Otospermophilus Beecheyi)

ESTIMATING SEX-BIAS IN DISPERSAL IN THREE CENTRAL

VALLEY POPULATIONS OF CALIFORNIA GROUND

SQUIRRELS (OTOSPERMOPHILUS BEECHEYI)

USING MICROSATELLITE-BASED

ANALYSES

A Thesis Presented to the Faculty of California State University, Stanislaus

In Partial Fulfillment of the Requirements for the Degree of Master of Science in Ecology and Sustainability

By Ashleigh Glover June 2018

CERTIFICATION OF APPROVAL

ESTIMATING SEX-BIAS IN DISPERSAL IN THREE CENTRAL

VALLEY POPULATIONS OF CALIFORNIA GROUND

SQUIRRELS (OTOSPERMOPHILUS BEECHEYI)

USING MICROSATELLITE-BASED

ANALYSES by Ashleigh Glover

Signed Certification of Approval page is on file with the University Library

Dr. Jennifer Cooper Date Full-time Lecturer in Biological Sciences

Dr. Andrew G. Gardner Date Assistant Professor of Botany

Dr. Ann K. Kohlhaas Date Professor of Zoology

DEDICATION

To my loving parents, Jay and Barbara Glover, my supportive boyfriend,

Anthony, and my beautiful Belle, who fills every day with happiness and excitement.

ACKNOWLEDGEMENTS

I would like to sincerely thank my advisor, Dr. Jennifer Cooper, for her unwavering support and guidance. I could not be the person or scientist that I am today without her. I would also like to thank my thesis committee members, Drs.

Andrew Gardner and Ann Kohlhaas, for their valuable insights, which greatly improved the quality of this thesis. Next, I would like to thank the past and present members of our “Squirrel Team” for their hard work in the field and the lab. In addition, I would like to thank the management personnel of the San Luis National

Wildlife Refuge Complex for allowing us to conduct our fieldwork, year after year.

This project could not have been completed without financial support, so I would like to thank the funding organizations at California State University, Stanislaus that supported my research: the Office of Research and Sponsored Programs, the Center for Excellence in Graduate Education, the Biology Research Committee, and the

Graduate Equity Fellowship program. Furthermore, I would like to thank Dr. Jennifer

Smith at Mills College in Oakland, CA for providing us with the primer sequences so that I could amplify all of the microsatellite loci used in this study. Last, but certainly not least, I would like to thank my loving family and friends. Their constant support and encouragement has helped me turn my childhood dreams into a reality.

TABLE OF CONTENTS PAGE

Dedication ...... iv

Acknowledgements ...... v

List of Tables ...... vii

List of Figures ...... viii

Abstract ...... ix

Estimating sex-bias in dispersal in three Central Valley populations of California ground squirrels (Otospermophilus beecheyi) using microsatellite-based analyses 1

Introduction ...... 1 Hypotheses ...... 4 Methods...... 7 Results ...... 21 Discussion ...... 33 References ...... 39

Relatedness and grouping behavior in three Central Valley populations of California ground squirrels (Otospermophilus beecheyi) ...... 49

Introduction ...... 49 Hypotheses ...... 53 Methods...... 55 Results ...... 60 Discussion ...... 68 References ...... 74

Appendices

A. Raw multi-locus genotypes ...... 82 B. Individual burrow assignments ...... 100 C. Test for departure from Hardy-Weinberg equilibrium ...... 107 D. Test for linkage disequilibrium ...... 108 E. Mean log probability and delta K plots from STRUCTURE analyses ...... 111 F. Bar plots from STRUCTURE analyses when LOCPRIOR model used ..... 117 G. Correlograms from sex-specific spatial autocorrelation analyses ...... 118 H. Parentage assignment from CERVUS analyses ...... 121 vi

LIST OF TABLES

TABLE PAGE

Estimating sex-bias in dispersal in three Central Valley populations of California ground squirrels (Otospermophilus beecheyi) using microsatellite-based analyses

1. The 10 autosomal microsatellite markers used in this study ...... 10

2. Demographics of genotyped California ground squirrels ...... 22

3. Summary diversity statistics for 3 Central Valley populations of California ground squirrels used in dispersal pattern analyses ...... 23

4. Pairwise genetic differentiation (FST) among 3 Central Valley populations of California ground squirrels ...... 24

5. AMOVA results when all juveniles excluded from analysis ...... 29

6. Sex-specific statistics when all juveniles excluded from analyses ...... 29

7. Sex-specific statistics when juveniles captured post-May included in analyses 30

Relatedness and grouping behavior in three Central Valley populations of California ground squirrels (Otospermophilus beecheyi)

1. Candidate parent simulation parameters for CERVUS analyses ...... 58

2. Summary diversity statistics for 3 Central Valley populations of California ground squirrels used in social grouping and mating system analyses ...... 61

3. Percent of offspring assigned putative parents from CERVUS analyses ...... 63

4. Estimate of dispersal distances within 2 Central Valley populations of California ground squirrels ...... 65

5. Coefficient of relatedness between offspring with shared father ...... 66

6. Coefficient of relatedness between offspring with shared mother ...... 68

vii

LIST OF FIGURES

FIGURE PAGE

Estimating sex-bias in dispersal in three Central Valley populations of California ground squirrels (Otospermophilus beecheyi) using microsatellite-based analyses

1. Map depicting the location of San Joaquin River NWR, Merced NWR, and San Luis NWR within the Central Valley of California ...... 8

2. Maps of burrow system locations within each refuge ...... 15

3. Bar plots from STRUCTURE analyses ...... 25

4. Results of test for isolation by distance pattern within each refuge ...... 27

5. Tests for sex-specific isolation by distance patterns within each refuge ...... 31

Relatedness and grouping behavior in three Central Valley populations of California ground squirrels (Otospermophilus beecheyi)

1. Average relatedness within burrow systems within each refuge ...... 62

viii

ABSTRACT

The California ground squirrel (Otospermophilus beecheyi) is a diurnal rodent native to California. Although abundant throughout their range, their social grouping and dispersal patterns, as well as mating system, are not well described. To date, there are no population genetic studies on this species. I used seven microsatellite markers to determine population genetic structure, test for the presence of a sex-bias in dispersal, examine patterns of relatedness within burrow systems, and describe the mating system within three wildlife refuges located in the Central Valley of California. There was no evidence of genetic differentiation among burrow systems within each refuge, and no biologically significant isolation by distance pattern was observed in any refuge. A weak male-bias in dispersal was detected. Average relatedness within burrow systems was approximately zero for adult males, adult females, and all individuals. There was evidence of both sexes mating with multiple partners within a breeding season. These results suggest that there is a single panmictic population within each refuge in which both males and females are highly dispersive and can disperse over long distances, that California ground squirrels are not remaining in highly social family groups and are aggregating due to some other factor, such as high population density or food resources, and that this species exhibits a polygynandrous mating system. California ground squirrels are the largest agricultural pests in California. Studies of genetic diversity and dispersal patterns such as this could be used to formulate effective management strategies.

ESTIMATING SEX-BIAS IN DISPERSAL IN THREE CENTRAL VALLEY

POPULATIONS OF CALIFORNIA GROUND SQUIRRELS

(OTOSPERMOPHILUS BEECHEYI) USING MICROSATELLITE-BASED

ANALYSES

Introduction

Natal dispersal is a common feature of animal life cycles. It is clear that a behavioral trait that is ubiquitous among most taxa must be maintained by strong selective forces, especially as the costs of dispersal are not trivial. For example, dispersal can require large energy expenditure, and migration through unknown habitat increases the risk of mortality from starvation or predation (Perrin and

Mazalov 1999; Cote and Clobert 2010).

The avoidance of kin competition and inbreeding are some of the chief explanations for the commonality of dispersal (Greenwood 1980; Cote and Clobert

2010). Competition for resources or mates amongst kin, either between parents and offspring or among offspring, decreases the inclusive fitness of individuals and is alleviated by dispersal (Gyllenberg et al. 2008; Cote and Clobert 2010; Perrin and

Mazalov 2000). The first analytical models for dispersal strategies under kin competition, developed by Hamilton and May (1977), predicted that kin competition itself could promote changes in dispersal of a species despite the risks associated with dispersing (Ridley and Sutherland 2002; Cote and Clobert 2010; Gyllenberg et al.

2008). In addition, Brom et al. (2016) used analytical models to show that kin

competition is important in the evolution of sex-biased dispersal and potentially even more influential than inbreeding avoidance.

The hypothesis of inbreeding avoidance has been supported by the observance of sex-biases in dispersal, whereby one sex usually disperses further and/or more frequently than the opposite sex (Trochet et al. 2016). Both the dispersing and the philopatric sex are predicted to benefit from such a system, because the cost of inbreeding can be high due to the cumulative effects of deleterious recessive alleles in homozygous offspring (Zajitschek et al. 2009). However, inbreeding avoidance alone does not determine which sex will disperse, because the primarily dispersing sex varies among taxonomic groups (Shaw and Kokko 2014; Brom et al. 2016; Gros et al. 2009). It has been suggested by Greenwood (1980) that, when the benefits of dispersal and philopatry are unequal between the sexes, mating system plays an important role in determining which direction the bias takes. In a monogamous mating system, which is typical in birds, males devote substantial energy to resource acquisition, territory defense and parental care (Greenwood 1980), making philopatry more beneficial. In a polygynous mating system, which is typical in mammals, males participate little in parental care, making resource acquisition and territory defense more important to female fitness. This dichotomy favors female philopatry and male dispersal (Perrin and Mazalov 1999; Lawson Handley and Perrin 2007). Another possible explanation for sex-biased dispersal is unequal dispersal costs between the sexes. If this is the case, the sex with lower costs will more likely be the dispersive sex (Gros et al. 2009), even if the benefits of dispersal are equal for the sexes.

Rodents are a species-rich group of mammals with varying levels of social structure, from solitary to large social units, and diverse mating systems. Although male-biased dispersal is typical in mammals, female-biased and sex-unbiased dispersal has also been documented in this group (Wauters et al. 2011; Lawson

Handley and Perrin 2007). For example, Wauters et al. (2011) found no sex bias in dispersal in three populations of Eurasian red squirrels (Sciurus vulgaris) in Northern

Belgium. In contrast, Devillard et al. (2004) found that, as social complexity increased, the magnitude of male-biased dispersal increased in polygynous ground dwelling sciurids (ground squirrels, marmots and prairie dogs).

The California ground squirrel (CGS; Otospermophilus beecheyi) is a widespread mammal species native to California. The agricultural-based economy of

California provides added food resources for this abundant rodent: McGrann et al.

(2014) found a significant positive relationship between CGS burrow density and percent cover of adjacent perennial fruit and nut crops in the Sacramento Valley of

California. Further, they found a significant negative relationship between burrow density and percent cover of grasslands. CGS were 8.40 times more likely to occur adjacent to perennial nut crops than adjacent to grasslands. This, in combination with the species’ high reproductive potential, has allowed natural population densities to increase greatly (Marsh 1998; McGrann et al. 2014; Horn and Fitch 1946).

To date, no population genetic studies have been conducted on O. beecheyi, and direct observations of natal dispersal have been limited (Boellstorff and Owings

1995; Smith et al. 2016). In this study, individuals that are captured as juveniles in

one location (natal site) and later captured ten or more average home range distances away are considered to have dispersed. Typically, ground squirrel home ranges are small, approximately 25m in diameter (Festa-Bianchet and Boag 1982; Holekamp and Sherman 1989). Boellstorff and Owings (1995) reported one population of CGS adhering to this home range size, but Owings et al. (1977) observed another population with larger home ranges of approximately 70m in diameter. This variation suggests that home range size is dependent on habitat and population density.

In this study, I propose to characterize the population structure and natal dispersal patterns of three Central Valley populations (Figure 1) of CGS using microsatellite-based genetic analyses, and test hypotheses regarding dispersal distance and sex bias in dispersal. Because the dispersal pattern has not been formally described in this species, the results from this study will add to our understanding of mammalian social and grouping behavior.

Hypotheses

Isolation by Distance

Isolation by distance, first discussed by Sewell Wright (1943), is a common pattern in population genetics: as geographic distance increases between populations, genetic differentiation between those populations also increases. This linear pattern is only detectable when the regional effects of gene flow and genetic drift are at equilibrium (Phillipsen et al. 2015). The same effect can be observed within populations when genetic distance between individuals is regressed against geographic distance of sampling locations (Meirmans 2012). This genetic signature

results from gene flow within populations, or “short-distance dispersal”. Isolation by distance effects can only be detected if the sampling occurs along a continuum and if the sampling scale is appropriate; for instance, samples along the continuum must be separated by a distance less than that of the average natal dispersal event (van Strien et al. 2015; Phillipsen et al. 2015). Therefore, testing for the presence of isolation by distance within populations can provide an estimate of dispersal distance in this species. Our sampling scale is different for each refuge, but ranges from 0.75 to 5.5 miles (1207 to 8851 meters) in length. Therefore, I expect to capture individual, within-population dispersal events in CGS. I predict that an isolation by distance

(IBD) pattern will be observed within each of the three populations due to short- distance dispersal of individuals.

Sex-Bias in Dispersal

Dispersal in mammals is male-biased (Greenwood 1980) with some exceptions, including the African wild dog (Lycaon pictus) and chimpanzee (Pan troglodytes), which exhibit female-biased dispersal (Dobson 1981). Observational studies have demonstrated predominantly male-biased dispersal in several species of ground squirrels, including the CGS (Dobson 1979; Boellstorff and Owings 1995),

Belding’s ground squirrel (Spermophilus beldingi, Holekamp 1984) and Richardson’s ground squirrel (Spermophilus richardsonii, Greenwood 1980). Although male- biased dispersal is the general rule in mammals, it is not uncommon for a proportion of females to disperse from their natal location and a proportion of males to exhibit philopatry (Greenwood 1980; Dobson 1979; Holekamp 1984; Cooper et al. 2010;

Lawson Handley and Perrin 2007). Neuhaus (2006) observed 30.8 % of yearling male

Columbian ground squirrels (Spermophilus columbianus) remaining at their natal sites in two Canadian populations, and Boellstorff and Owings (1995) reported anecdotal evidence for limited male philopatry in a single population of CGS.

I expect to complement the observational data with microsatellite-based, F- statistical genetic estimates of male-biased dispersal in three populations of O. beecheyi. Wright’s FST is a parameter measuring genetic differentiation between and within populations. For species that exhibit within-population dispersal, as the value of pairwise FST increases, the more genetic variation between two samples can be explained by the non-random mating patterns imposed by social grouping (which results in sub-population structure). Therefore, when pairwise FST values are computed separately for adult males and adult females among sub-populations, the direction of sex-bias in dispersal can be determined, because the sex with higher dispersal will exhibit lower average sub-population FST values (Prugnolle and de

Meeus 2002). Juveniles are excluded from these analyses because they are pre- dispersal and would add statistical noise, decreasing the ability to detect a sex-bias in dispersal (Gauffre et al. 2009). Sex-specific dispersal rates can also be calculated using FST estimates by the ratio of sex-specific FST estimators after dispersal over FST estimated before dispersal (Vitalis 2002). If this species exhibits male-biased dispersal within populations, then male-specific FST values within each population will be smaller than female-specific FST values. Although comparing sex-specific FST estimates will be the primary analysis to test for a sex-bias in dispersal, I will perform

additional sex-specific analyses, for which contrasting patterns between males and females can be used to estimate the direction of the sex-bias in dispersal: Analysis of

Molecular Variance (AMOVA), FIS, average relatedness, mean of the assignment index (mAIc), variance of the assignment index (vAIc), isolation by distance, and spatial autocorrelation.

Methods

This project is an extension of “Population genetic estimates of agricultural- based population growth in California ground squirrels” (J. Cooper, PI), ongoing since 2015. We trap and release CGS at three National Wildlife Refuges (NWR) in

Stanislaus County, located in California’s Central Valley: San Luis NWR, San

Joaquin River NWR and Merced NWR (Figure 1). Within each refuge, we sample individuals along a continuum from a central location deep within the refuges to the border between the refuges and the adjacent farmland. High population densities have been observed at all locations, but density varies at locations within refuges. Each of these National Wildlife Refuges exhibits a mosaic of habitat types, including riparian and wetland, oak motte and shrub chaparral, and open grasslands, which house a variety of wildlife for public viewing (Figure 2). San Luis NWR is comprised of riparian woodlands dominated by willows, cottonwoods, and oaks, wetlands, grasslands, and vernal pools. San Joaquin River NWR is primarily comprised of riparian woodlands. Merced NWR is also comprised of riparian woodlands, as well as wetlands, grasslands, vernal pools, and over 800 acres of managed cropland.

Population densities of CGS seem to be positively dependent on the presence of extensive grassland microhabitat (J. Cooper, pers. obs.).

Figure 1: Map depicting the location of San Joaquin River NWR, Merced NWR, and San Luis NWR within the Central Valley of California.

Field Data Collection

We use Tomahawk traps to capture individuals in multiple locations throughout each refuge, and we record GPS data for each trap location. To habituate naive animals to entering the traps, we pre-bait with sunflower seeds mixed with peanut butter. Weekly trapping sessions typically last 7-8 hours; traps are triggered at dawn, and checked every 2 hours. Captured squirrels are handled in the field with thick canvas handling bags (Koprowski 2002) and manual restraint. Individuals are sexed and assigned to an age/reproductive status category (juvenile, sexually mature,

pregnant/lactating). We sample 1mm of ear tissue, and store samples in lysis buffer for later genetic analysis. For this study, samples were collected from 2015 to 2017.

We uniquely mark individuals using numbered ear tags (National Band and Tag Co).

Individuals are released within 1m of the capture site. Approval for these research activities has been granted by the state of California (SCP 012683), San Luis National

Wildlife Refuge Complex (81650-15) and the CSU Stanislaus Animal Welfare

Committee (1415-003 AW).

Genetic Data Collection

For samples collected in 2015, I extracted DNA from tissue using DNeasy

Blood and Tissue spin columns (Qiagen). For the remainder of the samples, I used the ammonium acetate extraction protocol as described by Osterburg et al. (1975) for

DNA isolation. After extraction, I assess the quantity and quality of the DNA using a

NanoDrop 2000 spectrophotometer. The acceptable DNA concentration range is 50-

200 ng/mL, and the ideal A260:A280 ratio is approximately 1.8 (Pikor et al. 2011).

Although no genetic markers have been developed for CGS, numerous microsatellite loci have been developed for the closely related Mohave ground squirrel

(Xerospermophilus mohavensis; Bell and Matocq 2010) and round-tailed ground squirrel (Xerospermophilus tereticaudus; Munroe and Koprowski 2011a, 2011b,

2014). Because the genetic divergence between these species is modest, cross- amplification of several microsatellite loci has been successful after primer optimization using non-labeled oligos (Smith, unpublished data; Table 1).

Table 1: The 10 autosomal microsatellite markers used in this study. TA = optimal 0 annealing temperature ( C); [MgCl2] = final MgCl2 concentration (mM). DNA = amount of template DNA added (ul/rxn); *indicates DNA is at a 1:10 dilution.

Original Markers Primer Sequences TA [MgCl2] DNA Species Reference Urocitellus Nunes et D4 F 5': AGCAAGACCCTAAGCAAC 55 1.5 1.5 beldingi al. 2015 R 5': AGCACCCTGTTACAAAGG Spermophilus Stevens et GS22 F 5': TCCCAGAGAACAACATCAACAG 55 1.5 1.5 columbianus al. 1997 R 5': TCCGCACAGGTCTTGGACTT U. brunneus May et al. IGS-6 F 5': GGGCATTAATTCCAGGACTT 57 1.5 1.5 brunneus 1997 R 5': GGGCTGGAATTAAAGGTATCA Marmota Kyle et al. 2g2 F 5': TGAACTGGGTCTTGAGGTCT 55 1.5 1.0* caligata 2004 R 5': GTCTGCTCTGCTCTCCATCA Nunes et A116 F 5': TCTGTCTCACCTCCTGTGTC 57 1.5 1.0* U. beldingi al. 2015 R 5': GCAAACTCACCTCTAAGATGG Da Silva MA018 F 5': ATCCGTCCAATAAAGAAATTC 56 1.5 1.0* M. marmota et al. 2003 R 5': GTTTCTTGTGGCTCAGTGGTCAGATG Goossens SS-Bibl18 F 5': ATGGTCATGGAAGGGAAG 55 1.5 1.0* M. marmota et al. 1998 R 5': GGCATCTTCACAGTTGATCT Hanslik & Kruckenh ST10 F 5': TTGTGATCCTCCAGGGAGTT 57 1.5 1.5 S. citellus -auser R 5': GTGATTTCCAAACCCCATTC 2000 Nunes et B108 F 5': GGAGCGTCAATGGAGAGG 58 2.0 1.0* U. beldingi al. 2015 R 5': GGCAGAAGGCAGAACTGG S. Stevens et GS20 F 5': TCCAGAGTTTTTCAGACACA 55 1.5 1.0* columbianus al. 1997 R 5': GCCCAGCCATCACCCTCACC

DNA amplification at microsatellite loci was accomplished with polymerase chain reaction (PCR). The total PCR volume was 12 µl and contained final concentrations of the following reagents (optimized for each primer set, Table 1): 1.5-

2.0 mM of MgCl2, 0.5 µM of fluorescently labeled forward primer, 0.5 µM of reverse primer, and 3.35 µl of 125U Top Taq Master Mix (Qiagen). I performed all polymerase chain reactions in an Applied Biosystems 2720 thermal cycler with the

following thermal profile: a 5-min denaturation step at 94°C followed by 34 cycles of

30-s at 94°C, 45-s at annealing temperature, and 90-s at 72°C, finishing with a final

10-min extension at 72°C. I visualized PCR products on a 2% agarose gel to confirm successful amplification. I shipped amplicons to the University of Arizona Research

Lab for high-resolution fragment analysis with an ABI Prism 3730XL sequencer

(Applied Biosystems), and the data were returned to us in the form of raw trace files.

I reviewed the trace files were using PeakScanner 2 (Applied Biosystems) and visually assigned allele sizes (Appendix A). On average, approximately 7% of genotypes assigned per locus were replicated to assess assignment error rates. Raw alleles called were binned using the software TANDEM (Matchiner & Salzburger

2009), which uses a binning method originally described by Idury and Cardon (1997).

Data Analysis

Microsatellites. Because preliminary genotyping in a subset of individuals revealed that three of the microsatellites tested were monomorphic across all populations (GS20, B108, and ST10), only the remaining seven microsatellites were amplified in all individuals (San Joaquin River National Wildlife Refuge: n = 100;

Merced National Wildlife Refuge: n = 100; San Luis National Wildlife Refuge: n =

104). As null alleles within a locus cause equilibrium expectations to be violated, these seven loci were checked for the presence of null alleles in CERVUS v.3.0.7

(Kalinowski et al. 2007). I tested for conformity to Hardy-Weinberg equilibrium and linkage disequilibrium between loci with the software FSTAT v.2.9.3.2 (Goudet et al.

2002). These tests were corrected for multiple testing using Bonferroni correction. I

calculated the number and size range of alleles, number of individuals, and observed and expected heterozygosity within each refuge using Arlequin v.3.5.2.2 (Excoffier and Lischer 2010). I used the software CONVERT v.1.31 (Glaubitz 2004) to convert

Excel files into Arlequin input files.

