Crocodylus siamensis) and Saltwater crocodile (C. porosus) are two of the most endangered animals in Thailand. Their numbers have been reduced severely by hunting and habitat fragmentation. A reintroduction plan involving captive-bred populations that are used commercially is important and necessary as a conservation strategy to aid in the recovery of wild populations. Here, the genetic diversity and population structure of 69 individual crocodiles, mostly members of captive populations, were analyzed using both mitochondrial D-loop DNA and microsatellite markers. The overall haplotype diversity was 0.924–0.971 and the mean expected heterozygosity across 22 microsatellite loci was 0.578–0.701 for the two species. This agreed with the star-like shaped topology of the haplotype network, which suggests a high level of genetic diversity. The mean ratio of the number of alleles to the allelic range (M ratio) for the populations of both species was considerably lower than the threshold of 0.68, which was interpreted as indicative of a historical genetic bottleneck. Microsatellite markers provided evidence of introgression for three individual crocodiles, which suggest that hybridization might have occurred between C. siamensis and C. porosus. D-loop sequence analysis detected bi-directional hybridization between male and female individuals of the parent species. Therefore, identification of genetically non-hybrid and hybrid individuals is important for long-term conservation management. Relatedness values were low within the captive populations, which supported their genetic integrity and the viability of a breeding and reintroduction management plan. This work constitutes the first step in establishing an appropriate source population from a scientifically managed perspective for an in situ/ex situ conservation program and reintroduction of crocodile individuals to the wild in Thailand.The Siamese crocodile ( Crocodylus siamensis) is a freshwater species that is commonly found in swamps and sheltered portions of rivers and streams in Thailand [C. porosus) inhabits more marine environments and typically resides in saline and brackish mangrove swamps, estuaries, deltas, lagoons, and lower stretches of rivers [C. siamensis and a report from a preliminary survey of only three C. porosus observed in the wild Thai populations [in situ/ex situ management are necessary for the recovery of wild populations of C. siamensis and C. porosus, with decisions made at the national policy level. A large number of C. siamensis individuals are maintained in 12 commercial captive facilities under the auspices of the Crocodile Co-operatives of Thailand and CITES regulations for the leather and food industries, whereas C. porosus has been managed as a captive population without commercial use. However, no new individuals have been introduced into these captive populations subsequent to their establishment and no historical information on the captive populations is available, which might lead to the loss of genetic variation. This might consequently hamper the adaptability of a captive-bred population to a new environment, the long-term sustainability of the population, and increase the probability of species extinction [in situ/ex situ management.The Siamese crocodile  and the American crocodile (C. acutus) and between the Cuban crocodile (C. rhombifer) and American crocodile [Crocodylus anthropogenic hybrids are a serious problem in the context of conservation management because the hybrids are highly similar in morphology to the parental species and might lead to introgression if included in a reintroduction program. Consequently, a genetic tool must be developed to identify and exclude hybrids before reintroduction.Conservation management of animal wildlife is impacted by hybridization, which can result from the possible invasion of one species into the habitat of another or involuntary mixing of species in the same farm. Such hybridization might lead to the genetic extinction of the species . Hybridirocodile –15. Hybrrocodile –20. MostC. siamensis and C. porosus, the C. siamensis captive-bred population is currently considered the most important source of individuals for a reintroduction program. The captive crocodile populations were derived from twelve major crocodile farms. These distribute crocodiles to sub-farms throughout the country to increase the Thai captive crocodile population. However, the present lack of genetic information impedes and undermines establishment of an effective long-term conservation management plan. In the study described herein, we examined genetic diversity and relatedness between captive-bred populations of C. siamensis and C. porosus, as well as wild C. siamensis individuals in the Boraphet Wetland Wildlife Reserve. We used mtDNA D-loop and microsatellite genotyping to assess the diversity of the gene pool to guide reintroductions to the wild and the formulation of in situ/ex situ management recommendations. The admixture between C. siamensis and C. porosus in captive populations is also discussed.Assessment of genetic diversity in