

explored the effect of unrepresentative sampling caused by excessive relatives in

the sample on genetic diversity characterization (2b), and developed a new

method for calculating the minimum sample size required for accurate

estimation of single-locus genetic diversity indices (2c).

3. Population status assessment (Chapter V). I applied the SF method to calculate

Nb in a breeding assemblage of E. calamita, H. molleri and P. perezi and

assessed the reliability of estimates (3a) by calibration with direct evidences of

breeding activity (3b) and by checking the convergence of results in replicated

subsampled analyses (3c). Additionally, I estimated annual Na for the three

species by means of CMR methods (3d). Finally, I combined both estimates to

calculate the Nb/Na ratio, which is an informative parameter about population

status (3e).

4. Regional connectivity characterization (Chapter VI). I integrated four

complementary genetic approaches to quantify regional patterns of gene flow in

E. calamita, H. molleri, P. perezi and P. cultripes in Central Spain (4a),

recorded individual displacements obtained during a 7-year local monitoring

program to fill knowledge gaps about dispersal potential in these species (4b),

and tested the effect of Sierra de Guadarrama as a current barrier to gene flow

for these four amphibian species (4c).

CHAPTER III

SPECIES ASSIGNMENT IN THE PELOPHYLAX RIDIBUNDUS X P. PEREZI HYBRIDOGENETIC COMPLEX BASED ON 16 NEWLY CHARACTERISED MICROSATELLITE MARKERS

Sánchez-Montes G, Recuero E, Gutiérrez-Rodríguez J, Gomez-Mestre I & Martínez-Solano I

Herpetological Journal (2016), 26 (2): 99-108

Pelophylax microsatellites and hybridisation

95

Abstract

Pelophylax perezi is an Iberian green waterfrog with high tolerance to habitat alteration that at

times shows local population growth and demographic expansion, even where other species

decline. However, pond destruction, invasive predators and hybridisation with other European

waterfrog species (P. ridibundus) threaten many of its populations across its range. Hybrids of

P. perezi and P. ridibundus (P. kl. grafi) can breed successfully with the former parental species

after discarding the whole P. perezi genome in the germinal line, thus representing a sexual

parasite for P. perezi. However, little is known about the extent of the contact zone of this

hybridogenetic complex. Due to the morphological similarity of the three taxa, molecular tools

are needed to delineate their respective ranges. Here we characterise a set of 16 microsatellite

markers specifically developed for P. perezi. These markers showed moderate to high

polymorphism (2–17 alleles/locus) in two populations from central Spain (n = 20 and n = 23),

allowing individual identification of frogs. Seven of these markers cross-amplified in

individuals of P. ridibundus from southern France (3–8 alleles/locus). These markers were used

to genotype samples along a transect from southern France to eastern Spain, encompassing both

pure and hybrid individuals. Sample assignment to each taxon was based on the new

microsatellite loci and compared with nuclear and mitochondrial sequence data. Our results

show that these markers are useful to distinguish P. ridibundus, P. perezi and the hybrid form P.

kl. grafi from each other, even when sample sizes are low. The newly characterised markers will

also be useful in demographic and phylogeographic studies in P. perezi and are thus a valuable

tool for evolutionary and conservation oriented research.

Key words: cross-amplification, hybridisation, microsatellites, Pelophylax kl. grafi, Pelophylax

perezi, Pelophylax ridibundus


97

Resumen

Pelophylax perezi es una especie de rana verde ibérica que muestra una gran tolerancia a la

degradación de hábitats y que, en ocasiones, muestra crecimientos poblacionales y expansión

demográfica en áreas donde otras especies están en declive. Sin embargo, algunas poblaciones a

lo largo de su área de distribución están amenazadas por la destrucción de charcas, la

introducción de especies invasoras y la hibridación con otra especie de rana verde europea (P.

ridibundus). Los híbridos de P. perezi y P. ridibundus (denominados P. kl. grafi) pueden

aparearse con éxito con individuos de P. perezi después de descartar la dotación genómica de P.

perezi en la línea germinal, y por tanto constituyen un parásito sexual para esta especie. Sin

embargo, se sabe muy poco sobre la extensión del área de contacto interespecífico en este

complejo hibridogenético. Debido a la similitud morfológica entre los tres taxones, es necesario

recurrir a herramientas moleculares para delinear sus respectivos rangos de distribución. En este

trabajo caracterizamos un juego de 16 microsatélites específicamente diseñados para P. perezi.

Los marcadores mostraron un polimorfismo medio-alto (2-17 alelos por locus) en dos

poblaciones del centro de España (n = 20 y n = 23), lo que permitió la identificación individual

de las ranas. Siete de estos marcadores amplificaron con éxito en individuos de P. ridibundus

del sur de Francia (3-8 alelos por locus). Estos siete marcadores se utilizaron para genotipar

muestras a lo largo de un transecto desde el sur de Francia al este de España, en el cual se

encontraron tanto híbridos como individuos puros de ambas especies parentales. Se utilizaron

los nuevos microsatélites para asignar cada muestra a uno de los tres taxones, y se compararon

estos resultados con datos de secuencias nucleares y mitocondriales. Los resultados sugieren

que los marcadores descritos son útiles para distinguir individuos pertenecientes a cada uno de

los tres taxones, incluso cuando los tamaños muestrales son pequeños. Estos nuevos marcadores

son útiles también para estudios demográficos y filogeográficos en P. perezi y, por tanto,

constituyen una herramienta valiosa para la investigación en los campos de la biología evolutiva

y la conservación de la biodiversidad.


99

Introduction

Perez’s Frog, Pelophylax perezi (López-Seoane 1885), is a medium sized green

waterfrog endemic to the Iberian Peninsula and southern France. It has been introduced

into the Balearic, Canary and Azores Archipelagos and in two localities in the UK

(Bosch et al. 2009). Pelophylax perezi shows great adaptability to breed in almost every

kind of water body and exhibits tolerance to a wide range of ecological and

physicochemical conditions. Moreover, its larvae respond to predators altering their

behaviour, shape and degree of pigmentation hence improving survival (Gomez-Mestre

& Díaz-Paniagua 2011). This plasticity and degree of tolerance to environmental

degradation may explain the success of this species in humanised areas. It is remarkable

that Perez’s frog persists or even thrives in the same locations where other amphibian

species show dramatic negative trends (Martínez-Solano et al. 2003a, b). However,

despite this resilience, P. perezi is locally also at risk from invasive predators due to

lack of innate recognition and habitat overlap (Cruz & Rebelo 2005; Gomez-Mestre &

Díaz-Paniagua 2011), habitat destruction and hybridisation with other species.

Demographic studies are essential to identify populations at risk of loss of genetic

diversity for conservation purposes. Relevant parameters such as breeding success,

effective population size and gene flow/admixture can be estimated with the help of

molecular markers. Among these, microsatellites are especially useful in local-scale

studies because of their high polymorphism (Hotz et al. 2001).

As in other Pelophylax species, P. perezi is susceptible to hybridisation and

introgression with other green frog species (Graf et al. 1977; Uzzell & Tunner 1983). In

fact, some areas in southern France and north-eastern Spain harbour viable P.

ridibundus x P. perezi hybrids (F1 hemiclones formed through hybridogenesis and

named as Pelophylax klepton grafi) that are found along with one or both parental

species in those locations, thus forming an hybridogenetic complex (Dubois & Ohler

1994; Arano et al. 1995; Crochet et al. 1995). Pelophylax kl. grafi is considered a

synonym of P. perezi by some authors (Frost 2014), but it discards the whole P. perezi

genome in its germinal line and is able to maintain a hybrid lineage by backcrossing

with P. perezi individuals. It thus represents a sexual parasite, capable of reducing

genetic diversity in populations of P. perezi. It originated either from hybridisation of P.

perezi with ancestral, isolated P. ridibundus populations, or from hybridisation with P.

kl. esculentus, another klepton involving P. ridibundus and P. lessonae (Crochet et al.

CHAPTER III

100

1995). Pelophylax kl. grafi is currently listed as ‘Near Threatened’ in IUCN’s Red List

of Threatened Species due to ‘an observed decline as a result of competition from the

introduced species P. ridibundus’ (Tejedo et al. 2009), i.e. by the introduction of P.

ridibundus from central and eastern Europe and Asia. However, the precise distribution

limits of the hybrids and their parental species in their contact zone are mostly unknown

because species identification in the field, based on morphological characters, is

problematic (see for instance, Petitot et al. 2014). Hybridogenesis typically implies

asymmetrical participation of the sexes from the different species of the complex, but its

biological mechanisms, the relative performance of individuals of the three taxa in

different conditions, and the ecological and evolutionary consequences remain largely

unexplored (Berger et al. 1988; Hotz et al. 1992). Molecular tools may thus be a

valuable conservation tool for delineating parental populations and hybrid zones and for

tracing the history of pure and hybrid lineages.

In this paper, we describe a set of 16 microsatellite loci specifically developed

and optimised for P. perezi and evaluate their utility for population studies as general

indicators of genetic diversity. We then assess cross-species amplification success in a

population of P. ridibundus from southern France as well as in additional putative

samples of P. perezi from the eastern part of the Iberian range, including the area north

of the Ebro River, where hybridisation events between the two species have been

previously detected (Arano et al. 1995). With help from additional molecular markers

(sequences of the mitochondrial gene cytochrome oxidase I and the nuclear gene

tyrosinase), we discuss the utility of the newly developed microsatellites in identifying

hybrids (P. kl. grafi) and both parental species of the complex (P. perezi and P.

ridibundus).

Materials and methods

An enriched partial genomic library was generated from DNA of a single tadpole of P.

perezi collected in Valdemanco, central Spain (40°51’ N, 3°38’ W). The library was

constructed at the Sequencing Genotyping Facility, Cornell Life Sciences Core

Laboratory Center (CLC) following the method described in Gutiérrez-Rodríguez et al.

(2014). A total of 60 loci containing microsatellite motifs (30 trimers and 30 tetramers)

between 4 and 10 repetitions long were selected for further screening.

Table III.1. Characterisation of 16 microsatellite loci in Pelophylax perezi, including primer sequences, labelling dye, repeated motif, multiplex reaction and size range.

Annealing temperature was 60°C in all cases. n = sample size, Na = allelic richness, HO and HE = observed and expected heterozygosities in the populations of Santo

Tomé/Cerceda, respectively. Cross-amplification in P. ridibundus (Pr) is indicated with the sign ‘+’. GB: GenBank accession numbers.

Locus Primer sequence Labelling

dye Repeated

motif Multiplex reaction

Size range (bp)

n Na HO HE Pr GB

Pper4.25 5' TCCCTTCTAGTGCTGTAACTTCG 3' 6-FAM (AGAT)8 1 199-385 20/23 11/17 0.8/0.87 0.84/0.92 - KT166015

5' AGTTCATCTGCAGTTCCTACATG 3' Pper4.15 5' ACATATTGTGCTGCTCCATCAAG 3' VIC (AGAT)8 1 177-236 20/23 8/11 0.8/0.96 0.84/0.86 - KT166016

5' AATTTCTTCAGTGCTGTCATGTC 3' Pper4.28 5' CATGTACAGCTGACTTTAGAGCC 3' NED (AAGG)5 1 201-251 20/23 2/5 0.3/0.61 0.48/0.62 - KT166017

5' TTCTTTCCAATTTGAGACTCGGG 3' Pper3.9 5' CAACATATCTTCCCGAATGAGGC 3' PET (AAG)6 1 201-256 20/23 6/7 0.6/0.87 0.56/0.76 - KT166018

5' GTTTCTCTCAGTCTAGTTGGTGC 3' Pper4.5 5' TGTGCGCTATCCTCTGTAGTTAG 3' VIC (AAAC)6 2 147-160 20/23 4/3 0.55/0.7 0.66/0.62 + KT166019

5' TGAATCCTGGCATTGTCATCTTG 3' Pper4.16 5' AGAGCAGATATACCACACTCCAG 3' NED (AGAT)9 2 139-184 20/23 5/10 0.7/0.83 0.74/0.85 - KT166020

5' ACCTCAAGCATTTATAGACCAGC 3' Pper3.24 5' ATGTGGAGACTATCAGCAGACAG 3' PET (AAC)7 2 251-274 20/23 6/7 0.5/0.83 0.61/0.78 + KT166021

5' CAAGTCTTGACTGTTCATACCGG 3' Pper4.20 5' TCTTAGCAGTGACAGATGTGAAC 3' VIC (AAGT)6 3 220-224 19/23 1/2 0/0.43 0/0.49 - KT166022

5' TCTTAGTGCAGATTAGGGACCTG 3' Pper3.22 5' ACTGTCATCTGGTCTGGTATCAC 3' NED (ACT)9 3 359-379 19/23 5/5 0.53/0.61 0.54/0.49 + KT166023

5' ACACTAATTGTCCTCCTGTAGAAC 3' Pper4.13 5' AGAGACCATATATCGGAGCCATC 3' PET (AGAT)10 3 442-494 19/23 5/11 0.79/0.78 0.74/0.87 - KT166024

5' TGGCAAATCACTCCACTTAACAG 3' Pper4.7 5' TACCTCTTCTGCTGATCTCTTGG 3' NED (AGAT)9 4 292-346 20/22 6/15 0.8/1 0.79/0.89 + KT166025

5' AAGCAATTTATCAAGCAGGAGGG 3' Pper3.1 5' TTGCCAGCAGAAGAGAACATTAC 3' PET (AGG)9 4 340-364 20/23 5/6 0.95/0.61 0.69/0.67 - KT166026

5' TCTCACAGACATCGCATTTGATC 3' Pper4.23 5' AGCTGTCAAAGGATGTCATGTTC 3' 6-FAM (AGAT)9 5 440-492 20/23 7/12 0.65/0.7 0.73/0.88 + KT166027

5' TCAGGTGAGAGATCGAAATACCC 3' Pper4.29 5' CTGTGCTACGAGGATTGTAATGG 3' VIC (AAAG)7 5 321-349 20/23 5/8 0.55/0.91 0.51/0.80 + KT166028

5' TTCATTCTCTGTGTCGTGAATGC 3' Pper3.23 5' ACTTGTATCATCTTTCTCTGCGC 3' NED (ACT)6 5 154-181 20/23 3/4 0.45/0.78 0.60/0.70 - KT166029

5' TTTCTGCCCAATTCTACTACTGC 3' Pper4.24 5' TTTCCCTATTGCCTATGAACTGC 3' PET (AGAT)10 5 203-262 20/23 7/9 0.85/0.91 0.80/0.84 + KT166030

5' AGTGCTATGGTTGGGATTTGAAC 3'

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Genomic DNA was extracted from tail tips of larvae and toe tips of newly

metamorphosed froglets and adult frogs with NucleoSpin Tissue-Kits (Macherey-

Nagel). PCR reactions were performed in a total volume of 15 μl, including

approximately 25 ng of template DNA, 5x GoTaq Flexi buffer (Promega), 3.33 mM

MgCl2, 0.33 mM dNTP, 0.33 μM of each primer and 0.5U GoTaq Flexi DNA

polymerase (Promega). PCR cycling consisted of initial denaturation (95°C, 5 min), 40

cycles of denaturation (95°C, 45 s), annealing (60°C, 45 s), and extension (72°C, 45 s),

with a final extension step (72°C, 10 min). PCR products were visualised on 2%

agarose gels.

Figure III.1. Map showing the approximate ranges of P. ridibundus (dotted area), P. perezi (grey),

and P. kl. grafi (mesh), indicating the location of sampled populations. Ranges of the two parental

species are based on IUCN assessments (Bosch et al. 2009; Kuzmin et al. 2009), whereas for P.

kl. grafi we incorporated information from Rivera et al. (2011). The course of the Ebro River, the

major corridor for dispersal of P. kl. grafi in Iberia, is also indicated. The contact between eastern

and western Iberian grafi nuclei through the Ebro River is assumed, but it is not fully documented.

Sampling localities are represented with different symbols based on taxonomic assignment of

individuals analysed: P. perezi (dark circles), P. ridibundus (white circle), and the hybridogenetic

complex (dark asterisks). STO = Santo Tomé, CER = Cerceda, BEA = Beauzelle, DAR = Darnius,

OIX = Oix, PRA = Prades, ULL = Ulldemolins, ARE = Ares del Maestre, NOG = Las Nogueras, PIN

= Pinoso.


103

Of the 60 pairs of primers tested, 20 amplified consistently, showing

unambiguous bands and were chosen for subsequent multiplex reactions. Forward

primers were labelled with fluorescent dyes (6-FAM, VIC, NED and PET) for use in

five multiplex reactions designed with Multiplex Manager v.1.2 (Holleley & Geerts

2009) (see Table III.1). PCR reactions were performed using Type-it Microsatellite

PCR kits (Qiagen). All reactions were run in a total volume of 15 μl, containing 7.5 μl

of Master Mix, 1.2 μl of each primer mix (0.16 μM of each primer, except primers for

Pper4.7, Pper3.1 and Pper4.23, which were added in double concentration, 0.32 μM),

and 5.3 μl of RNase-free H2O. The PCR cycling conditions were: 95°C for 5 min, 30

cycles at 95°C for 30 s, 60°C for 90 s, and 72°C for 30 s, with a final extension at 60°C

for 30 min. Genotyping was performed on an ABI PRISM 3730 sequencer with the

GeneScan 500 LIZ size standard (Applied Biosystems). Allele peaks were assigned

manually in GeneMapper v.4.0 (Applied Biosystems). Four of the 20 loci did not show

assignable peaks and were thus discarded.

These new 16 molecular markers (GenBank accession numbers in Table III.1)

were tested in 43 individuals from two Iberian populations of P. perezi in Central Spain

(Cerceda, Madrid, 40° 43’ N, 3° 57’ W, n = 23, and Santo Tomé del Puerto, Segovia,

41° 12’ N, 3° 35’ W, n = 20) (Fig. III.1). Additionally, samples were collected along a

northsouth transect from southern France, through Catalonia and Comunidad

Valenciana in eastern Spain, in order to capture pure parentals of P. ridibundus and P.

perezi and their hybrids, P. kl. grafi (see Fig. III.1 and Table III.2). Since most samples

were collected from metamorphs or larvae and thus morphological characters could not

be used to unambiguously diagnose species, species assignment was aided by

genotyping with mtDNA (cox1) and one nuclear marker, tyrosinase (tyr). These markers

were amplified using primers and protocols described in Recuero et al. (2007) and

Bossuyt & Milinkovitch (2000). For reference, we used samples of P. ridibundus from

Bosnia and Turkey, one sample of P. saharicus (a close relative of P. perezi, see for

instance Uzzell & Tunner 1983; Akın et al. 2010) from Morocco, and two additional

samples of P. perezi from Galicia (near the type locality of the species) and Madrid, in

central Spain (in this case, samples from two different localities in Madrid were

sequenced, one for each marker, see sample codes in Table III.2). Sequences were

edited with Sequencher v.5.0 (Gene Codes Corp., USA) and aligned by hand. Gene

trees for each marker were inferred with the software BEAST v.1.8.1 (Drummond et al.

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2012). Optimal partitioning strategies for each marker and associated models of

nucleotide substitution were simultaneously selected with the software PartitionFinder

v.1.1.1 (Lanfear et al. 2012). Three partitions were specified for cox1, corresponding to

first (HKY+G), second (TrNef) and third (HKY) codon positions; and two partitions

were defined in tyr sequences, corresponding to first plus third positions (K80+I), and

second positions (HKY+G). Analyses in BEAST were run specifying a Yule coalescent

prior and assuming a strict molecular clock. Parameter estimates were inspected to

check for convergence and adequate Effective Sample Sizes (ESSs) in Tracer v.1.6

(Rambaut et al. 2014); subsequently, after removing 10% of the resulting trees as burn-

in, the remaining trees were summarised with TreeAnnotator v.1.8.1 (distributed as part

of the BEAST package). All new sequences were deposited in GenBank under

accession numbers KT879303-KT879366.

MICRO-CHECKER v.2.2.3 (Van Oosterhout et al. 2004) was used to test for

evidence of stuttering, large allele dropout and presence of null alleles in each

population with sample size > 5. Number of alleles (Na), observed (HO) and expected

(HE) heterozygosity were calculated for each locus and population using GENALEX 6.5

(Peakall & Smouse 2012). We also used GENALEX to estimate the power of resolution

for individual identification of this set of microsatellite loci in the populations of Santo

Tomé and Cerceda by calculating the Probability of Identity (PI) and another, more

conservative estimate that accounts for possible relatives included in the sample

(PISibs) (Waits et al. 2001). Genepop v.4.3 (Raymond & Rousset 1995; Rousset 2008)

was used to test for deviations from Hardy-Weinberg equilibrium (HWE) and for

evidence of linkage disequilibrium (LD). The Markov chain was run with 10,000

dememorisation steps, 1,000 batches and 10,000 iterations per batch. The Bonferroni

sequential correction was applied to account for multiple tests (Rice 1989).

We used the software program NewHybrids (Anderson & Thompson 2002) to

test the utility of the newly developed markers to distinguish P. ridibundus, P. perezi,

and their hybrids. The analyses were performed using all available populations to

estimate the probability of assignment of each individual to three predefined genotypic

category classes: pure species 1, pure species 2, and F1 hybrids. Since P. kl. grafi

discards the whole P. perezi genome in its germ line before meiosis and thus only

includes the unrecombined P. ridibundus clonal genome in the gametes, backcrosses

with both parental species (and eventual F2 hybrids, which have not been reported yet)


105

are indistinguishable from previously defined categories (Graf et al. 1977; Graf & Polls-

Pelaz 1989; Lodé & Pagano 2000). Several short runs were first performed in order to

detect and avoid suboptimal local maximum likelihood regions (following the authors’

indications). Then a longer analysis (> 2.5 million sweeps) was run. Mean assignment

probability values for each individual were computed after a burn-in period of 240,000

sweeps, during which the maximum likelihood value scored in the short runs was

reached. Finally, we used GENALEX to identify private alleles diagnostic for each species

by calculating allele frequencies only in individuals with concordant information at

mitochondrial and nuclear sequences and microsatellites (i.e. excluding samples Rz181,

Rz184, Rz143, Rz144, Rz145, Rz161, Rz162, Rz304, Rz305, Rz308, see Table III.2).

Results

Locus Pper4.20 showed few alleles and was monomorphic in the population of Santo

Tomé. Only one locus showed homozygote excess in one of the central Spanish

populations (locus Pper4.23 in Cerceda). According to MICRO-CHECKER, this excess of

homozygotes was generalised in many allele size classes in this population, possibly

indicating the presence of null alleles rather than large allele dropout. The number of

alleles ranged from 1 to 11 in Santo Tomé and from 2 to 17 in Cerceda (Table III.1).

Mean allelic richness was 5.38 (SE = 0.59) in Santo Tomé and 8.25 (SE = 1.06) in

Cerceda. Observed and expected heterozygosities were generally higher in Cerceda than

in Santo Tomé (see Table III.1). Locus Pper4.23 in Cerceda was the only one to show

significant departure from HWE after applying the sequential Bonferroni correction.

Loci Pper4.13 and Pper4.23 were found to be consistently in linkage disequilibrium in

both populations, whereas locus Pper3.22 was in linkage disequilibrium with loci

Pper4.15 and Pper4.7, but only in Santo Tomé. The set of 16 loci allowed individual

identification, even when accounting for possible relatives included in the sample.

Moreover, just the combination of the five least informative loci was sufficient for

individual recognition with 95% confidence, and seven loci were enough when

accounting for relatives in the sample.

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106

Table III.2. Results of individual assignment analyses by means of mitochondrial (cox1), nuclear (tyr) and

seven microsatellite loci (prob.: assignment probabilities in NewHybrids analyses). The 14 alleles of each

microsatellite genotype are coded as private P. perezi allele (black), private P. ridibundus allele (white),

shared by P. perezi and P. ridibundus (grey), exclusive of mixed individuals (diagonal) or missing data

(horizontal). (*): for these two samples, mtDNA-based assignment is based on sequences from a different

marker (ND2, unpublished data).

We obtained mtDNA sequences from a total of 33 individuals and nuclear (tyr)

sequences of 31 individuals (Table III.2). Gene trees were well resolved. In the mtDNA

tree, fully supported clades (Bayesian Posterior Probabilities, BPPs: 1.0) included a

sister-group relationship between P. saharicus and a monophyletic group including

reference samples of P. perezi as well as all Iberian samples and two individuals from

Beauzelle (Table III.2, Fig. III.2); and a clade including all the remaining samples from

Sample Population mtDNA tyr Microsatellite (prob.)

Rz179 Beauzelle P. ridibundus P. ridibundus P. ridibundus (>0.99)

Rz181 Beauzelle P. perezi P. ridibundus P. ridibundus (>0.99)



Rz184 Beauzelle P. perezi P. ridibundus P. ridibundus (>0.99)




Rz188 Beauzelle P. ridibundus --- P. ridibundus (>0.99)

Rz143 Oix P. perezi --- P. ridibundus (0.97)

Rz144 Oix P. perezi P. ridibundus P. ridibundus (0.83)

Rz145 Oix P. perezi P. ridibundus P. ridibundus (0.98)

Rz161 Darnius P. perezi --- P. kl. grafi (0.86)

Rz162 Darnius P. perezi P. ridibundus P. kl. grafi (0.93)

Rz304 Prades P. perezi P. ridibundus P. ridibundus (0.64)

Rz305 Prades P. perezi P. ridibundus P. kl. grafi (0.99)

Rz306 Prades P. perezi P. perezi P. perezi (>0.99)

Rz307 Prades P. perezi * P. perezi P. perezi (>0.99)

Rz308 Prades P. perezi * P. ridibundus P. kl. grafi (0.98)

Rz193 Ulldemolins P. perezi P. perezi P. perezi (>0.99)

Rz194 Ulldemolins P. perezi --- P. perezi (>0.99)

Rz295 Ares del Maestre P. perezi P. perezi P. perezi (>0.99)



Rz279 Las Nogueras P. perezi P. perezi P. perezi (>0.99)



Rz273 Pinoso P. perezi P. perezi P. perezi (>0.99)



Rz18 Madrid --- P. perezi ---

Rz163 Madrid P. perezi --- ---

Rz166 A Coruña P. perezi P. perezi ---

LAR8 Morocco P. saharicus P. saharicus ---

24TU Turkey P. ridibundus P. ridibundus ---

BOS19.1 Bosnia P. ridibundus P. ridibundus ---

Diagnostic alleles


107

Beauzelle plus reference samples from Bosnia and Turkey. This ‘ridibundus’ clade was

further subdivided in two well-supported clades, one including one sample from

Beauzelle and the reference sample from Turkey, and a second clade including the

reference sample from Bosnia and the rest of the samples from Beauzelle. The tyr tree

also recovered a sister group relationship between P. saharicus and P. perezi, although

with low support (BPP: 0.78). The ‘perezi’ clade included reference samples from the

type locality and central Spain and all Iberian samples south of the Ebro River, as well

as the individual from Ulldemolins and two individuals from Prades. The rest of the

Iberian samples north of the Ebro River clustered with reference P. ridibundus samples

and those from Beauzelle (Table III.2, Fig. III.2). Some samples had discordant

mitochondrial and nuclear haplotypes, including two samples from Beauzelle, two

samples from Oix, one from Darnius and three from Prades (Table III.2). In all cases the

discordance involved the presence of ‘perezi’ mtDNA with ‘ridibundus’ nDNA. Three

of these individuals were identified as P. kl. grafi by NewHybrids, another three were

classified as P. ridibundus with high probability (> 0.9) and two additional individuals

had lower assignment probabilities to P. ridibundus (0.83 and 0.64, Table III.2). An

additional individual was identified as P. kl. grafi (Rz161) based on microsatellite data.

This individual had mtDNA of P. perezi, but unfortunately we could not amplify tyr. No

instances of cyto-nuclear discordance were identified south of the Ebro River, where all

individuals were assigned to P. perezi based on both mtDNA and nDNA.

Seven out of the 16 microsatellite loci cross-amplified in the samples from

Beauzelle (P. ridibundus). The number of alleles ranged from 3 to 8. Mean allelic

richness was 2.06 (SE = 0.67, n = 9). Potential null alleles were detected in loci

Pper3.22 and Pper4.24. Most of the 16 loci amplified in the Catalonian (Ulldemolins,

Prades, Oix, Darnius) and Valencian (Pinoso, Las Nogueras, Ares del Maestre) samples

(Fig. III.1). Mean allelic richness estimates were 2.75 in Girona (SE = 0.31, n = 5), 3.75

in Prades (SE = 0.39, n = 5), 2.81 in Ulldemolins (SE = 0.26, n = 2), 2.81 in Ares del

Maestre (SE = 0.36, n = 3), 2.31 in Las Nogueras (SE = 0.27, n = 3) and 2.63 in Pinoso

(SE = 0.32, n = 3).

Only the seven markers that successfully amplified in all populations were used

in the assignment analyses with NewHybrids. All samples from central Spain,

Ulldemolins and south of the Ebro River were consistently assigned to one of the

parental species (P. perezi), with probability > 0.99 in all cases. All samples from

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108

France were unequivocally identified as the other parental species (P. ridibundus),

including the two individuals with ‘perezi’ mtDNA. In the populations from Oix,

Darnius, and Prades, both parental species as well as some hybrids were detected. These

results were mostly in agreement with data from mitochondrial and nuclear markers,

although based on the distribution of diagnostic alleles (see Table III.2), haplotype

discordance in two individuals from Beauzelle, and in the three samples from Oix and

sample Rz304 from Prades, might indicate the presence of backcrosses of P. kl. grafi

Figure III.2. Gene trees for mitochondrial (cox1, top) and nuclear (tyr, bottom) markers in Pelophylax

samples analysed. Values on relevant nodes are Bayesian Posterior Probabilities. Sample codes as

in Table III.2. Scale in substitutions per site.


109

with P. ridibundus, and of individuals of P. kl. grafi that were misclassified as pure P.

ridibundus by NewHybrids, respectively. There was little overlap in allele frequencies

across species; we identified 78 private alleles (3 to 11 per locus in P. perezi and 2 to 8

in P. ridibundus) and only two alleles shared by both species (Table III.2 and Fig. III.3).

Discussion

The 16 newly characterised microsatellites showed moderate to high levels of

polymorphism in the samples of P. perezi from Cerceda and Santo Tomé, and

evidenced high resolution power as molecular tools in population genetic studies, even

allowing individual recognition. This is essential to provide accurate estimates of

genetic diversity, population structure, and gene flow in fine scale studies and to

calculate effective population sizes and perform parentage analyses. Two of the markers

(Pper4.13 and Pper4.23) were consistently in linkage disequilibrium. Comparison of

the original contigs (see GenBank Accessions) reveals very high similarity, suggesting

they in fact correspond to a single locus. However, Pper4.23 cross-amplified in P.

0

2

4

6

8

10

12

14

16

18

20

Pper4.5 Pper3.24 Pper3.22 Pper4.7 Pper4.23 Pper4.29 Pper4.24

Nu

mb

er

of

all

ele

s

Locus

Figure III.3. Private and shared alleles found in each locus in the subset of individuals consistently assigned to P. perezi (n = 13) or P. ridibundus (n = 7) based on concordance between mitochondrial, nuclear and microsatellite data (see Table III.2). Black bars: private P. perezi alleles, white bars: private P. ridibundus alleles, grey bars: alleles shared by both species.

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110

ridibundus samples, whereas Pper4.13 did not. Therefore, we decided to report results

from both markers, although it is advisable to exclude Pper4.23, which showed null

alleles in some samples, when studying P. perezi or klepton grafi.

Additionally, these new microsatellites can also help clarify unresolved issues in

the P. perezi x P. ridibundus hybridogenetic complex. Most existing genetic studies on

the P. kl. grafi system are based on allozyme data (Crochet et al. 1995; Lodé & Pagano

2000; Pagano et al. 2001a, b; Schmeller et al. 2007). Microsatellite-based studies can

reveal more genetic diversity than allozymes (Hotz et al. 2001) and do not require

euthanising animals. Monomorphic discriminative markers may be employed to

differentiate between the three taxa, but polymorphic microsatellites can reveal fine

scale reproductive interactions. They thus provide better tools to trace the origin and

frequency of hybridisation and introgression events and to identify taxa in the complex

and delineate their respective ranges. Field discrimination between P. perezi, P.

ridibundus and P. kl. grafi is challenging. Morphological characters based on the shape

of vomerine teeth, the extent of interdigital membranes and some morphometric

characters distinguishing each taxon (Crochet et al. 1995) are used in some field guides

(Rivera et al. 2011; Ferrer & Filella 2012). However, some of these meristic characters

have overlapping ranges across species and so they are not fully discriminant. In

addition, no diagnostic characters consistently differentiating species have been

identified in tadpoles. Dubious reports of P. ridibundus in Catalonia are probably

related to identification problems (Rivera et al. 2011). The use of molecular tools is thus

essential for species assignment and subsequent range delimitation within this complex.

The subset of seven microsatellite loci that crossamplified in all samples was

useful for the assignment of individuals to the three taxa in the complex. In general, we

obtained high assignment probabilities and those assignments were, in most cases, in

concordance with independent mitochondrial and nuclear data (see Table III.2). All the

Iberian samples south of the Ebro River were identified as pure P. perezi by the three

independent molecular marker sets. Results of NewHybrids were consistent with

species identification based on sequences of the nuclear marker tyr, including three

samples identified as P. kl. grafi by NewHybrids that had tyr haplotypes characteristic

of P. ridibundus but mtDNA of P. perezi and are thus either hybrids or backcrosses

(Rz162 from Darnius, and Rz305 and Rz308 from Prades, Table III.2). Other instances

of cyto-nuclear discordance identifying individuals as hybrids or backcrosses include


111

two individuals from Beauzelle (Rz181 and Rz184) that were consistently assigned to

P. ridibundus but had mtDNA characteristic of P. perezi, and three Iberian individuals

assigned with uncertainty to P. ridibundus (samples Rz144-145 from Oix and Rz304

from Prades, see Table III.2). While based on our limited dataset it would be

preliminary to identify taxon-diagnostic alleles, it is worth noting that only in the

inferred area of hybridisation between the Ebro Delta and the southern slopes of the

Pyrenees, private alleles of both parental species appear simultaneously in four putative

P. kl. grafi individuals and in four additional specimens assigned with low probability

to P. ridibundus (in Oix and Prades, see Table III.2). All these individuals have P.

perezi mitochondrial DNA, suggesting they are either hybrids or backcrosses and

indicating that the hybrids might originate preferentially from matings between P.

ridibundus or P. kl. esculentus males and P. perezi females, perhaps for behavioural

reasons. All these eight samples show some alleles that are not found in any of the

individuals that are consistently assigned to either of the parental species (see Table

III.2). These alleles are found mostly in locus Pper3.22, but also in Pper4.23 and

Pper4.29.

It should be noted that our reference sample for P. ridibundus (Beauzelle, near

Toulouse) is geographically close to population 45 in Pagano et al. (2001a, b). These

authors found both P. ridibundus (considered allochthonous to this region) and P. kl.

grafi in this location. Our sample included two individuals with nuclear (tyr and

microsatellites) P. ridibundus genotypes but P. perezi mtDNA (Rz181 and Rz184,

Table III.2). These individuals are either P. kl. grafi that NewHybrids failed to identify

as such, or grafi-ridibundus backcrosses (or perhaps F2 hybrids, which have not been

reported yet in P. kl. grafi although there are some records in P. kl. esculentus, see Hotz

et al. 1992). Lack of P. perezi individuals in this location both in Pagano et al. (2001a,

b) and in our current work could reflect sampling biases but also its displacement by

invasive P. ridibundus. Furthermore, Pagano et al. (2001a, b) reported that some of the

P. ridibundus individuals analysed at this location carried a rare allele (MPI-j), which

occurs in minor to moderate frequency in Pelophylax populations from Anatolia (see

Pagano et al. 1997). This is consistent with our finding of very similar mtDNA

haplotypes in one of the samples from Beauzelle (Rz186) and the reference sample from

Turkey (24TU) (see Fig. III.2). Further testing of these new markers with additional

samples of P. ridibundus across its extensive range, as well as in related taxa, will help

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112

refine our preliminary assessment of potential species diagnostic alleles and aid in the

tracking of sources of introductions as well as in studies on the outcome of processes of

interspecific hybridisation (Holsbeek & Jooris 2010; Luquet et al. 2011).

In Catalonia, previous studies have reported the presence of P. kl. grafi along

two major river basins, Ebro and Segre, as well as in the Llobregat Delta and other

coastal areas (Alt and Baix Empordà, Arano & Llorente 1995; Rivera et al. 2011; Ferrer

& Filella 2012). Our data confirm the Alt Empordà records (Darnius), and extend the

presence of P. kl. grafi to the neighbouring region of La Garrotxa (Oix, in the Llierca

basin, although microsatellite data were not conclusive in this case) and in upper

reaches of the Llobregat River (Prades), suggesting a more widespread presence of the

klepton along this river. Lack of evidence for P. kl. grafi individuals in the population

of Ulldemolins, north of the Ebro River, may represent a sampling artefact (n = 2), since

it is located within the known range of P. kl. grafi (Fig. III.1). On the other hand, their

absence in this population could also be the consequence of a fragmented distribution,

as a result of ecological and/or anthropic factors.

Previous studies have reported P. kl. grafi hybrids in southern France and in

north-eastern Spain, north of the Ebro River and along its course. There are records in

Catalonia, Basque Country, Navarre and Zaragoza, suggesting that P. kl grafi hybrids

and/or their parental species may have crossed the Pyrenees through two routes, at the

eastern and western ends of these mountains. However, it is unclear whether the hybrid

complex originated in France or in Iberia, with subsequent dispersal across the

Pyrenees, or independently in both regions. In addition, it is still unknown whether the

P. ridibundus genome entered the complex from native or introduced P. ridibundus

populations or even from P. kl. esculentus hybrids. The newly characterised

microsatellites, along with other markers, will help address these questions.

The combination of mitochondrial and the newly developed nuclear markers has

proven useful for species assignment and will help test hypotheses about the origin and

evolutionary history of hybrid lineages. Our preliminary results suggest that the new

microsatellites are useful to distinguish between the two pure lineages of coexisting

waterfrogs, P. ridibundus and P. perezi, as well as the hybrid form P. kl. grafi, even

when sample sizes are low. These markers can be also used to perform demographic

and phylogeographic studies in P. perezi and are thus a valuable tool for evolutionary

and conservation studies.


113

Acknowledgements

B. Álvarez and I. Rey (Tissue and DNA collection, MNCNCSIC), D. Buckley, A. Perdices and

S. Perea provided some reference samples. We thank M. García París, the Grande- Revuelta and

Mayor-Sánchez families, J.C. Monzó, and V. Sancho for help during fieldwork and S.

Bogdanowicz at Cornell University for help with the microsatellite library. The editor and one

anonymous reviewer provided valuable comments on a previous draft. This research was funded

by grants CGL2008-04271-C02-01/BOS, and CGL2011-28300 (Ministerio de Ciencia e

Innovación -MICINN-), Ministerio de Economía y Competitividad -MEC-, Spain, and FEDER)

to IMS. G. Sánchez-Montes is funded by a predoctoral grant provided by the Asociación de

Amigos de la Universidad de Navarra. E. Recuero was supported by a DGAPA-UNAM

postdoctoral fellowship. J. Gutiérrez-Rodríguez was supported by the Consejo Superior de

Investigaciones Científicas of Spain (CSIC) and the European Social Fund (ESF) (JAE-pre PhD

fellowship). IMS was funded by the project ‘Biodiversity, Ecology and Global Change’, co-

financed by North Portugal Regional Operational Programme 2007/2013 (ON.2-O Novo Norte),

under the National Strategic Reference Framework (NSRF), through the European Regional

Development Fund (ERDF) and is currently supported by funding from the Spanish Severo

Ochoa Program (SEV- 2012-0262).

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CHAPTER IV

EFFECTS OF SAMPLE SIZE AND FULL SIBS ON GENETIC DIVERSITY CHARACTERIZATION: A CASE STUDY OF THREE SYNTOPIC IBERIAN POND-BREEDING AMPHIBIANS

Sánchez-Montes G, Ariño AH, Vizmanos JL, Wang J & Martínez-Solano I

Journal of Heredity (2017), esx038. doi: 10.1093/jhered/esx038

Sample size and full sibs in genetic diversity

119

Abstract

Accurate characterization of genetic diversity is essential for understanding population

demography, predicting future trends and implementing efficient conservation policies. For that

purpose, molecular markers are routinely developed for non-model species, but key questions

regarding sampling design, like calculation of minimum sample sizes or the effect of relatives in

the sample, are often neglected. We used accumulation curves and sibship analyses to explore

how these two factors affect marker performance in the characterization of genetic diversity. We

illustrate this approach with the analysis of an empirical dataset including newly optimized

microsatellite sets for three Iberian amphibian species: Hyla molleri, Epidalea calamita and

Pelophylax perezi. We studied 17-21 populations per species (total n = 547, 652 and 516

individuals, respectively), including a reference locality in which the effect of sample size was

explored using larger samples (77-96 individuals). As expected, FIS and tests for Hardy-

Weinberg equilibrium and linkage disequilibrium were affected by the presence of full sibs, and

most initially inferred disequilibria were no longer statistically significant when full siblings

were removed from the sample. We estimated that to obtain reliable estimates, the minimum

sample size (potentially including full sibs) was close to 20 for expected heterozygosity (HE),

and between 50 and 80 for allelic richness (AR). Our pilot study based on a reference

population provided a rigorous assessment of marker properties and the effects of sample size

and presence of full sibs in the sample. These examples illustrate the advantages of this

approach to produce robust and reliable results for downstream analyses.

Keywords: Accumulation curves, Allelic richness, Diversity profile, Expected heterozygosity,

Minimum sample size, Sibship analysis.


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Resumen

Caracterizar con precisión la diversidad genética de una población es esencial para comprender

su demografía, predecir futuras tendencias poblacionales y planificar medidas de conservación

eficaces. Con el objetivo de caracterizar la diversidad genética de poblaciones naturales, los

investigadores desarrollan marcadores moleculares para especies no modelo, pero en ocasiones

no se tienen en cuenta cuestiones clave relacionadas con el diseño muestral, como el cálculo del

tamaño mínimo de muestra o el efecto que causan sobre las estimas poblacionales los

individuos emparentados presentes en la muestra. En este capítulo utilizamos curvas de

acumulación y análisis de pedigrís para explorar cómo afectan estos dos factores a la utilidad de

los marcadores genéticos para la caracterización de la diversidad genética. Como ejemplo,

analizamos una base de datos empíricos obtenida a partir de microsatélites optimizados para tres

especies de anfibios ibéricos: Hyla molleri, Epidalea calamita y Pelophylax perezi. Estudiamos

17-21 poblaciones de cada especie (con un tamaño muestral total de n = 547, 652 y 516

individuos, respectivamente) incluyendo una localidad de referencia, en la que exploramos el

efecto del tamaño muestral utilizando unas muestras mayores (77-96 individuos). Como

esperábamos, tanto el índice FIS como los tests de equilibrio de Hardy-Weinberg y desequilibrio

de ligamiento se vieron afectados por la presencia de hermanos en la muestra, y la mayor parte

del desequilibrio pierde su significación cuando se eliminan los hermanos de los análisis.

Nuestros cálculos indican que el tamaño mínimo de muestra (incluyendo potenciales hermanos)

necesario para obtener estimas fiables de diversidad genética es cercano a los 20 individuos en

el caso de la heterocigosidad esperada (HE), aunque fueron necesarios entre 50 y 80 individuos

para estimar la riqueza alélica (AR). Nuestro estudio piloto basado en una población de

referencia permite evaluar con rigor las propiedades de cada marcador molecular

individualmente y explorar los efectos del tamaño muestral y de la presencia de hermanos en la

muestra. Estos ejemplos ilustran las ventajas de emplear este método para producir resultados

robustos y fiables en análisis genéticos posteriores.


123

Introduction

Accurate characterization of genetic diversity is a key step towards understanding the

ecological and evolutionary histories of populations and, consequently, to predict future

trends and implement efficient conservation measures (Hamilton 2009; Habel et al.

2015). The continuous improvement of molecular techniques and computation power,

associated with the development of model-based statistical analysis methods, are greatly

expanding our ability to estimate demographic parameters and the universe of

hypotheses that can be tested about genetic processes (Excoffier & Heckel 2006;

Buckley 2009; Guichoux et al. 2011). As a consequence, complex questions regarding

the detection of cryptic diversity, quantification of gene flow and population status

assessment have become approachable in recent times (Broquet & Petit 2009;

Segelbacher et al. 2010; Luikart et al. 2010; Marko & Hart 2011; Arntzen et al. 2013;

Fahey et al. 2014). In a scenario of global biodiversity loss, the possibility of early

identification of genetically impoverished and/or isolated populations is paramount for

informing management policies (Tallmon et al. 2004; Scherer et al. 2012). Thus,

accurate evaluation of the amount and spatial distribution of genetic diversity is

essential for research and conservation issues. For that purpose, new molecular markers

are routinely optimized for non-model species (Guichoux et al. 2011; Gallardo et al.

2012; Habel et al. 2014). However, questions of sampling design with potential

consequences on the reliability of inferences, like calculation of the minimum sample

size or the effect of excessive relatives in the sample, are often neglected.

Different indexes are commonly used to summarize genetic diversity. Most of

these indexes rely either on allele counts, like allelic richness (AR), or on allelic

frequencies, like observed and expected heterozygosity (HO and HE). Indeed, AR and

HE represent two particular cases of a potentially continuous diversity measurement

profile, in which rare alleles are more or less accounted for (Chao & Jost 2015). While

AR can be more useful to evaluate the evolutionary potential of populations (Petit et al.

1998; Leberg 2002; Pruett & Winker 2008), accurate estimation of allelic and genotypic

frequencies is more important for many other downstream analyses (Allendorf & Phelps

1981; Cornuet & Luikart 1996; Jones & Wang 2010b). It has been documented that AR

is heavily dependent on sample size (Banks et al. 2000; Foulley & Ollivier 2006;

Miyamoto et al. 2008; Pruett & Winker 2008). Comparing AR across populations with

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124

different sample sizes is possible by means of rarefaction methods (El Mousadik & Petit

1996; Kalinowski 2004; Pruett & Winker 2008), but the accuracy of estimates is still

limited by the smallest sample in the dataset. In contrast, 20-30 genetic samples have

proven sufficient for estimating HE in some empirical studies (Miyamoto et al. 2008;

Pruett & Winker 2008; Hale et al. 2012). However, these studies assessed the

‘sufficiency’ of sample either visually for separate markers (Hale et al. 2012) or by

exploring the approximation to final combined multilocus estimates (Miyamoto et al.

2008; Pruett & Winker 2008). To our knowledge, no method has been applied to

calculate threshold-based minimum sample sizes for individual markers, but this

information could improve the efficiency of ecological, evolutionary or conservation

studies (including long-term genetic monitoring programs) by aiding in the process of

marker set selection.

The sufficiency of sample has important implications for the accuracy and

precision of genetic estimates, but it is difficult to assess empirically (Fitzpatrick 2009;

Buerkle & Gompert 2013; Chao & Jost 2015). In fact, the minimum sample size is

marker-, species-, and even population-dependent so it should be addressed through

pilot studies, but these are often expensive and time-consuming (Taberlet & Luikart

1999). Alternatively, the performance of genetic markers can be supervised by

exploring how cumulative curves approach final estimates obtained from a large sample

of a reference population (e.g. Miyamoto et al. 2008). Different measures can be used to

characterize the approximation of subsample estimates to final estimates, such as the

root mean square error of estimates (Miyamoto et al. 2008; Pruett & Winker 2008) or

the successive slopes of the accumulation curve (Ariño et al. 2008, Chao et al. 2013).

Here we adapt a method originally derived for diversity accumulation curves (Ariño et

al. 2002) to calculate the minimum sample size required for each marker to estimate AR

and HE. This method could be routinely performed in reference populations to test the

suitability of molecular markers to address ecological and conservation questions, and

so inform marker set choice and sampling design. We complement this approach with

the calculation of diversity profile curves as proposed in Chao & Jost (2015).

Similarly, the presence of excessive relatives in the sample can also bias

population inferences. All natural populations contain relatives, so including relatives is

necessary for representative sampling. Unfortunately, knowing the exact proportion of

relatives of each class in a wild population is practically impossible. Therefore, it is


125

difficult to assess whether a sample, even with known or inferred genealogical

relationships among individuals, represents the population from which it was drawn

(Waples & Anderson 2017). In samples with an excess of relatives, alleles present in

large (or small) families might be over- (or under-) represented, thus leading to

inaccurate estimation of population allelic frequencies (Jourdan-Pineau et al. 2012). An

excess (compared to random sampling) of relatives in the sample is a frequent problem

when tissue sampling is performed among early stage individuals in iteroparous species

with overlapping generations, a scenario in which the aggregation of single cohort

relatives (especially full sibs) is common in many taxa (Goldberg & Waits 2010).