Population structure. To estimate genetic structure between refuges, I calculated pairwise FST in Arlequin v.3.5.2.2 (Excoffier and Lischer 2010). My research goal was to examine dispersal patterns and population structure at a fine scale, thus all subsequent analyses were performed within each refuge, thereby treating each refuge as a replicate population. This was appropriate, as the refuges are separated by a distance of at least six miles, and direct dispersal of an individual between two refuges is unlikely. Pairwise FST and Analysis of Molecular Variance

(AMOVA; global FST) were used to estimate genetic structure within each refuge in

Arlequin v.3.5.2.2 (Excoffier and Lischer 2010) with 10,000 permutations to identify statistical significance. This analysis requires the a priori assignment of individuals to subpopulations. Because no population genetic studies exist for CGS to guide determination of “subpopulations,” a home range containing a cohesive burrow system was assumed to be a subpopulation within each refuge. Smith et al. (2016) reported an upper bound of home range size to be approximately 5,466 m2 (73.9 m long), however a single burrow system was reported to be 266 m long. To account for potential large outlier home range sizes, I doubled the upper bound home range size in this study. Therefore, all squirrels captured within a 10,932-m2 area were assumed to belong to the same burrow system (Figure 2). GPS data were not available for all

genotyped individuals, and those individuals were excluded from all analyses that required assignment to a burrow system. This left a sample size of n = 64 (San

Joaquin River NWR), n = 22 (Merced NWR), and n = 103 (San Luis NWR;

Appendix B). Because FST can under-estimate differentiation when using highly polymorphic loci (Bird et al. 2011), I also calculated pairwise bias-corrected Jost’s D

(Dest) with 9,999 permutations to identify statistical significance in GenAlEx v.6.503

(Peakall and Smouse 2006, 2012).

To test the assumption that burrow systems were statistically significant genetic clusters, I performed individual-based genetic clustering analyses within each refuge using STRUCTURE v.2.3.4 (Pritchard et al. 2000). This approach estimates genetic clusters in the absence of a priori population designations, while minimizing linkage disequilibrium (LD) and maximizing conformity to Hardy-Weinberg equilibrium. Two separate clustering analyses were conducted within each refuge, and the results were compared: the first analysis used no geographic location prior to estimate the number of genetic clusters; the second analysis used the LOCPRIOR model (Hubisz et al. 2009), which uses spatial information to group individuals in clusters with the consideration that individuals sampled together are more likely from the same genetic cluster. The LOCPRIOR model can detect subtle population structure, but will not falsely detect structure (Porras-Hurtado et al. 2013), thus guarding against Type I error. For all cluster analyses, I used two models simultaneously: the admixture model (which considers that individuals can have an admixed ancestry) and the correlated allele frequencies model (which assumes that

closely related populations have similar allele frequencies due to shared ancestry).

The length of burn-in was 50,000, the number of MCMC replications was 200,000, the number of K clusters was set between 1-10, and 20 iterations per K value were performed. The most likely K was determined by comparing plots of Ln P(D) and ΔK in STRUCTURE HARVESTER v.0.6.94 (Earl and VonHoldt 2012). The highest point on these two plots is the most likely K value.

Figure 2: Maps of burrow system locations within each refuge. a) San Joaquin River NWR; b) Merced NWR; c) San Luis NWR. Each burrow system has multiple burrow entrances. See Appendix B for each individual’s burrow assignment. Approximate refuge borders are outlined in red.

Figure 2 continued: Maps of burrow system locations within each refuge. a) San Joaquin River NWR; b) Merced NWR; c) San Luis NWR. Each burrow system has multiple burrow entrances. See Appendix B for each individual’s burrow assignment. Approximate refuge borders are outlined in red.

Isolation by distance. To test for the presence of an isolation by distance

(IBD) pattern within each refuge, I regressed FST/(1 – FST) against the log of geographic distance between each dyad of individuals in GenAlEx v.6.503 (Peakall

and Smouse 2006, 2012) and performed Mantel tests to test for a statistical relationship between the genetic and geographic distance matrices with 9,999 permutations within each refuge.

Sex-bias in dispersal. To test for a sex-bias in dispersal within each refuge, I performed several analyses separately for each sex. Only post-dispersal individuals, i.e. adults, were used in the analyses. Juveniles and individuals for whom sex and/or age were unknown were excluded. Timing of dispersal for CGS varies by geographic locality. The exact time of year that juveniles within each refuge begin to disperse is unknown; however, observational studies suggest that, for this species, juvenile dispersal begins in the latter part of May in the San Joaquin Valley (Stroud 1982).

Therefore, analyses to uncover a sex-bias in dispersal were performed with all juveniles classified as pre-dispersal, regardless of when they were captured, and with juveniles captured after May of each sampling year classified as post-dispersal, and the results were compared.

First, I performed AMOVAs (global FST) for each sex within each refuge to estimate genetic variance components associated with population subdivisions (i.e. within burrow systems and among burrow systems). These were performed in

GenAlEx v.6.503 (Peakall and Smouse 2006, 2012). Statistical significance was determined by comparing the observed global FST value with a null distribution generated via 9,999 permutations. Each sex-specific dataset included only burrow systems containing a minimum of two adult individuals of the sex being analyzed.

Because there were not at least two burrow systems with a minimum of two adult

individuals for each sex in Merced NWR, no AMOVA analyses were conducted for that population. In this approach, the sex with the majority of variation partitioned among burrow systems is inferred to be the more philopatric sex. In contrast, the majority of variation should be partitioned within burrow systems in the dispersive sex, as a consequence of dispersal-mediated gene flow (Cooper et al. 2010).

Second, I used the software FSTAT v.2.9.3.2 (Goudet et al. 2002) to calculate sex-specific FST, FIS, relatedness, mean assignment index (mAIc), and variance of the assignment index (vAIc) values. If a sex-bias in dispersal exists, the direction of the bias in dispersal can be detected using these statistics (Goudet et al. 2002). In this approach (similar to the AMOVA approach described above) the sex with the lower average subpopulation FST value is inferred to be the dispersive sex. Wright’s FIS is a measurement of excess homozygosity, departing from the heterozygosity expected under a system of random mating (Spuhler and Kluckhohn 1953). A representative population sample will include both philopatric residents and immigrants of the dispersive sex; due to the Wahlund effect, a heterozygote deficit and a positive FIS will result. Therefore, the sex with the higher FIS is the dispersing sex. Genetic relatedness is connected to FST by the equation r = 2FST/(1 + FIT). Because FIT is identical between the dispersing and philopatric sex, average relatedness, like FST, should be lower in the dispersing sex. An assignment index (AIc), or the probability that an individual’s genotype should appear in a sample, can be calculated for each individual; a positive value indicates a genotype more likely to occur in its sample

(i.e., a resident) while a negative value indicates a genotype less likely to occur in its

sample (i.e., a disperser). Therefore, the philopatric sex will have a higher mean AIc

(mAIc) than the dispersing sex. Finally, because the sample will contain immigrants

(lower AIc values) and residents (higher AIc values), the dispersing sex with have a larger variance in the assignment index (vAIc). Statistical significance was assessed by comparing the observed statistics to null distributions created via 10,000 permutations. For the F-statistical and assignment approaches, the dataset was composed of burrow systems that contained at least one adult individual of each sex.

Third, an analysis to uncover an isolation by distance pattern was performed for males and females separately. I calculated pairwise FST among burrow systems within each refuge and regressed FST/(1 – FST) against the natural log of the geographic distance between them in GENEPOP v.4.7 (Rousset 2008). Mantel tests with 10,000 permutations were performed to test for statistical significance within each refuge. In this approach, a strong positive correlation between geographic distance and genetic distance should be observed in the philopatric sex, while the correlation should be weak or null in the dispersing sex (Dalerum et al. 2007).

Lastly, I performed spatial autocorrelation analyses for each sex separately within each refuge in GenAlEx v.6.503 (Peakall and Smouse 2006, 2012). Spatial autocorrelation coefficients (r), the square of genetic distance against geographic distance for each dyad of individuals, were calculated and plotted against distance class. Each distance class is bound by a lower and upper value of distance, typically meters or kilometers. The (r) for a dyad is plotted against the distance class in which the dyad’s pairwise comparison of geographic distance falls between the lower and

upper value of that particular distance class. If a dyad’s pairwise comparison of geographic distance does not fall within the lower and upper value of any distance class, that data point is ignored in the analysis. These lower and upper values can be customized or defaulted to even distance classes, and the researcher specifies the number of distance classes used based on the spatial scale of their study. In this study,

I used even distance classes, and the number of distance classes used varied by refuge because spatial scales varied by refuge (Appendix G). Statistical significance of either negative or positive autocorrelation was determined by a bootstrap procedure resampling against 9,999 permutations, with the null hypothesis of a random spatial distribution of genotypes. If the null hypothesis is rejected, the sex that exhibits higher relatedness at smaller distance classes is inferred to be the philopatric sex

(Double et al. 2005).

Migration rates. To estimate male dispersal rates within San Joaquin River

NWR and San Luis NWR, AMOVAs were performed to generate FST estimates for the pre-dispersal generation (juveniles of both sexes). Statistical significance was determined by comparing the observed global FST value with a null distribution generated via 9,999 permutations. These fixation indices were compared with the FST estimates for the post-dispersers (adult males) as described by Vitalis (2002).

Results

Demographics

Demographic data for individuals used in this study for each refuge are presented in Table 2.

Table 2: Demographics of genotyped California ground squirrels. SJRNWR = San Joaquin River National Wildlife Refuge; MNWR = Merced National Wildlife Refuge; SLNWR = San Luis National Wildlife River. A total of 6 individuals across all populations escaped before sex and age could be determined, and are not included in this table.

Population Adult Males Adult Females Juvenile Males Juvenile Females Total SJRNWR 9 30 28 30 97 MNWR 13 17 32 36 98 SLNWR 9 8 53 33 103

Microsatellites

The microsatellites used in this study were subjected to quality tests. The rate of null alleles for all loci was below 7% in all populations; all loci conformed to

Hardy-Weinberg equilibrium after Bonferroni correction (Appendix C), and no combinations of loci were significantly in linkage disequilibrium after Bonferroni correction (Appendix D), thus all microsatellite loci were retained. Summary diversity statistics are presented in Table 3. The microsatellite markers were highly polymorphic. The average number of alleles per locus was 11.0 (SD ± 4.0) in San

Joaquin River NWR, 11.0 (SD ± 4.1) in Merced NWR, and 10.7 (SD ± 3.2) in San

Luis NWR.

Table 3: Summary diversity statistics for 3 Central Valley populations of California ground squirrels used in dispersal pattern analyses. Allele sizes given in base pairs; a = number of alleles; n = sample size; H0 = observed heterozygosity; HE = expected heterozygosity; SJRNWR = San Joaquin River National Wildlife Refuge; MNWR = Merced National Wildlife Refuge; SLNWR = San Luis National Wildlife Refuge.

Population Parameter D4 GS22 IGS-6 2g2 A116 MA018 SS-Bibl18

SJRNWR Size Range 278 - 302 165 - 185 100 - 130 101 - 153 309 - 323 307 - 323 136 - 152 a 13 8 15 17 6 9 9

n 99 100 97 100 100 99 100

Ho 0.80 0.83 0.87 0.86 0.49 0.86 0.77

HE 0.83 0.81 0.85 0.85 0.53 0.86 0.84

MNWR Size Range 276 - 308 165 - 189 100 - 128 97 - 159 309 - 321 305 - 325 136 - 152 a 14 9 12 18 5 10 9

n 100 100 99 100 100 97 100

HO 0.78 0.80 0.86 0.86 0.70 0.79 0.82

HE 0.89 0.79 0.88 0.89 0.71 0.80 0.83

SLNWR Size Range 272 - 304 165 - 185 100 - 126 113 - 155 309 - 321 305 - 325 136 - 150 a 15 9 13 13 6 11 8

n 100 104 103 104 102 100 104

HO 0.74 0.67 0.83 0.88 0.44 0.89 0.87

HE 0.85 0.70 0.80 0.86 0.49 0.83 0.84

Population Structure

Pairwise estimates of FST among refuges (Table 4) reveal modest differentiation that is statistically significant. Pairwise estimates of FST among burrow systems within refuges also revealed weak to modest differentiation, although only two pairwise comparisons were significant after Bonferroni correction (between burrows A3 and A8 in San Joaquin River NWR: FST = 0.074, p<0.001; between burrows A5 and A8 in San Joaquin River NWR: FST = 0.070, p<0.001). Pairwise estimates of Dest indicated more differentiation than FST, although only three pairwise

comparisons were significant after Bonferroni correction (between burrows A3 and

A8 in San Joaquin River NWR: Dest = 0.283, p<0.001; between burrows A5 and A8 in San Joaquin River NWR: Dest = 0.272, p<0.001; between burrows C1 and C4 in

San Luis NWR: Dest = 0.100, p=0.001). AMOVA analyses indicated that almost all variation existed within individuals (San Joaquin River NWR: 97.82%; Merced

NWR: 94.57%; San Luis NWR: 99.53%) and almost no variation existed among burrow systems (San Joaquin River NWR: 4.60%; Merced NWR: 2.20%; San Luis

NWR: 1.73%). Inbreeding coefficients (FIS) were low and not statistically significant

(San Joaquin River NWR: FIS = -0.025, p = 0.881; Merced NWR: FIS = 0.033, p =

0.221; San Luis NWR: FIS = -0.013, p = 0.750).

Table 4: Pairwise genetic differentiation (FST) among 3 Central Valley populations of California ground squirrels. FST values are below the diagonal. P-values (statistically significant when P<0.05 as estimated over 10,100 permutations) are above the diagonal. SJRNWR = San Joaquin River National Wildlife Refuge; MNWR = Merced National Wildlife Refuge; SLNWR = San Luis National Wildlife Refuge.

SJRNWR MNWR SLNWR SJRNWR - <0.001 <0.001 MNWR 0.059 - <0.001 SLNWR 0.059 0.084 -

STRUCTURE analyses identified two genetic clusters within each refuge

(Figure 3). The Ln P(D) and ΔK plots were in agreement that the most likely K = 2

(Appendix E). When the LOCPRIOR model was used, STRUCTURE analyses identified four genetic clusters within San Joaquin River NWR and three genetic clusters within Merced NWR and within San Luis NWR (Appendix F). Although the number of genetic clusters identified differed between the analyses with and without

geographic locations provided, the LOCPRIOR model did not detect any fine-scale population structure; individuals within a cluster were sampled from across the entire sampling scale within each refuge. a)

Figure 3: Bar plots from STRUCTURE analyses. a) San Joaquin River NWR; b) Merced NWR; c) San Luis NWR. Each bar represents a single individual, and each color represents a distinct genetic cluster. The bars are filled by colors representing the likelihood of membership to each genetic cluster.

Because STRUCTURE analyses did not support the assumption of burrow systems as subpopulations or breeding groups within each refuge, estimates of population structure were made using the two clusters identified by STRUCTURE to see if the results differed from estimates made between burrow systems. Pairwise FST between the two clusters within each refuge also indicated weak differentiation and was only statistically significant in San Luis NWR (FST = 0.011, p<0.01). AMOVA analyses similarly indicated that almost all variation existed within individuals (San

Joaquin River NWR: 97.85%; Merced NWR: 96.68%; San Luis NWR: 98.62%).

Because these results did not differ meaningfully from the results generated by using burrow systems as subpopulations, all subsequent analyses were performed between burrow systems within each refuge.

Isolation by Distance

Mantel tests showed a significant effect of IBD in San Joaquin River NWR and San Luis NWR (Figure 4). However, the regression coefficients were small (San

Joaquin River NWR: r = 0.157, R2 = 0.0248, p<0.001; Merced NWR: r = -0.127, R2 =

0.0161, p = 0.072; San Luis NWR: r = 0.134, R2 = 0.0181, p = 0.004).

y = 0.1516x + 3.3017 5.0 R² = 0.0248 p<0.001 4.0 )

ST 3.0

/(1-F 2.0 ST F 1.0

0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 Log of distance (km)

5.0

4.0 )

ST 3.0

/(1-F 2.0 ST y = -0.2681x + 3.532 F R² = 0.0161 1.0 p = 0.072 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Log of distance (km)

Figure 4: Results of test for isolation by distance pattern within each refuge. Pairwise FST/(1 – FST) regressed against the log of distance for each dyad of individuals in GenAlEx v.6.503 in 3 Central Valley populations of Otospermophilus beecheyi: a) San Joaquin River NWR; b) Merced NWR; c) San Luis NWR.

5.0

4.0

3.0

2.0

Fst/(1-Fst) y = 0.2563x + 3.2097 1.0 R² = 0.0181 p = 0.004 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Log of distance (km)

Figure 4 continued: Results of test for isolation by distance pattern within each refuge. Pairwise FST/(1 – FST) regressed against the log of distance for each dyad of individuals in GenAlEx v.6.503 in 3 Central Valley populations of Otospermophilus beecheyi: a) San Joaquin River NWR; b) Merced NWR; c) San Luis NWR.

Sex-Bias in Dispersal

Sex-specific AMOVA analyses did not indicate a sex-bias in dispersal (Table

5). In both males and females within each refuge, almost all variation existed within burrow systems (≥ 87.88%). This pattern was the same when juveniles sampled after

May of each year were classified as post-dispersal and included in the analysis: in both males and females within each refuge, almost all variation existed within burrow systems (≥ 91.49%; results not shown).

Table 5: AMOVA results when all juveniles excluded from analysis. SS = sum of squares; df = degrees of freedom; Var = estimated variance. SJRNWR = San Joaquin River National Wildlife Refuge; SLNWR = San Luis National Wildlife Refuge. All juveniles were excluded from the analysis, regardless of when they were sampled.

Population Sex Source df SS Var. % of variation

SJRNWR Adult females Among burrow systems 7 23.76 0.12349 4.40 Within burrow systems 44 118.18 2.68595 95.60

Adult males Among burrow systems 1 4.03 0.33464 12.12

Within burrow systems 8 19.42 2.42708 87.88

SLNWR Adult females Among burrow systems 1 3.25 0.14583 5.19 Within burrow systems 6 16.00 2.66667 94.81

Adult males Among burrow systems 2 5.17 0.02154 0.86 Within burrow systems 11 27.33 2.48485 99.14

When all juveniles were excluded from the analyses, sex-specific FST, FIS, relatedness, mean assignment index (mAIc), and variance of the assignment index

(vAIc) values did not indicate a sex-bias in dispersal (Table 6); none of the tests yielded statistically significant results.

Table 6: Sex-specific statistics when all juveniles excluded from analyses. Estimates of average among-burrow system relatedness (r), FST, FIS, and mean and variance of the assignment index (mAIc and vAIc) within 3 Central Valley populations of Otospermophilus beecheyi. Tests for significant differences (P) are 1-tailed and based on 10,000 permutations. SJRNWR = San Joaquin River National Wildlife Refuge; MNWR = Merced NWR; SLNWR = San Luis National Wildlife Refuge. F = Female; M = Male. Negative FST values have been corrected to 0.00.

r F F mAIc vAIc ST IS

Population F M P F M P F M P F M P F M P SJRNWR 0.08 0.15 0.68 0.04 0.08 0.68 0.04 -0.02 0.65 0.02 -0.04 0.47 4.50 1.44 0.65

MNWR 0.18 0.33 0.76 0.06 0.16 0.74 -0.12 -0.24 0.76 -0.24 0.36 0.55 3.74 0.24 0.83

SLNWR -0.09 0.26 0.98 0.00 0.13 0.98 -0.02 -0.17 0.79 -0.33 0.26 0.86 1.05 0.45 0.89

However, when juveniles captured after May of each year were included in the analyses, sex-specific FIS, mean assignment index (mAIc), and variance of the assignment index (vAIc) values indicated a significant male-bias in dispersal in San

Luis NWR (Table 7).

Table 7: Sex-specific estimates when juveniles captured post-May included in analyses. Estimates of average among-burrow system relatedness (r), FST, FIS, and mean and variance of the assignment index (mAIc and vAIc) within 3 Central Valley populations of Otospermophilus beecheyi. Tests for significant differences (P) are 1- tailed and based on 10,000 permutations. SJRNWR = San Joaquin River National Wildlife Refuge; MNWR = Merced NWR; SLNWR = San Luis National Wildlife Refuge. F = Female; M = Male.

r F F mAIc vAIc ST IS Population F M P F M P F M P F M P F M P

SJRNWR 0.13 0.12 0.39 0.07 0.06 0.36 -0.03 -0.07 0.69 0.15 -0.24 0.24 4.83 2.41 0.94

MNWR 0.13 0.01 0.19 0.07 0.01 0.18 -0.01 0.01 0.49 0.10 -0.11 0.41 4.36 1.64 0.89

SLNWR 0.08 0.03 0.27 0.04 0.02 0.31 -0.09 0.02 0.01 0.95 -0.69 0.003 6.49 9.82 0.03

A sex-bias in dispersal was not detectable from the sex-specific isolation by distance analyses when all juveniles were excluded from the analyses (Figure 5).

Mantel tests found no significant effect when sex-specific pairwise FST/(1 – FST) between burrow systems was regressed against the natural log of the distance between pairs of burrow systems (all p-values > 0.12).

a) 0.4 Adult 0.3 Females

0.2

) Adult ST 0.1 Males /(1-F 0 Linear ST

F (Adult -0.1 Males)

-0.2 Linear (Adult Females) -0.3 -7.0 -6.0 -5.0 -4.0 -3.0 -2.0 -1.0 0.0 Ln of distance (km)

0.18 Adult 0.16 Females 0.14 Adult Males

) 0.12 ST 0.1

/(1-F 0.08 Linear

ST (Adult F 0.06 Males)

0.04 Linear (Adult 0.02 Females) 0 -6.0 -5.5 -5.0 -4.5 -4.0 -3.5 -3.0 Ln of distance (km)

Figure 5: Tests for sex-specific isolation by distance patterns within each refuge. Pairwise FST/(1 – FST) regressed against the natural log of distance among Otospermophilus beecheyi burrow systems in GENEPOP in 3 Central Valley populations: a) San Joaquin River NWR, females: R2 = 0.0848, p = 0.7348; males: R2 = 0.0423, p = 0.2438; b) Merced NWR, females: R2 = 0.113, p = 0.2122; males: R2 = 0.961, p = 0.1696; c) San Luis NWR, females: R2 = 0.00008, p = 0.1168; males: R2 = 0.0028, p = 0.1121.

0.5

0.4 Adult Females 0.3 ) Adult Males ST 0.2

/(1-F 0.1

ST Linear (Adult F Males) 0.0

-0.1 Linear (Adult Females) -0.2 -6.5 -6.0 -5.5 -5.0 -4.5 -4.0 -3.5 -3.0 Ln of distance (km)

Figure 5 continued: Tests for sex-specific isolation by distance patterns within each refuge. Pairwise FST/(1 – FST) regressed against the natural log of distance among Otospermophilus beecheyi burrow systems in GENEPOP in 3 Central Valley populations: a) San Joaquin River NWR, females: R2 = 0.0848, p = 0.7348; males: R2 = 0.0423, p = 0.2438; b) Merced NWR, females: R2 = 0.113, p = 0.2122; males: R2 = 0.961, p = 0.1696; c) San Luis NWR, females: R2 = 0.00008, p = 0.1168; males: R2 = 0.0028, p = 0.1121.

Spatial autocorrelation analyses also indicated no sex-bias in dispersal when all juveniles were excluded from the analyses. No pattern of fine scale genetic structure was present for either sex in any of the refuges (Appendix G).

Migration Rates

The Vitalis methodology could not be used to estimate male dispersal rates in

San Joaquin River NWR or San Luis NWR. The method is reliant upon contrasting variance components for males and females (Cooper et al. 2010); these were the same in both populations (Table 5).

Discussion

The results from this study indicate high levels of genetic diversity within each refuge. The average number of alleles per locus and average observed heterozygosity was high (Table 3). No evidence of heterozygote deficiency was found in any population (all p>0.10), suggesting little biologically significant inbreeding; although, unless populations are highly structured with high levels of consanguineous matings, small sets of microsatellite markers may not be a good predictor of inbreeding (Balloux et al. 2004). Collectively, this suggests great future adaptive potential in these populations (Ellegren and Galtier 2016), although it is beyond the scope of this study to address this; formal analyses of effective population size would shed light on this question.

