RRS) approaches show great potential for resolving the structure of wild populations. However, the population structure of species that have shown rapid demographic recovery following severe population bottlenecks may still prove difficult to resolve due to high gene flow between subpopulations. Here, we tested the effectiveness of the RRS method Genotyping‐By‐Sequencing (GBS) for describing the population structure of the New Zealand fur seal , a species that was heavily exploited by the 19th century commercial sealing industry and has since rapidly recolonized most of its former range from a few isolated colonies. Using 26,026 neutral single nucleotide polymorphisms (SNPs), we assessed genetic variation within and between NZFS colonies. We identified low levels of population differentiation across the species range  suggesting a state of near panmixia. Nonetheless, we observed subtle population substructure between West Coast and Southern East Coast colonies and a weak, but significant (p = 0.01), isolation‐by‐distance pattern among the eight colonies studied. Furthermore, our demographic reconstructions supported severe bottlenecks with potential 10‐fold and 250‐fold declines in response to Polynesian and European hunting, respectively. Finally, we were able to assign individuals treated as unknowns to their regions of origin with high confidence (96%) using our SNP data. Our results indicate that while it may be difficult to detect population structure in species that have experienced rapid recovery, next‐generation markers and methods are powerful tools for resolving fine‐scale structure and informing conservation and management efforts.Next‐generation reduced representation sequencing ( These analyses allow the quantification of parameters relevant to endangered populations such as genetic diversity, effective population sizes, gene flow and the inference of past population histories  allow the characterization of large numbers of molecular markers, increasing the resolution of comparisons of individuals within and between populations  methods in evolutionary biology  has undergone a rapid recolonization following near extinction. Prior to the arrival of humans, NZFS were distributed throughout the east and west coasts of New Zealand's North and South Islands  . SNP loci were then called using the Stacks “denovo_map.pl” pipeline , using a minimum stack depth (‐m) of 5 and default parameters for mismatches between loci within samples (‐M = 2) and when building the catalog (–n = 1). The “populations” program in Stacks was used to filter out monomorphic loci and sort individuals into separate files for each colony. 180,303 biallelic SNPs remained after this filtering. PLINK 1.9  was estimated for each colony and region using Arlequin v. 3.5 , using the LOCPRIOR option to assist with clustering of faint‐structure data  value of STRUCTURE 2.2 and the ∆K . This program estimates ancestry in a model‐based manner where individuals are considered unrelated and uses a cross‐validation procedure to determine the best number of possible genetic groups present in the dataset.We used the Bayesian cluster methodology implemented in STRUCTURE 2.2  approaches with microsatellite . Overall, FST values were low (0.003–0.022) and mostly significantly different from zero (p < 0.05) between each pair of colonies , had relatively high pairwise FST values when compared with every other colony except Ohau Point. The Mantel test revealed a weak, but significant IBD signal  and the ad‐hoc statistic ∆K, Bayesian clustering analysis for 167 NZFS implemented in STRUCTURE identified two main clusters along a west to east genetic gradient  while NZ‐North‐East colonies (CP and OP) showed more pronounced admixture (q = 0.59 and 0.51). While ADMIXTURE and STRUCTURE results were consistent in describing a west to east genetic gradient, ADMIXTURE supported a single cluster  approach strongly supported a scenario of Polynesian‐ and European‐induced declines followed by range‐wide recolonization and population divergence with a posterior probability of 1. Our estimates supported severe bottlenecks with potential 10‐fold and 250‐fold declines in response to Polynesian and European hunting, respectively  and OP (0%–7%) drove these averages down  were on average higher , and further suggest that NZ‐North‐East colonies (CP and OP) are sites of genetic admixture between these two main clusters. This observed structure was further supported by constructing a PCA from a matrix of estimated genomic relationships , which is consistent with previous results based on microsatellite data . Higher estimates of He derived from microsatellite markers may be a consequence of ascertainment bias caused by selecting the most polymorphic markers  and to breeding colonies (42%–53%) were on average higher for all SNP datasets used here than for microsatellite markers  , which is consistent with Emami‐Khoyi et al. (Ne of 116. However, we estimated much larger pre‐European and pre‐Polynesian Ne of ~570,000 and ~4,900,000, which is one to two orders of magnitude higher than previous estimates of historical Ne (Dussex et al., Ne estimates seem more realistic with an Ne of 600–1,000 predicted following demographic recovery after cessation of sealing. The estimate for the post‐European surviving population seems extremely low, yet not unlikely based on the history of near extermination of this and other fur seal species in the 1830s (Lalas & Bradshaw, Ne in this surviving population compared to the larger Ne in modern populations may be due to unsampled populations that might have contributed genetic diversity to the modern populations and possibly range expansions of other species and, as yet, undetected hybridization (Lancaster et al., Ne of modern populations.The subtle population structuring seen here for NZFS is most likely due to the high vagility of the species and its recolonization history (Dussex et al., i et al.  who estiWhile theory predicts that demographic decline can result in population structuring via the effects of genetic drift (Charlesworth, 5To date, few studies have compared the performance of microsatellite and SNPs for highly vagile species. Overall, while the genetic structure described here was not substantially different from that examined with microsatellite loci, a larger number of markers allowed for better assignments of pups to their genetic cluster and colony of origin. This was especially true for colonies that were outside of the zone of admixture.Our results, however, suggest that because of the ascertainment bias associated with the selection of microsatellite loci, over‐estimation of population structuring is possible when using such markers. SNP loci are thus not only less biased but also offer more statistical power. Nevertheless, due to the species’ recolonization history and high vagility, describing finer‐scale structuring remains challenging.Recent advances in whole genome sequencing present new avenues for research in NZFS. For instance, hunting may have impacted fur seal functional diversity, either via the effects of drift or via artificial selection pressures imposed by hunting. While the effective population size may not have been significantly affected, modern functional genetic diversity may not be representative of the prehuman functional diversity as the shift of genetic composition in southern New Zealand since human arrival suggests (Salis et al., None declared.N. Dussex designed research, performed population structure analyses, and wrote main body of text. H.R. Taylor advised on population genetic theory and lab procedures, analysed sequencing output, reviewed, and contributed to writing the paper. W. R. Stovall designed research, performed lab work and bioinformatics processing of data, performed population structure analyses, and wrote main body of text. K. Rutherford performed bioinformatics processing of data, analysed sequencing output, and oversaw data management and archiving. K. G. Dodds advised on visualization of results, and assisted with post‐sequencing data management. S. M. Clarke assisted with formulation of project, performed GBS and Illumina sequencing, and reviewed and contributed to writing the paper. N.J. Gemmell designed research, performed and oversaw field sampling, and supervised project. All authors contributed substantially to the writing and refinement of the final text.https://doi.org/10.5061/dryad.63nm407.Raw sequence data, processed data and scripts are available from the Dryad Digital Repository:  Click here for additional data file.


Brachionus plicatilis. Here, we investigate the genomic basis of adaptation to environmental unpredictability in these experimental populations. We identified and genotyped genome-wide polymorphisms in 169 clones from both selective regimes after seven cycles of selection using genotyping by sequencing (GBS). Additionally, we used GBS data from the 270 field clones from which the laboratory populations were established. This GBS dataset was used to identify candidate SNPs under selection. A total of 76 SNPs showed divergent selection, three of which are candidates for being under selection in the particular unpredictable fluctuation pattern studied. Most of the remaining SNPs showed strong signals of adaptation to laboratory conditions. Furthermore, a genotype-phenotype association approach revealed five SNPs associated with two key life-history traits in the adaptation to unpredictability. Our results contribute to elucidating the genomic basis for adaptation to unpredictable environments and lay the groundwork for future evolution studies in rotifers.Elucidating the genetic basis of phenotypic variation in response to different environments is key to understanding how populations evolve. Facultatively sexual rotifers can develop adaptive responses to fluctuating environments. In a previous evolution experiment, diapause-related traits changed rapidly in response to two selective regimes (predictable vs unpredictable) in laboratory populations of the rotifer  Anthropogenic effects and climate change have been identified as amplifiers of the variation in environmental fluctuations, and this effect is likely to further increase in the near future3. For that reason, adaptations to unpredictable fluctuations are particularly challenging to understand5. Research is needed to know whether organismal adaptive responses will allow them to cope with the upcoming increase in environmental unpredictability. Consequently, (1) understanding organisms’ evolutionary responses to unpredictability and (2) unraveling the molecular mechanisms and the genomic basis underlying these adaptations are key topics in evolutionary and conservation biology6.Unraveling the genetic mechanisms underlying organisms’ phenotypic variation is essential to understanding how organisms adaptively respond to environmental fluctuations4. One of them is phenotypic plasticity, in which the same genotype produces different phenotypes depending on the environmental conditions7. Another mode of evolutionary response is adaptive tracking, in which natural selection acts recurrently on the standing heritable variation8. A third adaptive response is bet hedging, a life-history strategy that has been linked to unpredictable fluctuations9. This response occurs when a selected trait in a genotype increases the geometric mean of fitness at the cost of a decrease in the arithmetic mean, thus reducing fitness variance12. Despite the relevance of the ubiquitous challenge posed by environmental fluctuations and the diversity of adaptive responses displayed by organisms, empirical studies elucidating the genomic basis of these responses are still scarce.Various adaptive responses have been