Estimates obtained from such samples may not be representative of the whole

population, which can sometimes lead to biased conclusions (Anderson & Dunham

2008; Goldberg & Waits 2010; Rodríguez-Ramilo & Wang 2012; Rodríguez-Ramilo et

al. 2014). It has been suggested that removing siblings from the samples can reduce bias

in unsupervised Bayesian clustering programs such as STRUCTURE (Anderson &

Dunham 2008; Rodríguez-Ramilo & Wang 2012), although this approach might often

be counter-productive in certain circumstances (Waples & Anderson 2017). However,

the effect of removing full sibs from genetic samples on genetic diversity indexes (such

as AR and HE) and in commonly employed tests of genotypic proportions such as

Hardy-Weinberg equilibrium (HWE) and linkage disequilibrium (LD) has not been

explored in wild populations.

Here we introduce a method for calculating the minimum sample size required

to assess the genetic diversity at each individual marker in a dataset, and explore the

effect of full sibs on genetic diversity characterization. We used specifically optimized

microsatellite markers to score multilocus genotypes for three co-distributed pond-

breeding amphibians: the Iberian treefrog (Hyla molleri), the natterjack toad (Epidalea

calamita) and the Iberian green waterfrog (Pelophylax perezi). These three species are

iteroparous, with overlapping generations, and molecular protocols are required to

obtain information about their demography, mating system and genetic structure. We

estimated several genetic diversity indexes in 17-21 populations per species and

assessed the effect of the presence of full sibs in the samples by comparing results

including or excluding full sibs. We also used large samples (n = 77-96 individuals) in a

reference population where the three species co-occur to explore the effect of sample

size on single-locus AR and HE estimates and to calculate minimum required sample

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126

sizes for each marker. We discuss the benefits of this approach for establishing efficient

sampling design protocols in conservation genetics studies.


Tissue sampling

Between 2010 and 2015 we collected larval tissue samples of H. molleri (n = 547), E.

calamita (n = 652) and P. perezi (n = 516) in 17-21 localities per species along both

slopes of Sierra de Guadarrama, in the Iberian Central System, encompassing different

habitat types and with altitudes ranging between 875 and 1720 m.a.s.l. (see Table IV.1

Figure IV.1. Topographic map showing location of the study area in the Iberian Peninsula and sampling localities. See Table IV.1 for abbreviations.


127

and Fig. IV.1). In one of the localities (Valdemanco) we collected 77 to 96 tadpoles of

each of the three species. In the remaining locations, 19 to 36 tadpoles per locality were

collected (Table IV.1). For each species in each locality, we used nets to sample larvae

from the same year cohort. Surveys were performed uniformly throughout the water

surface and samples included individuals of different body sizes, to minimize potential

sampling biases arising from the aggregative behavior of full sib tadpoles. Small

tadpoles were euthanized and preserved in absolute ethanol. In the case of large

tadpoles, tail tips were clipped and stored in absolute ethanol for subsequent DNA

extraction, and larvae were released back in the same pond of capture.

Table IV.1. List of localities included in the present study. For each locality, the abbreviation (Abr),

geographic coordinates and sample sizes for each species including (and excluding) full sibs are

displayed. aIn Cabanillas de La Sierra, three samples of P. perezi were obtained in different years

(2010/2013/2014).

Locality Abr Coordinates H. molleri E. calamita P. perezi

Alameda del Valle ALA 40.91º N 3.85º W - (-) 24 (13) -

Arcones ARC 41.13º N 3.73º W 30 (27) - (-) 19 (14)

Berrocal BRC 41.06º N 3.98º W - (-) 30 (6) -

Boceguillas BOC 41.31º N 3.66º W - (-) 20 (1) -

Bustarviejo BUS 40.85º N 3.68º W 30 (29) 28 (19) 30 (17)

Cabanillas de la Sierraa CAB 40.85º N 3.65º W 22 (19) 30 (26) 20/27/30 (20)/(20)/(15)

Cerceda CER 40.72º N 3.96º W 20 (16) 30 (14) 23 (18)

Collado Hermoso HER 41.05º N 3.93º W 23 (7) - (-) 32 (28)

Colmenar Viejo COL 40.69º N 3.83º W 21 (18) 30 (7) -

Dehesa de Roblellano ROB 40.86º N 3.63º W 30 (20) 36 (33) 23 (4)

El Berrueco BER 40.93º N 3.57º W 21 (18) 29 (3) 20 (8)

Fuenterrebollo FUE 41.33º N 3.93º W 20 (12) - (-) 20 (10)

Gargantilla del Lozoya GAR 40.95º N 3.72º W - (-) 30 (27) -

Gascones GAS 41.01º N 3.65º W 21 (19) - (-) -

La Pradera de Navalhorno PRA 40.88º N 4.03º W 22 (9) 30 (11) 23 (19)

Lozoyuela LOZ 40.92º N 3.65º W - (-) 28 (17) -

Medianillos MED 40.76º N 3.68º W 21 (9) - (-) 25 (20)

Muñoveros

MUN 41.20º N 3.95º W - (-) 32 (16) -

Navafría NAV 41.06º N 3.83º W - (-) 30 (10) -

Navalafuente NVL 40.81º N 3.68º W - (-) 30 (5) -

Puerto de Canencia CAN 40.87º N 3.76º W 25 (22) 28 (26) 22 (19)

Puerto de La Morcuera MOR 40.84º N 3.83º W 30 (24) 20 (11) 22 (15)

Puerto del Medio Celemín CEL 40.88º N 3.66º W - (-) 30 (21) -

Rascafría RAS 40.85º N 3.91º W 20 (18) - (-) 22 (20)

Santo Tomé del Puerto STO 41.19º N 3.59º W - (-) 30 (8) 21 (17)

Sauquillo de Cabezas SAU 41.19º N 4.06º W 20 (12) - (-) 22 (10)

Soto del Real SOT 40.76º N 3.80º W 20 (18) 30 (14) -

Torrecaballeros TOR 41.00º N 4.02º W 34 (28) - (-) -

Turrubuelo TUR 41.32º N 3.59º W 21 (19) - (-) 21 (15)

Valdemanco VAL 40.85º N 3.64º W 96 (88) 77 (27) 94 (58)

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DNA extraction and genotyping

Two enriched partial genomic libraries, one for H. molleri and another for E. calamita,

were prepared at the Sequencing Genotyping Facility, Cornell Life Sciences Core

Laboratory Center (CLC) (New York, NY) following the method described in

Gutiérrez-Rodríguez & Martínez-Solano (2013). They were generated from DNA of

one tadpole of H. molleri collected in Arzila, Portugal (40.20º N, 8.65º W) and one

adult male of E. calamita collected in Valdemanco, central Spain (40.85º N, 3.64º W).

From each of the two libraries, 60 loci containing microsatellite motifs (30 trimers and

30 tetramers) between 5 and 12 repetitions long were selected for further screening.

Although some tri-nucleotides might be under selection, we do not expect that it would

dramatically affect our results, except if selection was very strong, which is highly

unlikely. This expectation is further supported by the similar polymorphism and

diversity profiles shown by the tri- and tetra-nucleotide loci in our study (see Table IV.2

and Appendix 1), and also by other demographic analyses performed with different

subsets of loci (data not shown). For DNA purification, optimization of multiplex

reactions, genotyping and allele scoring, we followed the methods described in

Sánchez-Montes et al. (2016). Final sets of markers consisted of 18 and 16 newly

developed microsatellite loci for H. molleri and E. calamita, respectively (see Appendix

1), and 15 previously optimized markers for P. perezi (Sánchez-Montes et al. 2016).

These sets of markers were used to genotype the samples of each species. We selected a

subsample for repetition of the DNA amplification process (between 3.7% and 17.8% of

the sample in each species) to check for consistency of genotype calling.

Characterization of genetic diversity and effect of full sibs

For characterization of genetic diversity, allelic richness (AR), observed (HO) and

expected (HE) heterozygosity and FIS were calculated for each locus in each population

using GENALEX 6.5 (Peakall & Smouse 2006). Tests for departures from HWE and

evidence of LD were performed with GENEPOP v.4.3 (Raymond & Rousset 1995;

Rousset 2008), with 10,000 dememorisation steps, 1,000 batches and 10,000 iterations

per batch. The Bonferroni sequential correction was applied to account for multiple

testing (Rice 1989). The presence of null alleles was assessed with MICRO-CHECKER

v.2.2.3 (Van Oosterhout et al. 2004). We calculated the information content of the


129

markers by means of their informativeness for genetic relationship (R Info) using the

software KININFOR (Wang 2006). The other information indexes calculated by the

program were highly correlated with R Info in the three species (data not shown).

Sibship analyses were performed in COLONY (Jones & Wang 2010a) to identify full sibs

in each locality and to infer mistyping rates due to allele dropout and false allele

scoring. All analyses for genetic diversity characterization were conducted both on the

original genotype data (referred to as the complete samples) and on the data after

excluding all but one of the identified full sibs in every full sib family from each

population (referred to as the reduced samples).

Table IV.2. Mean (and standard deviation) of several indexes averaged across all sampled populations for

every marker of H. molleri (Hmol), E. calamita (Ecal) and P. perezi (Pper). For those measures affected by

the presence of full sibs in the sample (FIS, HW), estimates obtained in the reduced samples are also

displayed for comparison. AR = allelic richness, HO and HE = observed and expected heterozygosity. HW:

number of populations in which significant departures from Hardy-Weinberg equilibrium were detected.

Marker Complete samples

Reduced samples

AR HO HE FIS HW

FIS HW

Hmol3.7 1.05 (0.22) 0 (0.01) 0 (0.01) - - 0

- - 0

Hmol3.28 3.95 (1.10) 0.71 (0.15) 0.61 (0.11) -0.17 (0.23) 2

-0.18 (0.23) 0

Hmol4.2 2.75 (0.64) 0.50 (0.12) 0.45 (0.10) -0.12 (0.15) 0

-0.14 (0.18) 0

Hmol3.9 2.95 (0.94) 0.32 (0.19) 0.31 (0.16) -0.01 (0.22) 0

-0.02 (0.25) 0

Hmol3.3 3.05 (0.60) 0.37 (0.15) 0.35 (0.12) -0.04 (0.17) 0

-0.02 (0.23) 0

Hmol4.12 10.8 (3.09) 0.86 (0.12) 0.81 (0.10) -0.06 (0.12) 1

-0.05 (0.13) 0

Hmol4.16 8.50 (2.69) 0.83 (0.11) 0.78 (0.08) -0.05 (0.10) 0

-0.09 (0.12) 0

Hmol4.1 7.60 (2.19) 0.79 (0.09) 0.75 (0.07) -0.06 (0.09) 1

-0.06 (0.10) 0

Hmol4.9 4.30 (1.08) 0.65 (0.12) 0.60 (0.08) -0.09 (0.17) 0

-0.05 (0.18) 0

Hmol4.10 9.05 (2.93) 0.86 (0.09) 0.81 (0.06) -0.06 (0.07) 2

-0.05 (0.08) 0

Hmol3.22 6.30 (1.26) 0.80 (0.12) 0.75 (0.07) -0.07 (0.13) 1

-0.06 (0.15) 0

Hmol4.22 2.05 (0.39) 0.34 (0.18) 0.30 (0.15) -0.10 (0.18) 0

-0.10 (0.17) 0

Hmol3.15 3.85 (0.67) 0.61 (0.14) 0.59 (0.06) -0.02 (0.20) 0

-0.03 (0.22) 0

Hmol4.27 3.30 (0.73) 0.55 (0.20) 0.57 (0.11) 0.06 (0.26) 2

0.05 (0.26) 0

Hmol3.8 4.05 (1.05) 0.57 (0.15) 0.55 (0.13) -0.04 (0.20) 1

-0.07 (0.21) 1

Hmol4.11 2.15 (0.49) 0.27 (0.15) 0.27 (0.14) -0.02 (0.21) 0

-0.01 (0.23) 0

Hmol4.8 10.25 (3.18) 0.88 (0.09) 0.82 (0.06) -0.07 (0.10) 1

-0.07 (0.11) 0

Hmol4.29 10.35 (3.33) 0.86 (0.12) 0.83 (0.07) -0.04 (0.13) 3

-0.06 (0.11) 0

Ecal4.21 7.43 (2.09) 0.58 (0.17) 0.75 (0.07) 0.22 (0.21) 12

0.19 (0.19) 1

Ecal4.20 16.48 (5.65) 0.96 (0.04) 0.89 (0.04) -0.08 (0.08) 10

-0.11 (0.22) 0

Ecal4.8 15.57 (5.90) 0.89 (0.12) 0.86 (0.08) -0.04 (0.11) 5

-0.08 (0.25) 0

Ecal4.29 7.81 (1.72) 0.86 (0.08) 0.80 (0.07) -0.09 (0.16) 2

-0.13 (0.24) 0

Ecal4.16 4.38 (1.07) 0.61 (0.12) 0.57 (0.11) -0.09 (0.14) 0

-0.05 (0.21) 0

Ecal4.18 7.05 (1.32) 0.85 (0.07) 0.79 (0.05) -0.07 (0.12) 2

-0.10 (0.26) 0

Ecal4.3 9.76 (3.22) 0.82 (0.10) 0.81 (0.09) -0.02 (0.09) 6

0 (0.10) 0

Ecal4.6 7.05 (1.63) 0.65 (0.16) 0.77 (0.10) 0.15 (0.24) 11

0.17 (0.25) 4

Ecal4.14 9.05 (2.69) 0.57 (0.18) 0.82 (0.05) 0.30 (0.24) 18

0.28 (0.36) 9

Ecal4.2 16.81 (7.15) 0.71 (0.19) 0.88 (0.05) 0.20 (0.23) 20

0.11 (0.42) 11

Ecal3.26 12.76 (4.60) 0.63 (0.18) 0.85 (0.08) 0.25 (0.23) 17

0.23 (0.36) 11

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Table IV.2 (cont.)

Effect of sample size

We explored the effect of sample size on the estimates of AR and HE for each locus in

the locality with the highest total sample size for the three species (i.e. the complete

samples from locality Valdemanco, see Table IV.1). In order to compute approximate

95% confidence intervals for final AR and HE estimates (i.e. for the estimates obtained

with the complete samples), we randomly produced 10,000 bootstrap samples for each

locus, each with the same number of individuals as the whole sample. We calculated

Simpson’s complementary diversity index in PAST v. 3 (Hammer et al. 2001); this index

is identical to HE (HE 1-DS, where DS is Simpson’s dominance). We also quantified

the rate of approximation to final AR and HE obtained by the molecular markers with

increasing sample size, using 10,000 jackknifed subsamples of one, two, three...n

individuals of the total sample, and obtained AR and HE accumulation curves for each

locus. We used diversity functions in ESTIMATES (v.9.1.0, Colwell & Elsensohn 2014);

this software provides expected S that is identical to AR, and 1/DS that we converted to

HE.

Marker Complete samples Reduced samples

AR HO HE FIS HW FIS HW

Ecal4.24 8.95 (2.56) 0.87 (0.10) 0.82 (0.05) -0.07 (0.13) 4 -0.11 (0.24) 0

Ecal3.4 5.38 (1.56) 0.7 (0.19) 0.67 (0.18) -0.06 (0.11) 2 -0.08 (0.16) 0

Ecal3.29 4.24 (1.37) 0.44 (0.14) 0.48 (0.13) 0.08 (0.21) 3 0.03 (0.31) 0

Ecal3.19 6.57 (1.96) 0.43 (0.18) 0.73 (0.13) 0.42 (0.21) 15 0.37 (0.34) 8

Ecal4.26 21.14 (9.67) 0.95 (0.07) 0.90 (0.05) -0.06 (0.09) 12 -0.09 (0.23) 0

Pper4.25 13.16 (5.11) 0.88 (0.09) 0.86 (0.08) -0.03 (0.09) 2 -0.06 (0.13) 0

Pper4.15 8.79 (2.64) 0.81 (0.13) 0.80 (0.08) -0.01 (0.11) 0 -0.05 (0.11) 0

Pper4.28 4.00 (1.63) 0.55 (0.17) 0.52 (0.12) -0.06 (0.21) 0 -0.07 (0.2) 0

Pper3.9 6.21 (1.55) 0.71 (0.14) 0.69 (0.10) -0.02 (0.13) 0 -0.05 (0.1) 0

Pper4.5 3.11 (0.46) 0.64 (0.09) 0.63 (0.04) -0.02 (0.14) 0 -0.03 (0.16) 0

Pper4.16 7.95 (2.30) 0.81 (0.10) 0.79 (0.06) -0.03 (0.12) 0 -0.01 (0.16) 0

Pper3.24 6.21 (1.65) 0.77 (0.17) 0.74 (0.12) -0.04 (0.16) 1 -0.06 (0.17) 1

Pper4.20 2.05 (0.23) 0.39 (0.16) 0.40 (0.12) 0.05 (0.32) 1 0.06 (0.32) 0

Pper3.22 3.68 (1.16) 0.44 (0.12) 0.42 (0.11) -0.05 (0.11) 0 -0.05 (0.12) 0

Pper4.13 9.58 (3.61) 0.82 (0.13) 0.81 (0.13) -0.02 (0.08) 1 -0.04 (0.15) 0

Pper4.7 11.63 (4.76) 0.83 (0.21) 0.84 (0.08) 0.03 (0.22) 5 0.02 (0.21) 1

Pper3.1 5.74 (1.79) 0.70 (0.15) 0.72 (0.07) 0.02 (0.20) 4 0.01 (0.24) 1

Pper4.29 6.05 (1.90) 0.76 (0.18) 0.67 (0.14) -0.13 (0.11) 1 -0.14 (0.12) 0

Pper3.23 4.89 (1.05) 0.67 (0.16) 0.67 (0.07) -0.01 (0.23) 2 0.03 (0.27) 1

Pper4.24 9.21 (2.80) 0.82 (0.16) 0.81 (0.09) -0.01 (0.18) 2 0 (0.2) 1


131

We used R (R Development Core Team 2009) to inspect the accumulation

curves looking for asymptotic stabilization of AR and HE (see Appendix 2). Our

criterion for defining ‘sufficient samples’ was to minimize a Type-II (β) error (Snedecor

& Cochran 1989) by selecting the first point along the section of curve that would

persistently exceed the lower bound of the (bootstrapped) confidence interval of the

final estimate, while no further points would consistently fall below. We summarized in

boxplots the observed minimal sample sizes for each locus necessary to approximate

final estimates of AR and HE. For comparison with our results, we also obtained

empirical and Chao’s diversity profiles for each marker for values 0 ≤ q ≥ 3, by

adapting the R script in Appendix S8 in Chao & Jost (2015). The parameter q defines

the sensitivity of the diversity estimate to the rarest categories in the sample, and most

of the variation in the diversity profile is expected to be comprised within the interval q

= [0,3] (Chao & Jost 2015). The empirical profile at q = 0 corresponds to AR measured

as the total number of alleles (analogous to species richness in Chao & Jost 2015), and

at q = 2 it approximates Simpson’s diversity index (Chao et al. 2015) which, when

calculated as the complement of Simpson’s dominance, is analogous to HE, as stated

above.

Results

Characterization of genetic diversity and effect of full sibs

Almost all microsatellite markers were polymorphic in nearly all sampled populations

(see Appendix 1). The only exception was Hmol3.7, which was monomorphic in all

populations except for CAN (see Appendix 1). Although neither average FIS, nor

minimum sample sizes could be calculated for this locus, we report primer information

and amplification conditions because this marker might result more informative at

larger-scale studies. Genetic diversity measures obtained with the reduced samples were

very similar to those obtained with the complete samples (see Appendix 1), although FIS

estimates changed slightly (see Table IV.2 and Supplementary Appendix 1). FIS and the

allelic dropout rate (inferred from COLONY analyses) were highly correlated in the three

species (H. molleri: Spearman’s rho = 0.57, p = 0.015; E. calamita: rho = 0.85, p <

0.001; P. perezi: rho = 0.70, p = 0.005) although the trend was clearer in E. calamita,

which showed the highest variance in the values of both FIS and allelic dropout rate (see

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132

Appendix 4). However, FIS was not correlated with false allele rate in any of the three

species.

Four markers of the H. molleri set and five markers of the P. perezi set showed

significant deviations from HWE in more than one population in the complete samples

(Table IV.2). However, after removing full sibs from the samples, no locus departed

from HWE in more than one population (out of 20 and 17 total localities of H. molleri

and P. perezi, respectively, see Table IV.2 and Appendix 1). Only one marker (out of 18

total loci) in the H. molleri set (Hmol3.15) and four loci (out of 15 total loci) in the P.

perezi set (Pper4.7, Pper3.1, Pper3.23 and Pper4.24) showed evidence of null alleles in

more than one population (three populations at most), and these effects mostly remained

after removing full sibs from the sample (see Appendix 1). In contrast, almost all loci in

the E. calamita set were found to be out of HWE in some populations when using the

complete samples. Five of them (Ecal4.6, Ecal4.14, Ecal4.2, Ecal3.26 and Ecal3.19,

out of 16 total loci) still showed departures from HWE in 4-11 populations after

removing full sibs from the samples (Table IV.2 and Appendix 1). According to MICRO-

CHECKER results, these five loci, as well as Ecal4.21, showed evidence of null alleles in

many populations (see Appendix 1).

A few pairs of loci showed evidence of linkage disequilibrium (LD) across some

populations in the complete datasets after applying the Bonferroni correction. A pair of

loci in the H. molleri set, three pairs of loci in the E. calamita set, and three pairs of loci

in the P. perezi set were in LD in more than 20% of the populations. The most

widespread disequilibrium involved markers Ecal4.20 and Ecal3.26, which were in LD

in 14 populations. However, none of these disequilibria remained significant in the

reduced samples (data not shown).

Effect of sample size

Minimum sample sizes required for approaching final estimates of AR and HE in each

locus are summarized in the boxplots of Fig. IV.2. Median values ranged between 50

and 80 individuals for characterization of AR in each species, while less than 20

individuals were sufficient to estimate HE. Minimum sample sizes required for

estimation of HE were highly correlated with marker polymorphism, measured as AR, in


133

the three species (H. molleri: Spearman’s rho = 0.79, p < 0.001; E. calamita: rho = 0.67,

p = 0.005; P. perezi: rho = 0.74, p = 0.002, see Fig. IV.2). In contrast, minimum sample

sizes required for estimation of AR were negatively correlated with marker AR,

although only significantly in the case of E. calamita (H. molleri: rho = -0.28, p =

0.270; E. calamita: rho = -0.61, p = 0.013; P. perezi: rho = -0.09, p = 0.738, see Fig.

IV.2). Loci in the three marker sets showed different diversity profiles (see Appendix

3). The least polymorphic loci in each set showed flat profiles, but the most

polymorphic loci showed a more or less decreasing function along the range of q.

Profiles obtained applying Chao’s correction for sampling bias were very similar to

empirical profiles in most cases, although some highly polymorphic loci showed

differences at q = 0, like Hmol4.8 (15 observed alleles vs. 23 alleles estimated by

Figure IV.2. Minimum sample sizes (i.e. minimum number of individuals) required to obtain final

estimates of HE (grey) and AR (white) in the complete samples from Valdemanco. Scatterplots in the

top panel show the minimum sample sizes (y-axis) required to estimate each parameter for each

marker individually (grey dots: HE, white dots: AR), while each marker is represented in the x-axis by

the polymorphism (AR) shown in Valdemanco. Minimum sample sizes required for estimation of HE

were highly correlated with marker polymorphism, measured as AR, in the three species (H. molleri:

Spearman’s rho = 0.79, p < 0.001; E. calamita: rho = 0.67, p = 0.005; P. perezi: rho = 0.74, p =

0.002). In contrast, minimum sample sizes required for estimation of AR were negatively correlated

with marker AR, although only significantly in the case of E. calamita (H. molleri: rho = -0.28, p =

0.270; E. calamita: rho = -0.61, p = 0.013; P. perezi: rho = -0.09, p = 0.738). Boxplots (bottom panel)

summarize the minimum sample sizes for the marker set of each species.

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Chao’s correction), Ecal4.26 (42 observed vs. 45 estimated alleles) or Pper4.7 (20

observed vs. 28 estimated alleles, see Appendix 3).

Discussion

A thorough empirical assessment of marker polymorphism and performance is a key

step to evaluate their adequacy for genetic diversity characterization and therefore to

inform marker set choice for future studies (Matson et al. 2008; Queirós et al. 2015).

The moderate to high polymorphism observed in our marker sets (Table IV.2) suggests

that a high power of resolution could be obtained by combining a subset of the most

polymorphic markers in a single (or two) multiplex reaction(s), which might be useful

e.g. for management purposes (Cornuet & Luikart 1996; Holleley & Geerts 2009;

Harrison et al. 2013; Queirós et al. 2015). However, in studies including genetically

impoverished populations, for instance near range borders (Rowe et al. 1999;

Edenhamn et al. 2000; Allentoft et al. 2009), more loci could be necessary to attain a

similar power of resolution, and these loci could be selected from each set after testing

their degree of polymorphism in the area of interest. Marker set composition should

therefore be assessed before conducting the sampling, to guarantee unbiased

comparison among populations (i.e. using the same marker set for all sampling

localities) while also avoiding problems caused by insufficient marker information.

Mistyping rates are also essential to assess the practical utility of newly developed

markers, but this information is often overlooked (Pompanon et al. 2005; Lampa et al.

2013). Inferred error rates in our markers rarely exceeded 0.05, except for the six

markers of E. calamita in which we also detected evidence of null alleles (Appendix 1).

These markers showed dropout rates between 0.09 and 0.32 (Appendix 1). In all three

species, dropout rates inferred by COLONY were highly correlated with FIS, but this trend

was more obvious in the case of E. calamita than in H. molleri and P. perezi, because

larger variance was observed in the former species (Appendix 4). These results

highlight the usefulness of pedigree reconstruction in COLONY for the estimation of error

rates since they are in agreement with HWE tests, which are based on FIS (Waples

2015).

Our analyses of marker genotypes across many populations allowed assessing

the effect of sampling full sibs on estimates of genetic diversity, which may be


135

problematic when pedigree information is not available (Allendorf & Phelps 1981;

Goldberg & Waits 2010). We identified full sibs in each population after reconstruction

of one- or two-generation pedigrees (Jones & Wang 2010b) and found that samples

from some localities were mostly composed of full sibs (see Table IV.1), thus

potentially misleading some downstream analyses (Anderson & Dunham 2008;

Jourdan-Pineau et al. 2012; Rodríguez-Ramilo & Wang 2012). However, removing all

relatives from the sample is not always a good solution, because the degree of

nonrandomness (with respect to sibship frequency) in empirical samples is unknown

(Waples & Anderson 2017). More theoretical work, coupled with empirical data, is

needed to derive guidelines about how best to account for this factor. Here we report

some preliminary conclusions drawn from both theoretical (see Appendix 5) and

empirical work, with consistent results across species and populations.

The presence of full sibs in our samples did not significantly affect estimates of

genetic diversity (AR, HO and HE), although there were slight variations in FIS estimates

(Table IV.2). Theoretically, full sibs in the sample are expected to affect the genotype

distributions (see Appendix 5). For this reason, FIS, HWE and LD are most affected,

although the pattern of change is complex and dependent on the mating system

(Goldberg & Waits 2010). As expected, tests for HWE and LD were strongly affected

by the presence of full sibs in the samples (Waples 2015), and most initially inferred

disequilibria were no longer significant after removing full sibs (Table IV.2). While this

could also be caused by the lower statistical power in some reduced samples due to

reduced sample sizes, some consistent departures from HWE were still detected in

many reduced samples of E. calamita (see Appendix 1). Five loci (Ecal4.6, Ecal4.14,

Ecal4.2, Ecal3.26 and Ecal3.19) departed from the expected HWE in more than 15% of

populations in the reduced samples. Disequilibria in these five loci, as well as in

Ecal4.21, were probably due to the presence of null alleles, as indicated by analyses

with MICRO-CHECKER (Appendix 1). These six markers are highly informative and can

be useful in some analyses accounting for genotyping errors (such as sibship analyses in

COLONY), but otherwise they should only be used when downstream analyses are robust

to violation of HWE assumptions. Altogether, these results suggest that genetic

diversity indexes (AR, HO, HE) are not affected by the presence of close relatives in the

sample, at least in the absence of strongly unbalanced data structure (i.e. when there are

not very large families combined with unrelated individuals in the same sample), such

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136

as in our case (see also Waples & Anderson 2017). In contrast, the presence of close

relatives in the sample strongly affects the results of tests of HWE and LD, especially in

small samples/populations.

On the other hand, accounting for the minimum sample size required for genetic

diversity characterization is crucial for the accuracy of results and the efficient design of

monitoring programs (Wang 2002). Here we have adapted methods based on diversity

accumulation curves (Ariño et al. 1996; Ariño et al. 2008) by observing the rate at

which jackknifed subsamples approach the confidence interval of bootstrapped

replicates of the entire dataset and can no longer be statistically separated from each

other at a pre-specified significance level (see Appendix 2). Our threshold criterion was

useful for defining a realistic minimum sample size in most markers, although it was

dependent on the width of the 95% confidence interval (CI) of final estimates. As a

consequence, in the case of markers with very narrow 95% CI, large sample sizes were

required to reach the lower bound of the 95% CI. This resulted in an artificially inflated

minimum sample size for AR estimation in some markers (see, for example, Hmol3.3,

Ecal3.19 or Pper3.24 in Appendix 2). Conversely, for some indexes with a very wide

95% CI, inferred minimum sample sizes were artificially low (e.g. HE curves for

Hmol3.9, Ecal3.22 or Pper3.22 in Appendix 2). Too wide (or narrow) 95% CIs in

highly (or very little) polymorphic loci probably caused the negative relationship

between AR and the minimum sample size for AR estimation (Fig. IV.2).

Furthermore, although our total sample sizes in Valdemanco can be considered

large enough to characterize genetic diversity in pond-breeding amphibian populations,

our final estimates cannot be taken as actual population parameters. As a consequence,

these minimum sample sizes cannot be regarded as generally applicable to other

systems. Rather, our goal is double: to encourage the general use of a simple method to

explore the rate of approximation to final genetic diversity estimates with cumulative

sample size (such as those applied in Miyamoto et al. 2008, Pruett & Winker 2008,

Hale et al. 2012, Chao & Jost 2015, or in this paper), and to empirically calculate

minimum sample size. Our method could be easily adapted to sequential sampling

schemes where additional individuals are genotyped, and their alleles added to the pool

at each step. Thus, additional sampling is no longer necessary when the added

individual(s) do not significantly improve the estimates of AR and HE. This way,

minimum sample sizes can be defined when required (e.g. for the design of sampling


137

protocols). Nevertheless, since AR and HE are two particular cases of the continuous

diversity measurement, we also followed Chao & Jost (2015)’s proposal of reporting the

continuous diversity profile at the most relevant values of q. As expected, the most

polymorphic loci in our datasets also showed more rare alleles and, as a consequence,

their diversity profile varied through the range of q. In contrast, the profiles of the least

polymorphic loci were largely flat (Appendix 3). This is in agreement with the observed

positive correlation between marker polymorphism and the minimum sample size

required for HE estimation (Fig. IV.2). Empirical profiles were markedly similar to

Chao’s profiles in most markers, suggesting that our empirical accumulation curves of

AR and HE did not dramatically underestimate diversity (Appendix 3). However, we

found differences in the profiles of some markers with alleles at low frequencies, like

Hmol4.8, Ecal4.2, Ecal4.26 or Pper4.7 (Appendix 3), which concordantly showed wide

95% CIs in their corresponding accumulation curves for AR estimation (Appendix 2).

Highly polymorphic loci are usually associated with rare alleles, and therefore higher

sample sizes are required to estimate AR (but not necessary HE) with these markers.

These results support the usefulness of our method for reliable minimum sample size

calculation and also for detecting possible diversity underestimations caused by loci

with rare alleles.

Our results highlight that the presence of full sibs can slightly alter FIS estimates

and affect tests of HWE and LD, but also that AR, HO and HE are not affected by the

presence of small full sib families. We proved that some genotypic disequilibria are no

longer significant after removing full sibs from the samples, therefore allowing

detection of truly problematic markers (e. g. those presenting null alleles). On the other

hand, the minimum sample size is dependent on the marker(s) selected and should also

be assessed in each case for the configuration of the final marker set (Harrison et al.

2013). The required sample size for genetic diversity characterization can be optimized

from an exhaustively sampled population by means of accumulation curves and some

threshold criterion. This methodology is easy to apply to any empirical dataset and can

be readily used to help design sampling protocols for genetic monitoring studies. These

two aspects are basic for the efficient design of ecological studies aiming to obtain

reliable and comparable inferences about demography and genetic diversity distribution

in non-model species.

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Acknowledgements

We thank M. Peñalver, L. San José, J. Gutiérrez, E. Iranzo, L. Carrera, C. Valero, M. Rojo, G.

Rodríguez, J. Agüera, A. Sabalza, N. Escribano, R. Goñi, R. Santiso and I. Miqueleiz for help

during fieldwork. R. Waples, W. Sherwin and an anonymous reviewer provided valuable

comments on a previous version of this manuscript. Authorizations for animal tissue sampling

were provided by Consejería de Medio Ambiente y Ordenación del Territorio, Comunidad de

Madrid and Consejería de Fomento y Medio Ambiente, Junta de Castilla y León (Spain). This

work was supported by Ministerio de Ciencia e Innovación, Spain, and FEDER (grant number

CGL2008-04271-C02-01/BOS) and Ministerio de Economía y Competitividad, Spain, and

FEDER (grant number CGL2011-28300) to IMS, who was also supported by funding from the

Spanish Ramón y Cajal (RYC-2007-1668) and Severo Ochoa (SEV-2012-0262) programs. G.

Sánchez-Montes was funded by a predoctoral grant provided by the Asociación de Amigos de la

Universidad de Navarra and benefited from funding from the Programa de ayudas de

movilidad de la Asociación de Amigos de la Universidad de Navarra.

Data accessibility

Sequences of contigs containing newly developed microsatellite loci were deposited in the

NCBI GenBank with accession numbers from KY964693 to KY964726. Microsatellite

genotype data for the three species are available from the Dryad Digital

Repository: http://dx.doi.org/10.5061/dryad.f65s7.

http://dx.doi.org/10.5061/dryad.f65s7


139

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CHAPTER V

RELIABLE EFFECTIVE/CENSUS POPULATION SIZE RATIOS IN SEASONAL-BREEDING SPECIES: OPPORTUNITY FOR INTEGRATIVE DEMOGRAPHIC INFERENCES BASED ON CAPTURE-MARK-RECAPTURE DATA AND MULTILOCUS GENOTYPES

Sánchez-Montes G, Wang J, Ariño AH, Vizmanos JL & Martínez-Solano I

Ecology and Evolution (Accepted pending minor review)

Effective/census population size in seasonal breeders

145

Abstract

The ratio of the effective number of breeders (Nb) to the adult census size (Na) approximates the

strength of genetic stochasticity of a population in maintaining genetic diversity in one

reproductive season. This information is relevant for assessing population status, understanding

evolutionary processes operating at local scales and unraveling how life-history traits affect

lineage differentiation. However, our knowledge on Nb/Na ratios in nature is limited because

estimation of both parameters is challenging. The sibship frequency (SF) method is adequate for

reliable Nb estimation because it is based on sibship and parentage reconstruction, thereby

providing demographic inferences that can be compared with field-based information. In

addition, capture-mark-recapture (CMR) robust design methods are well suited for Na

estimation in seasonal-breeding species. Here, we argue that integrating both methods is an

optimum means of estimating the Nb/Na ratio. We illustrate this approach by using tadpole

genotype samples of three pond-breeding amphibian species (Epidalea calamita, Hyla molleri

and Pelophylax perezi, n = 73-96 single-cohort tadpoles per species genotyped at 15-17

microsatellite loci) coupled with candidate parental genotypes (n = 94-300 adult individuals per

species) to estimate Nb by the SF method in a locality in Central Spain. We then assess the

reliability of Nb estimates by comparison of sibship and parentage inferences with field-based

information and check for the convergence of results in replicated subsampled analyses. Finally,

we use CMR data from a 6-year monitoring program to estimate annual Na for the three species

and calculate the Nb/Na ratio. Reliable Nb/Na ratios were obtained for E. calamita (Nb/Na = 0.18-

0.28) and P. perezi (0.5). On the other hand, in the case of H. molleri, Na could not be

appropriately estimated and genetic information proved insufficient for reliable Nb estimation.

This work shows that integrative demographic studies taking full advantage of SF and CMR

methods can provide accurate estimates of the Nb/Na ratio in seasonal-breeding species such as

pond-breeding amphibians. Importantly, the SF method allows for ready evaluation of the

reliability of results. This represents a good opportunity for obtaining reliable demographic

inferences with wide applications for evolutionary and conservation research.

Keywords: COLONY, Demography, Egg string counts, Marker information, Mating system,

Monogamy, Number of mates, Polygamy, Sample size, Sibship size prior.


147

Resumen

El cociente entre el número efectivo de reproductores (Nb) y el número de adultos de una

población (Na) ofrece información acerca de la intensidad de la estocasticidad genética en el

mantenimiento de la diversidad genética durante una temporada reproductora determinada en

una población. Esta información es importante para evaluar el estado de conservación de las

poblaciones, entender los procesos evolutivos que operan a pequeña escala espacial y temporal

y estudiar cómo afectan las características vitales propias de cada especie a la divergencia de los

linajes a lo largo del tiempo. Sin embargo, tanto Nb como Na son complejos de estimar en

poblaciones silvestres, por lo que nuestro conocimiento sobre el cociente Nb/Na en la naturaleza

es limitado. Afortunadamente, el método de “sibship frequency” (SF) es adecuado para obtener

estimas fiables de Nb, ya que se basa en la reconstrucción de paternidades y otras relaciones de

parentesco, por lo que proporciona inferencias demográficas que pueden ser comparadas con

observaciones directas de campo. Además, los métodos de captura-marcaje-recaptura (CMR)

que utilizan una metodología de “diseño robusto” son adecuados para estimar Na en especies

con reproducción estacional. En este capítulo argumentamos que integrar ambos métodos es una

manera óptima de estimar el cociente Nb/Na. Para ilustrar esta afirmación utilizamos genotipos

de renacuajos de tres especies de anfibios ibéricos (Epidalea calamita, Hyla molleri y

Pelophylax perezi, n = 73-96 individuos de cada especie genotipados en 15-17 microsatelites)

junto con genotipos de individuos adultos como posibles padres y madres (n = 94-300

individuos adultos por especies) para estimar Nb utilizando el método SF en una localidad del

centro de España. Posteriormente evaluamos la fiabilidad de nuestras estimas de Nb mediante la

comparación de los pedigrís reconstruidos durante los análisis con información directa obtenida

en el campo, y mediante la exploración de la convergencia de los resultados en análisis

replicados empleando diferentes tamaños de muestra e información genética. Finalmente,

utilizamos datos de CMR procedentes de un programa de seguimiento desarrollado durante seis

años para estimar Na en las tres especies y calcular el cociente Nb/Na. Se obtuvieron cocientes

Nb/Na fiables para E. calamita (Nb/Na = 0,18-0,28) y P. perezi (0,5). Sin embargo, en el caso de

H. molleri no se pudo estimar Na, y tampoco la información genética fue suficiente para estimar

Nb con precisión. Este trabajo demuestra que los estudios demográficos que integran métodos de

SF y CMR son capaces de proporcionar estimas fiables del cociente Nb/Na en especies de

reproducción estacional, como es el caso de los anfibios que se reproducen en medios

temporales. También es importante destacar que el método SF permite evaluar la fiabilidad de

los resultados de manera eficaz, lo que representa una gran oportunidad para obtener inferencias

demográficas fiables aplicables en investigación evolutiva y de conservación.


149

Introduction

The effective size and the census size of a population are two conceptually different

demographic parameters. The effective population size (Ne) is a theoretical number that

was proposed to measure the strength of inbreeding and genetic drift experienced by

finite populations (Wright 1931; Crow & Kimura 1970; Waples et al. 2013).

Accordingly, Ne is defined as the size of an ‘idealized population’ that experiences the

same rate of inbreeding or genetic drift as the real population of study (Wright 1931).

Since both effects act to reduce genetic diversity, the absolute value of Ne is directly

proportional to the capacity of the population to maintain genetic diversity

(Charlesworth 2009; Waples & Antao 2014; Ruzzante et al. 2016; Wang et al. 2016).

The census size, in contrast, is the total number of individuals in the population or,

alternatively, the number of potentially-breeding adults (Na) of the population

(Frankham 1995). Therefore, the ratio between Ne and Na can be considered as a

measure of the potential of the population as a genetic reservoir, standardized by the

abundance of adult individuals (Frankham 1995; Palstra & Ruzzante 2008; Palstra &

Fraser 2012; Bernos & Fraser 2016).

The relevance of the Ne/Na ratio in evolutionary and conservation biology is

based on three major facts. First, it measures the relative performance of a population

against inbreeding and genetic drift, thus providing relevant information about its

conservation status (Frankham 1995; Palstra & Fraser 2012). A high ratio (close to a

value of one) suggests that most adults of the population contribute (nearly equally in

expectation) to the next generation, approaching an idealized panmictic scenario. In

contrast, a low Ne/Na ratio (much smaller than one) implies a high variance in breeding

success among adults and leads potentially to genetic impoverishment driven by

stochastic processes (Banks et al. 2013). Second, the Ne/Na ratio is conditioned by life-

history traits that reflect species-specific reproductive constraints, which might

ultimately have implications for lineage differentiation (Waples et al. 2013; Waples

2016). As a consequence, differences in Ne/Na ratios across species might explain part

of the variance in diversification rates among different taxa. Third, in demographically

stable populations, life-history traits can have a major effect on the Ne/Na ratio, and

therefore, species-specific Ne/Na ratios could be used to estimate Ne from adult

abundance data (or vice versa) in these populations (Bernos & Fraser 2016).

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Nevertheless, the Ne/Na ratio tends to increase in small populations due to genetic

compensation, and thus variance in the Ne/Na ratio among different populations can be

informative about the main evolutionary processes affecting them (Palstra & Ruzzante

2008; Beebee 2009; Bernos & Fraser 2016). Because of the broad informative content

of the Ne/Na ratio, a large number of studies have addressed its calculation across a wide

variety of taxa (Frankham 1995; Palstra & Fraser 2012). However, our knowledge about

the variation of Ne/Na ratios in nature is still limited, because estimation and

interpretation of both Ne and Na are challenging (Palstra & Fraser 2012). In addition,

diverse methods are often employed by different researchers to estimate both Ne and Na,

further complicating comparison among studies.

Direct calculation of Ne requires comprehensive demographic information

(Caballero 1994; Vucetich & Waite 1998; Waples et al. 2011) and indirect methods

(like single-sample genetic methods) are widely used (Schwartz et al. 1998; Wang

2005; Luikart et al. 2010; Wang et al. 2016). Especially, the linkage disequilibrium

(LD) and the sibship frequency (SF) methods have proven the most reliable (Beebee

2009; Wang 2016). In species with overlapping generations, estimation of Ne by these

single-sample methods requires additional demographic information difficult to obtain

in wild populations (Nunney 1993; Waples 2005; Wang et al. 2010; Waples et al. 2013,

2014; Waples & Antao 2014). However, if all individuals in the genetic sample belong

to the same cohort, the effective number of breeding individuals (Nb) producing that

cohort can be readily estimated by these methods (Waples 2005; Wang 2009; Waples et

al. 2013, 2014; Waples & Antao 2014). Although Nb retains only part of the information

of Ne (for example, it does not account for age-variation in breeding success), it can be

used to estimate the ability of the population to maintain genetic diversity (Waples

2005; Waples et al. 2013; Waples & Antao 2014; Kamath et al. 2015). Thus, the Nb/Na

ratio can be used as an estimate of a single-season effective/census population size ratio.

Although some methods based on direct counts, acoustic surveys or

extrapolations from evidences of breeding activity have been employed to estimate Na,

individual-based capture-mark-recapture (CMR) methods provide the most accurate

insights about population size variation (Clutton-Brock & Sheldon 2010). CMR studies

are time-consuming, but current techniques include a wide range of sophisticated

sampling designs that can be applied to different types of data (Lebreton et al. 1992;

Tavecchia et al. 2009). In particular, robust design frameworks, which rely on nested


151

CMR sessions, are especially powerful in the case of Na estimation (Pollock 1982;

Kendall et al. 1995). The efficiency of robust design analyses can be maximized when

the capture of individuals is concentrated in short time periods, during which population

closure can be assumed (Kendall & Nichols 1995; Kendall et al. 1997). This is the case

of seasonal-breeding species, in which adult individuals congregate during a few weeks

every year (e.g. in lekking aggregations); this allows the concentration of sampling

sessions, facilitating annual estimation of Na.

In this regard, pond-breeding amphibians in temperate latitudes represent an

excellent study system because their seasonal aggregative breeding behaviour facilitates

annual Na estimation by robust design CMR methods. Similarly, the spatial and

temporal clustering of tadpoles of the same cohort in the breeding sites makes them

especially suitable for Nb estimation (Waples 2005; Wang 2009; Beebee 2009). The SF

method, implemented in COLONY (Wang 2009; Jones & Wang 2010), is especially

convenient for Nb estimation in seasonal-breeding species. This method has proved

accurate for Nb estimation when sample size is close to, or higher than, real Nb (Beebee

2009; Ackerman et al. 2016; Wang 2016), or when highly informative markers are

used. In addition, the SF method is based on sibship reconstruction, which provides

demographic inferences (e.g. the number of mating pairs or the average polygamy of

each sex) that can be compared with evidences of breeding activity such as egg string

counts, direct mating observations or individual records of permanence in the breeding

sites. This calibration with field information allows the supervision of reconstructed

sibship relationships, on which the estimate of Nb is based. Furthermore, inclusion of

genotype information of candidate sires and dams potentially increases the robustness of

sibship reconstruction, thereby improving Nb estimates. Finally, replicated analyses

varying the analytical settings and the genetic information employed (i.e. the numbers

of sampled markers and individuals) can be used to check for the convergence of

results.

In summary, we argue that demographic approaches integrating SF estimation of

Nb and CMR estimates of Na provide a good opportunity for producing reliable Nb/Na

ratios for seasonal species, such as temperate pond-breeding amphibians. Here we show

the results of such an integrative study, including field- and molecular-based approaches

for three sympatric anuran species differing in their life-history traits: the natterjack toad

Epidalea calamita (Laurenti, 1768); the Iberian treefrog Hyla molleri Bedriaga, 1889

CHAPTER V

152

and Perez’s frog Pelophylax perezi (López-Seoane, 1885). We monitored a breeding

assemblage of the three species in Central Spain with CMR methods, integrating field-

based information with genotype data from newly optimized sets of microsatellite

markers in order to estimate the Nb/Na ratio.

Specifically, we aimed to:

1) Estimate annual Na (number of adult males and females separately) of the

three species in a breeding locality using CMR robust design methods.

2) Estimate Nb of the three species using the SF method and assess the

reliability of these estimates by comparing reconstructed families with

independent information about each species’ phenology and evidences of

breeding activity, and by checking for the consistency of results in replicated

analyses with different priors, number of markers, and sample sizes.

3) Calculate the corresponding Nb/Na ratio for each species.


Study area and CMR monitoring program

Our area of study comprises the vicinities of Laguna de Valdemanco, a temporary

aquatic system that extends across a maximum surface area of 12,800 m2 (when

adjacent meadows are flooded in early spring), with one meter of maximum depth. This

pond is located in the foothills of La Cabrera ridge, 1055 m above sea level, between the

towns of La Cabrera and Valdemanco (Madrid, Spain). It is surrounded by

Mediterranean forest dominated by gum rockrose (Cistus ladanifer). A 6-year

monitoring program of the amphibian community of this locality was developed

between 2010 and 2015, with CMR sessions performed every year in Laguna de

Valdemanco and in some additional minor breeding sites at a distance between 270 and

800 meters from the main pond (Sánchez-Montes & Martínez-Solano 2011). Six

amphibian species breed regularly in the pond (Pleurodeles waltl, Triturus marmoratus,

Pelobates cultripes, Epidalea calamita, Hyla molleri and Pelophylax perezi), and

dispersive individuals of Alytes cisternasii, Bufo spinosus and Discoglossus galganoi

were also recorded occasionally. We addressed the estimation of the Nb/Na ratio for E.


153

calamita, H. molleri and P. perezi, three of the species for which CMR work proved

most successful, based on the recapture rates obtained.

The annual number of egg strings of E. calamita was also recorded. Among the

three targeted species, this is the only one that lays clutches mainly in shallow water

thus allowing exhaustive counts (García-París et al. 2004). Counts were performed

every year during the whole E. calamita breeding season, from the appearance of the

first strings to the end of the mating period, when the puddles and shallow areas

selected by this species for egg-laying finally dried. Egg strings were counted

repeatedly during the season, their position was recorded and their development was

revised in subsequent visits to avoid overestimation.

CMR estimates of Na

As part of the monitoring program, nocturnal CMR sessions were performed during the

breeding season of each species every year from 2010 to 2015. The entire water surface

of Laguna de Valdemanco, shores and nearby areas were sampled on foot without time

limit, in order to maximize the number of captures. Adult individuals were captured by

hand or with the help of dip nets, sexed based on external morphological features and

marked with an 8 mm AVID M.U.S.I.C transponder (EzID, Greeley, Colorado, USA),

with a unique identity code readable with an AVID Minitracker II device. Three

phalanges of a toe of every marked individual were clipped and stored in absolute

ethanol for genetic analyses (see below). Toe clipping in these three species did not

affect survival of individuals, as suggested by the observed rapid healing (see also

McCarthy & Parris 2004). Bone samples were also used for skeletochronological

studies (Sánchez-Montes, unpublished data). All individuals were released back in their

place of capture after processing.