The weak genetic differentiation observed among refuges might be expected given that O. beecheyi has a large, continuous distribution, spanning from Northern

California all the way into Mexico. Within each refuge, lack of genetic differentiation is evident. Pairwise FST and Dest estimates were weak to moderate but overall not statistically significant, and AMOVA analyses partitioned most of the genetic variation within individuals. Although STRUCTURE analyses indicated two genetic clusters within each refuge, individuals within a cluster were sampled from across the entire span of each refuge (over several kilometers), and estimates of genetic differentiation between the two clusters were weak. In addition to a lack of spatial structure within each refuge, the bar plots from the STRUCTURE analyses illustrate admixture of two clades of O. beecheyi: Southern O. beecheyi and Central O.

beecheyi (Phuong et al. 2014). Furthermore, Mantel tests indicated a significant, but weak, isolation by distance pattern within San Joaquin River NWR and San Luis

NWR. Collectively, these findings support the presence of a single, panmictic population within each refuge in which individuals are highly dispersive and potentially disperse long distances regularly (Kool et al. 2013). This finding is supported by Evans and Holdenried (1943), who reported the observational long distance dispersal (approximately 1.1 km) of a small subset of individuals in a single population of O. beecheyi in Alameda County, California. However, because analyses between burrow systems were completed with smaller sample sizes due to the lack of

GPS coordinates for some individuals, it is possible that the true allele frequencies within each location were not captured and the lack of population structure is spurious (Puechmaille 2016). Therefore, these results should be interpreted with caution.

When all juveniles were excluded from analyses, regardless of when they were sampled, the majority of analyses performed to test for a sex-bias in dispersal seemingly point to female-biased dispersal: i.e., male FST estimates were larger than female FST estimates in all three refuges; male FIS estimates were smaller than female

FIS estimates in all three refuges; average male relatedness was higher than average female relatedness in all three refuges; male mAIc estimates were larger than female mAIc estimates in Merced NWR and San Luis NWR; male vAIc estimates were smaller than female vAIc estimates in all three refuges. However, there was no significant difference between the male and female estimates for all of these analyses,

suggesting a lack of a sex-bias in dispersal in these three populations of O. beecheyi.

If dispersal is female-biased in this species, which would contradict previous observational studies (Dobson 1979; Boellstorff and Owings 1995), lack of statistical significance could be due to small sample sizes or the power of the microsatellite markers used to detect a subtle sex-bias. Using simulations, Goudet et al. (2002) demonstrated that all tests (i.e. vAIc, mAIc, and FST) had low power when less than eight microsatellite loci were used, and 80% power was only obtained when adding more loci if sampling was exhaustive; if sampling was not exhaustive, 80% power was never reached, no matter how many microsatellite loci were used.

Interestingly, when juveniles captured after May of each year were categorized as post-dispersal and included in the analyses, sex-specific FIS, mean assignment index (mAIc), and variance of the assignment index (vAIc) values indicated a significant male-bias in dispersal, but only in San Luis NWR. These results differ from the lack of a sex-bias found when all juveniles were excluded from the analyses. Including post-dispersal juveniles increased samples sizes within each refuge; after adding the post-dispersal juveniles, sample sizes for both males and females were largest in San Luis NWR (n = 58 for males, n = 42 for females).

Therefore, it is likely that the lack of a sex-bias found when all juveniles were excluded was a sample size effect and not a true lack of a sex-bias in dispersal.

However, even with increased sample sizes, not all tests were able to detect a male- bias in dispersal within each refuge. When gene flow is high, subpopulations are less differentiated and immigrants are less distinct. Assignment index tests (i.e., mAIc and

vAIc) are sensitive to the presence of a smaller number of immigrants within a sample. However, the ability of FST to detect a sex-bias in dispersal is dependent upon immigrants constituting a larger proportion of the sample; when sample size is small, very few immigrants are likely sampled and, thus, pass undetected. In addition, the ability of all indices to detect a sex-bias in dispersal greatly diminishes as the intensity of the bias decreases (Goudet et al. 2002). Therefore, it is likely that within

San Joaquin River NWR and Merced NWR, even though adding post-dispersal juveniles increased sample sizes, sample sizes were still too small for even the assignment indices to detect a significant sex-bias in dispersal. Within San Luis

NWR, the inability of FST to detect a sex-bias in dispersal is most likely the result of the bias intensity and not sample size because males and females, although slightly less than males, are dispersing at high rates, homogenizing allele frequencies among burrow systems. Within these three Central Valley populations of CGS, dispersal is most likely male-biased, but the bias is weak. This could explain why male-biased dispersal was only detected in San Luis NWR, where sampling was more exhaustive, and only by some of the tests performed.

Whereas short-distance dispersal is likely to occur and exhibit a sex-bias when avoiding kin competition is important at the local scale, long distance dispersal can result in response to crowding conditions, and selective pressures are not expected to differ among sexes (Fontanillas et al. 2004). Populations were observed in high densities within each refuge (J. Cooper, pers. obs.); therefore, the potentially weak male-bias in dispersal and lack of strong isolation by distance pattern could be a

response to crowded conditions within each refuge, although formal analyses of population densities need to be conducted to confirm this. To confirm whether a weak male-bias in dispersal exists in this species as observed in this study, future studies should be conducted with larger sample sizes per burrow system. In addition, future studies would benefit from using mitochondrial DNA markers in combination with microsatellite markers to test for a sex-bias in dispersal. Cooper et al. (2010) demonstrated that mitochondrial DNA haplotype frequency data were able to detect a sex-bias in dispersal in a Texas population of collared peccaries (Pecari tajacu), which exhibited high levels of gene flow, when microsatellite data could not.

Collectively, the results from this study suggest that migration rates of both sexes are higher and individuals of both sexes disperse farther than previously believed. Although O. beecheyi is listed as a species of “Least Concern” by the

International Union for the Conservation of Nature and Natural Resources Red List of

Threatened Species (Smith et al. 2016), assessing the genetic diversity and dispersal patterns of this species is critical. Because CGS populations are often found within close proximity with agricultural lands, crop raiding is ubiquitous; O. beecheyi is responsible for approximately 12-16 million dollars in annual losses in California

(Marsh 1998), although no recent studies have addressed this topic. Efforts to control populations of O. beecheyi over the past century have proven largely unsuccessful

(Smith et al. 2016) because individuals rapidly move from nearby populations into controlled areas (Stroud 1982). Therefore, population genetic studies assessing genetic diversity and dispersal patterns in this species could be used to formulate

more effective management strategies. Because O. beecheyi populations have been unaffected by human encroachment on the natural habitat (Marsh 1998), it is possible that the ability of males and females to disperse long distances is an adaptive strategy for coping with environmental stochasticity, such as periodic droughts creating localized declines in crop food abundance. In addition, the development of agriculture in the Central Valley over a century ago may have radically changed natural food distribution patterns, potentially causing dispersal strategies to change in comparison to the ancestral behavior patterns (Phuong et al. 2017).

REFERENCES

Balloux F, Amos W, Coulson T. 2004. Does heterozygosity estimate inbreeding in

real populations?. Molecular Ecology. 13:3021-3031.

Bell KC and Matocq MD. 2010. Development and characterization of polymorphic

microsatellite loci in the Mohave ground squirrel (Xerospermophilus

mohavensis). Conservation Genetics Resources. 2(1):197-199.

Bird CE, Karl SA, Smouse PE, Toonen RJ. 2011. Detecting and measuring genetic

differentiation. Phylogeography and Population Genetics in Crustacea.

19:31-55.

Boellstorff D and Owings D. 1995. Home range, population structure, and spatial

organization of California ground squirrels. Journal of Mammalogy.

76(2):551-561.

Brom T, Massot M, Legendre S, Laloi D. 2016. Kin competition drives the evolution

of sex- biased dispersal under monandry and polyandry, not under monogamy.

Animal Behaviour. 113:157-166.

Cooper JD, Waser PM, Gopurenko D, Hellgren EC, Gabor TM, DeWoody JA. 2010.

Measuring sex-biased dispersal in social mammals: comparisons of nuclear

and mitochondrial genes in collared peccaries. Journal of Mammalogy.

91(6):1413-1424.

Cote J and Clobert J. 2010. Risky dispersal: avoiding kin competition despite

uncertainty. Ecology. 91(5):1485-1493.

Da Silva A, Luikart G, Allaine D, Gautier P, Taberlet P, Pompanon F. 2003. Isolation

and characterization of microsatellites in European alpine marmots (Marmota

marmota). Molecular Ecology Resources. 3:189-190.

Dalerum F, Loxterman J, Shults B, Kunkel K, Cook JA. 2007. Sex-specific dispersal

patterns of wolverines: insights from microsatellite markers. Journal of

Mammalogy. 88(3):793-800.

Devillard S, Allaine D, Gaillard JM, Pontier D. 2004. Does social complexity lead to

a sex-biased dispersal in polygynous mammals? A test on ground-dwelling

sciurids. Behavioral Ecology. 15(1):83-87.

Dobson FS. 1979. An experimental study of dispersal in the California ground

squirrel. Ecology. 60(6):1103-1109.

Dobson FS. 1981. An experimental examination of an artificial dispersal sink.

Journal of Mammalogy. 62(1):74-81.

Double MC, Peakall R, Beck NR, Cockburn A. 2005. Dispersal, philopatry, and

infidelity: dissecting local genetic structure in superb fairy-wrens (Malurs

cyaneus). Evolution. 59:625-635.

Earl DA and VonHoldt DM. 2012. STRUCTURE HARVESTER: a website and

program for visualizing STRUCTURE output and implementing the Evanno

method. Conservation Genetic Resources. 4:359-361.

Ellegren H and Galtier N. 2016. Determinants of genetic diversity. Nat Rev Genet.

17:422-433.

Evans FC and Holdenried R. 1943. A population study of the Beechey ground

squirrel in Central California. Journal of Mammalogy. 24(2):231-260.

Excoffier L and Lischer HEL. 2010. Arlequin suite ver 3.5: a new series of programs

to perform population genetics analyses under Linux and Windows.

Molecular Ecology Resources. 10:564-567.

Festa-Bianchet M and Boag DA. 1982. Territoriality in adult female Columbian

ground squirrels. Canadian Journal of Zoology. 60:1060-1066.

Fontanillas P, Petit E, Perrin N. 2004. Estimating sex-specific dispersal rates with

autosomal markers in hierarchically structured populations. Evolution.

58(4):886-894.

Gauffre B, Petit E, Brodier S, Bretagnolle V, Cosson JF. 2009. Sex-biased dispersal

patterns depend on the spatial scale in a social rodent. Proc. R. Soc. B.

276:3487-3494.

Glaubitz JC. 2004. CONVERT: a user friendly program to reformat diploid genotypic

data for commonly used population genetic software packages. Molecular

Ecology Notes. 4:309-310.

Goossens B, Graziani L, Waits LP, Farand E, Magnolon S, Coulon J, Bel MC,

Taberlet P, Allaine D. 1998. Extra-pair paternity in the monogamous Alpine

marmot revealed by nuclear DNA microsatellite analysis. Behavioral Ecology

and Sociobiology. 43:281-288.

Goudet J, Perrin N, Waser PM. 2002. Tests for sex-biased dispersal using bi-

parentally inherited genetic markers. Molecular Ecology. 11:1103-1114.

Greenwood P. 1980. Mating systems, philopatry and dispersal in birds and mammals.

Animal Behaviour. 28:1140-1162.

Gros A, Poethke HJ, Hovestadt T. 2009. Sex-specific spatio-temporal variability in

reproductive success promotes the evolution of sex-biased dispersal.

Theoretical Population Biology. 76:13-18.

Gyllenberg M, Kisdi E, Utz M. 2008. Evolution of condition-dependent dispersal

under kin competition. Journal of Mathematical Biology. 57:285-307.

Hamilton WD and May RM. 1977. Dispersal in stable habitats. Nature. 269:578-581.

Hanslik S and Kruckenhauer L. 2000. Microsatellite loci for two European sciurid

species (Marmota marmota, Spermophilus citellus). Molecular Ecology.

9:2163-2165.

Holekamp K. 1984. Natal dispersal in Belding’s ground squirrels (Spermophilus

beldingi). Behavioral Ecology and Sociobiology. 16(1):21-30.

Holekamp K and Sherman PW. 1989. Why male ground squirrels disperse: A

multilevel analysis explains why only males leave home. American Scientist.

77:232-239.

Horn E and Fitch H. 1946. Trapping the California ground squirrel. Journal of

Mammalogy. 27(3):220-224.

Hubisz MJ, Falush D, Stephens M, Pritchard JK. 2009. Inferring weak population

structure with the assistance of sample group information. Molecular Ecology

Resources. 9:1322-1332.

Idury RM and Cardon LR. 1997. A simple method for automated allele binning in

microsatellite markers. Genome Research. 7:1104-1109.

Kalinowski S, Taper ML, Marshall TC. 2007. Revising how the computer program

CERVUS accommodates genotyping error increases success in paternity

assignment. Molecular Ecology. 16:1099-1106.

Kool JT, Moilanen A, Treml EA. 2013. Population connectivity: Recent advances

and new perspectives. Landscape Ecology. 28:165-185.

Koprowski JL. 2002. Handling tree squirrels with a safe and efficient restraint.

Wildlife Society Bulletin. 30(1):101-103.

Kyle CJ, Karels TJ, Clark B, Strobeck C, Hik DS, Davis CS. 2004. Isolation and

characterization of microsatellite markers in hoary marmots (Marmota

caligata). Molecular Ecology Resources. 4:749-751.

Lawson Handley LJ and Perrin N. 2007. Advances in our understanding of

mammalian sex-biased dispersal. Molecular Ecology. 16:1559-1578.

Marsh RE. 1998. Historical review of ground squirrel crop damage in California.

International Biodeterioration & Biodegradation. 42:93-99.

Matschiner M and Salzburger W. 2009. TANDEM: integrating automated allele

binning into genetics and genomics workflows. Bioinformatics. 25:1982-1983.

May B, Gavin TA, Sherman PW, Korves TM. 1997. Characterization of

microsatellite loci in the Northern Idaho ground squirrel Spermophilus

brunneus brunneus. Molecular Ecology. 6:399-400.

McGrann M, Van Vuren D, Ordenana M. 2014. Influence of adjacent crop type on

occurrence of California Ground squirrels on levees in the Sacramento Valley,

California. Wildlife Society Bulletin. 38(1):111-115.

Meirmans P. 2012. The trouble with isolation by distance. Molecular Ecology.

21:2839-2846.

Munroe KE and Koprowski JL. 2011a. Genetic mating system of round-tailed ground

squirrels (Spermophilus tereticaudus): not just another polygynous ground

squirrel. Behavioral Ecology and Sociobiology. 65: 1811-1824.

Munroe KE and Koprowski JL. 2011b. Sociality, Bateman's gradients, and the

polygynandrous genetic mating system of round-tailed ground squirrels

(Xerospermophilus tereticaudus). Behavioral Ecology and Sociobiology.

65(9):1811-1824.

Munroe KE and Koprowski JL. 2014. Levels of social behaviors and genetic structure

in a population of round-tailed ground squirrels (Xerospermophilus

tereticaudus). Behavioral Ecology and Sociobiology. 68(4): 629-638.

Neuhaus P. 2006. Causes and consequences of sex-biased dispersal in Columbian

ground squirrel, Spermophilus columbinaus. Behaviour. 143(8):1013-1031.

Nunes S, Weidenbach JN, Lafler MR, Dever JA. 2015. Sibling relatedness and social

play in juvenile ground squirrels. Behavioral Ecology and Sociobiology.

69:357-369.

Osterburg H, Allen J, Finch C. 1975. The use of ammonium acetate in the

precipitation of ribonucleic acid. Biochemical Journal. 147:367-368.

Owings DH, Borchert MB, Virginia R. 1977. The behaviour of California ground

squirrels. Animal Behaviour. 25:221-230.

Peakall R and Smouse PE. 2006. GenAlEx 6: genetic analysis in Excel. Population

genetic software for teaching and research. Molecular Ecology Notes. 6:228-

295.

Peakall R and Smouse PE. 2012. GenAlEx 6.5: genetic analysis in Excel. Population

genetic software for teaching and research – an update. Bioinformatics.

28(19):2537-2539.

Perrin N and Mazalov V. 1999. Dispersal and inbreeding avoidance. The American

Naturalist. 154(3):282-292.

Perrin N and Mazalov V. 2000. Local competition, inbreeding, and the evolution of

sex-biased dispersal. The American Naturalist. 155(1):116-127.

Phillipsen IC, Kirk EH, Bogan MT, Mims MC, Olden JD, Lytle DA. 2015. Dispersal

ability and habitat requirements determine landscape-level genetic patterns in

desert aquatic insects. Molecular Ecology. 24:54-69.

Phuong MA, Lim MCW, Wait DR, Rowe KC, Moritz C. 2014. Delimiting species in

the genus Otospermophilus (Rodentia: Sciuridae), using genetics, ecology,

and morphology. Biological Journal of the Linnean Society. 113:1136-1151.

Phuong MA, Bi K, Moritz C. 2017. Range instability leads to cytonuclear

discordance in a morphologically cryptic ground squirrel species complex.

Molecular Ecology. 26:4743-4755.

Pikor L, Enfield K, Cameron H, Lam W. 2011. DNA extraction from paraffin

embedded material for genetic and epigenetic analyses. Journal of Visualized

Experiments. 49, e2763, doi: 10.3791/2763.

Porras-Hurtado L, Ruiz Y, Santos C, Phillips C, Carracedo A, Lareu M. 2013. An

overview of STRUCTURE: applications, parameter settings, and supporting

software. Frontiers in Genetics. 4:Article 98.

Pritchard JK, Stephens M, Donnelly P. 2000. Inference of population structure using

multilocus genotype data. Genetics. 155:945-959.

Prugnolle F and De Meeus T. 2002. Inferring sex-biased dispersal from population

genetic tools: a review. Heredity 88:161–165.

Puechmaille SJ. 2016. The program structure does not reliably recover the correct

population structure when sampling is uneven: subsampling and new

estimators alleviate the problem. Molecular Ecology Resources. 16:608-627.

Ridley J and Sutherland WJ. 2002. Kin competition within groups: the offspring

depreciation hypothesis. Proceedings: Biological Sciences. 269(1509):2559-

2564.

Rousset F. 2008. GENEPOP’007: a complete re-implementation of the GENEPOP

software for Windows and Linux. Molecular Ecology Resources. 8:103-106.

Shaw AK and Kokko H. 2014. Mate finding, Allee effects and selection for sex-

biased dispersal. Journal of Animal Ecology. 83:1256-1267.

Smith JE. California ground squirrel primers. N.d. TS. Mills College.

Smith JE, Long DJ, Russell ID, Newcomb KL, Munoz VD. 2016. Otospermophilus

beecheyi (Rodentia: Sciuridae). Mammalian Species. 48(939):91-108.

Spuhler JN and Kluckhohn C. 1953. Inbreeding coefficients of the Ramah Navaho

population. Human Biology. 25(4).295-317.

Stevens S, Coffin J, Strobeck C. 1997. Microsatellite loci in Columbian ground

squirrels Spermophilus columbianus. Molecular Ecology. 6:493-495.

Stroud, DC. 1982. Dispersal and some implications for control of the California

ground squirrel. Proceedings of the Tenth Vertebrate Pest Conference (1982).

Paper 42.

Trochet A, Courtois EA, Stevens VM, Baguette M, Chaine A, Schmeller DS, Clobert

J. 2016. Evolution of sex-biased dispersal. The Quarterly Review of Biology.

91(3):297-320.

Van Strien MJ, Holderegger R, Van Heck HJ. 2015. Isolation-by-distance in

landscapes: considerations for landscape genetics. Heredity. 114:27-37.

Vitalis R. 2002. Sex-specific genetic differentiation and coalescence times: estimating

sex-biased dispersal rates. Molecular Ecology 11:125–138.

Wauters LA, Preatoni D, Martinoli A, Verbeylen G, Matthysen E. 2011. No sex bias

in natal dispersal of Eurasian red squirrels. Mammalian Biology. 76:369-372.

Wright S. 1943. Isolation by distance. Genetics. 28:114-138.

Zajistschek S, Zajitschek F, Brooks R. 2009. Demographic costs of inbreeding

revealed by sex-specific genetic rescue effects. Evolutionary Biology. 9:289,

doi:10.1186/1471-2148-9-289.

RELATEDNESS AND GROUPING BEHAVIOR IN THREE CENTRAL VALLEY

POPULATIONS OF CALIFORNIA GROUND SQUIRRELS

(OTOSPERMOPHILUS BEECHEYI)

Introduction

Sociality in animals is influenced by grouping of individuals. In mammals, social groups can range from small pair-bond units to large aggregations. The size and composition of social groups has various effects on behavior and varies by species (Silk 2007). Armitage (1981) created an index of varying degrees of sociality, which includes five categories, using life history traits for 18 ground-dwelling sciurid species. Rank 1 species are essentially asocial; rank 2 species aggregate but live individually in favorable habitats; in rank 3 species, a single male defends a group of individual females within his territory; in rank 4 species, a male defends a group of females that share burrows; and rank 5 species form multi-harem colonies.

In social mammals (i.e. ranks 2-5), groups often consist of related individuals.

Smith (2014) found that 84% of mammal species examined (n = 44) showed some degree of preference for maintaining spatial proximity to genetic kin. Maintaining proximity to kin affords costs and benefits to group members. For example, when offspring remain within their natal home range, either via delayed dispersal or philopatry, the cost to parents can be significant due to reproductive competition and reduced access to limited resources (Rayor and Armitage 1991; Alexander 1974;

West-Eberhard 1975). The avoidance of kin competition is one of the chief

explanations for the commonality of dispersal despite the risks of death from starvation and/or predation associated with dispersing and is important in the evolution of sex-biased dispersal (Greenwood 1980; Cote and Clobert 2010; Brom et al. 2016). However, maintaining proximity to kin can be beneficial to individuals

(Smith 2014). For example, extended parental investment in offspring can increase the probability of offspring survival and reproduction, thereby increasing parental individual fitness (Waser and Jones 1983; Armitage 1987). In addition, anti-predator behaviors, such as alarm calling and learned olfactory cues, serve to protect individuals within a group (Armitage 1981; Smith et al. 2016). For example,

California ground squirrels (CGS; Otospermophilus beecheyi) use alarm calls to warn conspecifics of threats: chattering warns nearby squirrels of a terrestrial predator while whistling indicates an aerial predator is near. In addition, adult CGS will chew skin sheds from rattlesnakes, the primary predator of this species, and then lick their fur to apply the scent of the snake. Females will also lick their young’s fur to apply the snake’s scent to provide an olfactory cue for the juveniles in an effort to thwart predation by rattlesnakes (Smith et al. 2016). Therefore, assessing relatedness between individuals can shed light on the space use and social structure of species.

Spatial distributions, in addition to influencing the degree of sociality within a species, can influence the mating system of a species (Munroe and Koprowski 2011).