natural and captive populations is an important step to understand population structure, history, and hybrid status better when developing breeding programs for conservation management of threatened species –23. MoleC. siamensis and 17 adult C. porosus from 12 captive populations in nine provinces of Thailand . Permission was granted by the farm owners and the Crocodile Co-operatives of Thailand. Five C. siamensis were captured from the Boraphet Wetland Wildlife Reserve , and all crocodiles were released immediately in the same area after sample collection. This research was conducted under the authority of the Department of Fisheries, Ministry of Agriculture and Cooperatives, Thailand. Detailed information on the sampled individuals is presented in C. siamensis or C. porosus on the basis of morphology [Samples were collected from 47 adult rphology ,27 and mrphology . A piecerphology . Whole g5′-TGCCATGTTCGCATCCATCC-3′) and mt12srRNAr2 (5′-CCAGAGGCTAGGCGTCGTGG-3′), which were designed based on five crocodilian mtDNA sequences: C. siamensis (GenBank accession number: EF581857), C. porosus (GenBank accession number: AJ810453), C. niloticus (GenBank accession number: AJ810452), Alligator mississippiensis (GenBank accession number: Y13113), and Gavialis gangeticus (GenBank accession number: AB079596). PCR amplification was performed using 15 μl of 1× ThermoPol buffer that contained 1.5 mM MgCl2, 0.2 mM dNTPs, 5.0 μM primers, 0.5 U of Taq polymerase , and 25 ng of genomic DNA. The PCR conditions were as follows: initial denaturation at 94°C for 3 min, followed by 40 cycles of 94°C for 30 s, 55°C for 30 s, and 72°C for 1 min 30 s, and then a final extension at 72°C for 5 min. The PCR products were detected by electrophoresis on 1% agarose gels. The PCR products were cloned using the pTG19-T vector . The nucleotide sequences of the DNA fragments were determined by the DNA sequencing service of First Base Laboratories Sdn Bhd . The BLASTn and BLASTx programs (http://blast.ncbi.nlm.nih.gov/Blast.cgi) were used to search nucleotide sequences in the National Center for Biotechnology Information (NCBI) database to confirm the identity of the DNA fragments amplified in the present study. The sequences generated in this study were deposited in the DNA Data Bank of Japan (DDBJ)  .C. siamensis and 17 for C. porosus individuals), 20 sequences from other crocodile species available in the GenBank database, and one sequence from a turtle Pelodiscus sinensis (GenBank accession number: AY687385) as the outgroup, using the default parameters of the Molecular Evolutionary Genetics Analysis 6 (MEGA6) software  and nucleotide (π) diversity [D [D* and F* tests [Fs [Multiple sequence alignment was performed for 69 sequences generated in this study . All university  were caliversity . Tests orsity [D  and Fu aF* tests , were peests [Fs  was calcests [Fs . Signifiests [Fs . The Marests [Fs .C. siamensis and C. porosus. Most of the microsatellite loci were located in different linkage groups of the Saltwater crocodile genome. PCR amplification was performed using 15 μl of 1× ThermoPol buffer that contained 1.5 mM MgCl2, 0.2 mM dNTPs, 5.0 μM primers, 0.5 U of Taq polymerase , and 25 ng of genomic DNA. The PCR conditions were as follows: initial denaturation at 95°C for 3 min; followed by four cycles of 95°C for 20 s, 65°C for 20 s, and 72°C for 30 s; followed by four cycles of 95°C for 20 s, 62°C for 20 s, and 72°C for 30 s; followed by eight cycles of 95°C for 20 s, 60°C for 20 s, and 72°C for 30 s; followed by 24 cycles of 95°C for 20 s, 55°C for 20 s, and 72°C for 30 s; and a final extension at 72°C for 7 min. The PCR products were separated on a 6% denaturing polyacrylamide gel (w/v) and visualized by silver staining as described previously [http://www.gelanalyzer.com/index.html). DNA fragments were extracted from the silver-stained gels and cloned into the pTG19-T vector (Vivantis Technologies). Nucleotide sequencing was carried out to confirm the identity of the DNA fragments amplified in the present study. Nucleotide sequences of all microsatellite loci for C. siamensis and C. porosus were deposited in DDBJ , expected heterozygosity (He), Hardy–Weinberg equilibrium, and linkage disequilibrium were calculated for each locus and for each captive population using Arlequin version 3.5.2.2. Shannon’s information index (I) was calculated for each locus for each species using GENALEX version 6.5 [FST) was estimated to determine pairwise population differentiation with corrected P values using Arlequin version 3.5.2.2. The state of heterozygosity excess and shift in allelic frequency distributions in genetically bottlenecked populations was tested using BOTTLENECK version 1.2.02 [M ratio test [M ratio is the mean number of alleles in a population divided by the allelic size range, and can indicate reductions in both recent and historical population sizes. Phylogenetic analysis of all microsatellite loci was performed using the unweighted pair group with arithmetic mean (UPGMA) clustering method using NTSYSpc version 2.1 . Jaccard’s coefficient was used to estimate genetic