Castanea sativa, a species of high commercial value in Europe and a significant component of the Caucasian forests’ biodiversity. In contrast to the European range, neither the historical biogeography nor the population genetic structure of the species in its isolated Caucasian range has been clarified. Here, based on a survey of 21 natural populations from the Caucasus and a single one from Europe, we provide a likely biogeographic reconstruction and genetic diversity details. By applying Bayesian inference, species distribution modelling and fossil pollen data, we estimated (i) the time of the Caucasian—European divergence during the Middle Pleistocene, (ii) the time of divergence among Caucasian lineages and (iii) outlined the glacial refugia for species. The climate changes related to the Early–Middle Pleistocene Transition are proposed as the major drivers of the intraspecific divergence and European–Caucasian disjunction for the species, while the impact of the last glacial cycle was of marginal importance.Due to global climate cooling and aridification since the Paleogene, members of the Neogene flora were extirpated from the Northern Hemisphere or were confined to a few refugial areas. For some species, the final reduction/extinction came in the Pleistocene, but some others have survived climatic transformations up to the present. This has occurred in  Sweet chestnut is a remnant of once widely spread ancient Neogene flora, which makes it a relic species. It is a highly economically and ecologically important tree distributed and cultivated mostly in the Mediterranean Basin. The species is also present in the Caucasus region, where it forms natural forests of high conservation value. In this work, we reconstructed the evolutionary history of this species in the Caucasus. We demonstrated that populations retained high genetic diversity and moderate differentiation, partly due to high gene flow and less human impact in the past, in comparison to the European populations. The split between European and Caucasian ranges occurred during the Middle Pleistocene. Based on genetic information, the Caucasian populations of the species were grouped into four genetic clusters which were formed long before the last glacial period. The early Cenozoic vegetation of the Northern Hemisphere mid-latitudes consisted mainly of mixed mesophytic forests with tropical to warm–temperate taxa, which were widespread during the Paleogene and much of the Early Neogene. However, during the Neogene  cooling and aridification occurred and forced pronounced floristic and structural changes in the vegetation in the Northern Hemisphere. Many species of this ancient flora became extinct, and a few survived to become relics that are currently found in several refugia for Neogene species including the Colchic and Hyrcanian forests of the Caucasus .The Caucasus ecoregion stretches between the Black and the Caspian seas and includes the North Caucasus (Russia), the South Caucasus , north-eastern Turkey and north-west Iran . The comCastanea sativa) is a thermophilous tree species of high ecological, economic and cultural importance with a complex natural history resulting from domestication  explored the species’ geographic pattern of genetic differentiation, (ii) reconstructed the divergence history of the species, (iii) delineated the location of the probable glacial refugia and (iv) reconstructed the species dynamics of the Holocene expansion in the region.Here we investigate the evolutionary history of C. sativa from the South Caucasus (17 in Georgia and 4 in Azerbaijan) were analysed , the number of private alleles (Pa), observed heterozygosity (Ho) and unbiased estimation of expected heterozygosity (uHe). Allelic richness (Ar) was estimated in FSTAT 2.9.4  in Genepop v 4.7.5  using thAT 2.9.4 .He, Ar, Fis and Fst ) between different sets of populations: (i) among populations from the Lesser Caucasus (LC), LR, West Greater Caucasus (WGC), Central Greater Caucasus (CGC) and East Greater Caucasus (EGC) [see] to search for sub-regional patterns; (ii) between Western Georgian populations located in the GC and LC (WGC/LC) excluding populations from the LR  to search for diversity pattern specific to different mountain ranges, and (iii) between populations on the west  and on the east (CGC and EGC) of the region to search for differences in diversity patterns among populations that likely derive from Colchis and Hyrcanian refugial areas, respectively. Statistical significance was calculated in FSTAT 2.9.4 based on 9999 permutations.We tested the statistical difference in parameters of genetic structure (Fis) including ‘null alleles’ correction based on the individual inbreeding model using the Bayesian approach implemented in INEst 2.2 (5 MCMC (Markov Chain Monte Carlo) iterations, storing every 200th value with burn-in steps of 5 × 104. Two independent runs were done for each population, selecting different models according to the Deviance Information Criterion (DIC), that is, the inbred population model  and the random mating model (nb). Then, to estimate the extent of divergence among the Caucasian populations and regions, Wright’s fixation index (FST) was calculated using FreeNA with ‘Excluding Null Alleles’ (ENA) correction for the presence of null alleles. The significance of differentiation was tested using bootstrapping resampling over loci method with 104 replications.Finally, we estimated the inbreeding coefficient (NEst 2.2 . ParametK ranging from 1 to 23 (the whole dataset) or 1 to 22 (the Caucasian populations), and (iii) 105 burn-in periods followed by 1 × 106 MCMC iterations. To infer the optimal number of clusters that best tune with our dataset, the Best K, we employed different common approaches (K approach (X|K) Q) which appears to be more accurate and reliable than commonly applied methods  values were interpolated across the landscape using QGIS 3.16.3 ‘Hannover’ . This model locates the studied populations in the geogenetic space independently of their true geographic locations and, at the same time, estimates the gene flow. Simulations were run using 10 initial fast runs, each for 105 MCMC iterations and one long run with 106 iterations with a sampling of every 103. The inferred geogenetic location of the populations and their admixture sources were superimposed on the observed population sampling locations. The uncertainty in the locations of the populations was visualized with 50 % of credible ellipses and inferences about the source of admixture were made for each population to detect the pattern of gene flow and the possibility of LDD. The overall performance of MCMC was validated by exploring the posterior probability trace, while the ability of the model to describe our data was evaluated by comparison of parametric versus observed covariance matrix.To more deeply explore the spatial pattern of the population differentiation, we used the geogenetic approach implemented in SpaceMix, which allows for the visualization of complex histories of gene flow among populations, including inferring the long-distance dispersal (LDD) of genes and its effect on the genetic structure . For dating the divergence points among genetic lineages, we selected individuals that showed at least 80% of ancestry to the respective cluster, hereafter called a lineage. As a result, the gene pool representing the LC (Lineage I) consisted of 62 individuals, the gene pool of the WGC (Lineage II) consisted of 72 individuals, the CGC (Lineage III) consisted of 65 individuals, and the population EGC4 that represents the most easterly located and most genetically distant Caucasian gene pool consisted of 40 individuals (Lineage IV). The European gene pool consisted of 27 individuals (Lineage V).For a probabilistic analysis of alternative hypotheses for the history of divergence of sweet chestnut lineages from the South Caucasus, we used the approximate Bayesian computation (ABC) procedure combined with supervised machine learning—Random Forest algorithm implemented in R application DIYABC-Random Forest v1.2.1, hereafter ABC-RF (t2 generations ago among Lineage I (LC), Lineage IV (EGC—population ECG4) and Lineage V (Europe). Further, at t1 generations ago, Lineage II (GC) diverged from Lineage I. The last event included the admixture ta generations ago between Lineages II and IV that resulted in Lineage III. Scenario 2 was a modification of Scenario 1 in the way that it assumes admixture between the same lineages but without any previous divergence of Linage II from Linage I. Scenario 3 presented the differentiation of all genetic lineages throughout a single divergence event t1 generations ago. Scenario 4 refers directly to the K = 4 structure and west–east differentiation and shows that Lineages 2 and 4 diverged from Lineages 1 and 3, respectively. Scenarios 5–8 assumed several topologies that describe different hierarchies of divergence and ancestry for the current lineages.Eight scenarios regarding the topology of population ancestry were constructed based on the patterns of genetic differentiation revealed by STRUCTURE . Based o4 simulated training data set per scenario for model selection at the first step of ABC-RF analysis. After testing, the number of trees in the constructed random forests was set to 2000 as this was large enough to ensure a stable estimation [see]. Next, the most likely scenario was evaluated based on a classification vote for each scenario, representing the number of times a scenario is selected in a forest of n trees and the posterior probability estimator. The compatibility between scenarios and/or associated priors and the observed data was assessed using the linear discriminant analysis (LDA). After processing the scenario of choice, the second step of ABC-RF analysis included parameter estimation for the most likely scenario. To do this, we performed independent ABC-RF treatments for each parameter of interest based on an extended training data set as 105 simulated datasets, 2000 trees with 1000 out-of-bag samples used as a test, five axes of PLS  derived from the PLS processed on the summary statistics, and summarized using the 130 statistics that described the genetic diversity within the populations, between pairs or per triples of populations, averaged over all loci used. We inferred the mean and the median as point estimates for each parameter and computed global and local accuracy metrics corresponding to global and local Normalized Mean Absolute Error and the 90% coverage.We used a 2 × 10µ) and the mean parameter of the geometric distribution (P) used to model the length of mutation events. We assumed uniform distribution for the mean mutation rate and γ for the remaining genetic parameters; their ranges are shown in To evaluate whether our training set is sufficient for ABC-RF analysis, we applied procedures recommended by C. sativa of 100 years was used, similar to ABC-RF analysis provides the time from the demographic event (split or merging) in the number of generations that needs to be converted into absolute time to make a correlation with possible causal factors . This stage of the analysis is prone to bias due to the unknown true generation time for most species and therefore requires careful consideration. The generation time used in this study is a proxy for the true generation time, which has not yet been established for sweet chestnut. However, we prefer not to use the age at first reproduction applied by some investigators as a surrogate for generation time in trees, which are long-lived species. Age at first reproduction would significantly bias the results by underestimating the true generation time and consequently underestimating the time since common ancestry and other demographic events. Hence, a generation time for C. sativa in the past was estimated to support our demographic estimations and track the location of potential glacial