In mammals, polygyny, or the mating of a male with multiple females, has historically been the most commonly identified mating system (Krebs and Davies

1993). However, advances in genetic technology have allowed more accurate

assignment of parentage to be completed so that the mating system in many mammalian species has been redefined as polygynandrous, or the mating with multiple partners for both males and females (Munroe and Koprowski 2011). The mating of a female with multiple males can result in multiple paternity within a single litter, which leads to fertility assurance and increased genetic diversity of offspring, thereby increasing the genetic variability and evolutionary potential of the population

(Shurtliff et al. 2005; Valenzuela 2000). Alternatively, polygyny increases genetic relatedness within groups and genetic differentiation among groups (Nunney 1993) as well as reduces effective population size (Sugg and Chesser 1994). Therefore, identifying the mating system within a species can allow predictions to be made about the viability of populations within a species. Polygyny is likely to arise as the mating system when reproductive females aggregate due to ecological factors (i.e., predation avoidance or food availability) and a male is able to monopolize mating opportunities with the clustered females through male-male competition; when males cannot monopolize mating opportunities so that neither sex is restricted to a single mate, the mating system tends to be polygynandrous (Emlen and Oring 1977). A polygynandrous mating system allows females to avoid aggressive behaviors from courting males; because there is no mate guarding by males, females are not at risk of being attacked or injured by subordinate males attempting to mate (Wolff and

Macdonald 2004; Munroe and Koprowski 2011).

Ground-dwelling sciurids are a diverse group of rodents with varying degrees of sociality, from asocial (e.g., Franklin’s ground squirrel, Poliocitellus franklinii) to

large colonies that exhibit cohesive behaviors such as communication (e.g., black- tailed prairie dogs, Cynomys ludovicianus; Munroe and Koprowski 2011). This group also exhibits various mating systems, from monogamous (e.g., Olympic marmot,

Marmota olympus; Allaine 2000) to polygynous (e.g., arctic ground squirrel,

Urocitellus parryii; Munroe and Koprowski 2011) to polygynandrous (e.g., yellow- pine chipmunk, Tamias amoenus; Schulte-Hostedde et al. 2002). Within this group, most species of ground squirrels are classified as rank 2 or 3 on Armitage’s (1981) sociality scale as well as exhibit a polygynous mating system (Munroe and

Koprowski 2011). However, due to the advances in genetic technology revealing the prevalence of multiple paternity litters, the mating system of several ground squirrel species (e.g., Richardson’s ground squirrel; Urocitellus richardsonii; Munroe and

Koprowski 2011) has been revised to polygynandrous, and the number of revisions is expected to increase (Zeh and Zeh 2001).

The CGS is a widespread mammal species native to California. The social organization of this species remains poorly understood (Smith et al. 2016), but observational studies indicate that this species is a rank 2 species (i.e., lives individually in favorable habitats but will aggregate when population densities are high; Munroe and Koprowski 2011; Boellstorff et al. 1994). Population densities vary by location, but previous studies have documented predominately amicable interactions among same-sex burrow mates, suggesting that this species is facultatively social; when population densities are high and individuals cannot live solitarily, they will maintain shared burrow systems and remain amicable with burrow

mates (Smith et al. 2016; Owings et al. 1977; Boellstorff and Owings 1995). Multiple paternity within eight out of nine (89%) litters tested has been demonstrated in a single population of O. beecheyi, located in Alameda, California, using allozyme data

(Boellstorff et al. 1994), suggesting a polygynandrous mating system. However, further investigation of this occurrence using additional molecular markers is warranted. In addition, females were observed in one Santa Cruz, California population of O. beecheyi mating with multiple males within a single breeding system

(Holekamp and Nunes 1989), also suggesting a polygynandrous mating system.

In this study, I propose to test the hypothesis that this species exhibits a polygynandrous mating system, using microsatellite DNA markers to determine patterns of parentage in three Central Valley populations of CGS. I will also calculate coefficients of relatedness (r) to investigate relatedness networks within burrow systems in order to extend our understanding of CGS social structure.

Hypotheses

Sociality

To test the hypothesis that O. beecheyi is a rank 2 species (i.e., lives individually in favorable habitats but will aggregate when population densities are high; Munroe and Koprowski 2011; Boellstorff et al. 1994), I will first calculate the average relatedness within burrow systems within each refuge. I will perform these calculations for adult males, adult females, and all individuals separately. If O. beecheyi is a rank 2 species, then average relatedness within burrow systems for all groups will be approximately zero (i.e., unrelated). This would suggest that

individuals are dispersing away from their natal site and not remaining in family groups. Second, I will assign parentage to offspring using microsatellite DNA markers and calculate the distance between the site where a parent was sampled as a juvenile and the site where their offspring were sampled in subsequent years as an estimate of dispersal distance. If O. beecheyi is a rank 2 species, then there will be no difference between adult male and adult female dispersal distance. This would suggest a lack of a sex-bias in dispersal and that both sexes are dispersing away from their natal site and not remaining in family groups.

Mating System

To test the hypothesis that O. beecheyi exhibits a polygynandrous mating system, I will first identify males that were assigned as putative fathers to more than one offspring in each year and calculate the coefficient of relatedness between each dyad of offspring for each of these males. If a male mated with multiple females within a breeding season, then some of his offspring will be identified as half- siblings. Second, I will identify females that were assigned as putative mothers to more than one offspring in each year. Because this species is monoestrous (Smith et al. 2016), these offspring are assumed to belong to the same litter. I will calculate the coefficient of relatedness between each dyad of offspring for these females. If a female mated with multiple males within a breeding season, then multiple paternity will be observed in her litter: some of her offspring will be identified as half-siblings.

Therefore, if O. beecheyi exhibits a polygynandrous mating system, then multiple matings for males and females will be observed within a single breeding season.

Methods

Field Data Collection

We sample individuals within three Central Valley populations, as described in chapter one (Estimating sex-bias in dispersal in three Central Valley populations of

California ground squirrels (Otospermophilus beecheyi) using microsatellite-based analyses).

Genetic Data Collection

Genetic data collection is conducted as described in chapter one (Estimating sex-bias in dispersal in three Central Valley populations of California ground squirrels (Otospermophilus beecheyi) using microsatellite-based analyses).

Data Analysis

Microsatellites. Because preliminary genotyping in a subset of individuals revealed that three of our microsatellites were monomorphic across all populations

(GS20, B108, and ST10), only the remaining seven microsatellites were amplified in all individuals (San Joaquin River National Wildlife Refuge (NWR): n = 100; Merced

National Wildlife Refuge: n = 100; San Luis National Wildlife Refuge: n = 104).

These seven loci were checked for the presence of null alleles in CERVUS v.3.0.7

(Kalinowski et al. 2007). Any locus with high rates of null alleles (>10%) were excluded from downstream analyses. Conformity to Hardy-Weinberg equilibrium and linkage disequilibrium between loci were tested in FSTAT v.2.9.3.2 (Goudet et al.

2002). These tests were corrected for multiple testing using Bonferroni correction.

Molecular markers exhibiting linkage disequilibrium (i.e. the non-random association

of alleles at different loci) and departure from Hardy-Weinberg equilibrium are typically under selection (i.e. non-neutral), which usually decreases polymorphism of the markers (Kirk and Freeland 2011). Because neutral markers are not under selection, they are able to accumulate higher levels of polymorphism; in order to eliminate individuals as candidate parents of offspring with high likelihood, parentage analyses require highly polymorphic markers. In addition, because most statistics of parentage analysis assume independence among loci, markers in linkage disequilibrium should be avoided (Jones et al. 2010). The number and size range of alleles, number of individuals, and observed and expected heterozygosity were calculated within each refuge using Arlequin v.3.5.2.2 (Excoffier and Lischer 2010).

The software CONVERT v.1.31 (Glaubitz 2004) was used to convert Excel files into

Arlequin input files.

Relatedness within burrow systems. To calculate average relatedness within burrow systems within each refuge, I used the software STORM v.2.0 (Frasier 2008), which calculates the pairwise relatedness for each dyad of individuals based on the method described by Li et al. (1993) within a burrow system, and then computes the average of all pairwise comparisons. Based on this method, total relatedness is rxy =

! ! ! ! (!), where rxy(l) is the relatedness at each locus, w(l) is the weight for ! !" each locus based on the number of alleles in each locus, and W is the sum of the weights for all loci used (Frasier 2008). Three separate analyses were performed within each refuge. In the first analysis, I calculated the average relatedness within burrow systems for all individuals (i.e., juveniles and adults, males and females). In

subsequent analyses, I calculated average relatedness within burrow systems for each sex separately and only included adults because they are the post-dispersers. Each sex-specific data set was comprised of burrow systems that included at least two adult individuals of the sex being analyzed.

Parentage assignment. To assign parentage, I used the likelihood-based computer program CERVUS v.3.0.7 (Kalinowski et al. 2007). Analyses were performed separately for each year (2015-2017) within each refuge. Juveniles that were sampled in a particular year were considered candidate parents in the following years. For example, juveniles sampled in 2015 were added to the candidate parent pool for juveniles captured in 2016 and 2017. CERVUS calculates a log-likelihood ratio (LOD) score for each parent-offspring dyad. If the LOD score equals 0, the putative parent is equally likely to be the true parent as a randomly selected individual in the population of the same sex. If the LOD score is positive, the putative parent is more likely to be the true parent than a randomly selected individual in the population of the same sex. If the LOD score is negative, the putative parent is not likely the true parent. To test for statistical significance, a delta (Δ) critical value is calculated for strict (95%) and relaxed (80%) confidence levels based on computer simulations of parental assignment with allele frequencies observed in the population. These critical values are used to determine confidence in parentage assignment in the actual data set. If only one candidate parent has a positive LOD score, Δ equals the LOD score. If more than one candidate parent has a positive LOD score, Δ equals the difference between the two largest LOD scores (Jones et al. 2010; Shurtliff et al. 2005). The

computer simulation parameters for all analyses utilized 100,000 cycles and 100% of loci typed with a genotyping error rate of 0.015. The proportion of incorrectly assigned genotypes out of all replicated genotypes determined this rate. The computer simulations also require the number of candidate parents to be provided as well as the estimated percentage of the total number of candidate parents present in the entire population that these candidate parents comprise. These values differed by year and refuge because of differences in the number of adults sampled between refuges (Table

1). No analysis was performed for 2017 in San Luis NWR because no juveniles were genotyped from 2017. These were conservative estimates because of the relatively small number of adults sampled within each refuge.

Table 1: Candidate parent simulation parameters for CERVUS analyses. SJRNWR = San Joaquin River NWR; MNWR = Merced NWR; SLNWR = San Luis NWR.

Number of Number of Estimated % of the Total Year Population Candidate Mothers Candidate Fathers Number of Candidate Parents SJRNWR 30 9 80 2015 MNWR 17 13 75 SLNWR 8 9 70 SJRNWR 52 23 90 2016 MNWR 39 34 90 SLNWR 29 31 90 SJRNWR 52 24 95 2017 MNWR 50 43 95

Dispersal distance. To estimate dispersal distance within O. beecheyi, I calculated the linear distance between the GPS coordinates of the site where an individual was first sampled as a juvenile and the site where their offspring (as assigned by CERVUS) in subsequent years was sampled using Google Maps

(https://www.google.com/maps/). I calculated the average distance and standard deviation for males and females, and performed a two-tailed t-test to test the statistical significance between the two means to determine if a sex-bias in dispersal exists.

Multiple paternity and maternity. I used the maximum likelihood software program ML-RELATE (Kalinowski et al. 2006) to calculate coefficients of relatedness (r) between each pair of individuals within each refuge as well as classify each pair of individuals as unrelated, half-siblings, full-siblings or parent-offspring using a 95% confidence level and 10,000 randomizations. This calculation of relatedness relies on the method described by Thompson (1975): r = k1/2 + k2, where km is the probability that individuals with a genealogical relationship (i.e., parent- offspring, full-siblings, etc.) have m alleles identical by descent; however ML-

RELATE can modify this equation to accommodate loci with null alleles (Wagner et al. 2006). The coefficient of relatedness between offspring assigned to a single male from CERVUS was used to determine the relationship (i.e. full-sibling, half-sibling) of the offspring in order to test whether individual males mated with multiple females within each refuge across the three sampling years. Statistical significance was determined by testing the null hypothesis that the r values did not differ from 0 (i.e. the offspring were unrelated) using 10,000 simulations. An r value of 0.5 indicates the pair are either parent-offspring or full-siblings, and an r value of 0.25 indicates that the pair are half-siblings (De Ruiter and Geffen 1998). A statistically significant half-sibling relationship between two offspring with the same father in a given year

indicates the offspring have different mothers, which suggests that the male mated with multiple females. This approach was also used to explore patterns of female parentage: the coefficient of relatedness between offspring assigned to a single female was used to determine the relationship of the offspring in order to test whether individual females mated with multiple males within each refuge across the three sampling years. A statistically significant half-sibling relationship between two offspring with the same mother in a given year indicates multiple paternity within the litter as most females are monestrous (Smith et al. 2016). Multiple mates for both males and females is evidence of a polygynandrous mating system.

Results

Microsatellites

All microsatellite loci were retained after quality tests. The rate of null alleles for all loci was below 7% in all populations. All loci conformed to Hardy-Weinberg equilibrium after Bonferroni correction (Appendix C). No combinations of loci were significantly in linkage disequilibrium after Bonferroni correction (Appendix D).

Summary diversity statistics are presented in Table 2. The microsatellite markers were highly polymorphic: the average number of alleles per locus was 11.0 (SD ±

4.0) in San Joaquin River NWR, 11.0 (SD ± 4.1) in Merced NWR, and 10.7 (SD ±

3.2) in San Luis NWR.

Table 2: Summary diversity statistics for 3 Central Valley populations of California ground squirrels used in social grouping and mating system analyses. Allele sizes given in base pairs; a = number of alleles; n = sample size; H0 = observed heterozygosity; HE = expected heterozygosity; SJRNWR = San Joaquin River National Wildlife Refuge; MNWR = Merced National Wildlife Refuge; SLNWR = San Luis National Wildlife Refuge.

Population Parameter D4 GS22 IGS-6 2g2 A116 MA018 SS-Bibl18

SJRNWR Size Range 278 - 302 165 - 185 100 - 130 101 - 153 309 - 323 307 - 323 136 - 152 a 13 8 15 17 6 9 9

n 99 100 97 100 100 99 100

Ho 0.80 0.83 0.87 0.86 0.49 0.86 0.77

HE 0.83 0.81 0.85 0.85 0.53 0.86 0.84

MNWR Size Range 276 - 308 165 - 189 100 - 128 97 - 159 309 - 321 305 - 325 136 - 152 a 14 9 12 18 5 10 9

n 100 100 99 100 100 97 100

HO 0.78 0.80 0.86 0.86 0.70 0.79 0.82

HE 0.89 0.79 0.88 0.89 0.71 0.80 0.83

SLNWR Size Range 272 - 304 165 - 185 100 - 126 113 - 155 309 - 321 305 - 325 136 - 150 a 15 9 13 13 6 11 8

n 100 104 103 104 102 100 104

HO 0.74 0.67 0.83 0.88 0.44 0.89 0.87

HE 0.85 0.70 0.80 0.86 0.49 0.83 0.84

Relatedness Within Burrow Systems

Average relatedness within burrow systems for each sex category within each refuge is presented in Figure 1. Within San Joaquin River NWR, the average relatedness within burrow systems when all individuals were included in the analysis was r = -0.216 (SD ± 0.179). The average relatedness within burrow systems for adult males was r = -0.153 (SD ± 0.219) and r = -0.004 (SD ± 0.198) for adult females. A negative r value indicates that the dyad is less related in ancestry than the average

(Wang 2014). Within Merced NWR, the average relatedness within burrow systems

when all individuals were included in the analysis was r = 0.012 (SD ± 0.101). The average relatedness within burrow systems for adult males was r = 0.009 (standard deviation could not be calculated because only one burrow system contained at least two adult males) and r = -0.021 (SD ± 0.028) for adult females. Within San Luis

NWR, the average relatedness within burrow systems when all individuals were included in the analysis was r = -0.006 (SD ± 0.232). The average relatedness within burrow systems for adult males was r = 0.140 (SD ± 0.138) and r = -0.052 (SD ±

0.153) for adult females.

Average Relatedness Within Burrow Systems

0.4 0.3 0.2 0.1 0 ** Adult Males -0.1 Adult Females -0.2 -0.3 All Individuals Average Relatedness Relatedness Average -0.4 -0.5 SJRNWR MNWR SLNWR Sex Category

Figure 1: Average relatedness within burrow systems within each refuge. The Li et al. (1993) method was used in the software STORM; SJRNWR = San Joaquin River NWR; MNWR = Merced NWR; SLNWR = San Luis NWR. Error bars are ±1 SD. **SD could not be calculated for the male-specific estimate of average relatedness within Merced NWR because only one burrow system contained at least two adult males and were included in the analysis.

Parentage Assignment

Parent assignment results from CERVUS analyses are presented in Appendix

H. Only those offspring that had at least one parent assigned with strict (95%) or relaxed (80%) confidence are listed. No juveniles were genotyped from San Luis

NWR in 2017. On average, CERVUS was able to assign putative mothers to 58.3%

(± 36.3%) and putative fathers to 50.9% (± 43.1%) of all offspring sampled in San

Joaquin River NWR, putative mothers to 27.5% (± 23.8%) and putative fathers to

14.1% (± 12.4%) of all offspring sampled in Merced NWR, and putative mothers to

24.4% (± 8.2%) and putative fathers to 22.1% (± 4.9%) of all offspring sampled in

San Luis NWR (Table 3).

Table 3: Percent of offspring assigned putative parents from CERVUS analyses. a) SJRNWR = San Joaquin River NWR; b) MNWR = Merced NWR; c) SLNWR = San Luis NWR. Note: No juveniles were genotyped from SLNWR in 2017.

Percent of Offspring Assigned a Percent of Offspring Assigned a Population Year Putative Mother Putative Father 2015 41.7 19.4 SJRNWR 2016 100.0 100.0 2017 33.3 33.3 2015 39.5 23.3 MNWR 2016 42.9 19.0 2017 0.0 0.0 2015 18.6 18.6 SLNWR 2016 30.2 25.6 2017 n/a n/a

Dispersal Distance

GPS data were not available for all individuals within each refuge, so estimates of dispersal distances were performed with reduced sample sizes (San

Joaquin River NWR: n = 64; Merced NWR: n = 22; San Luis NWR: n = 103), and dispersal distances could not be determined for two parent-offspring pairs in San

Joaquin River NWR and seven pairs in Merced NWR. Within San Joaquin River

NWR, one male (individual 28) was captured as a juvenile in 2015 and was assigned as the putative father of offspring in subsequent trapping years. Within San Luis

NWR, eight females and four males were captured as juveniles in 2015 and were assigned as putative parents of offspring in subsequent trapping years. For females, the average distance between parent and offspring was 1.77 km (± 1.29 km). For males, the average distance between parent and offspring was 1.64 km (±1.57 km).

There was no significant difference between the mean dispersal distances for males and females, t(10) = 2.23, two-tail p = 0.881.

Table 4: Estimate of dispersal distances within 2 Central Valley populations of California ground squirrels. Individuals were captured in 2015 as juveniles and were assigned as putative parents by CERVUS in a subsequent year. Distance = linear distance (km) between location where parent was trapped as a juvenile and location where their offspring were trapped in subsequent years. SJRNWR = San Joaquin River NWR; SLNWR = San Luis National Wildlife Refuge.

Offspring (Year Sex of Distance Population Parent Sex of Parent Captured) Offspring (km) SJRNWR 28 Male 361 (2017) Male 2.02 87 Female 267 (2016) Male 3.59 89 Female 245 (2016) Male 0.43 89 Female 258 (2016) Male 2.36 91 Female 248 (2016) Male 0.21 102 Female 262 (2016) Male 2.32 SLNWR 118 Female 252 (2016) Female 0.28 115 Female 268 (2016) Male 2.79 119 Female 224 (2016) Male 2.18 97 Male 247 (2016) Male 0.39 98 Male 241 (2016) Female 0.29 110 Male 222 (2016) Male 3.52 116 Male 258 (2016) Male 2.36

Multiple Paternity and Maternity

The multiple offspring of a single father are presented in Table 5. Within San

Joaquin River NWR, one male in 2015 (individual 293) was assigned as the putative father of two offspring (individuals 1 and 8). These offspring are likely full-siblings (r

= 0.65, p = 0.0001), indicating that they have the same mother. One male in 2017

(individual 321) was assigned as the putative father of three offspring (individuals

357, 360, and 385). Two of the offspring pairs (357 and 360, 357 and 385) are likely half-siblings (r = 0.34, p = 0.0174 and r = 0.31, p = 0.0330 respectively), indicating that individual 357 is from a different litter and does not have the same mother as

individuals 360 and 385, who are likely full-siblings. Within Merced NWR, four males in 2015 (individuals 49, 79, 173 and 299) were assigned as putative fathers to at least two offspring (53 and 55; 70 and 73; 65 and 66; 39 and 62, respectively). Of these four offspring pairs, two pairs are likely full-siblings while the remaining two pairs are likely half-siblings. Individual 173’s offspring are putatively half-siblings, although the relationship was not statistically significant (albeit hardly not significant; r = 0.32, p = 0.0613). Individual 299’s offspring pair (39 and 62) are likely half- siblings (r = 0.32, p = 0.0315). Within San Luis NWR, one male in 2015 (individual

242) was assigned as the putative father of two offspring (individuals 84 and 123).

These offspring are likely full-siblings (r = 0.52, p = 0.0133), indicating that they have the same mother.

Table 5: Coefficient of relatedness between offspring with shared father. Results of paternity assignment from CERVUS analyses and the coefficient of relatedness (r), calculated in ML-RELATE after Thompson (1975), between the putative father’s offspring are presented. Tests for statistical significance were performed with 10,000 simulations. SJRNWR = San Joaquin River NWR; MNWR = Merced NWR; SLNWR = San Luis NWR. HS = Half Sibling; FS = Full Sibling.

Putative Population Litter Year Putative Father Offspring Offspring Pair r Relationship P-value 2015 293 1, 8 1, 8 0.65 FS 0.0001 2017 321 357, 360, 385 357, 360 0.34 HS 0.0174 SJRNWR 357, 385 0.31 HS 0.0330

360, 385 0.50 FS 0.0165 2015 49 53, 55 53, 55 0.41 FS 0.0036 2015 79 70, 73 70, 73 0.45 FS 0.0016 MNWR 2015 173 65, 66 65, 66 0.32 HS 0.0613 2015 299 39, 62 39, 62 0.32 HS 0.0315 SLNWR 2015 242 84, 123 84, 123 0.52 FS 0.0133

The multiple offspring of a single mother are presented in Table 6. Within San

Joaquin River NWR, two individuals (146 and 148) in 2015 were assigned as the putative mothers of more than one offspring (individuals 12, 15, and 18; 10 and 26, respectively). In individual 146’s litter, one offspring pair (12 and 18) are likely half- siblings (r = 0.30, p = 0.0019) while the other two offspring pairs are likely full- siblings, indicating multiple paternity in this litter. Individual 148’s offspring (10 and

26) are likely half-siblings (r = 0.27, p = 0.0189), indicating multiple paternity in this litter. Within Merced NWR, one individual (78) in 2015 was assigned as the putative mother of two offspring (69 and 71). These offspring are likely half-siblings (r =

0.32, p = 0.0273). Within San Luis NWR, two individuals (229 and 276) in 2015 and two individuals (89 and 221) in 2016 were assigned as the putative mothers of at least two offspring. Individual 229’s offspring are likely full-siblings while individual 276,

89, and 221’s offspring are likely half-siblings, indicating multiple paternity in these three litters; the half-sibling relationships for individual 89 and 221’s offspring pairs were not significant, although hardly (p = 0.0616 and p = 0.0568, respectively).

Table 6: Coefficient of relatedness between offspring with shared mother. Results of maternity assignment from CERVUS analyses and the coefficient of relatedness (r), calculated in ML-RELATE after Thompson (1975), between the putative mother’s offspring are presented. Tests for statistical significance were performed with 10,000 simulations. SJRNWR = San Joaquin River NWR; MNWR = Merced NWR; SLNWR = San Luis NWR. HS = Half Sibling; FS = Full Sibling.

