Petromyzon marinus), native to the northern Atlantic, naturally migrated into Lake Champlain and expanded their range into the Great Lakes via man-made shipping canals.Uncovering the mechanisms underlying rapid genetic adaptation can provide insight into adaptive evolution and shed light on conservation, invasive species control, and natural resource management. However, it can be difficult to experimentally explore rapid adaptation due to the challenges associated with propagating and maintaining species in captive environments for long periods of time. By contrast, many introduced species have experienced strong selection when colonizing environments that differ substantially from their native range and thus provide a “natural experiment” for studying rapid genetic adaptation. One such example occurred when sea lamprey  for sea lamprey collected from native (Connecticut River), native but recently colonized (Lake Champlain), and invasive (Lake Michigan) populations, assessed genetic differentiation between all populations, and identified candidate genes that responded to selection imposed by the novel environments. We observed a 14 and 24% reduction in genetic diversity in Lake Michigan and Lake Champlain populations, respectively, compared to individuals from the Connecticut River, suggesting that sea lamprey populations underwent a genetic bottleneck during colonization. Additionally, we identified 121 and 43 outlier genes in comparisons between Lake Michigan and Connecticut River and between Lake Champlain and Connecticut River, respectively. Six outlier genes that contained synonymous SNPs in their coding regions and two genes that contained nonsynonymous SNPs may underlie the rapid evolution of growth , reproduction , and bioenergetics .Utilizing 368,886 genome-wide single nucleotide polymorphisms (SNPs), we calculated genome-wide levels of genetic diversity  illustrate the utility in applying genomic approaches for the successful management of introduced species.By identifying the genomic basis of rapid adaptation to novel environments, we demonstrate that populations of invasive species can be a useful study system for understanding adaptive evolution. Furthermore, the reduction in genome-wide levels of genetic diversity associated with colonization coupled with the identification of outlier genes underlying key life history traits known to have changed in invasive sea lamprey populations (e.g.The online version contains supplementary material available at 10.1186/s12864-021-07553-x. Petromyzon marinus), a parasitic, jawless vertebrate native to the northern Atlantic, invaded the Laurentian Great Lakes.Invasive species can rapidly establish and spread in new environments that often have substantially different abiotic and biotic conditions than found throughout their native range , 2. This, ammocoetes) transform into parasitic juveniles [Clupea harengus), mackerel (Scomber scombrus), and Atlantic salmon  [, salmon), sea lamprey rely on a pheromone produced by larvae in streams and rivers as a cue for migration [In their native range, which includes most of the northern Atlantic and surrounding regions , sea lamuveniles  and migro salar) . Juvenilo salar) . The parigration –15. Thusigration , 12, 16.Salvelinus namaycush), burbot (Lota lota), lake whitefish (Coregonus clupeaformis), and walleye (Sander vitreus). In order to rehabilitate local fisheries, 3-trifluoromethyl-4-nitrophenol (TFM), a pesticide that targets the larval stage of sea lamprey, has been applied in Lake Michigan since 1960 and in Lake Champlain since 1990 [Sea lamprey are native to the northern Atlantic coast , and mignce 1990 , 24.The ecology and environmental conditions found in the Great Lakes are substantially different from those found throughout the sea lamprey’s native range , and hav, occurring in fewer than 200 years in invasive sea lamprey) to new environments in both sea lamprey and other species. To achieve this objective, we sampled larval sea lamprey from three locations: (1) the Connecticut River, a native-range tributary of the Atlantic Ocean, (2) Lake Michigan, where sea lamprey are invasive, and (3) Lake Champlain, an additional freshwater environment that sea lamprey colonized through natural migrations , and (2) what genes have responded to selection in the novel environments and how do they relate to the documented phenotypic shifts in life history traits?While these differences in life history characteristics of invasive sea lamprey are suggestive of genetic adaptation, many of these traits may simply be a plastic response to different environments –29. Detecations:  the Connn = 16, Lake Champlain: n = 13, Connecticut River: n = 14). We also sampled liver tissue from a subset of the same individuals (42%), but those data were only used for validating our outlier loci , sea lamprey collected from Lake Michigan and Connecticut River clustered as one population while those from Lake Champlain were assigned to the other  populations, Lake Michigan and Lake Champlain individuals were still correctly assigned to their sampling locations, although there may be some subtle population structure within Connecticut River  and the additional cluster only illustrates a small number of Connecticut River individuals with mixed ancestry.We collected 565 larval sea lamprey from the Manistee River in Lake Michigan, 517 larval sea lamprey from Corbeau Creek and the LaPlatte River in Lake Champlain, and 404 larval sea lamprey from the Connecticut River in 2016  across the genome was 0.261 for Connecticut River, 0.225 for Lake Michigan, and 0.200 for Lake Champlain. Using a randomization test . These differences represent a 14% reduction in genome-wide levels of genetic diversity for Lake Michigan and a 24% reduction for Lake Champlain compared to the native range, Connecticut River. Mean observed heterozygosity and mean nucleotide diversity (π) for each chromosome were highly correlated  and RALA (RAS like proto-oncogene A), were found to be in common in both comparisons involving the native, anadromous population  , and genome scans based on k-nearest neighbor (kNN) techniques  and two out of 43 outlier genes in comparison between Lake Champlain and Connecticut River  may potentially explain changes in growth, reproduction, and bioenergetics in sea lamprey in response to novel ecological conditions  of 6.39 and codes for a transmembrane receptor for growth hormone, can directly affect the growth rate of fish [PGR, which has a Z(FST) of 5.51 and encodes a nuclear progesterone receptor, plays a crucial role in the development of testis, the arrangement of spermatogenic cysts, sperm count, sperm motility, and ovulation [PGR allele, while Lake Michigan is nearly fixed for the other  = 5.05), encoding a STAR-related lipid transfer protein, and cilia-related TTC25 (Z(FST) = 6.13), encoding a tetratricopeptide repeat domain-containing protein, may also affect the motility, maturation, or fertilization ability of sperm [ of fish   of 5.83 and codes for enzymes essential for the catabolism of ketone bodies that serve as an important fuel during starvation  of 5.05, which encodes an enzyme that promotes the release of glucose-1-phosphate from liver glycogen stores  of 5.05, DIN4, is found to be activated under sugar starvation  of 5.72, codes for an ornithine mitochondrial transporter, which is a key component in urea cycle essential for nitrogen metabolism, and may suggest a shift in diet [SLC25A15 may be a reflection of changes in sea lamprey’s prey and their capacity of adjusting nitrogen metabolism depending on which host fishes are available. Additionally, SLC25A15 could also be potentially related to osmoregulation, ammonia detoxification, or even as a response to TFM treatment since the protein is located within mitochondria [Lastly,  in diet  , not a single outlier SNP was found between these two entirely-freshwater populations , yet we think the absence of outliers in the comparison between Lake Michigan and Lake Champlain may have been more likely to be driven by relatively high coancestry. Nevertheless, the two entirely-freshwater populations have likely experienced moderate levels of genetic drift, either via founder effects or higher variance in reproductive success, given the observation that mean FST was highest between these two populations.A total of 121 outlier genes were identified in the comparison between Lake Michigan and Connecticut River and a total of 43 outlier genes were identified in the comparison between Lake Champlain and Connecticut River. While the highest genome-wide level of genetic differentiation across all loci were detected in the comparison between Lake Michigan and Lake Champlain  and sea lamprey collected from Lake Michigan, Lake Champlain and Connecticut River clearly clustered into three distinct groups  were significantly different from each other , with the highest genetic diversity in the native-range Connecticut River population, followed by the Lake Michigan and Lake Champlain populations  was identified as an outlier in the comparison between Lake Michigan and Connecticut River. GHR encodes a transmembrane growth hormone receptor. The dimerization of the growth hormone receptor occurs upon the binding of growth hormone, and a signal transduction pathway involved in growth is turned on. As such, GHR plays a crucial role in fish growth [PGR, STARD10, and TTC25, could explain the lower fecundity observed in Lake Michigan sea lamprey since these genes are known to affect ovulation and sperm production and quality [PGR and STARD10, but did show evidence of adaptive evolution at TTC25  was detected in the comparison between these two populations. This result may suggest that changes in life history strategies essential for sea lamprey’s colonization in Lake Champlain may be mediated by plasticity or the genetic differentiation between these two populations driving changes in life history strategies is not strong enough to be detected.Invasive sea lamprey in the Great Lakes exhibit a faster growth rate, a shorter larval stage, a smaller adult body size, and a lower fecundity , 26. Seah growth  and may  quality , 38. The, OXCT1 and PYGL) and one containing synonymous outlier SNPs  that support this hypothesis at the genomic level. OXCT1 is involved in the catabolism of a primary fuel under starvation , PYGL participates in the production of glucose from liver glycogen stores, and DIN4 is activated under sugar starvation. While OXCT1 is identified in the comparison between Lake Michigan and Connecticut River, PYGL and DIN4 are detected in the comparison between Lake Champlain and Connecticut River, suggesting that sea lamprey in both Lake Michigan and Lake Champlain have responded to the selection imposed by the differences in food sources and availability in these novel, freshwater environments.In the Great Lakes, sea lamprey spend more time feeding on a single host, and more sea lamprey are found to attach to one adult fish than in the Atlantic Ocean , 26. Thi+-K+-ATPase may engage in sea lamprey’s osmoregulation [, SNPs) to support this hypothesis. However, both growth hormone and glycogen metabolism have been shown to be related to seawater adaptation [GHR and PYGL may also play a role in the successful colonization of freshwater environments. Our inability to uncover a clearer genetic signal related to sea lamprey’s adaptation to freshwater environments could be due to a lack of power . Alternatively, sea lamprey may respond to changes in salinity plastically by differentially expressing relevant genes at the transcriptomic level, and the selection imposed by differences in salinity may not be strong enough to shift allele frequencies among populations at the genomic level [Instead of migrating to the ocean, Lake Michigan and Lake Champlain sea lamprey spend their entire life in freshwater and never acclimate to salt water environments. Previous work has shown that 11-deoxycortisol, Na-K-Cl cotransporter 1, and Nagulation –55. We waptation –58, suggic level , 59.While we have identified a series of outlier genes that may be driving sea lamprey’s rapid adaption to novel environments, several outstanding questions remain. Although our study identified outlier genes that are likely driving rapid adaptation at the genomic level, we do not know yet how these genes perform at the proteomic and metabolic level to support variation at the