CMR sessions were planned to fulfill the assumptions of the robust design

method (Pollock 1982), by minimizing the time span between secondary sampling

occasions (in this case, within each breeding season, with a maximum time span of 46

days, but typically less), relative to the time span between primary samples (in this case,

between different years). Our final CMR datasets included capture histories for 542

adult E. calamita (141 females, 401 males), 415 adult H. molleri (57 females, 358

males), and 190 adult P. perezi (94 females, 96 males) marked between 2010 and 2015.

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The total number of captures was 1,513 for E. calamita (1-17 captures per individual in

26 total CMR sessions), 526 for H. molleri (1-4 captures per individual in 17 sessions)

and 312 for P. perezi (1-6 captures per individual in 19 sessions). Return rates (the

proportion of individuals captured more than once) were 0.58, 0.23 and 0.41 for E.

calamita, H. molleri and P. perezi, respectively.

We analyzed inter-annual variation in Na using the robust design method

implemented in MARK (White & Burnham 1999). Different models were generated by

applying constraints (time and/or sex dependence) to annual survival (S). Since no time

limit was imposed to standardize capture effort in the CMR sessions, individual

probability of capture was always modelled as dependent of sex and time. The

probability of capture was set equal to the probability of recapture in all models (i.e. we

did not introduce a trap-dependence factor in any model). We tested different models

assuming that the probability of temporary emigration/immigration was either 1)

dependent of the last probable state of the individual (Markovian), 2) independent of the

last probable state of the individual (random), or 3) absent (i.e. temporary

emigration/immigration forced to zero). These temporary immigration/emigration

probabilities could reflect actual temporary displacements out of the area of study or

individuals skipping a breeding season (i.e. interannual changes in state between

‘breeder’ and ‘non-breeder’, Muths et al. 2006, 2013; Cayuela et al. 2014, 2016). Since

no optimum goodness-of-fit tests have been proposed for robust design models, we

tested for the most common causes of departures from the Cormack-Jolly-Seber (CJS)

model assumptions among secondary occasions (Schwarz & Stobo 1997). We thus used

U-CARE (Choquet et al. 2009) to test for ‘transience’ and ‘trap-dependence’ effects in

each year in which the required minimum number of three and four CMR sessions,

respectively, were available (i.e. in four, two and three years for E. calamita, H. molleri

and P. perezi, respectively). Models were ranked based on the Akaike Information

Criterion corrected for small sample sizes (AICc, Akaike 1974; Burnham & Anderson

2002), and estimates of Na were obtained by weighted averaging estimates from the

candidate models.

Table V.1. Sample sizes (n) employed for SF analyses and estimates (with 95% CIs) of Nb and Na obtained for each species. Also, the total number of sires and dams inferred

in SF analyses (in parentheses the number of inferred parents included in the genotyped samples of candidate parents) are shown for each species, along with the egg string

counts for E. calamita. Nb/Na was calculated by dividing the point SF estimate of Nb by the sum of Na point estimates for males and females in each species (total Na). Non-

estimable parameters are indicated with ‘-’.

Species Year n

Nb Na Inferred

number of sires

Inferred number of dams

Nb/Na Egg

string counts tadpoles males females males females total

E. calamita 2013 77

198 102 51 (35-78) 138 (133-143) 43 (28-58) 181 23 (15) 23 (16) 0.28 46

2015 73 52 (35-80) 162 (158-165) 125 (78-172) 287 31 (27) 29 (22) 0.18 104

H. molleri 2013 96 48 48 131 (97-179) 126 (102-150) - - 52 (18) 51 (5) - -

P. perezi 2010 94 47 48 69 (49-98) 69 (30-108) 68 (4-133) 137 37 (17) 38 (24) 0.50 -

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Genetic estimates of Nb

We obtained four single-cohort tadpole genotype samples for Nb estimation: two

samples for E. calamita in 2013 and 2015 (n = 77 and 73 tadpoles, respectively), one for

H. molleri in 2013 (n = 96) and one for P. perezi in 2010 (n = 94, Table V.1). Tadpole

genotypes of E. calamita (2013), H. molleri and P. perezi were obtained from Sánchez-

Montes et al. (2017), and we included an additional sample of tadpoles of E. calamita

(2015). Tadpoles were sampled in a comprehensive survey across the entire surface of

the breeding pond (Sánchez-Montes et al. 2017). We also used a subsample of the tissue

collection obtained during the 6-year monitoring program as candidate parents for SF

analyses (Table V.1). This subsample included adult males and females that had been

captured in the area of study within a period from one year before to one year after the

breeding season when tadpoles were collected (including both the 2013 and 2015

breeding seasons in the case of E. calamita, see Table V.1). All individuals were

genotyped using three sets of 15-17 polymorphic microsatellites specifically designed

for each species following the methods described in Sánchez-Montes et al. (2016,

2017). Basic properties of the three sets of markers and genetic diversity estimates

obtained in Laguna de Valdemanco can be found in Sánchez-Montes et al. (2017).

We used tadpole and adult genotypes of each species to reconstruct sibship and

parentage and to obtain estimates of Nb using SF analyses in COLONY Version 2.0.6.1

(Jones & Wang 2010). We calculated the probability that the progenitors of the

offspring samples were among the genotyped adult individuals using Na estimates from

CMR analyses, by dividing the sample size of candidate fathers (mothers) of each

species by the estimated Na of males (females) in the corresponding year. Since no

estimate of Na was available for females of H. molleri, we used the same probability as

for males (i.e. 48/126 = 0.38). We also performed additional analyses with different

probabilities of parents being present in the genotyped samples (0.5 for both sexes of E.

calamita and 0.2 for H. molleri and P. perezi) to check for the dependence of results on

these prior probabilities. Based on previously estimated error rates (Sánchez-Montes et

al. 2017), we used a genotyping error rate of 0.05 for every marker in E. calamita and

of 0.01 in each of the remaining two species. Since offspring samples represent a single

year cohort, the assumption of the mating system of the species required for COLONY

analyses refers to the possibility of multiple matings within a single breeding season.

Low rates of double-clutching females have been reported in some E. calamita


157

populations in Sweden and UK (Silverin & Andrén 1992; Denton & Beebee 1996) and

double- and multiple-clutching have been observed in some Hyla species in Europe

(Broquet et al. 2009; Cadeddu & Castellano 2012). However, it is unknown whether

females of H. molleri and P. perezi lay more than one clutch per year (sequential

polyandry) or whether there are multiple paternities within each clutch (simultaneous

polyandry), although the former scenario seems more likely (Lengagne & Joly 2010;

Byrne & Roberts 2012). Accordingly, we conservatively performed all analyses by

assuming polygamy in both sexes in the three species, with ‘very long’ run length and

‘very high’ precision settings.

Additionally, we explored the effects of using different sibship size priors,

number of markers and sample sizes on sibship and parentage reconstruction and on Nb

estimates. First, we compared the effect of using different sibship size priors, from an

average sibship size of one up to five, or without any prior information. The average

sibship size is the mean number of offspring sired by each breeding male (paternal

sibship size) and female (maternal sibship size). Setting a low average value for both

sexes (i.e. = 1) may help discourage false full and half sib assignations, improving Nb

estimation, when marker information is insufficient (Wang 2016). High average values

are only expected in samples obtained from a low number of potential breeders or in

cases of strong male (or female) dominance. Second, we explored the effect of marker

information by performing jackknifed replicates for each number of loci in each species

dataset, from one locus to the complete set, either using or not using a prior sibship size

= 1. Third, we performed replicates at different-sized jackknifed offspring (tadpole)

subsamples (but using the complete candidate parental samples), either using or not

using a sibship size prior = 1. For all these analyses, we used custom-generated R (R

Development Core Team 2009) scripts (see Appendix 6) to run COLONY for multiple

input files (settings: ‘medium’ run length, ‘high’ precision, 10 replicates for each

analysis in each species), record estimates of Nb, and calculate the average number of

different mates per inferred breeder of each sex (a measure of the degree of polygamy)

from inferred families. In all analyses using a sibship size prior, we set a ‘weak’ prior in

order to aid but not force family reconstruction.

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Figure V.1. Annual estimates of Na (with 95% CI) obtained in Laguna de Valdemanco for the three

species, by sex (males: black circles; females: grey circles). Some Na could not be estimated (see

Appendix 7). Dark grey bars show annual counts of egg strings of E. calamita. Blue bars show

monthly cumulative rainfall data from the Barajas weather station (Madrid, about 40 km south from

Laguna de Valdemanco). A sharp decrease in precipitation is apparent in 2012, especially in the early

months of the year, when the breeding activity of the three species is concentrated.


159

Results

CMR estimates of Na

For each species, the top three ranked models encompassed more than 99% of the

weight based on AICc scores (see Appendix 7). We did not detect consistent departures

from CJS model assumptions among secondary occasions in any of the species,

although males of E. calamita showed evidences of ‘transience’ effects in 2011 and

2013 and ‘trap-dependence’ in 2011 (results not shown). Estimates of Na were

concordant in most years across different models (Appendix 7). Values obtained after

weighted averaging across candidate models are shown in Table V.1 (for the year of the

tadpole genetic sampling in each species) and Figure V.1. Estimated number of males of

E. calamita and H. molleri were similar, around 150 individuals every year, although

extreme high and low estimates were also obtained in some years (Fig. V.1).

Unfortunately, the number of females of H. molleri could not be estimated due to their

low recapture rate (0.04). Estimates of Na in E. calamita and H. molleri clearly

outnumbered those in P. perezi. The sex-ratio of P. perezi in 2010 was very close to 1:1,

and in the case of E. calamita it was male-biased in most years, especially in 2013 (Fig

V.1). Precision of Na estimates, based on 95% CIs, improved with cumulative data from

successive years in all three species, but especially in males of E. calamita, for which

highly precise estimates were obtained from 2013 to 2015. For H. molleri and P. perezi,

population declines became apparent in the period from 2012 to 2015. The year 2012

was unusually dry in Laguna de Valdemanco, as reflected in a sharp drop in egg string

counts of E. calamita (from an average of 60 to only five egg strings, Fig. V.1).

Genetic estimates of Nb

Estimates of Nb for E. calamita were slightly over 50 in both years 2013 and 2015

(Table V.1). For H. molleri and P. perezi, Nb estimates were 131 and 69, respectively

(Table V.1). These values, obtained with very long runs of the full datasets, were

concordant with those obtained with medium length runs in the replicated analyses in

the case of E. calamita and P. perezi, but not in H. molleri (as shown by the comparison

of Nb values in Table V.1 with final Nb values in Figs. V.2, V.3 and V.5). Between 46

and 87% of the inferred parents in reconstructed families of E. calamita and P. perezi

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160

were among the genotyped candidate fathers and mothers, but only 35% of the inferred

sires and 10% of the inferred dams of H. molleri were included in the candidate parental

samples (Table V.1). These values were not affected by the use of a different prior

probability for a true parent being included in the genotyped candidates (results not

shown). The estimated average sibship sizes (and ranges) were 3.35 (1-6) for both sexes

of E. calamita in 2013, 2.35 (1-8) and 2.52 (1-7) for paternal (p) and maternal (m)

sibship sizes of E. calamita in 2015, 1.85 (1-5, p) and 1.88 (1-5, m) for H. molleri and

2.54 (1-7, p) and 2.47 (1-9, m) for P. perezi (Appendix 8). We found low levels of

Figure V.2. Estimates of Nb (dots) and average number of mates per breeder male (black lines) and

female (grey lines) using different sibship size prior values (from one to five) or no prior (no).

Estimates are averaged among ten replicates for each prior value (Nb: harmonic mean; average

number of mates: arithmetic mean). Error bars represent 95% CIs. Note that, in the case of E.

calamita, no variance among replicates was observed in all estimates in 2013 and in most estimates

in 2015.


161

polygamy in E. calamita both in 2013 and 2015 (Figs. V.2, V.4 and V.6). According to

inferred parentage relationships, 83-90% of the successfully breeding males and 86-

87% of the successfully breeding females of E. calamita mated with only one partner in

each breeding season (Appendix 8). In contrast, higher polygamy levels were inferred in

H. molleri (50% of inferred sires and 49% of inferred dams were polygamous) and P.

perezi (46% of inferred sires and 42% of inferred dams were polygamous, see Figs. V.2,

V.4 and V.6 and Appendix 8).

Nevertheless, parentage assignment errors are possible due to limited parental

sampling and marker information. For this reason, checking for the convergence of

Figure V.3. Harmonic means (with 95% CIs) of point estimates of Nb obtained in ten replicated SF

analyses using a sibship size prior = 1 (white dots) or no prior (black dots) with increasing marker

information.

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results with different analytical settings and different amounts of marker information is

critical to assess the reliability of estimates. Using paternal and maternal sibship size

priors = 1 resulted in an increase in Nb estimates and a proportional decrease in the

average number of mates per breeder in the three species (Fig. V.2). Using prior sibship

size values between two and five yielded similar results to using no sibship size prior

(Fig. V.2). Similar patterns were observed when comparing Nb and polygamy rates

either using a sibship size prior = 1 or no prior at increasing levels of marker

information (Figs. V.3 and V.4): the use of the prior reduced inferred polygamy levels

and increased Nb estimates in the three species. In the case of E. calamita and P. perezi,

using the prior raised estimates of Nb when little marker information was provided (less

Figure V.4. Arithmetic means (with 95% CIs) of the average number of mates per breeder male (dark

lines) and female (grey lines) obtained in ten replicated SF analyses using a sibship size prior = 1

(dashed lines) or no prior (solid lines) with increasing marker information.


163

than eight markers), thus approaching the convergent final estimates obtained with the

full marker set. However, estimates in analyses with and without the sibship size prior

in H. molleri did not reach convergence (Figs. V.3 and V.4). There was also a clear

convergence of estimates of Nb and (to a lesser extent) polygamy levels in E. calamita

and P. perezi with increasing sample size (Figs. V.5 and V.6). At low sample sizes (less

than 30 larval genotypes, Fig. V.5), Nb estimates remained stable in E. calamita, but

decreased in P. perezi. Results in H. molleri were, again, increasingly divergent with

increasing sample size.

Figure V.5. Harmonic means (with 95% CIs) of point estimates of Nb obtained in ten replicated SF

analyses with different subsample sizes using a sibship size prior = 1 (white dots) or no prior (black

dots).

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Discussion

Estimation of the Nb/Na ratio is critically dependent on the accuracy of estimates of both

Nb and Na. Independent field-based information and replicated analyses play an

invaluable role to assess the reliability of results. In the case of E. calamita we obtained

Nb/Na ratios of 0.28 and 0.18 in 2013 and 2015, respectively (Table V.1). These values

are higher than the effective/census size ratios reported by Rowe & Beebee (2004, they

calculated Ne rather than Nb) and Beebee (2009) for similar census-sized British

populations (i.e. with Na = 100-300), although both studies reported even higher ratios

Figure V.6. Arithmetic means (with 95% CIs) of the average number of mates per breeding male

(dark lines) and female (grey lines) obtained with different subsample sizes in ten replicated SF

analyses using a sibship size prior = 1 (dashed lines) or no prior (solid lines). Note the difference in

axe scales.


165

for small populations (> 0.5 and 1, respectively). However, these two studies did not

estimate Na by CMR methods, but instead estimated the total number of breeding adults

from counts of egg strings, on the basis that females of E. calamita usually lay a single

egg string per year (Denton & Beebee 1993). In our population, counts of egg strings of

E. calamita provided a minimum estimate for the number of successfully mating

females in the years of tadpole sampling (46 in 2013 and 104 in 2015) that very closely

matched the number of potential breeding females estimated by the CMR method (43 in

2013 and 125 in 2015, see Table V.1). These results are concordant with a female

breeding success close to one in our population, and support the hypothesis that counts

of egg strings are a good surrogate for the number of breeding females. On the other

hand, the estimated number of females and egg string counts clearly outnumbered the

actual number of dams inferred in SF analyses (23 in 2013 and 29 in 2015, Table V.1

and Appendix 8). This indicates that our offspring samples did not include a

comprehensive representation of all the mating pairs of the year, probably because of

the high mortality rate observed at the egg stage, due to early desiccation of the

ephemeral water bodies selected for breeding (we estimated a minimum of 16% of egg

strings lost due to early pond desiccation in our study area, unpublished obs.). The high

risk of breeding failure in E. calamita could result in differences in Nb depending on the

sampling stage (e.g. eggs or metamorphic individuals), in contrast to species with

preference for more predictable breeding sites (Phillipsen et al. 2010).

Effective/census size ratios in ranid frogs are typically higher than those reported

for bufonid species (Hoffman et al. 2004; Schmeller & Merilä 2007). In our P. perezi

population, we obtained an Nb/Na ratio of 0.5 (Table V.1). This value is within the range

reported for other ranid frogs (Brede & Beebee 2006; Schmeller & Merilä 2007;

Phillipsen et al. 2010; Ficetola et al. 2010), although this is the first study integrating

both SF estimates of Nb and CMR estimates of Na. In H. molleri, only the number of

adult males (126) could be estimated (Table V.1, Fig. V.1), so we could not calculate

the Nb/Na ratio in this species. Our sampling design does not seem to be optimally suited

to provide reliable estimates of the number of adult females in H. molleri (see also

Pellet et al. 2007; Broquet et al. 2009). A specific CMR sampling scheme suited to the

elusive breeding behaviour of females of H. molleri should be adopted in the future to

increase recapture rate.

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The accuracy of Nb estimates obtained by SF analyses depends on the correct

reconstruction of families, which can be hindered when genetic information is scarce or

the sample size is small compared to the real (unknown) Nb of the population (Wang

2016). Analyses of such datasets usually lead to unreliable family reconstruction mainly

due to type I error inflation caused by misidentification of unrelated or loosely-related

(e.g. cousins) individuals as full or half sibs (Wang 2016). In fact, false half sib

assignations are far more common than false full sib identifications in cases of low

marker information (Ackerman et al. 2016). This leads to inflated levels of polygamy

and biased Nb estimates. For this reason, both exploration of inferred families and

comparison with field observations of breeding activity are crucial crosscheck points to

identify possible analytical artifacts.

In our study area, inferred levels of polygamy varied among different species.

Oviposition in these three anuran species usually takes place when the female is in

amplexus with only one male (Arak 1988; García-París et al. 2004; Lengagne & Joly

2010). This suggests that each egg mass/string is only sired by one male and one

female, but there is no empirical evidence for this, and thus this question should be

further addressed with the help of markers such as the microsatellites used here. During

our 6-year monitoring program, we detected individual males and females of H. molleri

and P. perezi and males of E. calamita that remained in the breeding site during more

than 30 days in a single breeding season, thereby providing some chances for multiple

mating (Byrne & Roberts 2012). In contrast, we only detected two females of E.

calamita which remained more than eight days in Laguna de Valdemanco in a single

breeding season (11 and 15 days, respectively). Accordingly, we found low levels of

female (but also male) polygamy in the reconstructed families of E. calamita, both in

2013 and 2015 (Figs. V.2, V.4, and V.6). The average full sibship size in reconstructed

families of E. calamita was higher than 2.3 in both years, and most inferred parents

were identified among the genotyped adults (Table V.1 and Appendix 8). These results

were independent of the use of any sibship size prior, thus supporting the reliability of

mating system inferences. Epidalea calamita lays clutches in ephemeral puddles

(therefore reducing interspecific but increasing intraspecific competition), taking

advantage of their fast larval development (Gomez-Mestre & Tejedo 2002). Immediate

occupancy of these ephemeral sites after heavy rainfalls is, therefore, critical for

maximizing the opportunities for larvae to survive until metamorphosis. Within-year


167

monogamy might be a consequence of this breeding behavior. In contrast, we obtained

higher polygamy rates in P. perezi and H. molleri (Figs. V.2, V.4 and V.6 and Appendix

8), which is concordant with the longer time spent at the breeding site by individuals of

both sexes in these species. In view of the relatively large Nb/Na ratio observed in P.

perezi (Table V.1), polyandry could be interpreted as a strategy that allows this species

to maintain relatively high levels of genetic diversity in scenarios of low abundance

(Lengagne & Joly 2010; Byrne & Roberts 2012). Similar genetic compensation effects

have been previously documented in other anuran species (Beebee 2009; Hinkson &

Richter 2016). Alternatively, polyandry could be a consequence of a risk-spreading

strategy involving spatial and temporal division of clutches (Byrne & Roberts 2012).

In cases of artificially inflated polygamy in family reconstructions, setting a

sibship size prior = 1 could aid sibship reconstruction by preventing false sib

assignments. In the case of E. calamita and P. perezi, replicated analyses with different

sibship size priors, number of markers and sample sizes were highly convergent (Figs.

V.3 to V.6), supporting the reliability of our results. Thus, it was possible to compare

the final results (obtained with the complete dataset and full marker information) with

estimates obtained with subsampled datasets. In both species use of the lowest sibship

size prior (i.e. = 1) led to better Nb estimates (i.e. closer to final estimates) in cases of

both low sample size and low marker information (Figs. V.3 and V.5). In addition, in H.

molleri the use of a low sibship size prior also reduced polygamy levels and increased

the inferred number of parents and the corresponding Nb estimate. However, the lack of

final convergent results calls for caution when interpreting these Nb estimates and

highlights the need of additional genetic information to assess the magnitude of the

effect of using the sibship size prior in this species. Integrative studies addressing Nb

estimation by the SF method complemented with simulation studies will help provide

general guidelines for the use of sibship size priors in SF analyses.

Extension to Ne/Nc estimation

We have focused on the genetic estimate of Nb, a parameter that intuitively relates to the

number of breeders of the season (Waples & Antao 2014). Amphibians are typically

iteroparous breeders but different species show a wide range of variation in longevity

(García-París et al. 2004). Our most time-distant recaptures so far are two years for H.

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molleri, five years for P. perezi and six years for E. calamita. All these individuals were

initially marked as sexually mature adults, so time-distant recaptures are an

underestimate of their actual lifespan (Docampo & Milagrosa-Vega 1991; Patón et al.

1991; Banks et al. 1993; Esteban et al. 1996; Leskovar et al. 2006; Pellet et al. 2006a,

b). The integration of age information (for instance, from skeletochronological studies)

(Esteban et al. 1996; Friedl & Klump 1997; Leskovar et al. 2006; Sinsch 2015) into SF

analyses would allow calculation of key parameters, like generation length and age-

variation in breeding success (Wang et al. 2010). In consequence, the effective size in a

generation (Ne) could be estimated and compared with census size inferences based on

Na estimates (Waples 2005; Waples et al. 2011). Since Na is estimated from captures of

adult individuals in the breeding sites, the variation of Na over time will be due to

mortality/natality processes and the variation in attendance to breeding sites driven by

internal (e.g. energetic state) and environmental (e.g. meteorological conditions) factors

(Muths et al. 2006, 2013; Cayuela et al. 2014, 2016).

The ratio Ne/Na is more informative about evolutionary processes affecting

populations at larger temporal scales. Distinguishing between intrinsic reproductive

features and adaptive demographic strategies will require further exploration of these

patterns in a network of populations. The increasing accessibility to hundreds of

species-specific molecular markers and the analytical versatility of SF analyses in

COLONY for multiple species and mating systems, coupled with unparalleled

computation power, provide great opportunities for integrative demographic research.

This information will be in turn cornerstone for the interpretation of patterns of genetic

structure at larger scales and thus for the implementation of effective conservation

policies.

Acknowledgements

We thank M. Peñalver, L. San José, J. Gutiérrez, E. Iranzo, C. Valero, M. Rojo, G. Rodríguez,

J. Agüera, A. Sabalza, M. E. Guinea, J. Franco, J. Yanes, I. Vedia and A. Vilches for help

during fieldwork. Trent Garner and members of the Ecology, Evolution, and Development

Group at EBD provided valuable feedback on a preliminary draft of the manuscript. G.

Sánchez-Montes was funded by a predoctoral grant provided by the Asociación de Amigos de la

Universidad de Navarra and also benefited from funding from the Programa de ayudas de

movilidad de la Asociación de Amigos de la Universidad de Navarra. This research was funded

by grants CGL2008-04271-C02-01/BOS, and CGL2011-28300 (Ministerio de Ciencia e

Innovación -MICINN-, Ministerio de Economía y Competitividad -MEC-, Spain, and FEDER)


169

to IMS, who was supported by funding from the Spanish Severo Ochoa Program (SEV-2012-

0262). Authorizations for animal capture, marking and tissue sampling were provided by

Consejería de Medio Ambiente y Ordenación del Territorio. Comunidad de Madrid (Spain).

Data accessibility

The dryad archive (doi: TBO*) contains new microsatellite genotype data of the three species

and the CMR capture histories.

* Provisional repository available at: https://goo.gl/6n3pcu

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CHAPTER VI

MOUNTAINS AS BARRIERS TO GENE FLOW IN AMPHIBIANS: QUANTIFYING THE DIFFERENTIAL EFFECT OF A MAJOR MOUNTAIN RIDGE ON THE GENETIC STRUCTURE OF FOUR SYMPATRIC SPECIES WITH DIFFERENT LIFE HISTORY TRAITS

Sánchez-Montes G, Wang J, Ariño AH & Martínez-Solano I

Journal of Biogeography (Under review)

Mountains as barriers to gene flow

177

Abstract

Mountains, along with rivers and oceans, are the main topographic factors traditionally

associated with biogeographic breaks. Nevertheless, mountains usually act as more or less

permeable filters, which are more or less restrictive to gene flow in species differing in life

history traits. Studies comparing the genetic structure of species with different life history traits

in a shared landscape can thus provide comprehensive insights into the current and historical

role of mountains as barriers to gene flow. In this chapter, we test the role of a mountain range

(Sierra de Guadarrama, Central Spain) as a major barrier to gene flow in four co-distributed

taxa with different life history traits: Epidalea calamita, Hyla molleri, Pelophylax perezi and

Pelobates cultripes. We used larval genotypes of the four species scored at 15-18 microsatellite

loci (13-19 populations/species and 19-36 individuals/population) sampled on the northern and

southern slopes of Sierra de Guadarrama to describe genetic structure based on FST, migration

rates per generation, clustering algorithms and resistance by elevation surfaces. We also

obtained direct observations of individual displacements as a proxy of dispersal potential during

a seven-year monitoring project based on capture-mark-recapture (CMR). All species traveled

longer distances than those reported in the study area for P. cultripes (0.71 km). Individuals of

E. calamita traveled up to 3.55 km, followed by H. molleri (up to 2.84 km) and P. perezi (1.51

km). Pairwise FST estimates showed lower overall connectivity in P. cultripes. Average

migration rates per generation were low in all species, with some exceptions in populations of

H. molleri and P. cultripes located on the same slope. Clustering algorithms consistently

recovered well-differentiated population groups of P. cultripes in northern vs southern slopes,

but widely admixed areas were observed in the other three species, especially near mountain

passes. Resistance by elevation surfaces showed a strong barrier effect of Sierra de

Guadarrama in P. cultripes and suggested a potential role of topography in the genetic structure

of E. calamita and H. molleri. Altogether, our results show that Sierra de Guadarrama is

currently acting as a strong barrier to gene flow for P. cultripes and, to a lesser extent, for E.

calamita, H. molleri and P. perezi. This differential effect can be at least partly explained in

terms of their different life history traits, including dispersal potential. Our findings support the

general role of the Central System as a key feature shaping population connectivity and the

distribution of genetic variation in amphibian communities.

Keywords: Connectivity, Dispersal, Isolation by distance, Genetic clustering, Landscape

genetics, Migration rates per generation.


179

Resumen

Las montañas son, junto con los ríos y los océanos, los principales elementos topográficos

tradicionalmente asociados con discontinuidades biogeográficas. Sin embargo, las montañas

actúan normalmente como filtros semipermeables, que ejercen un efecto barrera al flujo génico

de diferente intensidad en especies con diferentes características vitales. Por tanto, los estudios

que comparan la estructura genética en especies con diferentes características vitales en un

mismo paisaje pueden proporcionar una visión más completa sobre el papel de las montañas

como barreras al flujo génico, tanto en el presente como en el pasado. En este capítulo se

estudia el papel de un sistema montañoso (la Sierra de Guadarrama, en España central) como

barrera al flujo génico en cuatro especies simpátricas con diferentes características vitales:

Epidalea calamita, Hyla molleri, Pelophylax perezi y Pelobates cultripes. Se utilizaron

genotipos de larvas de las cuatro especies (13-19 poblaciones/especie y 19-36

individuos/población genotipados en 15-18 microsatélites) recolectadas en poblaciones situadas

en las laderas norte y sur de la Sierra de Guadarrama para describir su estructura genética

mediante la estima de FST, tasas de migración por generación, análisis de afinidad y modelos

basados en superficies de resistencia por elevación. También se registraron desplazamientos de

individuos de estas especies durante siete años con un proyecto de monitorización basado en

captura-marcaje-recaptura (CMR), para tener una estima de la capacidad de dispersión de cada

especie. Todas las especies mostraron desplazamientos más largos que los registrados en

individuos de P. cultripes en el área de estudio (hasta 0,71 km). Epidalea calamita fue la

especie en la que se registraron mayores desplazamientos individuales (hasta 3,55 km), seguida

por H. molleri (hasta 2,84 km) y P. perezi (1,51 km). Las estimas de FST entre pares de

poblaciones sugieren una menor conectividad regional en el caso de P. cultripes. Las estimas de

tasas de migración por generación fueron bajas en todas las especies, aunque con algunas

excepciones en poblaciones de H. molleri y P. cultripes localizadas en la misma ladera de la

sierra. Los análisis de afinidad mostraron una clara diferenciación entre las poblaciones

localizadas en las laderas norte y sur de la Sierra de Guadarrama en P. cultripes, pero en las

otras tres especies se observaron amplias zonas de mezcla, especialmente en áreas cercanas a los

puertos de montaña. Los análisis de resistencia por elevación mostraron un fuerte efecto barrera

de la Sierra de Guadarrama en P. cultripes y sugieren un posible papel de la topografía en la

estructura genética de E. calamita y H. molleri. En conjunto, los resultados muestran que la

Sierra de Guadarrama actúa como una importante barrera al flujo génico para P. cultripes y, en

menor medida, para E. calamita, H. molleri y P. perezi. Este efecto diferente puede explicarse,

al menos en parte, por las diferentes características vitales que presentan las cuatro especies,

incluyendo su capacidad de dispersión. Por tanto, los resultados sugieren que el Sistema Central

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es un elemento clave para entender la conectividad regional y la distribución de la variación

genética en comunidades de anfibios.


181

Introduction

Identifying long-term barriers to gene flow is a major goal of historical biogeography.

Mountains, along with rivers and oceans, are the main topographic factors traditionally

associated with biogeographic breaks. A significant effect of mountains in restricting

gene flow among populations has been reported in a variety of taxa including plants

(Wei et al. 2013), amphibians (Lougheed et al. 1999; Funk et al. 2005; Emel & Storfer

2012; Vörös et al. 2016), and mammals (Janssens et al. 2008; Zalewski et al. 2009). As

a consequence of this barrier effect, gene flow across mountain ridges is sometimes

directed through mountain passes (Pagacz 2016), thus potentially establishing similar

connectivity corridors for different species.

In amphibians, slope and elevation have been shown to affect population

connectivity (Arntzen 1978; Funk et al. 2005; Martínez-Solano & González 2008;

Richards-Zawacki 2009; Emel & Storfer 2014; McCartney-Melstad & Shaffer 2015;

Pereira et al. 2016). Mountain ridges are commonly regarded as barriers to amphibian

gene flow (Lougheed et al. 1999; Funk et al. 2005; Emel & Storfer 2012), and their

effect may have been especially intense during the peaks of the different glacial periods.

Nevertheless, mountains do not usually act as absolute barriers but rather as more or

less permeable filters, which are more or less restrictive to gene flow in species

differing in life history traits associated with their ecological performance. For example,

species with different dispersal potential, breeding behaviour or physiological

constraints on traits affecting their altitudinal range limits are expected to respond

differently to topography, and in consequence will show some differences in their

patterns of spatial genetic structure across shared landscape features (Steele et al. 2009;

Richardson 2012). In the long term, these differences in patterns of regional

connectivity may scale up, with implications for lineage differentiation and speciation.

Studies comparing the genetic structure of species with different life history traits in a

shared landscape can thus provide comprehensive insights into the current and historical

role of mountains as barriers to gene flow.

The Iberian Peninsula is one of the best examples of the ‘refugia within refugia’

paradigm, where a prominent role of topographic features has been invoked to explain

current patterns of endemism at the specific and intraspecific levels (Gomez & Lunt

2007; Abellán & Svenning 2014). In particular, the orientation of major mountain

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ranges along west-east axes has been hypothesized to constrain latitudinal population

expansion/contraction events in response to climatic changes during the Pleistocene.

Among these, the Central System mountains have been often considered to represent a

historical barrier to gene flow across different taxonomic groups. For instance, the

ranges of several amphibian species find their distributional limit in the Iberian Central

System (Martínez-Solano et al. 2006; Arntzen & Espregueira Themudo 2008; Díaz-

Rodríguez et al. 2015; Reino et al. 2017). Furthermore, the Central System mountains

separate well differentiated intraspecific clades in other species (Gonçalves et al. 2009;

Gutiérrez-Rodríguez et al. 2017a).

However, concordance between topographic features and genetic breaks does

not unambiguously imply a causal role of the putative barrier in shaping genetic

structure. Similar patterns can arise because of the confluence of lineages near the

hypothesized barrier that underwent previous differentiation in other areas.

Furthermore, despite broad scale concordance in some cases, comparative studies often

reveal differences in the relative strength of putative barriers across taxa, implying

individual responses may be mediated by differences in key life history traits.

Therefore, assessing the differential role of a putative barrier in shaping genetic

structure across taxa requires addressing 1) whether the putative barrier acts as a barrier

in the present, disrupting patterns of population connectivity, and 2) the consistency of

the barrier effect across species with different life history traits (e.g. Richardson 2012).

To answer these questions, several molecular-based approaches have been proposed that

allow testing the relative effect of different landscape features on regional patterns of

gene flow (Cushman et al. 2006; Landguth et al. 2010; Blair et al. 2012). These

approaches will provide more robust inferences under a comparative approach, since

species with differences in life history traits like size, activity patterns, longevity,

reproductive investment or habitat and breeding site preferences are expected to show

different population dynamics and ecological requirements, and therefore will respond

differently to sharp ecological gradients such as those associated with high mountain

ridges. In addition, differences in the dispersal potential across species have an obvious

impact on regional patterns of population connectivity. However, this information is

generally unavailable and thus rarely accounted for. Nevertheless, direct field

observations on individual spatial displacements in wild populations recorded in long-

term capture-mark-recapture (CMR) studies can provide key information to understand


183

how local dynamics scale up to shape patterns of regional structure in different species

(Berry et al. 2004; Fedy et al. 2008; Frei et al. 2016; Pagacz 2016; Reid et al. 2016).

Here we explicitly test the potential role of Sierra de Guadarrama (a segment of

the Iberian Central System) as a major barrier to gene flow in four sympatric amphibian

species: the natterjack toad Epidalea calamita, the Iberian treefrog Hyla molleri, Perez’s

frog Pelophylax perezi and the Western spadefoot toad Pelobates cultripes. These four

species have different morphology, life history traits, habitat preferences and altitudinal

distribution limits (see Table VI.1), and thus they are expected to be differentially

affected by Sierra de Guadarrama in terms of regional connectivity. We complement a

previous study providing information on dispersal potential in one of the species (P.

cultripes) with new data on the other three species based on a seven-year CMR study in

a locality in the southern slope of Sierra de Guadarrama, which reveals differences in

dispersal potential among the four species (Fig. VI.1). To further investigate differences

in the relative permeability of Sierra the Guadarrama as a barrier to gene flow, we

sampled 13-19 populations per species and inferred patterns of genetic structure based

on four different genetic approaches: calculation of pairwise genetic distances (FST),

estimation of migration rates per generation and the use of genetic clustering algorithms

and resistance by elevation surfaces. We discuss observed differences in the relative

role of Sierra de Guadarrama in shaping regional patterns of genetic structure in the

four species in regard to their life history traits and dispersal potential.

Table VI.1. Differences in morphology, life history traits, habitat preferences, movement capabilities and topographic distributional limits among E. calamita, H. molleri, P. perezi

and P. cultripes. SVL: snout-to-vent length; Longv.: longevity; Matur.: age of sexual maturation; Veg. cover prefer.: vegetation cover preference; Disp.: maximum recorded

dispersal; Mig.: maximum recorded migration; Alt.: Maximum recorded elevation across the species’ range of distribution (in metres above sea level).

Species SVL

range (mm)

Activity Longv. (years)

Matur. (years)

Breeding site

selection

Length of

larval period

Veg. cover prefer.

Disp. (m)

Mig. (m)

Alt. References

E. calamita 31.3-98 nocturnal 10-17 2-3 lentic 24-54

days grassland 4,411 2,600 2,500

Beebee (1983), Boomsma & Arntzen (1985), Banks & Beebee (1987), Banks et al. (1993), Denton & Beebee (1993), Tejedo et al. (1997), Gomez-Mestre & Tejedo (2002), García-París et al. (2004), Leskovar et al. (2006), Sinsch et al. (2010), Oromi et al. (2012), Trochet et al. (2014).

H. molleri 35-45 preferentially

nocturnal - - lentic

3

months

forest,

shrubland,

grassland

- - 2,140

Barbadillo (1987), García et al. (1987), Márquez-M. de Orense & Tejedo-Madueño (1990), García-París et al. (2004), Márquez et al. (2005), Martínez-Solano (2006).

P. perezi 41.6-110 diurnal and

nocturnal 4-6 1-3

lotic and

lentic

2-4

months

forest,

shrubland,

grassland

- - 2,380

Díaz-Paniagua (1986), Lizana et al. (1987), Docampo & Milagrosa-Vega (1988, 1991), Patón et al. (1991), Real & Antúnez (1991), Báez & Luis (1994), Esteban et al. (1996), Fernández-Cardenete et al. (2000), Díaz-Paniagua et al. (2005), Trochet et al. (2014).

P. cultripes 36.8-125 nocturnal 12 2 lotic and

lentic

3-4

months

shrubland,

grassland 710 - 1,770

Salvador et al. (1986), Álvarez et al. (1990), Cejudo (1990), Talavera (1990), Lizana et al. (1994), Díaz-Paniagua et al. (2005), Leclair et al. (2005), Marangoni & Tejedo (2007), Trochet et al. (2014), Gutiérrez-Rodríguez et al. (2017b).


185


Study area, targeted species and dataset collection

The study was conducted in the Sierra de Guadarrama mountain range, in the eastern

end of the Iberian Central System (Fig. VI.2). This massif runs in a SW-NE direction

and marks the limit between the Spanish provinces of Segovia (on the northern slope)

and Madrid (on the southern slope). It has 13 peaks above 2000 m.a.s.l. (the highest

elevation is Peñalara at 2428 m.a.s.l.), and the lowest elevations are found at both

extremes in the Alto del León (SW, 1510 m.a.s.l.) and Somosierra (NE, 1445 m.a.s.l.)

passes (Fig. VI.2). Three additional passes are located in Navacerrada (1858 m.a.s.l.),

Figure VI.1. Map of the Valdemanco area (Madrid, Spain, see inset) showing the location of the main

breeding site (A: Laguna de Valdemanco, photograph in the lower left corner) and four secondary

breeding sites (B: a water trough 230 m away from A, C: a quarry with ephemeral ponds 395 m away

from A, D: an abandoned swimming pool 680 m away from A, and E: a mining pond 710 m away from

A). The pie chart in Laguna de Valdemanco (A) shows the number of individuals of each species

(white: E. calamita, black: H. molleri, light grey: P. perezi, dark grey: P. cultripes) that were marked

and recaptured only in A. Photographs of these species are shown on the right, with E. calamita, H.

molleri, P. perezi and P. cultripes from top to bottom, respectively. Pie charts in B, C, D and E show

the number of individuals of each species for which the longest recorded displacement was from A to

B, C, D or E, respectively (i.e. every individual is represented in only one pie chart: the chart

corresponding to the most distant breeding site from A where it was captured). Recorded

displacements of P. cultripes are summarized from Gutiérrez-Rodríguez et al. (2017b).

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186

Cotos (1829 m) and Navafría (1774 m, see Fig. VI.2). Regional climate is

Mediterranean with cold winters and mild dry summers, although the asymmetry of the

massif results in heterogeneity of microclimates among different areas (López-Sáez et

al. 2014). Average annual rainfall in the Navacerrada meteorological station (see the

location of the Navacerrada mountain pass in Fig. VI.2) is 1223 mm, although mean

values vary substantially among different months, from 23 mm in July to 176 mm in

November (AEMET, 2017). Lower elevations in Sierra de Guadarrama are covered by

Figure VI.2. Patterns of genetic structure obtained in structure with K = 2 for the four species in Sierra de Guadarrama. For each sampled population (see Table VI.2 for abbreviations), colours of pie charts represent the proportion of alleles corresponding to each of the two inferred clusters (represented by black and white colours, respectively) obtained in admixture analyses. The locations of the five lowest mountain passes are indicated with a star. Background colours represent altitudinal ranges and the highest reported limits for P. cultripes (1770 m), H. molleri (2140 m) and P. perezi (2380 m).


187

forests of oak trees (Quercus ilex subsp. ballota, Q. pyrenaica) and pines (Pinus

sylvestris, P. nigra). Above 1600 m.a.s.l. the landscape is dominated by shrubs, and

alpine grasslands and meadows occupy the highest altitudes (López-Sáez et al. 2014).

Table VI.2. List of sampled localities for each species (Ecal: E. calamita, Hmol: H. molleri, Pper: P. perezi

and Pcul: P. cultripes), with their abbreviations (Abr), geographic coordinates (Coord), elevation in m.a.s.l.

(Elev), and the number of tadpole tissue samples obtained in each locality (Sample size). Further

information about the E. calamita, H. molleri and P. perezi samples can be found in Sánchez-Montes et al.

(2017).

Locality Abr Coord Elev Sample size

Ecal Hmol Pper Pcul

Alameda del Valle ALA 40.91º N 3.85º W 1104 - 24 - -

Arcones ARC 41.13º N 3.73º W 1142 30 - 19 -

Arroyo Tejada TEJ 40.67º N 3.74º W 850 - - - 30

Berrocal BRC 41.06º N 3.98º W 1098 - 30 - -

Bustarviejo BUS 40.85º N 3.68º W 1092 30 28 30 21

Cabanillas de la Sierra CAB 40.85º N 3.65º W 1009 22 30 20 27

Cerceda CER 40.72º N 3.96º W 1031 20 30 23 30

Collado Hermoso HER 41.05º N 3.93º W 1193 23 - 32 20

Colmenar Viejo COL 40.69º N 3.83º W 854 21 30 - 30

Dehesa de Roblellano ROB 40.86º N 3.63º W 1072 30 36 23 29

El Berrueco BER 40.93º N 3.57º W 927 21 29 20 30

Fuenterrebollo FUE 41.33º N 3.93º W 909 20 - 20 31

Gargantilla del Lozoya GAR 40.95º N 3.72º W 1074 - 30 - -

Gascones GAS 41.01º N 3.65º W 1035 21 - - -

La Pradera de Navalhorno PRA 40.88º N 4.03º W 1192 22 30 23 30

Lozoyuela LOZ 40.92º N 3.65º W 1107 - 28 - -

Medianillos MED 40.76º N 3.68º W 933 21 - 25 -

Muñoveros MUN 41.20º N 3.95º W 906 - 32 - -

Navafría NAV 41.06º N 3.83º W 1180 - 30 - -

Puerto de Canencia CAN 40.81º N 3.68º W 1477 25 28 22 -

Puerto de La Morcuera MOR 40.87º N 3.76º W 1720 30 20 22 -

Puerto del Medio Celemín CEL 40.84º N 3.83º W 1248 - 30 - -

Rascafría RAS 40.88º N 3.66º W 1516 20 - 22 -

Santo Tomé del Puerto STO 40.85º N 3.91º W 1121 - 30 21 30

Sauquillo de Cabezas SAU 41.19º N 3.59º W 911 20 - 22 -

Soto del Real SOT 41.19º N 4.06º W 936 20 30 - 30

Torrecaballeros TOR 40.76º N 3.80º W 1127 34 - - -

Turrubuelo TUR 41.00º N 4.02º W 1042 21 - 21 30

Up to 15 amphibian species can be found in Sierra de Guadarrama, although

many of them become rare above mid elevations (1000-1500 m.a.s.l., Martínez-Solano

2006). We focused our study on four anuran species that are widely distributed across

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both slopes of Sierra de Guadarrama: E. calamita, H. molleri, P. perezi and P.

cultripes. Populations of the four species in Sierra de Guadarrama have been reported

at maximum altitudes of 2200 m.a.s.l. for E. calamita, 2140 m for H. molleri, 2170 m

for P. perezi and 1470 m for P. cultripes (Martínez-Solano 2006). These altitudinal

limits are surpassed in other areas of the Iberian distribution range of E. calamita (up to

2500 m, García-París et al. 2004), P. perezi (up to 2380 m, Fernández-Cardenete et al.

2000) and P. cultripes (up to 1770 m, Cejudo 1990). These four species show additional

differences in life history traits with potential implications for their regional persistence

and the connectivity of their populations across high mountain ridges (Table VI.1).

Some of these traits, like a larger size, increased longevity, facultative diurnal activity,

fast larval development or high dispersal potential might be advantageous for

maintaining gene flow in alpine environmental conditions, and therefore differences in

regional genetic structure would be expected.

To obtain inferences about individual dispersive patterns, we recorded direct

observations of displacements as part of a seven-year (2010-2016) CMR monitoring

project performed in an assemblage of the four species near the locality of Valdemanco,

Madrid (see Fig. VI.1). Laguna de Valdemanco and other secondary breeding sites

nearby were surveyed on a yearly basis since 2010, with multiple CMR sessions

performed every year for each species. In each CMR session, all sexually mature

individuals found during visual encounter surveys were captured, sexed based on

morphological characters and marked with an AVID M.U.S.I.C transponder (EzID,

Greeley, Colorado, USA), supplied with an identity code readable with an AVID

Minitracker II device. During this seven-year period we performed 219 CMR sessions,

and marked 1086 adult E. calamita (427 of them were further recaptured in at least one

subsequent CMR session, with a maximum of 23 recaptures per individual), 599 H.

molleri (153 were further recaptured, up to a maximum of seven recaptures per

individual) and 662 P. perezi (325 were further recaptured, up to a maximum of 10

recaptures per individual). Dispersal events of marked adults of the three species from

Laguna de Valdemanco to nearby breeding sites were recorded from direct visual

encounters during the monitoring program (see Fig. VI.1). The minimum cumulative

distances covered by each individual were calculated by summing the distances between

consecutive recorded locations. To compute these cumulative distances we only

accounted for movements longer than the longitude of the main axis of the Laguna de


189

Valdemanco flooding area (i.e. 125 m). During the same seven-year period, 824 adult P.

cultripes were marked in the study area (440 were further recaptured, up to a maximum

of 17 recaptures per individual); recorded displacements were reported in Gutiérrez-

Rodríguez et al. (2017b).

We used larval genotypes of the four species (15-18 microsatellite loci per

species, n = 19-36 individuals per population) from 13-19 populations per species

across both slopes of Sierra de Guadarrama (Table VI.2, Fig. VI.2). Genotypes of E.

calamita, H. molleri and P. perezi were obtained from the dataset published in Sánchez-

Montes et al. (2017). From that dataset we excluded sample localities containing less

than six non-full sib individuals to avoid unreliable inferences derived from few full sib

families in some genetic samples (Anderson & Dunham 2008; Rodríguez-Ramilo &

Wang 2012; Sánchez-Montes et al. 2017). We also excluded Laguna de Valdemanco

from the dataset because tissue sampling in that locality was more exhaustive than in the

remaining populations to address different research questions (Sánchez-Montes et al.

2017). Additionally, we obtained larval samples of P. cultripes in 13 localities across

the study area (total n = 368, between 20 and 31 individuals per population, see Table

VI.2 and Fig. VI.2) following the survey method described in Sánchez-Montes et al.

(2017). We used 16 published microsatellite loci (Gutiérrez-Rodríguez & Martínez-

Solano 2013) to genotype the samples of P. cultripes following the laboratory and allele

calling procedures described in Sánchez-Montes et al. (2016).

Genetic analyses

Pairwise population genetic distances and tests of IBD

We used the G-statistics subroutine in GENALEX (Peakall & Smouse 2006) to estimate

FST values (Wright 1943, 1951) between all pairs of populations in each species and

performed 9999 permutations to assess the significance of each value after applying the

Bonferroni correction for multiple tests as 0.05/n, where n is the number of pairwise

comparisons in each species. Since individual genotype data in each population were

obtained from single cohort tadpoles, we assessed the possible effect caused by an

excess of full sibs in the samples by recalculating FST estimates after identifying and

removing all but one member of each inferred full sib family using COLONY (Jones &

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Wang 2010; Sánchez-Montes et al. 2017). Although removing all relatives from the

samples is not recommended because it can introduce additional bias in some genetic

analyses, we only compared the analysis results of FST by including and excluding full

siblings for exploratory purposes (Sánchez-Montes et al. 2017; Waples & Anderson

2017). We then used CoDiDi (Wang 2015) to test for the utility of each marker set for

unbiased FST or GST (Nei 1973) estimation. This program calculates the correlation

between gene diversity and GST across markers for each dataset. A significantly

negative correlation implies that mutation rate is at least as important as migration rate

in determining the genetic divergence of the populations, and therefore multilocus

average GST values might underestimate the actual genetic differentiation between

populations (Wang 2015).