Putative Population Litter Year Putative Mother Offspring Offspring Pair r Relationship P-value 2015 146 12, 15, 18 12, 15 0.51 FS 0.0003 12, 18 0.30 HS 0.0019 SJRNWR

15, 18 0.74 FS 0.0001

2015 148 10, 26 10, 26 0.27 HS 0.0189 MNWR 2015 78 69, 71 69, 71 0.32 HS 0.0273 2015 229 119, 125 119, 125 0.50 FS 0.0313 2015 276 106, 126 106, 126 0.23 HS 0.0415 SLNWR 2016 89 245, 258 245, 258 0.29 HS 0.0616 2016 221 220, 250 220, 250 0.31 HS 0.0568

Overall, evidence of multiple paternity was found in 2 out of 2 litters (100%),

1 out of 1 litters (100%), and 1 out of 2 litters (50%) in San Joaquin River NWR,

Merced NWR, and San Luis NWR, respectively, in 2015. In 2016, evidence of multiple paternity was found in 0 out of 2 litters (0%) in San Luis NWR (although only slightly, the tests were not significant). The percentage of litters with multiple paternity cannot be determined for 2016 in San Joaquin River NWR or Merced NWR, nor for 2017 within any refuge because no putative mothers were assigned to more than one offspring.

Discussion

The results from this study support the categorization of O. beecheyi as a rank

2 (i.e., individuals live solitarily but will aggregate when population densities are high) species on Armitage’s (1981) sociality index. Average relatedness within

burrow systems was approximately zero (i.e., individuals are unrelated) within each refuge. When adult males and adult females were analyzed separately, average relatedness within burrow systems was approximately zero as well, although average relatedness within burrow systems for adult males in San Luis NWR was slightly higher (r = 0.140). Because variation was high among estimates of average relatedness within individual burrow systems, the standard deviations are high across all groups and refuges. Therefore, future studies should incorporate larger sample sizes. The pattern of low average relatedness within burrow systems across all groups and refuges suggests that individuals are not remaining in highly social family units but exhibit highly aggregated, overlapping home ranges, possibly due to high population densities. Population densities have been observed to be high within each refuge (J. Cooper, pers. obs.), although actual population densities within each refuge need to be quantified.

In addition to the analyses of average relatedness within burrow systems, the categorization of O. beecheyi as a rank 2 species is also supported by the results of the dispersal distance analysis. This analysis was dependent upon parent-offspring pairs to be identified by CERVUS. The ability of CERVUS to assign parents to offspring was highly variable among the refuges. This is probably due to the differences in the number of adults sampled within each refuge. San Joaquin River

NWR had the highest number of adults sampled, followed by Merced NWR, and San

Luis NWR. Consequently, more offspring were assigned putative parents, on average, in San Joaquin River NWR, followed by Merced NWR, and San Luis NWR. Within

San Luis NWR, both males and females dispersed short distances (< 0.5 km) and longer distances than would be predicted by their body size (> 2.0 km), possibly because individuals do not remain in highly social family units and prefer to live solitarily. Because of the combination of short distance and long distance dispersal accomplished by both males and females, the average dispersal distances for both sexes revealed large standard deviations. Nevertheless, the average of the short and long dispersal distances accomplished by females was slightly larger than that of males, which could explain why average adult male relatedness within burrow systems was slightly higher than adult female average relatedness within burrow systems within San Luis NWR. The lack of a significant difference between average male and female dispersal distances also suggests a lack of a sex-bias in dispersal.

The lack of a sex-bias in dispersal within each refuge is an exception to the typical pattern of male-biased dispersal in mammals (Greenwood 1980). The results from chapter one (Estimating sex-bias in dispersal in three Central Valley populations of

California ground squirrels (Otospermophilus beecheyi) using microsatellite-based analyses) suggest that these Central Valley populations of CGS exhibit weak male- biased dispersal, so the lack of a sex bias in dispersal based on the results of this analysis could be due to the small sample size. The magnitude of sex-bias in dispersal is theoretically expected to increase with increased social complexity because of the interaction between the benefits of cooperation and the costs of inbreeding (Lawson-

Handley and Perrin 2007). For example, Devillard et al. (2004) found that, as social complexity increased, the magnitude of male-biased dispersal increased in

polygynous ground dwelling sciurids (ground squirrels, marmots and prairie dogs).

Therefore, the lack of family units indicative of a highly social species could explain the lack of a strong sex-bias in dispersal in these populations of O. beecheyi.

Interestingly, within San Luis NWR, the distances between individual 89 and her two offspring (individuals 245 and 258) is highly variable: 0.43 and 2.36 km, respectively.

This could indicate secondary dispersal for individual 89, in which her offspring

(individual 258) dispersed with her. However, this explanation is not likely.

Alternatively, this could indicate that offspring 258, although sampled as a juvenile, had already dispersed away from their mother and sibling at the time of capture. If this is true, then the estimated dispersal distances could be those of the offspring, and not the parents. In that case, when average dispersal distance is calculated for the male and female offspring, the average is 0.29 km (± 0.01 km) for females and 2.02 km (±1.25 km) for males. There is a significant difference between the mean dispersal distances for males and females, t(9) = 2.26, two-tail p = 0.002, suggesting male-biased dispersal. However, these sample sizes are small, so increased sample sizes in combination with analysis of mark-recapture data are necessary to address the discrepancy in the sex-bias in dispersal results of this analysis.

The results from this study provide evidence that multiple paternity occurred in 100% of the litters in San Joaquin River NWR, 100% of the litters in Merced

NWR, and 50% of the litters in San Luis NWR within the 2015 breeding season.

Even though the total numbers of litters identified within each refuge were small, the results from this study confirm the high incidence of multiple paternity reported in

one population of O. beecheyi by Boellstorff et al. (1994) using allozyme data (89% of litters). This, in combination with the observance of two males fathering offspring with more than one female within a breeding season, supports the classification of a polygynandrous mating system in the populations of CGS in this study. A polygynandrous mating system can provide several benefits: it can increase fertility assurance for females by eliminating genetic incompatibility, increase genetic diversity of offspring, and decrease genetic differentiation among groups, thereby increasing the sustainability of populations (Munroe and Koprowski 2011).

Therefore, the presence of a polygynandrous mating system as well as high observed heterozygosity and lack of genetic differentiation among burrow systems (see chapter one: Estimating sex-bias in dispersal in three Central Valley populations of California ground squirrels (Otospermophilus beecheyi) using microsatellite-based analyses) suggests high adaptive potential in this species.

Disentangling the relationship between sociality, mating system, and dispersal patterns can help us make predictions about the evolutionary trajectory of a species, which is becoming increasingly important in the wake of global climate change

(Parmesan et al. 2000) and human encroachment. Population densities vary throughout their range, but O. beecheyi is considered widespread and abundant

(Smith et al. 2016). Because this species has been unaffected by human encroachment into their natural habitat (Marsh 1998), they are an excellent system to study the relationship between sociality, mating system, and dispersal patterns as a predictor of adaptive potential. In this study, the populations of O. beecheyi were characterized by

a polygynandrous mating system, low levels of sociality, and short and long distance dispersal accomplished by both males and females. Sociality and mating system are linked (Lawson-Handley and Perrin 2007); social interactions within and between the sexes affect selection on the mating patterns in both sexes, forming coevolutionary feedback loops (Alonzo 2010). However, it is unclear whether sociality and/or mating system led to the lack of a strong sex-bias in dispersal observed and whether any of these life history strategies correlate with population density, or if population density is the result of ecological factors, such as microhabitat. Future studies examining many populations of CGS with varying population densities and from varying microhabitats can begin to disentangle these interactions and shed light on the applicability of using these life history traits as predictors of adaptive potential.

REFERENCES

Alexander RD. 1974. The evolution of social behavior. Annual Review of Ecology

and Systematics. 5:325-383.

Allaine D. 2000. Sociality, mating system and reproductive skew in marmots:

evidence and hypotheses. Behavioral Processes. 51:21-34.

Alonzo SH. 2010. Social and coevolutionary feedbacks between mating and parental

investment. Trends in Ecology and Evolution. 25:99-108.

Armitage KB. 1981. Sociality as a life-history tactic of ground squirrels. Oecologia.

48:36-49.

Armitage KB. 1987. Social dynamics of mammals: reproductive success, kinship and

individual fitness. Trends in Ecology and Evolution. 2:279-284.

Boellstorff DE, Owings DH, Penedo MCT, Hersek MJ. 1994. Reproductive

behaviour and multiple paternity of California ground squirrels. Animal

Behaviour. 47:1057-1064.

Boellstorff D and Owings D. 1995. Home range, population structure, and spatial

organization of California ground squirrels. Journal of Mammalogy.

76(2):551-561.

Brom T, Massot M, Legendre S, Laloi D. 2016. Kin competition drives the evolution

of sex- biased dispersal under monandry and polyandry, not under monogamy.

Animal Behaviour. 113:157-166.

Cote J and Clobert J. 2010. Risky dispersal: avoiding kin competition despite

uncertainty. Ecology. 91(5):1485-1493.

De Ruiter JR and Geffen E. 1998. Relatedness of matrilines, dispersing males and

social groups in long-tailed macaques (Macaca fascicularis). The Royal

Society Publishing. 265:79-87.

Devillard S, Allaine D, Gaillard JM, Pontier D. 2004. Does social complexity lead to

a sex-biased dispersal in polygynous mammals? A test on ground-dwelling

sciurids. Behavioral Ecology. 15(1):83-87.

Emlen ST and Oring LW. 1977. Ecology, sexual selection, and the evolution of

mating systems. Science. 197:215-223.

Excoffier L and Lischer HE. 2010. Arlequin suite ver 3.5: a new series of programs to

perform population genetics analyses under Linux and Windows. Molecular

Ecology Resources. 10:564-567.

Frasier TR. 2008. STORM: software for testing hypotheses of relatedness and mating

patterns. Molecular Ecology Resources. 8:1263-1266.

Glaubitz JC. 2004. CONVERT: a user friendly program to reformat diploid genotypic

data for commonly used population genetic software packages. Molecular

Ecology Notes. 4:309-310.

Goudet J, Perrin N, Waser PM. 2002. Tests for sex-biased dispersal using bi-

parentally inherited genetic markers. Molecular Ecology. 11:1103-1114.

Greenwood P. 1980. Mating systems, philopatry and dispersal in birds and mammals.

Animal Behaviour. 28:1140-1162.

Hamilton WD. 1964. The genetical evolution of social behaviour, I and II. Journal of

Theoretical Biology. 7:1-52.

Holekamp KE and Nunes S. 1989. Seasonal variation in body weight, fat, and

behavior of California ground squirrels (Spermophilus beecheyi). Canadian

Journal of Zoology. 67:1425-1433.

Jones AG, Small CM, Paczolt KA, Ratterman NL. 2010. A practical guide to methods

of parentage analysis. Molecular Ecology Resources. 10:6-30.

Kalinowski S, Wagner A, Taper M. 2006. ML-RELATE: a computer program for

maximum likelihood estimation of relatedness and relationship. Molecular

Ecology Notes. 6(2):576-579.

Kalinowski S, Taper ML, Marshall TC. 2007. Revising how the computer program

CERVUS accommodates genotyping error increases success in paternity

assignment. Molecular Ecology. 16:1099-1106.

Kirk H and Freeland JR. 2011. Applications and implications of neutral versus non-

neutral markers in molecular ecology. International Journal of Molecular

Sciences. 12:3966-3988.

Krebs JR and Davies NB. 1993. An introduction to behavioural ecology. Blackwell

Science, Oxford.

Lawson-Handley LJ and Perrin N. 2007. Advances in our understanding of

mammalian sex-biased dispersal. Molecular Ecology. 16:1559-1578.

Li CC, Weeks DE, Chakravarti A. 1993. Similarity of DNA fingerprints due to

chance and relatedness. Human Heredity. 43:45-52.

Marsh RE. 1998. Historical review of ground squirrel crop damage in California.

International Biodeterioration & Biodegradation. 42:93-99.

Munroe KE and Koprowski JL. 2011. Sociality, Bateman’s gradients, and the

polygynandrous genetic mating system of round-tailed ground squirrels

(Xerospermophilus tereticaudus). Behavioral Ecology and Sociobiology.

65:1811-1824.

Nunney L. 1993. The influence of mating systems and overlapping generations on

effective population size. Evolution. 47:1329-1341.

Owings DH, Borchert MB, Virginia R. 1977. The behaviour of California ground

squirrels. Animal Behaviour. 25:221-230.

Parmesan C, Root TL, Willig MR. 2000. Impacts of extreme weather and climate on

terrestrial biota. American Meteorological Society. 81:443-450.

Rayor LS and Armitage KB. 1991. Social behavior and space-use of young of

ground-dwelling squirrel species with different levels of sociality. Ethology

Ecology & Evolution. 3:185-205.

Schulte-Hostedde AI, Millar JS, Gibbs HL. 2002. Female-biased sexual size

dimorphism in the yellow-pine chipmunk (Tamias amoenus): sex-specific

patterns of annual reproductive success and survival. Evolution. 56:2519-

2529.

Shurtliff QR, Pearse DE, Rogers DS. 2005. Parentage analysis of the canyon mouse

(Peromyscus crinitus): evidence for multiple paternity. Journal of

Mammalogy. 86:531-540.

Silk J. 2007. The adaptive value of sociality in mammalian groups. Philosophical

Transactions of the Royal Society B. 362:539-559.

Smith JE. 2014. Hamilton’s legacy: kinship, cooperation and social tolerance in

mammalian groups. Animal Behaviour. 92:291-304.

Smith JE, Long DJ, Russell ID, Newcomb KL, Munoz VD. 2016. Otospermophilus

beecheyi (Rodentia: Sciuridae). Mammalian Species. 48:91-108.

Thompson EA. 1975. Estimation of pairwise relationships. Annals of Human

Genetics. 39:173-188.

Sugg DW and Chesser RK. 1994. Effective population sizes with multiple paternity.

Genetics. 137:1147-1155.

Valenzuela N. 2000. Multiple paternity in side-neck turtles Podocnemis expansa:

evidence from microsatellite DNA data. Molecular Ecology. 9:99-105.

Wagner AP, Creel S, Kalinowski ST. 2006. Estimating relatedness and relationships

using microsatellite loci with null alleles. Heredity. 97:336-345.

Wang J. 2014. Marker-based estimates of relatedness and inbreeding coefficients: an

assessment of current methods. Journal of Evolutionary Biology. 27:518-530.

Waser PM and Jones MT. 1983. Natal philopatry among solitary mammals. Quarterly

Review of Biology. 58:355-390.

West-Eberhard MJ. 1975. The evolution of social behavior by kin selection.

Quarterly Review of Biology. 50:1-33.

Wolff JO and Macdonald DW. 2004. Promiscuous females protect their offspring.

Trends in Ecology & Evolution. 19:127-134.

Zeh JA and Zeh DW. 2001. Reproductive mode and the genetic benefits of

polyandry. Animal Behaviour. 61:1051-1063.

APPENDICES

APPENDIX A

RAW MULTI-LOCUS GENOTYPES

Appendix A: Raw genotypes (in basepairs) of Otospermophilus beecheyi within 3 Central Valley populations visually assigned after reviewing trace files using Peakscanner 2 (Applied Biosystems). Pop = Population. SJ = San Joaquin River National Wildlife Refuge; MC = Merced National Wildlife Refuge; SL = San Luis National Wildlife Refuge. ID = Individual. Sex: M = Male, F = Female, U = Unknown; Age: J = Juvenile, A = Adult, U = Unknown. Missing genotypes are left blank.

Locus

D4 GS22 IGS-6 2g2 A116 MA018 SS-Bibl18

Pop ID Sex Age Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2

SJ 1 F J 283.6 283.6 170.8 179.1 101.9 119.1 129.3 129.3 311.1 317.0 318.1 318.1 140.0 146.6 2 M J 283.6 291.8 170.7 179.0 101.8 103.7 131.6 139.6 317.1 317.1 310.0 318.1 141.2 146.6

3 F J 282.8 282.8 170.8 174.8 101.7 101.7 122.1 129.3 311.5 316.5 312.6 314.0 145.4 148.7

4 F J 279.9 283.6 170.6 181.0 103.7 124.7 127.3 130.8 317.6 317.6 312.4 316.3 135.8 141.1

5 M J 279.6 291.5 181.5 186.6 101.7 109.1 130.8 130.8 315.3 316.9 310.4 316.4 136.0 141.1

6 F J 283.5 283.5 170.9 181.2 101.9 111.3 129.3 139.4 312.2 316.2 308.5 312.5 150.9 152.9

7 M J 287.2 295.4 170.9 175.1 101.7 101.7 130.8 139.4 311.4 317.1 308.3 313.4 141.0 145.4

8 F J 283.5 291.7 170.7 178.9 101.8 118.9 129.3 129.3 317.5 317.5 309.9 318.1 140.9 140.9

9 M J 283.4 295.4 179.4 183.5 101.5 118.7 129.4 139.4 317.5 317.5 317.2 317.2 150.9 150.9

10 F J 289.6 291.5 179.4 185.7 101.6 124.6 127.2 129.1 316.5 316.5 312.4 321.2 141.9 141.9

11 F J 283.4 283.4 182.2 184.2 101.5 101.5 127.2 129.3 311.2 313.0 310.3 321.3 146.5 148.5

12 M J 285.5 285.5 171.9 180.2 103.6 111.2 137.4 139.6 314.2 316.2 308.2 318.1 140.1 144.4

13 F J 283.3 287.1 175.0 175.0 101.5 101.5 124.9 129.4 311.3 316.1 306.4 312.4 145.5 148.7

Locus

D4 GS22 IGS-6 2g2 A116 MA018 SS-Bibl18

Pop ID Sex Age Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2

SJ 14 F J 279.7 291.5 182.2 186.5 101.7 117.7 129.0 131.1 315.1 317.1 306.5 316.4 135.8 141.1 15 M J 285.5 285.5 181.3 183.4 101.8 103.7 139.6 139.6 314.1 316.1 310.3 318.3 139.8 144.3

16 M J 283.6 283.6 179.2 181.3 111.1 118.9 127.5 129.5 312.2 316.2 314.3 316.2 135.8 150.7

17 F J 283.3 289.6 183.3 185.4 111.0 117.6 115.2 129.5 313.1 316.1 308.1 308.1 142.3 152.8

18 F J 285.0 285.0 181.4 183.5 101.5 103.4 137.4 139.6 315.5 317.3 310.0 318.2 142.1 144.3

19 M J 283.5 287.3 175.1 175.1 101.5 120.7 124.8 135.2 311.3 316.3 313.4 318.2 145.5 148.7

20 M J 287.1 295.3 183.3 186.5 101.7 120.8 130.8 139.6 311.5 316.3 308.2 310.3 141.1 146.6

21 F J 285.3 299.1 179.1 186.3 117.7 120.7 118.3 137.6 316.4 316.4 312.1 317.1 139.8 144.3

22 M J 283.4 287.3 184.3 186.5 101.8 103.7 139.6 139.6 311.4 316.3 312.2 314.2 137.9 140.0

23 M J 283.5 287.3 179.3 183.5 101.9 101.9 127.1 129.2 316.3 316.3 308.4 312.5 141.0 152.8

24 F J 295.3 295.3 179.2 183.5 103.7 120.9 129.5 139.6 312.2 316.3 310.2 310.2 144.4 150.9

25 F J 295.0 295.0 164.2 178.9 103.9 116.1 139.7 141.0 311.4 316.4 312.7 312.7 145.0 145.0

26 F J 287.2 289.1 185.6 185.6 109.5 114.1 135.2 139.4 311.1 316.1 310.0 322.1 140.2 142.3

27 M J 283.4 287.2 183.6 185.7 120.8 128.7 129.6 139.7 311.5 316.3 308.2 309.9 142.4 144.4

28 M J 283.5 287.8 181.4 185.5 103.4 128.6 129.5 131.6 311.1 311.1 308.2 308.2 142.3 146.4

29 F J 291.1 295.5 179.1 185.5 124.6 128.5 131.1 139.6 312.1 316.1 308.5 312.2 144.4 150.9

30 F J 283.3 287.7 175.8 186.3 120.6 120.6 130.6 139.6 311.9 316.0 308.5 310.3 144.3 150.7

31 U U 283.4 287.1 183.4 185.5 115.7 120.6 129.3 139.6 311.2 325.9 308.4 310.1 144.6 146.6

Locus

D4 GS22 IGS-6 2g2 A116 MA018 SS-Bibl18

Pop ID Sex Age Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2

SJ 32 F J 296.6 296.6 183.4 185.5 103.4 115.6 120.2 129.4 311.2 311.2 312.5 312.5 142.3 146.6 128 F A 279.6 283.5 181.1 183.3 101.7 103.5 122.2 127.2 312.1 316.0 312.4 314.2 145.3 145.3

129 M A 283.3 285.3 179.9 184.1 103.6 114.9 125.1 125.1 316.2 316.2 312.5 317.5 139.8 145.5

130 M A 287.2 291.6 184.2 184.2 101.5 101.5 139.4 139.4 316.0 316.0 306.4 320.2 140.0 144.4

131 M J 283.2 283.2 179.3 183.5 103.5 109.1 130.8 139.6 314.4 316.2 306.5 310.2 141.1 150.9

132 F J 283.3 283.3 179.2 183.4 101.7 121.5 134.9 139.6 311.2 316.1 312.3 312.3 145.5 145.5

133 F J 283.5 287.2 171.9 180.3 101.7 107.4 124.8 131.6 317.2 317.2 308.2 316.0 145.5 148.7

134 F J 283.2 283.2 180.1 184.3 107.1 117.6 133.0 139.4 311.2 315.9 306.7 314.5 144.5 144.5

135 F J 279.5 289.5 181.3 185.5 101.8 103.7 134.8 139.6 317.3 317.3 308.4 312.5 141.3 145.6

136 F A 279.8 301.0 181.2 183.4 101.8 107.6 124.8 137.7 311.5 311.5 308.7 312.6 144.4 144.4

137 F A 285.3 287.1 181.2 181.2 101.9 124.8 127.0 129.1 311.4 316.5 312.7 321.7 141.2 153.0

138 F A 283.2 287.0 179.1 183.3 101.8 107.5 122.8 124.8 311.3 311.3 308.7 312.8 145.6 145.6

139 F A 283.1 287.0 175.1 181.4 120.9 128.7 124.8 135.3 311.2 316.1 306.6 313.6 145.5 145.5

140 M A 278.5 282.5 183.3 183.3 103.8 109.4 130.8 130.8 316.2 316.2 144.5 144.5

141 F A 287.5 290.0 181.1 181.1 101.9 117.8 139.6 139.6 316.5 317.6 308.6 313.7 142.2 152.9

142 F A 283.5 295.4 180.0 180.0 101.9 109.4 130.8 139.6 315.3 317.3 306.7 317.6 144.5 144.5

143 F A 283.4 291.7 169.8 184.6 129.5 129.5 315.4 315.4 306.7 306.7 141.2 145.7

144 F A 283.4 293.5 184.3 186.4 101.8 103.7 124.9 131.1 316.5 316.5 322.1 322.1 140.1 142.3

Locus

D4 GS22 IGS-6 2g2 A116 MA018 SS-Bibl18

Pop ID Sex Age Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2

SJ 145 F A 282.6 284.6 171.7 179.0 103.7 111.2 128.5 130.5 316.2 316.2 310.4 318.4 140.1 144.4

146 F A 283.6 285.6 170.9 181.3 103.5 111.1 139.6 139.6 316.1 316.1 310.4 318.4 142.2 144.4

147 M J 286.4 289.3 170.8 183.4 111.2 120.9 129.6 139.7 316.1 316.1 310.5 316.4 141.2 141.2

148 F A 185.4 185.4 131.0 135.2 311.8 316.3 308.7 321.7 142.1 142.1

149 F A 283.4 301.0 179.0 181.1 127.1 129.1 312.4 315.3 306.6 313.8 139.9 139.9

150 M A 282.1 282.1 180.2 184.3 101.8 101.8 129.4 137.6 311.3 311.3 312.6 318.5 144.9 144.9

151 M A 291.3 295.4 171.7 184.3 101.8 103.7 131.1 135.2 316.3 316.3 306.7 312.8 142.3 148.9

270 F A 289.8 295.6 183.3 185.5 101.5 111.2 131.1 137.7 311.4 314.1 317.4 320.2 148.7 148.7

271 F A 283.4 293.5 181.1 185.4 103.6 103.6 131.3 137.6 311.3 317.3 310.3 320.4 141.2 144.4

272 F A 279.7 283.5 177.2 184.4 115.8 115.8 130.8 139.4 316.5 316.5 310.3 318.5 138.0 142.3

290 F A 282.9 286.7 179.3 183.4 101.9 124.8 129.3 137.3 311.2 317.1 306.5 317.7 141.6 141.6

291 M A 282.9 287.3 177.0 179.3 101.7 103.4 127.2 130.7 311.4 311.4 308.6 313.8 139.8 144.4

292 F A 283.2 293.2 183.3 185.4 101.7 101.7 131.0 139.4 311.4 311.4 312.6 320.3 145.6 145.6

293 M A 283.3 287.1 164.5 179.0 103.7 118.8 129.1 139.4 316.1 316.1 308.5 318.4 140.1 144.5

301 F A 286.9 301.1 175.0 186.6 103.5 120.7 129.5 139.5 316.3 316.3 310.4 312.7 145.6 148.7

302 M A 283.6 287.0 174.9 183.4 100.0 119.7 135.3 139.4 316.5 316.5 306.6 313.9 144.4 148.7

303 U U 282.2 282.2 179.2 183.5 103.7 122.8 131.0 137.3 311.2 317.5 310.5 312.6 144.5 146.6

304 F A 283.5 291.0 179.1 179.1 101.8 117.9 129.4 137.3 316.7 316.7 312.5 315.6 140.0 146.7

Locus

D4 GS22 IGS-6 2g2 A116 MA018 SS-Bibl18

Pop ID Sex Age Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2

SJ 319 F A 282.6 295.2 179.0 183.3 118.6 118.6 116.3 129.5 316.7 316.7 308.7 308.7 138.0 144.5 320 F A 279.8 295.5 177.0 183.4 117.8 124.7 122.1 129.5 311.6 324.5 312.5 318.4 144.6 146.8