similarity [r (very good fit); 0.8 ≤ r < 0.9 (good fit); 0.7 ≤ r < 0.8 (poor fit); r < 0.7 (very poor fit). Principal component analysis (PCA) was performed to visualize the overall relationship across individuals in the populations using GENALEX version 6.5.Allelic frequency, number of specific alleles, observed heterozygosity (sion 6.5 . Polymorsion 6.5 . Reductin 1.2.02 . The Wiln 1.2.02 . This tetio test  was perfmilarity . Bootstrmilarity . Mantel’K) was varied from 1 to 28, with 25 replicates for each value of K. The most likely number of clusters was determined by plotting the log probability of the data (ln Pr(X|K) [K values tested and selecting K at which the value of ln Pr(X|K) stabilized. The ΔK method [r) [The model-based clustering method implemented in STRUCTURE version 2.3.3  was used Pr(X|K)  across tK method  was applK method . In addithod [r)  were estthod [r)  as implethod [r) .C. siamensis and C. porosus D-loop into 263 bp in length, which corresponded to the fragment at positions 16,525 to 16,807 bp of the C. siamensis mitochondrial genome (GenBank accession number: EF581859), were compiled into data sets for C. siamensis and C. porosus. This sequence region was located in D-loop domain III, which is commonly used for the analysis of genetic diversity and population structure [C. siamensis data set and 41 polymorphic sites (comprising 22 transitions and 19 transversions) in the C. porosus data set. The number of haplotypes was 35 for C. siamensis and 14 for C. porosus. The overall haplotype and nucleotide diversities were 0.924 ± 0.031 and 0.031 ± 0.009, respectively, for C. siamensis and 0.971 ± 0.032 and 0.021 ± 0.006, respectively, for C. porosus  differed from shared nucleotide sequences of the haplotype of the mitochondrial genome of C. siamensis (GenBank accession number: EF581859) that had been described previously [C. siamensis (CSI38) was distinguished from CSI44 by one mutational step. However, one haplotype (CSI17) was distinguished from CSI44 by at least 50 mutational steps with a missing haplotype. The most common haplotype of C. porosus differed from the haplotype of the mitochondrial genome of C. porosus (GenBank accession number: DQ273698) that had been described previously [C. siamensis and C. porosus, together with those for 20 crocodile species obtained from the GenBank database, indicated that most sequences of C. siamensis and all sequences of C. porosus each formed a monophyletic clade. However, CSI17 was placed as a sister clade to C. rhombifer (GenBank accession number: NC_024513)  .D , Fu’s Fs , Fu and Li’s F* , and Fu and Li’s D*  were all negative and significant for the D-loop sequences of C. siamensis. Similarly, Tajima’s D , Fu’s Fs , Fu and Li’s F* , and Fu and Li’s D*  were all negative and significant for the mtDNA D-loop sequences of C. porosus.Tajima’s C. siamensis and 82 private alleles in C. porosus, were detected among all loci, with a mean number of alleles per locus of 13.59  to 1.000 (CpP4501) (mean 0.486 ± 0.306) and He values ranged from 0.038 (CpP3008) to 0.949 (CpP501) (mean 0.578 ± 0.323). The Ho values of C. porosus ranged from 0.059 (CpP3001) to 0.941 (CpP1409) (mean 0.591 ± 0.253) and He values ranged from 0.059 (CpP3001) to 0.939 (CpP501) (mean 0.701 ± 0.221) , and 0.999 and 0.996, respectively, in C. porosus  as determined by Wilcoxon sign-rank tests   Tables. division  Tables. nk tests . Althoug porosus . These M) mean 0.6 ± 0.306C. siamensis and C. porosus) (r = 0.959). The C. porosus group comprised 16 C. porosus individuals and two C. siamensis individuals (CSI05 and CSI06), whereas the C. siamensis group contained 50 C. siamensis individuals and one C. porosus individual (CPO09)  . The cop (CPO09) . PCA rev (CPO09) .K = 2) on the basis of Evanno's ΔK  and one C. porosus individual (CPO09) showed high estimated levels of admixture, whereas low levels of admixture were estimated in CPO14, CSI08, CSI17, CSI18, CSI26, and CSI41. By contrast, STRUCTURE analysis based on the mean ln P(K) revealed one peak (K = 13)  of 2,346 pairs of crocodiles among the 69 sampled crocodiles were examined, and the mean pairwise value was −0.009. A total of 1,449 pairs showed r < 0; there were 892 pairs with 0.5 < r < 0; and five pairs with 1< r < 0.5  , which p(K = 13) . Clusterpulation . The rel r < 0.5 . The mea r < 0.5 .C. siamensis constitute 1% of the agricultural income in Thailand [in situ/ex situ management are sustainable. The first step toward establishment of a captive breeding and reintroduction program is the evaluation of genetic diversity based on analysis of mtDNA D-loop sequences and microsatellite markers as an indicator of the genetic status of captive crocodile populations in the main commercial farms.The meat, clothing, and medicinal products that are derived from Thailand . CrocodiC. porosus and C. siamensis. This finding was probably attributable to the high haplotype heterogeneity in the study population. Concomitantly, microsatellite analysis showed that the mean Ho and He were 0.486 and 0.578, respectively, for C. siamensis and 0.591 and 0.701, respectively, for