physiological, individual, and population level in recently colonized sea lamprey populations. Additionally, four large classes of genes were found to be related to cell cycle progression, cellular component assembly, cellular transporting, and cellular signaling. These genes together regulate the replication, transcription, and translation of DNA, the assembly of chromatin and ribosome, and some cellular enzymatic reactions. How these genes work concurrently and coordinate to help sea lamprey colonize new environments remains unknown and merits further investigation. Lastly, these genetic changes in life history traits may be driven by selection to escape treatment from the lampricide 3-trifluoromethyl-4-nitrophenol (TFM), which is applied in both Lake Michigan and Lake Champlain. Recent theoretical work suggests that a rapid decrease in the size and age at metamorphosis could be driven by TFM-induced selection . DetermiFST are false positives, and caution is warranted when using FST as an indicator for local adaptation [FST has been widely used to assess genetic differentiation between populations, there are some caveats for using it to detect local adaptation. The correlated coancestry among organisms living in the long one-dimensional landscapes may generate many false positives [FST as an estimator for genetic differentiation. Furthermore, a FST outlier may not necessarily indicate local adaptation because it could also be driven by global adaptation or genetic drift [FST, AFD, kNN-based approach, and nucleotide diversity within and between populations as an approach to discriminate genetic differentiation arising from local adaptation from that driven by other mechanisms. We also recommend using strict filtering criteria (here outliers were only considered if FST was greater than five standard deviations from the mean). This process almost certainly increases the type II error rate , but importantly serves to decrease the type I error rate .We cannot exclude the possibility that some of the outlier genes detected using aptation , 61, 62.ic drift . Therefo, bottlenecks, hybridization, polyploidy, genome modification) may affect rapid adaptation [Biological invasion has been closely observed in many systems , 63, 64,aptation . As globaptation , a betteUtilizing genome-wide SNPs called from RNA-seq data, we found that two sea lamprey populations lost genome-wide genetic diversity when colonizing novel, entirely freshwater environments. Evidence of a response to selection at eight outlier genes suggest that sea lamprey rapidly adapted to their novel, freshwater environments with changes in growth, reproduction, and bioenergetics. Overall, our study enhances our understanding of the genes underlying sea lamprey’s life history traits, which may aid with invasive species control in the Great Lakes, and also demonstrates how introduced species can be a useful study system for shedding light on rapid genetic adaptation.n = 16, Lake Champlain: n = 13, Connecticut River: n = 14) for RNA-seq after a four-month acclimation period in a common environment at the Aquaculture Research Lab at Purdue University  mRNA, we transferred samples from the −80 °C freezer into RNAlater-ICE solution , with a ratio of ten volumes of solution to one volume of sample mass, and kept the tissue samples in the solution for 16 h at −20 °C. We next weighed the thawed tissues, submerged them in Qiagen  lysis buffer, and homogenized them using a rotor-stator homogenizer. After extracting mRNA from 43 muscle and 19 liver tissue samples using Qiagen RNeasy Mini Kit, we prepared mRNA libraries and sequenced them at the Purdue Genomics Core Facility to generate a total of 1,385,780,178 paired-end RNA-seq reads   and liver  tissue samples, separately [With trimmed RNA-seq reads, we called SNPs with the variant discovery pipeline provided by the Genome Analysis Toolkit (GATK 3.8) . We firsparately . We starparately  followinparately . By addi, we used genotypes called from liver tissue as a validation for those called from muscle tissue, see Fig. , an alternative allele had to be present in at least two out of 41 muscle samples and one out of 19 liver samples).We used the SNPs that were identified in both the GATK joint genotyping workflow and the RNA-seq pipeline in all subsequent analyses. For a subset of 18 individuals, we sequenced both muscle and liver tissues. By independently calling SNPs with muscle and liver tissue samples from the same individuals, we were able to validate our SNP calling pipeline (i.e.K) to 2 to 10. Since sampling locations can assist the clustering for datasets with a relatively weak signal of structure when used as prior information, we chose to use location information .We first tested population structure using the program STRUCTURE (version 2.3) –34 usingn = 15; Lake Champlain: n = 12; Connecticut River: n = 14), we first excluded loci out of Hardy-Weinberg equilibrium identified using the package HWxtest in R 3.6.1  [, Lake Michigan, Lake Champlain, Connecticut River; π) and between all three pairwise comparisons  at each locus using the R package PopGenome [With the 41 muscle samples (Lake Michigan:  < 0.05) , 72 for  < 0.05) , 90 of wopGenome . We averFST for each locus in Hardy-Weinberg equilibrium among all three populations  using vcftools (version 0.1.16) [FST across all loci in each comparison. To detect significant outliers, we z-transformed FST in each comparison to get Z(FST), and defined loci with FST greater than five standard deviations from the mean as outlier loci  > 5) [FST, we also calculated pairwise allele frequency difference (AFD) at each locus, z-transformed AFD to get Z (AFD), and compared analysis results based on FST and AFD [k-nearest neighbor (kNN) techniques to assess whether an outlier locus is driven by selection [FST of all population comparisons, the kNN-based approach, which is implemented in the R package PopGenome [FST vector, which is calculated by subtracting the medoid from the pairwise FST vector, indicates local adaptation when positive but introgression or selection reducing genetic divergence when negative [, outlier genes) on which outlier loci were located by mapping outlier loci to the sea lamprey genome [https://www.ebi.ac.uk/QuickGO/), for each pairwise comparison using the metacoder package [We next calculated pairwise, unbiased  0.1.16) . To asse and AFD . We addielection . With paopGenome , 77, assopGenome . SNPs thnegative . After iy genome  and calc package ., ref ORF) and alternative  alleles, and translated the ref and alt ORFs for the same coding region into reference and alternative amino acid sequences, respectively. If reference and alternative amino acid sequences were identical, the SNP located at the outlier locus was classified as a synonymous change; otherwise, the SNP was classified as nonsynonymous. All outlier genes were assigned to one of two categories: those containing outlier SNPs causing synonymous substitutions or outlier SNPs causing nonsynonymous substitutions.By examining the position of outlier loci on the annotated sea lamprey genome , we deteAdditional file 1: Figure S1. Population structure of Lake Michigan, Lake Champlain, and Connecticut River sea lamprey . Likelihood values for K = 2, K = 4 and K = 10 are −11,693,170, −11,023,345, and −11,043,300, respectively. Figure S2. Randomization tests on observed heterozygosity among three populations. Observed heterozygosity (Ho) of each chromosome  and at SNPs (by SNP) calculated using 346,280 SNPs across the genome that are in Hardy-Weinberg equilibrium and in common to Lake Michigan (LM), Lake Champlain (LC) and Connecticut River (CT) populations differs significantly between each pairwise comparison . Figure S3. Observed heterozygosity and nucleotide diversity (π) of three populations. Sea lamprey have 99 chromosomes, of which the first 90 are assembled. Mean observed heterozygosity and mean nucleotide diversity across each chromosome are highly correlated in Lake Michigan (a), Lake Champlain (b), and Connecticut River (c) populations. Mean nucleotide diversity across the genome is indicated by dashed lines and nucleotide diversity of chromosomes is indicated by points (d). Nucleotide diversity (π) shows a similar pattern as observed heterozygosity, which is the highest in Connecticut River, followed by Lake Michigan and Lake Champlain (d). Figure S4. Z(FST) along 90 chromosomes in three pairwise comparisons. Sea lamprey have 99 chromosomes, 90 of which are assembled. In comparison to Fig. FST) of all SNPs in comparisons between Lake Michigan and Lake Champlain (a), Lake Michigan and Connecticut River (b), and Lake Champlain and Connecticut River (c), rather than mean Z(FST)  of SNPs on different chromosomes and alternating colors blue and black represent Z(FST) of SNPs on neighboring outlier genes. Outlier SNPs with Z(FST) but in purple and grey are those that are not located on any annotated gene. Figure S5. Mean read depth of each population and number of shared loci in each pairwise comparison. Mean read depths in three populations are almost identical (a) and numbers of shared loci used to calculate FST and AFD for three pairwise comparisons among Lake Michigan (LM), Lake Champlain (LC), and Connecticut River (CT) are almost identical (b). Figure S6. Correlations between FST and AFD in three pairwise comparisons. Figure S7. Gene ontology (GO) hierarchy networks. The GO hierarchy networks are constructed from GO terms associated with one outlier gene in comparisons between Lake Michigan and Connecticut River (a) and between Lake Champlain and Connecticut River (b) using the metacoder package in R. Branch and node colors indicate the biological process child term to which distal nodes belong and the central grey node represents the biological process level of the GO hierarchy. Figure S8. Z-transformed FST ), genomic regions, and allele frequency of SNPs for additional outlier genes. Z(FST) of all SNPs on outlier genes identified with muscle samples that may contribute to adaptation to local environments  in sea lamprey are shown along corresponding chromosomes with SNPs on introns, untranslated regions (UTR), and coding regions (CDS) indicated in different colors (left panels). Shifts in allele frequency of SNPs with the highest Z(FST) or causing nonsynonymous mutations on outlier genes in Lake Michigan, Lake Champlain, and Connecticut River populations are almost identical across different tissue types . The dark grey horizontal bar on the top left of the left panel represents the length of an outlier gene. STARD10 and TTC25 are associated with reproduction , and PYGL and DIN4 with bioenergetics . Figure S9.FST, nucleotide diversity within populations, and nucleotide diversity between populations at outlier and surrounding SNPs. Dashed lines represent average FST (black lines), nucleotide diversity within populations (blue lines), and nucleotide diversity between populations (red lines) of all SNPs 50 Kb upstream and downstream  from the outlier SNP with the highest FST. Points indicate FST (black), nucleotide diversity within populations (blue), and nucleotide diversity between populations (red) of outlier SNPs. Plots were qualitatively and nearly numerically identical for ±10 Kb, ±100 Kb, and ± 500 Kb windows . Single window averages were used instead of a standard sliding window approach because RNA-seq data generates SNP loci that are highly clustered within, but not between, genic regions. Where necessary, points were jittered for visual clarity. At all eight outlier genes, FST and nucleotide diversity between populations tend to increase while nucleotide diversity within populations tends to decrease at outlier SNPs in compaSTrison to the average, suggesting that divergence at these genes is driven by local adaptation. Table S1. Summary of tissue samples used for RNA-seq. Table S2. Summary of outlier SNPs and outlier genes. LM vs. CT stands for the pairwise comparison between Lake Michigan and Connecticut River populations, and LC vs. CT stands for the pairwise comparison between Lake Champlain and Connecticut River populations. See Table S3 for full gene names. Table S3. Full gene names of outlier genes retrieved from https://david.ncifcrf.gov/list.jsp. Table S4. Gene ontology (GO) terms of biological process associated with outlier genes retrieved from https://www.ebi.ac.uk/QuickGO/.prey K = , 4, 10. prey K = , 4, 10.