321 M A 279.9 295.5 181.3 183.4 103.5 116.8 101.8 122.1 316.0 324.1 308.4 312.5 144.6 144.6

322 F J 287.4 290.0 177.2 179.4 111.3 114.2 116.4 156.6 316.1 316.1 308.5 310.4 138.0 142.3

323 F A 283.7 285.7 183.4 183.4 103.5 118.8 139.4 143.7 315.8 315.8 310.6 317.8 140.1 151.0

324 F J 283.3 301.0 170.9 185.5 103.7 115.4 135.6 139.9 311.3 311.3 312.3 312.3 142.5 144.5

325 M J 287.1 293.4 177.2 181.3 103.7 107.4 129.4 139.6 312.7 316.5 312.4 321.5 142.3 144.5

326 M J 285.7 285.7 181.3 186.6 101.8 107.3 122.1 124.8 311.2 311.2 312.4 320.1 141.1 144.5

333 F A 283.7 295.8 170.8 183.4 101.6 103.6 139.6 139.6 316.1 316.1 308.9 318.0 145.5 145.5

334 M J 283.6 291.6 170.8 185.5 101.6 103.6 135.3 137.6 312.1 315 306.3 308.0 141.2 148.8

335 M J 282.9 301.0 184.3 184.3 111.4 124.8 116.4 139.4 316.1 316.1 310.6 319.3 142.3 148.9

336 F A 289.4 299.2 171.0 183.5 103.8 118.0 124.8 124.8 311.3 316.2 317.4 321.3 142.3 144.4

337 M J 283.3 287.3 181.1 183.2 109.3 124.7 101.6 129.6 310.3 316.2 315.5 319.4 140.1 144.5

338 U U 283.4 283.4 179.1 183.3 109.2 117.8 116.3 139.4 311.2 316.1 308.2 319.3 142.1 146.5

353 F A 287.4 287.4 181.4 183.5 101.5 101.5 135.2 139.6 316.3 316.3 312.2 320.2 139.9 145.4

354 F A 283.7 299.7 183.2 183.2 111.3 118.0 116.4 129.5 315.6 315.6 318.3 318.3 136.1 142.2

355 F A 282.0 294.9 165.3 182.2 99.8 114.4 116.3 129.3 316.3 316.3 308.6 318.5 144.6 144.6

356 M J 282.9 282.9 182.2 182.2 103.3 109.2 137.5 139.4 310.6 310.6 315.8 318.6 137.8 140.1

Locus

D4 GS22 IGS-6 2g2 A116 MA018 SS-Bibl18

Pop ID Sex Age Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2

SJ 357 F J 286.6 295.1 179.2 181.4 118.0 120.9 116.2 122.2 311.8 315.4 308.5 320.2 135.9 144.5 358 F A 283.8 301.0 170.9 175.0 101.5 103.4 129.5 129.5 311.5 316.5 306.6 312.2 141.0 148.7

359 M J 283.7 289.5 184.3 184.3 101.7 103.5 137.6 143.7 311.5 314.4 310.7 312.4 140.1 144.5

360 M J 283.6 295.4 183.4 183.4 109.2 117.7 122.3 129.4 316.7 316.7 308.2 308.2 140.2 144.5

361 M J 279.6 283.4 179.0 186.2 103.5 107.1 122.3 129.4 312.7 316.7 308.4 312.5 142.2 144.6

362 M J 279.6 293.5 181.2 185.4 101.6 103.4 129.5 139.6 311.2 317.3 308.8 312.7 135.9 141.1

363 F J 289.7 299.1 181.3 183.4 99.8 101.9 131.1 139.6 312.9 316.1 312.4 321.2 150.9 153.0

364 M J 286.9 295.2 184.2 184.2 103.5 111.0 116.3 139.4 312.1 316.1 312.4 320.2 138.0 142.2

365 M J 282.4 294.7 179.1 183.3 109.2 113.9 122.2 129.3 315.3 315.3 312.2 319.2 142.4 144.5

382 F J 285.3 291.6 175.7 182.1 101.5 109.1 135.0 139.4 312.0 316.0 306.7 317.8 135.9 140.1

383 F A 292.2 301.0 179.1 185.5 100.0 105.9 131.6 139.4 317.0 317.0 306.5 314.3 146.5 146.5

384 F J 283.9 301.0 181.3 183.4 107.4 111.2 127.2 130.6 312.5 312.5 312.5 314.4 143.3 145.8

385 F J 281.7 295.5 179.2 184.3 103.6 109.2 122.3 130.9 316.3 324.2 308.3 319.3 141.3 144.5

386 F J 283.2 301.0 181.3 183.5 103.5 117.8 129.1 139.6 316.6 316.6 308.5 315.3 138 144.4

387 M J 285.3 295.4 179.1 183.4 103.5 120.8 129.5 156.5 310.4 310.4 308.3 319.3 146.9 148.7 MC 33 F J 287.5 303.0 184.1 184.1 107.1 126.3 137.4 160.5 311.1 315.0 316.2 319.4 142.1 148.7 34 F A 283.3 303.0 165.4 184.2 107.3 126.5 122.6 137.6 324.9 324.9 318.4 319.7 139.8 142.1

35 M J 283.6 291.1 165.4 177.9 109.2 113.0 122.7 122.7 311.5 324.2 304.9 318.8 144.4 148.7

Locus

D4 GS22 IGS-6 2g2 A116 MA018 SS-Bibl18

Pop ID Sex Age Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2

MC 36 F J 281.3 281.3 165.3 165.3 111.1 117.7 124.5 130.7 311.3 315.3 308.2 319.3 144.4 144.4 37 M A 287.0 290.9 183.1 183.1 101.7 126.6 120.7 122.6 310.3 316.2 319.4 319.4 142.1 146.6

38 F J 283.3 293.4 184.1 184.1 111.0 111.0 160.3 160.3 311.1 315.0 304.9 320.0 142.2 146.5

39 M J 300.5 300.5 180.2 184.4 101.8 111.2 122.5 124.6 314.1 316.1 304.5 319.3 142.1 144.3

40 M J 293.4 295.3 167.4 186.6 109.3 113.9 133.3 137.2 311.7 315.7 319.5 319.5 140.0 144.3

41 M J 283.4 303.2 182.0 182.0 107.5 109.4 124.5 139.6 311.0 315.0 304.6 318.2 142.3 144.4

42 F J 291.6 293.5 165.5 184.1 101.5 109.1 133.3 139.6 311.1 314.0 318.6 320.7 144.4 146.6

43 F J 283.1 293.3 165.3 184.1 117.7 126.6 158.5 160.5 311.1 316.1 319.2 319.2 144.3 148.7

44 F J 295.4 299.2 165.3 165.3 113.2 113.2 116.2 158.2 311.4 316.3 316.1 318.1 144.4 146.6

45 F J 283.4 293.4 171.6 184.0 112.9 120.6 137.4 139.7 316.3 316.3 318.7 321.8 142.1 148.7

46 F J 298.8 298.8 165.2 180.1 112.8 126.3 101.5 133.4 316.3 316.3 140.1 146.5

47 F J 283.3 298.9 167.3 184.0 101.7 109.2 130.7 137.8 311.3 315.1 318.7 318.7 140.0 144.4

48 M A 275.9 287.3 164.3 166.5 101.8 111.2 122.9 139.6 316.0 323.7 135.8 144.3

49 M A 293.5 298.9 165.3 184.2 103.7 113.2 130.8 132.9 312.0 316.1 318.6 319.8 140.2 142.1

50 M J 307.3 307.3 165.2 167.3 117.9 120.8 127.1 139.6 311.1 315.0 304.4 310.3 144.3 148.7

51 M J 283.3 293.3 180.2 184.5 114.0 126.6 122.7 137.3 311.2 311.2 316.7 319.9 146.6 146.6

52 F J 293.5 293.5 167.3 186.4 103.5 109.2 122.6 133.5 311.2 315.1 319.2 319.2 144.3 146.6

53 F J 291.3 293.2 165.2 184.1 103.4 117.7 128.6 130.4 312.0 315.1 316.2 318.1 142.1 148.7

Locus

D4 GS22 IGS-6 2g2 A116 MA018 SS-Bibl18

Pop ID Sex Age Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2

MC 54 F J 283.2 298.9 165.3 186.4 101.5 111.0 137.6 160.5 315.0 315.0 304.6 318.3 140.0 144.4 55 F J 291.8 293.7 164.3 164.3 113.2 117.9 133.4 137.4 312.1 314.2 319.3 319.3 142.4 148.5

56 F J 290.9 307.2 164.4 179.1 109.4 120.9 123.0 137.8 310.8 314.9 304.3 317.2 144.4 152.9

57 M J 283.2 290.9 165.2 167.3 101.8 101.8 142.2 158.2 311.3 324.2 318.8 320.1 144.3 152.7

58 F J 283.2 298.9 164.4 189.7 110.9 110.9 130.6 133.7 312.3 314.1 316.1 318.2 137.9 146.4

59 F J 283.3 293.4 164.6 183.1 113.0 120.6 139.6 139.6 314.1 314.1 316.1 321.2 148.7 148.7

60 M J 275.7 287.1 166.6 183.0 101.7 107.3 122.6 139.2 311.2 316.1 304.5 308.4 135.8 142.1

61 F A 279.6 291.0 164.4 164.4 109.1 109.1 122.5 139.4 310.2 311.8 304.4 319.3 142.1 146.6

62 M J 300.6 303.4 184.0 184.0 111.3 111.3 124.6 139.8 311.2 315.9 315.5 319.4 145.6 145.6

63 M J 283.3 287.1 165.4 171.8 107.4 109.3 122.6 124.5 312.1 316.1 304.4 304.4 142.4 142.4

64 M J 283.1 300.7 167.4 182.2 107.3 112.9 139.6 139.6 311.2 324.0 142.1 142.1

65 M J 283.5 287.3 182.1 184.2 112.9 126.5 124.5 139.6 311.3 316.1 318.6 322.5 142.3 144.4

66 F J 283.3 287.1 180.0 184.0 101.7 113.1 122.6 124.7 315.3 317.3 318.5 319.7 140.0 140.0

67 F J 287.1 295.2 165.4 165.4 101.7 107.3 128.2 156.2 310.3 310.3 304.4 319.2 144.6 148.9

68 F J 283.4 303.2 164.6 185.7 111.4 113.3 130.4 160.7 310.1 312.0 318.0 321.9 140.0 144.5

69 M J 290.9 299.1 183.1 189.7 111.1 120.9 137.2 139.4 311.5 311.5 308.4 319.3 142.1 146.4

70 F J 283.8 299.4 164.5 189.6 113.3 118.0 131.6 133.7 311.2 316.1 308.5 318.2 138.0 144.6

71 M J 289.4 299.4 164.5 183.1 102.0 121.0 122.6 122.6 309.2 311.9 308.5 316.3 144.4 146.6

Locus

D4 GS22 IGS-6 2g2 A116 MA018 SS-Bibl18

Pop ID Sex Age Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2

MC 72 F J 291.0 307.3 166.7 179.3 117.8 120.9 122.5 139.8 311.8 315.0 304.5 318.1 139.8 144.2 73 M J 283.4 283.4 164.7 164.7 113.2 117.9 137.2 139.4 311.2 316.1 317.5 317.5 139.0 140.8

74 M J 300.5 300.5 183.3 183.3 111.0 112.9 158.2 158.2 310.9 314.0 318.3 319.4 140.0 144.2

75 M J 279.5 295.2 167.6 184.0 101.5 107.1 116.3 124.8 311.1 311.1 319.5 319.5 146.5 148.7

76 M J 291.0 299.1 164.6 189.7 111.0 111.0 131.5 133.6 312.2 315.1 319.1 319.1 142.2 144.3

77 F J 291.0 291.0 164.4 179.2 109.1 109.1 122.6 124.5 311.4 316.3 304.6 306.6 139.9 152.8

78 F A 290.9 299.1 179.1 182.9 120.6 120.6 122.5 139.4 311.2 316.1 308.6 316.4 136.0 144.4

79 M A 283.4 283.4 164.4 166.6 117.6 117.6 133.5 133.5 315.2 317.0 317.4 317.4 139.9 153.1

80 M J 299.1 303.2 164.4 166.5 107.2 117.7 133.7 139.6 314.4 316.4 304.3 316.2 144.4 146.5

81 M J 298.8 298.8 166.5 166.5 107.2 126.6 139.4 158.0 311.0 314.8 315.4 319.2 146.6 152.8

82 M J 298.9 298.9 166.4 183.0 107.2 111.0 158.0 158.0 309.2 311.9 308.4 320.1 139.9 142.2

83 F A 283.3 290.9 164.6 179.3 111.2 117.7 135.3 137.4 311.9 315.0 304.3 318.0 142.2 146.7

152 M J 283.5 299.1 164.5 183.3 109.1 111.1 129.1 139.4 315.3 315.3 315.4 319.3 142.4 146.7

153 F J 287.3 299.1 166.4 166.4 101.9 111.4 124.6 133.3 312.1 316.0 304.6 308.7 140.0 144.5

154 F A 288.3 300.3 167.3 184.1 101.8 107.5 122.5 130.4 311.2 324.1 318.0 319.3 138.0 142.2

155 F J 290.3 303.1 177.1 183.1 107.4 111.2 122.8 133.7 314.0 324.8 318.2 319.4 146.7 146.7

156 F A 296.0 300.3 164.2 166.3 101.8 109.2 128.6 137.6 316.1 316.1 304.4 320.0 142.3 144.6

157 F J 298.9 298.9 164.5 171.0 101.6 109.2 137.2 137.2 324.0 324.0 315.3 315.3 135.8 146.5

Locus

D4 GS22 IGS-6 2g2 A116 MA018 SS-Bibl18

Pop ID Sex Age Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2

MC 158 F A 280.2 292.9 183.4 183.4 101.8 114.0 130.3 132.3 311.3 316.2 315.8 319.4 142.3 146.7 159 F J 283.6 295.4 164.5 164.5 111.1 111.1 124.6 133.3 316.0 316.0 304.8 318.3 136.0 139.9

160 M J 291.6 299.0 179.2 183.4 111.1 111.1 131.4 133.5 315.2 324.1 304.6 306.7 146.7 146.7

161 F A 283.7 290.9 166.8 183.4 107.4 126.6 131.2 133.3 315.2 324.1 319.4 322.2 140.1 142.3

162 F A 282.7 290.8 166.4 179.1 107.5 118.0 122.7 139.6 311.3 324.1 304.5 315.4 146.6 153.0

163 M J 287.3 287.3 179.0 183.2 101.6 103.6 122.6 139.6 311.1 311.1 319.4 319.4 139.8 139.8

164 M J 287.3 301.0 179.2 183.4 139.4 160.3 314.8 314.8 317.5 319.4 142.2 142.2

165 M A 283.6 300.5 164.4 166.5 111.2 111.2 97.7 124.5 311.3 324.1 310.1 318.2 140.1 140.1

166 M A 281.4 289.1 179.1 183.5 109.0 109.0 129.4 159.0 313.9 313.9 316.2 319.3 140.1 148.8

167 M J 283.4 299.1 164.6 166.7 101.5 111.0 122.5 142.4 311.1 315.9 308.5 319.4 135.7 142.3

168 F A 294.9 303.5 164.2 185.4 109.3 113.0 131.3 133.4 311.3 324.1 306.9 318.3 140.1 146.6

169 M J 287.4 287.4 164.4 185.5 109.2 111.0 122.7 160.0 311.4 314.3 308.3 319.4 146.8 146.8

170 F A 283.4 303.2 164.2 183.3 101.6 111.1 122.6 122.6 314.2 314.2 315.5 318.2 146.5 146.5

171 M J 282.5 303.3 164.4 166.3 109.4 118.0 124.7 160.5 314.6 316.6 308.4 319.3 142.3 146.7

172 M A 282.9 292.7 164.4 183.1 113.2 117.8 122.7 124.6 312.9 316.4 319.5 319.5 146.7 146.7

173 M A 282.2 286.4 182.2 184.3 113.1 126.6 124.7 139.6 311.3 316.5 318.5 322.2 140.0 144.6

174 F A 299.5 299.5 179.0 184.3 109.2 113.0 98.1 133.5 310.5 316.5 306.5 316.4 140.1 148.7

175 F J 281.7 281.7 166.6 181.3 103.5 126.6 137.3 139.6 311.7 315.6 310.0 319.2 142.4 148.9

Locus

D4 GS22 IGS-6 2g2 A116 MA018 SS-Bibl18

Pop ID Sex Age Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2

MC 176 M J 283.9 283.9 164.4 183.4 100.0 116.4 122.6 160.5 312.0 312.0 316.4 318.3 144.4 150.9 177 F J 295.7 295.7 164.2 183.2 109.0 124.5 139.8 139.8 311.6 315.5 318.1 318.1 140.0 148.9

178 M J 286.9 298.8 183.9 186.5 101.7 112.9 122.6 124.6 311.4 316.3 310.1 313.5 142.2 148.7

179 M A 293.5 303.2 183.9 183.9 109.3 111.1 122.8 124.8 310.6 314.6 318.1 319.3 142.3 148.7

180 F J 287.3 299.1 164.4 183.1 109.1 113.7 124.8 124.8 311.2 316.1 304.6 317.4 142.2 152.8

181 F J 298.9 298.9 164.4 183.0 100.0 107.5 124.7 130.9 311.3 311.3 304.4 317.3 142.2 142.2

182 F J 299.2 303.4 183.9 183.9 113.1 118.0 124.7 130.5 311.6 316.7 304.5 304.5 144.6 148.9

183 U U 279.4 287.2 179.2 181.3 101.8 114.0 122.6 124.7 311.1 316.0 315.3 319.4 140.1 146.5

190 F A 299.4 303.3 164.2 166.7 111.1 117.8 122.7 124.7 311.2 315.0 308.4 316.3 144.3 144.3

191 M A 283.5 291.5 164.6 183.3 101.6 109.2 122.6 133.5 311.2 325.0 315.6 319.5 142.1 142.1

194 F J 283.0 300.7 166.6 181.2 101.5 112.9 124.5 162.3 311.2 314.2 318.3 319.5 142.2 148.7

202 F J 287.5 290.7 164.4 181.2 113.2 117.8 142.0 160.5 311.2 311.2 318.2 319.4 144.5 148.9

294 F A 287.5 300.8 164.4 166.7 109.1 111.9 97.7 133.3 315.4 315.4 310.6 318.7 144.5 146.7

295 M J 280.1 300.5 166.7 171.0 107.5 109.3 133.3 133.3 311.5 311.5 320.2 320.2 136.1 146.7

296 F J 287.1 294.7 164.6 170.9 100.0 106.1 115.9 133.3 311.1 311.1 310.3 320.5 142.2 146.7

297 F J 294.4 294.4 166.6 185.6 101.7 109.0 122.7 135.7 310.9 310.9 316.8 318.4 142.2 144.3

298 F A 280.5 301.0 166.5 181.4 113.9 117.7 124.7 139.8 311.2 311.2 310.1 319.1 142.4 148.7

299 M A 282.8 300.5 179.3 183.4 107.3 111.1 124.6 137.5 311.2 314.0 320.3 322.6 140.3 146.5

Locus

D4 GS22 IGS-6 2g2 A116 MA018 SS-Bibl18

Pop ID Sex Age Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2

MC 300 M A 283.9 289.6 164.2 179.1 107.2 108.9 133.5 139.6 310.6 313.5 319.3 319.3 139.9 142.2 327 F A 277.6 303.0 164.6 183.4 99.8 111.7 130.2 160.0 311.8 315.6 318.3 319.2 139.8 144.3

328 U U 302.9 302.9 184.2 186.4 101.8 117.6 162.5 162.5 311.7 315.6 315.4 320.3 144.5 146.7

329 F A 300.3 300.3 166.6 181.2 103.5 117.9 124.6 139.3 311.3 311.3 316.3 319.2 142.1 146.4

330 F J 288.9 300.8 166.3 182.9 103.8 109.4 133.5 139.8 315.6 315.6 310.8 318.9 140.1 142.2

331 M A 283.1 303.5 183.4 183.4 109.0 112.6 124.6 139.4 315.2 315.2 304.4 310.2 139.9 142.2

332 M J 281.7 294.6 164.2 164.2 99.8 125.9 124.7 135.6 310.3 310.3 310.4 310.4 139.9 142.2 SL 84 F J 285.5 291.6 170.8 183.1 109.6 112.4 119.7 127.6 316.2 318.0 312.3 318.1 144.4 148.9 85 M J 287.5 300.7 183.0 183.0 109.6 109.6 135.3 137.3 316.3 318.0 306.4 314.2 140.0 144.4