C. porosus. These values are similar to those of the Siamese crocodile population in Vietnam (Ho = 0.416 and He = 0.420) [Ho = 0.570 and He = 0.575) [C. siamensis. FST values were 0.073–0.240 (P < 0.05) from 50 pairwise captive/wild population comparisons in C. siamensis and 0.200–0.356 (P < 0.05) from five pairwise captive population comparisons in C. porosus, which might indicate high heterozygosity. This suggests that captive-bred Thai populations of both C. siamensis and C. porosus can be recommended for use in breeding programs and reintroduction plans.Although nucleotide diversity was low, analysis of mtDNA D-loop sequences indicated that haplotype diversity was relatively high in the sampled individuals of = 0.420)  and the C. siamensis haplotype group. The most common haplotype (CSI44) was closely related to the haplotype of three of the five individuals  that were sampled from the Boraphet Wetland Wildlife Reserve. Many unique haplotypes were also detected, which suggests that populations of C. siamensis had been isolated from each other. The star-like shaped topology of the haplotype network of C. siamensis suggests a high level of genetic heterogeneity. A similar pattern was observed for the C. porosus haplotype network, although wild individuals were not available for study. This star-like network might also imply the presence of a population bottleneck followed by a population expansion. Significant negative values for neutrality statistics also indicate population expansion [M ratio showed a signal for historical population reduction for both C. siamensis and C. porosus. Collectively, these results suggest that these captive crocodile populations underwent a recent population expansion. Crocodile hunting to collect individuals has occurred over the last 50 years [C. siamensis and C. porosus showed high haplotype heterogeneity. This suggests that individuals from various historically distinct lineages in the wild might have contributed to the established populations, leading to the presence of individuals with high levels of admixture. Range expansion and high haplotype diversity were aided by intentional human introduction in each captive population.Network analyses revealed the existence of four different haplotype groups, of which two were in the xpansion . This fi50 years ,61, with50 years . HoweverC. siamensis and C. porosus haplotype groups, which determined that the two crocodile species were genetically distinct [C. siamensis haplotypes. This finding corresponded with the results of the microsatellite phylogenetic analysis in which CSI17 was not placed in the cluster that contained most C. siamensis individuals. Cuban crocodiles have also been identified in captive breeding programs in Cambodia and Vietnam [C. rhombifer, if not a pure Cuban crocodile.A missing haplotype was detected between most distinct ,63. Howe Vietnam ,64, and  Vietnam ,65. The C. siamensis and C. porosus. Cluster analysis using the STRUCTURE software can help to determine the degree of hybridization by aggregating individuals into a single cluster relative to additional highly differentiated populations/species [C. siamensis and C. porosus individuals, respectively, and possessed a high proportion of private alleles. By contrast, CSI06 clustered with C. porosus. Observations on captive crocodiles suggest that bi-directional hybridization occurs between C. siamensis and C. porosus [C. porosus, which suggests that hybridization probably occurred between a female C. porosus and a male C. siamensis. By contrast, CSI05 and CSI06 shared haplotypes with C. siamensis, which suggest that they are descended from a female C. siamensis and a male C. porosus.The present microsatellite phylogenetic analyses and PCA indicated that three individuals  might be the result of interspecific hybridization between /species ,51. Usin/species , the thr porosus . This coCrocodylus [C. siamensis and C. porosus are always observed in the presence of anthropogenic impacts. This might reflect behavioral and geographic differences between the two species [Natural hybridization often occurs in the genus ocodylus ,19. Howe species ,12,15,19 species ,19. Hybr species ,19. This species . This scC. siamensis and C. porosus are genetically divergent, with partial introgression and hybrids between the two species. An alternative approach is required to identify hybrid or non-hybrid individuals in addition to karyotyping [1, F2, or backcross generations. The costs of molecular analytic methods have decreased greatly and the use of such procedures should be a prerequisite for breeding management and the establishment of a reintroduction program, rather than a karyological analysis approach. It is strongly recommended that researchers conduct routine molecular analyses to identify individuals of hybrid origin.The present D-loop and microsatellite data indicate that the captive populations of yotyping . A largeC. siamensis and 0.334 for C. porosus, and a high level of mean relatedness was indicated , implying that the samples were not closely related [C. porosus and C. siamensis were founded