We then used GENALEX to test for isolation by distance (IBD) patterns within

each of the two slopes of the mountain range by measuring the correlation between

genetic (FST) and geographic distances among all pairs of populations located within the

same slope in each species. Pairwise geographic distances were calculated from

Latitude/Longitude coordinates with GENALEX, which uses a modification of the

Haversine formula (Sinnott 1984). For each species, we performed two simple Mantel

tests, each one including only the populations located either on the northern or on the

southern slope of Sierra de Guadarrama, with 9999 permutations per test.

Migration rates per generation

We estimated migration rates per generation between all pairs of populations in each

species using BayesAss (Wilson & Rannala 2003). We ran five replicate analyses per

species with 1,000,000 burn-in and 10,000,000 iteration steps; adjusted mixing

parameters for allele frequencies (ΔA), inbreeding coefficients (ΔF) and migration rates

(ΔM) to situate acceptance rates in the Markov chain Monte Carlo (MCMC) runs

between 20-60%, in accordance with the authors’ instructions; and checked the

concordance of results by quantifying the differences among migration rate estimates

across the five different runs.


191

Clustering analyses

We employed three different clustering analyses to characterize the genetic structure of

the four species in Sierra de Guadarrama. For each method, we implemented two

different approaches with the aim of a) finding the number of clusters (K) best

explaining the variation in the data and b) focusing on K = 2 to assess the membership

probabilities of each individual (or population) to each of the two main inferred clusters.

First, we performed unsupervised Bayesian clustering analyses in structure

(Pritchard et al. 2000). We explored the maximum likelihood configurations for each

value of K, from one to the total number of sampled localities in each species. For each

K value we performed ten replicates using an admixture model with correlated allele

frequencies and 500,000 burn-in and 1,000,000 iteration steps (Pritchard et al. 2000;

Falush et al. 2003). We summarized clustering results using CLUMPAK (Kopelman et al.

2015) and explored the likelihood of different K values using the original (based on

likelihood scores, Pritchard et al. 2000) and the ΔK (‘Evanno’) methods (Evanno et al.

2005) in STRUCTURE HARVESTER (Earl & vonHoldt 2012). Second, we performed

discriminant analysis of principal components (DAPC, Jombart et al. 2010) using the R

package adegenet (Jombart 2008; R Development Core Team 2009). We selected the

minimum number of principal components required to account for at least 90% of the

variation contained in the data, explored the best value of K between one and 25 (thus

encompassing the total number of populations in each species) and computed individual

membership probabilities to each of the inferred clusters. Third, we used GENELAND

(Guillot et al. 2005) to perform spatially explicit clustering analyses. As in DAPC

analyses, we explored the best value of K between one and 25 to encompass the total

number of populations of each species. Then, we performed ten different runs for each

species with K = 2, each run implementing a correlated allele frequency model with

100000 iterations, a thinning of 100, and an uncertainty of 0.01 in spatial locations to

avoid individuals sampled in the same population being invariably assigned to the same

cluster altogether.

Landscape genetic analyses

We also employed a landscape genetics-based causal modeling approach (Cushman et

al. 2006, 2013) to test for the barrier effect of Sierra de Guadarrama, while also

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accounting for the roles of elevation and geographical distances on observed genetic

distances among populations. Causal modeling is based on a set of partial Mantel tests

aimed to assess the relative support for different candidate models explaining observed

genetic distances. The set of candidate models typically includes measures based on

Euclidean distances, the absolute barrier effect of a landscape feature and one or more

resistance surfaces assigning different resistance values to the landscape patches in the

area of study. The true model is expected to show significant correlation between the

relevant measure (distance, barrier or resistance) and observed genetic distances after

partialling out the effect of alternative measures, while the correlation of these

alternative measures with genetic distances after partialling out the effect of the true

relevant measure should not be significant (see Cushman et al. 2006, 2013). To

construct our elevation-based resistance measures, we first obtained a digital elevation

model of Sierra de Guadarrama with 200 metres of resolution from the Centro

Nacional de Información Geográfica (Instituto Geográfico Nacional, Ministerio de

Fomento, Gobierno de España, http://centrodedescargas.cnig.es/CentroDescargas/). We

then constructed four different resistance surfaces, all of them assuming a linear

relationship between elevation and resistance (resistance = elevation), but with this

linear effect starting at different minimum altitude thresholds (0, 1000, 1500 and 2000

m.a.s.l.), with elevations below that threshold having a resistance value equal to the

threshold. We used the R package POPGENREPORT (Adamack & Gruber 2014) to

calculate the least cost paths between all pairs of populations in each species with each

of the four pre-defined elevation-based resistance models using an eight-pixel nearest-

neighbour approach, and to construct the matrices of genetic (based on Nei’s GST) and

Euclidean distances. The resistance matrix for the barrier effect was constructed by

assigning a resistance value of ‘0’ to all pairwise comparisons involving populations

located on the same slope, and ‘1’ to all comparisons between populations located on

opposite slopes. Finally, we used the R package ECODIST (Goslee & Urban 2007) to

implement partial Mantel tests to assess the relative support for each model.


193

Results

Dispersal potential

In our seven-year CMR monitoring study, long cumulative movements were recorded in

some individuals of E. calamita (Fig. VI.3), but only two marked males were found in a

breeding site more than 400 metres away from Laguna de Valdemanco (see Fig. VI.1).

However, these two individuals moved at least two and five times, respectively,

between Laguna de Valdemanco and a mining pond located more than 700 metres away

during the seven-year period. These two and one additional male summed each one a

cumulative distance of more than 1420 m (up to a maximum of 3550 m), highlighting

the high dispersal capacity of this species (Fig. VI.3). We found several marked

individuals of H. molleri and P. perezi in different breeding sites more than 600 metres

away from Laguna de Valdemanco and not connected by aquatic corridors (see Fig.

VI.1), either in the same season or among different years. One male of H. molleri

moved at least four times between Laguna de Valdemanco and the mining pond in three

years, for a cumulative distance of 2840 m (Fig. VI.3). Several medium- and long-

distance displacements (between 680 and 1510 m) were also recorded in both males and

females of P. perezi (Fig. VI.3). Gutiérrez-Rodríguez et al. (2017b) reported eight

displacements of P. cultripes from Laguna de Valdemanco to nearby breeding sites, five

of them covering a distance of more than 700 metres (710 m, see Table VI.1 and Figs.

VI.1 and VI.3).

Genetic analyses

Pairwise population genetic distances and tests of IBD

We did not find any negative correlation between gene diversity and GST in any of the

four marker sets (results not shown), which supports the reliability of multilocus FST and

GST estimates in the four species. Therefore, FST values were averaged among all loci in

each species to estimate pairwise genetic distances between populations (Wang 2012,

2015). Additionally, FST estimates were not affected by the presence of full sibs in the

P. perezi samples, and only slight over- (in E. calamita and P. cultripes) or

underestimations (in H. molleri) were detected in the other species (see Appendix 9).

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Almost all pairwise FST estimates were significantly > 0 after applying the

Bonferroni correction (Fig. VI.4 and Appendix 9). The highest pairwise FST values (>

0.2) were obtained in P. cultripes, especially among populations located on different

slopes of Sierra de Guadarrama (Fig. VI.4 and Appendix 9). In this species BER was

the most differentiated population and scored the highest FST values, even with some

localities from the same slope like SOT and TEJ (Appendix 9). In H. molleri,

comparisons involving TOR or COL scored the highest pairwise FST values (maximum

Figure VI.3. Recorded cumulative distances covered by individuals of the four species in the

Valdemanco area (see Fig. VI.1). The number of individuals only recaptured at less than 100 meters

from the marking location (i.e. E. calamita: 400 individuals, H. molleri: 145, P. perezi: 269, P. cultripes:

419) was much higher than the number of dispersers in all species, so the lowest distance category of

each histogram (0-100 m) has been truncated for clarity (dashed line). Recorded displacements of P.

cultripes are summarized from Gutiérrez-Rodríguez et al. (2017b).


195

FST was 0.147), whereas in P. perezi the localities involving higher FST comparisons

with the remaining populations were BER and ARC (maximum FST = 0.142). The

maximum FST value in E. calamita was 0.082, and COL was the most differentiated

population (Appendix 9).

We found significant evidence of IBD within the northern slope (Segovia) in P.

cultripes (R = 0.762, p = 0.020), while E. calamita and H. molleri did not show

evidences of IBD (E. calamita: R = -0.056, p = 0.525; H. molleri: R = -0.302, p =

Figure VI.4. Relation between genetic (FST) and geographic distances among all pairs of populations

located on the southern (dark circles) or the northern slope (white circles) of Sierra de Guadarrama.

Pairwise distances involving CER and the remaining populations in the southern slope are

represented by black triangles.

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0.166) and P. perezi showed a significant negative relationship between genetic and

geographic distances (R = -0.403, p = 0.025). In the southern slope (Madrid) none of the

four species showed evidences of IBD (E. calamita: R = 0.323, p = 0.094; H. molleri: R

= 0.271, p = 0.088; P. perezi: R = 0.239, p = 0.234; P. cultripes: R = 0.347, p = 0.098),

although removing the extreme southwestern population of CER from the analyses

revealed significant IBD patterns in three of them (E. calamita: R = 0.593, p = 0.001; H.

molleri: R = 0.339, p = 0.044; P. perezi: R = 0.411, p = 0.131; P. cultripes: R = 0.407, p

= 0.018, see Fig. VI.4).

Migration rates per generation

Estimated migration rates per generation were concordant across the five replicate runs

in the four species. Mean (and maximum) differences in the estimated non-migrant

proportion of each population across the five replicates were 0.031 (0.156) in E.

calamita, 0.030 (0.133) in H. molleri, 0.012 (0.100) in P. perezi, and 0.047 (0.225) in P.

cultripes. Average pairwise migration rates were low in all species (~0.01) except

among populations located on the same slope in P. cultripes (mean = 0.03, see

Appendix 9). This high intra-slope average rate in P. cultripes was driven by some

widely connected populations both in the northern (FUE, STO and TUR) and in the

southern (CAB with COL, TEJ and ROB) slopes (see Appendix 9). Migration rates

dropped sharply beyond short geographic distances (c. 10 km) in P. perezi and,

especially, in E. calamita. In contrast, H. molleri and P. cultripes still maintained

migration rates close to 0.2 between some populations up to 40 km away, although these

high rates were only found among populations located within the same slope of Sierra

de Guadarrama (Fig. VI.5 and Appendix 9).

Clustering analyses

Unsupervised Bayesian clustering analyses in structure yielded increasing likelihood

values in models with increasing number of clusters (K), although tending to

stabilization at large K values (see Appendix 10). The ΔK method yielded K = 2 as the

optimal partition for E. calamita, P. perezi and P. cultripes (Fig. A10.1 in Appendix

10). Two clearly differentiated clusters, with little to no genetic admixture, were


197

recovered in P. cultripes, each located at either slope of Sierra de Guadarrama (Fig.

VI.2 and Appendix 10). In E. calamita, H. molleri and P. perezi, northern and southern

clusters separated by Sierra de Guadarrama were also recovered at K = 2, with admixed

populations near mountain passes (Fig. VI.2 and Appendix 10). In E. calamita, the

northern cluster was composed by all populations in the northern slope of Sierra de

Guadarrama, with admixed populations including SOT and CER (in the southwest) and

ALA (in the Lozoya Valley). The northern populations of MUN, BRC and PRA also

showed high admixture with the southern cluster (Fig. VI.2 and Appendix 10). In H.

molleri, almost every population clustered with the localities of its respective slope

Figure VI.5. Estimated migration rates as a function of geographic distance between populations

located in the same (dark circles) or on different slopes (white circles) of Sierra de Guadarrama.

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when K was set to two, although widely admixed populations were also found,

especially near the mountain passes of Somosierra, Cotos and Navacerrada (Fig. VI.2

and Appendix 10). The optimum partition in this species was K = 3, which resulted in

TOR and PRA clustering together differentially within the northern cluster. The

remaining populations received almost the same clustering assignation as with K = 2. In

P. perezi, the inferred northern cluster included all localities in the northern slope along

with RAS in the Lozoya Valley. The southern populations of CER, MED, MOR and

CAN also showed high admixture with the northern cluster (Fig. VI.2 and Appendix

10). Further partitions with K = 3 to 5 showed hierarchical structure in the four species

caused by differentiated populations within each slope of Sierra de Guadarrama, but

with little additional admixture across opposite slopes (Appendix 10).

Table VI.3. Results of the landscape genetic causal modeling approach. Partial Mantel tests evaluate the

effects of four different elevation-based resistance surfaces (Elev, Elev1000, Elev1500 and Elev2000, with

the linear relationship between elevation and resistance starting at 0, 1000, 1500 and 2000 m.a.s.l.,

respectively), a barrier effect (Bar) and Euclidean distances (Eucl) on observed genetic distances (Gen).

Models are named after the dependent variable (Gen) ~ the tested effect | and the partialled out

covariable. Significant results at the 0.05 level are marked in bold.

Model E. calamita H. molleri P. perezi P. cultripes

R p R p R p R p

Gen~Bar | Eucl 0.086 0.242 0.207 0.017 0.125 0.081 0.471 <0.001

Gen~Eucl | Bar 0.169 0.169 0.025 0.419 -0.076 0.653 -0.031 0.552

Gen~Elev | Eucl 0.131 0.229 0.281 0.026 -0.026 0.568 0.209 0.118

Gen~Elev | Bar 0.188 0.116 0.069 0.308 -0.109 0.738 -0.079 0.680

Gen~Eucl | Elev -0.060 0.623 -0.232 0.923 0.022 0.449 -0.131 0.786

Gen~Bar | Elev 0.030 0.404 0.145 0.059 0.147 0.060 0.452 0.001

Gen~Elev1000 | Eucl 0.181 0.141 0.317 0.011 -0.041 0.604 0.218 0.105

Gen~Elev1000 | Bar 0.201 0.100 0.075 0.296 -0.110 0.747 -0.069 0.654

Gen~Eucl | Elev1000 -0.119 0.738 -0.275 0.961 0.037 0.420 -0.150 0.817

Gen~Bar | Elev1000 0.026 0.420 0.146 0.064 0.147 0.065 0.452 0.001

Gen~Elev1500 | Eucl 0.250 0.002 0.175 0.038 -0.029 0.595 0.062 0.330

Gen~Elev1500 | Bar 0.187 0.146 0.033 0.392 -0.083 0.670 -0.044 0.577

Gen~Eucl | Elev1500 -0.231 0.995 -0.162 0.942 0.028 0.415 -0.039 0.609

Gen~Bar | Elev1500 0.067 0.300 0.197 0.022 0.130 0.085 0.470 0.001

Gen~Elev2000 | Eucl 0.120 0.158 -0.010 0.541 0.045 0.339 -0.020 0.556

Gen~Elev2000 | Bar 0.179 0.153 0.021 0.431 -0.075 0.641 -0.039 0.562

Gen~Eucl | Elev 2000 -0.098 0.785 0.024 0.400 -0.046 0.657 0.043 0.371

Gen~Bar | Elev2000 0.078 0.275 0.208 0.018 0.125 0.090 0.473 0.001


199

Best K values obtained in DAPC analyses were > 2 for all species, suggesting

strong genetic structure in the four species. The best K value was between four and

seven in E. calamita, K = 8 in H. molleri, between 7-8 in P. perezi and between 10-11 in

P. cultripes (results not shown). These high K values were more in agreement with the

original likelihood-based method in structure than with the ΔK method (see Fig. A10.1

in Appendix 10). Individual admixture results for K = 2 to 5 with DAPC were similar to

those obtained with structure in P. perezi and P. cultripes (see Appendix 10). In

contrast, the strong genetic differentiation of PRA and TOR drove the main clustering

partitions in H. molleri, and no strong structure was observed in E. calamita (Appendix

10).

Best K values obtained with GENELAND were largely concordant with the total

number of populations in each species (results not shown). These high K values were

again consistent with strong genetic structure, as in DAPC analyses and the original

method in structure. Results with K = 2 showed wide variation among different runs for

E. calamita, H. molleri and P. perezi, as shown in Fig. A10.10 in Appendix 10. While

the northern and southern clusters were clearly and consistently discriminated at K = 2

in the case of P. cultripes, results are more variable and inconsistent in the other three

species (Fig. A10.10 in Appendix 10).

Landscape genetic analyses

The landscape-based causal modeling approach revealed a strong effect of Sierra de

Guadarrama as a barrier to gene flow for P. cultripes, since the barrier effect showed

highly significant correlations with genetic distances after partialling out the remaining

candidate measures, while none of the remaining models showed significant support

(Table VI.3). The sets of partial Mantel tests suggested a potential role of elevation on

the genetic structure of E. calamita and H. molleri, although this effect was not fully

supported based on the expectations of causal modeling. In H. molleri, three of the four

resistance surfaces (with resistance = elevation starting at 0, 1000 and 1500 m.a.s.l.,

respectively) yielded significant relationships between genetic and resistance-based

least cost distances after partialling out the effect of Euclidean distances, but not after

partialling out the effect of the barrier (Table VI.3). Furthermore, the barrier effect

showed significant correlation with genetic distances after partialling out the effect of

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Euclidean distances and also after partialling out the resistance surfaces with the two

highest thresholds (i. e., 1500 and 2000 m.a.s.l., see Table VI.3). In E. calamita, on the

other hand, only the surface in which the linear effect of elevation started at 1500 m of

elevation was significantly supported after partialling out the effect of Euclidean

distances, but not after partialling out the barrier effect. None of the models tested in P.

perezi showed significant results (Table VI.3).

Discussion

Our results show that the effect of mountains in shaping regional patterns of genetic

structure can be quite different even in species that are commonly found in syntopy, like

our four study species, which often form breeding assemblages wherever their ranges

overlap in the Iberian Peninsula (Álvarez & Salvador 1984; Salvador & Carrascal

1990). These differences can be related to historical, intrinsic (life history traits), and/or

extrinsic factors (Wang et al. 2013); comparative studies can provide valuable insights

on their relative importance and illuminate the process of community assemblage.

Mountain systems act as long-term barriers to gene flow; however, their permeability as

barriers can be dynamic through time. Even with essentially the same topography, the

barrier effect of a mountain system may change dramatically from interglacial to glacial

periods (Pereira et al. 2016). Our results indicate that Sierra de Guadarrama is acting as

a current barrier to gene flow for P. cultripes and, to a lesser extent, for E. calamita, H.

molleri and P. perezi. If this effect is significant in the present interglacial period, it is

safe to assume that it was probably stronger during the Pleistocene, when glaciers

covered large areas in Sierra de Guadarrama (Domínguez-Villar et al. 2013). This

long-term effect could explain the phylogeographic breaks found in P. cultripes

(Gutiérrez-Rodríguez et al. 2017a) and H. molleri (Sánchez-Montes & Martínez-Solano,

unpublished data), two species showing a clear north-south subdivision in the Iberian

Peninsula and meeting at the Central System mountains.

All genetic approaches provided evidences of the current effect of Sierra de

Guadarrama as a barrier to gene flow, but the four species showed different patterns of

connectivity across the mountain ridge. Some of these differences can be explained in

terms of variation in some key life history traits, particularly dispersal potential, with

the less vagile species (Pelobates cultripes) showing the most pronounced genetic


201

break. Pelobates cultripes is a strictly nocturnal species with a long larval period, and it

shows the narrowest altitudinal range among the study species (Table VI.1). This may

reflect physiological constraints, although other factors, like their dependence on soils

adequate for their fossorial habits cannot be ruled out. Altogether, these traits could

favour a more pronounced phylopatric behaviour in this species, restricting regional

connectivity.

In contrast, we obtained high migration rates per generation at larger geographic

distances (up to 40 km) in P. cultripes, although only among populations located on the

same slope of Sierra de Guadarrama (Fig. VI.5 and Appendix 9). Although some

migration rate estimates could be imprecise due to the high number of populations

analyzed and the relatively low sample sizes, the estimated non-migrant fraction never

switched between the bounds of the prior distribution in none of the analyses, thus

supporting the reliability of our inferences (see Meirmans 2014). These high inferred

migration rates per generation might result from a very low number of migrants per year

in long-lived species, like P. cultripes, which can live up to 12 years in this area

(Talavera 1990, see Table VI.1). In this scenario, rare long dispersal events can easily

pass unnoticed for CMR studies using passive integrated transponder (PIT) tags, like in

our case, because typically large areas cannot be uniformly monitored. In any case, the

strong barrier effect exerted by Sierra de Guadarrama is well supported based on the

high overall population differentiation (Appendix 9) as well as the consistency of results

of the clustering and causal modeling approaches (Table VI.3, Fig. VI.2 and Appendix

10). The barrier effect may explain the absence or rarity of this species at higher (>

1500 m.a.s.l.) elevations (Cejudo 1990) and the strong phylogeographic signal

associated to the Central System (Gutiérrez-Rodríguez et al. 2017a). In fact, mountain

passes in Sierra de Guadarrama are above the higher reported altitudes for this species

except for both extremes (Somosierra and Alto del León, Fig. VI.2). Connectivity of

northern and southern clusters seems only possible through these two passes in

principle, although we did not detect admixed populations (Fig. VI.2).

We also found high migration rates per generation among some distant

populations in H. molleri, although only within the southern slope (Fig. VI.5 and

Appendix 9), probably due to the fragmented distribution of this species in the northern

slope of Sierra de Guadarrama (Márquez 2002), which was also reflected in the high

differentiation of the PRA and TOR populations (Appendix 10). The taxonomy of the

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Hyla arborea species group was recently revised, resulting in the recognition of the

Iberian H. molleri as a separate taxon at the species level (Stöck et al. 2008, 2012; Barth

et al. 2011; Gvozdík et al. 2015). Several important knowledge gaps about the life

history of this species remain to be filled (see Table VI.1). In this respect, dispersal

distances reported in this study represent the first direct records of medium-distance

dispersal for H. molleri (also for P. perezi) across a terrestrial landscape matrix. It

should be noted that, although reported movements involved adult individuals

displacing between breeding sites and thus potentially implying actual gene flow, we

could not verify that marked individuals bred at both sites, and therefore it is not

possible to properly distinguish between migration and dispersal movements. In any

case, our direct observations of recorded movements revealed the high dispersal

potential of H. molleri (Figs. VI.1 and VI.3), which probably favors regional population

connectivity (Fig. VI.4 and Appendix 9), and the colonization of highlands (more than

2100 m.a.s.l., see Table VI.1). However, our causal modeling analyses suggest a

potential role of elevation (and also of the mountain as a barrier) on the genetic

distances observed in our study area, implying that topography may to some extent

restrict across-slope gene flow in H. molleri (Table VI.3). These results are in

agreement with a role of Sierra de Guadarrama as a semi-permeable barrier to gene

flow in this species, as also suggested by the wide connected areas identified among the

two major (northern and southern) clusters obtained in clustering analyses (Fig. VI.2

and Appendix 10).

A similar scenario of Sierra de Guadarrama as a semi-permeable barrier to gene

flow was inferred for E. calamita and P. perezi. These two species showed high overall

connectivity across the study area (especially E. calamita, see Appendix 9) despite low

inferred migration rates per generation (Appendix 9), and also showed the widest

altitudinal range among the study species (Table VI.1). The high regional connectivity

observed in E. calamita and P. perezi is in line with the high inferred dispersal potential

of both species, based on CMR data (see Table VI.1 and Figs. VI.1 and VI.3). In

addition, two life history traits related to breeding site selection may contribute to

regional connectivity in the two species. On the one hand, E. calamita usually selects

ephemeral ponds for breeding, where competence is low because of the high mortality

risk associated with early drying but they perform well due to their extremely fast larval

development (by far the shortest among the four species, Table VI.1). This trait allows


203

E. calamita to successfully exploit extremely small and shallow (but also widely

available, even above the treeline at high altitudes) breeding sites, which represents an

advantage to colonize new areas and probably contributes to maintain high levels of

population connectivity. On the other hand, tadpoles of P. perezi require longer

hydroperiod ponds to complete their development (Table VI.1), but this species uses a

wide variety of breeding sites including streams, natural or artificial ponds, water

troughs and urban, degraded, salty or polluted areas (Egea-Serrano 2014). This

ecological breadth probably represents an advantage, allowing this species to maintain

high regional connectivity.

Overall, the good support for high K values in structure (based on the original

method, Appendix 10) and also in DAPC and GENELAND analyses indicate that the four

species also show different hierarchical levels of genetic substructure at finer spatial

scales (within slopes, Appendix 10). Future studies applying causal modeling

approaches on a broader range of resistance surfaces will yield further insights about the

role of other factors (vegetation cover and heterogeneity, land use/cover) in shaping

amphibian population connectivity at the landscape scale. Our integrative approach

combining field-based and molecular approaches to estimate population connectivity in

four co-distributed anurans allowed us to explicitly test for the first time the role of

Sierra de Guadarrama as a barrier to gene flow. Our results show that these mountains

have played a major role in disrupting historical and current connectivity across

populations on different slopes, but differently so depending on life history traits such as

breeding strategy and dispersal capacity. The mountains act as a full isolating barrier in

one species (P. cultripes), but they are semi-permeable in E. calamita, H. molleri and P.

perezi, with potential corridors located near mountain passes. These results highlight the

major role of the Central System Mountains as a key feature shaping historical patterns

of population connectivity across taxa, promoting population divergence and the

evolution and accumulation of endemicity.

Acknowledgements

We thank P. Gómez, M. Peñalver, L. San José, J. Gutiérrez, E. Iranzo, L. Carrera, C. Valero, M.

Rojo, G. Rodríguez, J. Agüera, A. Sabalza, N. Escribano, R. Goñi, R. Santiso and I. Miqueleiz

for help during fieldwork and J.W. Arntzen, Krystal Tolley and three anonymous reviewers for

useful suggestions to improve the manuscript. G. Sánchez-Montes was funded by a predoctoral

grant provided by the Asociación de Amigos de la Universidad de Navarra and benefited from

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funding from the Programa de ayudas de movilidad de la Asociación de Amigos de la

Universidad de Navarra. This research was funded by grants CGL2008-04271-C02-01/BOS,

and CGL2011-28300 (Ministerio de Ciencia e Innovación -MICINN-), Ministerio de Economía

y Competitividad -MEC-, Spain, and FEDER) to IMS, who is currently supported by funding

from the Spanish Severo Ochoa Program (SEV-2012-0262).

Data accessibility

The dryad archive (doi: TBO*) contains new microsatellite genotype data of P. cultripes.

* Provisional repository available at: https://goo.gl/6n3pcu


205

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CHAPTER VII

GENERAL DISCUSSION

General discussion

213

This dissertation integrates genetic analyses and individual-based monitoring

approaches to obtain reliable demographic inferences regarding local effective/census

size ratio (Nb/Na, Chapter V) and regional patterns of gene flow (Chapter VI). Assessing

the reliability of estimates obtained in wild populations, in which the actual, ‘true’

values are unknown, was possible due to a suited analytic design (replicated SF analyses

for Nb estimation, multi-year robust design analyses for Na estimation and multiple

genetic approaches for gene flow characterization) combined with direct fieldwork-

based records of breeding activity and cumulative movements. Prior to these analyses,

the suitability of the new molecular tools developed and applied in this work (species-

specific microsatellites) to address demographic and taxonomic questions was explored

in Chapters III and IV. In the following paragraphs, I summarize the main findings of

this dissertation and discuss their implications for taxonomic, demographic and

conservation research.

In the last two decades, species-specific microsatellites have proven their

usefulness as versatile tools for evolutionary research, by providing valuable insights on

population dynamics and the distribution of genetic diversity in a wide variety of taxa

(Selkoe & Toonen 2006). While the current trend is to generate large datasets with

thousands of genome-wide markers, like SNPs, microsatellites still play a relevant role

due to their high polymorphism, which provides high power of resolution in a

conveniently sized dataset (Hauser et al. 2011; Hess et al. 2011). This dissertation

contributes to the global toolkit of permanent molecular resources with three sets of new

microsatellite markers specifically optimized for three pond-breeding amphibian

species: E. calamita, H. molleri and P. perezi (n = 16, 18 and 15 markers, respectively,

Chapters III and IV). The optimization of species-specific microsatellites significantly

improves the efficiency of demographic analyses by avoiding problems associated with

cross-amplification of markers from more or less distantly related taxa, like low

amplification success or the presence of null alleles. The sets described in this

dissertation are the first markers specifically designed for H. molleri and P. perezi, and

complement markers previously developed for E. calamita (Rowe et al. 1997; Rogell et

al. 2005; Faucher et al. 2016). All the loci in the three sets were polymorphic in our

regional-scale multi-population sample (17-21 localities per species, 19-96 individuals

per locality), and showed values of mean allelic richness (AR) between 1.05 (marker

Hmol3.7) and 21.14 (marker Bcal4.26, see Table IV.2). Except for the noted case of

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Pper4.13 and Pper4.23 (Chapter III), no consistent evidences of linkage disequilibrium

(LD) across markers were detected, and therefore they can be regarded as unlinked loci

in population studies. Six of the markers in the E. calamita set showed evidences of null

alleles in some populations (Bcal4.21, Bcal4.6, Bcal4.14, Bcal4.2, Bcal3.26 and

Bcal3.19, Tables A1.1-A1.6), and thus their effect on downstream analyses should be

accounted for (e.g. by replicating analyses with subsampled numbers of markers, see

Chapter V). Combined multi-locus polymorphism in each set of markers was sufficient

to allow individual identification and therefore the three sets proved useful for

addressing fine-scale demographic questions.

In addition to the utility of the three microsatellite sets for demographic studies,

seven of the P. perezi markers also proved useful for taxonomic assignment in the P.

ridibundus x P. perezi hybridogenetic complex (Chapter III). Molecular resources are

essential for delineating the distribution ranges of both parental species and their hybrid

taxon P. kl. grafi, because species identification based on morphological features is

problematic (Crochet et al. 1995; Rivera et al. 2011; Ferrer & Filella 2012). Up to 78

private alleles of the two parental species, distributed among the seven microsatellite

markers, were identified in the dataset analyzed in Chapter III (Fig. III.3), even though

we focused on a relatively small area and sample sizes were small (Table III.2, Fig.

III.1). Although beyond the scope of this dissertation, the combination of the new

microsatellite markers with mitonuclear sequence information allowed us to expand our

knowledge about the geographic extension of the area of hybridization along the

Llobregat river and, probably, the Llierca basin (Chapter III). Future integrative studies

applying the high-resolution genetic tools presented in this dissertation to more

comprehensive sampling designs will shed light on the origin, prevalence and

demographic consequences of hybridogenesis in Western Palearctic waterfrogs.

At the intraspecific level, demographic research has greatly benefited from the

widespread availability of molecular resources, but several practical issues compromise

their application and the reliability of inferences. One example is the problem of

assessing the representativeness of a genetic sample, which is an essential albeit often

neglected requisite to obtain reliable demographic inferences (Waples 2015). Chapter

IV focused on two of the most common sources of bias affecting sample

representativeness, and therefore, compromising the reliability of subsequent

demographic inferences: sampling an excessive proportion of relatives and determining

General discussion

215

the minimum required number of sampled individuals. Regarding the former issue, it is

in fact difficult to establish what would be an ‘appropriate’ proportion of close relatives

in the sample, because the structure of the population is frequently the object of study in

empirical demographic studies, and therefore the real proportion of relatives in the

population is usually unknown (Waples & Anderson 2017). In a recent paper based on

simulations, Waples & Anderson (2017) found that removing all but one of the full

siblings in each full sib family in the sample, as previously suggested (for instance,

Goldberg & Waits 2010) could lead to even more severely biased estimates of allelic

frequencies, population differentiation and effective population size, except when the

original samples included some large families along with unrelated individuals. They

suggested removing relatives in large families but leaving small families intact as a

general guideline, although no solution worked best for all simulated scenarios (Waples

& Anderson 2017). Furthermore, pedigree information is seldom available in empirical

studies, and relatives are usually identified in a probabilistic (and therefore prone to

error) manner. Much insight on this as yet unresolved issue has been made using

computer simulations, but the potential biases associated with sampling excessive

relatives should also be addressed in empirical datasets. This dissertation contributes to

this topic by exploring the effect of close relatives on genetic diversity characterization

in an extensive multi-population dataset.

In Chapter IV of this dissertation, I compared estimates of the basic genetic

diversity indexes commonly used in population genetics studies, both including and

excluding full siblings in samples from 17-21 populations of E. calamita, H. molleri and

P. perezi (Tables A1.4-A1.6). Estimates of both observed (HO) and expected

heterozygosity (HE) and AR remained very similar, and only FIS estimates were slightly

affected by the presence of full sibs in the sample (Table IV.2). The observed effect of

close relatives on FIS estimates was accordingly reflected in the tests for Hardy-

Weinberg equilibrium (HWE) and LD, and could be theoretically explained (Appendix

5). These results reveal that, in the absence of strongly unbalanced data structure (i.e.

when there are not very large families combined with unrelated individuals in the

sample), genetic diversity characterization is robust to the presence of close relatives in

the sample. Nevertheless, the strong differences observed in the tests of HWE and LD in

some populations call for caution when checking for departures from expected

genotypic proportions, especially in small samples (Tables IV.2 and A1.4-A1.6). On the

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one hand, removing an excessive number of full sibs might improve results of both tests

in some populations, therefore allowing distinction between real departures from

equilibrium and sampling artifacts (Appendix 5). On the other hand, uncertainties

regarding inferences on relatedness caused by both limited genetic information and the

lack of direct pedigree information, coupled with insufficient sample sizes after the

removal of putative siblings, could result in a poor performance of HWE and LD tests in

some cases. Unfortunately, as stated above, no general guidelines can yet be proposed to

control for the effect of excessive relatives in genetic samples from wild populations.

However, it may be a good practice to attempt sibling identification and check their

effect on the particular demographic analyses employed in each study. Pilot studies are

extremely useful in this respect, and might save time and resources in demographic

research by aiding the design of representative sampling schemes, especially by

informing about the size of the effect of close relatives in the sample and providing

insights about the minimum sample size required for accurate genetic diversity

characterization.

In fact, assessing the sufficiency of sample is essential in any statistical

procedure (Cochran 1977). In demographic studies, characterization of genetic diversity

is usually addressed by the estimation of population-level indexes, like AR and HE,

calculated from combined multi-locus genotypes, which then allow further comparison

among populations and testing different demographic hypotheses. However, these basic

indexes of genetic diversity are differently affected by sample size. While samples of

~20 individuals per population have been shown sufficient to obtain consistent

(asymptotical) estimates of HE, AR is strongly dependent on sample size (Miyamoto et

al. 2008; Pruett & Winker 2008; Hale et al. 2012). Our results confirmed this pattern,

since samples of ~20 individuals were sufficient to obtain reliable estimates of HE,

while more than 50 individuals were required for estimating AR in the particular case

study presented in this dissertation (Fig. IV.2). This difference between the two indexes

stems from the fact that HE accounts for allelic frequencies, while AR measures the

number of different allelic classes. Both indexes, in turn, are two particular cases of a

continuous profile of diversity characterization (Chao & Jost 2015, see also Figs. A3.1-

A3.3). This difference between the two genetic diversity indexes must be taken into

account when designing demographic studies, since sample size requirement will

depend on which of the two measures is used to characterize genetic diversity.

General discussion

217

Furthermore, results in Chapter IV revealed that markers with different allelic

frequencies show widely different accumulation curves with increasing sample size,

also leading to differences in the minimum sample size required to approach asymptotic

estimates (Figs. A2.1-A2.3).

As a result, in genetic-based demographic studies, the minimum required sample

size depends on the research focus, the markers employed, and the genetic structure of

the target population(s). Therefore, the sufficiency of sample for the specific

demographic analyses in each case should be carefully checked in pilot studies. Such

studies inform on the minimum sample size required or recommended for the particular

aims of the study, but also aid in the selection of markers for the composition of an

adequate or optimal marker set, by exploring the behaviour of each individual marker in

genetic diversity characterization. This approach was illustrated in Chapter IV, where a

new method for calculating minimum sample size for single-locus genetic diversity

characterization was also introduced (Figs. A2.1-A2.3). Although establishing any

threshold criterion for calculating a minimum sample size implies some degree of

arbitrariness, inspection of accumulation curves to assess the performance of each

marker in estimating each index with increasing sample size represents a revealing

exploratory approach. This procedure allows exploring the structure of the data and also

aids in marker set composition and in making decisions on sample sizes when

necessary, e.g. for demographic studies or monitoring programs.

After exploring the performance of the three marker sets and the effect of full

siblings and sample size in the multi-population datasets, two demographic studies were

addressed in Chapters V and VI. In Chapter V, we demonstrated the advantages of

integrating genetic and individual-based capture-mark-recapture (CMR) data for

estimating local-scale demographic parameters. In particular, I proposed and developed

an integrative approach to estimate the Nb/Na ratio and to assess the reliability of

estimates. As argued in Chapters I and V, this ratio measures the effective size of a

population (in terms of the contribution of adult breeders to a single-cohort offspring)

relative to the abundance of sexually mature adults in that population (Frankham 1995;

Palstra & Fraser 2012). Consequently, it provides invaluable information for the

assessment of population status, because it accounts for possible reproductive

constraints in the population that remain undetected by census estimates because of

time-lag effects. However, estimation of Nb and Na is complicated, requiring intensive

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field work and genetic analyses to obtain accurate estimates of both parameters (see

Chapters I and V). For that reason, until recently, few studies have reported robust

estimates of the Nb/Na ratio, and as a consequence our knowledge of the variation of the

Nb/Na ratio in natural populations (and therefore its dependence on the life history traits

of different organisms) is fragmentary (Palstra & Fraser 2012).

Fortunately, recent developments in genetic methods for Nb estimation and CMR

formulations for Na estimation provide an excellent opportunity to fill this knowledge

gap, as illustrated in Chapter V, by combining the sibship frequency (SF) method to

estimate Nb (Wang 2009) and robust design models to estimate Na (Pollock 1982;

Kendall et al. 1995). The results presented in Chapter V show that reliable Nb/Na ratios

can be estimated in seasonal-breeding species, for which these two methods are

especially suited (see also Boxes 2 and 3 in Chapter I). Assessing the reliability of Nb

estimates is straightforward provided that field-based evidences of breeding activity can

be obtained to supervise/cross-check the families reconstructed in the SF analytical

procedure. This integrative approach is especially convenient for temperate amphibians,

in which seasonal sampling can be adapted to their breeding phenology. As illustrated in

Chapter V, individual-based data gathered during the CMR sessions provided

information about the average time spent by individuals of each sex in the breeding

sites, which then contributed to interpret the polygamy levels inferred in SF analyses

(Figs. V.4 and V.6). Also, tissue samples obtained at the time of marking adult

individuals allowed creating a dataset of genotyped candidate sires and dams, which

also aided family reconstruction (Appendix 8). Finally, egg string counts provided an

estimate of the maximum number of breeding females of E. calamita, which could then

be compared to the number of breeding males and females inferred in SF analyses

(Table V.1 and Fig. V.1). Comparing this field-based information to inferred sibship

and parentage relationships is a powerful strategy to assess the reliability of inferences,

on which the estimate of Nb is based (Wang 2009). In a similar way, comparison of SF

estimates in replicated subsampled analyses using different analytic settings and levels

of genetic information (i.e. varying the number of markers and the sample size)

represents a complementary approach to assess the reliability of inferences by checking

for the convergence of results, as also illustrated in Chapter V (Figs. V.3-V.6).

This dissertation applies for the first time an integrative demographic approach

for estimating the Nb/Na ratio in an assemblage of three Iberian pond-breeding

General discussion

219

amphibian species (E. calamita, H. molleri and P. perezi, Table V.1). The study yielded

reliable results for E. calamita and P. perezi, but the Nb/Na ratio could not be calculated

for H. molleri because the recapture rate of adult females was insufficient for estimating

Na and, additionally, no reliable Nb estimates could be obtained for this species (Chapter

V). Performing verification protocols capable of assessing the non-reliability of an Nb

estimate is an important checkpoint, but is often neglected. In the case study presented

in Chapter V, polygamy levels inferred for H. molleri in SF analyses (close to an

average of two mates per breeding male and female) were the highest among the three

species. Artificially inflated polygamy levels in reconstructed pedigrees can result from

false half sib assignations in cases of low marker information (Ackerman et al. 2016;

Wang 2016). However, our field-based information provided cues arguing against such

artifacts, because the wide temporal windows along which individuals of both sexes

were recorded at the breeding site offered opportunities for multiple mating. Also, while

we could not obtain direct evidences of multiple mating in H. molleri, and indeed there

is not such information about the mating system of this species in the literature,

polygamous behaviour has been documented in the closely related species H. arborea

(Broquet et al. 2009, see also Chapter I). Consequently, although the high polygamy

levels obtained in H. molleri may be partly caused by inaccurate family reconstruction,

our field-based information did not conclusively challenge the reliability of inferred

polygamy levels. In contrast, replicated subsampled analyses showed high sensitivity to

variations in the analytic settings (i.e. in the use or not of prior information about the

average family sibship size, see Figs. V.3-V.6). Furthermore, estimates of Nb increased

monotonically with increasing marker information and sample size, and did not reach

asymptotic stabilization as in the other two species, indicating sample and marker

information is insufficient to estimate Nb (Figs. V.3 and V.5). Thus, replicated analyses

were crucial to assess the non-reliability of H. molleri estimates, highlighting their

complementary role in the validation of demographic inferences when field-based

evidence is inconclusive.

On the other hand, reliable estimates were obtained for E. calamita and P.

perezi. Estimates of the Nb/Na ratio obtained for E. calamita in the two years of

sampling (0.28 in 2013 and 0.18 in 2015, see Table V.1) were higher than

effective/census size ratios documented for British populations of this species (Rowe &

Beebee 2004; Beebee 2009), although previous studies did not apply the comprehensive

CHAPTER VII

220

integrative approach described and applied here, and results are thus not directly

comparable across studies. The results here support the hypothesis that populations of

some bufonid species often perform far below their breeding potential in terms of the

long-term maintenance of genetic diversity (Scribner et al. 1997; Rowe & Beebee 2004;

Brede & Beebee 2006; Beebee 2009). This may be a consequence of a mating system

that is strongly constrained by their explosive breeding strategy, which is dependent on

unpredictable weather conditions and could favor monogamy within each breeding

season (Chapter V). This is in agreement with the low polygamy levels inferred from

family reconstruction in SF analyses (Figs. V.4 and V.6), which were further confirmed

by field-based evidences of breeding activity (see Chapter V). The possible implications

of this hypothesized weather-mediated reproductive constraint call for further research

to identify the main factors driving local evolutionary processes and long-term

persistence of populations of E. calamita. These constraints could also be more

generally involved in trait-mediated lineage differentiation in bufonids (Van Bocxlaer et

al. 2010; Liedtke et al. 2016). At any rate, observed differences between effective and

census population sizes in E. calamita show that accurate Nb estimation is critical to

properly inform population monitoring efforts in this species, because a high census size

is not necessarily associated with a healthy population in terms of its genetic diversity.

In contrast, a higher Nb/Na ratio was estimated for P. perezi (0.5, see Table V.1).

This ratio is at the upper end of the range reported for ranid frogs (0.06-0.68, see Brede

& Beebee 2006; Schmeller & Merilä 2007; Phillipsen et al. 2010; Ficetola et al. 2010),

although, as stated above, none of these studies applied the comprehensive integrative

approach presented in this dissertation. Inferred polygamy levels (close to an average of

1.5 mates per breeding male and female) were intermediate with respect to E. calamita

and H. molleri, and these values were consistent and convergent in replicated analyses,

and concordant with field-based evidences (Figs. V.4 and V.6). The population of P.

perezi studied in Chapter V showed higher Nb than E. calamita, despite much lower

census abundance (Table V.1). The subsequent question that arises is to what extent the

difference in Nb/Na ratios observed between the two species is caused by taxon-specific

reproductive constraints linked to life history traits or to population-specific genetic

compensation mechanisms (Beebee 2009; Hinkson & Richter 2016). More studies

applying this integrative demographic approach in networks of populations of different

General discussion

221

species will certainly provide key clues on this and related questions regarding natural

variation in Nb/Na ratios and its relationship with life history traits.

The last demographic study included in this dissertation (Chapter VI) employed

a multi-population dataset to explore the regional-scale connectivity of populations of

four pond-breeding amphibian species (the three species previously mentioned plus P.

cultripes, see Chapter I). A combination of genetic approaches was used to obtain

inferences about population differentiation (FST analyses), migration (migration rates

per generation) and genetic structure (clustering analyses), and to test the role of

topography on observed genetic distances among populations (causal modeling).

Altogether, these approaches provided evidences that Sierra de Guadarrama (a major

mountain massif in the Iberian Central System) is acting as a strong barrier to gene flow

for P. cultripes, and as a semi-permeable barrier for E. calamita, H. molleri and P.

perezi (Chapter VI). The Iberian Central System is considered one of the major

biogeographic barriers shaping the distribution of the Iberian batrachofauna (Martínez-

Solano et al. 2006; Arntzen & Espregueira Themudo 2008; Gonçalves et al. 2009; Díaz-

Rodríguez et al. 2015; Gutiérrez-Rodríguez et al. 2017; Reino et al. 2017). However, its

actual role as a current barrier to gene flow had not been assessed before.

Results in this dissertation suggest that gene flow across both slopes of Sierra de

Guadarrama is strongly impeded in P. cultripes (Table VI.3, Fig. VI.2). This is in

agreement with the strong phylogeographic discontinuity associated to the Iberian

Central System in this species (Gutiérrez-Rodríguez et al. 2017). This lack of inter-

slope gene flow may be linked to the fossorial habits and breeding strategy of this

species, or to possible physiological constraints (Table VI.1), since P. cultripes has not

been reported in elevations above 1770 m.a.s.l. (Cejudo 1990). The three major

mountain passes in Sierra de Guadarrama exceed this elevation, and only the passes at

both extremes of the massif are below this threshold altitude (Chapter VI). While

connectivity across slopes is, therefore, possible at these passes, none of our sampled

localities showed significant signs of admixture among populations in different slopes

(Figs. VI.2, A10.8-A10.10). On the other hand, the relatively high migration rates (close

to 0.2 in some cases, Table A9.8) inferred among populations in the same slope suggest

P. cultripes is capable of maintaining non-negligible levels of gene flow among

populations up to 40 km apart (Fig. VI.5). Although these inferred high dispersal

capabilities could not be confirmed with direct long-distance movement records (Fig.

CHAPTER VII

222

VI.3), all genetic evidences suggest that the mountain massif of Sierra de Guadarrama

indeed represents a strong topographic barrier for dispersal in this species.

In contrast, Sierra de Guadarrama acts as a semi-permeable barrier for E.

calamita, H. molleri and P. perezi. Results showed wide areas of genetic admixture

among populations on both slopes in the three species, associated with the main

mountain passes and the Lozoya Valley (Figs. VI.2 and A10.2-A10.7). Populations of

the three species in the Sierra de Guadarrama range have been reported at much higher

altitudes than the elevations of the mountain passes (Fernández-Cardenete et al. 2000;

García-París et al. 2004; Martínez-Solano 2006), and thus the massif was expected to

condition but not fully preclude gene flow across slopes. Accordingly, low average FST

values (< 0.07) were inferred in the three species (especially in E. calamita, see Tables

A9.1-A9.3 and Fig. VI.4), and high migration rates were estimated in some species, like

H. molleri (Tables A9.5-A9.7 and Fig. VI.5). Direct records of long-distance cumulative

movements supported the high dispersal capacity of the three species (Fig. VI.3). In

addition, the breeding strategies of these species could favour regional connectivity by

possibly maintaining metapopulation dynamics (H. molleri, see the case of the close

species H. arborea in Carlson & Edenhamn 2000; Arens et al. 2006), or allowing the

exploitation of ephemeral (E. calamita, see Table VI.1 and Beebee 1983) or a wide

variety of degraded and anthropized breeding sites (P. perezi, Egea-Serrano 2014).

Thus, the findings here support the idea that major topographic features such as

mountain massifs play an important role in shaping regional patterns of genetic structure

in amphibians. Furthermore, differences in regional connectivity between different

species may be associated to life history traits related to reproductive strategies, which

could help explain potential consequences for lineage diversification in the long term.

These results and the approach employed can be applied for the implementation and

validation of ‘biological corridors’ in conservation policies and environmental impact

assessments.

In summary, this dissertation contributes three new sets of highly polymorphic

microsatellite markers for three Iberian pond-breeding amphibian species, and

demonstrates their potential as a research tool for demographic studies. Application of

the three sets to extensive multi-population datasets provided details about the different

performance of individual markers, which represents an important piece of information

for aiding in marker set configuration optimally suited to specific research questions.

General discussion

223

Additionally, a new exploratory analytical method based on cumulative curves with

increasing sample size was proposed for designing sampling protocols in demographic

research, with wide application to different species and types of genetic information.

