Darevskia rostombekowi and other unisexual species of this genus reproduce normally via true parthenogenesis. Typically, diploid parthenogenetic reptiles exhibit some amount of clonal diversity. However, allozyme data from D. rostombekowi have suggested that this species consists of a single clone. Herein, we test this hypothesis by evaluating variation at three variable microsatellite loci for 42 specimens of D. rostombekowi from four populations in Armenia. Analyses based on single nucleotide polymorphisms of each locus reveal five genotypes or presumptive clones in this species. All individuals are heterozygous at the loci. The major clone occurs in 24 individuals and involves three populations. Four rare clones involve one or several individuals from one or two populations. Most variation owes to parent-specific single nucleotide polymorphisms, which occur as heterozygotes. This result fails to reject the hypothesis of a single hybridization founder event that resulted in the initial formation of one major clone. The other clones appear to have originated via post-formation microsatellite mutations of the major clone.The all-female Caucasian rock lizard  Questions concerning origin and evolution of parthenogenesis in lizards have received considerable attention in recent years –6. ParthIndotyphlops braminus [Sphyrna tiburo [Lepidophyma (L. reticulatum and L. flavimaculatum) [Leposoma percarinatum [Darevskia) or triploid (as in Aspidoscelis) and commonly exhibit high levels of nuclear gene diversity because of fixed heterozygosity at codominant loci [Among vertebrates, true parthenogenesis, where reproduction proceeds without male participation, has been detected in squamate reptiles, especially in lizards  and alsobraminus ,9. It ala tiburo . The Laca tiburo . In mosta tiburo ,13, but a tiburo . Only paculatum)  and the arinatum  did not arinatum , a new sarinatum . Becauseant loci . The morant loci .Cnemidophorus lemniscatus [Heteronotia binoei [Aspidoscelis neomexicana [A. tesselatus [Each parthenogenetic species usually consists of several clonal lineages, possibly caused by either mutations , multiple origins from different pairs of founders, rare new hybridization events, or some level of genetic recombination ,20,21. Cniscatus ,26 and Ha binoei . Aspidosmexicana  and A. tsselatus  exhibit Darevskia is of particular significance because its species have been the subjects of extensive ecological and biogeographical study, and because parthenogenesis has arisen several times within the group [Darevskia includes 24 bisexual species and seven parthenogenetic, diploid species of hybrid origin [Darevskia are in accordance with Parker et al.’s model [D. rostombekowi.The lacertid genus he group . Darevskd origin . Previoud origin –34. Gene’s model , with thDarevskia rostombekowi  and D. raddei . The parental species belong to different clades of Darevskia [Darevskia rostombekowi have a chromosome set of 2n = 38 [D. raddei [D. dahli, D. armeniaca, and D. unisexualis, in which several allozyme clones were found [D. portschinskii occurs in southern Georgia, northern Armenia and western Azerbaijan, and the maternal species D. raddei is distributed in Armenia and adjoining Georgia, western Azerbaijan, and northern/western Iran. Several discrete populations of D. rostombekowi occur in Lori and Tavush provinces of northern Armenia (elevation range 700–1500 m), and one small, isolated high-montane (2000 m) population along the southeastern shore of Lake Sevan (Gegharkunik province), in the vicinity of the village of Zagalu (renamed recently to Tsovak). The Sevan and Vardenis mountain ranges, which rise to more than 3000 m, isolate this population from the nearest populations inhabiting the foothills near the Kura River by Lake Gai-Gel’, Azerbaijan. The mountains are insurmountable geographical barriers for D. rostombekowi. No allozyme analysis exists for this population.g Murphy ) is one arevskia ,24 and tarevskia . Phyloge 2n = 38 , are cha 2n = 38  and exhi. raddei . This ware found . At presD. rostombekowi  originated from one southern Armenian population (Yeghegnadzor) of D. raddei [b; Cytb) from D. rostombekowi at Tsovak indicated a maternal origin from D. raddei at Yeghegnadzor [Cytb, which suggested the existence of a second matriline of D. rostombekowi specific to Tsovak. In addition, interpopulation comparisons of genetic variation in D. rostombekowi, based on multilocus DNA fingerprinting with microsatellite hybridization probes, and RAPD markers for the nuclear genome, indicated significant differences between lizards from northern Armenian and Tsovak [Analysis of mitochondrial DNA (mtDNA) showed that all northern matrilines of Armenian . raddei . Initialegnadzor , but subegnadzor –40 resold Tsovak ,42. Thesd Tsovak , highligD. rostombekowi including variation at three microsatellite-containing loci identifies genotypes of D. rostombekowi from four Armenian populations. We apply a new approach to genotyping the related parthenogenetic species D. dahli [D. rostombekowi—previously presumed to be a monoclonal parthenogenetic species—has several clones. New, the approach reveals parent-specific markers consisting of microsatellites and single nucleotide polymorphisms (SNPs) located out of the microsatellite cluster for each allele of each locus. The SNPs yield direct information about interspecific hybridization founder events, and microsatellite variability provides information concerning possible mutations in the initial hybrid clones that give rise to new genotypes. Interspecies allele comparisons involve homologous microsatellite loci from the parental bisexual lizard species D. raddei and D. portschinskii. All alleles present in D. rostombekowi also occur in the parental species.Our study examines the clonal diversity in D. dahli  and showD. rostombekowi (n = 42) from four populations, D. raddei (n = 65) from 13 populations, and D. portschinskii (n = 27) from two populations in Armenia were analyzed (D. rostombekowi as an Endangered Species by the IUCN (2009) and the species is not protected by CITES. The study was approved by the Ethics Committee of Moscow State University (Permit Number: 24–01) and was carried out in strict accordance with their ethical principles and scientific standards. All specimens were captured with a noose. Blood samples were taken from the tail veins of lizards under chloroform anesthesia, and then these lizards were released. DNA was isolated from lizard blood by using the standard phenol–chloroform extraction method with proteinase K, and resuspended in TE buffer, pH 8.0.DNA samples of analyzed . Sample Three loci, Du215, Du281, and Du323 were PCR-amplified using previously described primer pairs ,44. ThesD. rostombekowi [D. raddei and D. portschinskii were shown in Data on genetic variation of these loci in ombekowi  and on aThe number of alleles, allelic richness, as a measure of allele counts adjusted for sample size, and expected heterozygosity, as a measure of gene diversity, were calculated per locus and per population by using Fstat v.2.9.3.2 , GenePopD. rostombekowi were heterozygous at the three loci and contained two alleles that differed from each other in length and structure of the microsatellite clusters. Two of them, Du281 and Du323, had also single nucleotide variants (SNVs) in fixed positions of the flanking regions , Du215 had five alleles, Du281 had four, and Du323 had two , genotype 1 was most abundant; it was found in three populations. Genotypes 2 and 3 were represented by eight individuals each  in two and one population, respectively. Finally, rare genotypes 4 and 5 were represented by one individual each .D. rostombekowi varied from 0% to 75%  and D. portschinskii (n = 27) were given in D. raddei, and a little more for male D. portschinskii. Du215 and Du281 had heterozygote deficits. That of Du281 also occurred in male D. raddei. Female D. raddei had an excess of heterozygotes at Du323. Deviations from Hardy-Weinberg expectations probably owed to small sample sizes. The heterozygote deficits may have owed to a Wahlund effect due to the 65 individuals of D. raddei coming from 13 localities.Indices of gene diversity in males and females of the parental species D. rostombekowi originated from a single or multiple interspecies hybridization event(s), genotype-specific markers, formed by combinations of parent-specific SNVs of both alleles in Du281 and Du323 were evaluated  and D. portschinskii (n = 27). Sequencing obtained 37 alleles from D. raddei and 38 from D. portschinskii 2 formed Du323(rad)1 in D. raddei and allele Du323(rost)1 occurred in D. portschinskii as Du323(port)5. Similarly, alleles Du215(rost)1–4 were the same as Du215(port)7, 5, 10, 7, respectively, and Du215(rost)5 was the same as Du215(rad)12. Further, Du281(rost)1–3 had the motif (GATA)n, and specifically (GATA)2 GAGAT. The locus also appeared as Du281(rad)3 and Du281(rost)4 with a perfect (GATA)10; these coincided with alleles Du281(port)6 and 7, respectively. Due to the high mutation rate of microsatellite DNAs [D. rostombekowi and its parents. The dendrograms of genetic similarity for Du281 and Du323 loci, for which we found SNV markers, were shown in D. raddei, and others from paternal species D. portschinskii. For example, Du281(rost)1, 2, and 3 showed similarity with all Du281 alleles of D. raddei, and Du281(rost)4 was similar to two Du281 alleles of D. portschinskii. Du323(rost)1 had high similarity with Du323 alleles of D. portschinskii, and Du 323(rost)2 had high similarity with Du323 alleles of D. raddei.Alleles of Du215, Du281, and Du323 in chinskii . Microsaite DNAs , in someD. rostombekowi reject the hypothesis of monoclonality based on allozymes [D. armeniaca, D. dahli and D. unisexualis have more than one allozyme clone [D. dahli, whereas three microsatellite-containing loci detected 11 clones [D. rostombekowi, suggesting that it was formed by one