refugia for the species in the Caucasus. For this purpose, we employed the Species Distribution Modelling (SDM) approach based on the maximum entropy algorithm implemented in MAXENT 3.4.3  values were used to estimate the performance of each model  at 30 arc-sec resolution for the current period (1979–2013) were downloaded from CHELSA 1.2  to 25 (CsCAT41), with a mean value of 12.55 per population.All analysed nSSR loci were neutral, and no LD between each pair of loci was detected. Null alleles were presented in all loci with a frequency ranging from 0.009 (EMCs13) to 0.120 (CsCAT41) and an average value of 0.004 A) was noted in the two LC populations  and the lowest in the CGC and EGC . This pattern was also found in the private alleles frequency, where the highest Pa (1.75) was noted in LC populations and the lowest (0.25) in EGC. Observed heterozygosity (Ho) ranged from 0.583 (WGC5) to 0.413 (CGC3), while populations from the LC presented the highest level of uHe, reaching 0.594 in LC2, and the lowest was observed for the CGC stand (0.415 in CGC4). The highest Fis was observed in LC4, WCG6 and CGC3, reaching 0.111, 0.131 and 0.136, respectively. The remaining populations had relatively low values and remained below 0.1. According to DIC, the homozygosity excess in the populations with the highest Fis was likely due to inbreeding. See The highest average number of alleles per locus (Fst estimation with the ENA correction was slightly lower Fst = 0.073 (95% CI 0.052–0.092). Pairwise Fst ranged from 0.004 (WGC4/WGC3) to 0.266 (EGC4/CGC4). Generally, the most divergent was the most easterly located population, EGC4, which attained the highest values in pairwise comparisons and likely, the distinctiveness of this population affected the overall level of differentiation [see].The global genetic differentiation among populations was moderate, reaching 0.076 (95 % CI: 0.055–0.094), while the Ar and He among the groups of populations in three scenarios out of four tested [see]. No differences were found in any parameters of the genetic structure between populations from the LC and WGC. The global differentiation (Fst) among the groups of populations was significant only when comparing West Caucasian populations  to Central and East Greater Caucasian populations , reaching mean values of 0.046/0.095, respectively (P < 0.0369); no statistical difference was observed in Fis values [see].The regional-level analysis showed statistically significant differences in K = 3 according to the ΔK method, and K = 10 in the case of LnP(K) (Mean LnP(K) = −13 694.3; SD = 12.63179), while the approach of [see]. Both clustering approaches underpinned the unique genetic composition of the Macedonian population. Furthermore, the clustering at K = 5 delivered the same differentiation pattern as an analysis conducted for the South Caucasus subset of populations described below , the optimal number of homogenous groups in the Caucasian dataset (21 populations) was K = 10 (Mean LnP(K) = −13 185.36; SD = 140.42), which was too high to provide any other explanation but the idiosyncratic history of each population [see]. In contrast, Evanno’s ΔK method applied to the Caucasian populations (Georgia and Azerbaijan) produced an optimum K = 2 [see]. This grouping pattern assumed Western (Cluster I: WGC and LC) and Eastern (Cluster II: CGC and EGC) gene pools and clearly depicted admixture between them  to both clusters (Cluster I: 52% and Cluster II: 48%). However, an outlier from the general pattern was the CGC4 population, with a high proportion of Cluster I (56%) but located in the region of Cluster II.Based on LnP were grouped in Cluster I together with two from the WGC (WGC3 and WGC5) and one from the eastern edge of the LR (LR1), while Cluster II had wider spatial distribution and consisted of three populations from the WGC , LH2 from the western edge of the LR and single population from the CGC (CGC1). However, the majority of populations in both mountain ranges display a relatively high admixture with no predominance of any of the two clusters. This is well exemplified in populations WGC6, WGC7 and CGC1, which possessed almost equal Q-values to Clusters I and II. The highest Q-value of Cluster I was in population LC3 (61.8%), while Cluster II—WGC1 (61.4%). Cluster III was defined for populations distributed in the CGC (CGC2–CGC4) and the EGC (EGC1–EGC3). Here, we also noted ca. 20% of the genomes were attributed to Clusters I and II. At the easternmost part of the studied area, the population EGC4 of Cluster IV shows a high divergence from the other populations studied, confirmed by the highest values of pairwise Fst reaching >10%. The admixture of this distant genetic pool at 24.5 % and 22.4 % were detected in EGC3 and EGC2, respectively.Further details in the genetic structure were delivered by using K = 2 with the intermediate position occupied by CGC1 (The constructed geogenetic map of population relationships made by SpaceMix is given in  by CGC1 . SpaceMi by CGC1 . On the  by CGC1 .[see]. The second-best scenario obtained 280 (SD = 34.693) votes and it was Scenario 8 in which Linage 4 was ancestral to all Caucasian lineages.The classification votes and posterior probabilities estimated for the observed microsatellite dataset in all 10 replications of ABC-FR analyses were the highest for Scenario 1 having an average of 574 (SD = 31.522) votes out of 2000 RF-trees; the average posterior probability was PP = 0.708 (SD = 0.035). The global probability of incorrectly identified scenarios (prior error rates) in tested datasets was estimated to be 0.23942, and the highest-class error rates were in Scenarios 7, 6 and 2 . For Scenario 1, the posterior  error rate was, on average, 0.238 (SD = 0.0006). The fit of Scenario 1 to the observed dataset is illustrated by the projection of the simulated and observed datasets on the first two LDA axes t2 = 497.3 ka BP (95% CI: 243.1–856.7 ka BP), the ancestor lineage split into Lineages I, IV and V, which is primarily translated into a disjunction between the Caucasian and European gene pool. At t1 = 150.3 ka BP (95% CI: 79.2–195.6 ka BP), Lineage II, with the highest frequency in WGC, diverged from Lineage I which predominates in the LC. Finally, the admixture between Lineages II and IV dated at ta = 90.8 ka BP (95% CI: 29.4–157.7ka BP) led to Lineage III. The highest effective population size was estimated for Lineage I (Ne = 15 322), and the lowest for Lineage III  and the precipitation of the coldest quarter (Bio19), with a relative contribution reaching >54 % and >21 %, respectively. In addition, the annual mean temperature (Bio1), with a relative contribution of >11%, also had a significant impact on the projected species distribution in all models.The MAXENT model accuracy (AUC) for all presented SDM models was greater than 0.93 C. sativa across the studied landscape in different time slices is given in The potential distribution of The niche model for LIG indicates that the species covered approximately the same areas as the current range. Additionally, MAXENT modelled small pockets of the species occurrence in the Azerbaijan part of the GC (suitability up to 60 %), the LC (up to 40 %), central Armenia and southern territories of Turkey where the species is not present currently. The projected distribution in Hyrcania in LIG is broader than currently, with much higher suitability. There was a clear distributional gap between Hyrcanian forests and the GC in that period.C. sativa (40–60%), as did the North Caucasus  and high (90–100%) climatic suitability. For EH, the fossil pollen of sweet chestnut was reported in cores taken from sites beyond the projected range, that is, in the Javakheti Plateau and some locations in the Northern Caucasus, undoubtedly a result of long-distance pollen transport. Similarly, the cores from the Caspian Sea containing sweet chestnut pollen refer to wind transportation of pollen from the Hyrcanian forests.The compiled fossil pollen records retrieved from C. sativa occurred during the Middle Pleistocene . This corresponds to the Early–Middle Pleistocene Transition (EMPT) 1.2–0.6 Ma  and East Mediterranean (Greek) chestnut genepools. Similarly, our result indicates that the European and Caucasian lineages diverged due to the EMPT. Despite the limited sampling of the European range, our analysis has captured the signal of the serious transformation of the sweet chestnut distribution in Europe and Western Asia as a result of paleoenvironmental change following the EMPT.In our demographic reconstruction, we used a single population from Macedonia to represent the European genepool of sweet chestnut. Such a sampling scheme is a methodological caveat that has to be addressed due to potential bias. The most recent study (K = 2) that captured the major structure in the data , which represents the penultimate glacial of MIS 6  and Lineage II (the GC) occurred in the second part of the Middle Pleistocene , which display the highest gene diversity and number of private alleles [see]. Additionally, the genetic lineage from this area (Lineage I) had the largest ancestral effective population size as estimated by demographic analysis  among 21 studied populations of C. sativa in the South Caucasus computed in FreeNA. The significance was assessed based on 9,999 permutations. All values are in the range of 95% CI Regional differences in genetic structure parameters computed for the populations from the South Caucasus Posterior probability (PP) obtained for eight tested demographic scenarios of divergence tested in DIYABC-Random Forest. Scenario 1 was indicated as having the highest PP and assumed as the most probable one for populations of C. sativa in the Caucasus. Scenario choice was performed in ten replicates ABC-RF analyses based on 140,040 simulated training datasets. The table presents the accuracy metrics of prediction. The number of trees in the constructed random forests was set to 2,000. Standard deviations over the 10 replicate analyses are given in brackets for each metrics. The contribution of eight non-correlated bioclimatic variables. Estimation of the optimal number of genetic clusters of STRUCTURE results (K = 3) based on Evanno’s ΔK method for 22 studied populations of C. sativa from the South Caucasus and North Macedonia. A barplot for K = 3 inferred for 22 studied populations of C. sativa from the South Caucasus and North Macedonia based on the Evanno’s ΔK method. Estimation of the optimal number of genetic clusters of STRUCTURE results (K = 10) based on the mean probability method for 22 studied populations of C. sativa from the South Caucasus and North Macedonia. Estimation of the optimal number of genetic clusters of STRUCTURE results (K = 5) based on the method of C. sativa from the South Caucasus and North Macedonia. A barplot based on K = 5 inferred for 22 studied populations of C. sativa from the South Caucasus and North Macedonia based on an approach of  Estimation of the optimal number of genetic clusters of STRUCTURE results (K = 10) based on the mean probability method for 21 studied populations of C. sativa from the South Caucasus Estimation of the optimal number of genetic clusters of STRUCTURE results (K = 2) based on Evanno’s ΔK method for 21 studied populations of C. sativa from the South Caucasus. Estimation of the optimal number of genetic clusters for the South Caucasian populations of C. sativa (Georgia and Azerbaijan) based on the approach of  Scenario choice based on the Random Forest approach implemented in DIYABC-Random Forest: (A) Linear discriminant analysis (LDA) projection of the datasets from the training set and observed data on the two first LD axis; (B) prediction power indicating global prior error rate computed using out of bootstrap  for random forest versus the number of growing trees in the forest.plad059_suppl_Supplementary_MaterialClick here for additional data file.