initially by individuals of unknown ancestry. Mate pairings within representative captive crocodile populations might be determined to aid breeding and reintroduction programs. In general, breeding that is based on the minimization of relatedness generates maximum genetic diversity [C. siamensis and C. porosus in Thailand have proceeded in the absence of genetic diversity data, and have relied solely on morphological, ethological, demographic, and logistic information for the implementation of short-term management strategies [in situ/ex situ management decisions. The populations of both species exhibited high heterozygosity, which could be an indication of the sound establishment of the captive species populations. Specifically, 10 individuals of C. siamensis (19.231%) comprising six males and four females, and seven individuals of C. porosus (43.750%) consisting of four males and three females, were highlighted as genetically important for conservation and management, because they showed values of relatedness that were lower than that of the overall captive population. This provides flexibility in the implementation of breeding and reintroduction programs. However, one possible concern for reintroduction is the likelihood of disrupting locally adapted genetic materials through outbreeding among different source populations [in situ/ex situ conservation and reintroduction programs in Thailand. In addition, the present results provide reference data for further characterization of cryptic diversity, which directly impacts on the conservation prioritization of C. siamensis and C. porosus outside Thailand, such as in Southeast Asia.Inbreeding has negative impacts on both reproduction and survival ,68. The  related . These riversity . Prior trategies ,70. The ulations . This reulations . Althougin situ/ex situ management will aid the management of subsequent generations and maintain sustainable genetic diversity for long-term survival of the population. Therefore, it is highly recommended that the genetic status of C. siamensis and C. porosus populations are closely monitored using molecular genetic methods to improve in situ/ex situ management. We are also convinced that the future conservation management of C. siamensis will require the genetic identification of non-hybrid wild populations. Additional studies involving genome-wide scans will be required for an improved understanding of overall gene functions in different populations.The results of the present study indicate the status of genetic diversity in the extant Thai captive crocodile population. Accurate information on captive populations for breeding programs, reintroduction, or S1 FigNumbers indicate sample locality. Detailed information for all crocodile individuals is presented in (JPG)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 TableP < 0.001, ** P < 0.02, * P < 0.01, and ns = not significant.*** (DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 TableP values, with 110 permutations.The number indicates (DOCX)Click here for additional data file.S7 TableP values, with 110 permutations.The number indicates (DOCX)Click here for additional data file.S8 TableDetailed information for all crocodile individuals is presented in (DOCX)Click here for additional data file.S9 TableP values, with 110 permutations. Detailed information for all crocodile individuals is presented in The number indicates (DOCX)Click here for additional data file.S10 TableP values, with 110 permutations. Detailed information for all crocodile individuals is presented in The number indicates (DOCX)Click here for additional data file.S11 TableDetailed information for all crocodile individuals is presented in (DOCX)Click here for additional data file.S12 TableDetailed information for all crocodile individuals is presented in (DOCX)Click here for additional data file.S13 TableDetailed information for all crocodile individuals is presented in (DOCX)Click here for additional data file.


Triplostegia to elucidate the disjunction between the Himalaya–Hengduan Mountain region (HHM) and Taiwan (TW). Genetic structure of Triplostegia was investigated for 48 populations using sequences from five chloroplast loci and the ribosomal nuclear internal transcribed spacer. Divergence time estimation, ancestral area reconstruction, and species distribution modeling (SDM) were employed to examine the biogeographic history of Triplostegia. Substantial genetic differentiation among populations from southwestern China (SW), Central China (CC), and TW was detected. Triplostegia was inferred to have originated in SW, and diversification began during the late Miocene; CC was colonized in the mid‐Pliocene, and TW was finally colonized in the early Pleistocene. SDM suggested an expansion of climatically suitable areas during the Last Glacial Maximum and range contraction during the Last interglacial in Triplostegia. Disjunction between HHM and TW in Triplostegia is most likely the consequence of topographic isolation and postglacial contraction. The potential climatic suitability areas for Triplostegia by 2070s (2061–2080) are