Equus zebra zebra) is a subspecies of mountain zebra endemic to South Africa. The Cape mountain zebra experienced near extinction in the early 1900’s and their numbers have since recovered to more than 4,800 individuals. However, there are still threats to their long-term persistence. A previous study reported that Cape mountain zebra had low genetic diversity in three relict populations and that urgent conservation management actions were needed to mitigate the risk of further loss. As these suggestions went largely unheeded, we undertook the present study, fifteen years later to determine the impact of management on genetic diversity in three key populations. Our results show a substantial loss of heterozygosity across the Cape mountain zebra populations studied. The most severe losses occurred at De Hoop Nature Reserve where expected heterozygosity reduced by 22.85% from 0.385 to 0.297. This is alarming, as the De Hoop Nature Reserve was previously identified as the most genetically diverse population owing to its founders originating from two of the three remaining relict stocks. Furthermore, we observed a complete loss of multiple private alleles from all populations, and a related reduction in genetic structure across the subspecies. These losses could lead to inbreeding depression and reduce the evolutionary potential of the Cape mountain zebra. We recommend immediate implementation of evidence-based genetic management and monitoring to prevent further losses, which could jeopardise the long term survival of Cape mountain zebra, especially in the face of habitat and climate change and emerging diseases.The Cape mountain zebra ( Equus zebra zebra) provides a useful case study to help understand and advance the usefulness of genetic tools and monitoring for biodiversity conservation. The Cape mountain zebra is a subspecies of mountain zebra that is endemic to South Africa. The subspecies is listed as Vulnerable on the International Union for Conservation of Nature (IUCN) Red List [The Cape mountain zebra . HisRed List , 5, 6. HRed List , 7, 8. OThemeda trinadra is one of the main limiting factors to CMZ growth in many of the current populations [Following historical decline, CMZ numbers have since recovered to an estimated 2,650 individuals within the historical range ,6, due mulations , 11, 12.ulations . The popIn contrast, to the SANParks approach, CapeNature, the authority managing the remaining relict populations at KNR and GNR, has historically opted for a more conservative approach, citing low population numbers and lower growth rates as reasons not to remove animals from their reserves to establish populations elsewhere. The only exception to this rule was the population at DHNR, founded from a mix of MZNP and KNR individuals in the 1960s and 1970s. Thus, despite KNR and GNR containing a large proportion of the CMZ’s historic genetic variation [e = 0.239). In sharp contrast, the only known mixed population at DHNR had the highest genetic diversity . Howeve= 0.380, . Inbred = 0.380, , 14, 15.= 0.380, .To manage and monitor the evolutionary potential of the CMZ metapopulation, a Biodiversity Management Plan (BMP) for this species in South Africa was developed by various stakeholders. The purpose of the BMP is to ensure the long term survival of CMZ in nature, using a strategy underpinned by specific goals and objectives aimed at addressing the threats faced by this subspecies, such as population fragmentation, disease, inbreeding, hybridization with plains zebra and habitat loss , 9. It wHowever, it was recommended that introductions into either KNR and/or GNR populations be avoided due to the observed population structure, and mixing should only be considered at alternative locations which would be monitored. These plans were hindered, however, by recent ecological studies that suggested CMZ numbers in the GNR were too low to risk removal of individuals to seed mixed populations , 19, desAlthough the CMZ metapopulation continues to grow, it is yet to be determined whether management strategies have affected genetic diversity over time. Thus, this study aims to investigate temporal changes in genetic diversity in three key CMZ subpopulations by comparing present day 2015–2016) levels with those of samples collected from 1999 to 2001 . This re15–2016 lHowever, diversity may be maintained in larger populations experiencing rapid growth. Therefore, we expect that within one generation, populations with larger size and high growth rates  should maintain diversity, whereas populations with lower growth , and smaller size (KNR), would be expected to show a loss in genetic diversity. Furthermore, changes in genetic diversity such as loss of heterozygosity and rare alleles may also have consequences for how populations are structured relative to each other. The loss of shared alleles from populations would be expected to inflate genetic structure (differentiation), as seen in Moodley and Harley , whereasEthical approval was obtained from the Research Ethics and Scientific Committee (RESC) of the National Zoological Garden, South African National Biodiversity Institute , as well as the Animal Ethics Committee of University of the Free State (UFS-AED2017/0011). Permission was also obtained from the Department Agriculture Forestry and Fisheries of South Africa under Section 20 of the Animal Disease Act 1984 with (Ref: 12/11/1/1/8). The CMZ were chemically immobilised by helicopter. The dosages of sedation and reversal drugs as well as administration were carried out by a qualified, licensed veterinarian, registered with the South African Veterinary Council. Whole blood samples (5 ml) were collected by a qualified veterinarian from the MZNP (n = 75) and DHNR (n = 27) in 2016 and four samples were obtained from KNR in 2015. In addition, this study includes samples from MZNP (n = 12), DHNR (n = 15) and KNR (n = 9) collected between 1999 and 2001 [Taq DNA Polymerase Master Mix RED, forward and reverse primers (0.5 μM each), and 50 ng genomic DNA template. The conditions for PCR amplification were as follows: 5 min at 95°C denaturation, 30 cycles for 30 sec at 95°C, 30 sec at 55–60°C  following the manufacturer’s protocol for blood. We selected 14 cross-species microsatellite markers  used in the study conducted by Moodley and Harley, (2005). Polymerase Chain Reaction (PCR) amplification was conducted in a 12.5 μl reaction volume consisting of Ampliqon r), was estimated correcting for sample size through rarefaction using HP-RARE v. June-6-2006 [o) and expected heterozygosity (He) and number of private alleles per population was calculated using GenAlEx 6.5 [e or Ar) between the two time periods, a one tailed pairwise T-test (α = 0.05 and α = 0.1) was performed with a null hypothesis that no loss in diversity has occurred. Deviations from expected Hardy-Weinberg (HW) proportions were tested . We also tested for gametic disequilibrium between all pairs of loci using the exact test described by Guo and Thompson [MICRO-CHECKER software  was usede-6-2006 . Allele AlEx 6.5 , 24. To Thompson  in GenAle; i.e., gene diversity) and the theoretical expected heterozygosity assuming mutation-drift equilibrium (Heq) given the sample size and observed number of alleles (A). This test identifies populations that have recently undergone a decline in effective population size (Ne), resulting in a heterozygosity excess and deficit of rare alleles [e, occurring approximately 25–250 generations ago can be detected [The programme Bottleneck version 1.2.02  was used alleles . It was detected . Previoudetected , 27. Thidetected . Heterozdetected . Allelicdetected .eV). This method assumes temporal changes in allele frequencies are caused solely by genetic drift, based on the Wright Fisher model [F-statistic estimators, namely Fs [Fc [The two-sample temporal method , 33, 34 er model , 36. Theamely Fs  and Fc [y Fs [Fc . This anSince changes in genetic diversity can also affect genetic structure, we determined the relative structure (differentiation) among our three sampled populations at the time periods 1999–2001 and 2015–2016 using three methods. First, we used the Principal Component Analysis (PCA), which is a multivariate method using K-means clustering, and implemented in the R package Adegenet version 2.1.1 . We thenPr(X|K)) were averaged across runs and posterior probabilities were calculated. The K with the greatest increase in posterior probability  was ideity  to esti . We exce = 0.511 and 2015–2016: He = 0.338) in CMZ populations from both time periods  [r was 2.069, the average Ho was 0.327 (range of 0.115 to 0.531) and the average He was 0.503 (range of 0.356 to 0.720). In the 2015–2016 population, the Ar was 1.86, the average Ho was 0.274 (range of 0.061 to 0.621) and the average He was 0.337 (range of 0.119 to 0.627).Deviations from HW proportions, following Bonferroni correction , were on periods  comparedto 0.80) . In the r was observed for the DHNR population at both temporal periods compared to the MZNP and KNR populations . The number of private alleles for KNR, MZNP and DHNR in 1999–2001 was 0.143, 0.357 and 0.071 respectively, whereas the frequency of private alleles in these populations in 2015–2016 was 0.0  revealed a clear separation between MZNP and KNR for both the 1999–2001 and 2015–2016 time periods . The posqi) for 1999–2001 MZNP qi = 0.9639; 2015–2016 MZNP qi = 0.8465 and for the 1999–2001 KNR qi = 0.9715; 2015–2016 KNR qi = 0.9783. The 1999–2001 DHNR population indicated an approximately 50:50 mixed ancestry with qiMZNP = 0.5313 and qiKNR = 0.4687  and variation within populations was 45.58%. In the 2015–2016 time period variation was between MZNP and KNR populations was lower (FST = 35.35%) with much higher variation within populations (64.65%).In both time periods, MZNP and KNR were assigned to two distinct clusters with high individual coefficient of membership  and loss of private alleles among three key CMZ populations sampled at two time periods (1999–2001 and 2015–2016). These results suggest that despite a high metapopulation growth rate, the CMZ has lost a significant proportion of its genetic diversity within a single generation. At the population level, all three reserves lost genetic diversity (Ar and He), with a statistically significant loss being detected in DHNR. The loss of diversity in DHNR was compounded by decreases in Ho and an increase in the inbreeding co-efficient (FIS). In contrast, non-significant increases in Ho and a reduction in FIS were found in the relict populations MZNP and KNR. This slight increase in observed heterozygosity may reflect a sampling effect in KNR, since only four samples were genotyped from this reserve in the 2015–2016 time period. It is more likely due to several loci within these two populations reaching HWE, where Ho is not significantly different from He. Both KNR and