hybridization event. Identical SNVs but different microsatellite markers show that the hybridization event involved very similar female D. raddei and male D. portschinskii in one population.Given that lizards with identical genotypes represent a distinct clone, the several microsatellite clones of llozymes . Howeverllozymes . The obsme clone  and micr1 clones . IdenticD. rostombekowi formed first. Assuming that unisexual species originating from a single hybridization exhibit a widespread and common clone with a few rare clones [D. rostombekowi might be ancestral . However, the finding of a second mitotype of D. rostombekowi specific for the Tsovak population [D. raddei in the hybridization. To examine this possibility, additional genotype-specific markers from more microsatellite loci are necessary.Analysis of mitochondrial DNA shows that pulation  indicateDarevskia exhibit low levels of clonal diversity and a high degree of mtDNA similarity with respect to their parental species. Their recent origins appear to best explain these patterns, while they may have largely different ages. Darevsky [Darevskia originated in the Caucasus after the last glacial maximum and that all species have had the same amount of time to evolve new clones, approximately 5000–7000 years ago. However, a multilocus analysis for the parthenogens D. unisexualis, D. bendimahiensis and D. uzzeli dated their origins to between 200 000 and 70 000 years ago [Darevskia and these predate the last glacial maximum. Different parental lineages hybridized several times in different geographic regions [Cytb data. If this calculation is applicable to lizards, the age of D. rostombekowi would be approximately 200 000 years old (2% divergence) [Parthenogenetic species of Darevsky  suggesteears ago . These r regions . Further regions  providedergence) .Heteronotia binoei (Gekkonidae) [Aspidoscelis tessellatus and A. neomexicanus [D. dahli, D. armeniaca, and D. unisexualis [Darevskia rostombekowi has two times lower clonal diversity than D. dahli [D. rostombekowi may have a more recent origin than D. dahli, or it suffered a severe bottleneck.Electrophoretic studies of proteins and mitochondrial DNA have shown that parthenogenetic lizards tend to be multiclonal. A high level of variability of allozyme loci was discovered in konidae) . Geneticexicanus  and in psexualis . DarevskD. dahli . AlthougD. rostombekowi inhabiting the coast of Lake Sevan constitutes a chromosomal race and/or a clone that emerged because of chromosomal mutations occurring in the course of karyological evolution.Some cytogenetic and molecular characteristics of the genome of hybrid species differ from those of the bisexual species due to disturbance in the segregation of chromosomes. This results in cytogenetic variability, different genomic activity and mutations in some loci ,31,42,61D. rostombekowi. Future studies may determine whether the clonal diversity owes to a single or multiple interspecific hybridization events, and identify the role mutations play in altering the initial clone(s).In summary, our analyses discover multiclonal genetic structure in the parthenogenetic lizard S1 Fig*The number of alleles is shown in square brackets.(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.


Ctenomys sociabilis) and the Patagonian tuco-tuco (C. haigi). Previous analyses using microsatellites revealed no post-eruption changes in genetic variation in C. haigi, but an unexpected increase in variation in C. sociabilis. To explore this outcome further, we used targeted gene capture to sequence over 2,000 putatively neutral regions for both species. Our data revealed that, contrary to the microsatellite analyses, the eruption was associated with a small but significant decrease in genetic variation in both species. We suggest that genome-level analyses provide greater power than traditional molecular markers to detect the genetic consequences of population size changes, particularly changes that are recent, short-term, or modest in size. Consequently, genomic analyses promise to generate important new insights into the effects of specific environmental events on demography and genetic variation.Marked reductions in population size can trigger corresponding declines in genetic variation. Understanding the precise genetic consequences of such reductions, however, is often challenging due to the absence of robust pre- and post-reduction datasets. Here, we use heterochronous genomic data from samples obtained before and immediately after the 2011 eruption of the Puyehue-Cordón Caulle volcanic complex in Patagonia to explore the genetic impacts of this event on two parapatric species of rodents, the colonial tuco-tuco ( Accordingly, the genetic consequences of bottlenecks have been the focus of numerous empirical and theoretical studies (e.g.6). Such consequences can be difficult to assess, however, particularly when reductions in population size are modest in magnitude or duration. In part, this difficulty reflects the frequent use of traditional molecular markers such as microsatellites or mitochondrial loci, both of which may have limited power to detect changes in genetic variation over small spatial or temporal scales3. In contrast, examination of much larger numbers of markers drawn from across the genome should increase the ability to detect temporal and spatial differences in genetic variation5.Significant reductions in population size  and the parapatric Patagonian tuco-tuco (C. haigi) have been the subjects of extensive behavioral, ecological, demographic, and genetic research, including comparative analyses of genetic variation over multiple spatial and temporal scales15. In 2011, both species were significantly affected by the eruption of the Puyehue-Cordón Caulle volcanic complex in southern Chile, resulting in deceases in population density of ~40% for C. sociabilis and ~25% for C. haigi16 and providing a rare opportunity to compare directly levels of genetic variation before and after a natural population decline. Notably, previous analyses based on microsatellite markers16 suggested that this event was associated with a significant post-eruption increase in genetic variation in C. sociabilis but not in C. haigi.To evaluate the immediate genetic consequences of abrupt, naturally occurring reductions in population size, we generated a genome-wide panel of single-nucleotide polymorphisms for two species of ctenomyid rodents from the Limay Valley region of southwestern Argentina. The colonial tuco-tuco , the more immediate impacts of these demographic parameters are not well characterized. As a result, our analyses of the effects of the Puyehue-Cordón Caulle eruption should generate important new insights into the role of demographic processes in shaping short-term genomic responses to pronounced reductions in population size.To explore this unexpected outcome in greater detail, we employed targeted sequence capture to generate a large number of genome-wide markers for each study species. Specifically, we sought to determine if the expected greater resolution of these genomic analyses would reveal the same post-eruption increase in genetic variation in Ctenomys) studied have been the subjects of long-term field research on behavior, ecology, and demography. The colonial tuco-tuco (Ctenomys sociabilis) is endemic to an approximately 1000 square kilometer area in the western Limay Valley and adjacent hills of Neuquén Province, Argentina . In contrast, the Patagonian tuco-tuco (C. haigi) is much more widely distributed in the eastern Limay Valley and surrounding regions of Río Negro and Neuquén Provinces, Argentina . A focal study population for each species was established in 1992 on Estancia Rincon Grande (C. sociabilis) and Estancia San Ramon (C. haigi); these study sites are located a few hundred meters from one another, on opposite sides of the Rio Limay24.The two species of tuco-tucos  from the same population. No individual was included more than once in our analyses of genetic variation. To minimize potential kinship among the individuals examined, the pre-eruption samples were chosen to represent 13 different burrow systems, the majority of which (76.5%) were sampled only once. In contrast, the 31 post-eruption samples were from 9 burrows; of these, 6 of these burrows had multiple residents included in our post-eruption dataset. To add spatial perspective to our analyses, post-eruption samples were also obtained from 9 additional individuals from a second population of C. sociabilis at Estancia La Lonja, located about 3 km to the northwest of the focal Rincon Grande population. The La Lonja population had not been sampled prior to the eruption. Due to logistic challenges imposed by the substantive ash fall in the region, this was the only non-focal population of C. sociabilis that we were able to access following the eruption. Sampling of the focal study population of C. haigi at San Ramon consisted of 12 pre-eruption and 17 post-eruption (December 2012–January 2013) samples, with no material obtained from additional populations of this species.Tissue samples from 57 DNA was extracted from all tissue samples using the Qiagen DNEasy Blood and Tissue extraction kits. Extractions were performed immediately prior to the preparation of genomic libraries (see below) to minimize the possibility of sample contamination. Following extraction, DNA sample concentrations were measured using a Qubit fluorometer  and Nanodrop (ThermoFisher). Samples with low yields were re-extracted using a ZR Genomic DNA Tissue Miniprep kit (Zymo); this process, followed by ethanol precipitation, increased the concentrations of DNA in these extracts.et al.30, as modified by Crawford31. In brief, 580 ng of DNA from each individual was sheared using a BioRuptor Sonicator (Diagenode), after which a small quantity of the resulting fragmented DNA was visualized on a 1.2% agarose gel to verify