Finally, two demographic studies were developed in a multi-species, multi-scalar

framework, which illustrated the advantages of integrating multiple genetic- and field,

individual-based approaches to provide reliable inferences of the effective/census size

ratio and gene flow in different species. This novel integrative approach takes full

advantage of the most recent molecular and statistical methods available and proves

useful for addressing major challenges in evolutionary research and biodiversity

conservation.

CHAPTER VII

224

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CHAPTER VIII GENERAL CONCLUSIONS

General conclusions

229

General conclusions

1. The three new sets of microsatellite markers specifically developed for E.

calamita, H. molleri and P. perezi proved useful for fine-scale genetic diversity

characterization, and thus they can be readily applied in evolutionary,

demographic and conservation research.

2. Seven of the microsatellite markers developed for P. perezi also proved useful

for species assignment in the P. ridibundus x P. perezi hybridogenetic complex,

showing several private alleles for each of the two parental species.

3. The combination of microsatellite allele data with mitonuclear sequences

expanded the known distribution of the hybrid taxon P. kl. grafi in northeastern

Spain, along the Llobregat River and, probably, the Llierca basin, and thus

represents a valuable tool to delineate the range of this cryptic, little known

taxon.

4. In the absence of strongly unbalanced data structure, genetic diversity indexes

(AR, HO, HE) are not affected by an excess of close relatives in the genetic

sample.

5. In contrast, the presence of close relatives in the sample strongly affected the

results of tests of HWE and LD in some populations, which calls for caution

when assessing the adjustment of genetic samples to mendelian inheritance

assumptions, especially in small samples/populations.

6. A new method for calculating the minimum sample size required for estimating

AR and HE was devised in this dissertation relying on inspecting the shape of

cumulative curves with increasing sample size. The method provides useful

insights into the performance of individual markers and can be easily applied to

different types of molecular markers and in a wide variety of taxa.

7. The combination of genetic-based SF analyses and individual-based robust

design methods provided reliable estimates of the Nb/Na ratio in E. calamita and

P. perezi, but not in H. molleri, due to insufficient marker and capture-history

information.

CHAPTER VIII

230

8. The estimates of the Nb/Na ratio obtained for E. calamita in the same locality in

two different years were 0.28 and 0.18, and both estimates were associated with

a largely monogamous behaviour of both sexes within each of the two breeding

seasons.

9. The Nb/Na ratio obtained for P. perezi (0.5) was higher than the ratios recorded

in E. calamita (a species with a much larger local census population size), and

was also associated with higher polygamy levels (close to an average of 1.5

mates per breeding male and female).

10. A new extensive analytic design including verification protocols based on direct

field records and replicated analyses was devised in this dissertation and played

a crucial role in checking the reliability of Nb estimates.

11. The combination of genetic approaches based on population differentiation,

migration rates, genetic structure and landscape genetic causal modeling

provided evidences that Sierra de Guadarrama is acting as a strong barrier to

gene flow for P. cultripes, and as a semi-permeable barrier for E. calamita, H.

molleri and P. perezi.

12. Genetic inferences suggested high regional connectivity in E. calamita, H.

molleri and P. perezi, principally among populations located in the same slope

of Sierra de Guadarrama, which is in agreement with the high dispersal

capabilities confirmed by direct field-records of cumulative movements in these

three species.

APPENDIX 1

CHARACTERIZATION OF THE MICROSATELLITE SETS OF H. MOLLERI, E. CALAMITA AND P. PEREZI

Suppl. Material from Sánchez-Montes et al. (2017) Journal of Heredity (doi: 10.1093/jhered/esx038)

(Chapter IV)

Table A1.1. Characterization of the H. molleri microsatellite set, with multiplex combinations, primer sequences, repeated motifs and observed allele size ranges (in base pairs).

Annealing temperature was 60ºC in all cases. The mean (and standard deviation, SD) percentage of missing data, allelic dropout and false allele scoring rates across all

sample populations are shown for each marker. R Info: Informativeness for relationship. GB: GenBank accession numbers.

Locus Multiplex reaction

Primer sequences Repeated

motif Size range

(bp) Missing data (%)

Allele dropout

False alleles

R Info GB

Hmol3.7 1 5' GAAGGAAGGGCATTAAGAGGATG 3' (ACT)7 140 - 149 0.23 (1.02) - - 5.23E-09 KY964709 5' TCCTCTGGATTAACTCAGTAGGG 3' Hmol3.28 1 5' TGTACCAGAGCTTCTCCACTTAG 3' (AAT)10 188 - 203 0.5 (1.63) 0 (0.01) 0.06 (0.06) 0.02 KY964710 5' CCTACATTGGTCAGGATTAGGTAC 3' Hmol4.2 1 5' GCCGAAACGTAACTCTATGTACC 3' (ACAT)6 283 - 311 1.87 (3.75) 0.01 (0.02) 0.01 (0.02) 0.01 KY964711 5' TGACTTGCACTGGGACTTTAAAC 3' Hmol3.9 1 5' AACACAATCACAGTTAGCTTCCC 3' (ACT)7 442 - 451 0.56 (1.39) 0.03 (0.07) 0.01 (0.02) 0.00 KY964712 5' GTTGTCTAGAAGCAGAGTACCAC 3' Hmol3.3 2 5' AATAGGACTGAAAGGAACAACGC 3' (AAT)5 136 - 145 0.23 (1.02) 0.02 (0.04) 0.01 (0.02) 0.00 KY964713 5' AAGTGATCTGATCGGCTACTTTG 3' Hmol4.12 2 5' CTAAGTCATCTAGTGGTCCCTGG 3' (AGAT)8 228 - 344 2.22 (4.29) 0.01 (0.02) 0.04 (0.05) 0.07 KY964714 5' TTTACAAATGCGACGTTTCAACC 3' Hmol4.16 2 5' ATTTACTCAGGGAATGTGCATCC 3' (AGAT)9 147 - 235 0.24 (1.06) 0 (0.02) 0.03 (0.03) 0.05 KY964715 5' TCATGCTAACTGTGTTTATGTTGC 3' Hmol4.1 2 5' TGCAATGTATCTATTAGCCTCCAC 3' (AGAT)9 236 - 292 1.66 (2.67) 0.01 (0.04) 0.04 (0.04) 0.04 KY964716 5' GCCCATTTAAGCATACAGTCTAGC 3' Hmol4.9 3 5' GGACAACGTTCTGCAAGTTAATC 3' (AGAT)10 165 - 221 0.45 (1.23) 0 (0.01) 0.01 (0.02) 0.02 KY964717 5' TGTCTCTTCATGTTGGTGTGATC 3' Hmol4.10 3 5' TATTGCCCATATCCTCCCTTCTC 3' (AGAT)10 103 - 175 0.39 (1.23) 0 (0.01) 0.02 (0.03) 0.06 KY964718 5' ATGACATCACCTCATCAGCCAG 3' Hmol3.22 3 5' GACATCCATCATTCACATCCCTG 3' (AAT)10 294 - 324 0.84 (1.74) 0.01 (0.02) 0.04 (0.04) 0.04 KY964719 5' TTCTGCCTTCTCTTCCCATAGAC 3' Hmol4.22 4 5' GCTTCATCACCACTTAACCTGAG 3' (AAAC)6 236 - 244 0.73 (3.05) 0.01 (0.05) 0.03 (0.05) 0.00 KY964720 5' TGGACATGATCAGAGACCATTAC 3' Hmol3.15 4 5' TTTGTCTAGTGTCAGCCCTCTAG 3' (AAG)5 161 - 169 0 (0) 0.02 (0.03) 0.02 (0.03) 0.02 KY964721 5' AGCATACAGTGGCATATTTCAGC 3' Hmol4.27 4 5' GACGTCAATACCAAGTACGCTAG 3' (AGAT)6 204 - 220 1.21 (2.16) 0.06 (0.09) 0.04 (0.05) 0.02 KY964722 5' GTAAGTCAAGGGCCCTGAAGTC 3' Hmol3.8 4 5' ATAGTCTTATGCTTGTTGGGCTG 3' (ACT)12 258 - 279 1.36 (5.09) 0.03 (0.07) 0.04 (0.05) 0.02 KY964723 5' TATGGGAAACTGCACCACTCTTC 3' Hmol4.11 5 5' TTAAGCCTGAATGTATGGAATTGG 3' (AGAT)10 276 - 292 2.38 (3.44) 0.04 (0.08) 0.03 (0.03) 0.00 KY964724 5' TTTCGAGCATATTGATCCCTCCC 3' Hmol4.8 5 5' GTTGTGCTGACCTTGAAAGTATTG 3' (AGAT)10 384 - 441 2.49 (3.64) 0.01 (0.01) 0.02 (0.03) 0.07 KY964725 5' CTAGGCTTGATAATGGCAGTGTG 3' Hmol4.29 5 5' CTTTCCTTGGCTTCTTTATGCAC 3' (AGAT)6 356 - 461 3.58 (6.22) 0.02 (0.07) 0.04 (0.05) 0.07 KY964726 5' GTATGTGAGCTCTTTACTGCCTG 3'

Table A1.2. Characterization of the E. calamita microsatellite set, with multiplex combinations, primer sequences, repeated motifs and observed allele size ranges (in base

pairs). Annealing temperature was 60ºC in all cases. The mean (and standard deviation, SD) percentage of missing data, allelic dropout and false allele scoring rates across all

sample populations are shown for each marker. R Info: Informativeness for relationship. GB: GenBank accession numbers.



motif Size range

(bp) Missing data (%)

Allele dropout

False alleles

R Info GB

Ecal4.21 3 5' CACAGAAGGACAGTAGTTAGACG 3' (AGAT)9 80 - 128 2.61 (3.53) 0.13 (0.12) 0.02 (0.03) 0.04 KY964693 5' AGATCTGCTGGTTTACAAAGTGG 3' Ecal4.20 3 5' TGAGCAAATCCTCCAAACATGAG 3' (AAAG)10 238 - 314 1.3 (2.58) 0 (0) 0.03 (0.05) 0.09 KY964694 5' TTTGGCCTTTCAACCTTAATCCC 3' Ecal4.8 2 5' GACATCTGTTTGCGTTTCATTGG 3' (AGAT)8 362 - 448 0.38 (1.03) 0.01 (0.04) 0.03 (0.05) 0.08 KY964695 5' GCTAGTGTCATTTACTACAACAGC 3' Ecal4.29 2 5' ATGTTGAATGCTAAGCCGAAATG 3' (AGAT)10 122 - 174 0.16 (0.73) 0.01 (0.02) 0.03 (0.04) 0.05 KY964696 5' ACATACCTTCATTTGGCTGTGAG 3' Ecal4.16 2 5' GATAGCCCTCCATTCTAGTCTCC 3' (AAAT)5 164 - 184 0 (0) 0.01 (0.01) 0.01 (0.02) 0.02 KY964697 5' ATGGTTATGAACAGACATGCAAC 3' Ecal4.18 3 5' CTGGAAAGGTCATTGATTCAGGG 3' (AGAT)8 178 - 214 0.16 (0.73) 0.01 (0.01) 0.01 (0.03) 0.04 KY964698 5' AGACCCTGTGTAGTCATATACCC 3' Ecal4.3 2 5' AACAACCACCAGAACTAACATGG 3' (AGAT)6 305 - 357 0 (0) 0.01 (0.01) 0.02 (0.03) 0.06 KY964699 5' TGACGCAGATATGTATACAGTTGG 3' Ecal4.6 1 5' AGGGTGTCTGAATACTTTCCGTC 3' (AGAT)10 145 - 181 1.68 (2.39) 0.09 (0.09) 0.01 (0.01) 0.05 KY964700 5' TTGACAAAGGCCTCATTGAGAAG 3' Ecal4.14 1 5' TTACTTAGGCCCTGAACAGTGTC 3' (AGAT)8 426 - 476 5.05 (5.27) 0.21 (0.19) 0.03 (0.05) 0.06 KY964701 5' AATTGGCAATGATCAACGGTTTG 3' Ecal4.2 1 5' GACTGTTTCCTGGATGTGAATTTC 3' (AGAT)9 311 - 592 5.16 (5.3) 0.16 (0.17) 0.05 (0.09) 0.10 KY964702 5' ACAAGGATGATTACTTTGAGCAGG 3' Ecal3.26 2 5' GTGTATGGGCATCTTTAGAATGAG 3' (AAT)7 270 - 323 5.5 (5.96) 0.17 (0.14) 0.02 (0.04) 0.08 KY964703 5' TATCTGCCACTTTGAACGGTTTC 3' Ecal4.24 3 5' ATCAGGAGCCACTAGTACTGAAC 3' (AGAT)7 302 - 358 1.1 (1.7) 0.01 (0.02) 0.03 (0.05) 0.05 KY964704 5' ATGCCAGATGACACTACTCTTGG 3' Ecal3.4 3 5' TGACTATGGTGGGAAGGGTTAAG 3' (AAC)8 130 - 154 0.16 (0.73) 0 (0.01) 0.02 (0.03) 0.03 KY964705 5' AGGAAATTCTGGGACTCTGAGG 3' Ecal3.29 1 5' GCCAGGAATACTTCTTCACTCTG 3' (ACT)7 222 - 240 1.54 (3.47) 0.06 (0.11) 0.02 (0.03) 0.01 KY964706 5' TATCTGTTtGTTGATGGCAGACC 3' Ecal3.19 1 5' GCCATCCAATCCACAATCTCATC 3' (ACT)9 234 - 270 9.32 (5.46) 0.32 (0.22) 0.02 (0.03) 0.04 KY964707 5' ACCATTCCATACTTTGTGTGACG 3' Ecal4.26 1 5' CGGATCTAACCTTCATGTAACCAC 3' (AGAT)8 155 - 375 1.46 (2.86) 0 (0) 0.03 (0.04) 0.10 KY964708 5' AGAAAGTCTAGCTACACCTTTGG 3'

Table A1.3. Characterization of the P. perezi microsatellite set, with multiplex combinations, primer sequences, repeated motifs and observed allele size ranges (in base pairs).

Annealing temperature was 60ºC in all cases. The mean (and standard deviation, SD) percentage of missing data, allelic dropout and false allele scoring rates across all

sample populations are shown for each marker. R Info: Informativeness for relationship. Primer sequences, repeated motifs and GenBank accession numbers (GB) from

Sánchez-Montes et al. (2016).



motif

Size range

(bp)

Missing

data (%)

Allele

dropout

False

alleles R Info GB

Pper4.25 1 5' TCCCTTCTAGTGCTGTAACTTCG 3' (AGAT)8 183 - 403 0.58 (1.48) 0.01 (0.02) 0.05 (0.05) 0.09 KT166015 5' AGTTCATCTGCAGTTCCTACATG 3' Pper4.15 1 5' ACATATTGTGCTGCTCCATCAAG 3' (AGAT)8 177 - 249 0.06 (0.24) 0.01 (0.02) 0.03 (0.04) 0.06 KT166016 5' AATTTCTTCAGTGCTGTCATGTC 3' Pper4.28 1 5' CATGTACAGCTGACTTTAGAGCC 3' (AAGG)5 200 - 260 0.06 (0.24) 0.04 (0.1) 0.04 (0.04) 0.02 KT166017 5' TTCTTTCCAATTTGAGACTCGGG 3' Pper3.9 1 5' CAACATATCTTCCCGAATGAGGC 3' (AAG)6 191 - 262 0.06 (0.24) 0.02 (0.03) 0.03 (0.03) 0.03 KT166018 5' GTTTCTCTCAGTCTAGTTGGTGC 3' Pper4.5 2 5' TGTGCGCTATCCTCTGTAGTTAG 3' (AAAC)6 148 - 164 0.16 (0.72) 0.03 (0.06) 0.04 (0.05) 0.02 KT166019 5' TGAATCCTGGCATTGTCATCTTG 3' Pper4.16 2 5' AGAGCAGATATACCACACTCCAG 3' (AGAT)9 140 - 192 0.22 (0.74) 0.01 (0.02) 0.02 (0.04) 0.05 KT166020 5' ACCTCAAGCATTTATAGACCAGC 3' Pper3.24 2 5' ATGTGGAGACTATCAGCAGACAG 3' (AAC)7 248 - 278 1.18 (2.81) 0.02 (0.06) 0.05 (0.06) 0.04 KT166021 5' CAAGTCTTGACTGTTCATACCGG 3' Pper4.20 3 5' TCTTAGCAGTGACAGATGTGAAC 3' (AAGT)6 220 - 228 0 (0) 0.06 (0.18) 0.02 (0.05) 0.01 KT166022 5' TCTTAGTGCAGATTAGGGACCTG 3' Pper3.22 3 5' ACTGTCATCTGGTCTGGTATCAC 3' (ACT)9 358 - 382 0.42 (1.28) 0.01 (0.03) 0.03 (0.05) 0.01 KT166023 5' ACACTAATTGTCCTCCTGTAGAAC 3' Pper4.13 3 5' AGAGACCATATATCGGAGCCATC 3' (AGAT)10 425 - 513 0.42 (1.28) 0.01 (0.02) 0.06 (0.07) 0.06 KT166024 5' TGGCAAATCACTCCACTTAACAG 3' Pper4.7 4 5' TACCTCTTCTGCTGATCTCTTGG 3' (AGAT)9 280 - 364 1.42 (2.8) 0.05 (0.14) 0.02 (0.04) 0.08 KT166025 5' AAGCAATTTATCAAGCAGGAGGG 3' Pper3.1 4 5' TTGCCAGCAGAAGAGAACATTAC 3' (AGG)9 337 - 376 0.49 (1.35) 0.06 (0.13) 0.04 (0.06) 0.04 KT166026 5' TCTCACAGACATCGCATTTGATC 3' Pper4.29 5 5' CTGTGCTACGAGGATTGTAATGG 3' (AAAG)7 313 - 357 0.34 (1.03) 0 (0.02) 0.02 (0.03) 0.04 KT166028 5' TTCATTCTCTGTGTCGTGAATGC 3' Pper3.23 5 5' ACTTGTATCATCTTTCTCTGCGC 3' (ACT)6 154 - 196 0.34 (1.09) 0.03 (0.06) 0.03 (0.04) 0.03 KT166029 5' TTTCTGCCCAATTCTACTACTGC 3' Pper4.24 5 5' TTTCCCTATTGCCTATGAACTGC 3' (AGAT)10 195 - 339 0.67 (1.62) 0.05 (0.07) 0.05 (0.05) 0.07 KT166030 5' AGTGCTATGGTTGGGATTTGAAC 3'

Table A1.4. Characterization of 18 microsatellite loci in 20 H. molleri populations (Loc). For each population, several diversity and data quality measures are displayed both in

the complete and reduced (without full sibs) samples. AR = allelic richness, HO and HE = observed and expected heterozygosity. Missing (%) = Percentage of missing data.

Mistyping rates are calculated based on two estimates derived from sibship analyses in colony: allelic dropout (AD) and false allele (FA) scoring rates. Dev. HWP = Deviation

from Hardy-Weinberg Proportions.

Loc (sample size) Parameter Hmol

3.7 Hmol3.28

Hmol4.2

Hmol3.9

Hmol3.3

Hmol4.12

Hmol4.16

Hmol4.1

Hmol4.9

Hmol4.10

Hmol3.22

Hmol4.22

Hmol3.15

Hmol4.27

Hmol3.8

Hmol4.11

Hmol4.8

Hmol4.29

Arcones - complete (30)

AR 1 6 3 3 3 15 11 8 7 13 7 2 4 3 5 2 15 15 HO 0.00 0.83 0.47 0.23 0.10 0.90 0.83 0.87 0.60 0.93 0.67 0.50 0.60 0.70 0.67 0.28 0.83 0.97

HE 0.00 0.73 0.49 0.21 0.10 0.87 0.87 0.82 0.69 0.86 0.74 0.41 0.64 0.66 0.62 0.24 0.87 0.90

FIS

-0.14 0.04 -0.10 -0.04 -0.03 0.04 -0.06 0.13 -0.08 0.10 -0.23 0.06 -0.06 -0.08 -0.16 0.05 -0.07

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.3 3.3 3.3

AD rate

0.00 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.00 0.05 0.00 0.00 0.00 0.05 0.00

FA rate

0.04 0.03 0.04 0.03 0.07 0.02 0.06 0.00 0.00 0.05 0.04 0.02 0.00 0.00 0.04 0.06 0.03

Dev. HWP

No No No No No No No No No No No No No No No No No

Null alleles


Arcones - reduced (27)

AR 1 6 3 3 3 14 11 8 7 13 6 2 4 3 5 2 14 15 HO 0.00 0.85 0.48 0.22 0.11 0.89 0.81 0.89 0.56 0.93 0.63 0.52 0.63 0.74 0.67 0.31 0.81 0.96

HE 0.00 0.74 0.50 0.20 0.11 0.87 0.86 0.82 0.68 0.87 0.72 0.42 0.65 0.66 0.63 0.26 0.87 0.90

FIS

-0.16 0.03 -0.09 -0.05 -0.02 0.05 -0.09 0.19 -0.07 0.12 -0.24 0.03 -0.12 -0.06 -0.18 0.07 -0.07

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.7 3.7 3.7

Dev. HWP


Null alleles


Bustarviejo - complete (30)

AR 1 4 4 5 4 12 10 8 5 11 7 3 4 3 4 2 17 13 HO 0.00 0.90 0.63 0.21 0.33 0.87 0.93 0.80 0.73 0.87 0.90 0.27 0.70 0.40 0.63 0.13 0.90 0.96

HE 0.00 0.71 0.57 0.22 0.41 0.89 0.84 0.75 0.72 0.84 0.79 0.24 0.66 0.51 0.64 0.12 0.86 0.90

FIS

-0.26 -0.10 0.07 0.20 0.02 -0.11 -0.07 -0.02 -0.03 -0.14 -0.13 -0.07 0.22 0.01 -0.07 -0.04 -0.07

Missing (%) 0.0 3.3 10.0 3.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 13.3

AD rate

0.00 0.00 0.00 0.05 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.25 0.00 0.00 0.01 0.00

FA rate

0.15 0.00 0.03 0.00 0.00 0.02 0.07 0.00 0.05 0.07 0.00 0.00 0.13 0.03 0.00 0.11 0.06

Dev. HWP


Null alleles


Bustarviejo - reduced (29)

AR 1 4 4 5 4 12 10 8 5 11 7 3 4 3 4 2 17 13 HO 0.00 0.89 0.62 0.21 0.34 0.86 0.93 0.83 0.72 0.86 0.90 0.28 0.69 0.38 0.66 0.14 0.90 0.96

HE 0.00 0.71 0.57 0.23 0.43 0.89 0.84 0.76 0.72 0.84 0.79 0.24 0.64 0.51 0.65 0.13 0.86 0.90

FIS

-0.26 -0.08 0.07 0.19 0.03 -0.11 -0.09 0.00 -0.03 -0.13 -0.14 -0.07 0.25 -0.02 -0.07 -0.04 -0.07

Missing (%) 0.0 3.4 10.3 3.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 13.8

Dev. HWP


Null alleles



3.7 Hmol3.28

Hmol4.2

Hmol3.9

Hmol3.3

Hmol4.12

Hmol4.16

Hmol4.1

Hmol4.9

Hmol4.10

Hmol3.22

Hmol4.22

Hmol3.15

Hmol4.27

Hmol3.8

Hmol4.11

Hmol4.8

Hmol4.29

Cabanillas de la Sierra - complete

(22)

AR 1 4 3 3 3 13 10 7 5 11 7 2 5 3 5 2 7 11 HO 0.00 0.73 0.45 0.23 0.41 0.78 0.95 0.85 0.77 0.91 0.82 0.32 0.45 0.57 0.53 0.14 0.73 0.91 HE 0.00 0.61 0.44 0.37 0.34 0.88 0.88 0.78 0.67 0.85 0.69 0.27 0.57 0.58 0.67 0.21 0.72 0.88

FIS

-0.19 -0.04 0.38 -0.19 0.11 -0.09 -0.09 -0.16 -0.07 -0.18 -0.19 0.20 0.02 0.21 0.32 -0.01 -0.03

Missing (%) 0.0 0.0 0.0 0.0 0.0 18.2 0.0 9.1 0.0 0.0 0.0 13.6 0.0 4.5 22.7 4.5 0.0 0.0

AD rate

0.00 0.00 0.29 0.00 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.06 0.00 0.22 0.16 0.03 0.00

FA rate

0.15 0.00 0.04 0.00 0.06 0.02 0.00 0.00 0.09 0.08 0.04 0.03 0.08 0.06 0.00 0.01 0.00

Dev. HWP


Null alleles


Cabanillas de la Sierra - reduced

(19)

AR 1 4 3 3 3 13 10 7 5 10 7 2 5 3 4 2 7 11 HO 0.00 0.74 0.47 0.21 0.32 0.73 0.95 0.88 0.74 0.89 0.79 0.29 0.42 0.61 0.57 0.11 0.74 0.89 HE 0.00 0.63 0.46 0.38 0.28 0.85 0.87 0.79 0.66 0.86 0.70 0.25 0.55 0.61 0.67 0.20 0.72 0.89

FIS

-0.16 -0.03 0.45 -0.14 0.14 -0.09 -0.12 -0.11 -0.04 -0.13 -0.17 0.24 0.00 0.14 0.44 -0.03 -0.01

Missing (%) 0.0 0.0 0.0 0.0 0.0 21.1 0.0 10.5 0.0 0.0 0.0 10.5 0.0 5.3 26.3 5.3 0.0 0.0

Dev. HWP


Null alleles

No No Yes No No No No No No No No No No No No No No

Cerceda - complete (20)

AR 1 4 2 3 2 10 5 8 4 6 6 2 4 3 6 2 8 8 HO 0.00 0.85 0.47 0.05 0.50 0.80 0.55 0.70 0.65 0.80 0.85 0.05 0.45 0.58 0.50 0.15 0.80 0.53

HE 0.00 0.64 0.41 0.10 0.42 0.86 0.65 0.70 0.54 0.77 0.76 0.05 0.59 0.59 0.72 0.14 0.81 0.83

FIS

-0.34 -0.15 0.48 -0.19 0.07 0.15 -0.01 -0.20 -0.05 -0.11 -0.03 0.23 0.02 0.30 -0.08 0.01 0.36

Missing (%) 0.0 0.0 5.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5.0 0.0 0.0 0.0 25.0

AD rate

0.00 0.00 0.00 0.00 0.05 0.08 0.00 0.00 0.00 0.00 0.00 0.08 0.06 0.08 0.00 0.00 0.33

FA rate

0.07 0.01 0.00 0.00 0.03 0.02 0.00 0.00 0.00 0.06 0.00 0.05 0.20 0.00 0.05 0.00 0.15

Dev. HWP

No No No No No No No No No No No No No No No No Yes

Null alleles

No No No No No No No No No No No No No Yes No No Yes

Cerceda - reduced (16)

AR 1 4 2 3 2 10 5 7 4 6 6 2 4 3 6 2 8 8 HO 0.00 0.88 0.33 0.06 0.50 0.75 0.63 0.69 0.69 0.81 0.88 0.06 0.44 0.53 0.50 0.13 0.94 0.58

HE 0.00 0.62 0.36 0.12 0.43 0.86 0.65 0.71 0.57 0.77 0.75 0.06 0.61 0.58 0.73 0.12 0.83 0.79

FIS

-0.41 0.07 0.48 -0.16 0.13 0.04 0.03 -0.21 -0.06 -0.16 -0.03 0.28 0.09 0.32 -0.07 -0.13 0.26

Missing (%) 0.0 0.0 6.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6.3 0.0 0.0 0.0 25.0

Dev. HWP


Null alleles

No No No No No No No No No No No No No Yes No No Yes

Collado Hermoso - complete (23)

AR 1 3 2 2 2 9 5 6 4 7 6 2 3 3 4 2 9 8 HO 0.00 0.74 0.57 0.35 0.17 0.96 0.83 0.78 0.70 0.83 0.83 0.35 0.78 0.61 0.48 0.52 1.00 0.83

HE 0.00 0.59 0.48 0.29 0.23 0.87 0.73 0.71 0.65 0.80 0.75 0.49 0.59 0.59 0.50 0.39 0.86 0.82

FIS

-0.26 -0.17 -0.21 0.23 -0.10 -0.13 -0.10 -0.07 -0.03 -0.10 0.29 -0.33 -0.03 0.05 -0.35 -0.16 0.00

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

AD rate

0.00 0.00 0.00 0.12 0.00 0.00 0.00 0.00 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

FA rate

0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00

Dev. HWP

No No No No No No No No Yes No No No Yes No No Yes Yes

Null alleles



3.7 Hmol3.28

Hmol4.2

Hmol3.9

Hmol3.3

Hmol4.12

Hmol4.16

Hmol4.1

Hmol4.9

Hmol4.10

Hmol3.22

Hmol4.22

Hmol3.15

Hmol4.27

Hmol3.8

Hmol4.11

Hmol4.8

Hmol4.29

Collado Hermoso - reduced (7)

AR 1 3 2 2 2 7 4 6 4 4 5 2 3 3 3 2 7 6 HO 0.00 0.86 0.57 0.57 0.14 0.86 0.86 0.71 0.86 0.71 0.86 0.43 0.86 0.29 0.43 0.29 1.00 0.71

HE 0.00 0.60 0.49 0.41 0.34 0.84 0.66 0.71 0.70 0.70 0.70 0.46 0.57 0.36 0.36 0.24 0.82 0.63

FIS

-0.42 -0.17 -0.40 0.58 -0.02 -0.29 0.00 -0.22 -0.01 -0.22 0.07 -0.50 0.20 -0.20 -0.17 -0.23 -0.13

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Dev. HWP


Null alleles


Colmenar Viejo - complete (21)

AR 1 2 2 2 2 8 7 5 4 6 4 1 4 4 3 1 5 6 HO 0.00 0.48 0.48 0.14 0.57 0.71 0.81 0.81 0.67 0.71 0.75 0.00 0.62 0.52 0.62 0.00 0.81 0.80

HE 0.00 0.36 0.36 0.13 0.41 0.62 0.77 0.70 0.57 0.65 0.71 0.00 0.60 0.59 0.53 0.00 0.70 0.73

FIS

-0.31 -0.31 -0.08 -0.40 -0.16 -0.05 -0.15 -0.18 -0.09 -0.06

-0.02 0.11 -0.18

-0.16 -0.09

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.8 0.0 0.0 0.0 0.0 0.0 0.0 4.8

AD rate

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.05 0.00

0.00 0.00

FA rate

0.12 0.00 0.01 0.03 0.03 0.08 0.07 0.00 0.00 0.00

0.00 0.00 0.10

0.00 0.00

Dev. HWP

No No No No No No No No No No

No No No

No No

Null alleles


No No No

No No

Colmenar Viejo - reduced (18)

AR 1 2 2 2 2 7 7 5 4 6 4 1 4 4 3 1 5 6 HO 0.00 0.50 0.44 0.17 0.61 0.67 0.83 0.78 0.61 0.72 0.72 0.00 0.56 0.56 0.61 0.00 0.83 0.82

HE 0.00 0.38 0.35 0.15 0.42 0.57 0.77 0.71 0.54 0.67 0.71 0.00 0.58 0.57 0.54 0.00 0.69 0.74

FIS

-0.33 -0.29 -0.09 -0.44 -0.17 -0.08 -0.10 -0.13 -0.09 -0.02

0.03 0.03 -0.14

-0.21 -0.11

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5.6

Dev. HWP


No No No

No No

Null alleles


No No No

No No

Dehesa de Roblellano -

complete (30)

AR 1 4 2 4 3 10 6 7 3 10 8 2 3 3 6 2 12 12 HO 0.00 0.75 0.52 0.41 0.40 0.77 0.67 0.67 0.59 0.97 0.76 0.57 0.70 0.67 0.63 0.20 0.93 0.90 HE 0.00 0.62 0.50 0.40 0.34 0.79 0.74 0.72 0.53 0.85 0.79 0.46 0.56 0.65 0.73 0.18 0.86 0.84

FIS

-0.21 -0.04 -0.04 -0.18 0.03 0.10 0.07 -0.11 -0.14 0.04 -0.25 -0.25 -0.03 0.13 -0.11 -0.09 -0.07

Missing (%) 0.0 6.7 3.3 3.3 0.0 0.0 0.0 0.0 3.3 3.3 3.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0

AD rate

0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.09 0.00 0.00 0.01

FA rate

0.07 0.03 0.00 0.00 0.01 0.00 0.05 0.03 0.00 0.02 0.00 0.00 0.00 0.00 0.01 0.04 0.01

Dev. HWP

Yes No No No No No No No No No No No No Yes No No No

Null alleles


Dehesa de Roblellano - reduced (20)

AR 1 3 2 4 3 10 6 7 3 10 7 2 3 3 6 2 12 11 HO 0.00 0.67 0.63 0.37 0.45 0.75 0.75 0.65 0.47 0.95 0.74 0.50 0.70 0.70 0.55 0.25 1.00 0.90 HE 0.00 0.61 0.50 0.36 0.37 0.78 0.78 0.69 0.52 0.83 0.80 0.46 0.58 0.61 0.66 0.22 0.87 0.81

FIS

-0.10 -0.27 -0.02 -0.21 0.04 0.04 0.06 0.10 -0.14 0.07 -0.10 -0.21 -0.15 0.17 -0.14 -0.15 -0.11

Missing (%) 0.0 10.0 5.0 5.0 0.0 0.0 0.0 0.0 5.0 5.0 5.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Dev. HWP

No No No No No No No No No No No No No Yes No No No

Null alleles



3.7 Hmol3.28

Hmol4.2

Hmol3.9

Hmol3.3

Hmol4.12

Hmol4.16

Hmol4.1

Hmol4.9

Hmol4.10

Hmol3.22

Hmol4.22

Hmol3.15

Hmol4.27

Hmol3.8

Hmol4.11

Hmol4.8

Hmol4.29

El Berrueco - complete (21)

AR 1 4 3 3 3 11 11 7 5 9 5 2 4 4 3 2 9 8 HO 0.00 0.48 0.57 0.29 0.33 0.95 0.90 0.95 0.67 0.86 0.62 0.52 0.57 0.71 0.57 0.33 0.90 0.71

HE 0.00 0.54 0.47 0.25 0.33 0.80 0.86 0.81 0.63 0.82 0.74 0.39 0.56 0.58 0.44 0.28 0.82 0.77

FIS

0.11 -0.21 -0.14 -0.02 -0.20 -0.05 -0.18 -0.05 -0.04 0.16 -0.35 -0.02 -0.23 -0.30 -0.20 -0.11 0.08

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

AD rate

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.03

FA rate

0.00 0.00 0.00 0.00 0.09 0.10 0.02 0.02 0.04 0.00 0.02 0.00 0.00 0.00 0.05 0.00 0.10

Dev. HWP


Null alleles


El Berrueco - reduced (18)

AR 1 4 3 3 3 10 11 7 5 9 5 2 4 4 3 2 9 8 HO 0.00 0.39 0.50 0.28 0.33 0.94 0.94 0.94 0.67 0.89 0.56 0.56 0.50 0.67 0.67 0.39 0.89 0.78

HE 0.00 0.54 0.45 0.25 0.33 0.77 0.85 0.80 0.62 0.83 0.74 0.40 0.54 0.58 0.48 0.31 0.83 0.78

FIS

0.28 -0.11 -0.13 0.00 -0.22 -0.11 -0.19 -0.08 -0.08 0.25 -0.38 0.07 -0.15 -0.39 -0.24 -0.08 0.01

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Dev. HWP


Null alleles


Fuenterrebollo - complete (20)

AR 1 5 3 3 3 12 11 9 5 8 6 2 4 3 3 3 12 11 HO 0.00 0.65 0.45 0.15 0.65 0.90 0.80 1.00 0.85 0.75 0.70 0.35 0.55 0.58 0.30 0.25 1.00 0.95

HE 0.00 0.71 0.47 0.14 0.58 0.89 0.83 0.86 0.59 0.83 0.73 0.35 0.60 0.59 0.30 0.30 0.90 0.84

FIS

0.08 0.03 -0.06 -0.12 -0.02 0.04 -0.17 -0.45 0.09 0.04 0.00 0.08 0.01 0.00 0.16 -0.11 -0.13

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5.0 0.0 0.0 0.0 0.0

AD rate

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.07 0.00 0.00

FA rate

0.00 0.00 0.03 0.00 0.00 0.06 0.00 0.06 0.04 0.01 0.00 0.02 0.00 0.04 0.01 0.00 0.13

Dev. HWP


Null alleles


Fuenterrebollo - reduced (12)

AR 1 5 3 3 3 11 9 9 5 8 6 2 4 3 3 3 12 10 HO 0.00 0.75 0.58 0.25 0.58 0.92 0.75 1.00 0.75 0.75 0.58 0.33 0.50 0.73 0.50 0.33 1.00 0.92

HE 0.00 0.70 0.52 0.23 0.57 0.89 0.85 0.87 0.63 0.84 0.69 0.38 0.60 0.57 0.45 0.39 0.90 0.84

FIS

-0.07 -0.13 -0.11 -0.02 -0.04 0.11 -0.15 -0.19 0.11 0.16 0.11 0.17 -0.28 -0.12 0.15 -0.11 -0.09

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 8.3 0.0 0.0 0.0 0.0

Dev. HWP


Null alleles


Gascones - complete (21)

AR 1 5 3 4 3 12 12 11 5 14 8 2 4 3 4 2 12 14 HO 0.00 0.86 0.55 0.52 0.29 0.95 0.90 0.65 0.86 1.00 0.81 0.29 0.52 0.81 0.43 0.42 0.89 0.84

HE 0.00 0.73 0.41 0.43 0.38 0.91 0.85 0.80 0.71 0.89 0.82 0.36 0.59 0.64 0.67 0.33 0.88 0.88

FIS

-0.17 -0.34 -0.23 0.25 -0.05 -0.06 0.18 -0.21 -0.13 0.01 0.21 0.12 -0.26 0.36 -0.27 -0.02 0.04

Missing (%) 0.0 0.0 4.8 0.0 0.0 4.8 0.0 4.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 9.5 9.5 9.5

AD rate

0.00 0.00 0.00 0.13 0.01 0.00 0.18 0.00 0.00 0.00 0.21 0.04 0.00 0.18 0.00 0.00 0.01

FA rate

0.15 0.02 0.00 0.00 0.00 0.00 0.17 0.00 0.03 0.00 0.06 0.00 0.04 0.00 0.02 0.04 0.11

Dev. HWP


Null alleles



3.7 Hmol3.28

Hmol4.2

Hmol3.9

Hmol3.3

Hmol4.12

Hmol4.16

Hmol4.1

Hmol4.9

Hmol4.10

Hmol3.22

Hmol4.22

Hmol3.15

Hmol4.27

Hmol3.8

Hmol4.11

Hmol4.8

Hmol4.29

Gascones - reduced (19)

AR 1 5 3 4 3 12 12 9 5 13 8 2 4 3 4 2 12 13 HO 0.00 0.95 0.50 0.53 0.32 0.94 0.89 0.61 0.84 1.00 0.79 0.26 0.58 0.79 0.42 0.35 0.94 0.88

HE 0.00 0.75 0.39 0.43 0.41 0.91 0.85 0.78 0.72 0.88 0.81 0.36 0.62 0.63 0.68 0.29 0.89 0.88

FIS

-0.27 -0.29 -0.24 0.23 -0.04 -0.05 0.22 -0.17 -0.14 0.03 0.27 0.06 -0.25 0.38 -0.21 -0.05 -0.01

Missing (%) 0.0 0.0 5.3 0.0 0.0 5.3 0.0 5.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 10.5 10.5 10.5

Dev. HWP


Null alleles


La Pradera de Navalhorno - complete (22)

AR 1 2 2 3 3 4 5 4 2 5 4 2 5 2 2 2 7 6 HO 0.00 0.95 0.36 0.76 0.57 0.90 0.82 0.67 0.48 0.71 1.00 0.55 1.00 0.05 0.36 0.50 0.95 1.00 HE 0.00 0.50 0.40 0.56 0.45 0.72 0.68 0.59 0.47 0.74 0.75 0.43 0.67 0.28 0.30 0.42 0.79 0.77

FIS

-0.91 0.08 -0.35 -0.26 -0.25 -0.21 -0.13 -0.01 0.03 -0.34 -0.26 -0.48 0.83 -0.22 -0.20 -0.21 -0.30

Missing (%) 4.5 0.0 0.0 4.5 4.5 4.5 0.0 4.5 4.5 4.5 4.5 0.0 0.0 4.5 0.0 0.0 4.5 4.5

AD rate

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.17 0.00 0.00 0.00 0.00

FA rate

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.18 0.00 0.00 0.00 0.00 0.06 0.02

Dev. HWP

Yes No No No Yes No Yes No No Yes No No Yes No No No Yes

Null alleles

No No No No No No No No No No No No Yes No No No No

La Pradera de Navalhorno - reduced (9)

AR 1 2 2 3 3 4 5 4 2 5 4 2 5 2 2 2 6 6 HO 0.00 0.89 0.67 0.63 0.63 0.88 0.89 0.63 0.38 0.88 1.00 0.56 1.00 0.13 0.56 0.56 0.88 1.00 HE 0.00 0.49 0.44 0.48 0.46 0.72 0.69 0.55 0.43 0.74 0.74 0.40 0.69 0.30 0.40 0.40 0.81 0.79

FIS

-0.80 -0.50 -0.31 -0.36 -0.22 -0.29 -0.13 0.13 -0.18 -0.35 -0.38 -0.45 0.59 -0.38 -0.38 -0.08 -0.27

Missing (%) 11.1 0.0 0.0 11.1 11.1 11.1 0.0 11.1 11.1 11.1 11.1 0.0 0.0 11.1 0.0 0.0 11.1 11.1

Dev. HWP


Null alleles


Medianillos - complete (21)

AR 1 2 3 3 3 7 5 6 4 7 7 2 3 3 3 2 7 5 HO 0.00 0.52 0.52 0.33 0.24 1.00 0.71 0.71 0.52 0.95 0.90 0.14 0.52 0.33 0.62 0.24 0.76 0.76

HE 0.00 0.39 0.54 0.29 0.22 0.79 0.59 0.67 0.59 0.81 0.77 0.13 0.51 0.52 0.56 0.21 0.68 0.63

FIS

-0.35 0.03 -0.15 -0.10 -0.26 -0.21 -0.07 0.11 -0.18 -0.17 -0.08 -0.03 0.36 -0.10 -0.14 -0.13 -0.20

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

AD rate

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.22 0.00 0.00 0.00 0.00

FA rate

0.14 0.00 0.00 0.02 0.02 0.01 0.04 0.00 0.00 0.05 0.00 0.06 0.04 0.00 0.00 0.05 0.00

Dev. HWP

No No No No No No No No Yes No No No No No No No No

Null alleles


Medianillos - reduced (9)

AR 1 2 3 3 3 7 4 5 4 6 5 2 3 2 3 2 5 5 HO 0.00 0.44 0.44 0.44 0.44 1.00 0.67 0.78 0.56 0.89 0.89 0.11 0.67 0.33 0.56 0.22 0.67 0.78

HE 0.00 0.35 0.55 0.37 0.36 0.77 0.50 0.70 0.64 0.78 0.73 0.10 0.59 0.40 0.48 0.20 0.68 0.64

FIS

-0.29 0.19 -0.20 -0.22 -0.30 -0.33 -0.12 0.13 -0.14 -0.22 -0.06 -0.14 0.17 -0.17 -0.13 0.02 -0.22

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Dev. HWP


Null alleles



3.7 Hmol3.28

Hmol4.2

Hmol3.9

Hmol3.3

Hmol4.12

Hmol4.16

Hmol4.1

Hmol4.9

Hmol4.10

Hmol3.22

Hmol4.22

Hmol3.15

Hmol4.27

Hmol3.8

Hmol4.11

Hmol4.8

Hmol4.29

Puerto de Canencia -

complete (25)

AR 2 4 3 3 4 11 7 9 4 12 6 2 4 4 4 2 12 14 HO 0.04 0.76 0.60 0.52 0.40 0.96 0.84 0.75 0.44 0.96 0.92 0.48 0.68 0.64 0.68 0.13 0.87 0.96 HE 0.04 0.61 0.49 0.45 0.37 0.80 0.77 0.80 0.46 0.87 0.78 0.40 0.61 0.71 0.62 0.19 0.87 0.90

FIS -0.02 -0.24 -0.24 -0.16 -0.08 -0.19 -0.09 0.07 0.04 -0.10 -0.18 -0.19 -0.11 0.09 -0.10 0.33 0.01 -0.06

Missing (%) 0.0 0.0 0.0 0.0 0.0 4.0 0.0 4.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 8.0 8.0 0.0

AD rate 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.04 0.00 0.23 0.00 0.00

FA rate 0.00 0.02 0.03 0.00 0.02 0.08 0.00 0.11 0.00 0.11 0.03 0.03 0.00 0.00 0.06 0.03 0.00 0.09

Dev. HWP No No No No No No No No No No No No No No No No No No

Null alleles No No No No No No No No No No No No No No No No No No

Puerto de Canencia -

reduced (22)

AR 2 4 3 3 4 10 7 9 4 12 6 2 4 4 4 2 12 14 HO 0.05 0.77 0.59 0.45 0.45 1.00 0.86 0.81 0.45 0.95 0.91 0.45 0.68 0.64 0.68 0.15 0.85 0.95 HE 0.04 0.62 0.49 0.42 0.41 0.82 0.79 0.82 0.47 0.88 0.78 0.40 0.63 0.72 0.60 0.22 0.86 0.91

FIS -0.02 -0.24 -0.20 -0.07 -0.11 -0.21 -0.09 0.02 0.03 -0.08 -0.17 -0.15 -0.08 0.11 -0.14 0.31 0.01 -0.05

Missing (%) 0.0 0.0 0.0 0.0 0.0 4.5 0.0 4.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 9.1 9.1 0.0

Dev. HWP No No No No No No No No No No No No No No No No No No

Null alleles No No No No No No No No No No No No No No No No No No

Puerto de La Morcuera -

complete (30)

AR 1 4 3 2 3 11 11 5 4 10 6 2 3 5 4 2 11 11 HO 0.00 0.67 0.38 0.20 0.43 0.93 0.87 0.80 0.70 0.97 0.87 0.43 0.67 0.73 0.62 0.53 0.77 0.87 HE 0.00 0.58 0.35 0.23 0.50 0.83 0.84 0.70 0.56 0.87 0.80 0.41 0.64 0.59 0.61 0.46 0.81 0.84

FIS

-0.15 -0.09 0.13 0.12 -0.12 -0.03 -0.15 -0.25 -0.11 -0.09 -0.07 -0.04 -0.24 -0.02 -0.15 0.06 -0.04

Missing (%) 0.0 0.0 13.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.3 0.0 0.0 0.0

AD rate

0.00 0.00 0.10 0.12 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.00 0.03 0.01

FA rate

0.00 0.04 0.04 0.00 0.07 0.00 0.00 0.00 0.00 0.07 0.00 0.00 0.05 0.12 0.12 0.00 0.00

Dev. HWP


Null alleles


Puerto de La Morcuera -

reduced (24)

AR 1 4 3 2 3 10 11 5 4 9 6 2 3 5 4 2 10 10 HO 0.00 0.67 0.38 0.25 0.38 0.92 0.88 0.79 0.67 0.96 0.83 0.46 0.67 0.71 0.65 0.50 0.71 0.83 HE 0.00 0.55 0.32 0.22 0.50 0.84 0.83 0.70 0.55 0.87 0.80 0.39 0.64 0.56 0.61 0.44 0.80 0.81

FIS

-0.20 -0.20 -0.14 0.25 -0.09 -0.05 -0.14 -0.20 -0.11 -0.04 -0.16 -0.04 -0.27 -0.06 -0.13 0.11 -0.02

Missing (%) 0.0 0.0 12.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.2 0.0 0.0 0.0

Dev. HWP


Null alleles


Rascafría - complete (20)

AR 1 3 3 1 3 9 7 7 3 6 6 2 3 4 3 3 9 10 HO 0.00 0.55 0.55 0.00 0.35 0.47 0.85 0.79 0.55 0.80 0.75 0.40 0.45 0.60 0.65 0.37 0.89 0.95

HE 0.00 0.55 0.52 0.00 0.34 0.51 0.79 0.73 0.56 0.76 0.77 0.38 0.57 0.58 0.62 0.50 0.79 0.86

FIS

0.01 -0.05

-0.03 0.08 -0.08 -0.08 0.02 -0.05 0.03 -0.07 0.22 -0.04 -0.06 0.26 -0.13 -0.10

Missing (%) 0.0 0.0 0.0 0.0 0.0 5.0 0.0 5.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5.0 5.0 0.0

AD rate

0.00 0.05

0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.00 0.00 0.01 0.00 0.09 0.00 0.00

FA rate

0.10 0.00

0.00 0.09 0.00 0.00 0.00 0.05 0.18 0.05 0.00 0.06 0.19 0.02 0.00 0.00

Dev. HWP

No No

No No No No No No No No No No No No No No

Null alleles

No No



3.7 Hmol3.28

Hmol4.2

Hmol3.9

Hmol3.3

Hmol4.12

Hmol4.16

Hmol4.1

Hmol4.9

Hmol4.10

Hmol3.22

Hmol4.22

Hmol3.15

Hmol4.27

Hmol3.8

Hmol4.11

Hmol4.8

Hmol4.29

Rascafría - reduced (18)