MZNP, therefore, did not display significant heterozygous excess.In this study, we provide a rare test of the genetic consequences of different conservation management strategies among populations of a large mammal. Management recommendations from the BMP included (1) deliberate mixing of relict populations to maintain and improve genetic diversity (excluding KNR and GNR), (2) re-enforcement of existing populations prioritised over the establishment of new populations, (3) translocation of animals to other protected areas, (3) acquisition of land adjacent to protected areas with CMZ, (4) alteration in fire management in the habitat preferred by CMZ to increase availability of palatable grasses; and (5) formation of conservancies with adjacent landowners. However, our data provides support for a general reduction in genetic diversity  from Sardinia and Mesola (Ne = 2 to 8) as a consequence of bottlenecks and near-extinction [The N markers , 49. Biatinction .Of additional concern is the complete loss of private alleles from all three sampled population’s. This suggests rare and low frequency alleles have likely been lost genome wide. While a loss in such population-specific alleles may not have an effect on overall heterozygosity , it represents a loss of alleles potentially important for future adaptation and loss the uniqueness of the KNR and MZNP stocks. This means that a proportion of the historical diversity of the Cape mountain zebra, conserved through different conservation strategies and present in 1999–2001 has already been lost in the present generation. The loss of unique alleles in MZNP and KNR could also affect an individual or populations ability to adapt and cope with future environmental change. Equally worrying is that the third stock population, inhabiting the Gamkaberg Nature Reserve (GNR), with a growth rate even lower than that of KNR, could also be similarly affected.ST (0.544 to 0.354). Such a substantial shift or homogenization in allele frequencies within a single generation underscores the erosive effect of random genetic drift, even in populations that are expanding demographically, and threatens to undo much of the population benefits of a strategy to restore gene flow.The erosion of private alleles may have affected population genetic structure (differentiation) of these populations. Model-based and model-free algorithms all document a decrease in genetic differentiation between the KNR and MZNP stocks, which are now more similar to each other than they were a generation ago. Furthermore, the allele frequencies for DHNR, which were intermediate between its MZNP and KNR founding stocks in 1999–2001, are now clearly more Kammanassie-like, with 90% of genotyped individuals assigned to the KNR cluster . In addiMajor changes in the number of private alleles could also be brought about by non-random mating, where a handful of males dominate most of the breeding opportunities. This idea is corroborated by empirical data showing that in 2005, the population was already male-biased, with a deficit of females resulting in an excess of non-breeding males with limited reproductive potential. Population growth at DHNR has declined from 6.6% in 1995–1999 to 4.5% in 1999–2005 . Thus, DResults and the approach from this study could help design and implement management and conservation strategies in other species with only a few small populations remaining. In addition to census size, genetic monitoring of multiple metrics  can provide early detection of loss of diversity even when a population is large or growing.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S1 Fig(DOCX)Click here for additional data file.


We learn about population history and underlying evolutionary biology through patterns of genetic polymorphism. Many approaches to reconstruct evolutionary histories focus on a limited number of informative statistics describing distributions of allele frequencies or patterns of linkage disequilibrium. We show that many commonly used statistics are part of a broad family of two-locus moments whose expectation can be computed jointly and rapidly under a wide range of scenarios, including complex multi-population demographies with continuous migration and admixture events. A full inspection of these statistics reveals that widely used models of human history fail to predict simple patterns of linkage disequilibrium. To jointly capture the information contained in classical and novel statistics, we implemented a tractable likelihood-based inference framework for demographic history. Using this approach, we show that human evolutionary models that include archaic admixture in Africa, Asia, and Europe provide a much better description of patterns of genetic diversity across the human genome. We estimate that an unidentified, deeply diverged population admixed with modern humans within Africa both before and after the split of African and Eurasian populations, contributing 4 − 8% genetic ancestry to individuals in world-wide populations. Throughout human history, populations have expanded and contracted, split and merged, and exchanged migrants. Because these events affected genetic diversity, we can learn about human history by comparing predictions from evolutionary models to genetic data. Here, we show how to rapidly compute such predictions for a wide range of diversity measures within and across populations under complex demographic scenarios. While widely used models of human history accurately predict common measures of diversity, we show that they strongly underestimate the co-occurence of low frequency mutations within human populations in Asia, Europe, and Africa. Models allowing for archaic admixture, the relatively recent mixing of human populations with deeply diverged human lineages, resolve this discrepancy. We use such models to infer demographic models that include both recent and ancient features of human history. We recover the well-characterized admixture of Neanderthals in Eurasian populations, as well as admixture from an as-yet unknown diverged human population within Africa, further suggesting that admixture with deeply diverged lineages occurred multiple times in human history. By simultaneously testing model predictions for a broad range of diversity statistics, we can assess the robustness of common evolutionary models, identify missing historical events, and build more informed models of human demography. The study of genetic diversity in human populations has shed light on the origins of our species and our spread across the globe. With the growing abundance of sequencing data from contemporary and ancient humans, coupled with archaeological evidence and detailed models of human demography, we continue to refine our understanding of our intricate history. Accurate demographic models also serve as a statistical foundation for the identification of loci under natural selection and the design of biomedical and association studies.Whole-genome sequencing data are high dimensional and noisy. In order to make inferences of history and biology, we rely on summary statistics of variation across the entire genome and in many sequenced individuals. One such statistic that is commonly used for demographic inference is the distribution of SNP allele frequencies in one or more populations, called the sample or allele frequency spectrum (AFS) –4. AFS-bMeasures of LD are also informative about demographic history, mutation, recombination, and selection. A separate class of inference methods leverage observed LD across the genome to infer local recombination rates –8 and deWhile two-locus statistics have been extensively studied –22, mostIn this article, we show that the moment system of Hill and Robertson  can be eWe use this moment system together with likelihood-based optimization to infer multi-population demographic histories. We reexamine how well widely used models of human demographic history recover observed patterns of polymorphism, and find that these models underestimate LD among low frequency variants in each population, sometimes by a large amount. The inclusion of admixture from deeply diverged lineages in both Eurasian and African populations resolves these differences, and we infer an archaic lineage contributed ∼ 6 − 8% genetic ancestry in two populations in Africa. By jointly modeling a wide range of summary statistics across human populations, we can reveal important aspects of our history that are hidden from traditional analyses using individual statistics.To compute a large set of summary statistics for genetic data, we use mathematical properties of the Wright-Fisher model that are related to the look-down model of Donnelly and Kurtz , 25. To D2 can be extended to arbitrary moments of D, multiple populations, and even the full sampling distribution of two-locus haplotypes. Mathematical details and expanded discussion for each result are given in In this section, we therefore begin with evolution equations for heterozygosity and the frequency spectrum, then turn to recursions for low-order LD statistics and show that the classical Hill-Robertson  system ft, population size N(t) and mutation rate u, Wright  caximation , 27, 28.A/a at the left locus, and B/b at the right. There are four possible two-locus haplotypes, AB, Ab, aB, and ab, whose frequencies sum to 1 in the population. LD between two loci is measured as the covariance of their allele frequencies:D can also be interpreted as the probability of drawing two lineages from the population and observing one lineage of type AB and the other of type ab, minus the probability of observing the two cross types Ab and aB. As such, We will use this same intuition for the two-locus theory. First consider the model for two loci that each permit two alleles: alleles r of a recombination event between two loci in a given generation. Throughout, we assume r ≪ 1, so that higher order terms may be ignored. For loosely linked or unlinked linked loci (r = 1/2), higher order terms must be considered  . The posnsidered .r, u, and D is expected to be zero across the genome.To leading order in p is the allele frequency of A, and q is the allele frequency of B. The recursion isr, these take the formHowever, the second moment obertson  found a D and has received plenty of attention over the years. The second statistic includes a term z = (1 − 2p)(1 − 2q) whose magnitude is largest when there are rare alleles at both loci, and which is positive when p and q both correspond to the minor allele . Thus Dz statistic is contributed by pairs of variants where the rarest allele has frequency between 2 and 20%, while common variants comprises the bulk of D2.The three statistics in the Hill-Robertson system have a natural interpretation. The applications in this article focus on generalizing the Hill-Robertson equations to multi-population settings. However, we