described in organisms that inhabit fluctuating environments14 and explore the genomic basis of adaptation16 because it involves controlled laboratory conditions with a defined selective pressure. Typically, in this type of experiment, populations derived from a single ancestral genotype ─i.e., a genetically homogeneous population─ are exposed to different selective regimes17. However, an increasing number of studies have used a combination of several genotypes from one or more populations ─i.e., several ancestral genotypes or a polymorphic population─ to establish the experimental populations20. Evolutionary outcomes are expected to differ depending on whether the experimental populations are established from a single or from multiple genotypes. If the founding population is genetically monomorphic, adaptation to selective regimes could occur through the accumulation of de novo beneficial mutations. While not excluding this source of variation to fuel adaptive evolution, if the founding population is polymorphic ─and given the relative short-term scale of evolution experiments─ selection is expected to act mostly on the heritable standing variation23, which has been predicted to lead to rapid evolution in novel environments24.Experimental evolution is a powerful tool that can be used to address diverse questions in evolutionary biology25). These rotifers are facultatively sexual, combining asexual and sexual reproduction in their life cycle. Rotifer females reproduce asexually ─clonal proliferation─ until a specific environmental cue for sex initiation  induces some of these females to produce sexual daughters. These sexual females produce meiotic haploid eggs that develop into either haploid males, if they remain unfertilized, or into diploid diapausing eggs if they are fertilized28. Therefore, the product of sexual reproduction is a long-lived diapausing egg, so that sex is linked with diapause. Diapausing eggs are able to resist harsh environmental conditions such as desiccation, predation, freezing, etc.30. These eggs can remain viable in the sediments through at least several hydroperiods, forming diapausing egg banks, which are reservoirs of the past genetic diversity and adaptive phenotypic variation of the population31 and are essential for long-term population persistence32. In temperate climates, rotifer populations are typically ephemeral and recolonize the water column during the so-called planktonic growing season through the hatchlings of diapausing eggs, forming assemblages of clonal populations34. The rotifer species Brachionus plicatilis is a common inhabitant of salt lakes around the world36. These habitats are isolated from one another and are often characterized by strong seasonal fluctuations in the length of the growing season37 and can become unsuitable for periods of varying predictability38. These fluctuations are typical in the water bodies of the Mediterranean region39, which show a wide range of environmental predictability40. B. plicatilis populations in natural ponds are characterized by strong population structure and low levels of gene flow due to persistent founder effects42.Monogonont rotifers are common zooplankters of continental water bodies and are recognized as excellent model organisms for studies of experimental evolution . In addition, high genetic variance related to these traits has been found in natural populations of B. plicatilis44. The ease of experimental culturing and the range of natural environmental unpredictability in rotifer habitats make rotifers ideal organisms for investigating the genomic basis of adaptation to environmental unpredictability. However, despite progress in identifying adaptive responses in rotifer life-history traits to environmental unpredictability, studies on the genetic basis underlying these responses are almost nonexistent. In a recent study using field rotifer populations, Franch-Gras et al.45 identified several candidate single-nucleotide polymorphisms (SNPs) as part of the genomic basis of local adaptation to fluctuating environments, which constitute a database for future genomic studies45.Using a set of nine B. plicatilis to study the adaptation of life-history traits to unpredictable environments . Six genetically identical laboratory populations were established by placing together 30 rotifer clones from each of the nine field populations representing a broad range of environmental predictability used by Franch-Gras et al.43. Since the rotifer clones from these populations were pooled to create the laboratory populations, hereafter we will refer to this field set as the “origin population”. Three laboratory populations (functioning as replicates) were randomly assigned to each selective regime (predictable vs unpredictable). These populations were studied during seven cycles of selection under a common garden experimental approach, tracking the evolution of two key diapause-related life-history traits: (1) the timing of sex ─as a proxy of the timing of diapause─ and (2) the diapausing egg hatching fraction ─inversely related to diapause duration. Rotifers showed rapid adaptation to a particular unpredictable fluctuation pattern of hydroperiod length, displaying a divergent response in those life-history traits in the populations subjected to the unpredictable regime with respect to those subjected to the predictable regime. Populations subjected to the unpredictable selective regime showed both lower hatching fractions of diapausing eggs and earlier sex initiation, suggesting that bet-hedging strategies evolve as an adaptation to environmental unpredictability in these organisms46. Although we used hydroperiod length fluctuation as the factor of predictability in our experimental design, it should be noted that several other factors  might also affect the degree of predictability in the wild. Note that this experimental design involved testing for the effect of a particular pattern of unpredictable fluctuation in hydroperiod length, so we cannot generalize that the observed response is an adaptation to environmental unpredictability itself.In a previous evolution experiment, we used 47 to identify the genomic signatures of selection. This technique relies on the use of restriction enzymes to reduce genome complexity and allows the identification and genotyping of single-nucleotide polymorphisms (SNPs) in individuals from different populations49. Thus, we identified and screened thousands of genomic markers by using GBS in laboratory B. plicatilis populations that evolved under predictable and unpredictable hydroperiod selective regimes46 to study the genomic basis of their adaptation. Additionally, to identify putative genes under selection and estimate the impact of genetic drift in the experimental populations, we used both an available B. plicatilis genome assembly from one clone from one of the field populations45 and genome-wide data obtained with the same methodology from the nine B. plicatilis field populations45 used to found the experimental laboratory populations. This large GBS dataset was used to identify candidate loci for diversifying selection under these selective regimes in the experimental rotifer populations. Moreover, the initial GBS information allowed us to estimate drift during the duration of the experiment for each particular laboratory population, as they can be compared with the origin population.In this study, we used genotyping by sequencing (GBS)A total of 19 Gb of GBS raw data was obtained from the 169 clones from the six laboratory populations. A total of ca. 560 out of the ca. 915 million raw reads from the origin population plus the six laboratory populations were retained after quality filtering . After SNP calling and filtering, 6,107 SNPs were identified and genotyped Table  with an FST values between the origin and the six laboratory populations were low, ranging from 0.004 to 0.025  ─i.e., genetic diversity─ was maintained in all laboratory populations (ranging from 0.17 to 0.21) after the evolution experiment, except for one of the populations that evolved under the unpredictable regime (U2), which had the lowest genetic diversity . Overall, all populations analyzed were broadly in Hardy-Weinberg equilibrium  analysis with prior odds (PO) = 10 among the origin population and the two selective regimes  was associated with the timing of sex . Four SNPs in two scaffolds  were associated with the hatching fraction Fig. , with ansex Fig.  with an et al.’s45 criteria, additional analyses were performed using just those genes including a SNP in their coding region . GO enrichment analysis showed that they were enriched at high levels , such as the regulation of biological processes (GO:0050789), cellular processes (GO:0009987), single-organism processes (GO:0044699), the regulation of cellular processes (GO:0050794), binding (GO:0005488), and biological regulation (GO:0065007). The three candidate SNPs to be under selection between selective regimes were located in three genes: Ribosomal S6 kinase alpha-1-isoform X1, RNA-binding single-stranded-interacting 3 and Midasin.The 76 SNPs identified by BS1 as candidates of being under positive selection were located within 43 genes, 53.5% of which had putative associated functions  using an experimental evolution approach and genome-wide genotyping. Three out of 45 scaffolds had signatures of divergent selection in response to an unpredictable environmental fluctuation pattern. Most of the remaining scaffolds with signatures of directional selection experienced parallel allele changes in all six laboratory populations, suggesting adaptation to laboratory environmental conditions. These populations showed signatures of adaptation to the experimental unpredictable environment in their life-history traits RNA-binding single-stranded-interacting 3 (RBMS3), a member of the family of c-myc single-strand binding protein (MSSP) genes. These genes are involved in DNA replication, gene transcription, cell cycle progression and apoptosis in humans52 and regulate one of the major pathways promoting chondrogenesis in zebrafish53. The SNP S4644_2726 is located within the ribosomal S6 kinase alpha-1 isoform X1 gene and is involved in controlling growth and differentiation in human cells54. Finally, the SNP S78024_5745 was located in the Midasin gene, which encodes a nuclear chaperone involved in the assembly/disassembly of complex macromolecules in yeast55 and has been shown to be essential for the normal progression of female gametogenesis in Arabidopsis thaliana56. Regarding the genotype-phenotype association analysis, four SNPs putatively associated with the diapausing egg hatching fraction were found. Three of them were fully linked and located within the same gene ─kelch-like ECH-associated 1─ which is involved in responses to oxidative stress59. This gene has been described in several groups, including fish60, insects61, and mammals62.We found that three SNPs located in three different genes showed signatures of positive selection in response to the unpredictable environmental fluctuation pattern studied. The SNP S9060_3689 was located within the gene FST values  when compared to those of the original field populations . All but one population maintained their genetic diversity throughout the experiment. Nevertheless, the negligible loss of genetic diversity (He) and the minor variation in FST values among populations suggest that genetic drift was of minor importance in comparison to selection64.In general, the experimental populations showed weak genetic drift, as evidenced by the low genome-wide pairwise 66 given that (1) the laboratory populations were derived directly from the field and (2) the laboratory conditions other than the selective