Phrynosoma cornutum) to quantify relative support for different catalysts of diversification. Phylogenetic and clustering analyses of the GBS data indicate support for at least three primary populations. The spatial distribution of populations appears concordant with habitat type, with desert populations in AZ and NM showing the largest genetic divergence from the remaining populations. The mtDNA data also support a divergent desert population, but other relationships differ and suggest mtDNA introgression. Genotype–environment association with bioclimatic variables supports divergence along precipitation gradients more than along temperature gradients. Demographic analyses support a complex history, with introgression and gene flow playing an important role during diversification. Bayesian multispecies coalescent analyses with introgression (MSci) analyses also suggest that gene flow occurred between populations. Paleo-species distribution models support two southern refugia that geographically correspond to contemporary lineages. We find that divergence times are underestimated and population sizes are overestimated when introgression occurred and is ignored in coalescent analyses, and furthermore, inference of ancient introgression events and demographic history is sensitive to inclusion of a single recently admixed sample. Our analyses cannot refute the riverine barrier or glacial refugia hypotheses. Results also suggest that populations are continuing to diverge along habitat gradients. Finally, the strong evidence of admixture, gene flow, and mtDNA introgression among populations suggests that P. cornutum should be considered a single widespread species under the General Lineage Species Concept.The southwestern and central United States serve as an ideal region to test alternative hypotheses regarding biotic diversification. Genomic data can now be combined with sophisticated computational models to quantify the impacts of paleoclimate change, geographic features, and habitat heterogeneity on spatial patterns of genetic diversity. In this study, we combine thousands of genotyping-by-sequencing (GBS) loci with mtDNA sequences (ND1) from the Texas horned lizard ( SignificancePhrynosoma cornutum) as a model, our results suggest a complex demographic history that includes episodes of gene flow. Results also suggest that divergence is continuing along environmental axes and that adequate model choice is imperative for demographic hypothesis testing. This study can serve as a model for how genomic data and new analytical tools can be used to test traditional evolutionary hypotheses throughout geologically and climatically diverse regions.Many studies have documented cryptic diversity in diverse taxa inhabiting the arid regions of western North America, with divergence correlated with both Neogene vicariance and Pleistocene climate change. However, relatively few studies adopt a genomics approach and most implicitly assume that gene flow ceases once divergence begins. Using the Texas horned lizard  is spread across a diverse collection of ecological habitats making it an interesting candidate to examine adaptation and phylogeographic history. Although its range does consist of many smaller environmental niches  inhabiting similar arid ecological niches  approach. We first use concatenated and coalescent-based phylogenetic analyses, species delimitation analyses, and clustering to test the hypothesis that the genomic and mtDNA data support the presence of cryptic diversity, which has been demonstrated in other species of Phrynosoma with large geographic distributions. Second, we use genotype–environment association (GEA) analyses to test the hypothesis that a proportion of SNPs are statistically correlated with bioclimatic variables and that the environmental gradient between the plains and desert habitat may be driving adaptation and furthering genetic divergence  and by examining the phylogeographic and demographic history of P. cornutum samples and a single Phrynosomasolare outgroup. After processing the data in ipyrad (P. solare sequence used as the outgroup. Excluding the outgroup resulted in 27 variable (parsimony uninformative) sites and 100 parsimony informative characters.We obtained approximately 225 megabases of normalized GBS (nGBS) data from 75 n ipyrad , most inn ipyrad  online. Dquartets  analysis in RAxML-ng  yielded quartets  yielded quartets . Bootstrquartets  online.P. cornutum . The major split between two groups separating the western (DST) and eastern (STH+PLN) populations (K = 2) was recovered in virtually all analyses, and runs with the lowest cross-entropy levels supported the partition shown in To complement the phylogenetic analyses, we performed genetic clustering using sNMF in the R package LEA . After fc groups  online bc groups . We chosFst and Nei’s genetic distance estimates supported the split between the two groups inferred from the phylogenetic and sNMF analyses, separating the western (DST) and eastern populations (STH+PLN). Both Fst and genetic distance were higher between western and eastern populations than between the two eastern populations . The global model had an adjusted R2 of 0.017. RDA identified 29 outlier SNPs based on locus scores that were ±2.5 SD, eight associated with mean temperature of the driest quarter and 21 associated with precipitation seasonality , which can provide evidence that these lizards may be adapting to divergent climatic conditions. Correlations between SNPs and environmental variables was first performed through redundancy analysis (RDA) using the R package vegan. Our global model and first of two redundancy axes were significant , BIO17: precipitation of driest quarter , BIO15: precipitation seasonality , BIO19: precipitation of coldest quarter , and BIO2: mean diurnal range   to statiNe) estimates from the A00 analysis showed signs of both population growth and decline following divergence . The results differed most in their estimates for our DST (pop 1) population  as well as the most recent common ancestor of our ingroup . To minimize potential biases in parameter estimation, the following Ne values were from runs with KK104 removed. Our ingroup most recent common ancestor showed an Ne of approximately 625k with the descendant populations having Ne values of approximately 233k for DST and approximately 930k for the combined STH+PLN population. After the split of the STH+PLN populations, there was a reduction in Ne to STH (∼575k) and PLN (∼157k). These results are consistent with peripheral population expansion following divergence.Bayesian phylogenetics and phylogeography  was run vergence  online. Ne estimates due to admixture or mixed ancestry, we found evidence for biases in divergence times . AIC weights for the admixed data set strongly supported the refugia_barrier model (0.9980), whereas the nonadmixed data set favored the refugia_adj_2 model  consisted of approximately 86% of the MCMC samples. The subsamples corresponding to the same peak were noted to be similar between runs and those from different runs were combined to produce the posterior summary for that peak  model in BPP  to compaation SA . In othehat peak .φA=86.8%) trace back to node B (or branch TB), before taking the path TSR to the root of the tree. Sequences from STH will reach node C and then mostly (with probability 1−φC=93.5%) trace back to node B. Sequences from PLN will reach node D and mostly (with probability φD=93.5%) take the DCB route to reach B. Thus, most sequences from populations STH and PLN will be in the same ancestral population C by the time τC=τD ≈ 0.00017, whereas most sequences from DST will meet those from STH or PLN in ancestral population B by time τA=τB ≈ 0.00141. Note that, in BPP, both divergence (or introgression) times (τs) and population sizes (θs) are measured in units of expected number of mutations per site.We discuss the genetic history implied by Peak 1 for the nonadmixed data, and then examine the similarities and differences of Peak 2 and of the results from the admixed data. When we trace the history of the samples backward in time, Peak 1 implies the following a. The DSC at time τC=τD ≈ 0.00017, whereas most sequences from DST will meet those from STH or PLN in ancestral population A by time τA=τB ≈ 0.0014. Beyond nodes AB, the divergence times and population sizes on the paths to the root are similar between Peaks 1 and 2.Peak 2 for the nonadmixed data is a minor peak in the posterior b. It impτS ≈ τT, with near perfect matching of the parameters between the peaks: φ’A ≈ 1−φA, φ’B ≈ 1−φB, θ’A ≈ θB, and θ’B ≈ θA . Given the likelihood of over estimating divergence times from mtDNA due to substitution saturation owing to a quicker mtDNA mutation rate  may have roughly coincided geographically within refugial habitats that originated during the Pleistocene, in the Chihuahuan Desert to the east, and the Sonoran Desert to the west  and 7. Tene flow , 2019. Aene flow . UnfortuNe after the STH and PLN populations diverged, furthering the evidence for this northward expansion originating from the south. Interestingly, evidence from our population structure analysis indicates that members of this expanding PLN population do share ancestral genetic variation with our DST population. The existence of some highly admixed individuals  supports our demographic results and point toward secondary contact and gene flow post divergence. Taken together, these results suggest that climatic cycling during the Pleistocene was the most likely catalyst for range expansion and secondary contact. An alternative hypothesis for admixture may be due to human mediated movement of P. cornutum owing to its popularity as a pet and symbol of the American Southwest. Other studies have shown evidence of translocations with admixed individuals appearing far removed from boundary areas  of average annual rainfall in the western desert to over 50 in (127 cm) in the eastern reaches of the Great Plains  suggest that P. cornutum, along with P. asio and P. solare, exhibits substantial genetic divergence among populations  were also affected. Similar to MOMENTS, these differences can be explained by the impact of including a recent hybrid sample (KK104). Note that sequences sampled from two modern populations cannot coalesce until they are in the same ancestral population. Let t1–23 be the smallest sequence divergence between 1.DST and 2.STH (or 3.PLN), minimized across all loci and all sequence pairs at each locus. Then t1–23>τA. As KK104 appears to be a recent hybrid, the divergence time at some loci can be very small, and those small distances will force τA to be very small. Note that under the coalescent model, species divergence times and introgression times are determined mostly by the minimum, rather than the average, sequence divergence between species. The result suggests that hybrid samples should be avoided when one aims to infer ancient introgression history. Similarly, we suggest that the results from the nonadmixed data may represent a more realistic description of the history of divergences and introgressions for those lineages. We leave it to future studies to more thoroughly assess the impact of admixed samples on the estimation of divergence times under the MSC and MSci models.The recently developed MSci model was designed to explicitly accommodate both ILS and gene flow/introgression when estimating divergence times and effective population sizes . Given tτS ≈ τT in As discussed above, the peaks in the posterior of Both the MSci and isolation-with-migration models can be used with genomic data to account for