first outline generalizations to high-order moments and non-neutral evolution, leaving theoretical developments and simulations to the Appendix.D provide additional information about the distribution of two-locus haplotypes. Appendix S1.1 shows that the Hill-Robertson system can be extended to compute any moment of D, and presents recursions for those systems of arbitrary order Dm that closes under drift, recombination, and mutation.The existence of tractable higher-order moment equations for one-locus statistics  suggestsD2 system: the evolution of m, Appendix S1.4.1 shows the system closes and forms a hierarchy of moment equations, in that the Dm recursion contains the Dm−2 system, which itself contains the Dm−4 system, and so on .Given the analogy between the frequency spectrum and the Hill-Robertson equations, it is natural to study the connection between the moment equations for n , as in . All figion rate . This is−5 ≤ r ≤ 2 × 10−3 using the African-American recombination map estimated by Hinch et al. , we computed statistics and normalized by π2 in one population (we used π2(YRI)), and denote this set of normalized statistics Mi, and then estimated the likelihood asM and covariance matrix Σ  expansion proposed and inferred by Gutenkunst et al.  has beenet al. , 35, 38.Dz statistics, with Dz-curves from the model drastically underestimating observations. We refit the OOA model without including Dz statistics, and we inferred best-fit parameters that generally align with estimates using the joint AFS , we proLL < 1, as opposed to ΔLL = 1,730 between models with and without archaic admixture. ΔLL between the best fit model with archaic admixture and the fully saturated model (using observations as expectations) was 767. Consistent among the inferred models was the age of the split between diverged and modern human branches within Africa at ∼ 500 kya, though uncertainty remains with regard to the relationship between archaic human lineages in Africa and Eurasia. The sequencing of archaic genomes within Africa would clearly be helpful in resolving these topologies.We considered two topologies for the archaic branches: 1) both branches split independently from that leading to modern humans , and 2) We inferred an archaic population to have contributed measurably to Eurasian populations. This branch  split from the branch leading to modern humans 470 − 650 thousand years ago (kya), which contributed 1.2 ± 0.6% ancestry in modern CEU and CHB populations after the out-of-Africa split. This range of divergence dates from our maximum-likelihood model overlaps with previous estimates of the time of divergence between Neanderthals and human populations, estimated at 550 − 765 kya . The divWe chose a separate population trio to validate our inference and compare levels of archaic admixture with different representative populations. This second trio consisted of the Luhya in Webuye, Kenya (LWK), Kinh in Ho Chi Minh City, Vietnam (KHV), and British in England and Scotland (GBR). We inferred the KHV and GBR populations to have experienced comparable levels of migration from the putatively Neanderthal branch. However, the LWK population exhibited lower levels of admixture (∼ 6%) in comparison to YRI, possibly suggesting population differences in archaic admixture events within the African continent (Table A3).D2, Dz, π2). Studying these low-order multi-population statistics in a likelihood framework allowed us to infer a demographic model with archaic admixture, even without reference genomes from those diverged populations. We have also shown that higher order statistics may be computed through this same framework. Extending higher order two-locus moment systems to multiple populations would potentially provide further information about demography, particularly for past encounters with archaic lineages.The application presented here relied on the four-haplotype statistics  and the joint heterozygosity across sites and populations (π2). The one-population Dz statistic, in particular, has an interesting history, as it has come up in early work as a mathematical stepping-stone on the way to computing D2 [We described an infinite hierarchy of multi-locus summaries of genomic diversity that are easy to compute under arbitrary, multi-population demographies. Some of these statistics are familiar, including expected heterozygosity, uting D2 , but wasUsing this set of summary statistics, we explored a commonly used model of human demographic history derived from single-site AFS and validated using LD decay curves. While many statistics under this model fit the data well, the model dramatically underestimates levels of LD among rare alleles. Modeling archaic admixture worldwide resolved this discrepancy. We recovered the signal of Neanderthal admixture in Eurasian populations, and found evidence for substantial and long-lasting admixture from a deeply diverged lineage in two African populations that is consistent with evidence from previous studies –48, 57.This model deserve a more thorough investigation, including data from ancient humans and additional contemporary African populations. We leave this to future work for three reasons. First, proposing a detailed multi-population model of evolution in Africa will require carefully incorporating anthropological and archaeological evidence, which is a substantial endeavor. Second, the inclusion of two-locus statistics from ancient genomes will require vetting possible biases associated with ancient DNA sequencing, although we see no problem with using two-locus statistics in modern populations jointly with one-locus statistics in ancient DNA.D2, but it may impact analyses aiming to identify disease variants based on overrepresentation of rare variants in specific genes or pathways. Thus both statistical and population geneticists would benefit from including archaic admixture into baseline models of human genomic diversity.Third, and more importantly, archaic admixture can hide in the blind spot of classical statistics, and widely used demographic models for simulating genomes underestimate LD between low frequency variants in populations around the globe, especially in Africa. This large bias affects neither the distribution of allele frequencies nor the amount of correlation measured by S1 AppendixIn the Appendix, we provide detailed mathematical derivations, expanded discussions, supporting analyses, and supplemental figures and tables.(PDF)Click here for additional data file.


Rediviva longimanus bees exhibit elongated forelegs, a bizarre adaptation for collecting oil from floral spurs of their Diascia hosts. Furthermore, R. longimanus foreleg length (FLL) differs significantly among populations, which has been hypothesised to result from selection imposed by inter-population variation in Diascia floral spur length. Here, we used a pooled restriction site-associated DNA sequencing (pooled RAD-seq) approach to investigate the population genetic structure of R. longimanus and to test if phenotypic differences in FLL translate into increased genetic differentiation (i) between R. longimanus populations and (ii) between phenotypes across populations. We also inferred the effects of demographic processes on population genetic structure and tested for genetic markers underpinning local adaptation.Adaptation to local host plants may impact a pollinator’s population genetic structure by reducing gene flow and driving population genetic differentiation, representing an early stage of ecological speciation. South African FST = 0.165), though differentiation was not statistically associated with differences between populations in FLL. All populations exhibited very low genetic diversity and were inferred to have gone through recent bottleneck events, suggesting extremely low effective population sizes. Genetic differentiation between samples pooled by leg length (short versus long) rather than by population of origin was even higher (FST = 0.260) than between populations, suggesting reduced interbreeding between long and short-legged individuals. Signatures of selection were detected in 1119 (3.8%) of a total of 29,721 SNP markers,Populations showed marked genetic differentiation  on population genetic differentiation, short and long legged bees appeared to be partially differentiated, suggesting incipient ecological speciation. To test this hypothesis, greater resolution through the use of individual-based whole-genome analyses is now needed to quantify the degree of reproductive isolation between long and short legged bees between and even within populations.Populations of The online version of this article (10.1186/s12862-018-1313-z) contains supplementary material, which is available to authorized users. Ne) and the amount of gene flow among populations [Ne is a typical feature of rare or endangered species [Mating usually takes place among a subset of individuals of a species, typically within a portion of its distributional range, and, assuming limited dispersal, populations of that species inevitably become genetically structured . The populations . Low Ne  species  or of po species . Limited species –6, which species .FST, which relates the amount of genetic variation among populations to the total genetic variation over all populations [FST outlier loci [Host plant adaptation seems to be a common feature of many insect-plant interaction systems , 8–13. Aulations . Initialier loci , 17. Theier loci ). Host pier loci , 19, 20.Rediviva bees are striking examples of the bizarre morphology that host plant adaptation may generate. Females of many Rediviva species have evolved extremely elongated forelegs, sometimes longer than their entire body length [South African y length , 22. Fory length , 24. They length , 26.Rediviva females is an evolutionary highly labile trait which likely plays a role in Rediviva diversification [Rediviva varies not only between species but also between populations of the same species [Diascia [. Since most Rediviva use a range of Diascia [Rediviva FLL and host plant spur lengths is, however, still necessary for successful oil extraction as the main host, Diascia, only produces oil in the distal end of the spurs (but see [Foreleg length (FLL) of fication . Moreove species , 28, 29, Diascia , 22 or o Diascia , 30–32,  Diascia , Hollens(but see ) and thu.Other factors may impact the genetic structure of h Africa , 22, 35,h Africa , which ih Africa . Predomiispersal , which mirements , regionshis area . For exaite soil  whereas ndy soil . In addiRediviva longimanus. Specifically, in a population-based pooled RAD-seq dataset, we tested whether phenotypic differentiation in FLL translates into increased genetic differentiation between populations (isolation by adaptation: IBA) over the purely neutral evolutionary process of genetic drift (isolation by distance: IBD). Rediviva longimanus is among the Rediviva species (FLL = 6–23 mm) with the most extreme FLL (x̄=21 mm) and populations show noticeable differences in FLL, even over a small geographic scale [FST-outlier approach as well as a PCA-based outlier detection test to identify loci underpinning local adaptation.Here, we used a restriction site-associated DNA sequencing (RAD-seq) approach to investigate the population genetic structure and demography of e FLL x̄= mm and pRediviva longimanus is endemic to the Succulent Karoo in Western South Africa. Its distribution encompasses the Cederberg Mountains in the west, the Roggeveld Mountains in the east and the Nieuwoudtville area in the north [R. longimanus, though rare, is abundant enough to be sampled and where we expected to find differences in FLL even across a small geographic scale .he north . Samplincytochrome oxidase I ‘animal barcode’ region [Rediviva longimanus, for each sample included.DNA was extracted from the thorax, legs or head of females using a DTAB protocol (modified from ), which ’ region  and wereR. longimanus populations, we pooled individuals into four population pools corresponding to four of seven sampling locations , LC (N = 24), LF (N = 22), LI (N = 28). These sites encompassed the range of FLL in R. longimanus and differed significantly in mean relative FLL . In order to infer the population genetic structure and demography of ons Fig. , Table 1, LC N = 4, LF  was significantly different  from the short leg pool (x̄=6.4 ± 0.13 SD).In the second pooling scheme, we used samples from six of seven sites to generate two pools according to FLL, to which we refer as ls Table . One pooleg pools, we pooled bees showing the most extreme foreleg lengths, i.e. shortest and longest, independent of their population of origin, because we were interested in testing for genetic differentiation with respect to FLL across populations, which might be indicative of the initial stage of ecological speciation. Pooling individuals from different populations but with the same morphology, as in our two leg pools, has been recognised as a valid and highly useful approach, especially for the identification of candidate genes for host adaptation [leg pools comprised individuals from five of seven populations  ‘barcodes’ (10 bp) for each pool sample were attached to the DNA fragments prior to sequencing. Pooled libraries were run on an Illumina HiSeq2500 platform  to generate 125 bp single-end reads.For each pool, 1.5 μg of genomic DNA, normalised to a final DNA concentration of 60 ng/μl, was sent to Floragenex, Inc.  for RAD-seq. RAD-seq is an increasingly used –46 Next quencing . Subseququencing . RAD-seqquencing , 48. DNAR. longimanus or a close relative, we identified RAD loci de novo using denovo_map.pl in STACKS . Moreover, we remove highly repetitive stacks, loci with a log-likelihood below − 20 and confounding loci, i.e. multiple genomic loci matching a single catalogue locus.Following a quality control in FASTQC v. 0.11.5 , sequencbwa mem algorithm of BWA v. 0.7.12 [snp-frequency-diff.pl script of POPOOLATION2  and nucleotide diversity (Tajima’s Π) in NPSTAT v.1.0 [Genome-wide patterns of genetic diversity were assessed by calculating the population mutation rate , to account for a potential bias in our FST estimates due to selection. Confidence intervals (CI’s) for the FST estimates were inferred by bootstrapping 1000 times in the R package BOOTSTRAP v. 2017.2 [FST, we also assessed population genetic structure by principal component analysis (PCA) in the R package PCADAPT v. 3.0.4 [.Pairwise and overall genetic differentiation were estimated as the fixation index FST  in POPOO. 2017.2 . In addiv. 3.0.4 .population pools could be explained by differences in FLL  or geographic distance . We regressed the matrices of pairwise population genetic differentiation, transformed to FST/(1-FST), on relative FLL and on log-transformed geographic distances using a multiple matrix regression with randomisation (MMRR) analysis [R package ECODIST v. 1.2.2 [FST values within a dataset. Geographic distances between population pairs were inferred via the shortest path in GOOGLE EARTH v. 6.2 , we tested for a bottleneck in each population using FASTSIMCOAL2 v. 2.5.2.21 [Since estimates for Watterson’s 2.5.2.21 . We firs2.5.2.21 .AIC) and Akaike’s weight of evidence (w), as suggested by Excoffier et al. . In add et al. . FST calculations were repeated after excluding potential outlier loci, i.e. in the 5% tails of the FST distribution (see below). Bootstrapping was performed with 1000 replicates to generate CI’s for the FST estimate.We also computed population pools and leg pools datasets. In the first approach we extracted loci in the lower and upper tails (0.5% for the population pools and 5% for the leg pools) of the FST distribution, as calculated in POPOOLATION2  as candidates for divergent selection and the loci with the lowest FST values (lower tail) as candidates for balancing selection, in accordance with the rationale underlying FST-outlier detection tools such as BAYESCAN [We tested for signals of selection using two outlier detection approaches with both BAYESCAN  or LOSITBAYESCAN .. PCADAPT was run with 5 replicates for our best K (K = 1 for the leg pools and K = 3 for the population pools) and only SNPs identified across all runs were considered to be candidates under selection.In the second approach to infer signals of selection, we used PCADAPT v. 3.0.4 , which eFurther information about the methods used can be found in Additional file population pools  and 4,235,496 reads for the two leg pools. After filtering, we retained 8,232,334 reads for the population pools  and 3,602,671 reads for the two leg pools  for the leg pools to our reference  per population varied from 7362 to 9912 ) and log10 geographic distance was not significant  and relative FLL was also not significant .We then tested if population genetic differentiation was correlated with population-level differences in mean FLL or rather with geographic distance. The relationship between genetic differentiation (as R package PWR v. 1.2.2 [r = 0.90 to detect IBD and IBA. Our observed effects sizes (r) were clearly less than 0.90. Indeed, the statistical power of our analyses, given the observed effect sizes, was found to be low: 15% for IBD and 24% for IBA. Power analysis suggested that 10 or 8 populations would be needed to achieve power (1-β) of 0.80 to detect IBD or IBA, respectively.Because non-significant results may arise through lack of statistical power, we estimated the power of our current analyses and the sample size needed to reject the null hypothesis of no IBD and IBA using the v. 1.2.2 . This anpopulation pools  since this scenario’s AIC value was smaller than those for the two alternative demographic models: constant Ne and population expansion  was nearly twice as high as genetic differentiation among the population pools and dropped only slightly after excluding outlier loci . Hence, FLL might indeed play a non-negligible role in limiting gene flow (mating) between relatively long and relatively short-legged bees within and between populations.Genetic differentiation between the FST distribution for the population pools (0.5% threshold) and 652 in the FST tails for the leg pools . For comparison, genetic differentiation of other ground-nesting wild bee species is often only significant over greater (> 50 km) geographic distances [Pronounced population genetic structuring and differentiation were detected between all population pairs of istances , 66.Rediviva bees and potentially of other flying insects in the South African winter rainfall area due to the region’s harsh climatic conditions [Rediviva bees built their nests in the ground [Rediviva bees and further reduce gene flow among their populations.Marked genetic differentiation over a small geographic distance might be a general feature of nditions ; the peanditions , which fnditions , 39, 68.e ground  and mighNe could also account for the high population differentiation detected. A possibility is that bees are blown around by the harsh conditions of the Succulent Karoo, leading to high natal dispersal (i.e. near-panmixia), which, when coupled with very low Ne, could generate a pattern of consistently high population genetic differentiation independent of geography. Though plausible, we consider this scenario unlikely as it cannot easily account for the consistent relationship between foreleg length and local Diascia floral spur length observed in R. longimanus  and other Rediviva spp. [Low iva spp. , 29.Ne is, however, in agreement with personal observations of the rarity of the species , and could also explain our low genetic diversity estimates for R. longimanus. Genetic diversity, measured as Watterson’s θ and Tajima’s Π, were at least one order of magnitude lower than estimates in other Pool-seq studies of non-threatened species [R. longimanus, suggesting genetic bottlenecks, would also fit the low genetic diversity estimates inferred. During dry years, flower production can be very poor in the Succulent Karoo biome and most Rediviva species are likely to experience a marked reduction in population size, with some populations collapsing completely . Populations might thus frequently go through genetic bottlenecks, which would result in reduced genetic diversity, and which is probably only slowly restored once conditions become more favourable. The small, highly structured and potentially genetically depauperate R. longimanus populations are of conservation concern.Low  species , 70, tho species . Our demPooling of individuals for genetic analysis might also have inflated our estimates of genetic differentiation, as suggested by Anderson et al. , who fouR. longimanus and geographic distance or variation in FLL, probably because we analysed only a relative small number of populations and thus lacked statistical power. Rediviva longimanus populations are small, thus the issue of statistical power is difficult to resolve. Reciprocal transplant experiment would help to assess if differences in FLL are locally adaptive [R. longimanus.We did not detect an association between population genetic differentiation in adaptive . But theR. longimanus population genetic structure. First, populations are small in size , suggesting they are highly vulnerable to the effects of genetic drift. Moreover, we inferred population bottlenecks as the most likely demographic scenario for all populations studied. We also found low genetic diversity for all populations. Furthermore, populations seem to be significantly structured (average FST = 0.165), suggesting limited genetic exchange between populations, which would exacerbate the effects of drift.However, it is likely that genetic drift plays an important role in determining FST estimate between our leg pools. Though