AR 1 3 3 1 3 9 7 7 3 6 6 2 3 4 3 3 9 10 HO 0.00 0.56 0.50 0.00 0.33 0.47 0.89 0.82 0.56 0.78 0.72 0.39 0.44 0.61 0.61 0.35 0.88 0.94

HE 0.00 0.57 0.52 0.00 0.33 0.52 0.80 0.76 0.57 0.77 0.75 0.38 0.57 0.57 0.59 0.47 0.78 0.86

FIS

0.02 0.03

-0.01 0.10 -0.11 -0.09 0.02 -0.01 0.04 -0.04 0.22 -0.07 -0.03 0.25 -0.13 -0.10

Missing (%) 0.0 0.0 0.0 0.0 0.0 5.6 0.0 5.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5.6 5.6 0.0

Dev. HWP

No No


Null alleles

No No


Sauquillo de Cabezas -

complete (20)

AR 1 5 2 3 3 9 9 7 3 9 5 2 4 3 4 2 10 8 HO 0.00 0.85 0.75 0.25 0.15 1.00 1.00 0.75 0.70 0.90 0.95 0.60 0.55 0.40 0.95 0.15 1.00 0.80 HE 0.00 0.74 0.49 0.22 0.14 0.83 0.81 0.68 0.47 0.81 0.73 0.42 0.63 0.46 0.66 0.14 0.83 0.80

FIS

-0.15 -0.53 -0.12 -0.06 -0.20 -0.23 -0.10 -0.49 -0.11 -0.30 -0.43 0.12 0.13 -0.45 -0.08 -0.20 -0.01

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

AD rate

0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.07 0.02 0.00 0.00 0.00 0.00

FA rate

0.01 0.00 0.01 0.04 0.03 0.10 0.05 0.04 0.00 0.00 0.00 0.10 0.00 0.03 0.07 0.04 0.00

Dev. HWP


Null alleles


Sauquillo de Cabezas - reduced

(12)

AR 1 5 2 2 3 8 8 7 3 9 5 2 4 3 4 2 8 8 HO 0.00 0.83 0.67 0.08 0.25 1.00 1.00 0.83 0.75 0.83 0.92 0.50 0.58 0.17 0.92 0.08 1.00 0.92 HE 0.00 0.69 0.44 0.08 0.23 0.84 0.82 0.72 0.50 0.82 0.73 0.38 0.66 0.35 0.66 0.08 0.83 0.84

FIS

-0.20 -0.50 -0.04 -0.11 -0.19 -0.22 -0.16 -0.51 -0.02 -0.25 -0.33 0.11 0.52 -0.40 -0.04 -0.20 -0.09

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Dev. HWP


Null alleles

No No No No No No No No No No No No Yes No No No No

Soto del Real - complete (20)

AR 1 5 3 4 3 13 9 11 5 9 7 2 5 4 5 2 11 10 HO 0.00 0.65 0.30 0.65 0.25 0.85 0.90 0.80 0.65 0.75 0.75 0.10 0.80 0.50 0.75 0.11 0.95 0.79

HE 0.00 0.65 0.27 0.55 0.23 0.85 0.83 0.82 0.65 0.82 0.80 0.10 0.62 0.64 0.66 0.10 0.85 0.86

FIS

0.00 -0.13 -0.18 -0.10 0.00 -0.08 0.03 0.00 0.09 0.07 -0.05 -0.29 0.22 -0.13 -0.06 -0.11 0.09

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 10.0 5.0 5.0

AD rate

0.03 0.00 0.00 0.00 0.04 0.00 0.00 0.04 0.00 0.00 0.00 0.00 0.18 0.00 0.00 0.00 0.03

FA rate

0.06 0.04 0.07 0.04 0.21 0.00 0.04 0.04 0.00 0.06 0.00 0.00 0.08 0.00 0.02 0.00 0.00

Dev. HWP


Null alleles


Soto del Real - reduced (18)

AR 1 5 3 4 3 13 9 11 5 8 7 2 5 4 5 2 11 10 HO 0.00 0.61 0.33 0.67 0.28 0.83 0.94 0.78 0.61 0.72 0.78 0.11 0.83 0.44 0.72 0.12 1.00 0.78

HE 0.00 0.63 0.29 0.54 0.25 0.87 0.83 0.82 0.65 0.82 0.80 0.10 0.63 0.63 0.66 0.11 0.86 0.86

FIS

0.03 -0.15 -0.24 -0.12 0.04 -0.14 0.05 0.05 0.12 0.03 -0.06 -0.33 0.30 -0.09 -0.06 -0.16 0.10

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5.6 0.0 0.0

Dev. HWP


Null alleles



3.7 Hmol3.28

Hmol4.2

Hmol3.9

Hmol3.3

Hmol4.12

Hmol4.16

Hmol4.1

Hmol4.9

Hmol4.10

Hmol3.22

Hmol4.22

Hmol3.15

Hmol4.27

Hmol3.8

Hmol4.11

Hmol4.8

Hmol4.29

Torrecaballeros - complete (34)

AR 1 4 2 2 3 8 7 5 4 4 5 2 3 2 4 3 6 7 HO 0.00 0.50 0.21 0.41 0.50 0.79 0.68 0.76 0.79 0.79 0.53 0.18 0.47 0.18 0.41 0.27 0.97 0.76

HE 0.00 0.54 0.18 0.50 0.54 0.79 0.69 0.74 0.68 0.71 0.49 0.16 0.39 0.29 0.37 0.44 0.76 0.70

FIS

0.07 -0.11 0.17 0.08 -0.01 0.02 -0.04 -0.16 -0.11 -0.08 -0.10 -0.21 0.39 -0.11 0.38 -0.27 -0.08

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.9 2.9 2.9

AD rate

0.00 0.00 0.09 0.00 0.02 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.19 0.00 0.25 0.00 0.00

FA rate

0.00 0.04 0.00 0.00 0.05 0.05 0.00 0.02 0.00 0.00 0.00 0.06 0.00 0.03 0.04 0.02 0.01

Dev. HWP


Null alleles

No No No No No No No No No No No No No No Yes No No

Torrecaballeros - reduced (28)

AR 1 4 2 2 3 8 6 5 4 4 5 2 3 2 3 3 6 6 HO 0.00 0.50 0.21 0.32 0.50 0.79 0.68 0.79 0.79 0.79 0.46 0.14 0.46 0.21 0.46 0.29 0.96 0.71

HE 0.00 0.53 0.19 0.50 0.56 0.79 0.67 0.74 0.70 0.72 0.44 0.13 0.38 0.34 0.40 0.46 0.76 0.69

FIS

0.05 -0.12 0.36 0.11 0.00 -0.01 -0.06 -0.12 -0.09 -0.05 -0.08 -0.21 0.36 -0.15 0.39 -0.27 -0.04

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Dev. HWP


Null alleles

No No No No No No No No No No No No No No Yes No No

Turrubuelo - complete (21)

AR 1 5 3 2 4 14 8 11 5 10 8 2 4 4 4 2 11 13 HO 0.00 0.81 0.57 0.33 0.29 0.85 0.80 0.90 0.57 0.90 0.81 0.38 0.57 0.81 0.48 0.48 0.76 0.95

HE 0.00 0.76 0.54 0.46 0.33 0.89 0.79 0.82 0.65 0.84 0.77 0.41 0.65 0.64 0.39 0.49 0.83 0.89

FIS

-0.07 -0.05 0.27 0.13 0.04 -0.01 -0.10 0.13 -0.08 -0.06 0.07 0.12 -0.27 -0.21 0.03 0.09 -0.06

Missing (%) 0.0 0.0 0.0 0.0 0.0 4.8 4.8 4.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

AD rate

0.00 0.00 0.14 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.06 0.00 0.00 0.00 0.02 0.00 0.00

FA rate

0.01 0.00 0.00 0.07 0.00 0.03 0.05 0.00 0.00 0.01 0.07 0.00 0.08 0.07 0.10 0.00 0.00

Dev. HWP


Null alleles


Turrubuelo - reduced (19)

AR 1 5 3 2 3 14 8 11 5 10 8 2 4 4 4 2 11 13 HO 0.00 0.79 0.58 0.32 0.26 0.83 0.83 0.94 0.53 0.89 0.84 0.37 0.63 0.79 0.42 0.42 0.74 0.95

HE 0.00 0.76 0.55 0.47 0.32 0.88 0.81 0.82 0.65 0.84 0.78 0.41 0.64 0.63 0.36 0.49 0.83 0.89

FIS

-0.04 -0.06 0.32 0.17 0.05 -0.03 -0.15 0.19 -0.06 -0.08 0.10 0.01 -0.26 -0.18 0.14 0.11 -0.06

Missing (%) 0.0 0.0 0.0 0.0 0.0 5.3 5.3 5.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Dev. HWP


Null alleles


Valdemanco - complete (96)

AR 1 4 4 4 4 18 14 11 5 14 8 3 4 3 5 3 15 17 HO 0.00 0.63 0.54 0.29 0.41 0.88 0.86 0.85 0.57 0.82 0.91 0.24 0.47 0.70 0.47 0.22 0.82 0.98

HE 0.00 0.67 0.55 0.32 0.40 0.89 0.86 0.82 0.64 0.86 0.82 0.22 0.61 0.64 0.50 0.20 0.86 0.91

FIS

0.06 0.02 0.08 -0.01 0.01 -0.01 -0.04 0.11 0.04 -0.12 -0.11 0.23 -0.09 0.06 -0.07 0.05 -0.08

Missing (%) 0.0 0.0 1.0 0.0 0.0 3.1 0.0 1.0 1.0 0.0 4.2 1.0 0.0 5.2 1.0 4.2 11.5 3.1

AD rate

0.01 0.01 0.01 0.00 0.00 0.00 0.00 0.02 0.01 0.01 0.00 0.08 0.00 0.11 0.02 0.00 0.00

FA rate

0.07 0.01 0.01 0.00 0.04 0.01 0.01 0.02 0.01 0.03 0.00 0.03 0.04 0.04 0.00 0.02 0.02

Dev. HWP


Null alleles

No No No No No No No No No No No Yes No No No No No


3.7 Hmol3.28

Hmol4.2

Hmol3.9

Hmol3.3

Hmol4.12

Hmol4.16

Hmol4.1

Hmol4.9

Hmol4.10

Hmol3.22

Hmol4.22

Hmol3.15

Hmol4.27

Hmol3.8

Hmol4.11

Hmol4.8

Hmol4.29

Valdemanco - reduced (88)

AR 1 4 4 4 4 17 14 11 5 14 8 3 4 3 5 3 15 17 HO 0.00 0.63 0.55 0.28 0.42 0.89 0.85 0.87 0.57 0.84 0.92 0.24 0.48 0.71 0.49 0.20 0.81 0.98

HE 0.00 0.66 0.55 0.30 0.41 0.89 0.86 0.82 0.64 0.87 0.82 0.22 0.60 0.64 0.52 0.19 0.86 0.90

FIS

0.06 0.00 0.05 -0.01 -0.01 0.01 -0.07 0.10 0.03 -0.12 -0.11 0.20 -0.10 0.05 -0.05 0.06 -0.08

Missing (%) 0.0 0.0 0.0 0.0 0.0 3.4 0.0 1.1 1.1 0.0 4.5 1.1 0.0 5.7 1.1 4.5 11.4 3.4

Dev. HWP


Null alleles

No No No No No No No No No No No Yes No No No No No

Table A1.5. Characterization of 16 microsatellite loci in 21 E. calamita populations (Loc). For each population, several diversity and data quality measures are displayed both in




Loc (sample size) Parameter Ecal4.21

Ecal4.20

Ecal4.8

Ecal4.29

Ecal4.16

Ecal4.18

Ecal4.3

Ecal4.6

Ecal4.14

Ecal4.2

Ecal3.26

Ecal4.24

Ecal3.4

Ecal3.29

Ecal3.19

Ecal4.26

Alameda del Valle - complete (24)

AR 6 15 12 8 4 6 6 6 8 10 11 10 5 3 7 21 HO 0.83 0.96 0.96 0.83 0.71 0.75 0.79 0.71 0.42 0.43 0.32 0.96 0.63 0.29 0.50 0.96

HE 0.71 0.89 0.87 0.80 0.60 0.80 0.72 0.74 0.78 0.84 0.80 0.77 0.55 0.29 0.83 0.92

FIS -0.17 -0.07 -0.10 -0.04 -0.17 0.06 -0.10 0.05 0.46 0.49 0.60 -0.24 -0.14 0.00 0.40 -0.04

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 20.8 12.5 8.3 0.0 0.0 0.0 8.3 0.0

AD rate 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.04 0.24 0.54 0.52 0.00 0.00 0.00 0.22 0.00

FA rate 0.00 0.01 0.00 0.00 0.03 0.00 0.00 0.01 0.00 0.14 0.06 0.05 0.04 0.08 0.00 0.02

Dev. HWP No No No No No No No No Yes Yes Yes No No No Yes Yes

Null alleles No No No No No No No No Yes Yes Yes No No No Yes No

Alameda del Valle - reduced (13)

AR 5 14 12 8 4 6 6 6 7 8 9 9 4 3 7 16 HO 0.85 0.92 0.92 1.00 0.62 0.69 0.62 0.77 0.36 0.38 0.45 1.00 0.54 0.31 0.58 1.00

HE 0.73 0.91 0.89 0.83 0.55 0.82 0.72 0.77 0.70 0.83 0.83 0.76 0.49 0.33 0.82 0.91

FIS -0.16 -0.01 -0.04 -0.21 -0.11 0.15 0.15 0.00 0.48 0.54 0.45 -0.31 -0.10 0.06 0.29 -0.10

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 15.4 0.0 15.4 0.0 0.0 0.0 7.7 0.0

Dev. HWP No No No No No No No No Yes Yes Yes No No No No No

Null alleles No No No No No No No No Yes Yes Yes No No No No No

Berrocal - complete (30) AR 5 13 11 7 3 6 6 7 9 12 9 9 4 4 4 15

HO 0.50 0.97 0.93 0.70 0.80 0.87 0.57 1.00 0.63 1.00 0.87 1.00 0.50 0.40 0.50 1.00

HE 0.67 0.87 0.81 0.70 0.61 0.74 0.69 0.77 0.86 0.90 0.86 0.84 0.51 0.41 0.61 0.89

FIS 0.26 -0.11 -0.15 0.00 -0.32 -0.17 0.18 -0.29 0.26 -0.12 -0.01 -0.20 0.02 0.03 0.19 -0.12

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 13.3 0.0

AD rate 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.13 0.00

FA rate 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01

Dev. HWP Yes Yes No No No No Yes Yes Yes Yes Yes No No No No Yes

Null alleles Yes No No No No No No No Yes No No No No No No No

Berrocal - reduced (6) AR 5 9 7 5 2 6 5 7 7 9 9 8 3 3 4 11

HO 0.67 0.83 0.83 0.67 0.67 1.00 0.67 1.00 0.67 1.00 0.67 1.00 0.33 0.33 0.83 1.00

HE 0.61 0.86 0.76 0.76 0.44 0.83 0.67 0.82 0.83 0.88 0.88 0.83 0.29 0.29 0.65 0.90

FIS -0.09 0.03 -0.09 0.13 -0.50 -0.20 0.00 -0.22 0.20 -0.14 0.24 -0.20 -0.14 -0.14 -0.28 -0.11

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Dev. HWP No No No No No No No No No No No No No No No No

Null alleles No No No No No No No No No No Yes No No No No No


Ecal4.20

Ecal4.8

Ecal4.29

Ecal4.16

Ecal4.18

Ecal4.3

Ecal4.6

Ecal4.14

Ecal4.2

Ecal3.26

Ecal4.24

Ecal3.4

Ecal3.29

Ecal3.19

Ecal4.26

Boceguillas - complete (20) AR 3 4 4 3 2 4 3 2 4 4 4 4 1 2 2 4

HO 0.45 1.00 1.00 1.00 0.40 1.00 0.70 0.55 1.00 1.00 1.00 1.00 0.00 0.55 0.18 1.00

HE 0.65 0.75 0.74 0.61 0.32 0.73 0.61 0.40 0.74 0.75 0.74 0.74 0.00 0.40 0.49 0.75

FIS 0.30 -0.34 -0.36 -0.63 -0.25 -0.37 -0.15 -0.38 -0.35 -0.34 -0.34 -0.35

-0.38 0.64 -0.34

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 15.0 0.0

AD rate 0.28 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.60 0.00

FA rate 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.02 0.00

Dev. HWP Yes Yes Yes Yes No Yes No No Yes Yes Yes Yes No No No Yes

Null alleles No No No No No No No No No No No No No No Yes No

Boceguillas - reduced (1) AR 1 2 2 2 1 2 1 1 2 2 2 2 1 2 1 2

HO 0.00 1.00 1.00 1.00 0.00 1.00 0.00 0.00 1.00 1.00 1.00 1.00 0.00 1.00 0.00 1.00

HE 0.00 0.50 0.50 0.50 0.00 0.50 0.00 0.00 0.50 0.50 0.50 0.50 0.00 0.50 0.00 0.50

FIS

-1.00 -1.00 -1.00

-1.00

-1.00 -1.00 -1.00 -1.00

-1.00

-1.00

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0


Null alleles No No No No No No No No No No No No No No No No

Bustarviejo - complete (28) AR 12 21 22 9 5 8 12 8 10 19 15 11 7 5 8 26

HO 0.59 0.93 0.96 0.93 0.64 0.79 0.93 0.75 0.48 0.74 0.62 0.85 0.71 0.54 0.61 0.96

HE 0.84 0.92 0.90 0.85 0.65 0.84 0.90 0.85 0.85 0.91 0.89 0.83 0.80 0.62 0.85 0.95

FIS 0.30 0.00 -0.07 -0.09 0.00 0.06 -0.04 0.11 0.43 0.19 0.31 -0.02 0.10 0.13 0.29 -0.01

Missing (%) 3.6 3.6 0.0 0.0 0.0 0.0 0.0 0.0 10.7 3.6 7.1 3.6 0.0 0.0 0.0 0.0

AD rate 0.14 0.00 0.00 0.00 0.03 0.01 0.00 0.06 0.45 0.10 0.14 0.01 0.03 0.03 0.19 0.00

FA rate 0.00 0.02 0.10 0.09 0.02 0.00 0.00 0.00 0.15 0.00 0.01 0.03 0.00 0.05 0.00 0.00

Dev. HWP Yes Yes No No No No No No Yes Yes Yes No No No Yes Yes

Null alleles Yes No No No No No No No Yes Yes Yes No No No Yes No

Bustarviejo - reduced (19) AR 11 20 18 9 4 8 12 8 10 19 14 10 7 5 8 26

HO 0.67 0.89 0.95 0.89 0.58 0.84 0.89 0.79 0.56 0.68 0.61 0.79 0.74 0.42 0.63 0.95

HE 0.84 0.92 0.91 0.85 0.60 0.85 0.90 0.85 0.87 0.93 0.89 0.81 0.78 0.59 0.86 0.95

FIS 0.20 0.03 -0.05 -0.05 0.03 0.01 0.00 0.07 0.35 0.26 0.31 0.02 0.06 0.28 0.26 0.00

Missing (%) 5.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 15.8 0.0 5.3 0.0 0.0 0.0 0.0 0.0

Dev. HWP No No No No No No No No No Yes Yes No No No No No


Cabanillas de la Sierra - complete (30)

AR 8 24 24 8 5 8 13 8 11 26 20 12 6 5 7 28 HO 0.47 0.97 0.90 0.90 0.63 0.90 0.83 0.77 0.64 0.81 0.57 0.77 0.77 0.53 0.56 0.93

HE 0.70 0.93 0.92 0.85 0.68 0.85 0.87 0.81 0.86 0.94 0.92 0.84 0.76 0.49 0.81 0.94

FIS 0.33 -0.04 0.02 -0.06 0.07 -0.06 0.04 0.05 0.25 0.14 0.39 0.09 -0.01 -0.10 0.31 0.01

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6.7 10.0 0.0 0.0 0.0 0.0 16.7 0.0

AD rate 0.25 0.00 0.00 0.00 0.04 0.00 0.00 0.00 0.18 0.18 0.31 0.08 0.00 0.00 0.24 0.00

FA rate 0.09 0.12 0.17 0.16 0.00 0.07 0.06 0.02 0.07 0.31 0.11 0.13 0.00 0.04 0.10 0.11


Null alleles Yes No No No No No No No Yes Yes Yes No No No No No


Ecal4.20

Ecal4.8

Ecal4.29

Ecal4.16

Ecal4.18

Ecal4.3

Ecal4.6

Ecal4.14

Ecal4.2

Ecal3.26

Ecal4.24

Ecal3.4

Ecal3.29

Ecal3.19

Ecal4.26

Cabanillas de la Sierra - reduced (26)

AR 8 23 23 8 5 8 13 8 11 26 18 11 6 5 7 26 HO 0.42 0.96 0.88 0.88 0.62 0.92 0.85 0.77 0.67 0.87 0.58 0.77 0.81 0.50 0.52 0.92

HE 0.69 0.92 0.91 0.86 0.68 0.85 0.86 0.79 0.86 0.95 0.92 0.82 0.77 0.47 0.81 0.94

FIS 0.39 -0.04 0.03 -0.03 0.10 -0.09 0.02 0.03 0.23 0.08 0.38 0.06 -0.05 -0.06 0.36 0.02

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7.7 11.5 0.0 0.0 0.0 0.0 19.2 0.0



Cerceda - complete (30) AR 8 18 15 8 4 7 12 7 10 22 17 10 7 5 8 23

HO 0.77 1.00 0.97 0.83 0.53 0.80 0.80 0.61 0.59 0.66 0.66 0.93 0.70 0.35 0.19 1.00

HE 0.76 0.91 0.90 0.85 0.53 0.79 0.87 0.74 0.85 0.90 0.91 0.78 0.75 0.42 0.71 0.92

FIS -0.01 -0.10 -0.07 0.02 -0.01 -0.02 0.08 0.18 0.31 0.27 0.28 -0.20 0.06 0.18 0.74 -0.09

Missing (%) 0.0 3.3 0.0 0.0 0.0 0.0 0.0 6.7 3.3 3.3 3.3 0.0 0.0 13.3 10.0 0.0

AD rate 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.07 0.16 0.12 0.13 0.00 0.00 0.12 0.63 0.00

FA rate 0.00 0.00 0.00 0.00 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.04 0.04 0.03

Dev. HWP No Yes No No No No Yes Yes Yes Yes Yes No No No Yes Yes


Cerceda - reduced (14) AR 8 17 13 8 3 7 11 7 10 16 16 10 7 5 7 20

HO 0.71 1.00 0.93 0.79 0.29 0.64 0.86 0.62 0.46 0.69 0.57 0.93 0.71 0.54 0.31 1.00

HE 0.73 0.92 0.90 0.82 0.43 0.80 0.89 0.75 0.86 0.91 0.91 0.83 0.77 0.54 0.75 0.94

FIS 0.02 -0.09 -0.03 0.04 0.33 0.19 0.04 0.18 0.46 0.24 0.37 -0.11 0.07 0.00 0.59 -0.06

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7.1 7.1 7.1 0.0 0.0 0.0 7.1 7.1 0.0

Dev. HWP No No No No No No No No Yes Yes Yes No No No Yes No


Colmenar Viejo - complete (30)

AR 6 10 7 7 3 6 10 5 8 10 5 5 4 2 3 12 HO 0.28 0.93 0.43 0.80 0.30 0.73 0.90 0.73 0.33 0.80 0.43 0.83 0.73 0.13 0.14 0.77

HE 0.69 0.85 0.54 0.80 0.27 0.73 0.85 0.69 0.80 0.85 0.56 0.79 0.73 0.39 0.36 0.83

FIS 0.60 -0.09 0.20 0.01 -0.13 -0.01 -0.06 -0.07 0.58 0.06 0.22 -0.05 -0.01 0.66 0.60 0.07

Missing (%) 3.3 10.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 23.3 0.0 0.0 0.0 6.7 0.0

AD rate 0.25 0.00 0.06 0.01 0.00 0.00 0.02 0.01 0.29 0.00 0.09 0.00 0.00 0.49 0.37 0.00

FA rate 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02

Dev. HWP Yes Yes No Yes No No Yes Yes Yes Yes No No No Yes Yes Yes

Null alleles Yes No No No No No No No Yes No No No No Yes Yes No

Colmenar Viejo - reduced (7) AR 6 9 5 6 3 6 9 4 5 9 5 5 4 2 2 9

HO 0.57 1.00 0.43 0.71 0.43 0.86 0.86 0.57 0.43 0.86 0.43 0.86 1.00 0.14 0.00 0.71

HE 0.78 0.87 0.73 0.78 0.36 0.71 0.85 0.66 0.71 0.84 0.62 0.76 0.74 0.34 0.44 0.87

FIS 0.26 -0.15 0.42 0.08 -0.20 -0.20 -0.01 0.14 0.40 -0.02 0.31 -0.14 -0.34 0.58 1.00 0.18

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 14.3 0.0




Ecal4.20

Ecal4.8

Ecal4.29

Ecal4.16

Ecal4.18

Ecal4.3

Ecal4.6

Ecal4.14

Ecal4.2

Ecal3.26

Ecal4.24

Ecal3.4

Ecal3.29

Ecal3.19

Ecal4.26

Dehesa de Roblellano - complete (36)

AR 9 26 21 10 5 8 14 9 14 31 20 12 7 5 9 42 HO 0.53 0.94 0.89 0.81 0.61 0.83 0.86 0.74 0.64 0.73 0.66 0.86 0.86 0.50 0.59 1.00

HE 0.81 0.94 0.88 0.85 0.60 0.85 0.88 0.81 0.89 0.94 0.91 0.87 0.72 0.46 0.79 0.97

FIS 0.34 -0.01 -0.01 0.05 -0.02 0.02 0.02 0.09 0.29 0.23 0.28 0.01 -0.19 -0.09 0.25 -0.04

Missing (%) 5.6 5.6 0.0 0.0 0.0 0.0 0.0 5.6 8.3 8.3 11.1 2.8 0.0 0.0 11.1 0.0

AD rate 0.25 0.00 0.00 0.04 0.03 0.01 0.00 0.03 0.22 0.15 0.17 0.00 0.00 0.00 0.15 0.00

FA rate 0.06 0.05 0.04 0.05 0.00 0.07 0.00 0.01 0.09 0.07 0.00 0.01 0.00 0.01 0.02 0.05

Dev. HWP Yes No No No No No No No Yes Yes Yes No No No Yes No



AR 9 26 21 10 5 8 14 9 14 29 20 12 7 5 9 40 HO 0.52 0.94 0.88 0.82 0.61 0.82 0.85 0.71 0.60 0.80 0.66 0.84 0.85 0.52 0.59 1.00

HE 0.80 0.94 0.88 0.85 0.59 0.85 0.88 0.80 0.89 0.94 0.92 0.86 0.73 0.47 0.80 0.96

FIS 0.36 0.00 0.00 0.04 -0.03 0.04 0.04 0.12 0.32 0.15 0.29 0.02 -0.17 -0.10 0.27 -0.04

Missing (%) 6.1 6.1 0.0 0.0 0.0 0.0 0.0 6.1 9.1 9.1 12.1 3.0 0.0 0.0 12.1 0.0

Dev. HWP Yes No No No No No No No Yes Yes Yes No No No No No


El Berrueco - complete (29) AR 6 11 8 6 4 6 6 6 7 6 8 5 4 4 5 8

HO 0.83 1.00 0.86 0.97 0.66 0.83 0.79 0.93 0.79 0.43 0.71 0.57 0.83 0.48 0.59 1.00

HE 0.77 0.89 0.83 0.78 0.64 0.65 0.79 0.81 0.77 0.82 0.82 0.68 0.66 0.54 0.59 0.85

FIS -0.08 -0.12 -0.04 -0.24 -0.02 -0.27 0.00 -0.15 -0.02 0.47 0.13 0.17 -0.26 0.10 0.00 -0.17

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.4 20.7 3.4 3.4 0.0 6.9 6.9 10.3

AD rate 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.29 0.06 0.00 0.00 0.09 0.09 0.00

FA rate 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.08 0.00

Dev. HWP Yes Yes Yes No No No Yes No Yes Yes Yes No Yes No No Yes

Null alleles No No No No No No No No No Yes No No No No No No

El Berrueco - reduced (3) AR 5 6 5 4 2 4 5 4 3 2 2 3 4 3 2 5

HO 0.67 1.00 1.00 1.00 0.33 1.00 1.00 1.00 0.50 1.00 0.50 0.50 1.00 0.67 0.33 1.00

HE 0.78 0.83 0.78 0.72 0.50 0.67 0.78 0.72 0.63 0.50 0.38 0.63 0.72 0.61 0.28 0.78

FIS 0.14 -0.20 -0.29 -0.38 0.33 -0.50 -0.29 -0.38 0.20 -1.00 -0.33 0.20 -0.38 -0.09 -0.20 -0.29

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 33.3 66.7 33.3 33.3 0.0 0.0 0.0 0.0



Gargantilla del Lozoya - complete (30)

AR 9 21 20 10 4 9 9 9 8 21 14 13 5 7 7 26 HO 0.77 1.00 0.90 0.90 0.57 0.87 0.83 0.62 0.57 0.50 0.45 0.90 0.83 0.70 0.48 1.00

HE 0.84 0.92 0.88 0.83 0.56 0.83 0.86 0.83 0.78 0.92 0.86 0.87 0.75 0.71 0.84 0.94

FIS 0.08 -0.09 -0.02 -0.08 -0.01 -0.04 0.03 0.25 0.27 0.46 0.48 -0.04 -0.10 0.01 0.42 -0.06

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.3 6.7 6.7 3.3 0.0 3.3 0.0 3.3 0.0

AD rate 0.03 0.00 0.00 0.00 0.01 0.00 0.01 0.12 0.17 0.50 0.40 0.00 0.00 0.01 0.26 0.00

FA rate 0.10 0.04 0.12 0.05 0.05 0.01 0.05 0.00 0.00 0.17 0.07 0.17 0.00 0.08 0.00 0.00


Null alleles No No No No No No No Yes Yes Yes Yes No No No Yes No


Ecal4.20

Ecal4.8

Ecal4.29

Ecal4.16

Ecal4.18

Ecal4.3

Ecal4.6

Ecal4.14

Ecal4.2

Ecal3.26

Ecal4.24

Ecal3.4

Ecal3.29

Ecal3.19

Ecal4.26

Gargantilla del Lozoya - reduced (27)

AR 9 21 20 10 4 9 9 9 8 20 14 12 5 7 7 25 HO 0.78 1.00 0.93 0.89 0.56 0.85 0.81 0.62 0.56 0.54 0.42 0.89 0.81 0.67 0.50 1.00

HE 0.83 0.92 0.90 0.84 0.57 0.83 0.85 0.83 0.76 0.92 0.86 0.86 0.74 0.71 0.83 0.94

FIS 0.07 -0.08 -0.03 -0.06 0.03 -0.03 0.05 0.26 0.27 0.42 0.51 -0.03 -0.08 0.06 0.40 -0.06

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.7 7.4 3.7 3.7 0.0 3.7 0.0 3.7 0.0

Dev. HWP No No No No No No No No No Yes Yes No No No Yes No



AR 6 17 17 10 5 7 10 7 7 18 11 10 5 6 6 17 HO 0.57 0.97 1.00 0.80 0.60 0.90 0.93 0.48 0.60 0.70 0.70 0.93 0.83 0.53 0.44 1.00

HE 0.66 0.89 0.87 0.84 0.57 0.77 0.88 0.79 0.80 0.88 0.86 0.81 0.76 0.69 0.80 0.89

FIS 0.14 -0.08 -0.15 0.05 -0.05 -0.16 -0.07 0.39 0.25 0.21 0.18 -0.15 -0.10 0.22 0.44 -0.12

Missing (%) 0.0 0.0 0.0 0.0 0.0 3.3 0.0 3.3 0.0 0.0 0.0 0.0 0.0 0.0 10.0 0.0

AD rate 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.15 0.18 0.13 0.13 0.00 0.00 0.09 0.29 0.00

FA rate 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Dev. HWP No Yes No No No No Yes Yes Yes Yes Yes No No No Yes Yes



AR 6 13 14 9 5 6 9 7 7 13 10 8 5 6 5 14 HO 0.55 0.91 1.00 0.82 0.64 0.90 0.82 0.55 0.55 0.45 0.55 0.82 0.73 0.82 0.22 1.00

HE 0.65 0.89 0.90 0.86 0.64 0.75 0.85 0.80 0.81 0.91 0.86 0.83 0.72 0.71 0.71 0.91

FIS 0.16 -0.02 -0.12 0.05 0.01 -0.21 0.04 0.32 0.33 0.50 0.36 0.01 -0.01 -0.14 0.69 -0.10

Missing (%) 0.0 0.0 0.0 0.0 0.0 9.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 18.2 0.0

Dev. HWP No No No No No No No No No Yes Yes No No No Yes No


Lozoyuela - complete (28) AR 8 19 17 9 5 7 10 6 9 15 11 8 5 4 9 22

HO 0.52 0.89 0.81 0.71 0.64 0.79 0.82 0.75 0.54 0.46 0.48 0.81 0.82 0.46 0.41 0.92

HE 0.77 0.91 0.83 0.82 0.52 0.75 0.83 0.75 0.86 0.88 0.85 0.84 0.73 0.52 0.70 0.93

FIS 0.32 0.02 0.02 0.13 -0.24 -0.05 0.01 0.00 0.37 0.48 0.43 0.03 -0.13 0.11 0.42 0.01

Missing (%) 10.7 3.6 3.6 0.0 0.0 0.0 0.0 0.0 7.1 7.1 10.7 3.6 0.0 7.1 3.6 7.1

AD rate 0.18 0.00 0.05 0.00 0.00 0.03 0.03 0.01 0.17 0.33 0.22 0.05 0.00 0.00 0.33 0.00

FA rate 0.00 0.00 0.06 0.08 0.06 0.07 0.00 0.00 0.00 0.06 0.00 0.00 0.07 0.00 0.06 0.03

Dev. HWP Yes No No No No No No Yes Yes Yes Yes No No No Yes No


Lozoyuela - reduced (17) AR 7 17 15 9 4 6 10 5 9 14 11 8 4 4 9 20

HO 0.57 0.81 0.75 0.76 0.71 0.76 0.82 0.59 0.53 0.47 0.43 0.81 0.71 0.47 0.47 0.88

HE 0.70 0.91 0.86 0.81 0.53 0.77 0.85 0.70 0.85 0.91 0.85 0.84 0.72 0.46 0.73 0.92

FIS 0.18 0.11 0.13 0.06 -0.34 0.01 0.03 0.16 0.37 0.49 0.50 0.03 0.02 -0.01 0.35 0.04

Missing (%) 17.6 5.9 5.9 0.0 0.0 0.0 0.0 0.0 11.8 11.8 17.6 5.9 0.0 0.0 0.0 0.0

Dev. HWP No No No No No No No Yes No Yes Yes No No No Yes No



Ecal4.20

Ecal4.8

Ecal4.29

Ecal4.16

Ecal4.18

Ecal4.3

Ecal4.6

Ecal4.14

Ecal4.2

Ecal3.26

Ecal4.24

Ecal3.4

Ecal3.29

Ecal3.19

Ecal4.26

Muñoveros - complete (32) AR 8 17 18 8 4 7 13 8 13 19 12 9 7 5 9 27

HO 0.43 1.00 0.84 0.84 0.63 0.75 0.84 0.42 0.72 0.78 0.91 0.94 0.56 0.47 0.14 1.00

HE 0.82 0.90 0.88 0.84 0.54 0.82 0.86 0.83 0.86 0.90 0.88 0.85 0.65 0.55 0.78 0.93

FIS 0.47 -0.12 0.05 0.00 -0.15 0.09 0.02 0.49 0.16 0.13 -0.03 -0.10 0.14 0.15 0.82 -0.07

Missing (%) 6.3 0.0 3.1 0.0 0.0 0.0 0.0 3.1 9.4 0.0 0.0 0.0 0.0 0.0 9.4 0.0

AD rate 0.24 0.00 0.00 0.06 0.00 0.00 0.00 0.33 0.06 0.06 0.00 0.00 0.02 0.00 1.00 0.00

FA rate 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.07 0.02

Dev. HWP Yes No No No No No No Yes Yes Yes No No No Yes Yes No

Null alleles Yes No No No No No No Yes Yes Yes No No No No Yes No

Muñoveros - reduced (16) AR 7 15 14 7 3 7 13 7 13 17 12 8 7 5 8 22

HO 0.53 1.00 0.80 0.88 0.56 0.69 0.88 0.25 0.75 0.75 0.88 1.00 0.56 0.50 0.21 1.00

HE 0.80 0.90 0.90 0.83 0.54 0.82 0.89 0.82 0.85 0.91 0.88 0.84 0.65 0.57 0.77 0.94

FIS 0.34 -0.11 0.11 -0.05 -0.04 0.17 0.02 0.69 0.12 0.17 0.01 -0.19 0.14 0.13 0.72 -0.06

Missing (%) 6.3 0.0 6.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 12.5 0.0

Dev. HWP No No No No No No No Yes No No No No No No Yes No

Null alleles Yes No No No No No No Yes No Yes No No No No Yes No

Navafría - complete (30) AR 8 16 17 10 4 6 7 7 9 17 12 8 6 4 7 20

HO 0.54 0.90 0.83 0.97 0.67 0.87 0.70 0.53 0.63 0.86 0.85 0.79 0.67 0.43 0.38 1.00

HE 0.73 0.91 0.91 0.85 0.62 0.76 0.71 0.76 0.83 0.92 0.88 0.83 0.55 0.66 0.71 0.93

FIS 0.26 0.01 0.08 -0.13 -0.07 -0.14 0.01 0.30 0.24 0.06 0.04 0.05 -0.21 0.34 0.46 -0.07

Missing (%) 6.7 0.0 0.0 3.3 0.0 0.0 0.0 0.0 0.0 3.3 13.3 3.3 0.0 0.0 3.3 0.0

AD rate 0.14 0.00 0.00 0.00 0.00 0.02 0.03 0.13 0.17 0.00 0.00 0.00 0.00 0.18 0.25 0.00

FA rate 0.00 0.00 0.06 0.00 0.00 0.02 0.00 0.00 0.07 0.04 0.00 0.00 0.05 0.00 0.01 0.03

Dev. HWP Yes Yes Yes No No No No Yes No Yes Yes No No No Yes Yes

Null alleles Yes No No No No No No Yes Yes No No No No Yes Yes No

Navafría - reduced (10) AR 6 15 13 8 4 6 6 6 7 13 9 8 3 4 5 16

HO 0.56 0.90 1.00 1.00 0.80 0.80 0.70 0.70 0.70 0.78 0.88 0.90 0.80 0.40 0.44 1.00

HE 0.79 0.92 0.91 0.86 0.63 0.79 0.75 0.77 0.78 0.91 0.83 0.86 0.59 0.74 0.58 0.93

FIS 0.30 0.02 -0.10 -0.16 -0.28 -0.01 0.07 0.09 0.10 0.14 -0.06 -0.05 -0.37 0.46 0.23 -0.08

Missing (%) 10.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 10.0 20.0 0.0 0.0 0.0 10.0 0.0


Null alleles No No No No No No No No No No No No No Yes No No

Navalafuente - complete (30) AR 6 12 12 6 5 6 11 6 6 11 11 8 6 4 8 16

HO 0.83 1.00 0.73 0.90 0.50 0.93 1.00 0.34 0.10 0.60 0.60 0.87 0.87 0.27 0.77 1.00

HE 0.78 0.84 0.85 0.66 0.62 0.77 0.81 0.74 0.72 0.77 0.84 0.83 0.77 0.39 0.83 0.90

FIS -0.07 -0.20 0.13 -0.36 0.19 -0.21 -0.24 0.54 0.86 0.22 0.29 -0.05 -0.12 0.32 0.08 -0.11

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

AD rate 0.00 0.00 0.15 0.00 0.00 0.00 0.00 0.22 0.79 0.04 0.26 0.02 0.00 0.06 0.05 0.00

FA rate 0.02 0.00 0.00 0.03 0.00 0.00 0.04 0.00 0.02 0.00 0.05 0.00 0.00 0.00 0.00 0.06

Dev. HWP Yes Yes Yes No No Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Null alleles No No No No No No No Yes Yes Yes Yes No No Yes No No


Ecal4.20

Ecal4.8

Ecal4.29

Ecal4.16

Ecal4.18

Ecal4.3

Ecal4.6

Ecal4.14

Ecal4.2

Ecal3.26

Ecal4.24

Ecal3.4

Ecal3.29

Ecal3.19

Ecal4.26

Navalafuente - reduced (5) AR 5 8 7 4 3 6 6 5 4 8 7 6 5 4 7 8

HO 0.80 1.00 1.00 0.80 0.60 1.00 1.00 0.20 0.00 0.80 0.60 0.80 0.80 0.40 1.00 1.00

HE 0.76 0.86 0.84 0.58 0.62 0.80 0.80 0.74 0.72 0.86 0.82 0.80 0.76 0.58 0.84 0.86

FIS -0.05 -0.16 -0.19 -0.38 0.03 -0.25 -0.25 0.73 1.00 0.07 0.27 0.00 -0.05 0.31 -0.19 -0.16

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Dev. HWP No No No No No No No Yes Yes No No No No No No No

Null alleles No No No No No No No Yes Yes No No No No No No No

Puerto de Canencia - complete (28)

AR 10 23 21 8 6 10 13 8 11 25 18 12 6 6 9 28 HO 0.56 1.00 0.93 0.86 0.61 0.86 0.86 0.57 0.42 0.69 0.67 0.79 0.79 0.56 0.62 0.89

HE 0.82 0.92 0.90 0.83 0.58 0.85 0.89 0.85 0.85 0.94 0.92 0.85 0.75 0.55 0.84 0.95

FIS 0.32 -0.08 -0.03 -0.03 -0.05 -0.01 0.04 0.33 0.50 0.27 0.27 0.08 -0.05 -0.02 0.27 0.06

Missing (%) 3.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7.1 7.1 3.6 0.0 0.0 3.6 7.1 3.6

AD rate 0.17 0.00 0.00 0.00 0.00 0.00 0.02 0.18 0.55 0.28 0.23 0.03 0.00 0.00 0.19 0.02

FA rate 0.00 0.14 0.04 0.02 0.00 0.04 0.02 0.00 0.14 0.22 0.13 0.05 0.01 0.02 0.06 0.08

Dev. HWP No No No No No No No Yes Yes Yes No No No No No No

Null alleles Yes No No No No No No Yes Yes Yes Yes No No No Yes No

Puerto de Canencia - reduced (26)

AR 10 22 20 8 6 10 12 8 11 24 17 12 6 5 9 27 HO 0.56 1.00 0.92 0.85 0.62 0.88 0.85 0.54 0.42 0.71 0.68 0.77 0.81 0.52 0.63 0.88

HE 0.84 0.92 0.90 0.84 0.59 0.85 0.89 0.84 0.85 0.94 0.91 0.85 0.75 0.53 0.84 0.95

FIS 0.33 -0.08 -0.03 -0.01 -0.04 -0.04 0.05 0.36 0.51 0.25 0.25 0.10 -0.07 0.02 0.26 0.07

Missing (%) 3.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7.7 7.7 3.8 0.0 0.0 3.8 7.7 3.8

Dev. HWP No No No No No No No Yes Yes Yes No No No No No No


Puerto de La Morcuera - complete (20)

AR 5 11 14 7 3 6 6 7 6 11 8 5 3 2 6 10 HO 0.25 0.90 0.95 0.80 0.55 0.80 0.70 0.65 0.60 0.79 0.33 0.95 0.60 0.15 0.50 0.89

HE 0.62 0.87 0.90 0.75 0.47 0.80 0.65 0.80 0.77 0.85 0.83 0.74 0.52 0.14 0.80 0.85

FIS 0.59 -0.03 -0.06 -0.07 -0.18 0.00 -0.09 0.19 0.22 0.07 0.60 -0.28 -0.15 -0.08 0.38 -0.05

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5.0 10.0 5.0 0.0 0.0 10.0 5.0

AD rate 0.39 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.06 0.00 0.36 0.00 0.00 0.00 0.24 0.00

FA rate 0.00 0.00 0.00 0.08 0.00 0.00 0.13 0.00 0.02 0.00 0.00 0.00 0.03 0.02 0.03 0.00

Dev. HWP Yes No No No No No No No No No Yes No No No No No

Null alleles Yes No No No No No No No No No Yes No No No Yes No

Puerto de La Morcuera - reduced (11)

AR 4 11 13 6 3 6 5 6 6 9 8 5 3 2 6 9 HO 0.27 0.91 0.91 0.82 0.36 0.73 0.64 0.64 0.64 0.60 0.30 1.00 0.64 0.09 0.60 0.90

HE 0.67 0.87 0.89 0.77 0.37 0.80 0.69 0.77 0.79 0.81 0.85 0.74 0.54 0.09 0.81 0.85

FIS 0.59 -0.04 -0.02 -0.06 0.01 0.09 0.08 0.18 0.19 0.26 0.64 -0.35 -0.18 -0.05 0.25 -0.07

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 9.1 9.1 9.1 0.0 0.0 9.1 9.1

Dev. HWP No No No No No No No No No No Yes No No No No No

Null alleles Yes No No No No No No No No No Yes No No No No No


Ecal4.20

Ecal4.8

Ecal4.29

Ecal4.16

Ecal4.18

Ecal4.3

Ecal4.6

Ecal4.14

Ecal4.2

Ecal3.26

Ecal4.24

Ecal3.4

Ecal3.29

Ecal3.19

Ecal4.26

Puerto del Medio Celemín - complete (30)

AR 9 18 14 8 5 8 10 9 9 19 14 9 6 5 6 24 HO 0.59 0.97 0.93 0.97 0.53 0.97 0.80 0.70 0.54 0.70 0.50 0.83 0.77 0.57 0.29 0.97

HE 0.79 0.91 0.89 0.86 0.58 0.84 0.86 0.86 0.82 0.92 0.87 0.84 0.78 0.54 0.77 0.93

FIS 0.25 -0.06 -0.05 -0.13 0.07 -0.15 0.07 0.19 0.35 0.23 0.43 0.01 0.02 -0.05 0.62 -0.04

Missing (%) 10.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6.7 10.0 6.7 0.0 0.0 0.0 20.0 3.3

AD rate 0.15 0.00 0.00 0.00 0.05 0.00 0.03 0.11 0.21 0.13 0.24 0.00 0.01 0.00 0.48 0.00

FA rate 0.05 0.08 0.00 0.00 0.06 0.00 0.00 0.00 0.01 0.02 0.00 0.09 0.06 0.00 0.02 0.13

Dev. HWP Yes No No No No No No No Yes Yes Yes No No No Yes No


Puerto del Medio Celemín - reduced (21)

AR 9 17 14 8 5 8 10 9 9 18 14 9 5 5 6 21 HO 0.68 1.00 0.90 1.00 0.62 0.95 0.81 0.76 0.53 0.67 0.53 0.90 0.71 0.57 0.29 0.95

HE 0.76 0.91 0.90 0.86 0.61 0.83 0.87 0.86 0.84 0.92 0.90 0.84 0.78 0.55 0.79 0.92

FIS 0.10 -0.10 -0.01 -0.16 -0.02 -0.14 0.07 0.11 0.37 0.28 0.41 -0.07 0.09 -0.04 0.63 -0.03

Missing (%) 9.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 9.5 14.3 9.5 0.0 0.0 0.0 19.0 4.8



Santo Tomé del Puerto - complete (30)

AR 8 12 11 6 4 8 7 7 9 16 14 9 5 3 5 14 HO 0.73 1.00 1.00 0.83 0.73 0.77 0.83 0.43 0.67 1.00 0.77 0.97 0.60 0.50 0.40 0.80

HE 0.80 0.87 0.88 0.80 0.52 0.82 0.78 0.78 0.76 0.88 0.90 0.83 0.65 0.46 0.79 0.87

FIS 0.08 -0.15 -0.13 -0.04 -0.42 0.06 -0.06 0.45 0.12 -0.14 0.15 -0.17 0.08 -0.08 0.49 0.08

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6.7 0.0 0.0 0.0 0.0 0.0 0.0 16.7 0.0

AD rate 0.07 0.00 0.00 0.00 0.01 0.03 0.00 0.19 0.12 0.00 0.07 0.00 0.00 0.00 0.26 0.00

FA rate 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.00 0.01 0.00 0.00 0.00 0.03

Dev. HWP No No Yes No No No No Yes No Yes Yes Yes No No Yes Yes

Null alleles No No No No No No No Yes No No Yes No No No Yes No

Santo Tomé del Puerto - reduced (8)