Studying the genetic variation among different species distributed across their core and range‐edge habitats can provide valuable insights into how genetic variation changes across the species' distribution range. This information can be important for understanding local adaptation, as well as for conservation and management efforts. In this study, we have carried out genomic characterization of six species of Asian Pikas distributed along their core and range‐edge habitats in the Himalayas. We utilized a population genomics approach using ~28,000 genome‐wide SNP markers obtained from restriction‐site associated DNA sequencing. We identified low nucleotide diversity and high inbreeding coefficients in all six species across their core and range‐edge habitats. We also identified evidence of gene flow among genetically diverse species. Our results provide evidence of reduced genetic diversity in Asian pikas distributed across the Himalayas and the neighboring regions and indicate that recurrent gene flow is possibly a key mechanism for maintaining genetic diversity and adaptive potential in these pikas. However, full‐scale genomics studies that utilize whole‐genome sequencing approaches will be needed to quantify the direction and timing of gene flow and functional changes associated with introgressed regions in the genome. Our results represent an important step toward understanding the patterns and consequences of gene flow in species, sampled at the least studied, yet climatically vulnerable part of their habitat that can be further used to inform conservation strategies that promote connectivity and gene flow between populations. Our results provide evidence of reduced genetic diversity in Asian pikas distributed across the Himalayas and the neighboring regions and indicate that recurrent gene flow is possibly a key mechanism for maintaining genetic diversity and adaptive potential in these pikas. Of the six Asian pikas species reported from the Himalayas  of six species of pikas along their core and southern trailing range edges Table  and test22.1Tissue samples were collected during field surveys carried out in the year 2010–2014. Elevational transects were accessed via hiking and traps were set in locations where active pika presence was perceived based on fecal encounter rate and sighting frequencies. Seven sites in the eastern Himalayas and four sites in the western Himalayas were selected for trapping pikas and sampling  library was prepared following the protocol described in Peterson et al., 2.4O. curzoniae genome downloaded from the NCBI database . We then used the “gstacks” program in STACKS to identify SNPs within each population for each locus and then genotyped individuals at each identified SNP . We only selected genotypes with minimum allele coverage = 2, minimum percent of genotype data per population = 40, the maximum number of populations that can fail percent_genotypes = 3, and the minimum number of samples with rare allele = 2. We then created graphs to find samples with high missing data and removed samples with >50% missing genotypes. We further classified SNPs into various categories  using the utility scripts provided in STACKS and only kept “singleton” SNPs for downstream analysis. We then estimated pairwise linkage disequilibrium (LD) for each SNP pair using Plink v.1.09  > 0.5. Finally, we imputed missing data in the VCF using admixture ancestry relationships  as a measure of relative heterozygosity for each sample using a method of moments implemented in vcftools. The measure of inter‐species genetic diversity was calculated using the Fixation index (FST)  and geographic distance are correlated, we used scatter plots. We used kernel density estimates to demarcate distribution ranges for each population (95% utilization distribution contours). We then extracted the centroid for each of the distribution ranges. The geographic distances were defined as the Euclidean distance between centroids of pairs of population. In addition, we also calculated the nearest distances between pairs of populations using the vertices of the outermost ranges.To ascertain if estimates of genetic distance  and f4‐ratio statistics for all trios of species in the dataset. As we did not have an outgroup in this dataset, we used two separate internal outgroups (using a different subgenus) based on the phylogenetic tree generated above, (1) O. nubrica (subgenus Ochotona) to study the pattern of gene flow between trios of O. roylei, O. macrotis, and O. ladacensis (subgenus Conothoa) (2) O. roylei (subgenus Conothoa) to study the pattern of gene flow between trios of O. curzoniae, O. nubrica, and O. sikimaria (subgenus Ochotona). Third, we also inferred pairwise relatedness for each pair of species/population by calculating kinship coefficient using VCFTools.Second, we calculated Patterson's 33.1F) for each individual as a measure of observed heterozygosity across all samples. The inbreeding coefficient was generally high across all samples, regardless of the population being a “core” or “range‐edge”  between species ranged from 0.24 to 0.47 and was highest between O. macrotis (AP) and O. sikimaria  consisting of O. sikimaria, O. nubrica, and O. curzoniae. Interestingly, few samples from O. curzoniae appear intermediate to both clades.We first used the TimeTree database  and West Sikkim (WS) are similar, whereas the Arunachal population (AP) appears genetically distinct. We further calculated pairwise average genetic distance among these three populations of O. macrotis  the first gene flow between O. curzoniae and the base of O. ladacensis/O. macrotis split and (b) between Ladakh populations of O. ladacensis and O. macrotis.Results from the phylogenetic tree and inference of population and individual ancestries indicated the possibility of gene sharing among some of these species. Hence, we performed three different analyses to examine the possible evidence of gene sharing among different species (1) Treemix  O. roylei to study the pattern of gene flow between trios of O. curzoniae, O. nubrica, and O. sikimaria. For scenario 1, we found evidence of excess allele sharing between populations of O. macrotis from AP and WS, but not between AP and LK  that are distributed across the eastern and western Himalayas and neighboring high‐altitude mountain regions  which has been associated with elevation‐specific population decline and restricted dispersal  and geographic distance  was generally high for each species pair  with the population from Arunachal Pradesh  whereas very low genetic differentiation with the population from Ladakh  (FST = 0.03). Our results indicated that geographic distance cannot fully explain the genetic distance between populations/species of six species of Pikas in the Himalayas.Inter‐species pairwise genetic differentiation . Similarly, O. sikimaria, O. nubrica, and O. curzoniae were grouped into another cluster as these species are from different subgenus (Ochotona). Interestingly, few samples from O. curzoniae appear intermediate to both clades which is a possible indication that this species is possibly being exposed to gene flow. A recently published study has identified strong evidence of gene flow from O. curzoniae in their “core” habitat to ecologically distinct range‐edge species   was genetically similar to a geographically distant core population from Ladakh  in comparison to another core population from a geographically closer core population from Arunachal Pradesh. These results are consistent with patterns of genetic divergence (FST) we have identified among these three populations of O. macrotis (Table O. macrotis is contrary to the theory of isolation by distance (IBD), which states that genetic similarity decreases among populations as the geographic distance between them increases . We expect a similar case of gene flow in these two species that overlap in their distribution range in the western Himalayas. Consistent with the results of Treemix, the evidence of gene flow among populations of O. ladacensis and O. macrotis in Ladakh populations is also independently identified in ABBA‐BABA analysis ; formal analysis ; investigation ; methodology ; writing – original draft ; writing – review and editing . Melia G Romine: Investigation (supporting); methodology (supporting); writing – review and editing (supporting). Sunita Khatiwara: Methodology (supporting); writing – review and editing (supporting). Uma Ramakrishnan: Conceptualization ; investigation ; writing – review and editing . Sangeet Lamichhaney: Conceptualization ; investigation ; methodology (lead); writing – original draft (lead); writing – review and editing (lead).