gene flow when estimating divergence times and population sizes . The MScP. cornutum throughout the southwestern and central United States by using genomic data to examine the hard and soft allopatric forces that have shaped population genetic structure. We find evidence for an initial divergence during the Plio-Pleistocene (possibly the Miocene) that was likely driven by habitat fragmentation due to climate fluctuations, vicariance due to the Rio Grande, and potentially Lake Cabeza de Vaca, followed by a subsequent northward range expansion as the receding glaciation opened up novel habitats. This expansion facilitated divergence along sharp environmental clines and possible adaptation to a divergent niche space. Whether the population-level diversity uncovered through this study rises to the level of species will require further investigation , additional data , and dense population sampling, especially throughout Mexico. The evolutionary history presented here highlights the importance of both hard and soft allopatric forces in shaping a species through gene flow, as the lineage divergences appear at least partially influenced by a changing habitat and environmental niche. Finally, this study should serve as a foundation for the exploration of powerful new models of demographic inference that make use of genomic data sets.We investigated the history of diversification within P. cornutum were obtained from both museum specimens and field samples collected from multiple sites throughout KS, OK, CO, NM, TX, and AZ .Tissue samples (75) of , and AZ  online. MsII restriction enzyme and utilizes a subsequent normalization step after adapter ligation to remove fragments with a high number of copies. The method is particularly suited for species lacking a reference genome. Size-selected fragments were QC-ed and sequenced on an Illumina NextSeq flow cell (150 bp PE). Data were demultiplexed using Illumina bcl2fastq v. 2.17.1.14. Two samples  were excluded from further analysis due to a low number of reads. All nGBS data were uploaded to the SRA (BioProjectID = PRJNA780191).Genomic DNA was extracted from liver or muscle tissue using the Qiagen DNeasy Blood & Tissue Kit  following manufacturer protocols. DNA quantity and quality were measured on a NanoDrop spectrophotometer. Aliquots of DNA extracts were shipped to LGC Genomics  for library prep and sequencing using a modified GBS  approachipyrad v. 0.7.30 (cutadapt) and low-quality bases. Reads were then clustered within and between individuals based on 85% similarity, which is the default value recommended by the program authors. A minimum of 30 individuals per locus (∼39% of samples) was required to keep loci in the final assembly, resulting in a concatenated matrix of approximately 8 million base pairs and 57,459 retained loci. Default values were also used for the remaining parameters. We also performed additional assemblies using a clustering threshold of 90%, and the results were qualitatively similar.The raw, demultiplexed data were processed using . 0.7.30 . The demND1 gene, tRNA leucine, tRNA isoleucine, tRNA glutamine, and portions of 16S and tRNA methionine. PCR amplification was performed using previously published primers . Reactions (25 μl) consisted of the following: 2.5 μl 10× reaction buffer, 0.5 μl 10 mM dNTPs, 0.5 μl 10 μM forward primer (16dR), 0.5 μl 10 μM reverse primer (tMet), 0.125 μl taq DNA polymerase, 19.875 μl ddH2O, 1 μl template DNA. All PCRs were performed on a BIO-RAD T100 Thermal Cycler using the following cycling conditions: initial denaturation at 95 °C (30 s), 30 cycles of denaturation at 95 °C (30 s), annealing at 55 °C (1 min), and extension at 72 °C (1 min), followed by a final extension at 72 °C for 5 min and samples held indefinitely at 4 °C. Horizontal agarose gel electrophoresis (1%) was used to assess the success of reactions. Amplicons were enzymatically purified using ExoSAP-IT  following manufacturer’s recommendations. Purified products were sent to GENEWIZ  for Sanger sequencing. Due to the large fragment size, amplicons were sequenced in both directions. Raw sequence data were edited in FinchTV v. 1.5.0 . Aliview v. 1.26 (OL549193 - OL549266).We obtained new mtDNA sequences from all samples to compare with the GBS data. Approximately 1,400 bp of mtDNA were collected from each sample, encompassing the entire  primers  and the  v. 1.26  was used v. 1.26 . All newP. solare. We also performed 20 independent ML searches from ten distinct maximum parsimony and ten random starting trees to determine if multiple likelihood peaks were present in the data. Robinson–Foulds distances were calculated between the 20 unrooted trees. These analyses were performed using the full multilocus data versus individual SNPs.All phylogenetic analyses were implemented through the High-Performance Computing Center (HPCC) at The College of Staten Island (CUNY). We performed both concatenated and coalescent analyses on the genomic data, as both approaches have their strengths and weaknesses  and receP. solare. Similar to the ML analyses, all ExaBayes runs used the full loci including invariable sites.We also performed Bayesian phylogenetic analyses in ExaBayes v. 1.5 . ExaBayeDquartets  to assemble quartets into a full tree containing all taxa. Although SVDquartets can be used with multilocus sequence data, the method is particularly suited to large SNP data sets and has been recently used in other RADseq/GBS studies  and 100 nonparametric bootstrap replicates were used to assess nodal support. Trees were rooted using P. solare.Coalescent-based phylogenetic analysis was performed using SVquartets  in PAUP*Phrynosoma and other lizards . SNPs with minor allele frequency lower than 5%, and SNPs in strong linkage disequilibrium (r2 > 0.96) were removed from the data set.Population structure was analyzed using the nonnegative matrix factorization algorithm sNMF implemented in LEA v2.6.0, for which the number of genetic clusters, riterion . This crTRUCTURE , sNMF isFst values , with the correlation between population pairs as covariates, and Akaike weights calculated using the MuMIn package . Climate data were obtained from the WorldClim v2 database at the 2.5-min resolution .We also implemented RDA to assess correlation between SNPs and environmental variables using the R package vegan . RDA is vegan . The appvegan : mean tevegan . The sigP. cornutum (rooted with P. solare) following the results of the phylogenetic analyses  and clustering in sNMF. We chose to analyze three populations/lineages to represent the deepest divergences in the genealogy. We did not divide the Plains lineage into two populations due to the results of SVDquartets (see Results). However, all BPP analyses used individuals from only one of the two Plains lineages inferred by RAxML-ng and ExaBayes. For computational reasons, all analyses were run using 500 loci.We used BPP v4.1.3  to perfoτs) and the population sizes (θs). Both parameters are measured in the expected number of mutations per site. Four independent A11 analyses were run . The species model prior assumed uniform rooted trees, and the starting tree topology was based off the concatenated analyses. We specified an inverse gamma (IG) prior of IG for population sizes (θ) and IG for the divergence time at the root of the species tree (τ0). Runs were implemented using an initial burnin of 50,000 generations followed by sampling every five generations for 100,000 total samples. Convergence was assessed by examining consistency between runs. We then performed a series of species tree analyses in BPP (A01) using the same populations. Similar to previous analyses, runs were performed both with and without the admixed individual KK104 to quantify the potential impact of gene flow on species tree estimation. All A01 analyses used the same priors and sampling frequency as the A11 analyses. We compared the best tree and associated support values among runs. Finally, we performed multiple A00 analyses to estimate divergence times and effective population sizes (Ne) on the species tree inferred from the A01 analyses. Again, analyses included or excluded sample KK104 to determine how gene flow might influence divergence times and population sizes. The parameter settings and priors were identical to the other BPP analyses, except that we used an initial burnin of 200,000 followed by sampling every 20 generations for 100,000 total samples. Mixing, convergence, and ESS values (target >200) were assessed using Tracer v1.6.0  of P. cornutum and P. solare  estimate directly from the data (μ = τ/T). This calculation provided additional evidence either supporting or refuting previous rate hypotheses. We then compared our rate estimate to independently estimated genome-wide neutral substitution rate for lizards and squamates (−10 substitutions per site per year). To obtain estimates of Ne, we assumed a generation time of 2 years , we used MOMENTS  was used to determine the dimensions that would maximize segregating sites shared between samples when creating the folded JSFS; we also retained one SNP per locus to minimize linkage disequilibrium. MOMENTS is an efficient method of simulating the evolution of an allele frequency spectrum over time using differential equations. The basis of MOMENTS is similar to the diffusion approximation approach utilized in the program ∂a∂i and many of the models we tested were adapted from previously developed ∂a∂i and MOMENTS models  were used , and we optimized parameters using optimize_log_fmin, a simplex (a.k.a. amoeba) method in terms of log parameters. Optimized parameter sets of each replicate were used to simulate the 3D-JSFS, and the multinomial approach was used to estimate the log-likelihood of the 3D-JSFS given the model. We ranked models according to AIC (lowest to highest) and estimated the standard deviation for each parameter using the Godambe Information Matrix with bootstrapped spectra. Finally, we determined the best model by comparing the two top ranked models for each data set using a likelihood-ratio test if they were nested. It should be noted that although that practices that we employed are common , they can influence demographic inference. Projecting down the JSFS can result in composite likelihoods which can cause statistics such as AIC and BIC to favor more complex models . This model included multiple reticulations in the species tree. For the introgression probability parameter (φ), we specified a beta prior of . We ran four independent analyses using a burnin of 200,000, followed by 500,000 samples that were taken every two generations. All BPP MSci analyses were run under a strict clock model (default) using BPP v. 4.3.0. Convergence was assessed by examining the trace plots in Tracer and checking for consistency between runs. All MSci analyses used the same 500 loci as the BPP MSC analyses. We performed analyses both with and without the admixed/outlier sample KK104. When included, KK104 was assigned to the Desert (DST) population following the results from the phylogenetic analyses.Because several of our analyses suggested that gene flow was important throughout the evolutionary history of i in BPP  to estimP. cornutum for current climatic conditions and those of the LGM across the range of the species using ecological niche modeling. This methodology uses environmental data associated with occurrence records to estimate habitat suitability across the landscape by means of various program-specific algorithms  from the WorldClim data set . We deriGenome Biology and Evolution online.evab260_Supplementary_DataClick here for additional data file.