our leg pools dataset comprised individuals from different populations as well as with different leg lengths, the effect of population of origin per se on the FST estimate was probably slight. This is because we found all populations to exhibit a consistent, marked FST independent of geography and because we incorporated individuals from multiple – and often the same – populations into both the long and the short leg pools.Yet FLL might also have a non-negligible effect on genetic differentiation and result in reduced gene flow, as suggested by the high Rediviva bees preferentially use long spurred flowers and vice versa [R. longimanus. Nevertheless, examples where local adaptation to a host plant increases genetic differentiation and may finally lead to reproductive isolation have been documented for several other insects, in particular phytophagous insects, e.g. the walking stick insect Timema cristinae [Neochlamisus bebbianae [Rhagoletis pomonella, [How variation in FLL per se translates into increased genetic differentiation, potentially because of reduced gene flow and limited mating between long and short legged morphs, is unknown. Sexual selection acting on FLL seems unlikely since variation in FLL is only displayed by females and, in bees, males are usually not the choosy sex but rather undertake scramble competition for mates . Partialce versa , 34. Lonce versa . Howeverristinae , 76, theebbianae  or the amonella, , 20; seemonella,  for a moR. longimanus genome and thus likely missed important genes underpinning local adaptation. Whole genome sequencing rather than our RAD-seq approach would be more powerful to address the genetics of local adaptation and to identify candidate loci underlying FLL, which we assume to be locally adaptive and experience strong selection.Overall, we detected 1119 outlier loci, though there was little consistency in outlier loci identified by the different methods and datasets. Our pooled RAD-seq approach analysed only a small proportion of the We note, though, that selection may not always favour bees with the longest legs but rather may favour those with the best fitting legs. Simulations suggest that bees with legs much longer than the floral spurs of their hosts are unable to successfully collect oil . SelectiR. longimanus populations and low genetic diversity, likely because of low Ne and limited dispersal, compounded by recent bottleneck events. Genetic drift seemed to be important in structuring R. longimanus populations, but FLL might also reduce gene flow, as indicated by high genetic differentiation between our leg pools. Future studies including additional populations are required to test if neutral evolutionary processes such as genetic drift and migration or host plant adaptation are more important in structuring R. longimanus populations and whether FLL is associated with reduced gene flow and reproductive isolation. Nevertheless, our study is a first step to understand better the population genomics of an important pollinator in the Succulent Karoo biodiversity hotspot.We found pronounced genetic differentiation among Additional file 1:Rediviva longimanus population pools. (XLSX 10 kb)Pairwise test for significant differences in relative FLL between Additional file 2:population pool LC (Keiskie Mountains) and LF (Farm Papkuilsfontain). (PDF 180 kb)Picture showing the differences in FLL between Additional file 3:Rediviva longimanus leg pools. Mean FLL and standard deviations for the two leg pools are also indicated. (XLSX 10 kb)Locations and number of samples per location for the two Additional file 4:Additional methods. (DOCX 110 kb)Additional file 5:Rediviva longimanus population pools and leg pools. (XLSX 10 kb)Summary statistics for the number of reads sequenced, reads retained after filtering and reads successfully mapped to the consensus reference sequence for the Additional file 6:Rediviva longimanus population pools including only SNPs with a minor allele count of 6, minimum coverage of 20 and maximum coverage ≤ 98%. (XLSX 11 kb)Genetic diversity estimates for the Additional file 7:FST for the Rediviva longimanus population pools after excluding loci potentially under selection  and FST  under more stringent SNP filtering criteria . (XLSX 12 kb)Pairwise Additional file 8:Additional results for the outlier analysis, in particular for outlier annotation and GO enrichment. (DOCX 325 kb)Additional file 9:Rediviva longimanus population and leg pool data. The data set and methods with which the outliers were identified are also given. (XLSX 61 kb)BLAST and GO annotation of all 136 outliers identified for the


SNPs (Illumina goat DNA chip) to analyze the genetic diversity of 223 individuals belonging to eight French breeds . Analyses carried out included individual‐based approaches  and population‐based approaches (phylogenetic tree constructions). The results of the genetic diversity analyses revealed that French breeds are clearly differentiated, in particular, the Angora breed that originates from south west Asia. The Provençale breed shows a very original genetic pattern that could be the result of ancient admixture. Then, selection signatures were detected by identifying regions of outlying genetic differentiation between populations. Five genomic regions were detected under selection on chromosomes 5, 6, 11, 13 and 20, revealing mainly soft selective sweeps and a few hard selective sweeps and highlighting candidate genes that had been selected for during the evolutionary history of these breeds. Among them, two coat coloration genes (ADAMTS20 and ASIP) and one gene related to milk composition (CSN1S1) were involved.After domestication 11 000 years ago in Asia Minor, the goat followed human migration to Europe and Asia. It was then introduced in Africa and is now raised all over the world. In this study, we exploited a dataset composed of 54 000  The goat is one of the first grazing animals to be domesticated by humans in the Fertile Crescent due to its manageable size and the ability to adapt to difficult environmental conditions. According to archeological evidence Zeder  and geneIn metropolitan France, goats are traditionally bred for milk used for cheese production. Among dairy livestock species (with sheep and cattle), goat was the least productive one, and its livestock therefore was restricted mostly to regions with harsh environmental conditions (e.g. drylands and mountains). For this reason, historical records on French goat livestock are scarce. In particular, the establishment of goat herdbooks and official breeds started only in 1930, a century later than sheep and cattle, with the creation of the Alpine breed. Today, the advent of molecular genetic data offers the opportunity to inform this history through the signatures it left on the genetic diversity of contemporary populations.et al. et al. There are traditionally three regions for goat breeding in metropolitan France: western, southeastern and central France Spindler . Fourteeet al. et al. et al. et al. . In our study, we used this tool to genotype eight French goat breeds  to characterize their genetic diversity and relatedness. In addition, the marker density of this tool allows for the detection of selection signatures specific to French breeds to improve the knowledge about their evolutionary history.The recent availability of a medium density single nucleotide polymorphism (SNP) array for goats  developed by the International Goat Consortium  and Iranian Goat (n = 9)—were obtained from Alberto et al.  using the ‐‐pca command.For each population, admixture v1.23  and hapFLK (haplotypic approach) tests were performed using o et al.  and as iet al. hapflk web page. In parallel, allele frequencies of each population at each SNP were investigated using plink for each significant region.For each significant region, local trees were computed from the FLK and hapFLK statistics  for each significant region. Some of these genes were considered selection candidates based on their physical proximity to the most significant SNP and on their referenced biological effects in the literature.Candidate genes corresponding to protein‐coding genes were extracted from the feature table of the reference caprine genome , and structure analyses of the global sampled population, which represent the relationship between breeds .Two kinds of analyses were performed: intra‐breed analyses, which consider each animal individually to represent genetic variability within each breed (FIS and from 0.030 (Corse) to 0.142 (Angora) for FROH (Table FROH > 0.1) in the Angora, Poitevine and Pyrénées breeds than in the Alpine, Corse, Provençale and Saanen breeds. A runs of homozygosity (ROH) analysis  to 0.050 (Pyrénées) for sis Fig.  revealedA representation of the first and second principal components of the PCA is provided in Fig. admixture software . The clustering procedure implemented in admixture software at K = 8 . Reynold genetic distances and FST values ranged from 0.068 (the distance between Alpine and Corse) and 0.049  to 0.212 and 0.198 (the distance between Angora and Poitevine), with averages of 0.110 and 0.092 respectively. The Angora population was the most differentiated population with average distances of 0.185 and 0.169 for Reynolds distance and FST respectively, whereas the other breeds had lower average values (0.100 and 0.081 for Reynolds distance and FST respectively).Both computed genetic distances between each pair of populations Fig.  were vertreemix, including Iranian goat individuals from a domestic population and from a wild population of Capra aegagrus  revealed only two significant SNPs on chromosome 5 at positions 37.1 and 37.2 Mb, whereas the LD‐based approach (hapFLK) detected five significant regions across the six studied breeds Fig. . A detaiADAMTS20—related to coat color.The first region was very large (about 15 Mb) and was located between positions 34.7 and 49.6 Mb on chromosome 5. The breeds that were the most differentiated were Pyrénées and Fossés Fig. . SeventyCSN1S1—related to the secretion of the alpha‐s1 casein, responsible for milk coagulation.The second significant region was a bit smaller, spanning about 10 Mb and containing 72 candidate genes. It was located between positions 79.1 and 90.2 Mb on chromosome 6. Three breeds appeared to be differentiated at this location: Alpine, Corse and Pyrénées Fig. . This rePAX8, PSD4 and a cluster of genes coding for interleukins . However, we could not retain any particular functional candidate gene among the 59 candidate genes present in the region.The third significant region was less than 5 Mb long and was located between positions 44.9 and 49.3 Mb on chromosome 11. Only one breed was differentiated: Fossés Fig. . The proASIP gene, related to coat color.The fourth region was the largest region, spanning about 18 Mb. The signal was located between