Tetrastigma hemsleyanum is, in its native habitat, a widespread perennial herb of warm-temperate evergreen forest in subtropical China. Its ecological and medicinal values constitute a significant revenue for local human populations and ecosystem. Using 30,252 single nucleotide polymorphisms (SNPs) derived from reduced-representation genome sequencing in 156 samples from 24 sites, we conducted a landscape genomics study of the T. hemsleyanum to elucidate its genomic variation across multiple climate gradients and genomic vulnerability to future climate change. Multivariate methods identified that climatic variation explained more genomic variation than that of geographical distance, which implied that local adaptation to heterogeneous environment might represent an important source of genomic variation. Among these climate variables, winter precipitation was the strongest predictor of the contemporary genetic structure. FST outlier tests and environment association analysis totally identified 275 candidate adaptive SNPs along the genetic and environmental gradients. SNP annotations of these putatively adaptive loci uncovered gene functions associated with modulating flowering time and regulating plant response to abiotic stresses, which have implications for breeding and other special agricultural aims on the basis of these selection signatures. Critically, modelling revealed that the high genomic vulnerability of our focal species via a mismatch between current and future genotype-environment relationships located in central-northern region of the T. hemsleyanum’s range, where populations require proactive management efforts such as assistant adaptation to cope with ongoing climate change. Taken together, our results provide robust evidence of local climate adaption for T. hemsleyanum and further deepen our understanding of adaptation basis of herbs in subtropical China.Understanding adaptive genetic variation of plant populations and their vulnerabilities to climate change are critical to preserve biodiversity and subsequent management interventions. To this end, landscape genomics may represent a cost-efficient approach for investigating molecular signatures underlying local adaptation.  Local adaptation is ubiquitous in plants, which can result in the genetic divergence of populations cross the landscape . ClimateFST outlier analyses (OA) and environmental association analysis (EAA). OA studies screen SNPs with higher genetic divergence than expected among populations under a neutral model , one of the widespread components of China’s warm-temperate evergreen forest. This diploid, perennial herb is endemic to subtropical China  with a distribution that spans 18° of longitude, 13° of latitude and 1,000 m of elevation  . We chosTotal genomic DNA was isolated from the silica gel dried leaf material using Plantzol Reagent (Invitrogen). SLAF sequencing libraries were prepared using the HaeIII and RsaI restriction enzymes. The resulting 314 to 414 bp fragments  were excised and purified with the Qiagen Gel Extraction Kit. After the library quality inspection, paired-end sequencing (the read lengths were 126 bp) was conducted on an Illumina HiSeq 2500 platform .T. voinierianum reference genome . We aligned the reads to the e genome  using BWe genome . T. voinTK v.3.3  with HapTK v.3.3  and the TK v.3.3  with theTK v.3.3 , in a wihttp://envirem.github.io/ENVIREM_tutorial.html). All variables for downstream analysis in this study were obtained at 30-s resolution (∼1 km2), and were scaled, centered, log-transformed (if appropriate) and tested for normality. To reduce multicollinearity, climatic cells with sampling records from all 35 variables were checked for Spearman’s correlation coefficient in R. Ultimately, a subset of 11 variables (see r| < 0.75) were considered for environmental association analysis.We retrieved 35 bioclimatic variables see  for eachbles see  that lacHE), percentage of polymorphic loci (P) and Wright’s inbreeding coefficient (FIS) per population were estimated using the “populations” module of STACKS v.1.27  that varies from 2 to 10, we run 10 repetitions with a 10,000 burn-in steps of and 1,00,000 Markov chain Monte Carlo (MCMC) replications. The program was utilized applying independent allele frequencies and an admixture model. The most probable values of K were determined by ∆K method  with 10,000 permutations. In partial RDA, variance partitioning was carried out using population allele frequencies as the response variable in the two partial models that utilized the selected bioclimate variables as explanatory variables and the dbMEMs as conditioning variables and vice versa.We estimated the amount of genomic variation attributable to climate and geography by two different variance partitioning approaches—redundancy analysis (RDA) and generalized dissimilarity modelling (GDM). All climate variables were scaled and centered to account for difference in magnitude when calculating environmental distance. To test for the presence of regional climatic differences, the difference of each climate variable among the four genetic clusters (identified in STRUCTURE) were assessed using analysis of variance (ANOVA), followed by mic data  and permmic data . Using pmic data . We utilmic data . We geneFST matrix was generated using the R package hierfstat  and EAA (BayPass). The three OAs we adopted are based on different demographic assumptions. BAYESCAN v. 2.1 , false positives were expected for the OA and EAA methods even with a correction for population structure. As a result, we conducted two additional steps to control for false positives by GDM. Firstly, we randomly choose 200 SNPs from the full set of 30,252 SNPs, and incorporate the random sample as a “reference” in the GDM to investigate if explanatory power of a candidate SNP is higher than that of the reference group. Secondly, we integrated geographic distance in the GDMs to reveal if spatial isolation better explained the observed partial allelic turnover across climatic gradients. This acts as a second screening for loci that predominantly respond to neutral genetic processes . Overall, we tried to reduce false-positive rates, 1) by comparing the response of SNP-specific partial allelic turnover to climate variables with the response to geographic distance and 2) by comparing the response of each candidate SNP with reference SNP group to test whether a given locus explained a higher deviance within the GDM than a random sample of genetic variation.Given the strong interspecific genetic differentiation and significant correlation between genetic structure and environmental gradients in FST matrices using the package HIERFSTAT in R (We created the population pairwise TAT in R  and applT. voinierianum genome (T. voinierianum transcriptome (Vitis vinifera proteomes as described in The positions of candidate SNPs accompanying coding genes were identified based on gene models on the m genome  using BEm genome  and SnpEm genome . The gencriptome  and VitiFST of the 275 retained candidate SNPs under putative selection. The fitted models were predicted to climate in 2070 and two shared socioeconomic pathways (SSPs)—SSP370 and SSP580. As described in in situ allele frequencies shift at the robust set of climate-adaptive loci that a resident population required to response to local climate changes in 2070 (assuming no migration). Forward and reverse offsets were recently developed by T. hemsleyanum to all future grid cells within China. Then we identified minimum offset among genomic offsets estimated across all future grid cells (termed “forward offset”), which assumes unconstrained dispersal of T. hemsleyanum populations to any location within China. High values of forward offset indicate a low adaptation capacity of the population to all future climates of China and a high chance of extinction in current genotype–climate relationships across the landscape. Besides, the distance and initial bearing that populations would migrate/disperse to the grid cell that minimized forward offset were estimated in R package geosphere  to illustrate the full range of each band.For this threatened and endangered species, we are particularly interested in identifying spatial regions or populations at highest risk of future maladaptation, and where migration will be most effective to maintain the current status of adaptation. To do so, we extended the GDMs to estimate genomic offset, also called genomic vulnerability , which reosphere  using thc. 850 million reads was generated from all individuals with an average Q30 of 95.69% and a GC content of approximately 41.33% . High-quality SLAF tags  were identified throughout the genome with an average of 12.05-fold sequencing depth  was similar across populations  and varied from 0.045 (SW1) to 0.125 (EC2). The FIS values ranged from −0.044 (EC6) to 0.098 (EC1) with a mean value of 0.038. Significant pairwise genetic differentiation was detected for all population pairs , ranging from 0.079 (between CC3 and CC6) to 0.455 (between EC2 and SW1) ], Central [“CC” ], South [“SC”  and East China [“EC” ] , while 39.25% of the variation occurred within populations of T. hemsleyanum  . Bayesia) (K = 4 . The geo EC1–5)] . We obse EC1–5)] . AMOVA isleyanum .pRDA), four dbMEM were retained as sufficient to explain the geographic structure among populations , whereby ten climate variables were identified as significant predictive of genetic variation among populations , with onulations . Among tulations . dbMEM2 ulations . Cumulatulations , which iulations .p < 0.001; “combined fractions” in Using RDA, the climate (ten variables identified by forward selection) and geography both explained significant proportions of genetic variation  are exposed to greater growing degree days in comparison to populations from rest of the range; eastern coastal populations (cluster III) experience more winter precipitation; and southwestern populations (cluster I) experience highly potential evapotranspiration in warm and rainy seasons . Moreovep < 0.001) (The full GDM (climate + geography) model explained 17.6% of the deviance in spatial patterns of genetic variation among populations (< 0.001) . The mod< 0.001) . As with< 0.001)  indicateXTX and Selestim identified 55, 303, and 343 putatively adaptive SNPs, respectively. 20 SNPs were identified in all three tests and 87 in two tests, with the rest SNPs exclusively to BayeScan (9), BayENV XTX (204) and Selestim (254)  . Using Eim (254) . Collectim (254) .GDMs were independently applied to the 497 candidate SNPs to quantify partial allelic turnovers through climatic gradients . For eacAmong the 497 previous identified candidate SNPs, 6 SNPs (1.2%) showed non-significant allelic turnover response to any of the variables, and were not interpreted further. 50 of the 497 SNPs (10.1%) had less magnitude in partial allelic turnover associated with climate variables than with geographic distance . The “reference SNP group” explained 10.5% of the deviance in GDM model, and therefore the SNPs with explanatory power less than 10.5% were also excluded. Finally, we retained 275 SNPs for further functional annotation.T. voinierianum genome assembly, and reside in different genomic regions including coding, intron and intergenic regions. No candidate SNPs were found within regulatory regions. Of the 219 SNPs, 92 SNPs were predicted to fall within genes. 