AR 6 8 8 5 4 8 6 7 8 12 10 7 5 3 5 10 HO 0.75 1.00 1.00 0.88 0.75 0.75 0.75 0.63 0.75 1.00 0.63 1.00 0.63 0.25 0.50 0.88

HE 0.77 0.87 0.84 0.77 0.55 0.84 0.73 0.81 0.79 0.90 0.88 0.83 0.77 0.32 0.76 0.87

FIS 0.02 -0.15 -0.19 -0.13 -0.35 0.11 -0.02 0.23 0.05 -0.11 0.29 -0.21 0.18 0.22 0.35 -0.01

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 25.0 0.0


Null alleles No No No No No No No No No No Yes No No No No No

Soto del Real - complete (30) AR 6 13 14 7 6 7 13 7 7 13 14 8 7 3 6 19

HO 0.60 0.90 0.93 0.80 0.77 0.87 0.90 0.55 0.43 0.32 0.72 0.90 0.90 0.33 0.22 0.93

HE 0.71 0.83 0.88 0.71 0.71 0.79 0.85 0.79 0.81 0.86 0.87 0.82 0.79 0.37 0.69 0.88

FIS 0.15 -0.09 -0.06 -0.13 -0.08 -0.09 -0.06 0.31 0.47 0.63 0.17 -0.09 -0.13 0.10 0.68 -0.06

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.3 6.7 6.7 3.3 0.0 0.0 0.0 10.0 0.0

AD rate 0.08 0.00 0.00 0.02 0.00 0.00 0.00 0.16 0.23 0.46 0.11 0.00 0.00 0.10 0.47 0.00

FA rate 0.00 0.11 0.09 0.05 0.00 0.00 0.05 0.05 0.01 0.02 0.07 0.06 0.07 0.06 0.00 0.11

Dev. HWP No No No No No No No No Yes Yes No No No No Yes No



Ecal4.20

Ecal4.8

Ecal4.29

Ecal4.16

Ecal4.18

Ecal4.3

Ecal4.6

Ecal4.14

Ecal4.2

Ecal3.26

Ecal4.24

Ecal3.4

Ecal3.29

Ecal3.19

Ecal4.26

Soto del Real - reduced (14) AR 6 9 12 7 6 6 10 6 7 10 9 7 7 3 6 16

HO 0.50 0.93 0.93 0.86 0.79 0.93 0.93 0.69 0.38 0.31 0.71 0.93 0.93 0.36 0.08 1.00

HE 0.74 0.79 0.86 0.68 0.73 0.78 0.86 0.75 0.82 0.85 0.83 0.80 0.80 0.46 0.75 0.90

FIS 0.32 -0.17 -0.08 -0.25 -0.08 -0.19 -0.08 0.08 0.53 0.64 0.14 -0.16 -0.17 0.22 0.90 -0.12

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7.1 7.1 7.1 0.0 0.0 0.0 0.0 7.1 0.0

Dev. HWP No No No No No No No No Yes Yes No No No No Yes No

Null alleles Yes No No No No No No No Yes Yes No No No No Yes No

Valdemanco - complete (77) AR 10 25 28 9 6 8 14 9 15 28 20 11 7 5 7 42

HO 0.64 0.89 0.93 0.95 0.69 0.91 0.84 0.77 0.56 0.80 0.49 0.86 0.79 0.39 0.48 0.99

HE 0.74 0.92 0.93 0.83 0.71 0.85 0.87 0.83 0.89 0.93 0.93 0.86 0.79 0.41 0.80 0.96

FIS 0.13 0.02 -0.01 -0.14 0.03 -0.07 0.03 0.08 0.37 0.14 0.47 0.01 0.00 0.04 0.40 -0.03

Missing (%) 5.2 1.3 1.3 0.0 0.0 0.0 0.0 0.0 9.1 3.9 7.8 1.3 0.0 1.3 14.3 1.3

AD rate 0.04 0.00 0.00 0.00 0.00 0.00 0.01 0.02 0.11 0.08 0.18 0.00 0.00 0.01 0.19 0.00

FA rate 0.01 0.02 0.01 0.01 0.01 0.00 0.01 0.01 0.01 0.01 0.01 0.00 0.02 0.00 0.00 0.01

Dev. HWP No Yes No No No No No Yes Yes Yes Yes Yes No No Yes Yes


Valdemanco - reduced (27) AR 6 22 20 9 5 8 12 8 13 22 19 11 6 5 7 36

HO 0.48 0.93 0.96 0.96 0.63 0.93 0.78 0.70 0.60 0.81 0.42 0.81 0.85 0.44 0.35 0.96

HE 0.69 0.93 0.92 0.85 0.66 0.85 0.85 0.84 0.87 0.92 0.92 0.84 0.77 0.43 0.80 0.96

FIS 0.30 0.00 -0.05 -0.13 0.05 -0.09 0.09 0.16 0.31 0.12 0.54 0.03 -0.11 -0.03 0.57 -0.01

Missing (%) 7.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7.4 3.7 3.7 0.0 0.0 0.0 14.8 0.0

Dev. HWP No No No No No No No No Yes No Yes No No No Yes No


Table A1.6. Characterization of 15 microsatellite loci in 17 P. perezi populations (Loc). For each population, several diversity and data quality measures are displayed both in




Loc (sample size) Parameter Pper4.25

Pper4.15

Pper4.28

Pper3.9

Pper4.5

Pper4.16

Pper3.24

Pper4.20

Pper3.22

Pper4.13

Pper4.7

Pper3.1

Pper4.29

Pper3.23

Pper4.24

Arcones - complete (19) AR 4 3 2 3 3 6 2 2 3 4 5 3 4 3 6

HO 0.72 0.53 0.37 0.58 0.74 0.68 0.47 0.32 0.26 0.37 0.29 0.63 0.84 0.53 0.95

HE 0.63 0.60 0.36 0.63 0.59 0.74 0.45 0.39 0.31 0.36 0.68 0.57 0.73 0.49 0.74

FIS -0.15 0.13 -0.02 0.08 -0.25 0.07 -0.05 0.19 0.15 -0.02 0.57 -0.11 -0.16 -0.07 -0.28

Missing (%) 5.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 10.5 0.0 0.0 0.0 0.0

AD rate 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.09 0.09 0.00 0.30 0.00 0.00 0.04 0.00

FA rate 0.08 0.00 0.13 0.00 0.00 0.00 0.03 0.00 0.04 0.05 0.00 0.19 0.00 0.00 0.07

Dev. HWP No No No No No No No No No No Yes No No No No

Null alleles No No No No No No No No No No Yes No No No No

Arcones - reduced (14) AR 4 3 2 3 3 6 2 2 3 4 4 3 4 3 6

HO 0.85 0.57 0.43 0.64 0.71 0.71 0.50 0.29 0.29 0.36 0.31 0.71 0.79 0.57 0.93

HE 0.66 0.63 0.41 0.63 0.57 0.77 0.48 0.41 0.35 0.36 0.66 0.61 0.74 0.54 0.71

FIS -0.28 0.09 -0.05 -0.02 -0.25 0.07 -0.05 0.30 0.18 0.02 0.54 -0.16 -0.06 -0.06 -0.30

Missing (%) 7.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7.1 0.0 0.0 0.0 0.0



Bustarviejo - complete (30) AR 7 8 3 6 2 5 6 2 2 8 8 6 5 4 5

HO 0.73 0.73 0.77 0.77 0.50 0.83 0.89 0.77 0.43 0.93 0.90 0.55 0.86 0.53 0.37

HE 0.75 0.78 0.57 0.70 0.50 0.74 0.81 0.49 0.41 0.85 0.81 0.69 0.74 0.64 0.56

FIS 0.02 0.06 -0.35 -0.10 0.00 -0.13 -0.10 -0.58 -0.07 -0.10 -0.11 0.20 -0.16 0.16 0.34

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 6.7 0.0 0.0 0.0 3.3 3.3 3.3 0.0 0.0

AD rate 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.12 0.00 0.00 0.18

FA rate 0.00 0.05 0.02 0.03 0.00 0.03 0.00 0.00 0.00 0.00 0.07 0.02 0.02 0.00 0.00

Dev. HWP No No No No No No No Yes No No No No No No Yes

Null alleles No No No No No No No No No No No No No No Yes

Bustarviejo - reduced (17) AR 6 5 3 6 2 4 6 2 2 8 6 6 4 4 5

HO 0.71 0.71 0.71 0.71 0.53 0.82 0.88 0.76 0.35 0.88 0.94 0.56 0.81 0.47 0.35

HE 0.75 0.70 0.54 0.70 0.49 0.72 0.79 0.49 0.36 0.84 0.79 0.73 0.71 0.62 0.57

FIS 0.06 -0.01 -0.31 -0.01 -0.07 -0.14 -0.11 -0.55 0.02 -0.05 -0.20 0.23 -0.14 0.24 0.39

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 5.9 0.0 0.0 0.0 0.0 5.9 5.9 0.0 0.0

Dev. HWP No No No No No No No No No No No No No No No



Pper4.15

Pper4.28

Pper3.9

Pper4.5

Pper4.16

Pper3.24

Pper4.20

Pper3.22

Pper4.13

Pper4.7

Pper3.1

Pper4.29

Pper3.23

Pper4.24

Cabanillas de la Sierra 2010 - complete (20)

AR 13 9 6 8 3 9 7 2 4 13 16 6 6 5 10 HO 0.95 0.85 0.50 0.80 0.70 0.95 0.78 0.35 0.30 0.90 0.80 0.53 0.85 0.90 0.84

HE 0.90 0.81 0.49 0.77 0.65 0.77 0.81 0.40 0.27 0.90 0.91 0.78 0.73 0.73 0.86

FIS -0.06 -0.04 -0.03 -0.04 -0.08 -0.24 0.04 0.12 -0.11 0.00 0.12 0.32 -0.16 -0.23 0.02

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 10.0 0.0 0.0 0.0 0.0 5.0 0.0 0.0 5.0

AD rate 0.00 0.00 0.00 0.04 0.00 0.00 0.04 0.00 0.00 0.01 0.05 0.14 0.00 0.00 0.00

FA rate 0.07 0.08 0.10 0.00 0.00 0.04 0.17 0.00 0.04 0.12 0.00 0.00 0.00 0.00 0.06


Null alleles No No No No No No No No No No No Yes No No No

Cabanillas de la Sierra 2010 - reduced (20)

AR 13 9 6 8 3 9 7 2 4 13 16 6 6 5 10 HO 0.95 0.85 0.50 0.80 0.70 0.95 0.78 0.35 0.30 0.90 0.80 0.53 0.85 0.90 0.84

HE 0.90 0.81 0.49 0.77 0.65 0.77 0.81 0.40 0.27 0.90 0.91 0.78 0.73 0.73 0.86

FIS -0.06 -0.04 -0.03 -0.04 -0.08 -0.24 0.04 0.12 -0.11 0.00 0.12 0.32 -0.16 -0.23 0.02

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 10.0 0.0 0.0 0.0 0.0 5.0 0.0 0.0 5.0


Null alleles No No No No No No No No No No No Yes No No No


AR 19 9 6 7 3 10 8 2 5 12 15 8 9 5 12 HO 0.92 0.52 0.59 0.85 0.56 0.81 0.70 0.37 0.48 0.89 0.92 0.67 0.81 0.74 0.89

HE 0.92 0.67 0.57 0.74 0.66 0.82 0.83 0.44 0.40 0.90 0.84 0.73 0.80 0.76 0.86

FIS 0.00 0.23 -0.04 -0.15 0.16 0.00 0.15 0.17 -0.20 0.01 -0.09 0.09 -0.02 0.02 -0.03

Missing (%) 3.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.7 0.0 0.0 0.0 0.0

AD rate 0.01 0.09 0.00 0.01 0.15 0.00 0.03 0.02 0.00 0.00 0.00 0.01 0.00 0.00 0.00

FA rate 0.00 0.03 0.07 0.07 0.18 0.08 0.00 0.00 0.04 0.07 0.06 0.05 0.00 0.00 0.13


Null alleles No Yes No No No No No No No No No No No No No


AR 19 9 6 7 3 10 8 2 5 12 14 8 9 5 10 HO 0.95 0.60 0.60 0.90 0.45 0.80 0.75 0.40 0.50 0.90 0.95 0.65 0.80 0.75 0.90

HE 0.93 0.70 0.57 0.75 0.66 0.80 0.84 0.48 0.42 0.89 0.86 0.77 0.80 0.75 0.86

FIS -0.02 0.14 -0.05 -0.20 0.32 0.00 0.10 0.17 -0.20 -0.01 -0.10 0.15 0.00 0.00 -0.05

Missing (%) 5.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5.0 0.0 0.0 0.0 0.0


Null alleles No No No No No No No No No No No No No No No


AR 15 10 6 7 3 9 7 2 2 14 17 8 7 6 11 HO 0.77 0.90 0.60 0.73 0.77 0.67 0.87 0.30 0.27 0.83 0.90 0.73 0.87 0.73 0.93

HE 0.89 0.81 0.60 0.71 0.64 0.78 0.82 0.50 0.23 0.88 0.91 0.85 0.80 0.76 0.88

FIS 0.14 -0.11 0.00 -0.03 -0.19 0.15 -0.06 0.40 -0.15 0.05 0.02 0.13 -0.08 0.03 -0.06

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

AD rate 0.05 0.00 0.04 0.00 0.00 0.06 0.00 0.07 0.00 0.00 0.01 0.02 0.01 0.00 0.00

FA rate 0.10 0.00 0.00 0.00 0.00 0.00 0.05 0.00 0.00 0.05 0.00 0.04 0.00 0.03 0.03

Dev. HWP Yes No No No No No No No No Yes No Yes No No No

Null alleles Yes No No No No No No No No No No No No No No


Pper4.15

Pper4.28

Pper3.9

Pper4.5

Pper4.16

Pper3.24

Pper4.20

Pper3.22

Pper4.13

Pper4.7

Pper3.1

Pper4.29

Pper3.23

Pper4.24


AR 13 8 6 6 3 9 7 2 2 14 15 8 6 6 10 HO 0.67 0.93 0.53 0.80 0.80 0.67 0.87 0.27 0.27 0.87 0.87 0.73 0.93 0.73 0.87

HE 0.88 0.79 0.56 0.72 0.65 0.82 0.81 0.50 0.23 0.89 0.91 0.82 0.76 0.75 0.87

FIS 0.24 -0.18 0.06 -0.11 -0.23 0.18 -0.07 0.46 -0.15 0.02 0.04 0.11 -0.22 0.02 0.00

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0


Null alleles Yes No No No No No No No No No No No No No No

Cerceda - complete (23) AR 17 11 5 7 3 10 7 2 5 11 15 8 8 4 9

HO 0.87 0.96 0.61 0.87 0.70 0.83 0.83 0.43 0.61 0.78 1.00 0.78 0.91 0.78 0.91

HE 0.91 0.86 0.62 0.76 0.62 0.85 0.78 0.49 0.49 0.87 0.89 0.73 0.80 0.70 0.84

FIS 0.05 -0.12 0.02 -0.14 -0.12 0.02 -0.06 0.12 -0.24 0.10 -0.12 -0.07 -0.14 -0.11 -0.09

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.3 0.0 0.0 0.0 0.0

AD rate 0.03 0.00 0.08 0.00 0.00 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

FA rate 0.00 0.00 0.07 0.00 0.14 0.00 0.07 0.03 0.06 0.00 0.00 0.00 0.04 0.14 0.00



Cerceda - reduced (18) AR 16 11 5 7 3 10 7 2 5 11 14 8 8 4 9

HO 0.89 0.94 0.61 0.83 0.67 0.78 0.89 0.44 0.61 0.78 1.00 0.89 0.89 0.72 0.94

HE 0.90 0.85 0.64 0.78 0.59 0.85 0.79 0.50 0.49 0.87 0.90 0.77 0.81 0.71 0.83

FIS 0.02 -0.11 0.04 -0.07 -0.13 0.08 -0.12 0.11 -0.25 0.10 -0.11 -0.15 -0.10 -0.02 -0.14

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5.6 0.0 0.0 0.0 0.0



Collado Hermoso - complete (32)

AR 14 7 4 6 4 5 6 2 3 11 12 5 4 5 11 HO 0.97 0.91 0.75 0.66 0.68 0.81 0.94 0.28 0.41 0.84 0.97 0.81 0.78 0.66 0.78

HE 0.89 0.81 0.68 0.64 0.68 0.77 0.76 0.32 0.35 0.81 0.89 0.68 0.67 0.62 0.88

FIS -0.09 -0.12 -0.10 -0.02 0.00 -0.04 -0.24 0.13 -0.17 -0.05 -0.09 -0.20 -0.16 -0.06 0.11

Missing (%) 0.0 0.0 0.0 0.0 3.1 3.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

AD rate 0.00 0.00 0.00 0.03 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.04 0.10

FA rate 0.09 0.02 0.02 0.02 0.00 0.10 0.08 0.00 0.01 0.10 0.00 0.06 0.07 0.07 0.00



Collado Hermoso - reduced (28)

AR 14 7 4 5 4 5 6 2 3 11 12 5 4 5 11 HO 0.96 0.93 0.75 0.64 0.67 0.81 0.96 0.29 0.43 0.86 0.96 0.79 0.82 0.64 0.82

HE 0.89 0.82 0.67 0.64 0.69 0.78 0.77 0.29 0.36 0.82 0.90 0.68 0.69 0.62 0.89

FIS -0.09 -0.14 -0.12 -0.01 0.03 -0.04 -0.26 0.03 -0.18 -0.04 -0.08 -0.15 -0.19 -0.04 0.08

Missing (%) 0.0 0.0 0.0 0.0 3.6 3.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0




Pper4.15

Pper4.28

Pper3.9

Pper4.5

Pper4.16

Pper3.24

Pper4.20

Pper3.22

Pper4.13

Pper4.7

Pper3.1

Pper4.29

Pper3.23

Pper4.24

Dehesa de Roblellano - complete (23)

AR 8 6 4 6 3 4 5 2 2 6 7 5 5 4 6 HO 0.78 0.87 0.61 0.78 0.65 0.74 0.57 0.35 0.48 0.87 0.83 1.00 1.00 0.36 1.00

HE 0.81 0.75 0.66 0.78 0.66 0.59 0.65 0.29 0.48 0.80 0.81 0.76 0.73 0.70 0.77

FIS 0.04 -0.16 0.08 0.00 0.01 -0.24 0.13 -0.21 0.01 -0.09 -0.02 -0.31 -0.37 0.48 -0.29

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.3 0.0

AD rate 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.13 0.00

FA rate 0.03 0.00 0.00 0.02 0.00 0.00 0.03 0.00 0.18 0.00 0.00 0.00 0.00 0.00 0.00

Dev. HWP No No No No No No No No No No Yes No Yes Yes No

Null alleles No No No No No No No No No No No No No Yes No


AR 5 6 4 5 3 4 4 2 2 4 6 4 4 4 5 HO 1.00 1.00 0.75 0.75 0.75 0.75 0.50 0.25 0.25 1.00 0.75 1.00 1.00 0.25 1.00

HE 0.78 0.81 0.72 0.75 0.66 0.56 0.56 0.22 0.22 0.72 0.78 0.66 0.72 0.72 0.75

FIS -0.28 -0.23 -0.04 0.00 -0.14 -0.33 0.11 -0.14 -0.14 -0.39 0.04 -0.52 -0.39 0.65 -0.33

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0



El Berrueco - complete (20) AR 6 4 2 3 3 5 5 2 3 4 3 3 4 5 5

HO 1.00 0.80 0.50 0.75 0.75 0.80 1.00 0.50 0.50 0.75 0.20 0.55 0.80 0.85 0.70

HE 0.80 0.73 0.38 0.59 0.64 0.73 0.79 0.48 0.41 0.66 0.62 0.61 0.65 0.74 0.63

FIS -0.24 -0.09 -0.33 -0.27 -0.18 -0.09 -0.27 -0.04 -0.23 -0.13 0.67 0.10 -0.24 -0.15 -0.10

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

AD rate 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.55 0.07 0.00 0.00 0.00

FA rate 0.00 0.00 0.08 0.00 0.05 0.00 0.00 0.04 0.00 0.05 0.00 0.00 0.00 0.00 0.02

Dev. HWP No No No No No No Yes No No No Yes Yes No No No


El Berrueco - reduced (8) AR 6 4 2 3 3 5 5 2 3 4 3 3 4 4 5

HO 1.00 0.88 0.38 0.75 0.75 0.75 1.00 0.38 0.50 0.88 0.25 0.50 0.75 0.75 0.75

HE 0.77 0.73 0.30 0.57 0.65 0.68 0.77 0.49 0.41 0.63 0.59 0.53 0.66 0.71 0.66

FIS -0.29 -0.20 -0.23 -0.32 -0.16 -0.10 -0.31 0.24 -0.23 -0.38 0.58 0.06 -0.13 -0.05 -0.13

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0



Fuenterrebollo - complete (20)

AR 8 8 2 4 3 7 4 2 3 5 7 3 3 5 7 HO 0.85 0.75 0.50 0.30 0.70 1.00 0.50 0.40 0.30 0.80 0.84 0.70 0.25 0.65 1.00

HE 0.77 0.73 0.46 0.43 0.63 0.79 0.44 0.32 0.30 0.73 0.78 0.65 0.23 0.64 0.82

FIS -0.10 -0.02 -0.10 0.30 -0.10 -0.27 -0.14 -0.25 -0.01 -0.10 -0.09 -0.09 -0.10 -0.02 -0.22

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5.0 0.0 0.0 0.0 0.0

AD rate 0.00 0.00 0.00 0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.00 0.00 0.00

FA rate 0.00 0.05 0.00 0.04 0.07 0.00 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00




Pper4.15

Pper4.28

Pper3.9

Pper4.5

Pper4.16

Pper3.24

Pper4.20

Pper3.22

Pper4.13

Pper4.7

Pper3.1

Pper4.29

Pper3.23

Pper4.24

Fuenterrebollo - reduced (10) AR 6 7 2 4 3 7 4 2 3 5 7 3 3 5 7

HO 0.80 0.80 0.50 0.30 0.60 1.00 0.60 0.40 0.40 0.70 0.90 0.70 0.30 0.50 1.00

HE 0.76 0.76 0.46 0.35 0.60 0.80 0.48 0.32 0.40 0.71 0.77 0.66 0.27 0.62 0.83

FIS -0.06 -0.05 -0.10 0.13 -0.01 -0.26 -0.26 -0.25 -0.01 0.01 -0.18 -0.06 -0.13 0.19 -0.20

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0




AR 16 12 6 6 3 10 6 3 3 8 13 7 8 6 11 HO 0.91 0.87 0.83 0.57 0.48 0.78 0.83 0.48 0.39 0.70 0.87 0.83 0.91 0.61 0.57

HE 0.91 0.88 0.59 0.65 0.66 0.84 0.70 0.45 0.40 0.84 0.87 0.82 0.72 0.72 0.87

FIS 0.00 0.01 -0.39 0.13 0.27 0.07 -0.18 -0.06 0.03 0.17 0.00 -0.01 -0.27 0.16 0.35

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

AD rate 0.01 0.03 0.00 0.05 0.19 0.05 0.00 0.04 0.01 0.10 0.00 0.01 0.00 0.02 0.25

FA rate 0.12 0.13 0.05 0.06 0.05 0.00 0.00 0.04 0.00 0.08 0.00 0.05 0.05 0.00 0.06

Dev. HWP No No No No No No No No No No No No No No Yes



AR 16 11 6 6 3 10 6 3 3 8 13 7 8 6 11 HO 0.89 0.89 0.89 0.58 0.58 0.79 0.79 0.42 0.47 0.63 0.84 0.84 0.89 0.53 0.53

HE 0.91 0.87 0.62 0.64 0.66 0.82 0.71 0.45 0.46 0.83 0.87 0.83 0.73 0.73 0.87

FIS 0.01 -0.02 -0.45 0.09 0.12 0.04 -0.12 0.06 -0.03 0.24 0.03 -0.02 -0.23 0.28 0.39

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Dev. HWP No No No No No No No No No No No No No No Yes

Null alleles No No No No No No No No No Yes No No No Yes Yes

Medianillos - complete (25) AR 17 11 6 7 3 10 8 2 4 12 19 8 9 6 12

HO 0.88 0.88 0.76 0.88 0.72 0.72 0.92 0.56 0.32 0.96 1.00 0.76 0.76 0.40 0.64

HE 0.92 0.88 0.63 0.80 0.61 0.81 0.80 0.48 0.34 0.86 0.91 0.79 0.66 0.69 0.89

FIS 0.04 0.00 -0.21 -0.10 -0.19 0.11 -0.15 -0.17 0.06 -0.11 -0.10 0.04 -0.15 0.42 0.28

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

AD rate 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.00 0.19 0.16

FA rate 0.00 0.04 0.07 0.07 0.08 0.07 0.00 0.00 0.00 0.09 0.06 0.00 0.07 0.00 0.07

Dev. HWP Yes No No No No No No No No No No Yes No Yes No

Null alleles No No No No No No No No No No No No No Yes Yes

Medianillos - reduced (20) AR 16 10 5 6 3 10 8 2 4 12 18 8 8 6 10

HO 0.90 0.90 0.80 0.90 0.65 0.70 0.95 0.60 0.35 0.95 1.00 0.85 0.75 0.40 0.65

HE 0.92 0.88 0.64 0.79 0.61 0.80 0.79 0.48 0.34 0.86 0.91 0.78 0.67 0.70 0.88

FIS 0.02 -0.03 -0.25 -0.13 -0.06 0.13 -0.20 -0.25 -0.03 -0.11 -0.10 -0.09 -0.12 0.43 0.26

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Dev. HWP No No No No No No No No No No No No No Yes No

Null alleles No No No No No No No No No No No No No Yes Yes


Pper4.15

Pper4.28

Pper3.9

Pper4.5

Pper4.16

Pper3.24

Pper4.20

Pper3.22

Pper4.13

Pper4.7

Pper3.1

Pper4.29

Pper3.23

Pper4.24

Puerto de Canencia - complete (22)

AR 18 12 4 8 3 11 9 2 6 14 14 5 7 5 11 HO 0.95 0.95 0.64 0.86 0.64 0.91 0.86 0.36 0.50 0.91 0.91 0.59 0.55 0.64 0.77

HE 0.92 0.88 0.52 0.81 0.62 0.85 0.78 0.43 0.51 0.89 0.89 0.67 0.48 0.60 0.88

FIS -0.04 -0.09 -0.23 -0.06 -0.03 -0.07 -0.11 0.16 0.01 -0.02 -0.02 0.12 -0.13 -0.06 0.12

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

AD rate 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.07 0.01 0.00 0.03 0.05 0.00 0.00 0.08

FA rate 0.15 0.07 0.01 0.00 0.00 0.01 0.17 0.00 0.10 0.26 0.14 0.13 0.08 0.03 0.08



Puerto de Canencia - reduced (19)

AR 18 11 4 8 3 11 9 2 6 13 14 5 6 5 11 HO 0.95 0.95 0.58 0.89 0.63 0.89 0.84 0.42 0.53 0.95 0.89 0.53 0.58 0.68 0.79

HE 0.92 0.87 0.46 0.81 0.62 0.84 0.79 0.47 0.53 0.90 0.89 0.62 0.47 0.63 0.88

FIS -0.03 -0.09 -0.26 -0.11 -0.01 -0.06 -0.06 0.10 0.01 -0.05 0.00 0.15 -0.23 -0.09 0.11

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0



Puerto de La Morcuera - complete (22)

AR 10 8 2 6 3 7 7 2 4 9 9 3 5 4 6 HO 0.95 0.64 0.27 0.59 0.55 0.77 0.86 0.59 0.55 0.82 0.91 0.50 0.59 0.59 0.71

HE 0.87 0.80 0.24 0.67 0.54 0.80 0.75 0.49 0.56 0.82 0.81 0.64 0.52 0.58 0.78

FIS -0.09 0.20 -0.16 0.11 -0.01 0.04 -0.15 -0.20 0.03 0.01 -0.13 0.22 -0.13 -0.02 0.08

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.5

AD rate 0.00 0.05 0.00 0.00 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.08 0.05

FA rate 0.06 0.00 0.00 0.02 0.00 0.00 0.06 0.19 0.00 0.00 0.00 0.00 0.04 0.07 0.02



Puerto de La Morcuera - reduced (15)

AR 10 8 2 6 3 7 7 2 4 9 8 3 4 4 6 HO 0.93 0.60 0.13 0.80 0.53 0.67 0.93 0.60 0.53 0.80 0.87 0.40 0.67 0.60 0.67

HE 0.87 0.78 0.12 0.72 0.58 0.82 0.76 0.49 0.57 0.83 0.76 0.62 0.54 0.56 0.76

FIS -0.08 0.24 -0.07 -0.11 0.09 0.19 -0.22 -0.22 0.07 0.04 -0.14 0.36 -0.24 -0.07 0.12

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0



Rascafría - complete (22) AR 12 7 3 6 3 7 6 2 3 10 12 5 6 5 8

HO 0.82 0.86 0.18 0.64 0.64 0.77 0.50 0.45 0.68 0.77 0.91 0.41 0.64 0.82 0.86

HE 0.81 0.82 0.33 0.64 0.65 0.78 0.80 0.46 0.63 0.80 0.89 0.78 0.63 0.60 0.77

FIS -0.01 -0.05 0.46 0.00 0.01 0.01 0.37 0.02 -0.08 0.03 -0.02 0.48 -0.01 -0.36 -0.13

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

AD rate 0.04 0.00 0.42 0.00 0.00 0.02 0.22 0.00 0.00 0.01 0.00 0.56 0.00 0.00 0.00

FA rate 0.10 0.00 0.04 0.04 0.00 0.00 0.00 0.08 0.00 0.00 0.06 0.16 0.00 0.00 0.07

Dev. HWP No No No No No No No No No No No Yes No No No

Null alleles No No Yes No No No Yes No No No No Yes No No No


Pper4.15

Pper4.28

Pper3.9

Pper4.5

Pper4.16

Pper3.24

Pper4.20

Pper3.22

Pper4.13

Pper4.7

Pper3.1

Pper4.29

Pper3.23

Pper4.24

Rascafría - reduced (20) AR 12 7 3 6 3 7 6 2 3 10 11 5 6 5 8

HO 0.80 0.85 0.20 0.65 0.60 0.75 0.45 0.45 0.70 0.75 0.90 0.40 0.60 0.80 0.85

HE 0.80 0.82 0.36 0.65 0.63 0.79 0.79 0.47 0.63 0.79 0.89 0.77 0.63 0.59 0.78

FIS 0.00 -0.04 0.44 -0.01 0.05 0.05 0.43 0.04 -0.10 0.05 -0.02 0.48 0.04 -0.35 -0.10

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Dev. HWP No No No No No No Yes No No No No Yes No No No

Null alleles No No Yes No No No Yes No No No No Yes No No No

Santo Tomé del Puerto - complete (21)

AR 13 9 2 6 4 7 5 2 5 6 7 6 5 3 8 HO 0.81 0.81 0.33 0.57 0.52 0.71 0.50 0.00 0.52 0.81 0.76 0.95 0.57 0.48 0.86

HE 0.85 0.85 0.48 0.59 0.66 0.76 0.61 0.09 0.57 0.75 0.80 0.70 0.55 0.61 0.81

FIS 0.05 0.04 0.31 0.03 0.21 0.07 0.18 1.00 0.08 -0.08 0.05 -0.35 -0.04 0.22 -0.05

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 4.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

AD rate 0.05 0.00 0.13 0.06 0.12 0.00 0.15 0.79 0.00 0.00 0.02 0.00 0.00 0.13 0.04

FA rate 0.13 0.00 0.00 0.05 0.08 0.00 0.09 0.00 0.00 0.07 0.00 0.09 0.00 0.00 0.13


Null alleles No No No No No No No Yes No No No No No No No

Santo Tomé del Puerto - reduced (17)

AR 12 9 2 6 4 7 5 2 5 6 7 6 5 3 8 HO 0.88 0.82 0.35 0.65 0.47 0.71 0.56 0.00 0.53 0.76 0.76 0.94 0.53 0.47 0.88

HE 0.86 0.84 0.46 0.65 0.65 0.76 0.63 0.11 0.54 0.75 0.79 0.72 0.54 0.61 0.80

FIS -0.03 0.01 0.23 0.01 0.28 0.07 0.11 1.00 0.03 -0.03 0.03 -0.31 0.03 0.23 -0.10

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 5.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0


Null alleles No No No No No No No Yes No No No No No No No

Sauquillo de Cabezas - complete (22)

AR 12 9 3 7 3 8 5 2 4 7 10 7 8 5 10 HO 0.91 0.82 0.55 0.73 0.68 0.82 0.82 0.14 0.50 0.82 0.95 0.77 0.95 0.91 0.95

HE 0.88 0.84 0.52 0.63 0.66 0.84 0.75 0.13 0.47 0.79 0.86 0.73 0.80 0.64 0.86

FIS -0.03 0.02 -0.06 -0.15 -0.03 0.03 -0.09 -0.07 -0.06 -0.03 -0.12 -0.05 -0.20 -0.42 -0.10

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

AD rate 0.00 0.04 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00

FA rate 0.00 0.05 0.04 0.04 0.00 0.00 0.00 0.03 0.00 0.00 0.00 0.05 0.05 0.05 0.00



Sauquillo de Cabezas - reduced (10)

AR 8 8 3 6 3 8 5 2 4 6 9 7 6 5 8 HO 0.90 0.90 0.60 0.70 0.80 0.60 0.90 0.20 0.40 0.80 0.90 0.70 1.00 0.90 0.90

HE 0.84 0.82 0.52 0.65 0.66 0.82 0.78 0.18 0.48 0.73 0.84 0.73 0.77 0.60 0.84

FIS -0.08 -0.10 -0.17 -0.08 -0.21 0.27 -0.16 -0.11 0.16 -0.10 -0.08 0.04 -0.31 -0.50 -0.08

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0


Null alleles No No No No No Yes No No No No No No No No No


Pper4.15

Pper4.28

Pper3.9

Pper4.5

Pper4.16

Pper3.24

Pper4.20

Pper3.22

Pper4.13

Pper4.7

Pper3.1

Pper4.29

Pper3.23

Pper4.24

Turrubuelo - complete (21) AR 17 12 4 6 3 9 7 2 4 12 12 6 4 7 14

HO 1.00 0.95 0.62 0.76 0.67 1.00 0.86 0.33 0.45 0.90 0.90 0.76 0.57 0.81 0.90

HE 0.91 0.89 0.61 0.80 0.65 0.86 0.81 0.43 0.49 0.89 0.89 0.74 0.67 0.75 0.87

FIS -0.09 -0.07 -0.01 0.05 -0.02 -0.16 -0.05 0.22 0.08 -0.01 -0.01 -0.03 0.15 -0.09 -0.04

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.8 4.8 0.0 0.0 0.0 0.0 0.0

AD rate 0.00 0.00 0.02 0.00 0.01 0.00 0.00 0.13 0.08 0.00 0.00 0.01 0.07 0.00 0.00

FA rate 0.07 0.08 0.12 0.00 0.03 0.11 0.11 0.00 0.00 0.11 0.03 0.01 0.00 0.05 0.12



Turrubuelo - reduced (15) AR 14 12 4 6 3 9 7 2 4 11 11 6 4 7 11

HO 1.00 0.93 0.67 0.80 0.73 1.00 0.87 0.40 0.50 0.86 0.87 0.80 0.60 0.80 0.87

HE 0.89 0.89 0.60 0.80 0.66 0.86 0.80 0.39 0.49 0.88 0.86 0.70 0.62 0.75 0.85

FIS -0.13 -0.05 -0.10 0.00 -0.11 -0.16 -0.08 -0.02 -0.03 0.02 0.00 -0.14 0.04 -0.07 -0.02

Missing (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6.7 6.7 0.0 0.0 0.0 0.0 0.0



Valdemanco - complete (94) AR 24 12 6 9 4 12 8 2 5 16 20 7 8 6 13

HO 0.95 0.75 0.52 0.77 0.60 0.83 0.85 0.40 0.41 0.96 0.84 0.81 0.86 0.76 0.92

HE 0.92 0.75 0.52 0.78 0.66 0.87 0.85 0.43 0.39 0.90 0.90 0.78 0.78 0.72 0.91

FIS -0.03 0.00 0.00 0.00 0.10 0.05 0.00 0.07 -0.06 -0.06 0.07 -0.03 -0.10 -0.05 -0.02

Missing (%) 2.1 1.1 1.1 1.1 0.0 1.1 1.1 0.0 3.2 3.2 0.0 1.1 3.2 2.1 3.2

AD rate 0.01 0.00 0.02 0.00 0.01 0.00 0.00 0.01 0.00 0.01 0.02 0.05 0.00 0.00 0.00

FA rate 0.00 0.01 0.00 0.01 0.00 0.03 0.00 0.00 0.00 0.01 0.00 0.01 0.01 0.04 0.03



Valdemanco - reduced (58) AR 23 12 6 9 4 12 8 2 5 15 20 7 8 6 13

HO 0.91 0.77 0.51 0.77 0.60 0.83 0.85 0.40 0.43 0.93 0.88 0.81 0.86 0.73 0.91

HE 0.91 0.73 0.53 0.79 0.67 0.86 0.85 0.43 0.43 0.90 0.91 0.79 0.77 0.74 0.90

FIS 0.00 -0.06 0.05 0.02 0.11 0.04 0.01 0.09 -0.01 -0.03 0.03 -0.03 -0.11 0.00 -0.01

Missing (%) 3.4 1.7 1.7 1.7 0.0 1.7 0.0 0.0 3.4 3.4 0.0 1.7 5.2 3.4 5.2



APPENDIX 2

ACCUMULATION CURVES OF ALLELIC RICHNESS AND EXPECTED HETEROZYGOSITY AS A FUNCTION OF SAMPLE SIZE


(Chapter IV)

Accumulation curves

265

Figure A2.1. Accumulation curves of AR (dark lines) and HE (grey lines) as a function of sample size

(measured as number of individuals) for each marker in the H. molleri marker set. Jackknifed curves were

calculated from the complete samples in Valdemanco. Vertical dotted lines show the minimum sample size

at which the lower bound of the 95% confidence interval of each final estimate (shown as horizontal

dashed lines) is reached.

APPENDIX 2

266

Figure A2.1 (cont.). Accumulation curves of AR (dark lines) and HE (grey lines) as a function of sample

size (measured as number of individuals) for each marker in the H. molleri marker set. Jackknifed curves

were calculated from the complete samples in Valdemanco. Vertical dotted lines show the minimum

sample size at which the lower bound of the 95% confidence interval of each final estimate (shown as

horizontal dashed lines) is reached.

Accumulation curves

267


(measured as number of individuals) for each marker in the E. calamita marker set. Jackknifed curves




APPENDIX 2

268


size (measured as number of individuals) for each marker in the E. calamita marker set. Jackknifed curves




Accumulation curves

269


(measured as number of individuals) for each marker in the P. perezi marker set. Jackknifed curves were

calculated from the complete samples in Valdemanco. Vertical dotted lines show the minimum sample size

at which the lower bound of the 95% confidence interval of each final estimate (shown as horizontal

dashed lines) is reached.

APPENDIX 2

270


size (measured as number of individuals) for each marker in the P. perezi marker set. Jackknifed curves




APPENDIX 3

EMPIRICAL AND CHAO & JOST (2015) PROFILES


(Chapter IV)

Diversity profiles

273

Figure A3.1. Empirical (dotted line) and Chao & Jost (2015) profile (grey solid line) for each marker in the

H. molleri set. Profiles are obtained by estimating the effective number of species (Hill numbers) across a

range of diversity order q between 0 and 3.

APPENDIX 3

274

Figure A3.1 (cont.). Empirical (dotted line) and Chao & Jost (2015) profile (grey solid line) for each marker

in the H. molleri set. Profiles are obtained by estimating the effective number of species (Hill numbers)

across a range of diversity order q between 0 and 3.

Diversity profiles

275


E. calamita set. Profiles are obtained by estimating the effective number of species (Hill numbers) across a


APPENDIX 3

276


in the E. calamita set. Profiles are obtained by estimating the effective number of species (Hill numbers)


Diversity profiles

277


P. perezi set. Profiles are obtained by estimating the effective number of species (Hill numbers) across a


APPENDIX 3

278


in the P. perezi set. Profiles are obtained by estimating the effective number of species (Hill numbers)


APPENDIX 4

RELATIONSHIP BETWEEN FIS AND ERROR RATE ESTIMATES


(Chapter IV)

Effect of excessive relatives in the sample

281

Figure A4.1. Relationship between FIS and error rate estimates (empty dots: allelic dropout rate, solid

dots: false allele rate) obtained from sibship analyses for each marker in the three species. Note the

difference in axis scales in the E. calamita graph.

APPENDIX 5

EFFECT OF SAMPLING EXCESSIVE CLOSE RELATIVES ON FIS AND DEVIATION FROM HWE


(Chapter IV)


285

Wright’s (1931) FIS is the traditional and most popular statistic used in measuring the

distribution of genetic variation within and among individuals in a population. For a

population at Hardy-Weinberg equilibrium (HWE), homologous allelic copies are

independently distributed within and between individuals. In such a situation, FIS = 0.

For a population with subdivision (e.g. in social groups) or with close relative mating,

the two allelic copies within an individual are more probable to be identical in state than

those in different individuals. In such a situation, the observed homozygosity is higher

than that expected if the population is at HWE, leading to FIS > 0 (since 𝐹𝐼𝑆 = 1 −𝐻𝑂

𝐻𝐸,

where HO and HE are the observed and expected heterozygosity, respectively) (Nei

1977). In contrast, admixture and hybridization lead to FIS < 0.

The FIS of a population is usually unknown, and is estimated by the marker or

pedigree data of a sample of individuals drawn from the population. Here we show

analytically that sampling too many close relatives would lead to a reduced FIS estimate.

For a large population at HWE in which FIS = 0, a sample from it can yield a negative

FIS estimate if it contains excessive close relatives. These predictions are true no matter

whether pedigree or marker data are used in the estimation.

Denoting the probabilities of identity by descent (PIBD) for two homologous

genes drawn at random from an individual and between two individuals in a population

by F and θ, respectively, we have

𝐹𝐼𝑆 =𝐹−𝜃

1−𝜃 , (1)

by definition (Cockerham 1969, eqn 41; Weir 1996, p.176). In (1), F and θ are the

inbreeding coefficient of an individual and the coancestry between two individuals. If a

random sample (random with regard to genealogy) is taken from the population, then

unbiased estimates of F, θ, and thus FIS estimates would be obtained. However, if too

many (excessive) close relatives, such as full or half siblings, are included in a sample,

the PIBD between individuals in the population would be overestimated, from the true

value θ to θ’, while the estimated PIBD within individuals would remain unbiased as F.

As a result, 𝐹𝐼𝑆 would be expected to be decreased to

𝐹𝐼𝑆′ =

𝐹−𝜃′

1−𝜃′ (2)

APPENDIX 5

286

Equation (2) implies that 𝐹𝐼𝑆′ < 𝐹𝐼𝑆, because 𝜃′ > 𝜃. The larger the increase in PIBD

between sampled individuals, 𝜃′, due to the inclusion of a greater proportion of close

relatives, the smaller will be 𝐹𝐼𝑆′ relative to 𝐹𝐼𝑆.

For illustration, let us consider some numerical examples for a dioecious diploid

species in a large random mating population. It is expected that two homologous genes

at an autosomal locus are identical by descent with probabilities 0, 0 and ¼ when they

are in a single individual, in two unrelated individuals, and in two full siblings,

respectively. In a sample of individuals taken at random from the population, the

estimated PIBDs are expected to be F = 0 and 𝜃 = 0, and thus the estimated FIS is also

expected to be zero. In an inadequately drawn sample of individuals with a proportion

of δ full-sib pairs, the estimated PIBDs are expected to be F = 0, 𝜃 = (1 − 𝛿) × 0 +

𝛿 × 1/4 =𝛿

4, and the estimated FIS is expected to be

0−𝛿

4

1−𝛿

4

= −𝛿/(4 − 𝛿). Suppose a

sample has n=50 individuals, with 10 individuals taken from full sib family X, 20

individuals from full sib family Y, and the remaining 20 individuals from 20 different

and unrelated families. The proportion of full sib pairs in the sample is 𝛿 =

10×9/2+20×19/2

50×49/2= 0.1918, the estimated PIBDs are expected to be F = 0, 𝜃 = 0.1918 ×

1

4= 0.048, and the estimated FIS is expected to be

0−0.048

1−0.048= −0.0504, rather than the

expected value of zero.

As it can be seen from the examples, the inclusion of an excessive proportion of

relatives (in this case, full siblings) in a sample causes a reduction in the estimated FIS.

Conversely, including an excessively low proportion of full sibs in the sample (relative

to the true proportion in the population) results in an artificially inflated estimate of FIS.

Depending on the values of F and 𝜃, this bias may lead in some cases to false inferences

of negative inbreeding (and the false conclusion that the population is affected by

admixture (hybridization) or/and avoids close relative matings) or positive inbreeding

(false conclusion of positive assortative mating or population subdivision). For the same

reason, removing all but one of the full sibs in every full sib family in the sample does

not always eliminate the bias caused by unrepresentative proportion of full sibs in the

sample (Waples & Anderson 2017). In fact, it could lead to an underrepresentation of

full sibs in the sample and thus to the opposite bias, with 𝐹𝐼𝑆′ > 𝐹𝐼𝑆.


287

As a result, excessive close relatives in a sample cause an apparent decrease in

observed homozygotes and an apparent increase in observed heterozygotes at each locus

(i.e. higher HO/HE ratio). Excessive close relatives in a sample can also cause apparent

nonrandom associations between alleles in different loci. This leads to an increase in

statistically significant deviations from HWE across loci and evidences of LD, which

disappear when the excess of relatives is removed.

References


Cockerham CC (1969) Variance of gene frequencies. Evolution, 23, 72–84.

Nei M (1977) F-statistics and analysis of gene diversity in subdivided populations. Annals of

Human Genetics, 41, 225–233.



Weir BS (1996) Genetic data analysis II: Methods for discrete population genetic data. Sinauer

Assoc., Inc., Sunderland, MA, USA.

APPENDIX 6

R SCRIPTS FOR REPLICATED ANALYSES

Appendix S1 in Sánchez-Montes et al. Ecology and Evolution (accepted, pending minor review)

(Chapter V)

# -------------------------------------------------------------------------

# APPENDIX S1. R scripts for replicated analyses

# -------------------------------------------------------------------------

#

# R scripts employed for replicated analyses exploring different sibship size prior values and different amounts of marker information (i.e. subsampling the

# number of markers or the sample size).

# The three scripts use the same input file (named ‘inputfile.csv’) which should have 'm' offspring, 'x' candidate fathers and 'y' candidate mothers genotyped

# at 'n' loci, and arranged in the following format:

#

# ID;sex_stage;Loc1;Loc1_b;Loc2;Loc2_b;Loc3;Loc3_b;...;Locn;Locn_b;

# offspring1_ID;offspring;Loc1(allele1);Loc1(allele2);Loc2(allele1);Loc2(allele2);Loc3(allele1);Loc3(allele2);...;Locn(allele1);Locn(allele2)






# ...

# offspringm_ID;offspring;Loc1(allele1);Loc1(allele2);Loc2(allele1);Loc2(allele2);Loc3(allele1);Loc3(allele2);...;Locn(allele1);Locn(allele2)

# male1_ID;male;Loc1(allele1);Loc1(allele2);Loc2(allele1);Loc2(allele2);Loc3(allele1);Loc3(allele2);...;Locn(allele1);Locn(allele2)




# ...

# malex_ID;male;Loc1(allele1);Loc1(allele2);Loc2(allele1);Loc2(allele2);Loc3(allele1);Loc3(allele2);...;Locn(allele1);Locn(allele2)

# female1_ID;male;Loc1(allele1);Loc1(allele2);Loc2(allele1);Loc2(allele2);Loc3(allele1);Loc3(allele2);...;Locn(allele1);Locn(allele2)




# ...

# femaley_ID;male;Loc1(allele1);Loc1(allele2);Loc2(allele1);Loc2(allele2);Loc3(allele1);Loc3(allele2);...;Locn(allele1);Locn(allele2)

#

#

#

# The input file and the executable file of program COLONY (Colony2P.exe) should be placed in the working directory.

# Notations in the right hand of the scripts (following a #) indicate that specific values and settings need to be included in the corresponding coding line,

# following COLONY user guide.

# The specific settings included in these example scripts correspond to analyses of the E. calamita 2013 dataset of this paper.

#

#

# If you use the scripts for publishing papers, please cite Sánchez-Montes et al. 2017 Ecology & Evolution paper (Appendix S1).