fragment size distribution. Sonication was performed for four rounds at 90-second intervals to optimize fragment sizes of 450 bp. We then performed blunt-end repair using T4 DNA polymerase as per Meyer and Kircher32, after which reactions were cleaned using Ampure XP beads. We then ligated P5 and P7 adapters to each fragment and performed adapter fill-in with Bst polymerase. Following this, we measured DNA concentration using a Nanodrop and then conducted two rounds of indexing PCR reactions (11 cycles each) using a Phusion High Fidelity kit and standard Illumina indices. Every sample was then divided into two pools, each of which was indexed separately with a unique combination of P5 and P7 indices in order to reduce errors during sequencing30. We used experimental assays of indexing PCRs with different numbers of cycles, followed by analysis of the resulting fragment length distribution with a Bioanalyzer assay, to determine the number of cycles per reaction that would minimize the number of PCR duplicates and thus maximize read mapping33. Once indexing PCRs were complete, we quantified DNA concentrations again using Nanodrop, and then performed automated size selection  to isolate DNA fragments ranging in size from 580 to 610 bp. Fifty nanograms of each size-selected library were then aggregated in to a single pool in preparation for the targeted gene capture.DNA libraries for targeted gene capture were prepared following the dual index protocol of Kircher 31. We identified 2,027 target segments of nuclear DNA based on analyses of C. sociabilis and C. haigi transcriptomes31. Targeted regions were approximately 1,200 bp long and putatively neutral. Capture of these target regions was completed using the NimbleGen SeqCap protocol for hybridization, with several modifications. In brief, we performed an in-solution hybridization of our pooled libraries with the custom probes. After incubation at 47 °C for 70 hours, we washed and recovered the captured multiplex DNA sample using M-270 Streptavidin Dynabeads. We then amplified the captured DNA via ligator-mediated PCR using the HiFi HotStart ReadyMix (KAPA Biosystems). We performed three parallel rounds of ligator-mediated PCR, checking the resulting DNA concentrations after each PCR reaction (using Qubit) and adjusting the number of cycles in subsequent reactions in order to reduce PCR stochastic drift34. The first PCR reaction was performed for 13 cycles, while the second and third PCR reactions were run for 14 cycles. Amplification products were then pooled in equimolar amounts, cleaned with Ampure XP beads, and subjected to Bioanalyzer analysis to gauge the concentration and distribution of product sizes. Finally, to assess the success of hybridization, we performed a series of quantitative PCR reactions using five different primer pairs: three primer pairs had been designed to amplify regions targeted by our probes , while the remaining two primer pairs had been designed to amplify “control” regions not targeted by these probes . These qPCR reactions allowed us to quantify the rate of enrichment for target sequences, thereby confirming that our capture procedure was successful prior to submitting samples for sequencing. Finally, samples were sequenced with 300 bp paired-end reads on an Illumina HiSeq. 2500 RAPID platform at the UC Berkeley QB3 core facility; our samples were pooled with those from other projects for a total of 240 samples run across three sequencing lanes.Custom in-solution probes were designed using the NimbleGen SeqCap EZ Target Enrichment System; the details of this procedure are given in Crawford (2016)et al.35. Specifically, we used the FastX toolkit36 to trim and remove regions of sequence with quality scores below 20, after which we removed reads that were <20 bp in length. Quality distributions were visualized using FastQC37 and Galaxy38. We then merged all reads for the same individual using custom Bash and Python scripts, after which we used Bowtie239 to align these merged reads to the original transcriptome sequences used to design our bait capture probes. Following alignment, reads were processed with SAMtools40, which generated summary statistics for average sequence depth and cover for each individual. Based on these distributions, we used a custom Python script and SNPCleaner41 to further filter the sequences to ensure high confidence in our reads. Using these scripts, we allowed a maximum of one mismatched base per read and applied quality thresholds derived from those used by Bi et al.34 for working with both historical and modern DNA. Specifically, at the individual level, we eliminated any individuals with extremely low or high coverage . We then filtered out reads below the 5th or above the 95th percentiles of coverage depth for all samples, which was approximately 3X and 100X, respectively. Finally, we removed sites with a root mean square mapping quality below 10. Single nucleotide polymorphisms (SNPs) in the remaining reads were identified using the software package ANGSD42.We cleaned and verified the quality of the Illumina sequences obtained following the protocol of de Wit 43 to identify and remove any loci that appeared to be under directional selection. This program, which uses an FST-outlier method to identify signs of selection, was run for 50,000 simulations with a false discovery rate of 0.1. Additionally, we used a custom Python script to identify and remove any loci that were not in Hardy-Weinberg equilibrium. Collectively, these procedures ensured that the variation contained in our final data set reflected genome-wide consequences of demographic changes driven by neutral processes, rather than changes driven by directional selection.We generated multiple summary statistics to assess post-eruption changes in genetic variation. Prior to these analyses, we used LOSITANW) and nucleotide diversity (π) for each population. Finally, to determine if estimates of genetic diversity were influenced by the demographic structure of the population, we partitioned the data by sex and calculated mean heterozygosity for randomly selected subsets of males and females. For C. sociabilis, we also calculated mean heterozygosity and inbreeding coefficients using data from only a single randomly selected individual per burrow system in both pre- and post-eruption populations.To quantify genetic variation within populations, we used a custom Python script to calculate mean heterozygosity for each population sampled. To account for potential differences in heterozygosity resulting from the variable number of individuals sampled per population, we conducted a bootstrap analysis in which we subsampled an equal number of randomly selected individuals from each population; this process was repeated 100 times, after which we calculated mean heterozygosity for each population based on these subsamples. Similarly, to minimize the potential effects of loci with extremely high or low levels of heterozygosity, we randomly selected a subsample of 100 loci and then estimated heterozygosity based on this panel of SNPs; this process was repeated 100 times, after which mean heterozygosity was calculated across all iterations. To examine additional measures of genetic diversity, we plotted per-site distributions of Watterson’s theta  plots for both species using a custom Python script. We also generated PCA plots in which data for each species were subdivided by 1) sex, 2) age (juvenile or adult), and 3) burrow system (for C. sociabilis) to determine if any of those factors influenced estimates of genetic differentiation among populations. As another measure of genetic differentiation among populations, we conducted admixture analyses using a range of values for K, the number of putative populations. We ran these analyses using the a priori hypotheses of K = 2  and K = 3 for C. sociabilis , and K = 2 for C. haigi . We also explored outcomes for values up to K = 6 for both species to determine if a greater number of putative populations revealed unanticipated evidence of genetic differentiation.Next, we examined patterns of genetic diversity among populations using ngsToolsC. sociabilis from Rincon Grande, as well as the post-eruption population of this species from La Lonja. We randomly subsampled loci, starting with n = 50 and incrementing by 50 loci per iteration to a total of n = 1000. Following this, we then subsampled n = 1000 to n = 10,000 loci in 500 locus increments. In these simulations, subsampling was repeated 100 times for each value of n, with observed heterozygosity determined for each subsample. We then calculated the mean and associated standard deviation for estimates of heterozygosity obtained for each value of n.To explore the effects of the number of loci analyzed on estimates of genetic variation, we used a custom Python script to conduct a simulation in which we varied the number of loci used to characterize variation in the pre- and post-eruption populations of 47. Because differences in the numbers of individuals or alleles sampled can affect such distributions, we conducted SFS analyses using equal numbers of randomly selected pre- and post-eruption individuals; this subsampling was repeated 20 times for each time period, after which allele frequency distributions were averaged across iterations and pre- and post-eruption data sets were compared for each focal study population.To identify demographic processes that may have contributed to changes in pre- versus post-eruption patterns of genetic variation, we used custom Python scripts to generate folded site frequency spectra (SFS) for each species. SFS provide an overview of the distribution of allelic frequencies, which can reveal evidence of past demographic events such as reductions in population sizeC. sociabilis sampled. For both sets of analyses, we completed 100 iterations of random subsampling with equal numbers of individuals per population