31 (33.7%) out of the genic SNPs were exonic variants, including two identified in OA, 28 in EAA test (associated with climate variables) and one in both tests  equals to zero. The longest Dmin was predicted in central-north part of the range, whereas the shortest Dmin mainly occurred in the southern and northwestern range edge  locations had the distance to locations that minimized forward offset , could represent impact of climatic gradients that are highly related to space . While cow 6.4%; ; Table 2T. hemsleyanum, BIO19 and PETWeQ represented the most important predictors of genome-wide SNP variation when taking the effects of spatial isolation into account, which suggested that populations are responding to selective forces related to drought. This result agrees with the general consensus that T. hemsleyanum is best suited to moist hillsides or valleys and is a drought-sensitive species  and GDM (77.6%) analysis, which accords with the other genetic  and commFST outliers were detected in BayPass, and between 2.70% and 9.67% of the genotype-environment associations were overlapped among two or more variables  were not biased towards greater values  gene. In cotton, a single recessive mutation of a PPR gene reduces the heat accumulation  and served as top-ranked SNPs for growing degree days (9)  , suggestbHLH, MYB, WRKY, etc.) of transcription factors (TFs), for example, bHCH93, identified as a key player in regulating flowering  and anaphase-promoting complex (APC), both of which control cell cycle progression. Recent studies have begun to address the important role of cell cycle regulator, especially cyclins  and cyclin-dependent kinases (CDKs), in stimulus response of plants  receptor-like serine/threonine-protein kinase gene, associated with precipitation, appears to be involved in plant-pathogen interaction and developmental control  that realistically is not reachable given the seed and pollen dispersal limitations. On the other hand, the genomic offset (vulnerability) of T. hemsleyanum was generally low in the southern (trailing) edge, which is in contrast with some theoretical work (The influence of future shifts in climate across species’ distribution range is mediated by the collective potential of adapted populations to climate and/or migration in response. But few studies integrated intra-specific adaptation and migration when predicting how species responses to climate change but see . In thiscal work . These pcal work . The domcal work  that larcal work , mutual cal work . BesidesT. hemsleyanum in our predictions.Besides, our results further revealed substantial difference in the direction of migration that populations would follow to mitigate maladaptation to future climate challenges, and such pattern has been reported by several recent studies . While mT. hemsleyanum populations demonstrated maladaptation, we propose assisted gene flow strategy that involves the translocation of genotypes preadapted to future climate scenarios, particularly introducing from the moist and warm regions in southern margin of the mainland China. The genome-informed assisted gene flow can be beneficial for threatened species, as it may increase adaptive potential and alleviate inbreeding depression by introducing and increasing the frequency of adaptive alleles (Local populations vulnerable to future climate may occupy substantial unique adaptive genetic resources, thus special conservation and management efforts could be sensible as an insurance against such future genetic losses. For example, in central-north part of the range where  alleles .The reliability of genomic offsets as metrics of climate maladaptation was confirmed by recent works using data from population trend surveys and common garden experiments . But it


Cicer arietinum) was first domesticated in the Fertile Crescent about 10,000 years BP. Its subsequent diversification in Middle East, South Asia, Ethiopia, and the Western Mediterranean, however, remains obscure and cannot be resolved using only archeological and historical evidence. Moreover, chickpea has two market types: “desi” and “kabuli,” for which the geographic origin is a matter of debate.According to archaeological records, chickpea (To decipher chickpea history, we took the genetic data from 421 chickpea landraces unaffected by the green revolution and tested complex historical hypotheses of chickpea migration and admixture on two hierarchical spatial levels: within and between major regions of cultivation.For chickpea migration within regions, we developed popdisp, a Bayesian model of population dispersal from a regional representative center toward the sampling sites that considers geographical proximities between sites. This method confirmed that chickpea spreads within each geographical region along optimal geographical routes rather than by simple diffusion and estimated representative allele frequencies for each region. For chickpea migration between regions, we developed another model, migadmi, that takes allele frequencies of populations and evaluates multiple and nested admixture events. Applying this model to desi populations, we found both Indian and Middle Eastern traces in Ethiopian chickpea, suggesting the presence of a seaway from South Asia to Ethiopia. As for the origin of kabuli chickpeas, we found significant evidence for its origin from Turkey rather than Central Asia. Crop domestication is a unique form of biological coevolution with humans to establish new varieties with improved and beneficial phenotypes. Research in domestication is motivated not only by its economic and cultural importance for humans but also by solving fundamental questions, which remain an area of heated debate : For somhttps://github.com/uqrmaie1/admixtools). However, the story of the domestication of some crops goes beyond these limitations and requires the search in the admixture graph space, and new methods are needed to decipher more complex situations.Geographical origins of domestication of cultivated crop plants were first systematically described by Nikolai Vavilov , 1951. TCicer arietinum L.), a legume that serves as an essential source of high-quality protein (a), which differ in color and morphology (Сicer reticulatum and Cicer echinospermum), and kabuli was likely once selected from the local desis and then spread; however, the region of kabuli's origin is not known.One of the species with complex and partially obscure migration history is chickpea  to test the abovementioned ambiguities in chickpea's history and reconstruct migration routes of both desi and kabuli types. This historical germplasm collection contains landraces, which were collected in the 1920s–1930s, that is, before the Green Revolution, and possibly underwent less intensification of artificial selection than currently cultivated market varieties. Within the collection, we assembled a panel of 421 samples with geographical origins covering all centers of diversification and genotyped at 2,759 loci . In the The ambiguities in chickpea history cannot be resolved with conventional TreeMix  and MixMb). The first two PCs explain 72% of variance, and the first 6 PCs explain 90% of variance  demonstrated that the first principal component (PC) mostly reflects the difference between desi and kabuli subtypes; however, the separation is not strict, evincing the absence of any reproductive isolation between the subtypes b. The fiK), but the cross-validation error monotonically decreased with no minimum while increasing K from 1 to 20 . The six groups of samples obtained were almost isolated geographically; hence, we defined one-to-one correspondence between geographic regions  and genetic groups: Ethiopian, Indian, Uzbek-east, Uzbek-west, Mediterranean-and-Middle-East-north, and Mediterranean-and-Middle-East-south.To detect the underlying population structure, we used ADMIXTURE . We ran PCA plot c. The siThe compact location of admixture groups on the geography supports the presence of the strong geographical signal in genetic data. To reinforce this correspondence, we eliminate possible minor independent gene flows between regions or traces of contamination. Based on the ADMIXRURE analysis with six source populations, we filtered out samples with a contribution from any source population <60%. We considered such samples as highly mixed and not representative of any source population.Samples with disagreement between the genetic group and the geographic region were filtered out too. As a result, we worked with 294 samples; the ADMIXTURE analysis and geographical locations of the samples before filtration are shown in aSamples of two admixture groups (Mediterranean-and-Middle-East-north and Mediterranean-and-Middle-East-south) were split further into Mediterranean and Middle East, respectively. The Middle-East-north group was mostly located around the Black sea, so we called it the Black Sea population. Hierarchical clustering of the landraces based on SNP distance showed (supplementary fig. S2a).As the desi/kabuli signal was substantial in the data set, we further split accessions within each region into two groups; there are no kabulis among Ethiopian, Indian, and Uzbek-west landraces in our collection a.FST statistics (d). The similarity between desi and kabuli subpopulations within each population was higher than the similarity between different populations, which supports their separation. The PCA, ADMIXTURE, and FST results are in line with the previous study , which considers a scenario for dispersals within a geographic region and takes into account landrace-specific effects. The Bayesian hierarchical structure of the model was inspired by BayPass , mean allele frequencies in each population can be biased as the mean is sensitive to outliers. To get more robust estimates, we developed a model, “popdisp” a, which  BayPass , and the BayPass  and comp BayPass .a).We considered two scenarios for subsequent dispersal within the region. In the first scenario, dispersal within each region was sensible to the geographic landscape. As a result, the genetic relatedness in local landraces would be predicted by the geographic least-cost paths. This scenario was contrasted with simple diffusion so that genetic differences between landraces would be explained by geodesic distance. We called these two scenarios “routes” and “linear,” respectively a.Proceeding from the idea that the exact site from which chickpea dispersal began in a region is unknown and could be at any location, we inscribed the sampling sites into a rectangle, set back the 0.1 of side lengths from the boundaries, and defined 16 locations on a 4 × 4 even grid. Thus, in each region, we tested 16 origins instead of taking the 1 chosen as, for example, the ancient trade center. For each origin in the region, we considered two scenarios of dispersals (routes and linear) and took frequencies with the highest Bayesian information criterion (BIC) as the representative frequencies for the region.Sampling sites in the Mediterranean region are located on islands or around the sea; therefore, many of the least-cost paths between them pass over the water. The balance between movement costs on water and on land is challenging; hence, creating the distance matrix between samples in the Mediterranean region is difficult. Therefore, we decided to exclude populations from this region from our analysis. In contrast to the Mediterranean region, samples in the Black Sea population could be connected by the land paths. As a result, we worked with 11 populations: desi and kabuli in Turkey, Lebanese, Black Sea, and Uzbek-east regions and only desi (kabuli was not presented) in Indian, Ethiopian, and Uzbek-west regions.b). Moreover, all kabuli populations are close to their regional desis, but shifted in one direction along the first PC axis.We separately estimated allele frequencies in 11 populations under the routes and linear scenarios and discriminated between them by BIC values. In almost all cases (except the Ethiopia desi population), the route scenario was strongly favored  online. fig. 2Two alternative hypotheses exist about the chickpea colonization of Ethiopia. On the one hand, the spread of chickpea is inextricably linked with human history, and the origin of Ethiopian chickpeas can be proposed from the ethnic composition of Ethiopians. A haplotype sharing analysis points on Eurasian sources (such as Anatolian or Levant Neolithic ones) of contemporary Ethiopians , and Etha), we tested all possible origins of the Ethiopian desi population, considering it as a mixture of two or three sources. As source populations, we used all desi populations  with the known and fixed topology of phylogeny (b4), and the Black Sea population demonstrated the highest contributions to Ethiopian desi. This is consistent with the hypothetical path of chickpeas to Ethiopia from a recent study . The highest BIC value among all pairs of desi populations corresponds to the decomposition of Ethiopian desis into the Black Sea and Indian sources (fig. 3b6), the second highest to the (India + Lebanon) decomposition (fig. 37). When we tested the Ethiopian population as an admixture with three sources, the BIC value didn’t decrease comparing to the (India + Black Sea) admixture, so the Black Sea and Indian desi populations are the main