regime were identical and consistent across the selective regimes in the evolution experiment . This contrasts with the different and highly variable conditions experienced by the field populations66 from which the origin population was derived43. The selective regimes mimicked the extreme patterns of selection imposed by the environmental unpredictability in the hydroperiod length to which B. plicatilis natural populations are exposed40. We focused on the unpredictability of the hydroperiod length, as it is expected to exert a strong selection pressure on ecologically relevant traits of rotifer life history, such as the diapausing egg hatching fraction and the timing of sex67. However, unpredictability is likely generated by other environmental factors that might also be associated with hydroperiod length in natural populations  and could be expected to create additional selective pressures that were not explored here.A strong signal of adaptation to laboratory conditions during the selection experiment was found. This was derived from the number of candidate SNPs under selection with parallel allelic frequency changes identified in all six laboratory populations regardless of the selective regime. These results were not unexpected46. Although other experimental evolution studies have shown adaptation to a range of selective pressures in rotifers68 and other well-known cyclical parthenogens, such as Daphnia70, the genomic basis has been little explored. Genomic signatures of adaptation to environmental anthropogenic stressors were found in D. magna using an experimental evolution approach71; however, the genomic basis of adaptation to environmental unpredictability is essentially unknown. To the best of our knowledge, only a recent study involving rotifer field populations identified genomic signatures of local adaptation to unpredictable environments45. Nonetheless, the genetic variants that could be key in the adaptation to unpredictable environments are not shared between the two studies. It appears unlikely that only three genes underlie the genetic architecture of the life-history trait response to environmental unpredictability, given that such traits are likely to be affected by large numbers of genes72. In this regard, it is worth noting that several limitations constrain our results: (1) genes across the genome with small effects may not be detected as significant outliers given the power of evolution experiments, (2) we use a sample of SNPs on a draft genome, (3) the experiment is not designed to detect elements of adaptation due to idiosyncratic changes in each population, (4) selective pressures associated with habitat unpredictability in the field might differ from those in our experimental evolution approach73, and (5) we recognize that the adaptation was to a particular pattern of unpredictable environment rather than to environmental unpredictability itself. To assess adaptation to environmental unpredictability (and its genomic basis), several replicated patterns of unpredictability would have to be included (of course implying greater complexity in the logistics of the experiment). These limitations could explain the discrepancies found between the results reported by Franch-Gras et al.45 and those of the present study.Genomic signatures of rapid adaptation in laboratory populations through experimental evolution have been identified, supporting previous results showing rapid phenotypic divergence in rotifer populations, which evolved divergent life-history traits over a short time spanB. plicatilis45 allowed us to call SNPs more confidently and to identify putative genes under selection. Although tentative functionality has been assigned to most of the genes containing those SNPs (53.3%), the lack of functional annotation for many of the genes of B. plicatilis precludes the understanding of the mechanisms under selection in this rotifer species.The recent availability of a draft genome for B. plicatilis, building on previous results for ecologically relevant traits. Candidate SNPs under selection from a particular unpredictable fluctuation pattern were identified, some of them associated with key life-history traits in the life cycle of this rotifer species. Given that these responses allow monogonont rotifers to adapt to unpredictable environments, our results can serve as a basis for future studies focused on adaptation. In addition, the rotifer populations showed strong genomic signals of adaptation to laboratory conditions. Furthermore, this study shows the potential of using monogonont rotifer populations to evaluate signatures of adaptation in short-term experiments. Nonetheless, (1) a better understanding of the B. plicatilis genome and transcriptome and (2) experimental validation of the predicted genes are still necessary to elucidate the genomic basis of rotifer adaptation to unpredictable environments.In conclusion, we have shown genomic evidence of rapid adaptation in experimental populations of the rotifer et al.46, in which the effects of environmental unpredictability on diapause-related traits in rotifers were tested. Briefly, in that study, six genetically identical multiclonal laboratory populations of the rotifer B. plicatilis were founded by placing together asexual females from each of 30 clonal lines from nine Spanish Mediterranean salt ponds and lakes . These populations were subjected to two contrasting selective regimes (predictable vs unpredictable) during seven growing cycles of selection mimicking planktonic growing seasons. These growing cycles were interrupted by periods of habitat unsuitability ─dry periods─ .The populations analyzed were obtained from the last cycle of the evolution experiment described in Tarazona et al.46.After the seventh cycle of selection, a total of 30 clones from each of the six laboratory populations were used to found clonal lines  to carry out the DNA extraction. It is worth noting that 10 of these clones were also used to perform the life-history trait assays described in Tarazona −1 saline water, maintained at 20 °C, and fed the microalga Tetraselmis suecica) in 1.5 L plastic bottles for 15 days, starting from a low-density stock culture. On day 15, when an abundance of more than 15,000 individuals was reached in each bottle, the cultures were filtered out through a 30-µm Nytal mesh sieve. The retained rotifers were released and kept in saline water (12 g L−1) for 24 hours to purge their digestive tract and minimize contamination with microalgal DNA. Thereafter, the rotifers were retained on a 30-µm Nytal mesh sieve, which was frozen in liquid nitrogen. DNA extraction was performed on the frozen rotifer biomass using a JETFLEX Genomic DNA purification kit  following the manufacturer’s instructions. DNA quality was assessed on a 1% agarose gel, and the quantification of DNA was performed with a Qubit 2.0 fluorometer . A total of 169 clones were obtained with a sufficient DNA quality and concentration (>20 ng/µl of DNA) to apply the genotyping-by-sequencing protocol.To obtain sufficient biomass for DNA extractions, each individual experimental clone was grown under standard culture conditions  DNA samples were digested with the restriction enzyme ApeKI (GC[A-T]GC), (2) GBS libraries were constructed with unique barcodes for each clone, and (3) after pooling the samples, sequencing was carried out using an Illumina HiSeq. 2000/2500 .GBS libraries were prepared and sequenced at the Institute for Genomic Diversity  following the protocol described by Elshire www.bioinformatics.babraham.ac.uk/projects/fastqc/). Additionally, the published GBS data from the 270 clones used as founders of the experimental laboratory populations (hereafter origin population) were downloaded and employed for comparison (accession number SRP151997)45. SNPs were called from the raw DNA reads using the SNP calling v.2 pipeline as implemented in TASSEL 574. All reads were trimmed to 64 bp, and identical reads were collapsed into tags. Thereafter, these tags were aligned against a draft assembly of the B. plicatilis genome of 108 Mb (accession number REGN00000000)45 using the Burrows-Wheeler alignment tool (BWA)75, and the SNPs were called from the aligned tags. The default parameters from the TASSEL-GBS pipeline were used with some modifications intended to apply a more conservative approach: the minimum length of an aligned base pair (-aLen 30 instead of default 0) and the minimum locus coverage (-mnLCov 0.8 instead of default 0.1).Raw sequence data quality was analyzed using FastQC version 0.11.5  and VCFtools76 to construct the final “individual (clone) x genotype” matrix. The following parameters were set: (1) a minimum coverage of six reads to call a genotype, (2) a minimum of 50% of the individuals of each population genotyped, (3) a minimum allele frequency (–maf) higher than 1%, (4) only two alleles present , (5) average read depth <150 (–max-meanDP), and (6) heterozygotes in each SNP < 60%.The set of SNPs was filtered by quality using scripts modified from Franch-Gras 77 in R78 v. 3.2.2. To evaluate between-population differentiation and the effects of drift, the fixation index (FST) for each pairwise comparison was estimated. Additionally, the FST value between selective regimes was calculated by grouping the three replicates  under each selective regime. Other basic statistics, such as the expected heterozygosity (He), observed heterozygosity (Ho), and inbreeding coefficient (FIS), for the origin and six experimental laboratory populations were estimated. FST values, He, Ho and FIS were estimated using the R package “hierfstat”79. The function pairwise.fst within this package computes Nei’s pairwise FST between pairs of populations80. Hardy-Weinberg equilibrium (HWE) was tested using VCFtools for each population. File format conversions were performed using PGDSpider81 and Plink82 v.1.9.To assess the genome-wide genetic variation and differentiation, a principal component analysis (PCA) was performed on individual clone genotypes of all populations using the “adegenet” package83 v.2.1. This genome-scan method identifies markers that show stronger divergence patterns between groups than would be expected under neutral genetic processes. Moreover, it estimates the posterior probability that a given SNP is affected by selection. This FST-based method is a differentiation method that is robust to confounding demographic processes85 and has a low number of false positives86 relative to other methods. We carried out two BayeScan analyses, BS1 and BS2, using prior odds (PO) of 100 and 10 and a false discovery rate (FDR) of 0.05. PO refers to the ratio of posterior probabilities, which indicates the likelihood of the selection model in comparison to that of the neutral model88. Therefore, here, the selection model must be 10 or 100 times more likely than the neutral model for a locus to be considered as a candidate of being under selection. In BS1, the following groupings of populations were established: (1) origin populations, (2) the three replicate populations that evolved under the predictable regime (hereafter predictable populations), and (3) the three replicate populations that evolved under the unpredictable regime (hereafter unpredictable populations). In BS2, to assess the assignment of candidate SNPs to each selective regime (predictable vs unpredictable), we used two groupings: (1) predictable populations and (2) unpredictable populations. This design allowed us to observe parallel adaptive responses in replicate populations to obtain relatively robust results, but it did not allow us to identify idiosyncratic ─population-specific─ changes in the populations.Putative selected loci were identified in BayeScan46. We included clones with both phenotypic and genotypic data available . Linkage disequilibrium (LD) was also assessed for candidate SNPs associated with both phenotypic traits using Plink.Genotype-phenotype association analysis was performed for the two life-history traits ─timing of sex and hatching fraction of diapausing eggs─ obtained for each clone in our previous experimental evolution studyB. plicatilis genome45. Genes putatively associated with the candidate SNPs identified with BayeScan and those identified with GWAS were identified in the flanking regions of 0, 2.5 and 5 kbp upstream and downstream of the focal SNPs using BEDtools89. Additionally, a gene ontology (GO) enrichment analysis  was performed for those genes in which candidate SNPs under selection were found using Blast2GO90.Gene functions were retrieved from the current annotation of the Supplementary informationSupplementary Dataset S2Supplementary Dataset S1