to control for any effects due to differences in the actual number of individuals genotyped per population. In addition, to control for any potential effects due to sampling of multiple individuals per burrow system in C. sociabilis, we conducted 100 iterations of subsampling in which only one randomly chosen individual per burrow system was included in each population. To confirm that these analyses were not biased by outlier loci, we also randomly subsampled 100 loci; we repeated this procedure 100 times, after which we assessed the mean occurrence of private and shared alleles across iterations.Because patterns of allele sharing among populations may also reflect demographic factors such as bottlenecks, we determined the number of private alleles – alleles unique to the populations in which they were detected – for each population sampled, as well as the number of alleles shared between two, but not all three, of the populations of 49. We also generated pairwise mismatch distributions and their associated raggedness indices50 for each population using a custom Python script. The shapes of mismatch distributions can provide insights into recent demographic history, while raggedness indices can be used to compare such distributions quantitatively51. Finally, to determine if levels of inbreeding changed after the eruption, we calculated coefficients of inbreeding (F) for each individual using Plink52, after which we calculated mean values of f for each pre- and post-eruption population.To explore further potential signals of demographic change related to the volcanic eruption, we plotted distributions of Tajima’s D for each population sampled. This statistic, which can reflect both past demographic events and selection, was estimated across 100-bp sliding window regions using ngsTools; use of a distribution of values for Tajima’s D allows for more robust assessments of demographic history than a single mean estimate of this parameterC. sociabilis immediately following the 2011 eruption16. Specifically, we randomly designated 50% of individuals in each pre-eruption sample as survivors, after which we calculated heterozygosity for both the entire pre-eruption population and for the “surviving” post-eruption population. This procedure was repeated 100 times to generate a mean and standard deviation for heterozygosity in the simulated populations. Although this procedure did not take into account other demographic processes that may have affected post-eruption genetic variation, it provides a general estimate of expected changes in post-eruption heterozygosity due solely to stochastic loss of individuals from a population.To explore the potential impacts of demographic processes on post-eruption genetic variation in our study populations, we conducted a series of simulations. First, to quantify the expected effects of drift resulting from post-eruption declines in population size, we simulated an approximately 50% reduction in each of our pre-eruption study populations; this level of decline was largely consistent with that observed for 53 to examine post-eruption data from the Rincon Grande and La Lonja populations. This program allows the testing of different models of demographic history by generating Bayes Factors, derived from marginal likelihoods for different historical scenarios. We tested the following four hypotheses regarding post-eruption population structure at Rincon Grande and La Lonja: (1) panmixia (2) bi-directional movement (immigration and emigration) between the populations, (3) immigration only (La Lonja to Rincon Grande), and (4) emigration only (Rincon Grande to La Lonja). Although the power of our analyses may have been somewhat limited due to the assumption of constant population sizes and the failure to incorporate observed post-eruption demographic changes, tests of these hypotheses provide an important first step toward determining whether different patterns of migration may have shaped genetic variation in post-eruption populations of C. sociabilis.To examine the potential impacts of other demographic processes on genetic variation, we used MIGRATE-NC. sociabilis and 11.41 for C. haigi, with greater than 99.9% cover per individual of targeted areas for both species. No individuals had less than one-third or greater than three times the mean coverage, indicating that all animals fell within the suggested thresholds for filtering samples with extremely low or high coverage34. After examining all SNPs for evidence of departures from Hardy-Weinberg equilibrium, we excluded two sites from analyses for C. sociabilis and six sites from analyses for C. haigi. Similarly, we excluded an additional 24 loci for C. sociabilis and 4 for C. haigi that were identified by LOSITAN as potentially being under directional selection. Thus, the final data set consisted of 531 SNPs for C. sociabilis and 449 SNPs for C. haigi. Despite the overall greater number of variant sites analyzed for C. sociabilis, the mean number of variant sites per individual for this species (N = 16.35) was less than that for C. haigi (N = 34.55).Our sequence alignment resulted in a mean per-individual depth of coverage of 11.46 for C. sociabilis, mean heterozygosity across all sites decreased from 0.00167 pre-eruption to 0.00130 post-eruption; for C. haigi, these values were 0.00310 and 0.00274, respectively. For both species, this difference in variation was significant . The same outcome was obtained when comparing estimates of pre- and post-eruption heterozygosity for a randomly selected subsample of 100 loci per species , indicating that this outcome was not impacted by interspecific differences in the number of loci examined. This pattern of post-eruption reduction in genetic variation was also evident in the distributions of the per-site values of θW and π, both of which showed a shift toward lower values after the eruption . In contrast, there was no apparent difference in heterozygosity between males and females in the post-eruption population of this species at Rincon Grande ; this was the only population of C. sociabilis for which sample sizes were sufficient for comparisons of known-sex individuals. Among populations of C. sociabilis, post-eruption heterozygosity at La Lonja (0.00122) was significantly lower than either pre- or post-eruption heterozygosity at Rincon Grande . Pre-eruption samples from La Lonja were not available for analysis and thus we could not determine if the lower post-eruption variation in this population reflects consistent differences between La Lonja and Rincon Grande or if the impacts of the 2011 eruption were greater at La Lonja.A reduction in post-eruption variation was also detected for ST values across time periods were low for both C. sociabilis (0.00348) and C. haigi (−0.00042), suggesting little temporal differentiation between pre- and post-eruption populations. Similarly, principal component analyses failed to reveal strong temporal differentiation in either study species , and thus SFS analyses failed to reveal evidence of post-eruption changes in demography.Folded site frequency spectra , the pre- and post-eruption Rincon Grande populations shared the greatest number of alleles, while the post-eruption Rincon Grande and post-eruption La Lonja populations shared the fewest alleles . In both species, post-eruption populations were characterized by fewer private alleles Fig. , suggestC. sociabilis: −0.429; C. haigi: −0.892; two sample Kolmogorov–Smirnov tests; both p < 0.0001; Fig. Both pre- and post-eruption values of Tajima’s D  were generally negative for each study species, indicating an excess of low frequency alleles. Contrary to expectation, modal values of D decreased following the eruption in both study species . The mean value of F for the post-eruption population of C. sociabilis at La Lonja was −0.0143, which fell within the range of values reported for this species at Rincon Grande. In the absence of pre-eruption samples from La Lonja, we could not assess the impacts of the eruption on levels of inbreeding in this population. There was no significant difference between pre- and post-eruption values of F in the focal population of C. haigi . Thus, our analyses suggest that the 2011 eruption resulted in increased inbreeding within the focal study population of C. sociabilis but not the focal study population of C. haigi.Finally, mean values for individual coefficients of inbreeding (F) in the focal population of C. sociabilis at Rincon Grande did not result in a significant change in heterozygosity . Our Bayesian modeling analyses revealed that the most strongly supported demographic hypothesis was that of panmixia between the Rincon Grande and La Lonja populations (model probability = 0.986), with the hypotheses of full migration (model probability = 0.00467), immigration from La Lonja to Rincon Grande (model probability = 0.00667), and emigration from Rincon Grande to La Lonja (model probability = 0.00271) receiving considerably less support. Thus, we found little evidence to suggest that post-eruption changes in genetic variation in C. sociabilis were influenced by migration between populations.The simulated 50% reduction in size of the pre-eruption population of C. sociabilis and C. haigi the reduction in population size documented immediately following the 2011 eruption of the Puyehue-Cordón Caulle volcanic complex16 was associated with a decrease in genetic variation, as revealed by measures of mean heterozygosity across the multiple loci examined. In the more extensively sampled C. sociabilis, this decrease in genetic variation was evident throughout the focal population, with no apparent differences in response to the eruption based on the sex of the animals or the number of burrow systems sampled. Despite the post-eruption decrease in heterozygosity detected in each study species, we found little evidence of temporal or spatial genetic differentiation