86 M J 283.5 283.5 173.8 184.3 105.7 122.8 115.1 133.2 311.2 311.2 316.6 323.7 147.8 147.8

87 F J 285.6 293.7 173.0 179.3 109.4 109.4 129.4 137.0 316.0 316.0 310.0 318.2 142.5 150.9

88 M J 276.3 280.1 179.1 183.1 102.0 102.0 125.1 127.4 317.4 317.4 315.3 319.2 142.0 144.5

89 F J 285.7 287.6 168.5 183.2 105.7 109.5 124.9 131.2 316.0 316.0 312.2 315.4 135.7 144.3

90 M J 285.3 299.2 170.7 183.4 103.8 109.5 124.8 130.8 315.9 315.9 315.4 320.2 135.9 148.7

91 F J 285.4 300.6 183.0 183.0 109.4 117.1 122.7 127.3 317.0 317.0 312.5 315.6 135.8 144.4

92 F J 285.0 300.3 171.0 183.5 103.9 109.5 129.6 135.3 315.9 315.9 304.4 315.5 136.1 139.9

93 U U 283.3 283.3 165.2 165.2 120.5 122.6 316.5 324.4 317.7 317.7 138.0 140.1

94 M J 184.1 184.1 105.5 109.4 137.2 139.6 316.2 316.2 308.7 315.9 140.0 142.1

Locus

D4 GS22 IGS-6 2g2 A116 MA018 SS-Bibl18

Pop ID Sex Age Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2

SL 95 F J 287.7 300.5 178.2 184.5 107.4 109.2 124.5 135.2 310.3 314.2 315.9 321.9 135.9 151.0 96 F J 285.3 300.5 184.1 184.1 109.3 116.9 127.4 135.3 317.5 317.5 312.4 315.6 135.7 144.4

97 M J 285.3 285.3 184.0 184.0 105.5 109.4 124.5 135.2 312.2 317.1 312.7 315.9 144.4 146.4

98 M J 283.6 287.4 180.0 184.2 105.5 113.8 127.6 129.7 310.6 310.6 312.8 321.8 140.0 148.7

99 F J 279.6 285.2 171.9 184.3 103.8 109.4 122.6 124.5 316.3 316.3 304.8 316.0 136.0 151.1

100 F J 287.2 287.2 184.1 184.1 105.5 109.3 124.6 135.2 317.2 317.2 306.8 316.1 135.8 146.5

101 M J 279.5 291.3 177.9 186.5 107.5 109.3 119.9 129.3 312.5 316.5 321.5 323.5 142.2 151.1

102 F J 287.2 287.2 180.1 184.1 109.2 109.2 127.3 135.1 316.2 316.2 312.5 315.6 135.9 135.9

103 F J 287.2 287.2 184.0 184.0 105.6 109.4 124.7 124.7 317.1 317.1 315.7 315.7 136.0 146.6

104 F J 287.3 291.3 184.1 184.1 107.4 109.4 124.8 129.6 316.1 316.1 315.8 321.6 136.0 150.9

105 M J 281.2 281.2 182.1 184.1 101.8 109.4 115.2 137.1 317.2 317.2 315.5 315.5 139.9 147.8

106 M J 300.5 300.5 170.9 183.4 109.5 112.2 129.3 135.2 312.5 312.5 318.7 321.8 144.2 146.7

107 F J 291.5 291.5 171.7 184.3 109.1 109.1 124.7 135.4 142.1 146.6

108 M J 300.3 300.3 183.4 183.4 109.3 112.1 122.7 124.7 312.4 316.1 304.5 315.3 135.9 151.0

109 M J 285.2 285.2 176.8 183.0 105.6 109.4 125.2 127.3 308.1 317.2 146.7 151.0

110 M J 282.4 292.9 183.5 185.6 124.7 124.7 127.5 129.6 312.5 315.4 135.9 147.8

111 M J 283.5 287.3 179.1 181.2 105.6 114.0 130.6 130.6 316.2 316.2 308.5 314.6 135.9 148.7

112 M J 300.3 300.3 183.3 185.4 103.4 109.1 124.6 129.5 316.0 316.0 304.7 306.6 135.8 146.5

Locus

D4 GS22 IGS-6 2g2 A116 MA018 SS-Bibl18

Pop ID Sex Age Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2

SL 113 F J 282.4 286.6 184.3 184.3 103.3 103.3 125.2 127.3 317.6 317.6 306.4 312.4 142.2 146.7 114 M J 287.4 299.4 170.8 183.4 103.5 109.0 124.7 130.6 316.6 316.6 304.3 315.4 135.7 151.1

115 F J 279.3 285.4 181.3 183.2 105.3 109.3 124.6 130.6 316.1 316.1 315.3 320.1 136.1 148.7

116 M J 164.5 183.3 107.1 109.1 124.5 137.1 316.6 316.6 314.8 314.8 140.1 148.9

117 F J 300.5 300.5 183.2 183.2 109.0 112.0 129.5 130.8 316.1 316.1 304.6 306.5 135.9 151.0

118 F J 278.3 286.5 183.9 183.9 103.4 109.2 124.7 130.5 316.4 316.4 304.6 315.5 135.9 150.9

119 F J 279.8 286.9 176.8 184.1 107.6 109.5 127.3 135.5 312.3 317.2 315.5 315.5 135.9 144.3

120 M J 280.5 289.2 181.3 183.4 107.3 122.7 130.5 130.5 311.2 314.3 312.4 312.4 135.9 138.0

121 M J 285.3 300.5 184.0 184.0 109.3 116.8 124.6 127.3 316.2 316.2 312.5 315.6 135.9 144.4

122 M J 169.5 180.1 109.1 116.7 127.5 130.9 314.5 316.6 308.4 319.6 136.1 148.7

123 M J 285.4 300.5 184.0 184.0 103.5 109.2 119.7 127.4 316.5 316.5 312.5 323.4 142.2 144.4

124 M J 284.5 293.3 182.2 184.3 112.2 112.2 124.6 129.5 316.4 316.4 312.1 316.3 135.9 151.0

125 F J 184.3 184.3 107.1 109.2 129.5 135.2 317.6 317.6 144.5 146.6

126 F J 285.5 300.7 183.5 185.6 109.1 112.0 124.8 127.3 312.3 316.1 310.3 315.4 136.1 146.7

127 F J 285.3 285.3 181.3 183.3 109.3 116.9 124.6 135.2 316.1 316.1 315.2 315.2 135.9 142.4

218 M J 283.4 285.5 183.4 185.6 103.6 107.3 115.2 128.2 312.2 316.0 313.1 315.3 146.6 151.0

219 M A 283.2 287.0 183.6 185.8 109.2 116.7 135.2 135.2 316.3 318.0 314.3 315.3 135.9 142.1

220 M J 284.6 287.0 183.2 183.2 109.5 109.5 127.3 137.2 312.1 312.1 304.7 306.4 142.3 151.0

Locus

D4 GS22 IGS-6 2g2 A116 MA018 SS-Bibl18

Pop ID Sex Age Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2

SL 221 F A 287.3 300.5 170.7 183.0 103.5 109.2 119.7 127.3 312.3 316.8 306.7 314.3 135.9 151.2 222 M J 282.2 290.1 170.8 185.4 105.5 105.5 127.3 129.4 311.0 316.9 315.5 323.4 135.9 146.7

223 F A 287.3 300.5 179.1 183.2 109.1 113.7 124.8 158.2 312.3 316.1 313.4 315.2 136.1 146.7

224 M J 279.4 287.1 177.0 183.3 109.2 117.0 127.2 135.1 312.3 316.3 315.6 315.6 142.2 144.6

225 M J 286.8 300.9 183.0 185.9 105.4 120.7 127.4 129.5 312.2 317.1 310.7 314.2 136.0 151.0

226 M J 283.6 287.3 181.1 185.4 105.4 109.2 124.6 137.2 311.2 314.5 312.5 320.3 135.9 146.5

227 M J 285.0 291.4 183.0 183.0 105.3 109.2 129.4 129.4 312.3 317.3 315.4 318.2 146.7 146.7

228 F A 285.1 285.1 170.9 185.6 103.3 103.3 119.5 129.4 312.3 317.3 314.1 315.6 135.9 135.9

229 F A 279.9 285.5 176.7 183.0 107.4 107.4 129.4 135.3 312.1 316.1 313.5 315.5 144.5 146.4

230 M J 286.8 286.8 181.3 183.3 107.5 117.0 124.7 129.4 312.3 317.0 313.8 315.6 135.9 146.7

231 M A 283.1 287.0 183.3 183.3 103.4 105.5 124.7 124.7 312.4 316.4 310.2 315.3 142.3 148.7

232 F J 283.1 287.1 185.6 185.6 105.5 109.2 124.6 127.2 315.0 317.3 315.4 323.2 135.9 135.9

233 M A 283.1 287.1 170.0 185.6 105.4 109.3 124.8 137.2 312.1 316.0 313.3 315.4 142.2 146.7

234 F J 285.4 287.1 183.1 183.1 103.5 109.2 119.6 135.1 315.0 316.8 306.8 315.5 139.9 148.7

235 M J 285.1 300.3 183.3 183.3 105.3 109.2 130.6 137.3 312.3 312.3 306.4 314.1 148.7 148.7

236 M J 287.0 293.3 185.5 185.5 107.3 107.3 130.5 137.0 316.3 318.2 304.6 315.3 136.1 136.1

237 M J 283.5 287.3 170.9 179.0 105.6 117.0 125.2 127.3 316.3 316.3 314.3 315.6 136.0 136.0

238 F J 287.6 287.6 181.1 185.5 105.5 120.7 127.3 137.2 315.3 316.6 310.5 314.2 142.3 148.7

Locus

D4 GS22 IGS-6 2g2 A116 MA018 SS-Bibl18

Pop ID Sex Age Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2

SL 239 M J 285.4 300.5 183.2 183.2 109.3 117.0 129.4 137.2 312.3 316.9 313.4 315.2 136.0 146.6 240 M J 284.2 286.3 170.9 183.1 105.4 116.8 135.3 135.3 315.7 317.9 315.6 315.6 136.0 146.5

241 F J 287.2 287.2 179.0 183.3 103.6 105.3 127.5 137.3 311.4 311.4 319.2 321.2 139.9 148.7

242 M A 286.3 286.3 183.9 183.9 103.5 114.0 120.0 129.4 312.1 316.1 312.2 321.4 135.9 140.0

243 M J 275.8 299.7 183.3 183.3 109.3 116.9 124.5 137.2 316.1 317.9 312.2 315.5 142.2 142.2

244 M J 287.3 300.7 183.5 183.5 109.3 114.0 127.5 127.5 316.1 325.1 313.7 315.4 139.9 146.5

245 M J 285.4 285.4 168.5 183.2 105.3 109.2 125.2 127.5 316.4 318.4 313.7 315.9 136.1 144.4

246 M J 300.5 300.5 183.5 185.5 105.6 109.2 130.6 137.0 312.1 315.9 318.4 323.6 142.2 146.7

247 M J 285.4 285.4 184.3 186.1 105.4 114.9 135.1 137.2 317.3 317.3 144.4 151.1

248 M J 300.2 300.3 183.2 185.4 105.5 116.9 122.6 124.6 312.2 317.3 312.6 315.4 146.7 146.7

249 F J 287.6 299.7 170.7 183.4 109.2 113.8 127.1 135.3 316.1 316.1 315.3 323.3 144.5 148.9

250 M J 283.4 287.8 170.9 183.4 105.3 109.2 119.6 130.6 312.2 316.1 306.5 314.2 136.0 142.2

251 M J 287.3 300.5 185.5 185.5 103.8 105.6 119.7 124.6 316.2 317.8 315.3 323.2 135.7 148.7

252 F J 284.7 286.4 183.2 183.2 109.3 109.3 124.6 137.3 316.0 316.0 304.6 315.4 146.8 151.2

253 F J 287.4 300.5 183.4 185.4 107.4 109.2 124.7 130.6 312.2 316.2 315.6 323.1 144.3 146.7

254 M J 283.3 287.1 170.9 183.5 107.3 114.0 135.3 135.3 312.3 316.4 310.7 323.4 135.9 139.9

255 F J 285.7 287.0 171.8 185.5 107.3 109.1 124.6 135.2 312.3 316.4 315.3 323.3 144.2 149.1

256 M J 283.3 285.1 177.1 183.4 107.1 107.1 129.4 137.1 315.1 315.1 315.6 315.6 148.5 148.5

Locus

D4 GS22 IGS-6 2g2 A116 MA018 SS-Bibl18

Pop ID Sex Age Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2

SL 257 F A 287.0 291.1 185.6 185.6 105.3 105.3 129.4 137.3 316.2 316.2 306.7 314.2 140.2 144.4

258 M J 297.3 301.0 182.9 182.9 105.7 105.7 124.7 124.7 316.2 316.2 312.5 314.5 141.1 144.4

259 F J 283.8 285.7 179.2 183.5 107.4 109.3 129.6 129.6 311.4 316.3 315.3 320.1 136.1 142.2

260 M J 299.6 299.6 177.8 185.4 107.3 109.2 130.6 137.1 317.2 317.2 320.4 320.4 142.0 144.4

261 F J 283.0 300.8 181.1 186.3 105.5 109.0 130.4 137.4 315.8 315.8 306.8 315.5 142.0 147.8

262 M J 287.2 293.5 179.0 186.5 101.6 109.1 129.3 135.2 316.6 316.6 320.3 323.1 136.0 147.9

263 F J 285.4 299.4 170.7 183.0 105.5 115.9 129.5 137.3 316.5 316.5 312.5 315.2 141.2 151.0

264 M J 271.6 283.1 179.0 181.0 105.6 109.4 128.3 130.4 316.3 316.3 304.6 315.5 136.0 144.5

265 F J 282.9 300.8 181.3 186.6 101.6 109.3 129.6 129.6 316.3 316.3 308.6 323.5 144.4 147.9

266 M J 299.7 299.7 180.0 183.2 117.0 121.0 119.6 129.5 317.5 317.5 306.2 314.3 137.3 141.0

267 M J 285.0 299.1 176.9 178.8 105.5 109.5 131.7 137.1 317.6 317.6 318.2 320.1 142.3 142.3

268 M J 283.8 300.9 181.3 185.6 107.4 109.3 129.5 131.7 317.1 317.1 315.3 320.2 148.9 148.9

269 M J 283.8 285.0 181.1 181.1 100.0 107.8 127.5 129.6 316.1 316.1 306.3 308.4 135.6 144.5

273 M A 287.7 287.7 179.9 183.3 103.4 109.1 124.7 129.5 312.5 315.4 315.4 320.2 148.9 148.9

274 F A 283.5 287.4 179.1 183.1 101.9 105.6 127.5 130.3 316.2 316.2 313.8 315.6 135.8 144.6

275 M A 283.7 300.7 183.0 183.0 105.3 116.9 128.6 135.3 316.1 316.1 313.4 315.2 142.5 148.7

276 F A 300.3 303.6 171.1 185.4 103.9 109.4 127.3 129.5 312.1 312.1 313.7 315.7 144.5 146.5

277 M A 283.8 293.5 185.5 185.5 103.7 109.3 119.9 130.7 311.2 315.2 315.4 319.4 135.8 139.9

Locus

D4 GS22 IGS-6 2g2 A116 MA018 SS-Bibl18

Pop ID Sex Age Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2 Allele 1 Allele 2

SL 278 F A 275.9 299.3 181.3 183.4 101.6 109.2 130.6 135.3 317.1 317.1 312.6 314.5 142.3 147.8 279 M A 275.9 299.0 181.4 183.5 105.5 109.3 134.9 137.0 316.3 316.3 314.8 316.0 142.3 144.2

280 M A 281.8 291.1 183.3 185.5 109.1 109.1 128.5 135.1 316.4 316.4 310.5 317.6 142.0 146.7

100

APPENDIX B

INDIVIDUAL BURROW ASSIGNMENTS

Appendix B: Burrow assignment of individuals within 3 Central Valley populations of Otospermophilus beecheyi. ID = Individual; SJRNWR = San Joaquin River National Wildlife Refuge; MNWR = Merced National Wildlife Refuge; SLNWR = San Luis National Wildlife Refuge; U = Unknown. Alleles (measured in basepairs) are binned. Missing genotypes are left blank. Locus

ID Sex Age D4 GS22 IGS-6 2g2 A116 MA018 SS-Bibl18 SJRNWR: Burrow A1

137 Female Adult 286 288 181 181 102 126 125 127 311 315 313 323 142 152 272 Female Adult 280 284 177 183 116 116 129 137 315 315 311 319 138 142 Burrow A2

141 Female Adult 288 290 181 181 102 118 137 137 315 315 309 315 142 152 146 Female Adult 284 286 171 181 104 112 137 137 315 315 311 319 142 144 151 Male Adult 292 296 171 183 102 104 129 133 315 315 307 313 142 148 Burrow A3

147 Male Juvenile 288 290 171 183 112 122 127 137 315 315 311 317 142 142 322 Female Juvenile 288 290 177 179 112 116 115 153 315 315 309 311 138 142 335 Male Juvenile 284 302 183 183 112 126 115 137 315 315 311 321 142 148 140 Male Adult 278 284 183 183 104 110 129 129 315 315 144 144

321 Male Adult 280 296 181 183 104 118 101 121 315 321 309 313 144 144 354 Female Adult 284 300 183 183 112 118 115 127 315 315 319 319 136 142 385 Female Juvenile 282 296 179 183 104 110 121 129 315 321 309 321 142 144 293 Male Adult 284 288 165 179 104 120 127 137 315 315 309 319 140 144 320 Female Adult 280 296 177 183 118 126 121 127 311 321 313 319 144 146 357 Female Juvenile 288 296 179 181 118 122 115 121 311 315 309 321 136 144 364 Male Juvenile 288 296 183 183 104 112 115 137 311 315 313 321 138 142 365 Male Juvenile 282 296 179 183 110 114 121 127 315 315 313 321 142 144 360 Male Juvenile 284 296 183 183 110 118 121 127 315 315 309 309 140 144 Burrow A4

304 Female Adult 284 292 179 179 102 118 127 135 315 315 313 317 140 146 Burrow A5

319 Female Adult 284 296 179 183 120 120 115 127 315 315 309 309 138 144 355 Female Adult 282 296 165 181 100 116 115 127 315 315 309 319 144 144 386 Female Juvenile 284 302 181 183 104 118 127 137 315 315 309 317 138 144

101

ID Sex Age D4 GS22 IGS-6 2g2 A116 MA018 SS-Bibl18

356 Male Juvenile 284 284 181 181 104 110 135 137 311 311 317 319 138 140 337 Male Juvenile 284 288 181 183 110 126 101 127 309 315 317 321 140 144 Burrow A6

144 Female Adult 284 294 183 185 102 104 123 129 315 315 323 323 140 142 Burrow A7

148 Female Adult 185 185 129 133 311 315 309 323 142 142

142 Female Adult 284 296 179 179 102 110 129 137 315 315 307 319 144 144 Burrow A8

387 Male Juvenile 286 296 179 183 104 122 127 153 311 311 309 321 146 148 150 Male Adult 282 282 179 183 102 102 127 135 311 311 313 319 144 144 325 Male Juvenile 288 294 177 181 104 108 127 137 311 315 313 323 142 144 362 Male Juvenile 280 294 181 185 102 104 127 137 311 315 309 313 136 140 384 Female Juvenile 284 302 181 183 108 112 125 129 311 311 313 315 144 146 363 Female Juvenile 290 300 181 183 100 102 129 137 313 315 313 323 150 152 333 Female Adult 284 296 171 183 102 104 137 137 315 315 309 319 146 146 138 Female Adult 284 288 179 183 102 108 121 123 311 311 309 313 146 146 326 Male Juvenile 286 286 181 185 102 108 121 123 311 311 313 321 140 144 324 Female Juvenile 284 302 171 185 104 116 133 137 311 311 313 313 142 144 334 Male Juvenile 284 292 171 185 102 104 133 135 311 313 307 309 142 148 336 Female Adult 290 300 171 183 104 118 123 123 311 315 319 323 142 144 361 Male Juvenile 280 284 179 185 104 108 121 127 311 315 309 313 142 144 358 Female Adult 284 302 171 175 102 104 127 127 311 315 307 313 140 148 291 Male Adult 284 288 177 179 102 104 125 129 311 311 309 315 140 144 139 Female Adult 284 288 175 181 122 130 123 133 311 315 307 315 146 146 301 Female Adult 288 302 175 185 104 122 127 137 315 315 311 313 146 148 353 Female Adult 288 288 181 183 102 102 133 137 315 315 313 321 140 144 382 Female Juvenile 286 292 175 181 102 110 133 137 311 315 307 319 136 140 383 Female Adult 294 302 179 185 100 106 129 137 315 315 307 315 146 146 271 Female Adult 284 294 181 185 104 104 129 135 311 315 311 321 142 144 292 Female Adult 284 294 183 185 102 102 129 137 311 311 313 321 146 146 270 Female Adult 290 296 183 185 102 112 129 135 311 313 319 321 148 148 Burrow A9

290 Female Adult 284 288 179 183 102 126 127 135 311 315 307 319 142 142 303 U U 282 282 179 183 104 124 129 135 311 315 311 313 144 146

102

ID Sex Age D4 GS22 IGS-6 2g2 A116 MA018 SS-Bibl18

302 Male Adult 284 288 175 183 100 120 133 137 315 315 307 315 144 148 323 Female Adult 284 286 183 183 104 120 137 141 315 315 311 319 140 150 359 Male Juvenile 284 290 183 183 102 104 135 141 311 313 311 313 140 144 Burrow A10

30 Female Juvenile 284 288 175 185 122 122 129 137 311 315 309 311 144 150 24 Female Juvenile 296 296 179 183 104 122 127 137 311 315 311 311 144 150 28 Male Juvenile 284 288 181 185 104 130 127 129 311 311 309 309 142 146 29 Female Juvenile 292 296 179 185 126 130 129 137 311 315 309 313 144 150 Burrow A11

27 Male Juvenile 284 288 183 185 122 130 127 137 311 315 309 311 142 144 31 U U 284 288 183 185 116 122 127 137 311 323 309 311 144 146 143 Female Adult 284 292 171 183 127 127 315 315 307 307 142 146

145 Female Adult 284 286 171 179 104 112 127 129 315 315 311 319 140 144 149 Female Adult 284 302 179 181 125 127 311 315 307 315 140 140

ID Sex Age D4 GS22 IGS-6 2g2 A116 MA018 SS-Bibl18 MNWR: Burrow B1

298 Female Adult 280 302 167 181 114 118 123 137 311 311 311 321 142 148 300 Male Adult 284 290 165 179 108 110 131 137 311 313 321 321 140 142 299 Male Adult 284 302 179 183 108 112 123 135 311 313 321 325 140 146 329 Female Adult 302 302 167 181 104 118 123 137 311 311 317 321 142 146 330 Female Juvenile 290 302 167 183 104 110 131 137 315 315 311 321 140 142 327 Female Adult 278 304 165 183 100 112 129 157 311 315 319 321 140 144 Burrow B2

174 Female Adult 300 300 179 183 110 114 97 131 311 315 307 317 140 148 297 Female Juvenile 296 296 167 185 102 110 121 133 311 311 317 319 142 144 332 Male Juvenile 282 296 165 165 100 128 123 133 309 309 311 311 140 142 177 Female Juvenile 296 296 165 183 110 126 137 137 311 315 319 319 140 148 295 Male Juvenile 280 302 167 171 108 110 131 131 311 311 321 321 136 146 296 Female Juvenile 288 296 165 171 100 106 115 131 311 311 311 321 142 146 Burrow B3

152 Male Juvenile 284 300 165 183 110 112 127 137 315 315 317 321 142 146 165 Male Adult 284 302 165 167 112 112 97 123 311 321 311 319 140 140 294 Female Adult 288 302 165 167 110 112 97 131 315 315 311 319 144 146

103

ID Sex Age D4 GS22 IGS-6 2g2 A116 MA018 SS-Bibl18

Burrow B4

190 Female Adult 300 304 165 167 112 118 121 123 311 313 309 317 144 144 331 Male Adult 284 304 183 183 110 114 123 137 313 313 305 311 140 142 157 Female Juvenile 300 300 165 171 102 110 135 135 321 321 317 317 136 146 163 Male Juvenile 288 288 179 183 102 104 121 137 311 311 321 321 140 140 167 Male Juvenile 284 300 165 167 102 112 121 139 311 315 309 321 136 142 171 Male Juvenile 284 304 165 167 110 118 123 157 313 315 309 321 142 146 162 Female Adult 284 292 167 179 108 118 121 137 311 321 305 317 146 152