predicted to slightly shrink and move northward. With continued global warming and human‐induced deforestation, extinction risk may increase for the cold‐adapted species, and appropriate strategies should be employed for ecosystem conservation.Current disjunct patterns can result from long‐distance dispersal or postglacial contraction. We herein investigate the evolutionary history of  Generally, 10–12 individuals were arbitrarily selected from a population, whereas three to six individuals were collected from populations with few individuals  and ITS were amplified with primers described in Baldwin (http://www.biomed168.com/goods). The forward and reverse sequences were assembled in GENEIOUS 9.1.4 . Five cpDNA regions , and nucleotide diversity (π) were calculated in DNASP 5.0.0 , total gene diversity (HT), and the coefficients of differentiation  were estimated using PERMUT 1.0.0  that are geographically homogeneous and maximally differentiated from each other. To maximize the proportion of total genetic variance observed between groups (ΦCT), 1,000 iterations were run for K =  from each of 100 random initial conditions. To further quantify genetic differentiation among populations and groups, the hierarchical analysis of molecular variance (AMOVA) was estimated in ARLEQUIN 3.5.1  and Harpending's raggedness index (HRag) was tested using 1,000 parametric bootstrap replicates. If the expansion model was not rejected, the formula t = τ/2u was used to estimate the expansion time (t), where u = μkg . In addition, statistical significance of population expansion was further examined with two neutrality tests, Tajima's D , Bayesian inference (BI), and neighbor‐joining (NJ) methods, with gaps treated as missing data. The ML analysis was conducted using RAxML 8.2.8  for 41 Dipsacales species (including five Adoxaceae species as out‐groups) from GenBank  of 70.94 ± 11 Ma  following Moore and Donoghue  following Wang, Landrein et al.  following Bell and Donoghue ; Central China ; and Taiwan . SW and CC were defined as two separate regions based on their unique haplotypes and altitudinal gradients, despite their geographical proximity. Triplostegia distribution was inferred based on the specimen information from the Chinese Virtual Herbarium , Global Biodiversity Information Facility , and Specimens Database of Native Plants in Taiwan . The out‐group (Dipsacus asper) is found in both SW and CC, so we set its distribution as SW/CC. The number of maximum areas at each node was set to three. To account for phylogenetic uncertainty, 1,000 trees from BEAST were randomly chosen for BBM analysis. We set the root distribution to null, applied 10 MCMC chains with the JC + G model, and sampled the posterior distribution every 100 generations for 106 generations.We used the Bayesian Binary MCMC (BBM) method implemented in RASP 3.0.0 to estimate the possible ancestral areas of 2.8Triplostegia  to predict past, current, and future climatically suitable areas for http://www.worldclim.org; Hijmans, Cameron, Parra, Jones, & Jarvis, R| ≥ .85, and selected nine variables as climatic predictors to model the past, current, and future climatically suitable areas of Triplostegia . To model the paleoclimatic distribution of Triplostegia, we used three global climate models  for the Last Glacial Maximum , but only considered NCAR‐CCSM for the Last interglacial  because it is the sole GCM available in the database of WorldClim. To predict the future climatically suitable areas of Triplostegia in 2050s (average for 2041–2060) and 2070s (average for 2061–2080), we used three GCMs  and two greenhouse gas concentration scenarios as representative concentration pathways (RCPs) of 4.5  and 8.5 , representing the low and high gas concentration scenarios, respectively . RCP 8.5 may have larger cumulative concentrations of carbon dioxide than RCP 4.5, resulting in a different pattern of future climate change in response to various anthropogenic concentrations of greenhouse gases and other pollutants (http://www.ipcc.ch/report/ar5/).Current distribution of http://www.esri.com/software/arcgis/arcgis-for-desktop) and Google Earth  and omitted entries with implausible geographical coordinates using ArcGIS 10.2  ten independent replicates of cross‐validation procedures for model testing; (4) the maximum number of background points was set to 104; and (5) jackknife analyses of the regularized gain using training data were conducted to elucidate the importance of each predictor .As suggested by previous studies  curve (AUC). AUC was used to assess the accuracy of predictions, and models with AUC values larger than 0.7 were considered useful for our study , which indicates strong phylogeographic structure for the Triplostegia cpDNA haplotypes. The highest levels of cpDNA sequence diversity (π) were recorded within the SW group and the lowest levels in the CC group , and the lowest between CC and TW (FCT = 0.68***).We obtained 2,812 bp of cpDNA sequences with 430 bp of es Table  and threly Table . HT andup Table . DiffereHd and π for ITS were estimated to be 0.67 and 0.011, respectively , and