between populations of conspecifics, suggesting that the eruption did not lead to increased genetic structure. Because these results are based on analyses of a large number of putatively neutral markers distributed throughout the genome, we believe that our findings are indicative of the impact of the eruption on genetic variation in the study animals. Intriguingly, the decrease in genetic variation reported here for C. sociabilis contradicts previous findings regarding the impacts of the Puyehue-Cordón Caulle eruption; our prior analyses based on a much more limited suite of microsatellite loci16 had revealed an increase in post-eruption genetic variation in the focal study population for this species. This difference in outcomes raises intriguing questions regarding the abilities of different types of molecular markers, including associated differences in numbers of loci surveyed, to detect the genetic consequences of recent demographic changes.Our results indicate that in both 55. Due to the relatively modest declines in population size (~50%) observed16 and the short duration of this decline (<2 generations) prior to the collection of our post-eruption samples, we had not expected to find significant changes in post-eruption genetic variation in the focal study populations. As noted above, however, previous analyses based on microsatellite loci16 revealed a significant post-eruption increase in genetic variation in C. sociabilis, an unexpected result that, based on demographic modeling, appeared to reflect post-eruption changes in migration and gene flow. In contrast, the data set considered here revealed small but significant decreases in genetic variation in both study species, a finding that is more consistent with theoretical expectations . Given the considerably larger molecular data sets employed in this study, we expect that the reductions in post-eruption genetic variation reported here are more indicative of the genetic changes experienced by our study populations.Classical population genetics theory suggests that reductions in genetic variation associated with demographic bottlenecks are dependent on the magnitude and duration of the reduction in population size1, the exact processes by which such declines occur may vary. We found little evidence to suggest that selection was acting on the regions of the genome targeted for analysis. Very few of the loci examined (<5%) revealed departures from Hardy-Weinberg expectations or signals of directional selection. Further, because loci demonstrating departures from neutral expectations were excluded from analyses of genetic variation in our study populations, we do not believe that the post-eruption changes in variation reported here resulted from strong selective forces acting on these animals.Although reductions in population size are typically expected to lead to declines in genetic variationet al.56), we are not aware of any demonstrated or suggested links between ash produced by volcanic eruptions and increases in genomic mutation rates. Further, the average rate of mutation for mammalian genomes has been estimated at 2.2 × 10−9 per base pair per year57. Thus, given the limited temporal interval between our pre- and post-eruption sampling, this rate of mutational change could not have produced the observed post-eruption changes in genetic variation.Similarly, it is unlikely that the observed post-eruption changes in genetic variation were the result of altered mutation rates following the eruption. Although mutation rates can be affected by external factors  neither focal study population was in demographic equilibrium prior to the 2011 eruption and (2) the eruption did not substantially alter patterns of migration and gene flow in these populations.Although Hsu Ctenomys based on analyses of microsatellite and allozyme data67, our study offers the first direct comparison of pre- and post-bottleneck samples using genomic-level data. Accordingly, our analyses employed a much larger number of loci to detect changes in genetic variation. The presumably greater resolution offered by our data set is important given the relatively modest reductions in size and density detected in our focal study populations following the 2011 eruption. While studies of other ctenomyid species have examined the genetic consequences of decreases in population size of >90%65, our focal study populations experienced declines of only ~25–50%, suggesting that associated changes in genetic diversity would also be relatively modest. As a result, the use of a large panel of markers located throughout the genome should have increased our likelihood of detecting genetic signatures of these reductions in population size.Our analyses offer important insights into the genomic consequences of an abrupt reduction in size in natural populations of mammals. While apparent population bottlenecks have been reported for other species of et al.16, who reported a post-eruption increase in microsatellite heterozygosity in C. sociabilis. Although the same post-eruption samples were used in both studies, the pre-eruption samples examined differed: the samples analyzed here were collected in 2001, while those genotyped by Hsu et al.16 were collected during 1993–1998. It is possible that this difference in data sets contributed to the contrasting outcomes for these studies. However, given the generally low levels of genetic variation in both sets of pre-eruption samples , it seems unlikely that the temporal difference between these data sets underlies the reported differences post-eruption changes in genetic variation. Indeed, a marked reduction in population size in 1999  should have reduced pre-eruption variation among the individuals sampled here compared to those analyzed by Hsu et al.16.Particularly intriguing is the contrast between our results and those of Hsu et al.16 and this difference alone is likely to have affected the reliability of the post-eruption estimates of genetic diversity generated by each study2. Consistent with this, our analyses of the effects of number of loci on measures of genetic diversity revealed that the variance in estimates of mean heterozygosity decreased markedly as the number of loci employed increased, suggesting that larger numbers of these markers generate more robust metrics of genetic variation. Consequently, studies relying on a small number of markers likely show greater variance and more potential for summary statistics that are not representative of the true variation across the genome. In addition, microsatellite markers have been found to be unreliable for estimating genomic diversity, with marked differences in estimates of genetic variation generated with microsatellites versus SNPs69. Further, microsatellites have been found to have limited ability to detect complex demographic histories, including bottlenecks followed by rapid population growth70, and models demonstrating consistent tempo and mode in the evolution of microsatellites are lacking71. These limitations, combined with the presumptively greater analytical power of a large panel of genomic markers leads us to expect that estimates of genetic variation based on the latter provide a more accurate picture of pre- versus post-eruption changes in genetic variation in our study populations. If this supposition is correct, our analyses indicate that both C. sociabilis and C. haigi experienced decreases in genetic variation following the 2011 volcanic eruption.Instead, we posit that this difference in outcomes is due primarily to the greater power of our genomic-level markers to detect modest differences in genetic variation arising over short time periods. The number of markers employed in this study was more than two orders of magnitude greater than that used by Hsu C. sociabilis and C. haigi, analyses of genome-wide markers revealed small but significant reductions in post-eruption heterozygosity. Multiple lines of evidence supported the hypothesis that these reductions in genetic variation were associated with post-eruption declines in population size, indicating that even modest (~50%) reductions occurring over a span of 1–2 generations are sufficient to generate signals in genomic variation. Characterizing the impacts of such demographic changes has critical implications for understanding and for predicting how species will respond genetically to altered environmental conditions and thus may generate important insights into the conservation of species affected by such events.Our analyses of genetic variation in two species of ctenomyid rodents impacted by the 2011 eruption of the Puyehue-Cordón Caulle volcanic complex indicate that abrupt, naturally occurring reductions in population size can impact genetic variation over even brief time scales. For both Supplemental figures