Crassostrea gigas) is the world's most heavily cultivated shellfish species and has been introduced to all continents except Antarctica for aquaculture. We therefore used a medium‐density single nucleotide polymorphism (SNP) array to investigate the genetic structure of this species in Europe, where it was introduced during the 1960s and has since become a prolific invader of coastal ecosystems across the continent. We analyzed 21,499 polymorphic SNPs in 232 individuals from 23 localities spanning a latitudinal cline from Portugal to Norway and including the source populations of Japan and Canada. We confirmed the results of previous studies by finding clear support for a southern and a northern group, with the former being indistinguishable from the source populations indicating the absence of a pronounced founder effect. We furthermore conducted a large‐scale comparison of oysters sampled from the wild and from hatcheries to reveal substantial genetic differences including significantly higher levels of inbreeding in some but not all of the sampled hatchery cohorts. These findings were confirmed by a smaller but representative SNP dataset generated using restriction site‐associated DNA sequencing. We therefore conclude that genomic approaches can generate increasingly detailed insights into the genetics of wild and hatchery produced Pacific oysters.Cultivated bivalves are important not only because of their economic value, but also due to their impacts on natural ecosystems. The Pacific oyster ( In particular, the Pacific cupped oyster (Crassostrea gigas), which is native to the Pacific coast of eastern Asia, was introduced into many countries worldwide for commercial cultivation. Starting in the 1960s, C. gigas was introduced into Europe to support oyster farming after severe declines of the two previously cultivated oyster species–the Portuguese oyster (C. angulata) and the flat oyster  sequencing  from deep pedigrees . Whole genome sequencing of pooled genomic samples from eight European C. gigas populations led to the discovery of 1.2 million putative SNPs, of which 40,625 were printed on the array and 27,697 were validated as being polymorphic and of high quality. This array represents an important resource for selective breeding programs as well as more generally for population genetic studies of oysters. We therefore used it to investigate population genetic structure and inbreeding in C. gigas sampled from wild European populations and hatcheries. Specifically, we genotyped 192 individuals from 13 populations spanning a European latitudinal cline from Portugal in the south to Norway in the north. We then incorporated existing data from Gutierrez et al.  and from British Columbia in Canada (CAN).Pacific oyster samples were collected between November 2014 and March 2016 from 12 different sites along the Atlantic seaboard of mainland Europe as well as from one location in the Mediterranean  six wild populations from the southern group  for quality control and genotype calling. All thresholds for quality assessment were set to the values recommended in the Affymetrix best practice workflow (Supporting information Table 2.5http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). We then conducted a de novo assembly of the data and called genotypes using the Stacks 2.1 pipeline .” In the case of locations not connected by land, the shortest sailing distance between coasts was used. Second, we used the R package Adegenet version 2.1.1 .To investigate broad‐scale patterns of genetic differentiation, we used AMOVA to quantify the proportion of genomic variation attributable to each of five hierarchical levels of population substructure. As expected, over 95% of the total variation was partitioned within individuals. The remaining variance was mainly partitioned among the northern and southern groups, the source populations, and Portugal . Consistent with the AMOVA, a number of clear differences were apparent. First, the northern and southern groups clearly separated apart from one another, as did oysters from Portugal, although no genetic differences were apparent between the southern group and the two source populations Figure . FurtherWe next used a model‐based Bayesian clustering approach implemented in fineRADstructure to infer population structure via shared ancestry. The resulting cladogram and coancestry matrix shown in Figure 3.2b = 1.39, r2 = 0.79, p < 0.001, Figure b = 48.99, r2 = 0.99, p < 0.001). This indicates that, as expected, genomic inbreeding coefficients tend to be higher in populations with elevated levels of relatedness and shared coancestry.To explore patterns of inbreeding in wild populations and hatcheries, we calculated genomic inbreeding coefficients for all of the individuals in our dataset. Mean genomic inbreeding coefficients were strongly positively correlated with average pairwise relatedness values within populations . First of all, regardless of whether wild populations or hatcheries were considered, a significant tendency was found for genomic inbreeding coefficients to be higher in the northern than in the southern group . This is consistent with previous studies showing that genetic diversity is lower in the northern group (see Discussion). Second, genomic inbreeding coefficients were significantly higher in hatcheries versus wild populations . In line with this, identity disequilibrium was higher in individuals sampled from hatchery  than from wild populations , indicating that hatchery‐reared oysters have greater overall variance in inbreeding Although we did not expect our results to be strongly affected by ascertainment bias because oysters from both the northern and southern groups were used in the discovery panel for the SNP array . However, Pacific oysters also have one of the smallest documented effective to census population size ratios , but mortality rates within commercial oyster hatchery cultures can be as high as 98% between fertilization and the spat stage  or by maximizing numbers of effective breeders, while others actively avoid these practices , presumably to minimize the risk of disease transmission. In other cases, it may be desirable to maintain particular families or lineages that have been selected based on specific characteristics, even if this results in somewhat higher levels of consanguinity .It is important to recognize that not all of the hatchery‐reared oysters in our study showed higher levels of inbreeding than wild populations. By implication, inbreeding is not associated with hatchery propagation 4.3Moderate to high levels of inbreeding are known to negatively impact a multitude of commercially relevant fitness traits, from individual growth rate through harvest body size to survival, in many aquaculture organisms (Deng et al., C. gigas reference genome, which would allow inbreeding to be evaluated with even greater precision through the use of mapped genetic markers to quantify runs of homozygosity.With respect to the need for further exploration, it is worth bearing in mind that although our total sample size of oysters was reasonably large given the number of markers deployed, only around ten samples were analyzed on average from each population. While this is unlikely to have appreciably affected our inference of population structure (Willing, Dreyer, & Oosterhout, Having done so, a useful next step would be to evaluate in greater detail the effects of different levels of inbreeding on commercially important traits within hatcheries. So far, only a single study has evaluated the effects of inbreeding on adult traits in a commercial growing environment and this was based on a crossing design that maximized variation in inbreeding among families (Evans et al., Finally, a number of potential intervention strategies aimed at reducing inbreeding and increasing genetic diversity could be envisaged. The first obvious approach would be to incorporate individuals from wild populations into hatchery broodstocks, as also discussed by Lallias et al.  in the c4.4SNP arrays provide a cost‐effective and convenient route to genome‐wide investigations but can be prone to ascertainment bias when the samples used in the initial SNP discovery phase differ from those being interrogated on the array (Lachance & Tishkoff, C. gigas and C. angulata being negligible in our sample. Consequently, it appears that the Portuguese oysters could represent and inbred and isolated C. gigas population. However, we cannot discount the further possibility that we inadvertently sampled C. angulata from this location, as the two species are morphologically indistinguishable, the SNP array may include loci that cross amplify in C. angulata (Gagnaire et al., Fst comparisons involving our Portuguese sample were consistently high.Nevertheless, we conservatively took into account the possibility that ascertainment bias could be responsible for the ostensibly high level of inbreeding in the putatively hybrid Portuguese population. To test this possibility as well as to confirm our broader findings, we RAD sequenced a subset of individuals and repeated all of our analyses. Our previous results were largely confirmed, with very similar patterns of population genetic structure and inbreeding being obtained, lending further weight to our main conclusions. Furthermore, oysters from Portugal were again found to have relatively high genomic inbreeding coefficients based on the RAD data. As we would expect hybrids to be relatively outbred, this finding points toward hybridization between 5We harnessed some of the latest developments in genomics to shed new light on the population structure of Pacific oysters along a European latitudinal cline as well as to compare levels of inbreeding between wild and hatchery samples. The several orders of magnitude higher genetic resolution provided by the medium‐density SNP array allowed us not only to confirm previous findings (Faust et al., None Declared. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Syringodium filiforme, in the Florida Keys and subtropical Atlantic region. We used microsatellite markers to analyze 20 populations throughout the Florida Keys, South Florida, Bermuda and the Bahamas primarily to understand how genetic diversity of S. filiforme partitions across the Florida Keys archipelago. We found low allelic diversity within populations, detecting 35–106 alleles across all populations, and in some instances moderately high clonal diversity (R = 0.04–0.62). There was significant genetic differentiation between Atlantic and Gulf of Mexico (Gulf) populations  and evidence of population structure based on cluster assignment, dividing the region into two major genetic demes. We observed asymmetric patterns in gene flow, with a few instances in which there was higher than expected gene flow from Atlantic to Gulf populations. In South Florida, clustering into Gulf and Atlantic groups indicate dispersal in S. filiforme may be limited by historical or contemporary geographic and hydrologic barriers, though genetic admixture between populations suggests exchange may occur between narrow channels in the Florida Keys, or has occurred through other mechanisms in recent evolutionary history, maintaining regional connectivity. The variable genotypic diversity, low genetic diversity and evidence of population structure observed in populations of S. filiforme resemble the population genetics expected for a colonizer species.Evaluating genetic diversity of seagrasses provides insight into reproductive mode and adaptation potential, and is therefore integral to broader conservation strategies for coastal ecosystems. In this study, we assessed genetic diversity, population structure and gene flow in an opportunistic seagrass,  Genetic diversity is paramount to the long-term survival of populations, as genetic variation provides the basis for adaptation to environmental change via natural selection and confers short-term fitness advantages at the population level. Population structure and gene flow, which describe the level of genetic differentiation and connectivity between populations, are important components and drivers of genetic diversity. Quantifying genetic diversity within and among natural populations enhances conservation efforts because genetic patterns are difficult to predict given the complex suite of environmental and biological factors that contribute to genetic diversity and population structure . In seagSilene dioica, in the Gulf of Bothnia show strong differentiation when the supply of colonists is limited [Theory suggests early successional species are expected to have diminished genetic diversity due to founder effects and to develop strong population structure due to limited gene flow, while later successional species are typified by greater standing genetic diversity and weaker population structure . In terr limited , while o limited ,9. The rSyringodium filiforme, in the Florida Keys and subtropical Atlantic region.Seagrasses present an opportunity to study environmental and ecological determinants of genetic diversity because they comprise a globally distributed, paraphyletic taxon that has evolved from up to four independent lineages  and reprS. filiforme is widely distributed throughout the western tropical and subtropical Atlantic Ocean in shallow coastal and back reef environments [2 of South Florida coastline [ronments ,19, and oastline . These moastline ,22. Seagoastline –25 and hoastline –29 and Toastline . In ordeS. filiforme generally dominates early successional meadows because it has relatively high horizontal rhizome elongation rates [S. filiforme to quickly colonize bare areas through clonal propagation, but also through seed and vegetative fragment dispersal, especially following disturbance [Syringodium species as opportunistic, exhibiting a mixture of life history traits found in both colonizing and persistent species. Previous studies have characterized the genetic diversity of other common South Florida seagrasses, Thalassia testudinum and Halodule wrightii. In line with theory, T. testudinum, a late successional seagrass species, exhibited high genetic diversity within populations and weak genetic structure in Florida Bay and the Lower Keys  [H. wrightii partitioned among populations rather than within populations in a study focused on the Gulf of Mexico and Florida Bay [S. filiforme would exhibit genetic diversity and population structure patterns similar to those expected for colonizer species, and would therefore reveal high clonality, low genetic diversity and strong differentiation between populations throughout the Florida Keys and wider study area.on rates  and toleon rates . These tturbance . Based oturbance  categorier Keys) –37. As erida Bay . We hypoS. filiforme across the Florida Keys ranges from marginal and patchy in northeastern Florida Bay, to sparse in offshore intermixed beds on the Atlantic Ocean (hereafter referred to as Atlantic) side, to dense, monospecific stands along the Middle and Lower Keys on the Gulf of Mexico (hereafter referred to as Gulf) side [S. filiforme populations in the Atlantic and Gulf basins, leading us to predict these basins host genetically distinct populations due to limited opportunity for propagule exchange and gene flow across a physical barrier. We expected the Bahamas population to have high genetic connectivity with the Florida populations because of its relative proximity, but the Bermuda population to be genetically distinct from the Florida populations due to its geographic isolation.South Florida coastal waters exhibit particularly complex hydrology, especially around the Florida Keys, an archipelago that spans 350 km from the South Florida mainland to Key West, separating the Gulf of Mexico and Florida Bay from the Atlantic Ocean . The dislf) side ,41. The S. filiforme across the Florida Keys and subtropical Atlantic region. We examined 1) whether clonality varies within 100s of m2 and 1000s of m2 spatial scales; 2) the relative amounts of genetic diversity present within individuals and populations of S. filiforme; 3) the degree to which genetic differentiation among populations results in population structure; and 4) whether there are patterns in the magnitude and direction of gene flow between populations.In this study, we used species-specific microsatellite loci to assess genetic diversity, population genetic structure and connectivity via gene flow in 2 area to estimate clonal extent. After detecting unique genotypes within meters of each other, we reduced the sampling area in 2015 and modified the sampling area for Florida Bay and Bermuda in order to accommodate for greater patchiness of S. filiforme meadows. Genetic data collected with uneven but similar sampling schemes are comparable when using unique genotypes for regional analyses of genetic diversity and population structure [We sampled a total of 20 meadows, hereafter termed populations, in South Florida, the Bahamas and Bermuda  were sampled, respectively. Within each area, 24 leaves were collected from ramets (spaced 1.5 m apart) in a ~ 70 m2 sampling area.In 2014, we sampled eight populations in the Upper and Middle Keys on the Atlantic side, two populations on the Gulf side (Sprigger and Sluiceway) and a single population in Tampa Bay. In 2015, we sampled six populations in the Middle and Lower Keys on the Gulf side and one population in the northeastern portion of Florida Bay. Additionally in 2015, we sampled single populations in San Salvador, the Bahamas and Bailey’s Bay, Bermuda. Leaves of at least 50 individual Permits were required for sample collection in the Lower Florida Keys  and Florida Bay  in 2015 because sediment was collected in addition to seagrass plant tissue for supplemental analyses; sampling in these areas was conducted under FKNMS-2015-085 and EVER-2013-SCI-0058, respectively. Sampling in Bermuda was conducted under the Bermuda Dept. of Conservation Services License no. 15-04-16-22.-1. For some samples, DNA extraction was unsuccessful due to the poor tissue quality of senescing seagrass leaves, reducing the sample size for several sites.Total genomic DNA was extracted from the samples collected in 2014 using a DNeasy™ Plant Kit (QIAGEN) according to the manufacturer’s instructions. Extracted DNA was quantified on a Qubit® 2.0 Fluorometer (Invitrogen). Samples collected in 2015 were sent to the University of Wisconsin Biotechnology  for extraction and quantification. DNA was extracted from 40–50 mg of dried leaf tissue using the CTAB method as described in Saghai-Maroof et al. with minimal modification . FollowiA total of 17 microsatellite loci were amplified using fluorescently labeled primers . PCR wasG, the probability individuals sharing the same genotype were derived via separate sexual events, (Psex), and the probability of clonal identity, (Pgen), were estimated for each population using Genclone version 2.0 [R, the proportion of genetically distinct individuals (or genets) in the population, was calculated as R = (G-1)/(N-1) [The number of unique multi-locus genotypes (MLGs), sion 2.0 ,48. Uniqsion 2.0 . Genotyp1)/(N-1) .A, average number of alleles per locus, NA, and average allelic richness per locus standardized by smallest sample size, AR, were calculated using the ‘diveRsity’ package [Ho), expected heterozygosity (He), and deviation from Hardy-Weinberg equilibrium as measured by the inbreeding coefficient, FIS, were calculated in Genalex version 6.5 [Genepop version 4.2 [For the remainder of population genetic analyses, replicate MLGs were removed from the dataset to avoid allele frequency bias due to the presence of clones. The total number of alleles,  package  in R [52sion 6.5 ,54. We csion 4.2 ,56 and dGenodive [FST [D [Genodive using a covariance matrix based on individual allele frequencies to determine whether geographically proximal samples exhibit similar allele frequencies, but without the assumption of hierarchical genetic structure.An analysis of molecular variance (AMOVA) was performed first on all populations to assess overall genetic differentiation, and again with only populations bordering the archipelago  followed by 1,000,000 Markov Chain Monte Carlo repetitions. The most likely number of population clusters was determined by the ad hoc quantity, ΔK [clumpak [distruct was used for visual representation of the results [To determine the most likely number of population clusters, K, population assignment utilizing a Bayesian approach was performed in the genetic software program tructure . Admixtutity, ΔK . Complem[clumpak  were use results .Nm, was estimated using FST [Genalex and Genepop, respectively. Additionally, pairwise relative migration rates were estimated using Alcala’s Nm [Average total migration, sing FST  and raresing FST  in Genalala’s Nm  and direala’s Nm  using ‘dPgen ranged from 3.3 x 10−8 to 7.0 x 10−3, indicating there was a low probability of generating the observed genotypes under Hardy-Weinberg Equilibrium conditions. Psex ranged from 1.7 x 10−7 to 2.0 x 10−3, though there were higher values for Psex in the following Florida populations: Crane (0.094), Key West (0.15), Tampa Bay (0.063), and Florida Bay (0.19). The few instances in which Psex exceeded 0.05 occurred in populations dominated by few clones, thereby inflating Psex, and were unlikely to have greatly impacted the accuracy of heterozygosity estimates and other statistical analyses performed. The 37 instances  in which linkage disequilibrium was significant after a Bonferroni correction was applied  were also unlikely to affect subsequent population genetic analyses.For most populations, p-value < 0.05), most of which exhibited negative inbreeding coefficients. We found no significant effect of null alleles, except in populations with few genets (G ≤ 10) and loci for which all samples were homozygous for the same allele, or fixed. Excluding populations with few genets and loci with fixed alleles, the mean per locus significance of heterozygote deficiency due to null alleles across populations ranged from 0.276 to 0.827.Genotypic richness was highly variable among populations, ranging from 0.04 to 0.62 . GenotypFST = 0.149 ± 0.017, p-value = 0.001) and significant genetic differentiation between the Gulf and Atlantic populations . The results of pairwise population differentiation were consistent across both statistics, FST and Jost’s D , and low to high within Gulf populations (FST = 0.012–0.237). Pairwise differentiation values between Atlantic and Gulf populations ranged from 0.041 between Davis in the Upper Keys and Marathon