contributors to Ethiopian desis. However, more than half of Ethiopian desi's variance is not represented in ancestral populations, which is in line with the previous analysis, where Ethiopia represents a distinct cluster (b–d).At the same time, the Ethiopian population demonstrated a substantial affinity to Asian chickpea varieties: The cumulative impact of Indian and Uzbek-east desi is even higher than the cumulative impact from the Middle East populations (fig. 3 cluster b–d.c) and (India + Lebanon) (d) admixtures in chromosome-wide plots and found that some chromosomal regions come either only from the Black Sea population  or only from the Lebanese population . However, most of the regions assigned to the Black Sea population in the (India + Black Sea) admixture test are classified as a Lebanese contribution in the evaluation of India and Lebanon contributions , which means that these regions have no specificity to a particular population, but rather ancestrally belong to the Middle East branch. Chromosome decomposition also shows that some blocks in Ethiopian population have only Indian ancestry : They were estimated as of Indian origin when Indian desi participated in the analysis and, as own variance, when only Middle East desis were considered as the origin (fig. 3d).We compared the (India + Black Sea) c and (InLebanon) d admixtuTreeMix results indicated Turkey and India as the origins of Ethiopian desi, while MixMapper suggests that Ethiopian desi is a mixture of desi from Black Sea desi (74%) and India (26%)  online. d). We tested it as an admixture of all pairs of the five source desi populations. Among Asian populations, the Uzbek-east desi population had the dominant effect, while among Middle Eastern populations, the input of the Lebanese desis prevails (a3). The highest BIC value among all pairs of source populations corresponds to ancestries from Uzbek-east and Lebanese populations , and the second highest BIC is pointed to admixture from Uzbek-east and Turkey . At the chromosomal level, all the blocks with ancestries from Turkey coincide with blocks inferred as originating from the Lebanese population; however, there is a number of blocks with ancestry specifically assigned to the Lebanese population .The Uzbek-west population demonstrates a distinct admixture group and a separate branch on the neighbor-joining tree d. We tesprevails a1–3. TheTreeMix, MixMapper, and axmixtools predicted that Uzbek-west desis have ancestry from the Fertile Crescent populations: TreeMix traced ancestry to the Turkish–Lebanese clade, Mixmapper suggested Lebanese ancestry 68%;  online, Based on linguistic evidence, one may hypothesize that kabulis arose in Central Asia and are named after Kabul city (in modern Afghanistan). On the other hand, the West Asian (modern Turkey) domestication of kabulis (after desis) is also possible, as kabulis are distributed in regions neighboring to Turkey and have long been thought to be modern introductions to India and Ethiopia . Althougа, c and d), and the fourth hypothesis reflected its Central Asian origin (modern Uzbekistan) with a subsequent move back to the Middle East (fig. 5b). The BIC value for the fourth model was the lowest, while the model with the highest BIC value predicted the Turkish origin of kabulis; the second highest BIC value, the Lebanese origin of kabulis; the third highest BIC value, the Black Sea origin; and the lowest BIC value, the Uzbek-east origin of kabulis.Thus, four hypotheses were examined in this study: Three of them assumed the dispersal of kabuli chickpea from Turkey's Fertile Crescent, Turkey, Black Sea, or Lebanon specifically (fig. 5a and b). The analysis of the PCA plot (b) demonstrated the shift of all kabuli populations along the first PC axis and the direction of this shift is not “toward Uzbekistan.” TreeMix analysis did not reveal significant patterns of kabuli admixture, while MixMapper indicated the same pattern as we found  and GU132941 (PsbHLH). We took these sequences, performed the TBlastN search against C. arietinum genes, and found the match with basic helix–loop–helix protein A located at the LOC101506726 gene . We performed genome-wide association studies (GWAS) for the desi–kabuli contrast using rrBLUP and found one significant SNP, which is located 4,173 bp upstream of LOC101506726 (e). Therefore, in line with previous findings (f and g) showed that this region is inherited from Turkish kabulis and not from Uzbek-east kabulis (marked with the triangle in fig. 5f and g).The most pronounced difference between the desi and kabuli chickpea subtypes is the seed color. In legumes, this trait is Mendelian and controlled by the so-called A gene . For Pis01506726 e. Thereffindings  we consifindings f and g sWe have analyzed chickpea migration and admixture hypotheses directly by testing various dispersal scenarios partially based on historical evidence. We observed that the Ethiopian desi population was derived not solely from the Fertile Crescent, but almost equally from India and the Middle East (Black Sea–Lebanon).While the BIC value supports the Ethiopian desi as the admixture of two populations from India and the Black Sea region, on a fine scale, at the chromosomal level, we found regions that are specific for Lebanese impact. Therefore, we conclude that the Ethiopian population is a mosaic of ancestry from Indian, Lebanese, and Black Sea source populations. Likewise, a chromosomal admixture pattern for west Uzbek desis  has beenAnother question we addressed was the origin of kabuli, the light-colored chickpea type, which presumably originated from a local desi population. According to the analysis we performed, this region is Turkey. In line with To test the migration and admixture hypotheses, we developed two methods. The first model is “popdisp,” which estimates the representative allele frequencies in a population under the assumption of a type of spreading within the region. The spreading type should be provided to the popdisp as the covariance matrix between sampling sites. In our study, for each region, we assumed the spread from a representative center, and the center was used to get the representative allele frequencies of a region. We also suggested the heuristic to estimate covariance matrixes for this spread type. To avoid subjectivity, within each region, we tested 16 locations (on 4 × 4 even grid) as candidates for the center and took allele frequency estimates from the model with the highest BIC value. A user can analyze simpler  or more complex spread models if they can be translated into a covariance matrix.Medicago capable of long-distance transport with livestock or wind dispersed species. For the latter, we would expect distributions to track wind currents only, with no resulting signature of dispersal along historic trade routes.We considered two reasonable migration paths within a region: diffusion with and without considering the geographical landscape. Our analyses unambiguously favor the former scenario: The genetic relatedness between accessions was predicted by the geographic least-cost paths. The least-coast paths minimize the human movement cost between two given locations on a real landscape and could reflect human trade routes. In the future, it will be interesting to apply this approach to species with different dispersal strategies, for instance, comparing crops like round-seeded chickpea to human-associated weeds like spiky-podded The second model is “migadmi,” which estimates multiple and nested admixture hypotheses with more than two sources and demonstrates the admixture patterns along the chromosomes. Both models describe changes in allele frequencies in line with Wright–Fisher drift model and utilize logit transformation as in BEDASSLE  and CoDAC. arietinum L.) consists of 421 accessions from the VIR seed bank. These accessions were genotyped by sequencing (GBS), and 56,855 segregating single nucleotide polymorphisms (SNPs) were identified. These SNPs were further filtered to meet requirements for minor allele frequency (MAF) >3% and genotype call-rate >90%. A total of 2,579 SNPs in 421 accessions passed all filtering criteria and were retained for further analysis .The least-cost path is the explanatory framework for the movement of goods in archeology . This apx)  between sites. If a binary tree presents the spread paths from the center toward sampling sites, the covariance between two sites . Testing of popdisp on simulated data is provided in the The model describing population dispersals is implemented in Python package “popdisp” (a), that describes the historical diversification of a chickpea population within a geographic region. This model is not specific for chickpea; however, we describe it for this species. We hypothesize that each geographic region contains M sites, each characterized by one landrace subpopulation and one center , where chickpea was first introduced and spread from. Each site is characterized by individuals genotyped for N unlinked (independent) biallelic SNPs; the missing data are possible and do not require imputation. Assumption of independence means that we can apply the popdisp analysis for each SNP separately. For each biallelic SNP, we randomly assign reference and alternative states, and the popdisp method is insensitive for this assignment. We pooled the data from all individuals in a site, so that, for j-th site and i-th SNP, we defined the total counts of alternative allele, j-th site due to the missing data. We assume that the frequency of the alternative allele for i-th SNP in j-th site is We developed popdisp, a Bayesian hierarchical model a, that dWithin a region, we modelled the population spread from the ancestral site (center), which is characterized by respective frequency BEDASSLE :New variable quencies .j-th site are t is a time variable. To match these first two moments after ilr transformation of allele frequencies  to get parameter estimates. For each chickpea population (specific region and market class), we ran 3 Markov chain Monte Carlo (MCMC) chains of length 50,000 and traced the Gelman–Rubin convergence diagnostic (<1.1) and effective sample size.The complexity of the model does not allow the use of Gibbs sampling. Instead, we performed the algebraic inference of derivatives for log posterior distribution and run Hamiltonian Monte Carlo sampling algorithm  in pyhmcV matrix calculated for estimated routes and linear distances. Then we compared BIC between two estimates . Testing of migadmi on simulated data is provided in the The “migadmi” model describing migrations and admP populations characterized with vectors of log-balances of allele frequencies, which are obtained from the previous analysis. We denote log-balances of allele frequencies of i-th SNP in j-th populations with To test hypothetical migration routes of chickpea between regions, we created a model based on the same assumptions as used in the model for population spread within a region. We consider V and matrix D containing variances of differences between log-balances: A migration hypothesis is set by the binary tree, in which branch lengths are parameters. Based on the migration hypothesis, we construct the parametrized covariance matrix function  online:N is a number of SNPs, X is the matrix of log-balances for all SNPs and all populations, and where Likelihood (2) contains a unique scale parameter,  uniform  online, We assumed that SNPs within each window are probably linked and had evolved with a similar rate. This assumption allows us to avoid hood see  online:j-th and k-th populations, computed on SNPs from w-th window  admixture events happened long ago and all populations (both source and mixed) accumulated their own variance after the event; 2) the number of source populations in one event is not constrained, that is, can be higher than 2; 3) several admixture events can be analyzed simultaneously; and 4) admixtures can form a hierarchy, that is, a mixed population in one admixture event can be a source in another event.y be a mixture of Q sources .Let population h zq see  online. V and D matrices based on the tree, and 3) estimated parameters maximizing the objective function (4).To test an admixture hypothesis, we 1) constructed the corresponding tree with admixture events, 2) parametrized msad110_Supplementary_DataClick here for additional data file.