positions 49.0 and 66.5 Mb on chromosome 13. Three breeds were differentiated: Fossés, Poitevine and Saanen Fig. . This laCDH9 gene.The last region was the smallest one (less than 4 Mb), located between positions 46.5 and 50.1 Mb on chromosome 20. Only one breed was differentiated: Pyrénées Fig. . There wIn this study, we conducted an analysis of the genetic relationship among goat populations raised in France and identified five genomic regions that appear as selection signatures. For these two objectives, our results must be interpreted with caution for two main reasons.First, the individuals sampled were selected to ensure the representativeness of each breed, and we minimized the relationship between animals in the data by eliminating close relatives. However, the number of individuals within each breed remained rather small , although that is typical for such studies. In particular, due to the lack of knowledge of the pedigree for three breeds , we eliminated several closely related individuals, leading to a reduced sample size . This confers to each individual a high weight in the results.et al. et al. et al. et al. Second, the results obtained depended on the ascertainment bias of SNPs selected to be included on the caprine BeadChip (Tosser‐Klopp FST respectively), even when setting aside the Angora population (on average 0.086 and 0.066 for Reynolds distance and FST respectively). However, we noticed a low sample size effect in our study with the overestimation of genetic distances using the Reynolds approach in comparison with the Weir and Cockerham approach, which is not sensitive to sample size. PCA analysis confirmed the difference of the Angora population, as the first component explains 36.6% of the genetic variance and separates the Angora population from the others. Including the Iranian domestic population reveals its proximity to the Angora, consistent with its Turkish origin, and shows that these two populations separated a long time ago from the Western breeds considered here, possibly not long after domestication. Although the Angora breed in France is the result of a second wave of imports during the 1980s from several regions of the world . It also limits the identification of the origins of admixture, information for which could come from SNPs that would be specific to a few numbers of populations but that are not on the chip by design. Further work based on sequencing data will most likely be needed to investigate this further.Interestingly, a similar flow of ancestral genes for the Saanen and Fossés breeds was found when adding further migration edges in the sis Fig. . This coWe detected five significant regions along the caprine genome in our dataset. The selection signatures detected in this study thus could have resulted from natural selection or from an individual selection of breeders over the centuries. The latter concerns mainly production traits for economic reasons and morphologic traits for other reasons such as coat color, morphology, hardiness, etc. In each significant region, therefore, we looked at genes that could be related to one of these reasons more specifically.In our detection of selection signatures, we were looking for either ‘hard sweeps’, which means a rapid fixation of an initially rare variant in a population, or ‘soft sweeps’, which means a selection on standing variation. Indeed, the hapFLK differentiation‐based approach can detect these two selective signals.ADAMTS20 gene  was considered a good functional candidate, as it has been shown to modify coat color patterns in mice , which results from a defect in melanocyte development. Local examination of the allele and haplotype differentiation patterns in the signature . This would be consistent with the phenotypic diversity of the Fossés population, within which only some animals exhibit a belted phenotype.Among the 74 protein‐coding genes in the significant region on chromosome 5, the ure Fig.  reveals CSN1S1 gene (rank 7/72 in terms of position) was considered the best functional candidate for the selection signature. This gene codes for casein alpha s1, a protein largely responsible for milk coagulation, a fundamental step in the process of deriving cheese from raw milk. Indeed, many studies have focused on this very polymorphic locus because it is of great interest for the French dairy goat industry , a copy number variation gene related to coat coloration, was found quite close to the most significant SNP (rank 17/294). First characterized in mice in the early 1990s  and for the ASIP gene, the Kacchan population from the Pakistani breeds and the Alpine, Poitevine and Valdostana breeds from the Alpine group. The cluster of casein genes on chromosome 6 was also detected in the group of Alpine populations and in the eastern African populations. In contrast, regions on chromosomes 13 and 20 are specific to French breeds.In the current study, we confirmed three selection signatures detected by Bertolini et al.  and founADAMTS20, CSN1S1 and ASIP). Two of these selection signatures were reported for the first time. All the results obtained in this study can be useful for future breed management and pave the way to further genetic studies on the evolutionary history of goat populations.In our study, we characterized within‐ and between‐breed genetic diversity of eight French goat breeds with a medium density SNP chip. The analyses showed that the Angora breed is strongly different from the other French breeds due to its southwestern Asian origins. The other seven French goat breeds are genetically more closely related but still clearly differentiated. We also discovered that the Provençale breed shows a particular pattern of admixture, which will likely require sequencing data for interpretation. We detected five significant genomic regions differentially selected between breeds and three candidate genes potentially involved  lengths and number of ROH for each population.Click here for additional data file.Figure S4 Representation of the first seven principal components of the principal component analysis.Click here for additional data file.Figure S5 Weir & Cockerham FST values and Reynold's genetic distances between each pair of populations.Click here for additional data file.Figure S6 Maximum likelihood trees of the selected animals with Iranian goat and Bezoar ibex populations and (a) two, (b) three and (c) four unknown admixture events inferred.Click here for additional data file.Figure S7 Local population trees computed using the FLK statistic and the hapFLK statistic on the significant region of chromosome 5.Click here for additional data file.Figure S8 Local population trees computed using the FLK statistic and the hapFLK statistic on the significant region of chromosome 6.Click here for additional data file.Figure S9 Local population trees computed using the FLK statistic and the hapFLK statistic on the significant region of chromosome 11.Click here for additional data file.Figure S10 Local population trees computed using the FLK statistic and the hapFLK statistic on the significant region of chromosome 13.Click here for additional data file.Figure S11 Local population trees computed using the FLK statistic and the hapFLK statistic on the significant region of chromosome 20.Click here for additional data file.Table S1 Allele frequencies in each population at each SNP in the significant regions.Click here for additional data file.


FST values. Hatchery introgression was found to be minimal, with estimates ranging from 1.96% to 3.10% of wild individuals exhibiting membership proportions to a hatchery strain cluster exceeding 10% (q ≥ 0.10). Results from this investigation can be used to prioritize management efforts for Brook Trout in western New York State and act as a baseline to monitor future population trends.Although Brook Trout are distributed across most of eastern North America, population numbers have declined in many regions due to habitat loss, climate change, and competition with non‐native species. In New York State, Brook Trout habitat has been substantially reduced, with many areas showing complete extirpation of Brook Trout populations, predominantly in the western portion of the state. Small, fragmented populations are at risk of genetic diversity loss, inbreeding depression, and reduced fitness, leading to a greater potential for local extirpation. Genetic monitoring is a practical tool that can facilitate further conservation‐decision making regarding small populations. In this study, we used 12 microsatellite loci to examine 3,436 sampled Brook Trout, representing 75 sites from the Allegheny, Erie/Niagara, Genesee, Oswego, Lake Ontario, and Susquehanna drainage basins throughout western New York State. Three Brook Trout hatchery strains were also genetically characterized to evaluate the degree of hatchery introgression between wild populations and hatchery strains stocked in the region. Overall, estimates of genetic diversity varied widely: Allelic richness ranged from 2.23 to 7.485, and expected heterozygosity ranged from 0.402 to 0.766. As observed for Brook Trout in other regions, we found a high degree of genetic differentiation among populations, with all comparisons except one showing significant  The collected fin clips represented a mixed‐age sample of each population, preferentially including individuals of age one year or older to avoid biasing the diversity and relatedness calculations. Sampling took place at 75 sites, all located in stream habitats, distributed throughout six major drainage basins, including the Allegheny (27 sites), Erie/Niagara (6 sites), Genesee (24 sites), Susquehanna 16 sites), Oswego (1 site), and Lake Ontario (1 site) drainage basins , as well as from the two Brook Trout strains stocked by the Oswayo State Fish Hatchery, the Oswayo strain (N = 50), and the Tylersville strain (N = 50).The Oswayo State Fish Hatchery, operated by the Pennsylvania Fish and Boat Commission, stocks Brook Trout in Pennsylvania counties bordering New York State that could potentially come in contact with our study sites near the New York/Pennsylvania border, located in the Allegheny and Genesee basins. In order to detect hatchery individuals and hatchery introgression in wild Brook Trout populations, samples were collected from the Rome strain . The amplification protocol followed that of King et al. (Genomic DNA was extracted using the Mag‐Bind ® Tissue DNA Kit (Omega Bio‐tek) with the KingFisher Flex Magnetic Particle Processor (Thermo Fisher Scientific) as well as the Puregene  methods, following the manufacturers’ protocols. Samples were genotyped at 12 microsatellite markers developed in Brook Trout: g et al. . PCR pro2.3Because some populations were sampled during multiple field surveys, we performed an identity test in Cervus 3.0.7 , observed and expected heterozygosity , and