near the Middle Keys, to 0.330 between Conch in the Upper Keys and Crane in the Lower Keys. Florida Bay exhibited similar levels of differentiation between Gulf (FST = 0.144–0.273) and Atlantic (FST = 0.177–0.259) sites. Tampa Bay, the westernmost site sampled, exhibited high differentiation between Atlantic populations (FST = 0.236–0.37) and moderate to high differentiation between Gulf populations (FST = 0.101–0.279). The highest overall pairwise differentiation was found between the Bahamas and Tampa Bay, where FST = 0.531. The next greatest values were found between the Bahamas and Gulf populations (FST = 0.261–0.473), and values were moderate between the Bahamas and Atlantic populations (FST = 0.192 0.259). Bermuda pairwise differentiation with Atlantic and Gulf sites was moderate to high, with FST values ranging from 0.142 to 0.224 and 0.176 to 0.280, respectively.AMOVA revealed significant genetic differentiation between all populations , followed by K = 4 (ΔK = 20.84) number of population clusters. For K = 2, Atlantic and Gulf populations clustered separately, and Tampa Bay, Florida Bay and the Bahamas were assigned to the Gulf cluster  the level of clonality in S. filiforme, as measured by shared multilocus genotypes, to be highly variable among populations; 2) low allelic diversity and heterozygote excess in almost every population; 3) evidence of genetic differentiation and population structure, in which the sampled populations were assigned to two major demes separated by the Florida Keys archipelago; and 4) asymmetric gene flow patterns, though average migration rates across all populations exceeded one migrant per generation.Within the Florida Keys and subtropical Atlantic region, S. filiforme varied widely across sampling sites, though this cannot be completely explained by disparities in sampling area. Sample collections in 2014 were from within an area of ~ 2,500 m2, in which genotypic richness ranged from 0.37 to 0.62. Sample collections in 2015 were from within an area of ~ 500 m2, in which genotypic richness ranged from 0.05 to 0.43. Therefore, within each sampling scheme, we observed a wide range in clonality. In the larger areas sampled in 2014, we detected one genet present in two adjacent populations (Sprigger and Sluiceway), extending over hundreds of meters. For the 2015 collection sites, sampling in a smaller total area with shorter distances between each shoot sampled may have led to a decrease in detection efficiency of total genets and number of alleles present in the population [S. filiforme across the subtropical Atlantic region underscores the advantage of clonal reproduction in this environment. It is also possible that we observed an edge-of-range effect [S. filiforme, we would expect to find greater levels of diversity in Caribbean populations.Genotypic richness of pulation . Allelice effect  in whichS. filiforme populations in Florida may provide evidence of a phylogeographic break, which has been observed for a number of warm-temperate marine and intertidal organisms, related to increases in seawater temperature (and thereby northward shifts in temperate species’ range limits) associated with glacial retreats that occurred throughout the Pleistocene [The strongest population structure clearly develops in South Florida, where Tampa Bay, though hundreds of kilometers away from the Florida Keys, groups with Gulf populations, suggesting the Florida Keys archipelago presented historical barriers to gene flow between Gulf and Atlantic demes, and perhaps continues to impede gene flow with contemporary land configurations and sea levels. This is also supported by the relative migration and direction of gene flow calculations, which revealed asymmetric patterns in the magnitude and direction of genetic exchange. The Atlantic populations are strongly connected to one another, as are the Gulf populations, though to a lesser extent. The genetic disjunction between Atlantic and Gulf istocene –73.S. filiforme, a tropical species tolerant of warm seawater temperatures. During the Pleistocene, glacial advances exposed more of the Florida peninsula and may have restricted estuarine habitat to a small area within the western Gulf of Mexico [S. filiforme, changes in sea level that resulted in the final emergence of the Florida peninsula may have instigated the genetic break. McCommas [Bunodosoma carvernata, to this vicariant event based on estimated time since divergence. Without fossil evidence or molecular clock calculations, we can merely suggest the break we found in Florida was similarly initiated by prior fluctuations in sea level and maintained by contemporary ocean currents.Historical changes in sea level (and not necessarily temperature) may have been a primary factor contributing to the development of the genetic break for f Mexico , while gf Mexico . DependiMcCommas  attributS. filiforme, in which we detected relatively high gene flow from Atlantic populations to Gulf populations proximal to a break in the Middle Keys . Additionally, the Marathon population appeared more genetically similar to the Atlantic populations than to those in Gulf. This finding could reflect shared ancestry and relatively recent divergence between the Atlantic and Gulf populations, but does not exclude the possibility of genetic exchange occurring between Atlantic and Gulf populations across the archipelago via propagules or rafting vegetation.Interestingly, there are exceptions to the Atlantic-Gulf divide for S. filiforme in the Gulf of Mexico. In the latter half of the 20th century, Tampa Bay experienced a major decline (~ 70%) in historical seagrass coverage due to rapid population expansion and development along the coast [We found relative gene flow between proximal Florida Bay, Tampa Bay and Key West populations and other South Florida populations to be comparable to (and in some instances less than) gene flow levels observed between Florida and more distant non-Florida populations. These populations were also highly clonal, exhibiting the lowest genotypic richness values measured in this study. We sampled in the northeastern-most extent of Florida Bay in Blackwater Sound, an enclosed area with few hydrological connections to the greater Florida Bay or the Atlantic; we suspect the low gene flow and low genotypic richness are related to this isolation. Gene flow between the Key West population and other South Florida populations may be limited by hydrologic rather than topographic isolation: strong reversing tidal currents flowing between the Gulf of Mexico and the Florida Straits may prevent mixing between the Key West population and the further eastward Lower Keys populations. The Tampa Bay population, located roughly halfway up the Florida peninsula, approaches the northern range limit for he coast . Since ahe coast . It is uH. wrightii [K = 2 population clusters scenario, the Bermuda population contains genotypes with near equal membership to the Atlantic and Gulf clusters, while the Bahamas population shows complete membership to the Atlantic cluster. These results imply the Bahamas population groups with the Atlantic populations, though our previous analyses suggest relatively strong genetic differentiation and limited gene flow between the Bahamas and all other sites. Contemporary surface ocean currents directing the movement of propagules, and therefore genetic exchange between populations, may be responsible for these patterns [Though Bermuda is the furthest distance from all other populations, the greatest differentiation occurred between the Bahamas and Florida sites. High relatedness between Florida and Bermuda populations of wrightii  indicatepatterns . Based oPanulirus argus) larvae along a ‘recruitment conveyor’ in the Florida Keys, in which spawning larvae near the Yucatán Peninsula have been identified as source populations [The high genetic exchange within Atlantic populations as evidenced by high migration rates may be explained by hydrologic connections created by surface currents and eddies that form along the Florida Keys Atlantic coastline. The gene flow patterns observed here roughly agree with the modeled and observed movement of spiny lobster Click here for additional data file.S2 TableFST values are provided to the left of the diagonal and Jost's D values are provided to the right of the diagonal. Bold text indicates significance based on non-overlapping confidence intervals.(DOCX)Click here for additional data file.S3 TableValues represent the relative amount of gene flow from populations in the first column to receiving populations identified in the first row. For example, the highest amount of gene flow (1.000) occurs from Carysfort to Davis, while the lowest amount of gene flow occurs from the Bahamas to Key West (0.004). Bold text indicate significance based on non-overlapping 95% confidence intervals.(DOCX)Click here for additional data file.S1 FigAxis loading values are depicted for the two principle coordinate axes containing the greatest amount of variation, PC1 (18.3% variance) and PC2 (7.7% variance). Genotypes from each population group are distinguished by color and shape .(EPS)Click here for additional data file.


Larus bulleri) are endemic to New Zealand and are suspected to be undergoing substantial population declines. They primarily breed on open gravel beds in braided rivers of the South Island—a habitat that is diminishing and becoming increasingly modified. Although management of this species is increasing, little has been published on their movements and demographics. In this study, both mitochondrial DNA (mtDNA) control region domain I and nuclear single nucleotide polymorphisms (SNPs) were examined to help understand the connectivity and population structure of black-billed gulls across the country and to help inform management decisions. Mitochondrial DNA showed no population structure, with high haplotype and low nucleotide diversity, and analyses highlighted mitochondrial introgression with the closely related red-billed gulls (Larus novaehollandiae scopulinus). Nuclear DNA analyses, however, identified two groups, with Rotorua birds in the North Island being distinct from the rest of New Zealand, and isolation-by-distance evident across the South Island populations. Gene flow primarily occurs between nearby colonies with a stepwise movement across the landscape. The importance from a genetic perspective of the more isolated North Island birds  needs to be further evaluated. From our results, we infer that the South Island black-billed gull management should focus on maintaining several populations within each region rather than focusing on single specific colonies or river catchments. Future study is needed to investigate the genetic structure of populations at the northern limit of the species’ range, and identify the mechanisms behind, and extent of, the hybridisation between red-billed and black-billed gulls.Black-billed gulls ( The dynamics of gene flow, dispersal, and the potential causes of population structuring are important to understand when creating a management plan for a species . PopulatLarus bulleri) are endemic to New Zealand and exist primarily in the South Island [Larus novaehollandiae scopulinus), which primarily forages around marine environments, black-billed gulls mostly feed on terrestrial invertebrates, such as earthworms, larvae, and insects from ploughed farmlands, wet grass fields, and on mown hay meadows [Black-billed gulls/tarāpuka  and nuclear DNA markers differently, due to their manner of inheritance and recombination ,20. Due us gulls ,24.Genetic studies are increasingly being used to elucidate the population connectivity and ecology of seabirds, and the results from these studies are being applied