among all populations . Differences in ITS sequences were significant between each pair of geographical regions (Table FCT = 0.91***) and the lowest between CC and TW (FCT = 0.84***). Analysis of the ITS variation across all populations revealed that the total gene diversity (HT = 0.67) was much higher than the within‐population diversity (HS = 0.025). The coefficients of differentiation measured over the 48 populations were GST = 0.96 and NST = 0.99. Permutation test revealed that NST was significantly higher than GST (p < .05), which indicated a strong phylogeographic structure for ITS haplotypes. SAMOVA indicated that values of ΦCT reached a plateau at 0.88 for three groups . In addition, Tajima's D and Fu's FS values of cpDNA and ITS sequences were all negative in SW, which further indicated that populations in SW experienced sudden expansion , AMOVA  and contain additional derived and unique haplotypes . The SW origin of Triplostegia is further supported by our phylogenetic reconstruction based on nine complete chloroplast genomes  that represented populations from SW, CC, and TW, which strongly supported  initial divergence of the SW clade, and CC and TW populations grouped together  and/or islands (TW). Therefore, conservation actions are needed to protect these taxa, which are sensitive to climate change and habitat disturbance.Species distribution modeling only uses species occurrence data and environmental variables to predict the potential geographical distribution of species . Ancestral area reconstruction revealed that Triplostegia originated from SW, and diversification began during the late Miocene as a result of global cooling and uplift of the Hengduan Mts., then colonized CC in the mid‐Pliocene when global temperature further decreased, and finally dispersed to TW by land bridge at c. 2.15 Ma. Based on phylogeographic and SDM analyses, frequent gene flow might have existed between CC and TW populations with repeated continued distribution between two regions during the Pleistocene glacial cycles. Nevertheless, biotic exchange between SW and CC might have been isolated by altitudinal gradients triggered by dramatic physiographic changes in HHM. Our research represents the first comprehensive phylogeographic study on herbaceous plants to investigate the HHM‐TW disjunction and supports that postglacial range contraction together with topographic heterogeneity resulted in the HHM‐TW disjunction.In this study, we explored how geology and climate together influenced the evolutionary history of None declared.L.‐M.L., J.‐F.Y., and Z.‐D.C. planned and designed the research. Y.‐T.N. performed experiments. Y.‐T.N., J.‐Z.W., and T.Y. analyzed the data. Z.‐D.C., J.‐F.Y., L.‐M.L., and T.Y. conducted the field work. L.‐M.L., Z.‐D.C., Y.‐T.N., J.‐L.Z., X.‐X.W., and J.‐H.L. wrote the manuscript.MF737219–MF737340).All haplotype sequences identified in this study were deposited in GenBank (accession numbers  Click here for additional data file.


The history of human populations occupying the plains and mountain ridges separating Europe from Asia has been eventful, as these natural obstacles were crossed westward by multiple waves of Turkic and Uralic-speaking migrants as well as eastward by Europeans. Unfortunately, the material records of history of this region are not dense enough to reconstruct details of population history. These considerations stimulate growing interest to obtain a genetic picture of the demographic history of migrations and admixture in Northern Eurasia.We genotyped and analyzed 1076 individuals from 30 populations with geographical coverage spanning from Baltic Sea to Baikal Lake. Our dense sampling allowed us to describe in detail the population structure, provide insight into genomic history of numerous European and Asian populations, and significantly increase quantity of genetic data available for modern populations in region of North Eurasia. Our study doubles the amount of genome-wide profiles available for this region.We detected unusually high amount of shared identical-by-descent (IBD) genomic segments between several Siberian populations, such as Khanty and Ket, providing evidence of genetic relatedness across vast geographic distances and between speakers of different language families. Additionally, we observed excessive IBD sharing between Khanty and Bashkir, a group of Turkic speakers from Southern Urals region. While adding some weight to the “Finno-Ugric” origin of Bashkir, our studies highlighted that the Bashkir genepool lacks the main “core”, being a multi-layered amalgamation of Turkic, Ugric, Finnish and Indo-European contributions, which points at intricacy of genetic interface between Turkic and Uralic populations. Comparison of the genetic structure of Siberian ethnicities and the geography of the region they inhabit point at existence of the “Great Siberian Vortex” directing genetic exchanges in populations across the Siberian part of Asia.Slavic speakers of Eastern Europe are, in general, very similar in their genetic composition. Ukrainians, Belarusians and Russians have almost identical proportions