the inbreeding coefficient (f), were calculated with GDA 1.1  was calculated with FSTAT 2.9.3  for all wild populations and effective number of breeders (Nb) for the single‐cohort samples of hatchery strains in NeEstimator 2.01  with ML‐Relate  to determine the proportion of the population with family‐level relatedness values. As a comparison, maximum‐likelihood estimates of relationship  were determined between all pairs of individuals within each population using ML‐Relate. Line graphs of genetic diversity estimates, effective population size, and average relatedness were then generated with the ggplot2 package in R v.3.4.1  were assessed with the Kruskal–Wallis and pairwise Wilcoxon rank‐sum tests in R  was carried out in Arlequin 3.5  analysis to determine whether geographic distance was influencing the patterns of genetic differentiation among Brook Trout populations. Pairwise river distances were calculated using the R package RIVERDIST Tyers, . We conv Rousset  lineariz2.5To examine the degree of admixture among Brook Trout populations throughout western New York, we performed a Bayesian clustering analysis with STRUCTURE 2.3.4  examined varied based on the number of sampling sites within a given drainage basin : K = 1–30, Susquehanna: K = 1–18), and 10 replicates were performed for each K value.The default parameters  were applied with run lengths of 100,000 iterations as burn‐in and 500,000 additional iterations. An alternative parameter set was also applied for run lengths of 100,000 iterations as burn‐in and 250,000 iterations after the burn‐in. The alternative parameter set consisted of the independent allele frequency model, inferring alpha independently for each population, and setting the initial alpha prior to ~1/ by Wang  for dataK = 1–3), and populations within the Allegheny, Genesee, Lake Ontario, and Oswego basins were examined with all three hatchery strains (K = 1–5). Ten runs were performed for each K value with an initial burn‐in of 250,000 iterations and an additional 500,000 iterations after the burn‐in.To minimize the erroneous effects of IBD on population clustering by STRUCTURE  to a genetic cluster associated with a hatchery strain, with q ≥ 0.10 used as a minimum. Prior studies have determined this threshold q‐value to produce the highest proportion of correctly assigned pure and hybrid individuals  to the Rome hatchery strain and the number of years since the population was stocked by the NYDEC, either directly or via stocking an adjacent stream, using the most recent stocking year. A linear regression analysis was performed in R (R Core Team 2014) to evaluate the trend and significance of this relationship. Lastly, we examined the length of time in generations that backcrossing with a wild population would reduce the genetic signal of hatchery introgression. Using HYBRIDLAB 1.0  and a hatchery population , producing genotypic profiles for 50 wild‐hatchery hybrids. Ten generations of backcrossing with the wild population were then simulated, with 50 offspring produced for each generation. The resulting genotype data were then analyzed with STRUCTURE, K = 2, following the same methods as the population‐specific analyses described above.To better understand not only the degree of hatchery introgression in wild Brook Trout populations, but also the possible explanations for our results, we explored the influence of time on the levels of hatchery introgression in our study populations. Specifically, we examined the relationship between the mean membership proportions . Four individuals  failed to amplify and were removed from the data set, while individuals retained for further analysis displayed missing data at five or fewer loci. The identity test detected 15 sample pairs with exact allele matches across all genotyped loci. A majority of these duplicates originated from separate sampling events at the same locality. One matching pair (ACH‐38 and AHH‐42) was sampled from neighboring tributaries during separate survey dates, suggesting that Brook Trout individuals are capable of moving between adjacent tributaries. One individual from each of the 15 matching pairs was subsequently removed from the data set.p‐value of 0.00005. Significant disequilibrium occurred for locus SfoC28 in two populations (ASH and GCCT8), locus SfoD91 in two populations (GSB1 and GCHT8), and loci SfoD75, SfoC115, SfoD100, SfoC88, SfoB52, and SfoC113 in a single population each . The observed deviations were not consistent across populations, indicating that null alleles were not likely the cause of the disequilibrium, and therefore, all loci were retained for the remainder of the analyses.There were 10 instances of loci showing significant departures from Hardy–Weinberg expectations after applying the Bonferroni‐adjusted After removing 19 sampled individuals from the data set, a total of 3,417 wild and 150 hatchery Brook Trout individuals amplified consistently and displayed unique genotypes when all 12 markers were examined , and mean He and Ho were 0.630 (0.402–0.766) and 0.630 (0.437–0.752), respectively. There were 10 sites showing A and AR values of less than four alleles per locus (of sites with N > 30), including AJCT3, A2MT1, ABH, ANR, GSBT4, GSB, GPG, GRGC, SCCT5, and SCT3B. The ROM and TYL hatchery strains were also found to have A and AR estimates below 4 alleles per locus. In contrast, 11 sites exhibited over 7 alleles per locus for estimates of A: ACH, ALRT1, ACRT1, ASPSC, ASPEC, AIBRB, ESBT3, ESCT5, GAB, GWC, and SU482. Populations GAB, GWC, GBQSH, and SU482 all had Ho values above 0.75, while populations ABH and ANR were found to have the lowest Ho (<0.45). Inbreeding coefficient values (f) ranged from −0.103 in population AHH, suggesting heterozygote excess which is common in small populations . Estimates of Ne ranged from 3.8 (3.3–5.0) for site GCCT8 to 182.0  for site SCR, with an overall mean of 41.4 across wild populations , ABH , and AHH . In contrast, four populations exhibited estimates over 100, including SCR, ESBT3 , SLC , and the OSW hatchery strain . Overall, we found a significant positive correlation between Ne and AR , suggesting that genetic drift, rather than sampling error, was largely responsible for the variation in diversity metrics across Brook Trout populations.The wild Brook Trout populations were comprised of mixed‐age samples, and therefore, ns Table . Our Ner) pooled into corresponding relationship categories, but showed similar trends across populations . This population was also found to have the highest proportion of parent–offspring and full‐sibling relationships . Conversely, population GWC had the highest A and AR values and exhibited the lowest mean r value (0.051).The maximum‐likelihood estimates of relationship for parent–offspring, full‐sibling, and half‐sibling were generally higher than estimates of relatedness . Pairwise comparisons between the basins revealed that the Ne estimates for the Susquehanna basin were significantly higher than for the Allegheny (pairwise Wilcoxon rank‐sum test: p = 0.029).Although we did not find statistical differences among the four major basins for any of the diversity metrics . For the Susquehanna basin alone, this relationship was statistically significant for all diversity metrics .Examining all drainage basins together, we found a weak, but positive relationship between population density, defined as the number of populations sampled within a subwatershed, and 3.2FST calculations among Brook Trout sampling localities revealed high levels of genetic differentiation, with an average FST across all pairwise comparisons of 0.238. Overall, sites within the Genesee basin displayed the highest levels of differentiation (mean FST = 0.206), followed by the Erie/Niagara (mean FST = 0.205), the Allegheny (mean FST = 0.196), and the Susquehanna (mean FST = 0.164). Differences among the four major basins in within‐basin FST values were statistically significant (p < 0.001). Pairwise comparisons between basins revealed the Susquehanna to be significantly different from the other basins , while there was no statistical difference in pairwise comparisons between the Allegheny, Erie, and Genesee.Pairwise FST values, except one, were statistically significant (Bonferroni‐corrected p = 0.00002), based on 10,000 simulations (results are available in Dryad). The only nonsignificant comparison was found for sites SUT58 and SCR, with an FST value of 0.00435 (p = 0.03). Nine wild populations exhibited overall high levels of differentiation, with mean FST values over 0.30 across all pairwise comparisons, including Allegheny populations ANR (mean FST = 0.349) and ABH (mean FST = 0.333), Erie/Niagara population EC34A (mean FST = 0.304), Genesee populations GPG (mean FST = 0.318) and GC28B (mean FST = 0.305), Susquehanna populations SBC (mean FST = 0.324), SCT3B (mean FST = 0.308), and SCCT5 (mean FST = 0.307), and Lake Ontario population GU112 (mean FST = 0.303). The Rome strain (ROM) also showed high levels of differentiation when compared to the wild populations (mean FST = 0.323). These patterns can be visualized in Figure FST values are represented by a gradient from dark shades (high differentiation) to light shades (low differentiation). Populations with overall high levels of differentiation exhibited dark shades across a majority of their associated pairwise comparisons.All pairwise FST heat map by the clusters of light gray shades along the diagonal. These within‐watershed comparisons are associated with populations within the Middle and Lower Allegheny River watersheds (mean FST = 0.127), sites in the Cryder Creek and Dyke Creek watersheds within the Genesee basin (mean FST = 0.113), and sites in the Upper Cohocton watershed within the Susquehanna basin (mean FST = 0.142), indicating a higher degree of connectivity within these watersheds.Pockets of low differentiation can also be visualized in the FST values were further visualized with the PCoA . A majority of the incorrectly assigned individuals sampled from SUT58 were assigned to SCR and vice versa. Site SUT56 also displayed a low degree of correct assignments (69.6%), with incorrectly assigned individuals equally distributed between SUT58 and SCR. This low degree of resolution suggests that gene flow could be occurring at higher frequencies within these tributaries. None of the wild Brook Trout individuals were assigned to a hatchery strain and vice versa.Results from the individual‐based assignment test showed that individuals could be genetically assigned to the correct source population with a high degree of accuracy, where correctly assigned individuals are considered those assigned to the population where they were originally collected. Overall, 93.4% of Brook Trout individuals were correctly assigned to the population from which they were sampled Table . The assp = 0.001, R2 = 0.208) and