Amphioctopusaegina is an economically important species that has been intensively exploited in the marine areas along the Chinese coast. However, the genetic variation and population genetic structure, which would provide valuable information for their fisheries management, have rarely been investigated. In this study, the genetic variation within and among four A.aegina populations throughout its full distribution range were estimated based on mitochondrial cytochrome b DNA sequences. Our results indicated low (Qinzhou) to high (Dongshan) genetic diversities among the four populations. Analysis of molecular variance (AMOVA), ΦST statistics, phylogenetic tree and haplotype networks revealed two significant (p < 0.01) divergent lineages with a ΦST value of 0.7116 between them, one from a population in Qinzhou and the other from the remaining three populations of Dongshan, Huizhou and Zhanjiang. However, the low genetic distance (0.0032) and only two fixed substitutions between them suggest their recent divergence is possibly due to the last glacial period barriers to gene flow produced by the Leizhou Peninsula. The observed lineage divergence suggests that populations of A.aegina in China are genetically subdivided and may represent evolutionary lineages that should be managed individually. Understanding the population genetic structure of a target fishery species is an important component of successful and sustainable management of fishery resources . DetermiAmphioctopusaegina , commonly known as the marble or sand-bird octopus , P.charcoti , Adelieledonepolymorpha     , Octopusz, 1966)  and O.mii, 1920) .A.aegina sampled from coastal waters of Dongshan (Fujian Province), Huizhou (Guangdong Province), Zhanjiang (Guangdong Province), and Qinzhou (Guangxi Province) throughout its distribution range  DNA in nge Fig.  to examiA.aegina, from coastal waters of Dongshan, Huizhou, Zhanjiang and Qinzhou  designed specifically for this locus of the octopuses according to the previous studies  using a PTC-200 . Each reaction was performed under the following conditions: 5 min initial denaturation at 94 °C, 34 cycles of 30 s at 94 °C for denaturation, 45 s at 53 °C for annealing, and 1 min at 72 °C for extension, and a final extension at 72 °C for 5 min. All sets of PCRs included a negative control reaction in which all reagents were included, except for the template DNA. The amplification products were examined on a 1.5% agarose gel and stained with ethidium bromide. The Gel Extraction Mini Kit  was used to purify the PCR products. Thereafter, the PCR products were visualized using 1.5% agarose gels and sent for sequencing to Sangon Inc., Shanghai, China. The obtained sequences were submitted to GenBank of the NCBI database and provided the accession numbers OM282997-OM283070.A 820-bp fragment of the mitochondrial cyt-b were used to examine genetic differentiation of all  studies . The PCRHd), nucleotide diversity (pi) and mean number of pairwise differences (k) and their corresponding variances, were analyzed using DnaSP 5.10.01(BI) method implemented in MrBayes software  by incorporating the sequence divergence between haplotypes, and the significance of the covariance components was tested with 1000 permutations using ARLEQUIN v. 3.5 (IBD), pairwise values of ΦST/(1-ΦST) were plotted against the geographical distance, which was measured as the shortest coastal distance between two sampling sites of A.aegina. Mantel tests were applied to determine the strength and significance of their co-relationship using reduced major axis (RMA) regression using IBDWS  has been calibrated for the mitogenome of invertebrate taxa  ranged from 0-0.8048, nucleotide diversity (pi) ranged from 0-0.0016, and the average number of nucleotide differences (k) ranged from 0–1.3429 for each population   Fig. . In line84) Fig. . The thr84) Fig. . Such diA.aegina. The largest genetic distances were observed between Qinzhou and the other three populations, and the pairwise genetic distance between the two lineages was 0.0032. Using the molecular clock of 2.15–2.60% per million years usually applied for the mitochondrial cyt-b for molluscan species, the lineage A would have diverged from the lineage B about 110 ka BP   among the four populations were plotted against the geographical distances Fig. , and ManA.aegina sampled throughout its full distribution range in China using the mitochondrial cyt-b DNA sequences. The results show that A.aegina populations in China generally possess low (Qinzhou) to high (Dongshan) genetic diversity, which is usually observed in cephalopods  of low differentiation among populations due to their reproductive strategy of producing small eggs and planktonic paralarvae which usually leads to high larval dispersal, and hence high gene flow , a signiges Fig. , which cges Fig. , the paiges Fig. , and thees Table . The genes Table , which ies Table . More spes Table  claimed A.aegina. From the sampling localities of the specimens, Dongshan and Zhanjiang (807.25 km apart) and Huizhou and Zhanjiang (472.25 km apart) are much farther apart than Qinzhou and Zhanjiang (424.44 km). However, the corresponding genetic differences among these populations show an opposite trend as revealed by pairwise ΦST values (Table A.aegina populations. The Leizhou Peninsula stands as the third largest peninsula in China, while paired with Hainan Island, the second largest island in the country. Together, they create a natural geographic barrier between the Gulf of Tonkin and the remaining South China Sea, allowing only the passage of the slender and elongated Qinzhou Strait (A.aegina populations, as had already been indicated in several other marine taxa in this region (Many factors may potentially contribute to the genetic differentiation among marine populations such as geographic isolation, the influence of ocean currents, life history features, and ancient geological events . To evales Table . This suu Strait . Such phu Strait . For thiu Strait  even recang 807.2 km apartu Strait , during u Strait . Such a s region .A.aegina, which ranges from low to moderate levels, highlights the urgent need for conservation measures to be implemented for this significant species in the fishing industry. The recent but significant genetic break existing among populations prove the necessity for regarding Qinzhou and the remaining three populations as the significant separate evolutionarily units for future conservation and management.The observed genetic diversity in


Juniperus seravschanica Kom. is a species that grows widely in the mountain ranges from Central Asia to Oman. It is an important tree for the formation of shrub–forest massifs in mountainous areas and for draining and fixing soils from middle to high altitudes. A comprehensive study of the species’ genetic diversity and population structure is a basic approach to understanding the current status of J. seravschanica resources for the development of future conservation strategies. Samples from 15 populations of J. seravschanica were collected from the mountain ranges of Uzbekistan, Kyrgyzstan, and Kazakhstan. The genetic diversity and population structure of 15 Central Asian populations of J. seravschanica were assessed using 11 polymorphic simple sequence repeat (SSR) markers. Genetic diversity parameters, including the number of alleles (na), the effective number of alleles (ne), Shannon’s information index (I), the percentage of polymorphic loci (PPL), Nei’s genetic diversity index (Nei), principal coordinate analysis (PCoA), etc., were evaluated. The analysis of 15 J. seravschanica populations based on 11 polymorphic SSRs detected 35 alleles. The average PIC value was 0.432, and the highest value (0.662) was found in the JT_40 marker. Nei’s genetic diversity index for the J. seravschanica populations was 0.450, ranging from 0.407 (population 14) to 0.566 (population 4). The analysis of molecular variance (AMOVA) showed that 90.3% of total genetic variation is distributed within the population. Using the alleles of all the populations, the gene flow (Nm) was found to be 4.654. Population structure analysis revealed poor clustering in the studied populations and confirmed our AMOVA results. The output of this work can be efficiently used for the maintenance of the species across the Central Asian region. Juniperus L., belonging to the Cupressaceae family, are common in the mountain ranges of Central Asian countries, including Kazakhstan (seven species), Kyrgyzstan (4), Tajikistan (4), and Uzbekistan (3) /4. The heterozygosity (H) and polymorphic information content (PIC) values were calculated using Gene-Calc software [The number of alleles (na), effective number of alleles (ne), Shannon’s information index (I), observed heterozygosity (Ho), expected heterozygosity (He), percentage of polymorphic loci (PPL), Nei’s genetic diversity index (Nei), principal coordinate analysis (PCoA), and the analysis of molecular variance (AMOVA) were evaluated using the GenAlEx 6.5 package . In addisoftware .J.seravschanica populations. The burn-in period and MCMC (Markov chain Monte Carlo) replications were set to 100,000, and the iteration number was set to 3. The results of the structure analysis were evaluated using the web-based program Structure Harvester [The unweighted pair-group method with arithmetic means (UPGMA) based