ID Sex Age D4 GS22 IGS-6 2g2 A116 MA018 SS-Bibl18 SLNWR: Burrow C1

84 Female Juvenile 286 292 171 183 110 114 119 125 315 317 313 319 144 148 89 Female Juvenile 286 288 169 183 106 110 123 129 315 315 313 317 136 144 98 Male Juvenile 284 288 179 183 106 114 125 127 311 311 313 323 140 148 100 Female Juvenile 288 288 183 183 106 110 123 133 315 315 307 317 136 146 101 Male Juvenile 280 292 177 185 108 110 119 127 311 315 323 325 142 150 103 Female Juvenile 288 288 183 183 106 110 123 123 315 315 317 317 136 146 109 Male Juvenile 286 286 177 183 106 110 123 125 309 319 146 150

116 Male Juvenile 165 183 108 110 123 135 315 315 315 315 140 148

119 Female Juvenile 280 288 177 183 108 110 125 133 311 315 317 317 136 144 125 Female Juvenile 183 183 108 110 127 133 315 315 144 146

85 Male Juvenile 288 302 183 183 110 110 133 135 315 317 307 315 140 144 92 Female Juvenile 286 302 171 183 104 110 127 133 315 315 305 317 136 140 99 Female Juvenile 280 286 171 183 104 110 121 123 315 315 305 317 136 150 114 Male Juvenile 288 300 171 183 104 110 123 129 315 315 305 317 136 150 86 Male Juvenile 284 284 173 183 106 124 113 131 311 311 317 325 148 148 104 Female Juvenile 288 292 183 183 108 110 123 127 315 315 317 323 136 150 107 Female Juvenile 292 292 171 183 110 110 123 133 142 146

108 Male Juvenile 302 302 183 183 110 112 121 123 311 315 305 317 136 150 124 Male Juvenile 286 294 181 183 112 112 123 127 315 315 313 317 136 150 123 Male Juvenile 286 302 183 183 104 110 119 125 315 315 313 325 142 144 91 Female Juvenile 286 302 183 183 110 118 121 125 315 315 313 317 136 144 95 Female Juvenile 288 302 177 183 108 110 123 133 309 313 317 323 136 150 96 Female Juvenile 286 302 183 183 110 118 125 133 315 315 313 317 136 144

104

ID Sex Age D4 GS22 IGS-6 2g2 A116 MA018 SS-Bibl18

97 Male Juvenile 286 286 183 183 106 110 123 133 311 315 313 317 144 146 121 Male Juvenile 286 302 183 183 110 118 123 125 315 315 313 317 136 144 126 Female Juvenile 286 302 183 185 110 112 123 125 311 315 311 317 136 146 102 Female Juvenile 288 288 179 183 110 110 125 133 315 315 313 317 136 136 226 Male Juvenile 284 288 181 185 106 110 123 135 311 313 313 321 136 146 242 Male Adult 286 286 183 183 104 114 119 127 311 315 313 323 136 140 252 Female Juvenile 286 288 183 183 110 110 123 135 315 315 305 317 146 150 263 Female Juvenile 286 300 171 183 106 116 127 135 315 315 313 317 142 150 276 Female Adult 302 304 171 185 104 110 125 127 311 311 315 317 144 146 Burrow C2

87 Female Juvenile 286 294 173 179 110 110 127 135 315 315 311 319 142 150 105 Male Juvenile 282 282 181 183 102 110 113 135 315 315 317 317 140 148 110 Male Juvenile 282 294 183 185 126 126 125 127 311 315 136 148

256 Male Juvenile 284 286 177 183 108 108 127 135 313 313 317 317 148 148 120 Male Juvenile 280 290 181 183 108 124 129 129 311 313 313 313 136 138 Burrow C3

88 Male Juvenile 276 280 179 183 102 102 123 125 315 315 317 321 142 144 Burrow C4

90 Male Juvenile 286 300 171 183 104 110 123 129 315 315 317 321 136 148 112 Male Juvenile 302 302 183 185 104 110 123 127 315 315 305 307 136 146 111 Male Juvenile 284 288 179 181 106 114 129 129 315 315 309 315 136 148 117 Female Juvenile 302 302 183 183 110 112 127 129 315 315 305 307 136 150 127 Female Juvenile 286 286 181 183 110 118 123 133 315 315 317 317 136 142 236 Male Juvenile 288 294 185 185 108 108 129 135 315 317 305 317 136 136 248 Male Juvenile 302 302 183 185 106 118 121 123 311 315 313 317 146 146 241 Female Juvenile 288 288 179 183 104 106 125 135 311 311 321 323 140 148 251 Male Juvenile 288 302 185 185 104 106 119 123 315 317 317 325 136 148 277 Male Adult 284 294 185 185 104 110 119 129 311 313 317 321 136 140 221 Female Adult 288 302 171 183 104 110 119 125 311 315 307 315 136 150 228 Female Adult 286 286 171 185 104 104 117 127 311 315 315 317 136 136 238 Female Juvenile 288 288 181 185 106 122 125 135 315 315 311 315 142 148 246 Male Juvenile 302 302 183 185 106 110 129 135 311 315 319 325 142 146 267 Male Juvenile 286 300 177 179 106 110 129 135 315 315 319 321 142 142 Burrow C5

105

ID Sex Age D4 GS22 IGS-6 2g2 A116 MA018 SS-Bibl18

94 Male Juvenile 183 183 106 110 135 137 315 315 309 317 140 142

113 Female Juvenile 282 288 183 183 104 104 123 125 315 315 307 313 142 146 Burrow C6

115 Female Juvenile 280 286 181 183 106 110 123 129 315 315 317 321 136 148 118 Female Juvenile 278 288 183 183 104 110 123 129 315 315 305 317 136 150 122 Male Juvenile 169 179 110 118 125 129 313 315 309 321 136 148

106 Male Juvenile 302 302 171 183 110 112 127 133 311 311 319 323 144 146 218 Male Juvenile 284 286 183 185 104 108 113 127 311 315 313 317 146 150 231 Male Adult 284 288 183 183 104 106 123 123 311 315 311 317 142 148 274 Female Adult 284 288 179 183 102 106 125 129 315 315 315 317 136 144 275 Male Adult 284 302 183 183 106 118 127 133 315 315 315 317 142 148 Burrow C7

220 Male Juvenile 286 288 183 183 110 110 125 135 311 311 305 307 142 150 232 Female Juvenile 284 288 185 185 106 110 123 125 313 315 317 325 136 136 260 Male Juvenile 300 300 177 185 108 110 129 135 315 315 321 321 142 144 Burrow C8

222 Male Juvenile 282 290 171 185 106 106 125 127 311 315 317 325 136 146 227 Male Juvenile 286 292 183 183 106 110 127 127 311 315 317 319 146 146 258 Male Juvenile 298 302 183 183 106 106 123 123 315 315 313 315 140 144 259 Female Juvenile 284 286 179 183 108 110 127 127 311 315 317 321 136 142 219 Male Adult 284 288 183 185 110 118 133 133 315 317 315 317 136 142 266 Male Juvenile 300 300 179 183 118 122 119 127 315 315 307 315 138 140 280 Male Adult 282 292 183 185 110 110 127 133 315 315 311 319 142 146 257 Female Adult 288 292 185 185 106 106 127 135 315 315 307 315 140 144 229 Female Adult 280 286 177 183 108 108 127 133 311 315 315 317 144 146 250 Male Juvenile 284 288 171 183 106 110 119 129 311 315 307 315 136 142 234 Female Juvenile 286 288 183 183 104 110 119 133 313 315 307 317 140 148 269 Male Juvenile 284 286 181 181 100 108 125 127 315 315 307 309 136 144 279 Male Adult 276 300 181 183 106 110 133 135 315 315 315 317 142 144 Burrow C9

224 Male Juvenile 280 288 177 183 110 118 125 133 311 315 317 317 142 144 239 Male Juvenile 286 302 183 183 110 118 127 135 311 315 315 317 136 146 265 Female Juvenile 284 302 181 185 102 110 127 127 315 315 309 325 144 148 262 Male Juvenile 288 294 179 185 102 110 127 133 315 315 321 325 136 148

106

ID Sex Age D4 GS22 IGS-6 2g2 A116 MA018 SS-Bibl18

264 Male Juvenile 272 284 179 181 106 110 127 129 315 315 305 317 136 144 Burrow C10

225 Male Juvenile 288 302 183 185 106 122 125 127 311 315 311 315 136 150 233 Male Adult 284 288 171 185 106 110 123 135 311 315 315 317 142 146 245 Male Juvenile 286 286 169 183 106 110 123 125 315 317 315 317 136 144 247 Male Juvenile 286 286 183 185 106 116 133 135 315 315 144 150

223 Female Adult 288 302 179 183 110 114 123 155 311 315 315 317 136 146 230 Male Juvenile 288 288 181 183 108 118 123 127 311 315 315 317 136 146 244 Male Juvenile 288 302 183 183 110 114 125 125 315 321 315 317 140 146 255 Female Juvenile 286 288 171 185 108 110 123 133 311 315 317 325 144 148 235 Male Juvenile 286 302 183 183 106 110 129 135 311 311 307 315 148 148 249 Female Juvenile 288 300 171 183 110 114 125 133 315 315 317 325 144 148 273 Male Adult 288 288 179 183 104 110 123 127 311 315 317 321 148 148 Burrow C11

237 Male Juvenile 284 288 171 179 106 118 123 125 315 315 315 317 136 136 240 Male Juvenile 284 286 171 183 106 118 133 133 315 317 317 317 136 146 253 Female Juvenile 288 302 183 185 108 110 123 129 311 315 317 325 144 146 278 Female Adult 276 300 181 183 102 110 129 133 315 315 313 315 142 148 Burrow C12

243 Male Juvenile 276 300 183 183 110 118 123 135 315 317 313 317 142 142 254 Male Juvenile 284 288 171 183 108 114 133 133 311 315 311 325 136 140 Burrow C13

261 Female Juvenile 284 302 181 185 106 110 129 135 315 315 307 317 142 148 268 Male Juvenile 284 302 181 185 108 110 127 129 315 315 317 321 148 148

107

APPENDIX C

TEST FOR DEPARTURE FROM HARDY-WEINBERG EQUILIBRIUM

Appendix C: Summary statistics for each microsatellite locus for 3 Central Valley populations of Otospermophilus beecheyi from FSTAT v.2.9.3.2 analysis. SJRNWR = San Joaquin River National Wildlife Refuge; MNWR = Merced National Wildlife Refuge; SLNWR = San Luis National Wildlife Refuge; a = number of alleles; FIS = inbreeding coefficient; deviation from Hardy-Weinberg by exact test (P), where bolded values are significant at p<0.05, but none are significant after Bonferroni correction (p<0.00714286).

Population Locus a FIS P D4 13 0.043 0.2000 GS22 8 -0.019 0.7357 IGS-6 15 -0.013 0.7000 SJRNWR 2g2 17 -0.008 0.6071 A116 6 0.078 0.1929 MA018 9 0.007 0.5143 SS-Bibl18 9 0.079 0.0571 D4 14 0.126 0.0143 GS22 9 -0.015 0.6714 IGS-6 12 0.024 0.2929 MNWR 2g2 18 0.033 0.1929 A116 5 0.021 0.3357 MA018 10 0.012 0.5000 SS-Bibl18 9 0.010 0.4000 D4 15 0.127 0.0071 GS22 9 0.037 0.2571 IGS-6 13 -0.038 0.8643 SLNWR 2g2 13 -0.025 0.7929 A116 6 0.092 0.1571 MA018 11 -0.073 0.9857 SS-Bibl18 8 -0.027 0.8143

108

APPENDIX D

TEST FOR LINKAGE DISEQUILIBRIUM

Appendix D: Results from linkage disequilibrium analysis conducted in FSTAT v.2.9.3.2. Bolded p-values are significant at p<0.05, but no comparisons are significant after Bonferroni correction (p<0.00238095). SJRNWR = San Joaquin River National Wildlife Refuge; MNWR = Merced National Wildlife Refuge; SLNWR = San Luis National Wildlife Refuge.

Population Locus 1 Locus 2 P-Value D4 GS22 0.07143 D4 IGS-6 0.34286 D4 2g2 0.77857 D4 A116 0.00238 D4 MA018 0.15238 D4 SS-Bibl18 0.44286 GS22 IGS-6 0.11190 GS22 2g2 0.85952 GS22 A116 0.58571 GS22 MA018 0.03810 SJRNWR GS22 SS-Bibl18 0.24762 IGS-6 2g2 0.26429 IGS-6 A116 0.71190 IGS-6 MA018 0.00238 IGS-6 SS-Bibl18 0.05238 2g2 A116 0.04048 2g2 MA018 0.34286 2g2 SS-Bibl18 0.98095 A116 MA018 0.03571 A116 SS-Bibl18 0.92619 MA018 SS-Bibl18 0.64286 D4 GS22 0.22381 D4 IGS-6 0.63810 MNWR D4 2g2 0.19048 D4 A116 0.17143

109

Population Locus 1 Locus 2 P-Value D4 MA018 0.98095 D4 SS-Bibl18 0.83095 GS22 IGS-6 0.62381 GS22 2g2 0.30952 GS22 A116 0.82619 GS22 MA018 0.19048 MNWR GS22 SS-Bibl18 0.26667 IGS-6 2g2 0.18571 IGS-6 A116 0.11905 IGS-6 MA018 0.67381 IGS-6 SS-Bibl18 0.20238 2g2 A116 0.41905 2g2 MA018 0.97619

2g2 SS-Bibl18 0.31667 A116 MA018 0.76667 A116 SS-Bibl18 0.88333 MA018 SS-Bibl18 0.42857 D4 GS22 0.00476 D4 IGS-6 0.07381 D4 2g2 0.10714 SLNWR D4 A116 0.34762 D4 MA018 0.13095 D4 SS-Bibl18 0.15714 GS22 IGS-6 0.19286 GS22 2g2 0.85952 GS22 A116 0.01667 GS22 MA018 0.02619

GS22 SS-Bibl18 0.68095 IGS-6 2g2 0.11190 IGS-6 A116 0.79286 IGS-6 MA018 0.00952

110

Population Locus 1 Locus 2 P-Value IGS-6 SS-Bibl18 0.03810 2g2 A116 0.29524 2g2 MA018 0.75476 SLNWR 2g2 SS-Bibl18 0.12143 A116 MA018 0.35714 A116 SS-Bibl18 0.30952 MA018 SS-Bibl18 0.00238

111

APPENDIX E

MEAN LOG PROBABILITY AND DELTA K PLOTS FROM STRUCTURE

ANALYSES a)

Appendix E: Mean log probability of data and ΔK plots from STRUCTURE analyses depicting K=2 as the most likely number of genetic clusters for a) San Joaquin River NWR; b) Merced NWR; and c) San Luis NWR.

112

113

114

115

116

117

APPENDIX F

BAR PLOTS FROM STRUCTURE ANALYSES WHEN LOCPRIOR MODEL

USED a)

Appendix F: Results from STRUCTURE analyses using the LOCPRIOR model (geographic locations provided) for San Joaquin River NWR (a), Merced NWR (b), and San Luis NWR (c). Each bar represents a single individual, and each color represents a distinct genetic cluster. The bars are filled by colors representing the likelihood of membership to each genetic cluster.

118

APPENDIX G

CORRELOGRAMS FROM SEX-SPECIFIC SPATIAL AUTOCORRELATION

ANALYSES a)

San Joaquin River NWR - Males

0.500

r 0.000 r -0.500 U 1 2 3 4 5 6 7 8 9 L Geographic Distance Class (km)

San Joaquin River NWR - Females

0.200

r 0.000 r -0.200 U 1 2 3 4 5 6 7 8 9 L Geographic Distance Class (km)

Appendix G: Correlograms for each sex from spatial autocorrelation analyses within each refuge. The solid line represents the autocorrelation coefficient (r). The error bars represent the bootstrap 95% confidence intervals around the r estimate for that distance class. The dashed lines represent the upper and lower 95% confidence intervals around r = 0 (null hypothesis of no spatial structure) generated by 9,999 permutations of samples across the distance classes. a) San Joaquin River NWR; b) Merced NWR; c) San Luis NWR.

119

Merced NWR - Males

0.500

r 0.000 r -0.500 U 1 2 3 L Geographic Distance Class (km)

Merced NWR - Females

0.500

r 0.000 r -0.500 U 1 2 3 L Geographic Distance Class (km)

120

San Luis NWR - Males

0.200

r 0.000 r -0.200 U 1 2 3 L Geographic Distance Class (km)

San Luis NWR - Females

0.500

r 0.000 r -0.500 U 1 2 3 4 L Geographic Distance Class (km)

121

APPENDIX H

PARENTAGE ASSIGNMENT FROM CERVUS ANALYSES

Appendix H: Parentage assignment from CERVUS analyses in 3 Central Valley populations of Otospermophilus beecheyi for 3 years. a) SJRNWR = San Joaquin River NWR; b) MNWR = Merced NWR; c) SLNWR = San Luis NWR. LOD = log- likelihood ratio. Single asterisks indicate that a delta (Δ) value falls within the relaxed confidence level. Two asterisks indicate that a delta value falls within the strict confidence level. Offspring for which neither candidate parent was assigned within either the relaxed or strict confidence levels are not shown. a) SJRNWR Putative Putative Year Offspring Mother LOD Δ Father LOD Δ 1 333 -0.90 0.00 293 3.73 3.73** 2 333 3.55 0.16* 151 0.53 0.53** 3 358 4.85 3.41** 302 -3.28 0.00 7 333 -0.67 0.00 291 0.56 0.56** 8 145 0.77 0.01* 293 3.73 3.73** 10 148 0.70 0.70* 151 -4.28 0.00 12 146 5.03 2.88** 129 -0.92 0.00 14 304 1.17 1.17* 151 -5.00 0.00 15 146 4.71 0.21* 130 -4.11 0.00 2015 16 149 0.73 0.73* 293 -5.29 0.00 18 146 5.65 4.99** 150 -4.37 0.00 19 139 8.22 8.22** 302 2.58 2.58** 20 301 2.90 0.65* 291 -1.69 0.00 23 137 -0.92 0.00 291 0.02 0.02* 26 148 6.10 6.10** 130 -9.35 0.00 28 148 4.28 4.28** 291 -3.31 0.00 131 142 1.18 0.00 140 0.33 0.33* 132 292 3.74 3.33** 129 -4.32 0.00 133 138 2.36 2.36* 151 -4.46 0.00 2016 147 146 1.27 1.22* 27 3.46 3.46** 322 272 0.94 0.94* 12 -8.88 0.00

326 136 2.01 2.01** 22 -5.67 0.00 2017 334 271 -0.95 0.00 151 3.42 3.42** 335 354 1.38 1.38* 302 -5.08 0.00 357 30 -4.15 0.00 321 3.92 3.92**

122

359 323 1.79 1.79** 150 0.16 0.00 360 319 0.28 0.28* 321 5.43 5.43** 361 128 0.67 0.00 28 2.29 1.95** 2017 363 141 1.38 1.38* 131 -6.52 0.00 364 354 -2.53 0.00 22 4.10 4.10** 384 136 2.61 0.77* 16 1.71 1.71* 385 148 -2.52 0.00 321 5.94 5.61**

b) MNWR Putative Putative Year Offspring Mother LOD Δ Father LOD Δ 33 34 0.61 0.61* 37 -2.79 0.00 35 61 0.31 0.31* 172 -5.29 0.00 36 190 1.40 1.40* 48 -5.28 0.00 39 156 -2.19 0.00 299 0.14 0.14*

40 156 4.92 3.05** 49 -0.34 0.00 46 174 5.06 5.06** 49 3.23 3.23** 50 298 1.01 1.01* 48 -9.22 0.00 51 158 0.34 0.34* 299 -0.37 0.00

53 83 -1.54 0.00 49 4.34 4.34** 55 83 -0.04 0.00 49 2.45 2.45** 60 154 -4.76 0.00 48 7.76 7.76** 62 170 -2.17 0.00 299 3.37 3.37** 2015 63 61 0.10 0.10* 331 -3.62 0.00 64 298 5.30 3.24** 300 -1.18 0.00 65 161 -4.09 0.00 173 8.34 8.34** 66 34 -5.12 0.00 173 4.10 3.66**

67 156 2.67 2.67** 48 -4.46 0.00 68 168 4.57 0.68* 48 -0.10 0.00 69 78 1.73 1.73** 299 -2.21 0.00 70 190 -0.58 0.00 79 0.75 0.75*

71 78 4.95 4.92** 37 -2.94 0.00 72 78 0.88 0.39* 79 -4.02 0.00

123

Appendix H: Parentage assignment from CERVUS analyses in 3 Central Valley populations of Otospermophilus beecheyi for 3 years. a) SJRNWR = San Joaquin River NWR; b) MNWR = Merced NWR; c) SLNWR = San Luis NWR. LOD = log- likelihood ratio. Single asterisks indicate that a delta (Δ) value falls within the relaxed confidence level. Two asterisks indicate that a delta value falls within the strict confidence level. Offspring for which neither candidate parent was assigned within either the relaxed or strict confidence levels are not shown. b)MNWR 73 83 -0.21 0.00 79 1.80 1.80** 75 298 1.92 1.21* 299 -1.34 0.00 2015 77 162 1.05 0.38* 165 -9.50 0.00 81 162 0.86 0.86* 37 -0.12 0.00 152 156 0.20 0.20* 62 0.95 0.95* 153 190 4.09 3.67** 60 -2.24 0.00 163 66 2.50 2.50** 49 -3.49 0.00 164 33 3.99 3.70** 39 0.76 0.76* 169 52 1.06 1.06* 69 -7.66 0.00 2016 171 190 1.14 1.14* 172 3.35 3.35** 180 77 1.56 1.56* 65 3.02 0.00 181 327 -2.51 0.00 63 0.60 0.46* 194 298 4.95 4.37** 41 -0.09 0.00 202 55 0.45 0.21* 173 -1.37 0.00 c) SLNWR Putative Putative Year Offspring Mother LOD Δ Father LOD Δ 84 276 -8.94 0.00 242 0.15 0.15* 85 221 -5.35 0.00 219 0.61 0.61* 86 274 -9.43 0.00 231 0.42 0.42* 88 274 1.32 1.32** 231 -6.45 0.00 89 274 1.53 1.53** 242 -6.21 0.00 94 257 -2.99 0.00 279 2.65 2.65** 102 274 -0.37 0.00 219 1.12 1.12* 103 223 1.90 1.90** 231 -0.23 0.00 2015 106 276 1.29 1.29** 280 -4.12 0.00 108 223 0.38 0.38* 275 -9.70 0.00 119 229 6.82 6.82** 219 -0.88 0.00 122 274 -5.01 0.00 277 0.08 0.08* 123 221 -4.46 0.00 242 0.27 0.27* 125 229 3.81 3.81** 280 2.16 1.48** 126 276 2.40 2.18** 231 -7.47 0.00

124

2016 248 91 2.05 2.05** 233 -1.34 0.00 249 84 -2.49 0.00 279 1.52 1.52* 250 221 4.38 2.73** 275 -1.69 0.00 252 118 4.22 0.40* 97 1.93 0.00 253 125 -1.49 0.00 233 0.88 0.74* 258 89 0.11 0.11* 116 0.23 0.01* 261 115 -0.49 0.00 233 2.48 1.65* 262 102 0.17 0.17* 110 -0.07 0.00 263 99 -4.66 0.00 279 2.34 0.73* 264 274 3.89 3.89** 112 -5.31 0.00 267 87 5.79 5.79** 90 -3.41 0.00 268 115 0.91 0.91* 110 -5.42 0.00