The area of the Spanish Pyrenees is particularly interesting for studying the demographic dynamics of European rural areas given its orography, the main traditional rural condition of its population and the reported higher patterns of consanguinity of the region. Previous genetic studies suggest a gradient of genetic continuity of the area in the West to East axis. However, it has been shown that micro-population substructure can be detected when considering high-quality NGS data and using spatial explicit methods. In this work, we have analyzed the genome of 30 individuals sequenced at 40× from five different valleys in the Spanish Eastern Pyrenees (SEP) separated by less than 140 km along a west to east axis. Using haplotype-based methods and spatial analyses, we have been able to detect micro-population substructure within SEP not seen in previous studies. Linkage disequilibrium and autozygosity analyses suggest that the SEP populations show diverse demographic histories. In agreement with these results, demographic modeling by means of ABC-DL identify heterogeneity in their effective population sizes despite of their close geographic proximity, and suggests that the population substructure within SEP could have appeared around 2500 years ago. Overall, these results suggest that each rural population of the Pyrenees could represent a unique entity. In Europe, the transition from a rural to an urban world has been mainly triggered by the industrial revolution. This event promoted large-scale, differentiated and coordinated activities that were better accomplished by urban communities , which cIn Spain, rural areas comprised 68% of the total Spanish population by 1900 , and simThe main force explaining the higher inbreeding coefficients in Spanish rural areas compared to urban areas is geography . In partStudies using classical markers, such as blood markers, proteins, and HLA antigens  did not In the present study, we have characterized the genetic variation of the SEP rural population from five regions , Alt Urgell (U), Berguedà (B), Ripollès (R), and Garrotxa (G)) separated by less than 140 km along a west to east axis, making use, for the first time, of high-coverage whole-genome sequencing (WGS) data. This allowed us the use of powerful haplotype-based methods , with six samples per region. All sampled individuals were born and had all their grandparents born in the same sampled region. This dataset represents the oldest extract of the population, with an average age of ~76 years and equal proportions of both sexes   Supplem. After tSpanish exome data from  was acceA metric multidimensional scaling (MDS) analysis, also called classical multidimensional scaling  on an idIn order to identify genetic barriers, we developed an algorithm that models the shared co-ancestry matrix from Chromopainter/fineSTRUCTURE in terms of anisotropy within each geographic group see . In paraTo estimate the effective population size and the time of split of the SEP regions, we modeled their demographic history using a simple demographic model see Fig. . We usedSNVa − MAFSNVb | < 0.05). Runs of homozygosity (RoH) were quantified by means of two different approaches. For WGS data from SEP, we used the RoH as defined in [The decay of linkage disequilibrium (LD) was estimated for each population with the HR statistic . In ordefined in . For thefined in . This meIn order to describe the genetic relationships of the SEP samples with the European continent, we first performed a MDS analysis. SEP populations cluster with samples from the Iberian Peninsula  after 99,999 permutations), mainly distributing the SEP samples in the longitudinal axis in the second dimension. Chromopainter/fineSTRUCTURE also identified two main clusters that split the west to east axis . Out of the five considered populations, Alt Urgell was the one with the longest RoHs and Berguedà with the shortest ones . Following the observed population structure in previous analyses, we assumed a demographic topology that clustered PUB at one branch, and GR at the other. The ABC-DL analysis suggests that the overall observed population substructure among SEP regions appeared around 2500 years ago (95% CI from 2333.51 to 3087.88 years ago), independently of which centrality statistic was used (Table In agreement with previous results based on non-NGS data , 33, ourThe absence of differences between regions with respect to their patterns of Western Eurasian ancestry, as shown in the GLOBETROTTER results, suggests that geographic isolation within SEP is likely the cause of the identified substructure. This isolation should have appeared after major migrations into the region, or these had a very limited impact in the genetic makeup of SEP. In particular, it has been claimed that fine genetic variation has been shaped in Spain by linguistic and geopolitical boundaries at the time of Muslim rule in Spain . HoweverIn this work, we have described the genetic variation of five rural villages from the SEP through the analysis of high-coverage WGS data accompanied by detailed genealogical information. Our results suggest that geographically close SEP villages could show particular demographic histories. However, further analyses will be required to study if the observed pattern extends to other geographic regions of the Pyrenees, at both the Spanish and French side, as well as to other Spanish areas.Supplementary material


Esox lucius) was widely distributed in the high latitudes of the northern hemisphere. In China, northern pike was originally distributed only in the upper reaches of the Irtysh River in Xinjiang and has appeared in many water bodies outside the Irtysh River Basin in Northern Xinjiang. A total of four populations were collected from north to south in Xinjiang, including Irtysh River (RIR), Ulungu Lake (LUL), a small lake nearby Ulungu River (LJD), and Bosten Lake (LBO). We estimated population genomic parameters, performed gene flow analysis, and estimated the effective population size of each population. The proportion of individuals with high inbreeding coefficient (F ≥ 0.0625) accounted for 36.4% (44/121) of all sequenced individuals, approximately 4.5% (1/22) in LUL, 25.9% (7/27) in LBO, 42.9% (18/42) in RIR, and 60% (18/30) in LJD. RIR had the highest mean of genomic relatedness . Gene flow results showed that the population spreading was from RIR into two branches, one was LBO, and the other continued to split into LUL and LJD, and migration signal from LBO to LUL was detected. Our results suggested that the extinction risk of northern pike was very low in Xinjiang of China, and the controlled capture fishery of northern pike could be developed reasonably.Northern pike ( p < .05) of genomic relatedness , and individual pairs within LUL and LJD populations had relatively distant genomic relatedness (p < .05).Genomic pairwise relatedness of individual pairs within or between populations from the four sampling populations can be identified by coancestry coefficient and IBD, and validated by the consistency of the two parameters. Heat maps for estimates of the genomic pairwise coancestry coefficient and IBD analyses are shown in Figure 4 . Individual pairs within RIR population had the highest mean ( The studies in animals were reviewed and approved by the Committee for the Welfare and Ethics of Laboratory Animals of Heilongjiang River Fisheries Research Institute, CAFS .2.38H10O2) was used as an anesthetic to relieve the pain of the fish during handling and cutting a small piece of caudal fins. Then, fin samples were stored in absolute ethanol in a refrigerator at −20℃ until the extraction of genomic DNA.The wild fish were captured with standard gillnetting procedures. The standard gillnet was a sinking gillnet, 30 meters long and 3 meters high. The net had 3 mesh panels, each of which was 10 meters long. The mesh sizes were 10 cm, 12 cm, and 14 cm. RIR population  were captured from the mainstream of Irtysh River  in August 2019. LUL population  were captured from Ulungu Lake  in May 2019. LJD population  were captured in a small lake nearby Ulungu River  in October 2019. LBO population  were captured from Bosten lake  in September 2019. The 2‐phenoxyethanol , it was predicted that there were about 138,000 SLAFs. Paired‐end library construction was conducted as described in the Illumina sample preparation guide. All the 121 samples were sequenced on an Illumina HiSeq ×10 platform Genome Analyzer with a 2 × 126 bp setup (Illumina Inc.), according to the manufacturer's recommendations at the Experimental Department of Biomarker Technologies Corporation.All fin samples were digested overnight with proteinase K, and genomic DNA was extracted using standard phenol–chloroform protocol , with the settings . The SelectVariants and MarkDuplicates tool of GATK .The average observed heterozygosity (Ho) and the average expected heterozygosity (He) were estimated within population by using the R package “diveRsity”  was estimated by observed and expected autosomal homozygous genotype counts for each individual via PLINK software , were used to measure the genomic relatedness between any two individuals. The genomic coancestry coefficient between two individuals was estimated based on the half off‐diagonal value in the genomic additive relationship, which were obtained using a mixed model based on the quantitative genetics model  method using PLINK software , and the block size was set to 100 and calculated the proportion of variance correlated between populations explained by each model. The tree was visualized using the R script embedded in Treemix.Gaussian approximation was used to find genetic drift from genome‐wide allele frequency data and then to infer gene flows of historical population splits and mixtures for the four populations of northern pike by using Treemix software =fAB‐pq=fab‐fAbfaB. An extended approach for unbiased estimators for a large set of two‐locus statistics including D2 and σD2 was used to accurately estimate LD and calculate effective population size by Moments software  can be inferred by the linkage disequilibrium model, which requires accurate estimation of linkage disequilibrium (LD) statistics from sequencing data. For two loci, LD is typically given by the covariance or correlation of alleles co‐occurring on a haplotype. Allele A has frequency p in the population , and B has frequency q (b has frequency 1−q). The covariance is denoted D, 33.1p < .05). The value of He ranged from 0.314 to 0.317, and the highest diversity was observed in LUL and LJD populations, and the lowest diversity was found in RIR population (p < .05). The ranking of Pi results was similar to that of He, but the differences between populations were not significant (p > .05) in multiple comparisons; the detailed information is shown in Table The value of Ho within each sampling population ranged from 0.299 to 0.323 Table . The hig3.2F¯) estimated from genome‐wide SNP data were 0.083 for LJD, 0.063 for RIR, 0.039 for LBO, and 0.012 for LUL, respectively (Table F ≥ 0.0625). The value of inbreeding coefficient for the LUL population was F¯ = 0.012 ± 0.064, which was significantly lower than that of the other three populations in multiple comparisons (p < .05)  of all individuals  of genomic relatedness , and individual pairs within LUL and LJD populations had relatively distant genomic relatedness (p < .05).Genomic pairwise relatedness of individuals pairs within or between populations from the four sampling populations can be identified by coancestry coefficient and IBD, and validated by the consistency of the two parameters. Heat maps for estimates of the genomic pairwise coancestry coefficient