Phaseolus vulgaris, including 157 genotypes of the Mesoamerican, 77 of the southern Andean, and 12 of the Northern Peru–Ecuador gene pools, we identified five geographically distinct subpopulations. Three of these subpopulations belong to the Mesoamerican gene pool  and one each to the Northern Peru–Ecuador (PhI) and the southern Andean gene pools. The five subpopulations were distributed in different floristic provinces of the Neotropical seasonally dry forest and showed distinct distributions for temperature and rainfall resulting in decreased local potential evapotranspiration (PhI and southern Andes groups) compared with the two Mexican groups. Three of these subpopulations represent long-distance dispersal events from Mesoamerica into Northern Peru–Ecuador, southern Andes, and Central America and Colombia, in chronological order. Of particular note is that the dispersal to Northern Peru–Ecuador markedly predates the dispersal to the southern Andes (∼400 vs. ∼100 ky), consistent with the ancestral nature of the phaseolin seed protein and chloroplast sequences observed in the PhI group. Seed dispersal in common bean can be, therefore, described at different spatial and temporal scales, from localized, annual seed shattering to long‐distance, evolutionarily rare migration.The wild progenitor of common-bean has an exceptionally large distribution from northern Mexico to northwestern Argentina, unusual among crop wild progenitors. This research sought to document major events of range expansion that led to this distribution and associated environmental changes. Through the use of genotyping-by-sequencing  and geographic information systems applied to a sample of 246 accessions of wild  Phaseolus vulgaris) is one of the most important crops for human nutrition, being a source of proteins, vitamins, fibers, and essential micronutrients worldwide  and gene density (r = 0.74 P < 0.001). Nucleotide diversity (π), Tajima’s D, linkage blocks, and population differentiation (Fst) were analyzed for the entire sample and for three wild gene pools of common bean (π = 1.42e−5), twice that of the Andean gene pool (π = 6.46e−6), and only slightly higher (27%) than the Northern Peru–Ecuador gene pool (π = 1.05e−5). Regarding Tajima’s D, the Mesoamerican, and Northern Peru–Ecuador gene pools show positive values, D = 1.58 and 0.28, respectively. In contrast, the Andean gene pool showed a negative value (D = −0.45).After retaining only the wild ne pools  online. s genome  online amon bean . Among tFst) showed a strong differentiation between the three populations, with the highest value observed between the Northern Peru–Ecuador and Andean populations (Fst= ∼0.6), and the lowest between Northern Peru–Ecuador and Mesoamerican gene pools (Fst = ∼0.3). The Fst value between Andean and Mesoamerican gene pools (Fst = ∼0.34) confirmed previous estimations using whole genome sequence analysis of these populations (The Mesoamerican gene pool showed the highest number of haplotype blocks (951), with the shortest median (N50) length (246 kb). The Andean gene pool showed fewer haplotype blocks (245), but the highest N50 (921 kb). The Northern Peru–Ecuador population showed the lowest number of haplotype blocks (27) and an intermediate N50 length (694 kb) compared with the Mesoamerican and Andean groups. Population differentiation analysis  discriminated mainly between the Mesoamerican/Northern Peru–Ecuador groups and the Andean gene pool (P < 2e−16) negative correlation (r = −0.9) between these variables  was performed for the 246 ene pool A, followene pool A inset. ene pool A, suggesQ) cut-off ≥0.7 for assigning genotypes to a specific cluster. A similar threshold was used by P. vulgaris.Population structure analysis using the TESS3 program identified five subpopulations that best define our sample : three pUsing this cut-off, the majority of the individuals were clearly assigned to one specific cluster, even though some genotypes where admixed with subpopulations of either the same or another gene pool  online. Some genotypes showed to be miss-assigned, with accession G23523 from Mexico clustering with the Northern Peru–Ecuador gene pool, the MW G23508 clustering with the Andean gene pool, and the Northern Peru–Ecuador G23584 clustering with the Andean subpopulation. Clusters MW1 and MW2 showed to be composed only of accessions from Mexico, whereas cluster MW3 was composed of accessions from Guatemala, Honduras, Costa Rica, and Colombia .P. vulgaris showed similar patterns by using either neutral or coding variants . In addition, in both trees the nodes separating the Northern Peru–Ecuador and the AW/MW gene pools were both supported with a high bootstrap (>80), whereas the split between AW and MW populations was highly supported only in the phylogenetic tree built with neutral variants  variants. Despite small differences beween the two phylogenetic trees, the clustering of the major gene pools of wild variants .P. vulgaris across the genetic groups identified by TESS3. We hypothesized initially that the different genetic populations identified were subjected to similar climatic conditions and, thus, were found in similar environments. Instead, an ANOVA analysis of climatic distribution across genetic populations showed significant differences across these populations (P < 0.05), whereas there were no significant differences between the MW1 and MW2 populations located in Mexico. The Andean group showed the lowest annual rainfall, even though it was not significantly different than those of the North Peru–Ecuador group (PhI) and the MW1 population located in North Mexico  showed the highest value in the MW2 group, although no significant differences were observed between the MW1 and MW2 subpopulations B. The MWP. vulgaris wild gene pools in the collection analyzed, as observed by phylogenetic analysis, we used a constant effective population size  as a proxy for identifying the ancestral population in our demographic modeling process  clearly supported the Mesoamerican hypothesis of origin of this species, with a PP of 0.96 for the standard rejection algorithm, 1 for the mnlogistic, and 0.96 for the neural network approach . Model cAnalysis of model accuracy using cross-validation showed high predictive error (>20%) for the founder population size of the Northern Peru–Ecuador and Andean groups, whereas the times of divergence showed a lower predictive error for the Northern Peru–Ecuador (<15%) and Andean (<10%) gene pools divergence time when using the 0.5% and 1% sampled points nearest to the observed summary statistic  online. P. vulgaris is nothing short of extraordinary, especially among crop plants. Indeed, this distribution extends from northern Mexico  to northwestern Argentina  may get lost. In addition, genotype imputation in data sets with a relatively high percentage of missing data, such as GBS, increases the number of false heterozygous calls . Nevertheless, the lower diversity levels in the PhI and AW groups are due in part to the very narrow habitat they occupy on the western and eastern flanks, respectively, of the Andes Mountains. The other cause is, of course, the long-distance dispersal leading to genetic drift and selection.The three major gene pools showed different values for key population genetic variables. The Mesoamerican gene pool was the most diverse with a ectively . The redectively  but cannectively ; fig. 2 A similar pattern was observed in the number and size of haplotype blocks in the three wild gene pools, with the Mesoamerican having the shortest haplotype block lengths, and the Andean the longest one. Studies of haplotype blocks in human populations showed shorter haplotype blocks length in African population when compared with non-African ones, suggesting that population demography and evolution could shape the pattern of haplotype blocks length across the genome . These rP. vulgaris is located within the seasonally dry, Neotropical forest, one of the most threatened biomes on Earth . Thus, one might have expected similar environments in this habitat. Nevertheless, Banda-R et al. (2016) also documented 12 floristic groups within this dry forest, suggestive of different environments. In particular, groups MW1 and MW2 are found in the Mexican floristic province, MW3 in the Central America–Northern South America and Northern inter-Andean Valleys provinces, PhI in the Central inter-Andean Valleys province, and AW in the Piedmont and Apurimac-Mantaro provinces.Correlations between annual means of climate variables and the distribution of the five population groups , a range of ∼4 °C . The three long-distance dispersal all displayed a decrease in PET . Any need for adaptation beyond these ranges will probably require genetic diversity contained in other Phaseolus species, as illustrated by the adaptation to higher temperature provided by P. acutifolius and P. lunatus  and the A + M clade.Because the genus oamerica , it folloamerica . Howeveroamerica . In bothP. coccineus species. Based on these results, P. vulgaris and named this species Phaseolus debouckii  instead of using a set of SNPs widely distributed across the genome as in the current study. The lack of a common ancestor in the phylogenetic tree between MW and AW, along with the strong  species . This in species .Phaseolus genus , bean plants disperse their seeds through the explosive dehiscence of their pods. The dehiscence phenomenon lies at the intersection of genetic , the seeds are dispersed over a contiguous landscape composed of adjacent or sympatric suitable habitats. Although dispersal may not happen every year, it can take place relatively frequently allowing a certain degree of admixture between relatively distant populations and limit differentiation and, eventually, speciation. Potential agents of seed dispersal may be rodents, birds, and megafauna . This meP. vulgaris is the result of at least three long-distance migrations  from a core ancestral area in Mesoamerica. Other Phaseolus species have been subjected to these seed dispersal at different spatial scales. These include, for example, lima bean (P. lunatus), which has a wild progenitor distribution similar in magnitude to that of wild common bean, as it extends from Mexico to Argentina and was also domesticated twice . Our data suggest then that the current distribution of wild ed twice . They alfrijol de paloma (dove or pigeon bean) as birds damage pods to extract seeds from them .In the present study, the genotyping at a genome-wide level of some 250 individuals representative of wild common bean populations resulted in a more complete and comprehensive characterization of the genetic diversity, relatedness, and evolution of this species in comparison with previous studies. In addition, the integration of genetic and ecological data enabled the identification of patterns of dispersal and colonization leading to the extensive distribution of the wild ancestor of this crop across the Americas. It also clarifies that these migration has led to colonization