in conservation management ,27,28,29Blood samples from black-billed gull chicks were obtained in December and January during 2014/2015, 2015/2016, and 2016/2017. Sampling was carried out under collaboration with the Department of Conservation  following their approved best practice guidelines. Chicks were targeted as it is too disruptive to a breeding colony to capture adults, due to the high density of nests. Only one chick was sampled per nest to avoid sampling siblings. Blood (<20 µL) was taken from the brachial vein in the wing with a sterile needle (26G × 1/2″), collected in a glass capillary tube, and stored in 0.5 mL of Queen’s lysis buffer solution  .A phenol-chloroform extraction  was used® 9.0.5 [L. bulleri control region sequence (NCBI Accession No.: FM209657 used as a comparison to our sequences) was aligned with the black-billed gull sequences in Geneious®. The red-billed gull sequence was used to determine how closely this species matched the black-billed gull.Sequences were aligned with Geneious® 9.0.5 . Of the The mtDNA control region analyses showed the presence of two major clades, a small clade separated from a large clade without any geographical pattern . To unde® 9.0.5 [®.Sequences were aligned in Geneious® 9.0.5 . SequencSingle nucleotide polymorphisms (SNPs) may include variation at many thousands of loci, thereby giving a detailed estimate of genome-wide variation. We used a single enzyme digest Genotyping by Sequencing (GBS) approach to generate SNP data at AgResearch’s Animal Genomics Group laboratory . A total of 94  of the 100 phenol-chloroform extracted DNA samples mentioned above for the mtDNA control region were screened.Bioanalyser traces  indicative of single sequences in the region of interest were used to determine the best restriction enzyme to use for digesting genomic DNA. For black-billed gulls, the restriction enzyme ApeKI  was selected, and sample-specific barcode adapters were ligated to the sticky ends of fragments after digestion. Individually barcoded samples were pooled to create a single library, which was followed by multiple PCRs. A column purified pooled library of the PCRs was then size selected (193–313 bp size range) via a Pippin . Sequencing of library was done on Illumina HiSeq2500 with single-end reads, and 101 cycles in high-output mode (v4 chemistry) .A second GBS library was constructed due to a low sequencing depth using the above method. The size range used on the Pippin window was modified to 193 to 255 bp to give a subset of DNA fragments and therefore an increase in sequencing depth. This was run as an additional lane on the Illumina HiSeq2500 under the same conditions outlined above.https://blast.ncbi.nlm.nih.gov) against the NR database (ftp://ftp.ncbi.nlm.nih.gov/blast/db). A SNP loci catalogue was created as outlined in references [Methods outlined by reference  were folferences ,37. UNEAferences  trimmed https://github.com/AgResearch/KGD) was used for additional quality control, following outlines by reference [Kinship using GBS with Depth adjustment  outgroup. A median-joining network to explore relationships amongst haplotypes was created using PopART 1.7 (popart.otago.ac.nz).For phylogenetic analyses, the Akaike information criterion (AIC) in JModeltest2  was usedπ) and haplotype diversity (ɧ) provide insight into genetic diversity. All 69 sequenced individuals were initially included in the calculations; however, once introgression was identified, the calculations were rerun using only the individuals in the large clade , with the most commonly observed haplotype (BBG004_Ash) in this clade differing from the published sequence by one bp. The individuals in the smaller of these two clades closely matched the published red-billed control region sequence  with the most commonly observed haplotype (BBG002_Ash) in this clade also differing from the published red-billed gull sequence by one bp. The median-joining haplotype network showed the same split with 17 mutational steps between the two main clades with no clear separation of specific river catchments , or between river catchments . Trees for IQ-TREE and MrBayes were similar; therefore, only the Bayesian tree is shown along with posterior probability . The anatchments . The mostchments  was founOnce introgression was determined, the 11 individuals from the smaller of the two major clades  were remNo detailed analyses were done on the mitochondrial cytochrome b sequences. The seven cytochrome b sequences were examined to assist the explanation behind the two main clades seen in the Bayesian tree , specifiST values between populations were identical when regressed (y = 0.9327x − 5 × 10−6 R2 = 0.9983); the same number of optimal clusters were recorded in both fastSTRUCTURE and in DAPC; and clustering plots were visually indistinguishable. Therefore, values obtained with the 10% missing data per locus are presented throughout for consistency. The 10% missing data were chosen to represent the middle value of the three percentages .Unfiltered, 402,672 SNPs were obtained for 94 individuals, and contamination was not detected using the BLAST contamination check. Three different levels of filtering were performed where 5%, 10%, and 25% missing data were permitted per locus, giving a total of 15,095, 51,811, and 176,298 SNPs retained, respectively. The performance and outputs of all three datasets were compared with multiple analyses and the results were essentially identical: Pairwise FBased on the relatedness estimates derived from KGD, two individuals from Rotorua were highly divergent from all others sampled and appeared to be divergent enough to be red-billed gulls that had mistakenly been sampled A. This pThe average observed, and expected, heterozygosity was similar across sampling locations, though the Rotorua sampling site had lower allelic richness than the other populations, with a general trend of lower diversity present in the North and higher in the South Islands .The neighbour-joining tree of individuals created from SNP data splits the North and South Islands river catchments into two major clades A. AveragST was generally low, ranging from 0.002 and 0.043  than the South Island river catchments (0.002 to 0.011). All North Island values were significantly different from the South Island. The lowest FST values with a p-value <0.05 were between Hurunui and Ahuriri (0.004) and between Taramakau and Hurunui (0.004).Genetic variation between river catchments as measured by Fnd 0.043 . The higST measurements shows three clear groupings (p < 0.0001), and when tests were conducted among the South Island populations (excluding Rotorua and Wairarapa), the IBD values remained significant  there is mito-nuclear divergence in some black-billed gulls, due to historical introgression with red-billed gulls, and (2) there is low but significant population structure within black-billed gull breeding colonies across the country. A pattern of IBD is seen across their range, showing increasing isolation and lower diversity for the breeding populations in the North Island (particularly in Rotorua). These observations help us understand the spatial and breeding ecology of black-billed gulls, with important implications for the species’ management.In our study of black-billed gull genetic structure, results from mtDNA markers were discordant with nuclear DNA markers (SNPs). Mitochondrial DNA control region analyses showed no genetic structure between populations. The two major clades in the Bayesian tree and haplotype network  were alsMany factors influence genetic structure, such as gene flow, IBD, historical fragmentation, range expansion, long-distance colonisation, land/ice barriers, or non-breeding distribution ,59. For Thalassarche chrysostoma) showed no genetic structure in mtDNA or nuclear DNA because they forage and disperse over large areas during the non-breeding season [Thalassarche melanophris) formed three genetic groups, because they use similar areas during the breeding and non-breeding season [Seabirds typically cover vast distances to find food sources thereby increasing the chance of population mixing and hence gene flow and genetic structure . Contactg season . Black-bg season . Black-bg season . This woProcellaria aequinoctialis). The nuclear DNA analyses presented here show population structure with two clear clusters, Rotorua (Bay of Plenty region) as one group, and the remainder of the populations as another  . AlthougLarus gulls where 22 of 42 species are known to hybridise [Introgression is the exchange of genetic material between two species through hybridisation  and is oybridise ,24. Gullybridise . The smaybridise . In a reybridise .The divergence between the two clades—the primarily red-billed gull clade and the primarily black billed gull clade is ~6%. Using the mitochondrial divergence rate of 2% per million years as recommended in several recent studies ,76,77, tLarus marinus and Larus smithsonianus [The mechanisms behind introgression are complex, and many hypotheses have not been quantitatively tested . Hypothesonianus . Selectisonianus . Nuclearsonianus . In a cosonianus , sex-biaLarus occidentalis and Larus glaucescens showed that pure hybrid or mixed pairs had equal or better reproductive success than pure parental pairs, due to hybrid males having the best combined adaptive traits of both species [Rapid introgression of an advantageous trait can aid the survival of a species  and can  species . For the species . This el species ,81.A decision on whether the North Island populations should be managed as a separate unit from the South Island needs to be considered. The Rotorua population appears to be the least connected and therefore most divergent population. It also appears to have the lowest diversity—indicating it probably was colonised from the south and now has comparatively low connectivity to the other breeding sites. This comparative isolation means that it will be the least likely to be rescued by immigrants from other populations should it decline. However, the North Island holds only 1.6% of the total population, and the importance of these birds from a genetic perspective needs to be evaluated further. There appears to have been recent expansion into North Island breeding sites. This is unlikely to be from human mediated gene flow, as people are unlikely to have moved them deliberately, and black-billed gulls are not as closely “connected” or adapted to human activities or things like landfills as other species of gulls are . It is however possible that anthropogenic forest clearance has created increased habitat in the North Island facilitating a northwards expansion. For the South Island, sufficient movement between colonies and regions appears to be taking place to allow for gene flow, although this is most likely to occur from nearby colonies. It is therefore not necessary to focus on the protection or intensive management of single specific colonies, but rather to manage these populations within a metapopulation context .Future studies should focus on a more detailed analysis of the genetic structure of the North Island colonies . This would involve sampling a larger number of North Island colonies (particularly Auckland) than what was done in our study and identifying movements and gene flow among colonies along the northern edge of the black-billed gulls’ range. The mechanisms of the hybridisation between red-billed and black-billed gulls should be more closely studied, both genetically and with direct observations. This future research could also then examine what nuclear genes have introgressed from red-billed gulls into black-billed gulls, and potentially reveal genes related to hybrid compatibility, or differential adaptation .