and IBD analyses are shown in Figure According to the relationship inference criteria based on the coancestry coefficient Figure , 3 pairs3.4FST between any two of the four populations ranged from 0.008 to 0.051, showing low genetic differentiation . Population stratification was detected by PCA method. The 2D graphs for the first three PCs of these 121 individuals showed that the four populations were stratified, and only LJD and LUL showed a little bit overlapped. Among them, the RIR population had the highest genomic diversity . Analysis of the genetic structure suggested that the RIR population contained the genetic information of two presumed ancestor populations, while the other three populations were dominated by the same one ancestor population (K = 2)  Figure .3.5Gene flow results showed that the population splitting was from the RIR population into two branches, one was the LBO population, and the other continued to split into the LUL and LJD populations Figure . When Tr3.6We used two different software (Moments and NeEstimator) to estimate Ne of the four populations and obtained similar results. The ranking of Ne estimates for all four populations by these two methods was the same. However, the LUL estimate obtained by Moments was much higher than the value estimated by NeEstimator. Based on the number of haploid chromosomes chr = 25), the Ne correction factor was approximately equal to 1.245. LUL had the largest Ne , LJD was the second largest Ne , LBO was the third largest Ne , and RIR was the smallest Ne  . At the individual level, we found 42.9% (18/42) of the individuals in the RIR population were inbreeding offspring (F ≥ 0.0625). Comparatively, only LUL population had a lower inbreeding coefficient (F¯ = 0.012 ± 0.064). At present, it was difficult to obtain direct evidence of the reasons for the high inbreeding level of the populations. Our results suggested that these populations might be suffered by high‐intensity capture fishing in the earlier period. In fact, this could also be partially inferred from the capture records in the Irtysh River, as the output in 2006 was only 6% of the peak in the 1960s  was not commensurate with the size of the habitat. Under similar He and Ho estimates of the other populations , the high Ne of LJD population might be caused by multiple reasons, such as sampling bias, a large number of rare alleles, or even occurring sharply decline in population size due to habitat destruction or illegal fishing. Besides, this result partially supported our opinion that the northern pike populations in the water bodies surrounding Ulungu Lake might have connectivity with the LUL population by channels or other forms of waterways. One additional reason for the smallest estimated value of RIR was that the sample collected from the Irtysh River may not fully represent all fragmented habitats of the Irtysh River blocked by dams, which might underestimate the effective population size.As an aggressive carnivorous species, northern pike might be very destructive to the invasive water ecosystem. The introduction of nonnative species posed an increasing threat to the functional structure of the original aquatic ecosystem and might cause many ecological problems, such as extinction of native species through competition, predation, introduction of new pathogens, hybridization with native species, and changes in nutritional niche structure of ecosystem ; Formal analysis ; Methodology (lead); Software (lead); Validation (lead); Visualization (lead); Writing‐original draft (supporting); Writing‐review & editing (supporting). Tangbin Huo: Data curation ; Formal analysis ; Resources . Bo Ma: Data curation (supporting); Resources (supporting). Dan Song: Data curation ; Investigation ; Resources ; Writing‐original draft (supporting). Xiaofeng Zhang: Data curation ; Formal analysis (supporting); Investigation (supporting); Methodology (supporting); Software (supporting); Writing‐original draft (supporting). Guo Hu: Conceptualization (lead); Formal analysis (lead); Funding acquisition (lead); Investigation (lead); Methodology (lead); Project administration (lead); Resources (lead); Supervision (lead); Validation (lead); Writing‐original draft (lead); Writing‐review & editing (lead).


Sander vitreus) are a highly valued sportfish in the midwestern United States, a region characterized by postglacial recolonization from multiple lineages and an extensive history of stocking. We leveraged genomic data and recently developed analytical approaches to explore the population structure of walleye from two midwestern states, Minnesota and Wisconsin. We genotyped 954 walleye from 23 populations at ~20,000 loci using genotyping by sequencing and tested for patterns of population structure with single‐SNP and microhaplotype data. Populations from Minnesota and Wisconsin were highly differentiated from each other, with additional substructure found in each state. Population structure did not consistently adhere to drainage boundaries, as cases of high intra‐drainage and low inter‐drainage differentiation were observed. Low genetic structure was observed between populations from the upper Wisconsin and upper Chippewa river watersheds, which are found as few as 50 km apart and were likely homogenized through historical stocking. Nevertheless, we were able to differentiate these populations using microhaplotype‐based co‐ancestry analysis, providing increased resolution over previous microsatellite studies and our other single SNP‐based analyses. Although our results illustrate that walleye population structure has been influenced by past stocking practices, native ancestry still exists in most populations and walleye populations may be able to purge non‐native alleles and haplotypes in the absence of stocking. Our study is one of the first to use genomic tools to investigate the influence of stocking on population structure in a nonsalmonid fish and outlines a workflow leveraging recently developed analytical methods to improve resolution of complex population structure that will be highly applicable in many species and systems.Stocking of fish is an important tool for maintaining fisheries but can also significantly alter population genetic structure and erode the portfolio of within‐species diversity that is important for promoting resilience and adaptability. Walleye ( As the field of population genetics has transitioned to genomics, new opportunities for testing for the influences of stocking have emerged     from Wisconsin and Minnesota in the upper midwestern United States. Walleye are an important cultural, economic, and recreational resource in the Midwest and are one of the most sought‐after sportfishes in the region. Recently, however, declines in natural recruitment have been observed throughout the region  test for fine‐scale genetic boundaries across the landscape; (2) assess whether the genetic integrity of populations may have been altered due to stocking; and (3) define appropriate population genetic boundaries that can be integrated into future management plans. Our genomic analyses, especially the analysis incorporating microhaplotypes, provided increased resolution of population structure compared to previous studies based on microsatellites. This increased resolution of the intricate patterns of walleye population structure across Minnesota and Wisconsin provides information that can be used to conserve the genetic biocomplexity of walleye populations in the region.22.1The upper midwestern United States is composed of a high density of inland, glacial lakes connected by complex dendritic habitat and intersected by three continental‐scale drainages; 1) the Hudson Bay drainage; (2) the Mississippi River drainage; and (3) the Laurentian Great Lakes drainage  sequencing by Bootsma et al. , and rep2.2de novo construction of loci rather than aligning to a related reference genome as suggested by Paris et al., , selecting the first SNP when two or more SNPs at a locus had equal MAF estimates. Finally, any SNP with an FIS estimate >0.5 was removed.Genotypes were filtered in vcftools v0.1.15  genome  and expected (HE) heterozygosity, allelic richness (Ar), and FIS for each sampling location using diveRsity v1.9.90  was estimated with the bias‐corrected linkage disequilibrium method   of potential genetic groups. We used admixture to analyze three datasets, all sampled populations tested at K = 2 through K = 14 clusters, followed by hierarchical tests performed with only the populations sampled in Wisconsin (K = 2 through K = 10) and again using only populations sampled in Minnesota (K = 2 through K = 8). To examine support for each K, we applied fivefold cross‐validation to all tests.We then examined population structure with two distinct individual‐based methods. First, we used admixture v1.3 . Finally, the maximum‐likelihood tree‐building algorithm  to determine whether significant differences in co‐ancestry existed in this region where considerable stocking effort has occurred.The individual co‐ancestry matrix was constructed in RADpainter  and comparing the average differentiation of those loci to the average differentiation of the loci in the window. This sampling routine was conducted 1000 times for each window. If a window exceeded the 90th percentile of the sampling distribution, the number of bootstrap replicates was increased to 10,000. Contiguous windows that contained at least two loci and exceed the 99th percentile of the distribution after 10,000 bootstrap replicates were classified as significantly differentiated. We chose a minimum value of two loci per window to ensure that we could identify small but prolific windows. This analysis was conducted for the same three population groupings described above. We recognize that this sliding window analysis is relatively coarse due to the limited density of RAD data and does not take into account factors such as variation in recombination rate, which can influence the detection of highly differentiated regions  with BAYESCAN v2.1 , HO of 0.146 (0.141–0.184), and Ar of 1.58 (1.493–1.643)  than in Wisconsin populations  when compared in a two sample t‐test . This difference in diversity may reflect true population differences, but could also be a function of ascertainment bias, as 15 populations from the dataset are from Wisconsin compared to eight for Minnesota. Estimates of Ne were in the hundreds for twelve populations, with estimates >1000 for nine populations and <100 for two populations, Sanford Lake (#9) and Lake Koronis (#18) , which is the only population in our dataset from the Rock River drainage and was genetically unique, and the St. Louis River (#15), which is found in the Lake Superior drainage and was also genetically unique.Initial admixture results obtained while testing co‐ancestry of all samples indicated t Figure . Additio9 Figure . Moreove9 Figure  and fine9 Figure . Therefo1 Figure  but see FST = 0.053, 0.019–0.097) than that observed among samples from Wisconsin , on average .Within‐state comparisons revealed higher differentiation among samples from Minnesota  and Cut Foot Sioux Lake (#21) appeared to represent largely distinct genetic clusters  as it was from the nearest Wisconsin sample (DA = 0.014). In contrast to this signal of unique ancestry for the St. Louis River, Monmonier's algorithm, the dendrogram, admixture, and fineRADstructure results indicated that the other population sampled in the Lake Superior watershed, Lake Millicent (#14), possessed ancestry similar to ancestry observed in the upper Chippewa basin (#8–12), which is consistent with stocking records indicating that this population was founded from broodstock collected in