of regions with different climates than that of the region of origin . Furthermore, this distribution carries the unusual distinction that it includes the descendant of a potentially extinct ancestor (the PhI group of Ecuador and northern Peru) outside the ancestral area of the genus P. vulgaris accessions covering the entire geographical distribution of common bean from Northern Mexico to Northwestern Argentina were selected. All three gene pools of common bean were represented in our panel, with 175 accession of the Mesoamerican (MW), 84 of the Andean (AW), and 12 of the ancestral (PhI) gene pool from Northern Peru and Ecuador, the latter group characterized by the ancestral type I phaseolin  were included in each of the two libraries. Specific barcodes and adapters for CviAII were designed with the GBS barcoded adapter generator . The list of the barcode sequences used for multiplexing, the relative genotypes and the SRA accession number for each library are shown in DNA extraction and GBS Illumina library preparation followed the GBS protocol developed for common bean and described in http://www.ncbi.nlm.nih.gov/sra; last accessed October 26, 2017) under the accession numbers SRX2771627 and SRX2771628.The two libraries were first evaluated in one Illumina HiSeq2500 lane each to check the effectiveness of multiplexing at the UC Davis Genome Center, and then sequenced in four other Illumina HiSeq2500 lanes each at the QB3 Vincent J. Coates Genomics Sequencing Laboratory at the University of California, Berkeley, CA. All the sequencing was performed with the 100-bp single-end Illumina protocol. Raw reads are available at the NCBI Sequence Read Archive  after the barcode sequence, were then demultiplexed allowing no mismatch on the barcode sequence. Demultiplexed reads were aligned to the P. vulgaris reference genome sequence  or the adapter contaminants, and by quality trimming using a 5-bp sliding window and a cut-off quality (averaged on the 5-bp window) of 20. Reads longer than 30 bp after the preprocessing, and containing the overhang sequence of P. vulgaris, all the other Phaseolus species and the domesticated genotypes were removed from the data set prior to SNP filtering. In the last filtering step, we removed the genotypes with <10% of genotyped positions (-mind 0.1), kept only biallelic SNPs with <20% of missing data (-geno 0.8) and with a Minor Allele Frequency (MAF) higher than 0.05 (-maf 0.05). Variant statistics were calculated using VCFtools. Nucleotide diversity, Tajima’s D  ranging from 2 to 10 and 20% of masked genotypes (for computing the cross-entropy criterion). After manual evaluation of population clustering and geographic distribution of these populations for each value of K, we selected K = 5 as the number of population best representing the collection analyzed.Molecular PCA was performed using the adegenet package  implemenP. vulgaris genome . SNPhylo has been specifically designed for reconstructing phylogeny from SNPs data; it is able to collect representative SNPs for each linkage block, based on LD, in order to reduce marker redundancy. With both of these data sets, we selected an LD threshold of 0.4 for reducing marker redundancy in SNPs analysis. For both data sets, we performed alignments with the MUSCLE algorithm (P. coccineus) was used.Phylogenetic inference was performed with SNPhylo , analyzilgorithm  and infewww.r-project.org; last accessed October 26, 2017). Based on the geographical coordinates of the genotypes analyzed, we extracted climatic variable using the dismo R package. Annual rainfall and annual mean temperature were extracted from the WorldClim database  at 30 sec resolution. Annual PET was extracted from the Global Aridity and PET database . For identifying possible variations of climatic variables within the genetic groups identified by TESS3, we performed an analysis of variance (ANOVA) using only genotypes with a membership coefficient ≥ 0.7. Post hoc multiple comparisons were performed with the Tukey’s HSD test implemented in the agricolae R package.An analysis of the climatic distribution of the final 246 individuals retained for the final analysis was performed within the R statistical environment  and DendroPy (Demographic parameters were estimated with the abc package available on R (41). Prior to parameter inferences, we evaluated the accuracy of prediction of our model by cross-validation, implemented in the R package, on a subset of 1,000 randomly sampled simulations using the standard rejection algorithm. We evaluated this accuracy on three tolerance levels, using the 0.5%, 1%, and 5% sampled points nearest to the observed summary statistic. We inferred the time of divergence between the Ancestral and the main common bean group, and between the Andean and Mesoamerican gene pools using the standard rejection algorithm and the 1% sampled points nearest to the observed summary statistics. Confidence interval and highest posterior density (HPD) of the means of this points for each divergence time were calculated using the SciPy (DendroPy  python lMolecular Biology and Evolution online.Supplementary DataClick here for additional data file.


Erythrura gouldiae) is a threatened species in northern Australia, occupying the savannah woodlands of the biogeographically complex monsoon tropics. We present the most comprehensive population genetic analysis of diversity and structure the Gouldian finch using 16 microsatellite markers, mitochondrial control region and 3,389 SNPs from genotyping-by-sequencing. Mitochondrial diversity is compared across three related, co-distributed finches with different conservation threat-statuses. There was no evidence of genetic differentiation across the western part of the range in any of the molecular markers, and haplotype diversity but not richness was lower than a common co-distributed species. Individuals within the panmictic population in the west may be highly dispersive within this wide area, and we urge caution when interpreting anecdotal observations of changes to the distribution and/or flock sizes of Gouldian finch populations as evidence of overall changes to the population size of this species.Assessment of genetic diversity and connectivity between regions can inform conservation managers about risk of inbreeding, potential for adaptation and where population boundaries lie. The Gouldian finch ( Robust estimates of population parameters, such as size and connectivity, are of vital importance to effective conservation and wildlife management. Connectivity describes the movement of individuals, genes and behaviour between regions or groups of individuals, and the degree of connectivity can be used to define populations in a genetic and demographic sense . There iErythrura gouldiae) is one such species that could benefit from population genetic investigation, because the absence of robust population estimates has hindered conservation decision making. In the 1970s, Gouldian finch populations experienced strong declines (up to 87%), and became restricted to a few small areas in Western Australia and Northern Territory, and were virtually extirpated from Queensland [ad hoc observations [The Gouldian finch , observed and expected heterozygosities , and GenAlex v6.502 to calculate private alleles [We then used ARLEQUIN to tabulate the overall allelic richness  to estimate genetic structure between sampling localities in ARLEQUIN [ST can under-estimate differentiation at highly polymorphic loci [DEMEtics’ with p-values calculated by 1000 bootstrap resamples [a priori population definitions. We used STRUCTURE v 2.3.4 [a priori, but considers individuals that are sampled together to be more likely from a genetic cluster [K [To obtain measures of global differentiation and inbreeding, we conducted an Analysis of Molecular Variance (AMOVA) in ARLEQUIN  with 10,ARLEQUIN , with 10hic loci –34, for hic loci  using R esamples . Continuesamples . We alsoesamples . These t v 2.3.4  to estim v 2.3.4 ) against cluster . This me cluster . We useduster [K  using STuster [K .adegenet’ [find.clusters function to select clusters, which reduces the genetic data into Principal Components and runs a k-means clustering analysis (where k is the number of clusters) and weights results according to the Bayesian Information Criterion (BIC), and we retained all principal components for this analysis. Subsequently, we performed a Discriminant Analysis of Principal Components (dapc) using R-package ‘adegenet’ [a priori clusters and maximises the distances between them. We also ran dapc using the collection localities as prior groupings in an effort to explore geographic structuring by maximising the multivariate distances between sampling localities. Using the selected clusters, we ran model validation on ability of the model to correctly assign individuals to their clusters using optim.a.score and xvalDapc functions. For more information please see As an independent assessment of number of genetic clusters, we also ran a population genetic model-free ordination clustering method in R-package ‘degenet’ . These odegenet’ ,42. We udegenet’ ,42, whicSome movement information we have on Gouldian finches, based on band recoveries and radio-tracking, suggests that many individuals may be restricted to a 5km area, at least over short periods. Low recovery rates in the banding data suggest very limited natal philopatry in these birds, but we used genetic data as an independent test of this. If movement is limited, and natal philopatry is high, we might expect to see some evidence of spatial structure on the scale of a few kilometres. Spatial autocorrelation takes pairwise genetic distances and pairwise geographic distances between individuals and provides a measure of autocorrelation (r)–and by proxy genetic similarity—between them . Under rFor a subset of individuals (sample sizes in h) and nucleotide diversity (π) was calculated