on the genetic distance matrices was used for the reconstruction of the dendrogram of 15 populations in PAST software with 1000 bootstrap replications . The STRarvester , and thearvester  and Jakoarvester  calculatJ. seravschanica growing in the mountain ranges of four distinct Central Asian regions, including parts of Uzbekistan, South and North Kyrgyzstan, and Kazakhstan, were collected. The collected samples of 323 individuals from 15 natural populations were analyzed for genetic diversity and population structure using 11 polymorphic SSR markers. Genetic diversity results indicate that J. seravschanica in this region has a 0.450 value, with the lowest recorded value in North Kyrgyzstan (0.430) and the highest in Uzbekistan (0.473). The AMOVA results suggest that 90.3% of this genetic variation exists within populations and 9.7% between populations. The STRUCTURE outputs revealed a poor clustering in this species, confirming our AMOVA results. The Nm gene flow index was 4.654, suggesting a relatively high rate of gene flow. Population 5, collected in South Kyrgyzstan, was closest to the Zeravshan River and located in the middle of Coordinate 1 and Coordinate 2 eigenvalues of the PCoA plot, which indicates this population’s importance to the origin of this species. Accordingly, the obtained results on genetic diversity in J. seravschanica populations elucidated the populations with a high level of genetic diversity.In this study, 15 populations of


Austrochloritis kosciuszkoensis . Snails were collected in the Mt Buffalo and Alpine National Parks in Victoria, in a bid to understand how populations of this species are connected both within continuous habitat and between adjacent, yet separate environments. Utilising both mitochondrial DNA (mtDNA) and single nucleotide polymorphism (SNP) data, the degree of population structure was determined within and between sites. Very high levels of genetic divergence were found between the Mt Buffalo and Alpine snails, with no evidence for genetic exchange detected between the two regions, indicating speciation has possibly occurred between the two regions. Our analyses of the combined mtDNA and nDNA (generated from SNPs) data have revealed patterns of genetic diversity that are consistent with a history of long‐term isolation and limited connectivity. This history may be related to past cycles of changes to the climate over hundreds of thousands of years, which have, in part, caused the fragmentation of Australian forests. Within both regions, extremely limited gene flow between separate populations suggests that these land snails have very limited dispersal capabilities across existing landscape barriers, especially at Mt Buffalo: here, populations only 5 km apart from each other are genetically differentiated. The distinct genetic divergences and clearly reduced dispersal ability detected in this data explain the likely existence of at least two previously unnamed cryptic Austrochloritis species within a 30–50 km radius, and highlight the need for more concentrated efforts to understand population structure and gene flow in terrestrial invertebrates.The factors that influence population structure and connectivity are unknown for most terrestrial invertebrates but are of particular interest both for understanding the impacts of disturbance and for determining accurate levels of biodiversity and local endemism. The main objective of this study was to determine the historical patterns of genetic differentiation and contemporary gene flow in the terrestrial snail,  Austrochloritis kozsciouskoensis, to future disturbance events. Our study aimed to determine the historic patterns of genetic differentiation and gene flow of A. kozsciouskoensis populations in the Mt Buffalo and Alpine  National Parks in Victoria, and we found that there were signs of limited gene flow between populations within each region, with no evidence of genetic exchange present between the two regions. We have concluded that the Mt Buffalo and Kiewa populations likely represent individual cryptic species.We evaluated the potential response of a highly immobile native land snail species,  These a Harvey, , which i Harvey, . In Aust Harvey, . In natu Harvey, , often w Harvey, . The com Harvey, . Short‐r Harvey, . GeneticHedleyella falconeri have increased dispersal ability that may enable gene flow between populations in spite of habitat fragmentation . The genus Austrochloritis encompasses 34 described species, distributed throughout rainforests, wet sclerophyllous forests and woodlands in south‐eastern Australia  and single nucleotide polymorphism (SNP) approaches were utilised in this study. Studies using mtDNA have long been the mainstay for terrestrial SREs and have proven to be critical for assessing long‐term evolutionary patterns, such as allopatric differentiation driven by low dispersal ability in groups such as amphipods  and the neighbouring, albeit isolated, Mt Buffalo National Park . Mt Buffalo NP encompasses a mountain plateau with altitudes ranging from ~300 to ~1700 m with a variety of alpine, subalpine and mesic forest habitats distributed throughout the region. The area of the Kiewa and Ovens Rivers within the Alpine NP covers a similar range of altitudes, ~300 to ~1200 m, but offers a more continuous stretch of mesic forest habitats.Our study was undertaken on local populations of A. kosciuszkoensis were collected from moist forest habitats in Mt Buffalo NP and the adjacent regions of the Alpine NP (Taungurung and Gunaikurnai Country) along the Kiewa and Ovens Rivers , MN512673 (A. stanisici), MN512684 (A. abrotonus), and MN510584 (A. kanangra) from Genbank (https://www.ncbi.nlm.nih.gov/genbank/).Specimens of s Figure  in Victo2.2c oxidase (CO1) was selected and amplified using the primers LCO1490 and HCO2198  for Sanger sequencing. Samples were also sent to Diversity Arrays Technology (DArT) (https://www.diversityarrays.com/) in Canberra for DArT sequencing (DArTseq) , following the manufacturers' instructions. A 658 bp fragment of the Mitochondrial gene Cytochrome 2.32.3.1http://tree.bio.ed.ac.uk/software/figtree/). The Geneious addon RAxML , by running the dataset for 500,000 MCMC generations, a burn‐in of 0.10, thinning of 100 and a seed of 123 , and CLUMPAK , a visual representation of samples in genomic space, using the function ggplot. The package adegenet Jombart,  was thenA fixed difference analysis was performed with the use of the “dartR” and “pheatmap” packages  , using the functions genind2hierfstat, basic.stats, basicstats$n.ind.samp, basicstats$Ho, basicstats$Hs, basicstats$Fis, basicstats$overall and allelic.richness.Using the “hierfstat” package in R Goudet, , pairwisAn “isolation‐by‐distance” test was performed with 1000 permutations via the “ade4” package in R  between MTB sites and Kiewa sites respectively were calculated using BAYESASS 3.0.4  phylogram Figure  was spliAt the broader level, every haplotype was unique to its collection region. Forty mtDNA haplotypes were identified among 115 individuals; 24 haplotypes were unique to Mt Buffalo, 13 haplotypes were unique to Kiewa and 3 haplotypes were unique to the Ovens Figure . Interesp‐distance values between haplotypes  were evident between haplogroups . Support for this model was significantly greater than any other model of speciation tested Table , althoug3.4Based on the full SNP dataset, genetic diversity measurements Table  revealedFST values produced between these sites  than with the remaining sites, MTB4 (0.28 and 0.23) and MTB8 (0.37 and 0.33). The almost identical admixture shared between MTB1 and MTB2 suggests the presence of a single population on the southern slopes of Mt Buffalo along the Buckland River Valley. The differentiation between sample sites is further supported by isolation‐by‐distance plots  suggests that the However, the species delimitation methods here do not provide a clear answer to the number of taxa present in the sampled regions. While there is agreement that the Kiewa and Mt Buffalo populations are different taxa, many of the delimitation approaches also suggest the Harrietville/Ovens River and Mt Buffalo lineages represent different species, and the most extreme delimitation, generated by the coalescent approach, suggests that every cluster separated by Structure is a different species. These results highlight some of the inherent difficulties of delimiting species genetically ; formal analysis (lead); investigation (lead); visualization (lead); writing – original draft (lead); writing – review and editing . Orsi Decker: Conceptualization ; data curation (supporting); investigation (supporting); project administration ; resources ; supervision ; writing – original draft (supporting); writing – review and editing . Peter T. Green: Conceptualization ; funding acquisition (lead); project administration ; resources ; supervision ; writing – original draft (supporting); writing – review and editing . Frank Köhler: Conceptualization ; writing – review and editing . Adnan Moussalli: Conceptualization ; writing – review and editing . Nicholas P. Murphy: Conceptualization (lead); methodology (lead); project administration ; resources ; supervision ; writing – original draft (supporting); writing – review and editing .The authors declare no conflicts of interest.Appendix S1Click here for additional data file.