the Susquehanna basins . This relationship was approaching significance for the Allegheny basin , but was not significant for the Erie/Niagara basin , Erie/Niagara (population EC34A), and Genesee (population GPG) basins, making inferences of hatchery introgression inconclusive . Population ACH, which exhibited the highest level of genetic diversity in the Allegheny basin, showed a high degree of admixture, even with nonadjacent sites. Similarly, other high diversity populations were found to show elevated levels of admixture, such as ALRT1, while low diversity populations showed little admixture .For the Allegheny basin data set, which consisted of 27 sites and three hatchery strains, we concluded that the optimal number of genetic clusters, For the Erie/Niagara basin, we concluded that the six localities, in addition to the Rome strain, formed seven genetic clusters Figure . All popN = 24) were analyzed together with localities in the Oswego (GCC) and Lake Ontario (GU112) basins, in addition to the three hatchery strains, and produced 27 genetic clusters , sites SU482 and SKC, were also assigned to a single genetic cluster, and partial membership to the same cluster was observed for localities SLC/SAHB  and SDR/SBC .The 16 Susquehanna basin localities, combined with the Rome strain, formed 12 genetic clusters Figure . Localit3.4q‐values) above 0.10 to a genetic cluster associated with a hatchery strain, to be minimal. The basin‐wise STRUCTURE analyses using the alternative parameter set detected hatchery introgression in 1.96% of all wild Brook Trout individuals. We found similar results for the population‐specific analyses, examining each population individually along with the appropriate hatchery strain(s), which identified hatchery introgression in 3.10% and 2.08% of wild Brook Trout, applying the default and alternative parameter sets, respectively. Across all methods, a majority of the hatchery assignments were associated with the Rome strain (1.29%–1.73%) followed by the Oswayo strain (0.47%–1.20%) and the Tylersville strain , with multiple individuals showing evidence of wild‐hatchery introgression  and the number of years since stocking occurred by the NYDEC. Although this trend was not statistically significant for any of the methods , the mean hatchery q‐values calculated with the basin‐wide analysis showed a negative relationship that was approaching significance .Our linear regression analysis revealed a negative relationship between hatchery introgression in the wild Brook Trout populations examined , membership proportions associated with the hatchery strain did not exceed 0.10 for any of the offspring, suggesting that hatchery introgression was below detectable levels  and a mean He of 0.598 (0.454–0.649). Brook Trout in minimally disturbed areas of New Hampshire were found to have a mean AR of 5.4  and a mean He of 0.56 (0.34–0.70)  and a mean He of 0.749 (0.736–0.758). Differences in the genetic diversity among relatively intact Brook Trout populations may reflect the heterogeneous nature of the headwater streams in which they reside, with some streams providing larger amounts of habitat than others, and therefore able to support larger Brook Trout populations.Previous studies examining Brook Trout populations in predominantly high‐quality habitats with little anthropogenic disturbances have found varying levels of genetic diversity. In the Adirondack watershed in northern New York State, Bruce, Hare, Mitchell, and Wright  found a A was positive and approaching significance. Brook Trout populations with fewer neighboring populations, and thus fewer sources for new alleles, generally showed lower diversity values than those located in close proximity to many Brook Trout populations.Compared to the Brook Trout populations in high‐quality habitats described above, the populations in western New York State displayed slightly lower levels of allelic richness, but exhibited heterozygosity levels that were within the range of values reported in these studies. Although not statistically significant, the Allegheny basin was found to have the lowest mean diversity and effective population size estimates compared to the other major basins. One possible explanation for this observation is that the Allegheny contains a large number of sites that are geographically isolated from other Brook Trout populations due to extirpation. Across all basins, the relationship between population density and FST values, 100% correct assignment rates, and limited admixture. Without the presence of neighboring populations due to local extirpation, gene flow is limited for many Brook Trout populations in the Allegheny basin and is likely contributing to the low diversity levels observed.One example of a geographically isolated population is ABH, Bucher Hollow, which showed extremely low levels of allelic diversity and heterozygosity, represented in the box plots as an outlier or at the extreme end of the range  than populations unaffected by culverts . Torterotot et al. , the Susquehanna populations consistently showed moderate‐to‐high levels of allelic richness and heterozygosity, as well as large effective population sizes. These sites, predominantly located in the Upper Cohocton River watershed, were found to have low levels of genetic differentiation and high levels of incorrect assignment, suggesting high connectivity among populations. The only nonsignificant pairwise FST value detected in the data set was associated with a comparison between two sites in Punky Hollow subwatershed, SUT58 (an unnamed tributary) and SCR (Cohocton River), indicating that genetic partitioning was not detected between these two sites. Although Brook Trout have been extirpated from a large swath of the Susquehanna basin, the region that remains populated appears to have relatively high connectivity, which is likely contributing to the high levels of genetic diversity observed at these sites.The Susquehanna basin displayed the highest overall levels of genetic diversity relative to the other three basins, and was found to have significantly lower within‐basin FST values and high Bayesian admixture proportions. Sites within these watersheds also exhibited higher levels of genetic diversity. Gene flow among populations can maintain or increase the level of genetic variation within the populations involved, as new alleles are added and new genotypic combinations are created. This association between genetic diversity and population‐level admixture has been documented in other salmonids  to 296.7  to 99.4 , which is the receiving stream for Stoddard Creek (ASPSC), Beehunter Creek (ASPBC), and McIntosh Creek (ASPMC), which could explain the higher rate of introgression observed in these populations. Similarly, high proportions of membership to Pennsylvania State Fish Hatchery strains, the Oswayo and Tylersville strains, were found in the Allegheny basin (populations ALB1A) and the Genesee basin (populations GBQSH and GAB). These sites are all in close proximity to the Pennsylvania border, and potential hatchery introgression could be explained by nearby stocking locations along with the migration or human‐mediated dispersal of Brook Trout into New York State.Our study found a negative trend between the number of years since stocking occurred and the degree of wild‐hatchery introgression. Although approximately half of the populations examined in this study have been directly stocked in the past, most have not been stocked since the 1970s, and none of the sites are currently stocked. This suggests that if hatchery introgression was occurring in the past, the genetic signatures have been diluted to a low or nondetectable level after generations of backcrossing with wild individuals. Our simulation analysis confirmed this hypothesis, showing that after four generations of backcrossing with wild individuals, genetic signatures of hatchery introgression were no longer detected. This rapid dilution of hatchery alleles occurred when sample sizes were equal between hatchery/wild and hybrid/wild individuals, a scenario that would likely not occur in nature. Rather, sample sizes would likely be skewed toward fewer hatchery and hybrid individuals compared to wild individuals, further hastening the dilution effect of the hatchery alleles.One caveat to our analysis is that the Rome Brook Trout strain stocked in our study streams decades ago may not be genetically equivalent to the Rome samples currently examined due to years of genetic drift. Because historic specimens of the Rome Brook Trout strain were not available, we could not directly compare their genotypic profile against modern samples. However, the negative trend between hatchery introgression and stocking time, as well as the results of our simulated wild‐hatchery introgression and backcrossing analysis, provides increased confidence that the pattern of hatchery strain dilution in wild populations over time, as inferred in our study, reflects true processes and is not simply an artifact of genetic drift skewing the genotypic profile of hatchery individuals.5Although Brook Trout populations often show high degrees of differentiation, even within interconnected stream networks, isolated populations are at risk of reduced genetic diversity and inbreeding. With the widespread extirpation of Brook Trout throughout western New York State (Eastern Brook Trout Joint Venture, One of the principal goals of the Eastern Brook Trout Joint Venture is to identify and protect functional, stable Brook Trout populations and the surrounding habitat (Eastern Brook Trout Joint Venture, Assessments of neutral genetic variation, such as this study, can be useful in identifying populations to focus conservation efforts, and future investigations can make comparisons to the baseline values presented here to track the progress of Brook Trout conservation. Brook Trout are considered to be a keystone species in many headwater streams, holding the highest trophic position, and causing trophic cascades that affect the abundance of a wide range of organisms from insectivorous fish and salamanders to detritivorous insects (Tzilkowski, None declared.The authors made the following contributions: Stephanie Dowell Beer: performed laboratory work, analyses, and wrote the manuscript; Scott Cornett: involved in project design and planning, sample collection, and manuscript review; Peter Austerman: involved in project design and planning, sample collection, and manuscript review; Betsy Trometer: involved in project design and planning, sample collection, manuscript review, and provided funding; Thomas Hoffman: involved in project design, planning, and sample collection; and Meredith Bartron: involved in project design and planning, coordination of funding, and manuscript review. Click here for additional data file. Click here for additional data file.