using DnaSP v5.10.1 [R) based on the smallest sensible sample size  was calculated in Analytic Rarefaction 2.0 [We examined the mitochondrial genetic diversity from a subset of individuals across the five western populations, plus we explored the five individuals from Chidna in the east. Haplotype richness (H), haplotype diversity  and nucleotide diversity ϕST calculated with the Kimura 2-parameter model. All significance tests were run with 10,000 permutations. In addition, we examined the evolutionary relationships between haplotypes using a median-joining network .We conducted an AMOVA and population pairwise genetic differentiation measures in ARLEQUIN. We calculated differentiation between sampling localities using haplotype frequencies  using tWe also used a reduced-representation next generation sequencing approach to obtain SNPs from across the genome. We sequenced 285 individuals that included a subset of individuals from our six populations . Populations were randomised across three plates, and 12 individuals were duplicated across all three plates, to ensure there were no lane or library effects, which can cause artificial substructure in the data.Pst1; in silico digests using ‘SimRAD’ suggested this enzyme yields 952,644 cut-sites across the genome of the related Zebra finch Taeniopygia guttata [We sent DNA extracts to Cornell Institute of Genomic Diversity for library preparation and sequencing according to their in-house Genotyping-by-Sequencing (GBS) methodology . This is guttata ,52. EachRaw reads were processed into SNP genotypes using the reference-free bioinformatics pipeline designed for this particular methodology: Universal Network Enabled Analysis Kit (UNEAK)  in TASSEST between sampling localities using pairwise distance for the underlying distance matrix in ARLEQUIN [We measured heterozygosity, number of polymorphic sites per population using ARLEQUIN v.3.5.2.2 . We condARLEQUIN , and 10,Genetic clustering analysis was conducted in a Bayesian clustering programme FASTSTRUCTURE v1.0 designed to process large SNP datasets quickly . This prPoephila acuticauda) using previous data from the mitochondrial control region [Genetic diversity is reduced in threatened taxa, relative to non-threatened taxa reflecting population bottlenecks and small population sizes –61. We ul region . Measurel region , but musl region . We rarel region . Point el region , which aAfter quality filtering of the microsatellite dataset , we retaSummary diversity statistics for the microsatellite data are presented in R = 8.2, 6.14–10.0 95% CI, Bradshaw raw H = 12). The haplotype diversity was not significantly higher , but nucleotide diversity was significantly higher in Bradshaw than Wyndham .The sample sizes for populations using mtDNA were smaller than those employed in the microsatellite analysis. Of the 330bp fragment amplified, 14bp were polymorphic and we identified 20 haplotypes (KX858950-KX585969). There was considerable variation in levels of mitochondrial richness between the collection localities . BradshaGenotyping-by-sequencing (GBS) yielded 735,164,326 raw reads across three Illumina lanes containing 96 samples each, with an average of 2,552,653.91 reads per individual or 1.6X reads per predicted site. After stringent filtering, we retained 3839 SNPs with a minimum site depth of 5X and average 13.5X across 251 individuals. Like the microsatellites, measures of genetic diversity were consistent across the five main sampling localities . No sampIS and FIT) were low but statistically significant  based on permutation tests, and individual population inbreeding coefficients (FIS) are not consistent across populations (IS) using these markers . AMOVA based on mtDNA nucleotide diversity and haplotype frequency yielded slightly higher between population differentiation, but still with only 1.9% of genetic variation explained among sampling localities.Analysis of molecular variance (AMOVA) on the microsatellite data showed that most genetic variation was contained within individuals, with less than 1% of variation attributed to between population differences, and 4.2% among individuals within populations. Inbreeding coefficients , and nucleotide diversity was significantly differentiated  except with closest neighbour Yinberrie Hills, but none of these comparisons were significant after Bonferroni correction , and a significant effect in the microsatellite dataset  and SNP datasets , but the regression coefficients in both analyses was small. When the poorly sampled and distant Chidna locality was removed from the analysis there was no significant effect in the microsatellite dataset , SNP dataset , or mitochondrial dataset .Mantel tests showed evidence of Isolation-by-Distance (IBD) in the mitochondrial dataset  is two  is two , but thi) is two  across t) is two .find.clusters in ‘adegenet’[In the microsatellite and SNP dataset, the k-means clustering method adegenet’ found thThe spatial autocorrelation also revealed no pattern of fine scale genetic structure Fig A in . When thR = 21.2, 17.19–25.23% CI). Conversely, haplotype diversity and nucleotide diversity were significantly higher in the long-tailed finch .The haplotype richness observed in the Gouldian finch fell within the confidence intervals of the rarefied long-tailed finch estimate , but mitochondrial diversity estimates are significantly lower in the Gouldian finch than the long-tailed finch (P. acuticauda). Indeed, the widespread zebra-finch (Taeniopygia guttata) has similarly high estimates of diversity at mtDNA locus ND2 as those observed the long-tailed finch [Genetic diversity is an important indicator of future adaptive potential and risk of inbreeding . In the ed finch ,67. Broaed finch –70, therIS and FIT = 0.04) in the microsatellite dataset, but not the SNP dataset. This discrepancy is potentially because of large sampling variances associated with these highly polymorphic markers, in conjunction with our modest number of markers and population sampled, and therefore expected heterozygosities from SNP data may be more accurate [IS & FIT inbreeding coefficients in the more representative and statistically powerful SNP dataset suggests that inbreeding may not occur at significant levels in the wild Gouldian finch. Indeed, field observations suggest consanguineous matings may be rare, due to very low numbers of recaptured individuals between years, suggesting high levels of dispersal . That wspersal  [We found no evidence of genetic differentiation in the western range of the Gouldian finch from Mornington to Yinberrie Hills, a distance of 730 km, despite the bio-geographic complexity and vast distances involved in this part of Australia . Indeed,d hecki) . This isd hecki) ,15, S1 Ad hecki) , but equd hecki) .Genetic differentiation between the western range and our sampling locality in Queensland, Chidna, remains unclear. The Chidna locality was responsible for significant results in Mantel tests for IBD, despite Chidna showing no evidence of significant pairwise differentiation after Bonferroni correction. Discriminant Analysis of Principal Components on the SNP dataset identified Chidna as a distinct cluster, but our validations suggest model instability. However, clustering analyses found no evidence that there was more than one genetic cluster, suggesting Chidna is part of the same population. Because of the small sample size at Chidna it is unlikely that we have captured the true allele frequencies at the location, and any signal of differentiation (or lack-thereof) may be spurious ,71. HoweST) for microsatellite sampling schemes equivalent to ours and census population sizes of less than 500 individuals [ST <0.05) [The putative decline and fragmentation of the Gouldian finch the 1960s-1970s) is relatively recent (30–50 years and Gouldian finch generations), and there may not have been sufficient time to detect a reduction in gene-flow ,81. The 60s-1970sT <0.05) –86. CompT <0.05) ,17, we aT <0.05) ,86.em) [m<0.1) [Populations across the western range of the Gouldian finch are genetically interconnected, and exchange more than sufficient effective migrants to maintain the genetic diversity in each region (irrespective of stringency of Nem) ,87,88. B [m<0.1) ,90. Unle [m<0.1) . Therefo [m<0.1) . Banding [m<0.1) , S1 Appe [m<0.1) .m) between populations, we urge caution in the interpretation of spatially and temporally unsystematic estimates of population from anecdotal reports by bird-watchers. Our findings do not exclude the possibility that individual Gouldian finches may be capable of moving quite long distances. The genetic connectivity between the west and the populations in Queensland remain unresolved, but due to differences in land management practices and Gouldian finch density, movement patterns may be drastically different from what we have observed in the western range. Establishing patterns of genetic connectivity in Queensland remains a priority for adequately assessing the population status of the Gouldian finch in Queensland.All three molecular markers employed in this study provided congruent evidence about moderately high genetic diversity across the western range of the Gouldian finch, with no evidence of genetic differentiation despite biogeographic barriers. Although these data make it impossible to infer demographic connectivity Click here for additional data file.S2 AppendixDetailed methods for microsatellite amplification and quality checks.(DOCX)Click here for additional data file.S3 AppendixFigures and tables include uncorrected p-values for differences between each population comparison, pairwise estimates of genetic differentiation and spatial autocorrelation.(DOCX)Click here for additional data file.S4 AppendixText and figures describe the detailed methods and model checks associated with the analysis of the microsatellite and SNP data in adegenet.(DOCX)Click here for additional data file.