the upper Chippewa basin , a population on the border of Minnesota and Wisconsin in the Lake Superior watershed, and the rest of the populations in Wisconsin. Isolation between the St. Louis River and all other sampled populations was supported by admixture Figure  in which5, a popuasin #8–1, which iAlthough populations in Wisconsin generally displayed lower genetic structure than those in Minnesota, a subdivision between the lower reaches of the Wisconsin and Chippewa rivers in Wisconsin Figure  was idenFST values  for intra‐basin comparisons in this region than were inter‐basin comparisons. Furthermore, the neighbor‐joining tree constructed as part of this analysis identified two large groups whose configuration generally reflected the geographic distribution of individuals throughout this region , which was highly distinct in all analyses. This population had the lowest 3.3FST outliers did not reveal any consistent patterns, and many of the high FST loci were fixed or had low minor allele frequencies in most populations, with a few populations that were more variable. It is important to note that while type 1 error is generally lower with BAYESCAN than other similar programs  likely correspond to genetic differences between the ancient lineages that recolonized the landscape postglaciation  was more similar to the Wolf River population (#2) than to upriver populations in the same watershed, and the Eau Claire River population (#9) was different from all other populations in the study including upriver populations in the same watershed Figures , 3 and 4 was moreAlthough we believe the differences between upriver and downriver populations were not caused by stocking, there are many examples of altered population structure due to stocking in our dataset. The most conspicuous example of this pattern is in the upper Wisconsin and upper Chippewa watersheds; populations from these two watersheds are separated by about 1000 km of riverine habitat, but are geographically proximal over land, with <50 km between some populations that straddle the drainage boundary. Historically, many lakes near this watershed boundary have been stocked with fish from Art Oehmcke State Fish Hatchery . The Red Lake population, which was historically used as an egg‐take source for stocking programs, experienced substantial declines in abundance due to overexploitation . In this population, seven individuals had genetic ancestry consistent with stocking from Pike River (#16), 22 displayed putative Lake Superior ancestry, and no putative hybrids were observed Figures  and 4. PNotropis anogenus) populations in the region were genetically closer based on geography rather than watershed barriers.Although influences of undocumented historical stocking cannot definitively be excluded, the greater similarity of the Otter Tail Lake population to Mississippi headwaters populations instead of the Red Lake within the same basin may be due to different postglacial colonization patterns. Glacial Lake Agassiz, which covered much of the current Red River basin, once drained south via the Minnesota River valley to the Mississippi River Wright, , providi4.2FST outlier loci to differentiate closely related populations has provided increased resolution in some systems such as for sockeye salmon (Oncorhynchus nerka) populations in Alaska ; its sample forms a distinct cluster in admixture and fineRADstructure analyses, suggesting that potential remnant populations in southern Minnesota that may warrant their own genetic management unit. We also suggest that additional studies could be conducted to refine estimates of genetic structure by increasing sample resolution in areas such as the mainstem Mississippi and St. Croix watersheds, which were not represented in our study, and in the Red River headwaters and nearby Mississippi basin to further resolve relationships near this major watershed boundary.We suggest adopting genetically cognizant stocking protocols  was lower. We documented substantial evidence of stocking, including genetic homogenization of populations in the upper Wisconsin and Chippewa river drainages and hybridization between native and non‐native individuals in Red Lake. Our resolution for defining genetic structure was greater than that of previous microsatellite studies, and microhaplotype analysis allowed us to identify subtle genetic structure that was not present with microsatellite or single‐SNP analyses. Our study is one of the first to test for the influence of stocking on genetic structure outside of salmonids and outlines an approach for disentangling complex population structure that will be highly applicable to other species and systems.The authors declare no conflicts of interest.Table S1‐S5Click here for additional data file.Fig S1Click here for additional data file.Fig S2Click here for additional data file.Fig S3Click here for additional data file.Fig S4Click here for additional data file.


Astacus astacus) is a native European species in decline, with a contracting range and diminishing populations and abundance. Previous studies revealed this species significant genetic diversity in the south-eastern Europe, with populations from the western and the southern part of the Balkan Peninsula being the most divergent. However, sampling of populations from the western part of the Balkans was limited and insufficient for investigating genetic diversity and population divergence for the purpose of conservation planning and management. Thus, the major aim of this study was to fill in this knowledge gap by studying mitochondrial and microsatellite DNA diversity, using 413 noble crayfish from 18 populations from waterbodies in the western part of the Balkan Peninsula.The noble crayfish (COI and 16S sequences and population genetic structure was described using 15 microsatellite loci.Phylogenetic analysis of studied populations and their mitochondrial diversity were studied using A. astacus. The results suggest that the western part of the Balkans played an important role as microrefugia during the Pleistocene climate fluctuations, allowing the long term persistence of A. astacus populations in this region. These results will also be important to supporting conservation decision making and planning.Phylogeographic analysis revealed new divergent mitochondrial haplotypes for the populations in the westernmost part of the Balkan Peninsula in the tributaries of the Sava and Drava rivers. Microsatellite data indicated that these populations harbour an important component of genetic diversity within  Astacus astacus (noble crayfish) whose range and abundance have declined rapidly due to a negative anthropogenic influence upon their habitats , overfishing and adverse impacts of invasive alien crayfish species , and the 16S rRNA sequences by Large-scale studies of mitochondrial cytochrome oxidase subunit I , 5 U/µl of Antarctic phosphatase (New England Biolabs). The sequencing of purified PCR products was prepared by Macrogen, Inc. , with the same forward primers used for the gene amplifications.Mitochondrial ar/16Sbr  and LCO-ar/16Sbr  allowing species  availablCOI gene fragment was 655 bp long, while for 16S was 475 bp long. New 16S and COI sequences were submitted to GenBank and BOLD data bases, and their GenBank accession numbers are MW726211 –MW726336 and MW726338 –MW726635 for 16S and COI sequences, respectively (16S and COI genes of A. astacus were downloaded from GenBank (COI sequences from 16S sequences were 475 bp long). GenBank accession numbers of the haplotypes obtained in the present study, as well as the ones from Sequences were edited in SEQUENCHER v. 5.3  and aligned using MAFFT v.7.187 . The finCOI and 16S sequences, and used only those samples for which sequences of both genes were available. This made concatenated data set (used in phylogenetic reconstruction) smaller compared to the COI data set that was used for other analyses. The 350 bp long COI sequences and 475 bp 16S sequences from the same individual were concatenated and collapsed to unique haplotypes using FaBox  using the jModelTest  (16S. The tree prior was set as the birth-death and independent substitution models were assigned to each mtDNA gene. The Markov Chain Monte Carlo (MCMC) analysis run comprised 300,000,000 generations, sampled every 10,000 generations. In order to determine convergence, the Effective Sample Size (ESS) values were checked in Tracer (In order to reconstruct phylogenetic tree that will be comparable with trees obtained in previous studies  we concang FaBox . The fulinations  and the odelTest . The sel v.2.5.2 . Since t/s/Ma/l)  along win Tracer . The bes16S: HKY+I, COI: HKY+G). Two separate runs with four Metropolis-coupled Monte Carlo Markov chains (MMCM) were performed for 10,000,000 generations, and trees were sampled every 1,000 generations. After effective sample size  for each parameter was confirmed with Tracer, the first 25% of sampled trees were eliminated as burn-in, and a 50% majority-rule consensus tree was constructed, with nodal values representing the posterior probabilities. Sequences of Pontastacus leptodactylus (Acc. No. KX279350) were used as an outgroup.Additionally, the phylogenetic relationships were estimated using Bayesian analysis (BA) in MrBayes ver.3.2.  with priCOI sequences, and one including concatenated (16S+COI) sequences. The COI sequence data set I comprised 655 bp sequences obtained in this study, while data set II comprised 350 bp long sequences published in COI haplotypes obtained in the present study to the COI haplotypes obtained in their research and indirectly to the lineages sensusensuThe median-joining network (MJ) approach  was usedGenetic diversity indices  were calculated in program DNASP v.6 .A. astacus. Populations were grouped on the basis of major tributaries of the Danube River  and Prespa Lake. Standard AMOVA computations were performed with three hierarchical levels: among groups (river basins/tributaries), among populations within groups, and within populations. The variance components were tested statistically by non-parametric randomisation tests using 10,000 permutations. Genetic differentiation among populations and river catchments was analysed through estimation of pairwise values of ΦST.Analysis of molecular variance (AMOVA)  was perfA. astacus populations revealed that they possess much higher variability and wider allele size range at many loci than the eastern European (Czech Republic and Estonia) populations used in the study of A total of 19 species-specific tetranucleotide repeat microsatellite loci were amplified following the protocols and procedures described by FIS and pair-wise FST values (as estimated by Weir and Cockerham’s θ), for estimating expected (unbiased genetic diversity) and observed heterozygosities  and rarefied allelic richness (AR), and for testing the significance of differences in average values of AR, HE and HO among groups of populations . The private allelic richness (APrRar) was estimated by rarefaction method using HP Rare v.Feb-2-2009 (APr). GENEPOP v. 3.3 , implemented in PAST v. 4.05 , was usedistance  matrix bsoftware .e) was assessed from the microsatellite data using the coalescent-based approach implemented in Lamarc v.2.1  package , (b) COI data set including only sequences obtained in this study (655 bp long), (c) genotypes of 15 microsatellite loci. Mantel tests were performed at the individual level using an analogue of Nei’s genetic distances  aiming to get insight into diversity on the European level, and (b) genotypes from 15 microsatellite loci in order to get insight into local diversity on the Balkan Peninsula. First, sampling sites were connected through a