#

#-------------------------------------------------------------------

# Analyses for exploring different prior values

#-------------------------------------------------------------------

inputfile<-read.csv(file="inputfile.csv", header=T, sep=";")

outputfile<-"outputfile.txt"

cat("filename","prior","totmal","totfem","Polymal","Polyfem","Avgemal","Avgefem","Ne","Ne_min","Ne_max",file=outputfile,append=TRUE,sep = ",",fill=TRUE)

offspring<-subset(inputfile, inputfile$sex_stage=="offspring")

offspring<-offspring[c(-2)]

cmales<-subset(inputfile, inputfile$sex_stage=="male")

cmales<-cmales[c(-2)]

cfemales<-subset(inputfile, inputfile$sex_stage=="female")

cfemales<-cfemales[c(-2)]

for (i in 1:5) { #Set the desired prior values to explore

for (j in 1:10) { #Set the desired number of replicates for each prior value

filename<-paste("analysis",i,"prior",j,sep="_")

input.file<-paste(filename,".dat", sep="")

options(width=1000)

cat(filename, file=input.file, append=TRUE, sep = "\n")


cat(length(offspring$ID), file=input.file, append=TRUE, sep = "\n")

cat((ncol(offspring)-1)/2, file=input.file, append=TRUE, sep = "\n")

cat(sample(100000, 1), file=input.file, append=TRUE, sep = "\n")

cat(0, file=input.file, append=TRUE, sep = "\n") #0/1=Not updating/updating allele frequency

cat(2, file=input.file, append=TRUE, sep = "\n") #2/1=Dioecious/Monoecious species

cat(0, file=input.file, append=TRUE, sep = "\n") #0/1=Inbreeding absent/present

cat(0, file=input.file, append=TRUE, sep = "\n") #0/1=Diploid species/HaploDiploid species

cat("0 0", file=input.file, append=TRUE, sep = "\n") #0/1=Polygamy/Monogamy for males & females

cat(0, file=input.file, append=TRUE, sep = "\n") #0/1 = Clone inference = No/Yes

cat(0, file=input.file, append=TRUE, sep = "\n") #0/1=Scale full sibship=No/Yes

cat(paste(1, i, i, sep=" "), file=input.file, append=TRUE, sep = "\n") #0/1/2/3/4=No/Weak/Medium/Strong sibship prior; 4=Optimal sibship prior for Ne

cat(0, file=input.file, append=TRUE, sep = "\n") #0/1=Unknown/Known population allele frequency

cat(1, file=input.file, append=TRUE, sep = "\n") #Number of runs

cat(2, file=input.file, append=TRUE, sep = "\n") #1/2/3/4 = Short/Medium/Long/VeryLong run

cat(0, file=input.file, append=TRUE, sep = "\n") #0/1=Monitor method by Iterate#/Time in second

cat("100000", file=input.file, append=TRUE, sep = "\n") #Monitor interval in Iterate# / in seconds

cat(0, file=input.file, append=TRUE, sep = "\n") #0/1=DOS/Windows version

cat(1, file=input.file, append=TRUE, sep = "\n") #0/1/2=Pair-Likelihood-Score(PLS)/Full-Likelihood(FL)/FL-PLS-combined(FPLS) method

cat(1, file=input.file, append=TRUE, sep = "\n") #0/1/2/3=Low/Medium/High/VeryHigh precision

cat(" ", file=input.file, append=TRUE, sep = "\n")

cat(names(offspring)[seq(from=2, to=ncol(offspring), by=2)], file=input.file, append=TRUE, sep = ",", fill=TRUE)

cat(rep(0,ncol(offspring)/2), file=input.file, append=TRUE, sep = ",", fill=TRUE) #Marker types, 0/1=Codominant/Dominant

cat(rep(0.05,(ncol(offspring)-1)/2), file=input.file, append=TRUE, sep = ",", fill=TRUE) #Allelic dropout rate at each locus

cat(rep(0.05,(ncol(offspring)-1)/2), file=input.file, append=TRUE, sep = ",", fill=TRUE) #Other typing error rate at each locus


for (k in 1:nrow(offspring)){

cat(as.matrix(offspring[k,]), file=input.file, append=TRUE, sep = " ", fill=TRUE)

}


cat("0.66 0.61", file=input.file, append=TRUE, sep = " ", fill=TRUE) #probabilities that the father and mother of an offspring are included in candidates

cat(c(nrow(cmales),nrow(cfemales)), file=input.file, append=TRUE, sep = " ", fill=TRUE)


for (l in 1:nrow(cmales)){

cat(as.matrix(cmales[l,]), file=input.file, append=TRUE, sep = " ", fill=TRUE)

}


for (m in 1:nrow(cfemales)){

cat(as.matrix(cfemales[m,]), file=input.file, append=TRUE, sep = " ", fill=TRUE)

}


cat(0, file=input.file, append=TRUE, sep = "\n") #Number of offspring with known paternity

#IDs of known offspring-father dyad (if any)


cat(5, file=input.file, append=TRUE, sep = "\n") #Number of offspring with known maternity

cat("GSC081 BC09421", file=input.file, append=TRUE, sep = "\n") #IDs of known offspring-mother dyad (if any)






cat(0, file=input.file, append=TRUE, sep = "\n") #Number of known paternal sibship

#Size of known paternal sibship, and IDs of offspring in the sibship (if any)


cat(0, file=input.file, append=TRUE, sep = "\n") #Number of known maternal sibship

#Size of known maternal sibship, and IDs of offspring in the sibship (if any)


cat(0, file=input.file, append=TRUE, sep = "\n") #Number of offspring with known excluded paternity

#Offspring ID, number of excluded males, the IDs of excluded males


cat(0, file=input.file, append=TRUE, sep = "\n") #Number of offspring with known excluded maternity

#Offspring ID, number of excluded females, the IDs of excluded females


cat(0, file=input.file, append=TRUE, sep = "\n") #Number of offspring with known excluded paternal sibships

#Offspring ID, number of excluded paternal sibships, the IDs of excluded offspring


cat(0, file=input.file, append=TRUE, sep = "\n") #Number of offspring with known excluded maternal sibships

#Offspring ID, number of excluded maternal sibships, the IDs of excluded offspring

system(paste("Colony2p.exe IFN:", input.file, sep=""))

results<-read.delim(file=paste(filename,".BestCluster", sep=""), header=T, sep="")

male.matrix<-as.data.frame.matrix(table(results$FatherID, results$MotherID))

male.matrix$male_matings<-rowSums(male.matrix>0)

male.matrix<-male.matrix[!male.matrix$male_matings==0,]

mal<-data.frame(table(male.matrix$male_matings))

dimnames(mal)[[2]]<-c("N_matings", "N_indiv")

mal$sex<-c(rep("males", nrow(mal)))

female.matrix<-as.data.frame.matrix(table(results$MotherID, results$FatherID))

female.matrix$female_matings<-rowSums(female.matrix>0)

female.matrix<-female.matrix[!female.matrix$female_matings==0,]

femal<-data.frame(table(female.matrix$female_matings))

dimnames(femal)[[2]]<-c("N_matings", "N_indiv")

femal$sex<-c(rep("females", nrow(femal)))

total.males<-sum(mal$N_indiv)

total.females<-sum(femal$N_indiv)

poly.males<-subset(mal, !mal$N_matings==1)

poly.females<-subset(femal, !femal$N_matings==1)

Mult.mating.males<-sum(poly.males$N_indiv)

Mult.mating.females<-sum(poly.females$N_indiv)

Polygamy.rate.males<-Mult.mating.males/total.males

Polygamy.rate.females<-Mult.mating.females/total.females

mal$N_matings<-as.numeric(mal$N_matings)

mal$N_indiv<-as.numeric(mal$N_indiv)

total.matings.males<-sum(mal$N_matings*mal$N_indiv)

femal$N_matings<-as.numeric(femal$N_matings)

femal$N_indiv<-as.numeric(femal$N_indiv)

total.matings.females<-sum(femal$N_matings*femal$N_indiv)

Average.mating.males<-total.matings.males/total.males

Average.mating.females<-total.matings.females/total.females

Ne<-scan(paste(filename,".Ne",sep=""), what=list(character()))

cat(filename,i,total.males,total.females,Polygamy.rate.males,Polygamy.rate.females,Average.mating.males,Average.mating.females,file=outputfile,

Ne[[1]][18],Ne[[1]][21],Ne[[1]][24],append=TRUE,sep = ",",fill=TRUE)

file.remove(list.files(pattern=filename))

}

}

#--------------------------------------------------------------------------

# Analyses for subsampling the number of markers

#--------------------------------------------------------------------------



cat("filename","n_loci","totmal","totfem","Polymal","Polyfem","Avgemal","Avgefem","Ne","Ne_min","Ne_max",file=outputfile,append=TRUE,sep = ",",fill=TRUE)

for (i in 1:((ncol(inputfile)-2)/2)){

nloci<-i

for (j in 1:10) { #Set the desired number of replicates for each number of markers

colsample<-sample(seq(from=3, to=ncol(inputfile), by=2), i)

selection<-inputfile[c(1,2, rbind(colsample,colsample+1))]

offspring<-subset(selection, selection$sex_stage=="offspring")

offspring$drop<-rowSums(offspring[,3:ncol(offspring)])

offspring<-subset(offspring, offspring$drop>0)

offspring<-offspring[,1:(ncol(offspring)-1)]


cmales<-subset(selection, selection$sex_stage=="male")


cfemales<-subset(selection, selection$sex_stage=="female")


filename<-paste("analysis",i,"loci",j,sep="_")


options(width=1000)



cat(length(offspring$ID), file=input.file, append=TRUE, sep = "\n")

cat((ncol(offspring)-1)/2, file=input.file, append=TRUE, sep = "\n")









cat("1 1 1", file=input.file, append=TRUE, sep = "\n") #0/1/2/3/4=No/Weak/Medium/Strong sibship prior; 4=Optimal sibship prior for Ne










cat(names(offspring)[seq(from=2, to=ncol(offspring), by=2)], file=input.file, append=TRUE, sep = ",", fill=TRUE)

cat(rep(0,ncol(offspring)/2), file=input.file, append=TRUE, sep = ",", fill=TRUE) #Marker types, 0/1=Codominant/Dominant

cat(rep(0.05,(ncol(offspring)-1)/2), file=input.file, append=TRUE, sep = ",", fill=TRUE) #Allelic dropout rate at each locus

cat(rep(0.05,(ncol(offspring)-1)/2), file=input.file, append=TRUE, sep = ",", fill=TRUE) #Other typing error rate at each locus


for (k in 1:nrow(offspring)){

cat(as.matrix(offspring[k,]), file=input.file, append=TRUE, sep = " ", fill=TRUE)

}







}




}




























































cat(filename,nloci,total.males,total.females,Polygamy.rate.males,Polygamy.rate.females,Average.mating.males,Average.mating.females,file=outputfile,



}

}

#-------------------------------------------------------------------------------

# Analyses for subsampling the offspring sample size

#-------------------------------------------------------------------------------



cat("filename","samplesize","totmal","totfem","Polymal","Polyfem","Avgemal","Avgefem","Ne","Ne_min","Ne_max",file=outputfile,append=TRUE,sep = ",",fill=TRUE)

offspring<-subset(inputfile, inputfile$sex_stage=="offspring")


cmales<-subset(inputfile, inputfile$sex_stage=="male")


cfemales<-subset(inputfile, inputfile$sex_stage=="female")


for (i in c(10,20,30,40,60)){ #Set the desired sample sizes to explore

samplesize<-i

for (j in 1:10) { #Set the desired number of replicates for each sample size

rowsample<-offspring[sample(1:nrow(offspring), i, replace=FALSE),]

filename<-paste("analysis",i,"offspring",j,sep="_")


options(width=1000)



cat(length(rowsample$ID), file=input.file, append=TRUE, sep = "\n")

cat((ncol(rowsample)-1)/2, file=input.file, append=TRUE, sep = "\n")









cat("1 1 1", file=input.file, append=TRUE, sep = "\n") #0/1/2/3/4=No/Weak/Medium/Strong sibship prior; 4=Optimal sibship prior for Ne










cat(names(rowsample)[seq(from=2, to=ncol(rowsample), by=2)], file=input.file, append=TRUE, sep = ",", fill=TRUE)

cat(rep(0,ncol(rowsample)/2), file=input.file, append=TRUE, sep = ",", fill=TRUE) #Marker types, 0/1=Codominant/Dominant

cat(rep(0.05,(ncol(rowsample)-1)/2), file=input.file, append=TRUE, sep = ",", fill=TRUE) #Allelic dropout rate at each locus

cat(rep(0.05,(ncol(rowsample)-1)/2), file=input.file, append=TRUE, sep = ",", fill=TRUE) #Other typing error rate at each locus


for (k in 1:nrow(rowsample)){

cat(as.matrix(rowsample[k,]), file=input.file, append=TRUE, sep = " ", fill=TRUE)

}







}




}






#IDs of known offspring-mother dyad (if any)


















































cat(filename,samplesize,total.males,total.females,Polygamy.rate.males,Polygamy.rate.females,Average.mating.males,Average.mating.females,file=outputfile,



}

}

APPENDIX 7

SUMMARY TABLES OF CMR MODELS


(Chapter V)

Table A7.1. Summary table showing the output of the top three ranked models in each species, which cumulated > 99% of Corrected Akaike Information Criterion (AICc) weight. Models were

named following the parameterization of 1) the temporary emigration/immigration (emi/imm) as dependent (‘Markovian’) or independent (‘Random’) on the last state of the individual, or absent (i.e. fixed to zero, ‘No mov’) and 2) the annual survival rate as sex- (s), time- (t) dependent, or both (s*t), or constant (.). For each model, the table shows the total number of parameters (Num. of params.) of the model, and the estimates (with the 95% CI) of the average probability of survival and temporary emigration/immigration rates of males (m) and females (f) between consecutive breeding seasons from 2010 to 2015, and the Na by sex for each year. Estimates showing unreliably small or large standard errors were considered as non-estimable, and are indicated with ‘-’. Slight differences between the average of Na estimates of the models shown in this table and the estimates shown in Table V.1 are caused by the effect of additional models with low AICc weights on model-weighted-average estimates of Table V.1. Estimation of all parameters for females of H. molleri and Na for both sexes of P. perezi in 2012 was not attempted due to low recapture rates. For the same reason, * average probabilities of survival of males and females of P. perezi between the breeding seasons of 2011-2012 and 2012-2013 could not be distinguished, and so the corresponding survival probabilities for the biannual period 2011-2013 were calculated.

E. calamita H. molleri P. perezi

1 2 3 1 2 3 1 2 3

Model name

Random - S(.) Random - S(g) Random - S(t) No mov - S(.) No mov - S(t) Random - S(.) No mov - S(.) No mov - S(g) No mov - S(t) AICc

-693.83 -693.29 -687.79 -1140.06 -1133.92 -1132.82 251.75 253.78 259.61

AICc Weight

0.55 0.42 0.03 0.93 0.04 0.02 0.72 0.26 0.01 Num. of params.

75 76 79 25 29 29 50 51 53

Average survival 2010-2011

m 0.61 (0.56-0.66) 0.6 (0.55-0.66) 0.66 (0.49-0.8) 0.15 (0.11-0.22) 0.17 (0.06-0.4) 0.16 (0.09-0.27) 0.31 (0.25-0.38) 0.28 (0.2-0.38) 0.36 (0.19-0.56) f 0.61 (0.56-0.66) 0.79 (0.36-0.96) 0.66 (0.49-0.8)

0.31 (0.25-0.38) 0.34 (0.25-0.45) 0.36 (0.19-0.56)


m 0.61 (0.56-0.66) 0.6 (0.55-0.66) 0.52 (0.42-0.63) 0.15 (0.11-0.22) 0.04 (0.01-0.21) 0.16 (0.09-0.27)

0.31 (0.25-0.38)* 0.28 (0.2-0.38)*

0.34 (0.25-0.45)* 0.29 (0.2-0.41)*

f 0.61 (0.56-0.66) 0.79 (0.36-0.96) 0.52 (0.42-0.63)


m 0.61 (0.56-0.66) 0.6 (0.55-0.66) 0.7 (0.55-0.82) 0.15 (0.11-0.22) 0.24 (0.07-0.59) 0.16 (0.09-0.27) f 0.61 (0.56-0.66) 0.79 (0.36-0.96) 0.7 (0.55-0.82)

Average survival

2013-2014 m 0.61 (0.56-0.66) 0.6 (0.55-0.66) 0.6 (0.45-0.73) 0.15 (0.11-0.22) 0.21 (0.1-0.39) 0.16 (0.09-0.27) 0.31 (0.25-0.38) 0.28 (0.2-0.38) 0.34 (0.2-0.52) f 0.61 (0.56-0.66) 0.79 (0.36-0.96) 0.6 (0.45-0.73)

0.31 (0.25-0.38) 0.34 (0.25-0.45) 0.34 (0.2-0.52)


m 0.61 (0.56-0.66) 0.6 (0.55-0.66) 0.69 (0-1) 0.15 (0.11-0.22) 0.15 (0.07-0.29) 0.16 (0.09-0.27) 0.31 (0.25-0.38) 0.28 (0.2-0.38) 0.26 (0.12-0.46) f 0.61 (0.56-0.66) 0.79 (0.36-0.96) 0.69 (0-1)

0.31 (0.25-0.38) 0.34 (0.25-0.45) 0.26 (0.12-0.46)

Temporary emi/imm 2010-2011

m 0.14 (0.04-0.36) 0.13 (0.04-0.36) 0.16 (0.05-0.43) 0 (fixed) 0 (fixed) - 0 (fixed) 0 (fixed) 0 (fixed) f - - -

0 (fixed) 0 (fixed) 0 (fixed)


m 0.04 (0-1) 0.03 (0-1) - 0 (fixed) 0 (fixed) 0.85 (0.22-0.99) 0 (fixed) 0 (fixed) 0 (fixed) f 0.81 (0.37-0.97) 0.85 (0.45-0.97) 0.78 (0.32-0.96)



m 0.09 (0.02-0.27) 0.08 (0.02-0.27) 0.1 (0.03-0.3) 0 (fixed) 0 (fixed) 0.08 (0-1) 0 (fixed) 0 (fixed) 0 (fixed) f 0.69 (0.35-0.9) 0.8 (0.46-0.95) 0.7 (0.36-0.9)



m 0.5 (0.38-0.63) 0.5 (0.37-0.63) 0.5 (0.35-0.65) 0 (fixed) 0 (fixed) - 0 (fixed) 0 (fixed) 0 (fixed) f 0.84 (0.52-0.96) 0.9 (0.61-0.98) 0.84 (0.52-0.96)



m 0.19 (0.06-0.47) 0.17 (0.05-0.47) 0.28 (0-1) 0 (fixed) 0 (fixed) 0.06 (0-1) 0 (fixed) 0 (fixed) 0 (fixed) f 0.34 (0.05-0.82) 0.64 (0.2-0.93) 0.41 (0-1)


Na 2010 m 225 (116-539) 225 (116-539) 225 (116-539) 155 (99-293) 155 (99-293) 155 (99-293) 69 (46-132) 69 (46-132) 69 (46-132) f - - -

68 (36-186) 68 (36-186) 68 (36-186)

Na 2011 m 156 (154-163) 156 (154-163) 156 (154-163) 306 (228-444) 309 (227-455) 308 (228-448) 63 (48-98) 62 (47-97) 65 (48-104) f 159 (76-440) 159 (76-440) 159 (76-440)

76 (61-106) 76 (62-107) 77 (62-108)

Na 2012 m 128 (84-251) 128 (84-251) 120 (93-169) 134 (42-522) 53 (20-239) 39 (17-181) f 12 (7-50) 12 (7-50) 12 (7-50)

Na 2013 m 138 (135-146) 138 (135-146) 138 (135-146) 126 (108-158) 125 (108-157) 126 (108-158) 26 (23-36) 25 (23-36) 25 (23-36) f 43 (34-68) 43 (34-68) 43 (34-68)

27 (23-43) 28 (23-44) 27 (23-43)

Na 2014 m 70 (68-79) 70 (68-79) 70 (68-79) 144 (105-226) 155 (108-259) 146 (105-234) 20 (18-31) 20 (18-30) 20 (18-31) f 16 (12-47) 16 (12-47) 16 (12-47)

11 (9-24) 11 (9-25) 11 (9-26)

Na 2015 m 162 (160-169) 162 (160-169) 162 (160-169) 49 (39-78) 49 (39-81) 49 (39-81) - - - f 125 (94-194) 125 (94-194) 125 (94-194)

11 (10-23) 11 (10-23) 11 (10-22)

APPENDIX 8

INFERRED SIBSHIP AND PARENTAGE RELATIONSHIPS


(Chapter V)

Table A8.1. Inferred parentages for the tadpole samples (see ID codes in Dryad TBO1) of the three species (two different cohorts in the case of E. calamita). Inferred parents

included in the genotyped candidate parental samples are identified by their ID codes (see data in Dryad TBO1). Inferred parents which are not among the genotyped candidate

parents are coded with successive numbers (independent among different cohorts) following a * (sires) or a # (dams).

Epidalea calamita 2013

Epidalea calamita 2015

Hyla molleri

Pelophylax perezi Tadpole Inferred sire Inferred dam

Tadpole Inferred sire Inferred dam



GSC049 *1 BC09448

GSC492 BC09723 BC09715

GSH386 HY09379 #1

GS190 RP09060 #1 GSC050 *2 BC09486

GSC493 BC09719 BC09693

GSH387 *1 #2

GS191 *5 RP09086

GSC051 BC09328 BC09467

GSC494 BC09701 BC09726

GSH388 *2 #3

GS192 RP09026 #3 GSC052 BC09453 #1

GSC496 BC09778 BC09752

GSH389 *3 #4

GS193 *4 RP09045

GSC053 *2 BC09486

GSC498 BC09778 BC09728

GSH390 *4 #5

GS194 *14 RP09037 GSC054 BC09328 BC09467

GSC499 BC09574 BC09573

GSH391 *5 HY09374

GS195 RP09003 #13

GSC055 *2 BC09486

GSC500 BC09064 BC09787

GSH392 *6 #6

GS196 RP09061 RP09085 GSC056 BC09328 BC09467

GSC502 BC09776 BC09785

GSH393 *2 #1

GS197 RP09008 #6

GSC057 BC09057 BC09550

GSC504 BC09488 BC09779

GSH394 *1 #7

GS198 *1 RP09030 GSC058 *2 BC09486

GSC505 BC09740 BC09720

GSH395 *7 #8

GS199 *6 RP09158

GSC059 *1 BC09448

GSC506 BC09488 BC09779

GSH396 *8 #9

GS200 RP09060 #1 GSC060 *3 BC09426

GSC508 BC09705 BC09579

GSH397 *9 HY09412

GS201 *6 RP09158

GSC061 *4 BC09433

GSC510 BC09719 BC09693

GSH398 *10 #7

GS202 *13 RP09171 GSC062 BC09057 BC09550

GSC511 BC09705 BC09579

GSH399 HY09394 #10

GS203 *18 RP09067

GSC063 BC09141 BC09429

GSC512 BC09738 BC09762

GSH400 *11 #11

GS204 *14 #2 GSC064 BC09453 #1

GSC514 BC09778 BC09752

GSH401 HY09372 #12

GS205 *4 #9

GSC065 *1 BC09448

GSC516 BC09738 BC09762

GSH402 *6 HY09374

GS206 RP09006 #2 GSC066 BC09453 #1

GSC517 BC09371 BC09787

GSH403 HY09334 #13

GS207 *7 RP09033

GSC067 BC09141 BC09429

GSC518 BC09724 BC09697

GSH404 *9 #9

GS208 RP09064 RP09059 GSC068 BC09057 BC09550

GSC519 BC09738 BC09762

GSH405 *12 #14

GS209 *11 RP09067

GSC069 *3 BC09426

GSC520 BC09738 BC09762

GSH406 HY09399 #15

GS210 RP09060 #1 GSC070 BC09328 BC09467

GSC521 BC09724 BC09697

GSH407 HY09334 #16

GS211 RP09005 RP09066

GSC071 *4 BC09433

GSC522 BC09179 BC09430

GSH408 *13 #17

GS212 *2 #2 GSC072 BC09141 BC09429

GSC523 BC09179 BC09430

GSH409 *11 #14

GS213 *8 RP09067

GSC073 BC09328 BC09467

GSC524 BC09744 BC09755

GSH410 *14 #11

GS214 RP09026 #3 GSC074 *2 BC09486

GSC526 BC09744 BC09755

GSH411 *15 #18

GS215 RP09026 #3

GSC075 *4 BC09433

GSC528 BC09405 BC09790

GSH412 *16 #19

GS216 *8 #6 GSC076 *2 BC09486

GSC529 BC09405 BC09790

GSH413 HY09404 #20

GS217 *10 #10

GSC077 BC09453 #1

GSC530 BC09179 BC09430

GSH414 HY09404 #21

GS218 RP09024 RP09053 GSC078 *3 BC09426

GSC532 BC09405 BC09790

GSH415 *17 #22

GS219 *15 RP09050

GSC079 *1 BC09448

GSC534 BC09731 BC09733

GSH416 *18 #11

GS220 RP09006 #2 GSC080 *1 BC09448

GSC535 BC09179 BC09430

GSH417 HY09377 #23

GS221 RP09026 RP09040

GSC081 *5 BC09421

GSC536 BC09405 BC09790

GSH418 HY09379 #1

GS222 *1 #4 GSC082 *5 BC09421

GSC537 BC09405 BC09790

GSH419 *19 #5

GS223 *6 RP09158

GSC083 *5 BC09421

GSC538 BC09744 BC09755

GSH420 *20 #24

GS224 *10 #3 GSC084 *5 BC09421

GSC539 BC09179 BC09430

GSH421 *21 #25

GS225 RP09012 RP09152

Epidalea calamita 2013 Epidalea calamita 2015 Hyla molleri Pelophylax perezi Tadpole Inferred sire Inferred dam Tadpole Inferred sire Inferred dam Tadpole Inferred sire Inferred dam Tadpole Inferred sire Inferred dam

GSC085 *5 BC09421

GSC540 BC09744 BC09755

GSH422 *10 HY09418

GS226 *15 #12 GSC086 *6 #2

GSC541 BC09744 BC09755

GSH423 *19 #8

GS227 RP09064 RP09059

GSC087 *7 #3

GSC542 BC09105 #1

GSH424 *16 #4

GS228 RP09015 RP09040 GSC088 *6 #2

GSC544 *1 BC09604

GSH425 *22 #26

GS229 RP09061 RP09085

GSC089 BC09110 BC09451

GSC546 *1 BC09604

GSH426 *23 #27

GS230 *2 #2 GSC090 BC09110 BC09451

GSC547 BC09694 BC09708

GSH427 HY09406 #28

GS231 *8 RP09063

GSC091 *6 #2

GSC548 BC09694 BC09708

GSH428 *24 #1

GS232 RP09061 RP09051 GSC092 *5 BC09575

GSC550 BC09694 BC09708

GSH429 *20 #1

GS233 *2 #2

GSC093 BC09443 BC09575

GSC552 *1 BC09604

GSH430 *14 #10

GS234 RP09060 #1 GSC094 *6 #2

GSC553 BC09694 BC09708

GSH431 HY09371 #29

GS235 *17 RP09030

GSC095 BC09110 BC09451

GSC554 *1 BC09604

GSH432 HY09367 #30

GS236 *12 RP09016 GSC096 BC09110 BC09451

GSC556 BC09694 BC09708

GSH433 HY09393 #22

GS237 *19 RP09042

GSC097 BC09110 BC09451

GSC557 BC09694 BC09708

GSH434 *20 #11

GS238 RP09060 #1 GSC098 BC09334 #4

GSC558 *1 BC09604

GSH435 HY09377 #31

GS239 *9 RP09085

GSC099 BC09317 BC09591

GSC559 BC09153 BC09573

GSH436 *25 #4

GS240 *11 RP09171 GSC100 BC09317 BC09591

GSC560 *1 BC09604

GSH437 *26 #4

GS241 *9 RP09085

GSC101 *7 #1

GSC561 *1 BC09604

GSH438 *27 #32

GS242 *2 #2 GSC102 BC09334 #4

GSC562 BC09594 BC09571

GSH439 *11 #19

GS243 RP09060 #1

GSC103 BC09036 BC09593

GSC564 BC09594 BC09571

GSH440 *11 #11

GS244 *13 #6 GSC104 BC09414 BC09476

GSC565 BC09776 #2

GSH441 *27 #29

GS245 RP09061 RP09085

GSC105 BC09440 #5

GSC566 *2 #3

GSH442 HY09371 #33

GS246 *3 #5 GSC106 BC09334 #4

GSC568 BC09233 BC09426

GSH443 *28 HY09414

GS247 RP09006 #2

GSC107 BC09334 #4

GSC570 BC09589 #4

GSH444 HY09292 HY09405

GS248 RP09060 #1 GSC108 BC09057 #6

GSC572 BC09551 #5

GSH445 HY09372 #17

GS249 *12 #11

GSC109 BC09097 BC09311

GSC574 BC09233 BC09426

GSH446 HY09215 #34

GS250 RP09012 RP09152 GSC110 BC09440 #5

GSC575 *3 BC09426

GSH447 *19 #28

GS252 RP09006 #14

GSC111 BC09440 #5

GSC576 BC09771 BC09755

GSH448 HY09396 #22

GS253 *11 RP09067 GSC112 BC09036 BC09593

GSC578 BC09105 #1

GSH449 HY09338 #9

GS254 RP09008 #6

GSC113 BC09097 BC09311

GSC580 BC09163 BC09585

GSH450 HY09372 #35

GS255 RP09055 #8 GSC114 BC09424 BC09398

GSC582 BC09163 BC09585

GSH451 HY09399 #7

GS256 RP09058 #9

GSC115 BC09036 BC09708

GSC584 BC09163 BC09585

GSH452 HY09399 #36

GS257 *13 RP09057 GSC116 *7 #7

GSC585 BC09163 BC09585

GSH453 HY09394 #37

GS258 *16 RP09171

GSC117 BC09424 BC09398

GSC586 BC09163 BC09585

GSH454 HY09400 #38

GS259 *15 RP09036 GSC118 BC09334 #4

GSC587 *1 #6

GSH455 HY09379 #35

GS260 RP09064 RP09032

GSC119 BC09462 #3

GSC588 *4 #5

GSH456 *24 #39

GS261 RP09006 #14 GSC121 *8 BC09575

GSC590 BC09269 #7

GSH457 *29 #40

GS262 *4 #7

GSC122 BC09424 BC09398

GSC591 *4 #5

GSH458 *8 #39

GS263 RP09061 RP09051 GSC123 BC09317 BC09591

GSH459 *16 #39

GS264 RP09058 #9

GSC124 BC09036 BC09593

GSH460 HY09377 #31

GS265 RP09049 RP09180 GSC125 BC09330 BC09575

GSH461 *1 #37

GS266 *17 #8

GSC126 BC09414 BC09476

GSH462 HY09406 HY09414

GS267 RP09131 RP09066

Epidalea calamita 2013 Epidalea calamita 2015 Hyla molleri Pelophylax perezi Tadpole Inferred sire Inferred dam Tadpole Inferred sire Inferred dam Tadpole Inferred sire Inferred dam Tadpole Inferred sire Inferred dam

GSH463 *30 #8

GS268 RP09013 RP09057

GSH464 *17 #41

GS269 *13 RP09007

GSH465 *11 #38

GS270 RP09012 RP09152

GSH466 *20 #24

GS271 *3 #5

GSH467 HY09337 HY09412

GS272 RP09039 RP09063

GSH468 *26 #42

GS273 *3 RP09007

GSH469 *21 #5

GS274 *7 RP09042

GSH470 HY09400 #2

GS275 *8 RP09067

GSH471 *1 #43

GS276 RP09012 RP09152

GSH472 *6 #6

GS277 *20 RP09007

GSH473 *8 #6

GS278 *1 RP09051

GSH474 *31 #3

GS280 *3 #5

GSH475 *32 #44

GS281 RP09006 #2

GSH476 *33 #5

GS282 *10 #3

GSH477 *34 #35

GS283 *11 RP09067

GSH478 HY09394 #45

GS284 RP09061 RP09051

GSH479 HY09330 #46

GS285 *18 RP09063

GSH480 *26 #19

GSH481 *6 #27

1 Provisional repository available at: https://goo.gl/6n3pcu

APPENDIX 9

PAIRWISE FST ESTIMATES AND MIGRATION RATES PER GENERATION

Appendix S1 in Sánchez-Montes et al. Journal of Biogeography (Under review)

(Chapter VI)

Table A9.1. Pairwise FST estimates among populations of E. calamita. FST values significantly > 0 at the Bonferroni penalized level (i.e. p < 0.05/136 = 0.0004) are marked in

bold. Pairwise FST between populations located in different slopes of Sierra de Guadarrama are shaded in grey. See Table VI.2 for abbreviations.

CER COL SOT MOR CAN BUS CAB ROB CEL LOZ GAR ALA PRA BRC NAV MUN STO

CER -

COL 0.058 -

SOT 0.039 0.074 -

MOR 0.052 0.082 0.061 -

CAN 0.026 0.051 0.028 0.041 -

BUS 0.026 0.059 0.029 0.044 0.014 -

CAB 0.026 0.056 0.030 0.046 0.016 0.015 -

ROB 0.022 0.051 0.029 0.042 0.012 0.013 0.013 -

CEL 0.030 0.063 0.030 0.052 0.017 0.018 0.018 0.019 -

LOZ 0.037 0.058 0.039 0.055 0.022 0.026 0.024 0.022 0.032 -

GAR 0.026 0.049 0.033 0.046 0.012 0.015 0.019 0.014 0.021 0.023 -

ALA 0.047 0.082 0.044 0.050 0.030 0.034 0.034 0.032 0.039 0.041 0.036 -

PRA 0.042 0.077 0.047 0.062 0.029 0.031 0.036 0.029 0.033 0.045 0.031 0.049 -

BRC 0.040 0.080 0.047 0.081 0.039 0.043 0.041 0.041 0.036 0.051 0.042 0.066 0.058 -

NAV 0.039 0.080 0.042 0.071 0.034 0.036 0.034 0.034 0.028 0.047 0.035 0.050 0.034 0.046 -

MUN 0.030 0.066 0.044 0.057 0.024 0.024 0.027 0.026 0.023 0.041 0.026 0.047 0.030 0.043 0.029 -

STO 0.041 0.078 0.048 0.071 0.034 0.034 0.037 0.031 0.034 0.044 0.036 0.047 0.042 0.052 0.030 0.032 -

Table A9.2. Pairwise FST estimates among populations of H. molleri. All FST values were significantly > 0 at the Bonferroni penalized level (i.e. p < 0.05/171 = 0.0003, marked in

bold). Pairwise FST between populations located in different slopes of Sierra de Guadarrama are shaded in grey. See Table VI.2 for abbreviations.

CER COL SOT MED RAS MOR CAN BUS CAB ROB BER GAS PRA TOR HER ARC SAU FUE TUR

CER -

COL 0.074 -

SOT 0.051 0.075 -

MED 0.060 0.098 0.064 -

RAS 0.059 0.101 0.060 0.085 -

MOR 0.057 0.077 0.036 0.073 0.038 -

CAN 0.048 0.080 0.032 0.050 0.048 0.035 -

BUS 0.036 0.064 0.028 0.048 0.047 0.035 0.031 -

CAB 0.053 0.066 0.037 0.063 0.057 0.040 0.037 0.025 -

ROB 0.061 0.087 0.048 0.060 0.055 0.044 0.038 0.037 0.037 -

BER 0.069 0.089 0.052 0.069 0.058 0.047 0.043 0.043 0.054 0.053 -

GAS 0.042 0.073 0.027 0.062 0.041 0.028 0.024 0.023 0.028 0.031 0.039 -

PRA 0.104 0.144 0.084 0.117 0.087 0.079 0.075 0.076 0.082 0.085 0.069 0.064 -

TOR 0.119 0.147 0.091 0.135 0.124 0.098 0.086 0.096 0.099 0.102 0.102 0.079 0.095 -

HER 0.089 0.127 0.081 0.100 0.075 0.057 0.063 0.067 0.072 0.068 0.067 0.053 0.092 0.128 -

ARC 0.057 0.087 0.044 0.066 0.049 0.048 0.037 0.029 0.043 0.035 0.051 0.029 0.078 0.087 0.061 -

SAU 0.079 0.118 0.061 0.087 0.065 0.059 0.054 0.049 0.064 0.052 0.065 0.041 0.088 0.120 0.078 0.048 -

FUE 0.063 0.090 0.045 0.085 0.052 0.037 0.039 0.038 0.050 0.051 0.049 0.029 0.078 0.089 0.064 0.039 0.053 -

TUR 0.081 0.103 0.052 0.082 0.062 0.046 0.045 0.052 0.057 0.042 0.054 0.036 0.063 0.085 0.061 0.037 0.061 0.043 -

Table A9.3. Pairwise FST estimates among populations of P. perezi. All FST values were significantly > 0 at the Bonferroni penalized level (i.e. p < 0.05/105 = 0.0005, marked in

bold). Pairwise FST between populations located in different slopes of Sierra de Guadarrama are shaded in grey. See Table VI.2 for abbreviations.

CER MED RAS MOR CAN BUS CAB BER PRA HER ARC STO SAU FUE TUR

CER -

MED 0.033 -

RAS 0.046 0.041 -

MOR 0.057 0.043 0.043 -

CAN 0.049 0.035 0.043 0.040 -

BUS 0.070 0.059 0.074 0.074 0.061 -

CAB 0.042 0.034 0.043 0.050 0.039 0.056 -

BER 0.076 0.069 0.103 0.103 0.096 0.120 0.092 -

PRA 0.031 0.032 0.047 0.056 0.035 0.067 0.037 0.092 -

HER 0.050 0.036 0.050 0.062 0.042 0.081 0.047 0.105 0.030 -

ARC 0.088 0.079 0.099 0.123 0.092 0.120 0.101 0.128 0.077 0.092 -

STO 0.064 0.050 0.054 0.067 0.044 0.100 0.053 0.115 0.044 0.049 0.109 -

SAU 0.050 0.040 0.056 0.066 0.050 0.081 0.052 0.091 0.041 0.043 0.090 0.059 -

FUE 0.075 0.066 0.080 0.089 0.060 0.116 0.085 0.142 0.062 0.082 0.123 0.084 0.071 -

TUR 0.031 0.022 0.043 0.054 0.039 0.067 0.034 0.071 0.029 0.042 0.086 0.051 0.042 0.069 -

Table A9.4. Pairwise FST estimates among populations of P. cultripes. FST values significantly > 0 at the Bonferroni penalized level (i.e. p < 0.05/78 = 0.0006) are marked in

bold. Pairwise FST between populations located in different slopes of Sierra de Guadarrama are shaded in grey. See Table VI.2 for abbreviations.

CER COL TEJ SOT CAB BUS ROB BER STO PRA HER TUR FUE

CER -

COL 0.048 -

TEJ 0.102 0.065 -

SOT 0.088 0.048 0.084 -

CAB 0.066 0.041 0.054 0.059 -

BUS 0.079 0.051 0.089 0.095 0.085 -

ROB 0.055 0.027 0.079 0.068 0.037 0.052 -

BER 0.130 0.117 0.167 0.174 0.130 0.105 0.097 -

STO 0.125 0.097 0.151 0.130 0.110 0.108 0.111 0.198 -

PRA 0.168 0.118 0.137 0.141 0.123 0.122 0.139 0.234 0.087 -

HER 0.105 0.071 0.097 0.093 0.072 0.095 0.074 0.160 0.050 0.057 -

TUR 0.129 0.092 0.136 0.121 0.090 0.115 0.102 0.210 0.020 0.085 0.038 -

FUE 0.107 0.085 0.111 0.107 0.076 0.109 0.091 0.168 0.026 0.066 0.025 0.028 -

Figure A9.1. Correlation between FST values in the complete and reduced (i.e. excluding full sibs) samples

of the four species. Values are indicated for pairwise distances between populations located in the same

(dark circles) or in different slopes (white circles) of Sierra de Guadarrama. Dashed lines indicate x = y.

The slope of the adjusted linear regression was significantly > 1 in H. molleri and < 1 in E. calamita and P.

cultripes at the 95% confidence level. The slope was not significantly different from 1 in P. perezi at the

95% nominal level.

Table A9.5. Pairwise inferred migration rate estimates (and standard deviations of the marginal posterior distribution for each estimate) among populations of E. calamita.

Estimates refer to the fraction of individuals in population i (in rows) that are migrants derived from population j (in columns) per generation (Wilson & Rannala, 2003).

Therefore, values in the diagonal represent the proportion of individuals coming from the same population of sampling. Values > 0.1 are marked in bold. Pairwise migration

rates between populations located in different slopes of Sierra de Guadarrama are shaded in grey. See Table VI.2 for abbreviations.

CER COL SOT MOR CAN BUS CAB ROB CEL LOZ GAR ALA PRA BRC NAV MUN STO

CER 0.82 (0.03) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

COL 0.01 (0.01) 0.87 (0.02) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

SOT 0.01 (0.01) 0.01 (0.01) 0.87 (0.02) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

MOR 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.82 (0.03) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

CAN 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.69 (0.03) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

BUS 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.02) 0.67 (0.01) 0.01 (0.01) 0.01 (0.01) 0.03 (0.02) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

CAB 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.67 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

ROB 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.68 (0.02) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

CEL 0.01 (0.01) 0.01 (0.01) 0.02 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.79 (0.03) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

LOZ 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.82 (0.03) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

GAR 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.02) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.69 (0.04) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

ALA 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.83 (0.03) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

PRA 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.02 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.81 (0.03) 0.01 (0.01) 0.02 (0.01) 0.01 (0.01) 0.01 (0.01)

BRC 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.84 (0.02) 0.03 (0.01) 0.01 (0.01) 0.01 (0.01)

NAV 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.85 (0.03) 0.01 (0.01) 0.01 (0.01)

MUN 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.04 (0.02) 0.80 (0.03) 0.01 (0.01)

STO 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.86 (0.02)

Table A9.6. Pairwise inferred migration rate estimates (and standard deviations of the marginal posterior distribution for each estimate) among populations of H. molleri.




CER COL SOT MED RAS MOR CAN BUS CAB ROB BER GAS PRA TOR HER ARC

CER 0.81 (0.03) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.02 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

COL 0.01 (0.01) 0.84 (0.03) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

SOT 0.01 (0.01) 0.01 (0.01) 0.67 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.17 (0.03) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

MED 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.83 (0.03) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

RAS 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.81 (0.03) 0.02 (0.02) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

MOR 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.85 (0.03) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.02 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

CAN 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.67 (0.01) 0.19 (0.02) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

BUS 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.71 (0.03) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

CAB 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.67 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

ROB 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.67 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

BER 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.83 (0.03) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

GAS 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.14 (0.03) 0.01 (0.01) 0.01 (0.01) 0.02 (0.01) 0.68 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

PRA 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.83 (0.03) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

TOR 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.88 (0.02) 0.01 (0.01) 0.01 (0.01)

HER 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.80 (0.02) 0.06 (0.02)

ARC 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.05 (0.03) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.79 (0.03)

SAU 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

FUE 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.05 (0.02) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

TUR 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.02 (0.01) 0.01 (0.01) 0.01 (0.01) 0.02 (0.02) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.02 (0.01) 0.01 (0.01) 0.01 (0.01)

Table A9.6 (cont.). Pairwise inferred migration rate estimates (and standard deviations of the marginal posterior distribution for each estimate) among populations of H. molleri. Estimates refer to the fraction of individuals in population i (in rows) that are migrants derived from population j (in columns) per generation (Wilson & Rannala, 2003). Therefore, values in the diagonal represent the proportion of individuals coming from the same population of sampling. Values > 0.1 are marked in bold. Pairwise migration rates between populations located in different slopes of Sierra de Guadarrama are shaded in grey. See Table VI.2 for abbreviations.

SAU FUE TUR

CER 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

COL 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

SOT 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

MED 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

RAS 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

MOR 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

CAN 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

BUS 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

CAB 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

ROB 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

BER 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

GAS 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

PRA 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

TOR 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

HER 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

ARC 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

SAU 0.80 (0.03) 0.03 (0.02) 0.01 (0.01)

FUE 0.01 (0.01) 0.78 (0.03) 0.01 (0.01)

TUR 0.02 (0.01) 0.01 (0.01) 0.79 (0.03)

Table A9.7. Pairwise inferred migration rate estimates (and standard deviations of the marginal posterior distribution for each estimate) among populations of P. perezi.




CER MED RAS MOR CAN BUS CAB BER PRA HER ARC STO SAU FUE TUR

CER 0.79 (0.04) 0.01 (0.01) 0.02 (0.01) 0.02 (0.02) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.02 (0.02) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.02 (0.01) 0.01 (0.01) 0.01 (0.01)

MED 0.01 (0.01) 0.77 (0.04) 0.01 (0.01) 0.06 (0.04) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.02 (0.02) 0.01 (0.01) 0.02 (0.02) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

RAS 0.01 (0.01) 0.01 (0.01) 0.85 (0.03) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

MOR 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.86 (0.03) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

CAN 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.20 (0.02) 0.68 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

BUS 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.88 (0.02) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.02 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

CAB 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.67 (0.01) 0.01 (0.01) 0.02 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

BER 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.86 (0.03) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

PRA 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.02 (0.02) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.83 (0.03) 0.02 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.02 (0.01)

HER 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.88 (0.03) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

ARC 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.86 (0.03) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

STO 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.02 (0.01) 0.84 (0.03) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

SAU 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.05 (0.02) 0.80 (0.03) 0.01 (0.01) 0.01 (0.01)

FUE 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.02 (0.01) 0.01 (0.01) 0.85 (0.03) 0.01 (0.01)

TUR 0.01 (0.01) 0.07 (0.04) 0.04 (0.03) 0.03 (0.02) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.04 (0.03) 0.01 (0.01) 0.01 (0.01) 0.02 (0.02) 0.01 (0.01) 0.01 (0.01) 0.69 (0.02)

Table A9.8. Pairwise inferred migration rate estimates (and standard deviations of the marginal posterior distribution for each estimate) among populations of P. cultripes.




CER COL TEJ SOT CAB BUS ROB BER STO PRA HER TUR FUE

CER 0.89 (0.03) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

COL 0.02 (0.02) 0.70 (0.05) 0.01 (0.01) 0.01 (0.01) 0.20 (0.07) 0.01 (0.01) 0.01 (0.02) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

TEJ 0.01 (0.01) 0.02 (0.02) 0.79 (0.04) 0.01 (0.01) 0.11 (0.05) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

SOT 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.90 (0.02) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

CAB 0.01 (0.01) 0.01 (0.01) 0.02 (0.01) 0.01 (0.01) 0.87 (0.03) 0.01 (0.01) 0.01 (0.01) 0.02 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

BUS 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.69 (0.04) 0.20 (0.06) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

ROB 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.20 (0.05) 0.01 (0.01) 0.70 (0.04) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

BER 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.90 (0.02) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

STO 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.90 (0.02) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

PRA 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.90 (0.02) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)

HER 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.02 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.03 (0.02) 0.01 (0.01) 0.68 (0.01) 0.01 (0.01) 0.19 (0.03)

TUR 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.23 (0.02) 0.01 (0.01) 0.01 (0.01) 0.67 (0.01) 0.01 (0.01)

FUE 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.01 (0.01) 0.20 (0.04) 0.02 (0.01) 0.01 (0.01) 0.01 (0.01) 0.70 (0.03)

APPENDIX 10

RESULTS OF CLUSTERING ANALYSES

Appendix S2 in Sánchez-Montes et al. Journal of Biogeography (Under review)

(Chapter VI)

Results of clustering analyses

329

Figure A10.1. Likelihood of clustering partitions obtained with different number of clusters (K). Results of

the ΔK (‘Evanno’) method (blue) are shown along with the mean Ln probabilities, which correspond to the

original method (white dots, bars represent standard deviation), for each K value. Dotted lines mark ΔK =

0.

APPENDIX 10

330

Figure A10.2. Probability of assignment of each individual to each cluster, as inferred with structure for a

number of clusters (K) from 2 to 5 in E. calamita. Each vertical bar represents the membership coefficient

of each of the individuals sampled in 17 populations (indicated at the bottom, see abbreviations in Table

VI.2) to each of the clusters, which are depicted in different colours.

Figure A10.3. Probability of assignment of each individual to each cluster, as inferred with DAPC for a

number of clusters (K) from 2 to 5 in E. calamita. Each vertical bar represents the membership coefficient




331


number of clusters (K) from 2 to 5 in H. molleri. Each vertical bar represents the membership coefficient of

each of the individuals sampled in 19 populations (indicated at the bottom, see abbreviations in Table VI.2)

to each of the clusters, which are depicted in different colours.


number of clusters (K) from 2 to 5 in H. molleri. Each vertical bar represents the membership coefficient of



APPENDIX 10

332


number of clusters (K) from 2 to 5 in P. perezi. Each vertical bar represents the membership coefficient of




number of clusters (K) from 2 to 5 in P. perezi. Each vertical bar represents the membership coefficient of




333


number of clusters (K) from 2 to 5 in P. cultripes. Each vertical bar represents the membership coefficient




number of clusters (K) from 2 to 5 in P. cultripes. Each vertical bar represents the membership coefficient



APPENDIX 10

334

Figure A10.10. Results of ten different runs of spatial clustering analyses in GENELAND for each species

(Ecal: E. calamita, Hmol: H. molleri, Pper: P. perezi and Pcul: P. cultripes). For each run, the mean

posterior density (mpd) is shown above the map (latitude/longitude decimal geographic coordinates

indicated in the left/bottom axes, respectively) representing the sampled populations (black dots) and the

spatial assignment to each of the two inferred clusters (green or white).


335

Figure A10.10 (cont.). Results of ten different runs of spatial clustering analyses in GENELAND for each

species (Ecal: E. calamita, Hmol: H. molleri, Pper: P. perezi and Pcul: P. cultripes). For each run, the mean

posterior density (mpd) is shown above the map (latitude/longitude decimal geographic coordinates

indicated in the left/bottom axes, respectively) representing the sampled populations (black dots) and the

spatial assignment to each of the two inferred clusters (green or white).

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