Emberiza godlewskii, to determine the effects of major historical events, geographic barriers and past climatic changes on phylogenetic divergence and historical demographic dynamics of this species.Geological events and climatic changes played important roles in shaping population differentiation and distribution within species. In China, populations in many species have contracted and expanded responding to environmental changes with the uplift of the Qinghai-Tibet Plateau (QTP) and glacial cycles during Pleistocene. In this study, we analysed the population structure of Godlewski’s Bunting, A phylogeny based on concatenated mitochondrial and nuclear DNA datasets show two (northern and southern) clades approximately diverged 3.26 million years ago (Ma). The West Qinling Mountains serve as a dividing line between the two lineages. Both lineages experienced a recent demographic expansion during interglacial periods (marine isotope stages (MISs) 2–6). Bayesian skyline plots and the results of ecological niche modelling suggested a more intensive expansion of the northern lineage during the late Pleistocene, whereas the southern lineage was comparatively mild in population growth.E. godlewskii. Moreover, the West Qinling Mountains act as a dividing line between the two lineages, indicating a novel phylogeographic pattern of organisms in China. The difference in population expansion mode between two lineages resulted from different effects caused by the climate of the LGM and the subsequent habitat changes accompanying the arrival of a colder climate in northern and southern regions of China.Our results provide insights into the distribution patterns of avian taxa and the possible mechanisms for a south and north divergence model in China. The deep divergence may have been shaped by the uplift of the QTP. Habitat preferences might have facilitated the lineage divergence for The online version of this article (10.1186/s12862-019-1487-z) contains supplementary material, which is available to authorized users. Pica pica, which inhabit various open and semi-open habitats including short-grass areas but depend on trees and tall bushes, did not find major divergence between northern and southern populations [Cyanopica cyanus, which requires well-wooded habitat, shows a north-south genetic division in eastern China [The phylogeographic patterns of avian taxa in China are diverse . Among tulations . In contrn China . Multiplrn China , geograprn China , glacialrn China , habitatrn China . It is iZoogeographical studies have shown that the Himalayan Mountains and the southwestern mountain systems in China are important avian biodiversity hotspots in the Northern Hemisphere  and the Emberiza) are widely distributed across the Palearctic, from East Asia and the Himalayas to the Middle East and Africa [Emberiza godlewskii, shows distribution and habitat preferences typical of the genus. The species tends to select bushy and rocky hill slopes that are often near forests, thickets, ravines, and farm fields [styani based on differences in morphology and allopatric distribution from other subspecies. In our research, we included six subspecies and considered styani have a special status.Old World buntings  and Last Glacial Maximum (LGM).Although numerous species diversified in the Old World, there are few studies on the phylogeography of t et al.  studied E. godlewskii, and investigate demographic dynamics with hypothetical alternative scenarios. Our specific objectives are as follows: (1) To identify the factors driving the phylogeny of E. godlewskii across its distribution range, locate the divergence boundary and determine whether apparent gene flow and migration existed between the major lineages; (2) To reconstruct a detailed demographic history of the major lineages with approximate Bayesian computation (ABC) modeling [In this study, we incorporate an intensive sampling coverage and integrative approaches combining phylogeography with an isolation-with-migration (IM) model  to studymodeling  in ordermodeling  to exploWe collected 190 blood samples from 26 sites covering the major distribution of Godlewski’s Bunting in China . Cytochrome oxidase I (COI), cytochrome b (Cytb), the control region (CR) and two nuclear gene introns  were amplified by polymerase chain reaction (PCR). The primer pairs and PCR conditions with optimized annealing temperatures are described in Additional file We calculated the genetic parameters of the population, including the number of segregating sites (S), the number of haplotypes (Nhap), haplotype diversity (Hd), and nucleotide diversity (Pi) in DnaSP 5.0. The heterozygous sequences of the two nuclear genes were phased using the program PHASE  with theWe constructed the intraspecific phylogeny based on the concatenated mitochondrial haplotypes using a constant population size coalescent model in BEAST 1.8 . The optEmberiza rutila and Emberiza leucocephalos the outgroups. We conducted an analysis of molecular variance (AMOVA) based on FST to further determine where the major divergence occurred. We used AMOVA based on the concatenated mitochondrial set in Arlequin 3.5 [To test the validity of the major lineages revealed in the mtDNA, a species tree was estimated by *BEAST  based onquin 3.5  to diagnquin 3.5 .According to the phylogenetic results, we calculated the divergence time and reconstructed the demographic dynamics of the major lineages. Divergence times were estimated in BEAST 1.8  based onP values for these parameters were obtained from 1000 bootstrap samples.We calculated Tajima’s D , Fu’s FsTo explore whether the major phylogenetic lineages experienced historical fluctuation, we conducted Bayesian skyline plots (BSPs) using mtDNA in BEAST 1.8 to reconstruct the changes in effective population size through time since the most recent common ancestor (TMRCA). We ran the analysis for 100 million generations or more until the ESSs were larger than 200, with sampling every 10000 generations after discarding the first 10% of samples as burn-in. Population curves of demographic history through time were reconstructed in Tracer 1.5.To explore the demographic scenarios of divergence and expansion, we also applied approximate Bayesian computation (ABC) analysis in DIY-ABC, version 2.1.0.  with theTo better estimate gene flow and divergence time between the major lineages, we used IMa2 based on the IM model  with nuDE. godlewskii with 19 bio-climate variables from WorldClim [www.gbif.com), eBird, and our sampling sites, and after quality control steps to reduce spatial auto-correlation and sampling bias, we obtained 253 locations. We performed ENM analyses of the two different lineages based on the phylogenetic results, with 126 sites for the northern lineage and 127 sites for the southern lineage, with at least one of the longitude and latitude differences greater than 0.1° between every pair of locations. We constructed the model by randomly selecting 80% of the occurrence data, and the remaining 20% of the data were used to test the accuracy of the model in MaxEnt v3.3.3 k. The parameters were set to the default convergence threshold (10--5), 2000 maximum iterations and 10 replicates. MaxEnt was used to test the model by calculating the average area over ten replications under the receiver operating characteristic curve (AUC) and the binominal probabilities indicating the predictive ability of the model. Then, we reclassified the results into binary states under the 10% logistic threshold, indicating an unsuitable area, and higher than the threshold, indicating a suitable area.To explore whether genetic differentiation within regions occurred in response to distribution changes of suitable habitat during Pleistocene glacial-interglacial cycles, we employed ENMs to predict the distribution of the orldClim  during fP < 0.05) for the MUSK gene (Table We obtained 1103 base pairs (bp), 1323 bp, and 1094 bp of Cytb, COI and CR, respectively, for 190 individuals. For the nuclear gene introns, we obtained 509 bp and 558 bp of FIB and MUSK, repectively, for 101 individuals. All sequences were deposited in GenBank  and southern (clade B) clades in China Fig. . There wP values were significant.The results of the AMOVA based on the concatenated mtDNA showed that the maximum percentage (93.35%) of molecular variance resulted from between groups when the whole population localities were divided into northern and southern lineages. The variation percentages among the localities within group and within the populations were 0.89 and 5.75%, respectively Table , and allThe divergence time between clade A and clade B was dated to 3.26 (95% highest posterior density (HPD): 2.53–4.08) Ma, which was approximately in the late Pliocene  and southern clades , and the values of R2 were small and positive  for both the northern and southern clades, indicating an excess of low-frequency mutations relative to expectations under the standard neutral model. The mismatch distributions of the northern  and southern clades  fit the population expansion model, although the northern mismatch curve had two peaks. These results supported the model of historical demographic expansion  and north  of China, which are arid and semi-arid areas of the temperate zone. The southern lineage was distributed in the southeast of the Qinghai-Tibet Plateau (QTP) and the southwest  of China, corresponding to semi-humid areas of the subtropical zone. The belt in the narrow range from LHS to SP seems to act as the line dividing between the two lineages. The fragmentation of species distributions seems an important cause of allopatric divergence. Our ENM results indicated that there is a geographic boundary between the northern and southern clades. Moreover, IMa2 analysis confirmed a nearly null model of isolation without gene flow between the diverging populations, a gap between northern and southern populations. According to the divergence time estimated by the BEAST, the genetic differentiation might have been formed before the Pleistocene in a ‘vicariance’ model.As previously indicated by Päckert et al. , E. godlPerdix hodgsoniae, diverged from its ancestor approximately 3.6 Ma, corresponding to the early intensive uplift of the QTP. The changes in climate, ecology, and habitats following the uplift of the QTP during the Pleistocene were likely the most important factors driving the pattern of phylogeography in Asian birds [E. godlewskii was another important factor driving its expansion and shaping its distribution. The patterns of the south-north lineages are at least partly explained by ecological differences, especially in habitat preference.We propose that the deep divergence may be attributed to the uplift of the QTP, and a boundary corresponds to the West Qinling Orogenic Belt, located approximately 96°-106° E and 33°-37° N. The subduction collision and orogeny of this belt have been dated to the Silurian, but rapid uplift occurred in the late Pliocene . PhylogeE. godlewskii may be related to three geological events that occurred at different timescales in China. The first event was the early intensive uplift of the QTP, which may have been the major driver of the deep north-south split at 3.26 Ma, corresponding to the late Pliocene to early Pleistocene. The uplift of the QTP led to the geomorphological process of major downcutting in the Hengduan Mountain System [E. godlewskii during glaciation. The second event consisted of ecological and climatic changes. During the uplift and the following climate fluctuations, the southeastern flanks of the Himalayan and Hengduan Mountains were part of the monsoon realm [E. godlewskii. The third event was the gradual shift to a drier and colder climate on the QTP, with the development of glaciers accompanying the uplift [E. godlewskii was constricted into two lineages and dispersed to northern and southwestern China along both flanks of the western Qinling Mountains, which underwent extensive uplift along with the QTP and formed new basins for settlement [The distribution pattern of n System , includin System , and shen System , 47. Theon realm . These aon realm . Most ofon realm . The denon realm . These ce uplift . At thisttlement . The linttlement . HoweverAnother important question need to be addressed is whether apparent gene flow corresponding to secondary contact and admixture between the two lineages exits in a continuous time period. However, the results from IMa2 indicated no significant historical gene flow between the southern and northern lineages. Therefore, we simulated the demographic expansion of major lineages of this species based on a null model of isolation without migration (gene flow) between diverging populations with DIY-ABC, and the results indicated that both lineages underwent recent demographic expansions. The BSP indicated that the population experienced an expansion earlier than the LGM, predominantly during the interglacial periods MISs 2–6) . The nor . The noE. godlewskii rapidly expanded along with the suitable habitat in northern China in a short period of time, as indicated by the BSP. However, the glaciers in southwestern China retreated during MIS5 (0.11–0.071 Ma) [The diverse local-topography and heterogonous climates in China during the Pleistocene can be expected to have varied effects on species-distributions and historical demography. As revealed by Su et al. , the cli.071 Ma) , and a s.071 Ma) , corresp.071 Ma) . Pollen .071 Ma) . Therefo.071 Ma) .E. godlewskii, we provide new insights into the potential mechanisms of genetic divergence and historical dynamics of avian taxa in China. A south-north lineage divergence in E. godlewskii was identified and inferred related to geographic barrier of the West Qinling Mountains. The observed phylogeographic pattern may also have been driven by series geological events like uplift of QTP and subsequent environmental changes in glaciation. In particular, the two lineages showed different models of expansion in response to habitat changes during the Pleistocene glacial-interglacial cycles. Our findings may inform future investigations and potential taxonomic revisions.With a detailed phylogeographic structure of Godlewski’s Bunting, Additional file 1:Table S1 The sampling localities for Emberiza godlewskii. Table S2 The PCR primers used in this study. Table S3 Descriptions of prior settings for all parameters used in DIY-ABC. Table S4 Genbank Accession numbers for individuals of mitochondrial genes used in this study. Table S5 Genbank Accession numbers for individuals of nuclear genes used in this study. Table S6 Estimations of posterior distributions of parameters revealed by DIY-ABC for the best scenario of demographic history of northern and southern population, respectively. Figure S1 Demographic expansion and divergence models of Emberiza godlewskii. ABC results showing demographic history. Effective population sizes are marked in different colours and times of events  are indicated. Figure S2 (a) Schematic representation of ABC modelling of changes in population sizes. Na, ancestral population size; N1, current population size; Ne and Nb, population sizes between Na and N1; t2, old expansion time; tb, bottleneck time; t1, recent expansion time. (b) Posterior probabilities obtained by logistic regression of 1% of the closest simulated datasets for the northern and southern lineages, respectively, from left to right. (DOCX 279 kb)