network based on the Delaunay triangulation, in which the simple mismatch molecular distances between connected sampling sites were calculated based on the molecular data obtained from all individuals. Surface calculation was based on midpoints of edges derived from Delaunay triangulation. Surface heights were calculated based on residual genetic distances.GLS interpolation was performed using two datasets: (a) full https://qgis.org/en/site/) to generate molecular divergence surface image using the inverse distance weighted (IDW) algorithm, plotted over a map of Europe.The values of molecular distance were set in the mid-points of each connection in the network using the Alleles in Space (AIS) software . The rawsensusensuThe phylogenetic tree inferred by BEAST using concatenated data set indicated six mostly unsupported previously described genetic lineages New haplotypes from Serbian populations  grouped with haplotypes from Kosovo, Montenegro and Germany . HaplotyCOI data sets (A. astacus. The MJ network based on long COI sequences (655 bp) revealed the existence of 11 unique haplotypes, separated by different number of mutational steps (The median-joining (MJ) networks for concatenated data set , as wellata sets  and 4, wal steps . The anaal steps . The MJ al steps .COI haplotype ssh10) and Hap25 (COI haplotype Aas18) were separated by one base pair change (sensuCOI haplotype was recorded in Croatian populations from the Drava River system (MOT and JAN) as well as in the Romanian populations (BEZ and SOM) from the Tisa River system where also Lshm4/ssh5 was recorded  was closely related to Hap41 (COI haplotype Aas26) and Hap54 (COI haplotype ssh6) distributed also in Germany and Montenegro, respectively (A. astacus specimens from Serbian waterbodies had ssh4 (in concatenated data set Hap53) haplotype, while most of them possessed the Lshm3/ssh3 haplotype ) and haplotype Lshm10 ) were separated by five mutational steps, whereas 10 bp changes were observed between Lshm9 and Lshm3, and 11 between Lshm9 and Lshm6 . In the r change . Moreovege sensu, while tge sensu. The Lsmrecorded  and 4. Trecorded . Interesectively . Some ofaplotype  and 4. Baplotype . The remaplotype  and 4 thaplotype . The Lshaplotype , 3 and 4aplotype , 3 and 4COI sequences revealed that populations from the Danube’s tributaries Sava, Drava and Tisa rivers possessed higher number of haplotypes and nucleotide diversity compared to other river systems that were characterised by only one haplotype  and the Prespa Lake, values of genetic differentiation varied from 0.193 (Drava-Sava) to 1.00 , again demonstrating genetically different groups (data not shown).Genetic differentiation of populations based on pairwise comparison of ene flow . HoweverCOI sequences grouped according to specimen’s affiliation to major tributaries of the Danube River  and Prespa Lake, revealed that most of the variance is contained among population within groups  to 0.518 (Aast4_17)   .All microsatellite loci in studied crayfish populations were in linkage equilibrium (data not shown). Only a single population (JAN from Croatia) displayed significant deviation from expected HW proportions after applying sequential Bonferroni correction . MICROCHP), mean allelic richness (AR) and observed heterozygosity (HO), was on an average higher in the populations from the Black Sea basin than in the Adriatic Sea basin . Among the major tributaries of the Danube River, the highest average variation was observed in the Drava River populations , while the lowest average variation was recorded in the Tisa River populations   . Overall= 0.271) .The results of the hierarchical gene diversity analysis by AMOVA revealed that for the total data set, the highest percentage of variation was present within populations (48.21%), followed by variation among populations within river systems (37.18%) and among river systems (14.62%) .Of the total 180 alleles, 127 were shared by the Black Sea and Adriatic Sea basin populations, 11 alleles were confined to the Adriatic Sea basin and 42 alleles were found only in the Danube River basin of the Black Sea basin. Among major tributaries of the Danube, 26, 6, 8 and 2 alleles were confined to the Sava, Drava, Velika Morava and Tisa rivers, respectively  with pairwise estimates of FST ranging from 0.211 (between JAR and MOT populations in Croatia) to 0.838 (between Serbian GRL and Romanian BEZ populations) (FST ranging from 0.360 between Sava and Drava to 0.623 between Velika Morava and Tisa) but lower than their differentiation from the PRE population of the Adriatic Sea basin (average pair-wise FST from 0.578 between Drava and PRE to 0.736 between Tisa and PRE). Within the major tributaries of the Danube, populations were more differentiated in Sava, Velika Morava and Tisa  than in Drava (average pair-wise FST = 0.281) (data not shown).The overall level of genetic differentiation between all studied samples was high  . The avePCoA analysis provided good resolution of spatial population relationships reflecting their affiliation to the river systems . The Adre values of 4920, 4864 and 4742, respectively. Globally, the Croatian populations displayed the highest effective population sizes (mean Ne = 2951), while lower estimates appeared mainly in the eastern populations of Serbia , Romania (mean Ne = 272) and Albania (Ne = 491). The lowest Ne values were found in the Romanian population of BEZ (Ne = 11), and the Serbian populations of GAZ (Ne = 18) and GRL (N e = 9).Effective population size estimates varied strongly between populations and within river basins, ranging from 9 to 4920 . The higr = 0.317 (PMantel < 0.001) for 655 bp long COI sequences dataset; r = 0.096 (PMantel < 0.001) for 350 bp long COI sequences dataset; r = 0.493 (PMantel < 0.001) for microsatellite dataset). Alleles in Space (AIS) analysis was used to visualise the general genetic divergence on the European level for the COI data set   . Moderatcedonia) . Areas wA. astacus in Europe by analysing numerous unstudied populations in the western part of the Balkan Peninsula. Study revealed new haplotypes (both COI and 16S) that nested among haplotypes belonging to different lineages described in previous studies   . Climateuctions) , includihe north . The obsA. astacus populations persisted through glaciations on the Balkan Peninsula, in three refugia: (I) western part of the Balkan Peninsula  across Europe  thntenegro ; (III) sntenegro . These sr system . Also, hs Europe .COI data set reflected those relationships, while the analysis of longer COI sequences (655 bp) refined relations and indicated existence of undescribed diversity. These results uncovered how length of COI sequences could influence on discrimination and relationships between haplotypes. Even though short barcode sequences are suitable for species identification, frequently they are not accurate for resolving phylogenetic relationships  and Austropotamobius torrentium (varying from 1 to 36) (A. astacus exhibits lower genetic diversity (http://www.boldsystems.org) records, all published A. astacus COI sequences form a single BIN (barcode index number) (cluster), while other European crayfish species form 17, 5 and 5 BINs . Therefore, it can be inferred that all A. astacus mtDNA lineages/groups that have been described up to now  . Compareiversity . Based op to now  belong tlcanicus / A. balclcanicus . Future A. astacus populations to survive Pleistocene climate fluctuations.It is important to highlight the distribution range of haplotypes Lshm9 and Lshm10, that is limited to the westernmost part of the Drava and Sava tributaries in Croatia, and in the Sava River tributaries in Slovenia, likely indicating that this area of species distribution might have had an important role as microrefugia allowing COI haplotype Lshm2 . The observed genetic structure indicated complex evolutionary history of A. astacus in the Drava drainage that possibly played an important corridor for crayfish post-glacial range expansion. Moreover, it should be pointed out that both TOT and MOT are gravel pits that were part of the Drava River, so it is possible that these A. astacus populations represent remnant astacofauna formerly present in local river systems but now lost because of invasive species  and Prespa Lake, AMOVA for microsatellite revealed that most of genetic variation is within populations, which is similar to findings in A. astacus used to be widely distributed, and nowadays populations are isolated within drainages (tributaries/basins) but still retain a part of their original diversity, which is also consistent with high ΦST pairwise values.The AMOVA results are not congruent between  markers . While CA. astacus populations in the studied area was influenced by isolation by distance at a moderate level as observed using both COI and microsatellites datasets. However, isolation by distance probably was not the only factor that contributed to the observed genetic differences between populations; significant differences could be a consequence of landscape characteristics that produced geographically isolated population which have no surface water connection. Hydrogeography and complex landscape character of studied area played an import role onto the genetic differentiation of different freshwater taxa  also known for the occurrence of distinct freshwater taxa, such as amphipods, fish or insects (The  insects .FST = 0.512) among populations, however relatively low diversity within populations , indicating a long-term isolation of small refugial populations. This strong population subdivision may limit local adaptation and facilitate random genetic drift which might result in diminished evolutionary potential of A. astacus  and high differentiation , MOT, TOT and JAN (Drava), and in RES (Velika Morava) populations. These crayfish populations exhibit the highest values of average number of alleles per locus, allelic richness, expected and observed heterozygosity of all studied populations. Furthermore, those genetic diversity estimates were higher than the ones reported in previous studies where the same set of tetranucleotide microsatellite loci were used . When co Balkans , while l Balkans . The val Balkans . Since J Balkans . Further Balkans . Moreove Balkans .IS; proportional to the loss of genetic diversity and consequently loss of adaptive evolutionary potential of the species . Since A. astacus  that will require careful and balanced approach in order to avoid outbreeding and inbreeding depression (A. astacus populations will ensure their evolutionary potential and long-time survival.It is necessary to know the genetic structure of the species in order to be able to preserve its integrity and within species diversity . The comiorities . The respression . PreservA. astacus in Europe by including previously understudied populations and geographic regions. New haplotypes were discovered restricted to the western part of the Balkan Peninsula. Analyses of microsatellites revealed population structuring at both local, and larger geographic scales observed across the A. astacus distributional range and indicates a complex genetic structure and confirmed that populations in the western part of the Balkans harbour important component of genetic diversity of the species. This information will help inform future conservation and management programs.In the present study we updated the current knowledge on the mtDNA diversity of 10.7717/peerj.11838/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.11838/supp-2Supplemental Information 2Values at nodes represent posterior probabilities >0.5. Phylogenetic clades are represented as in Click here for additional data file.