of Caucasus and Northern European components and have virtually no Asian influence. We capitalized on wide geographic span of our sampling to address intriguing question about the place of origin of Russian Starovers, an enigmatic Eastern Orthodox Old Believers religious group relocated to Siberia in seventeenth century. A comparative reAdmix analysis, complemented by IBD sharing, placed their roots in the region of the Northern European Plain, occupied by North Russians and Finno-Ugric Komi and Karelian people. Russians from Novosibirsk and Russian Starover exhibit ancestral proportions close to that of European Eastern Slavs, however, they also include between five to 10 % of Central Siberian ancestry, not present at this level in their European counterparts.Our project has patched the hole in the genetic map of Eurasia: we demonstrated complexity of genetic structure of Northern Eurasians, existence of East-West and North-South genetic gradients, and assessed different inputs of ancient populations into modern populations.The online version of this article (10.1186/s12863-017-0578-3) contains supplementary material, which is available to authorized users. The phenotypic diversity of modern humans was shaped under the combined pressure of environment and social relations. Placing the studies of human genetic variation into a geographical context provides powerful insights into how historical events, patterns of migration, and natural selection have led to genetic distinctions between various present-day populations , 2. MoreWhile human genetic diversity has been sampled extensively in many areas of the globe –6, a sizHere we present high-quality genome-wide analysis of 30 diverse populations from Russia and neighbouring countries  were collected in course of study expeditions into different parts of Russia, Kazakhstan, Georgia, Uzbekistan and Kyrgyzstan. Samples were genotyped on the Illumina Infinium 370-Duo, 370-Quad, or 610-Quad arrays . The number of samples per population, source of the samples, and the type of microarray used are given in the Table DNA samples N = 076 were X chromosome or female samples with ≤20% heterozygous X chromosome markers. Across retained samples, 95% cut-off for SNP presence was imposed. .Geographic locations of the sampled populations are presented in the Fig. To provide a uniform representation of ethnic diversity in Russia we sampled broadly over the country see Fig.  and adjaThe “Extended” dataset includes all individuals genotyped in this study combined with selected previously published modern and ancient samples from Northern Eurasia, which extend geographic span of our study and provide necessary populational context for our analyses , the samples from the Volga-Ural region exhibit substantial admixture of European and Asian components . Importantly, for each of the populations, the ratio between components was characteristic of that population  samples - being genetically close to Slavs residing in Europe, also have between five to 10 % of Central Siberian ancestry (light blue).Asian genetic ancestry of the profiled populations is represented by four components. Two of them, light green (Beringian) and dark blue (East Siberian), are geographically confined to Northeast Asia, while showing only minor impact on populations west of Ural Mountains. On the contrary, East Asian (pink) and Central Siberian (light blue) components are also present in populations to the west of Ural.In Central Asian Turkic speakers, including Kazakh and Uzbek, East Asian genetic influence is dominant (>35%), while in Bashkir it is detected at somewhat lower levels (~ 20%). Importantly, in Western Turkic speakers, like Chuvash and Volga Tatar, the East Asian component was detected only in low amounts (~ 5%).The light blue genetic component dominates genetic landscape of populations inhabiting West and Central Siberia: Ugric-speaking Khanty and Mansi, Samoyedic speaking Selkups and linguistically isolated Ket. However, this ancestry component is present not only in Siberia, but also on the western side of Ural Mountains, though at somewhat lower frequencies - 20-30% in Komi (16% on average) and Udmurt (27% on average) who belong to the Permic branch of Uralic languages. Interestingly, similar levels of this ancestry component (16–23%) are also exhibited by Turkic speaking Chuvash (20% on average) and Bashkir (17% on average), while Tatar, who also reside in the Volga region and have related linguistic and cultural profiles, only show at most 15% (10% on average) of this genetic component. Even lower levels of this ancestry component (<5%) were observed in Turkic speakers of Central Asia.The Beringian component (light green) is confined exclusive to indigenous populations of Eskimo, Chukchi and Koryak. The East Siberian (dark blue) component is represented by Turkic and Samoyedic speakers of Central Siberian plateau: Yakut, Dolgan and Nganasan. This component is also found at moderate frequencies in Mongolic and Turkic speakers in Baikal region and Central Asia (5–15%), and, at low but discernible frequencies (1–5%), in Turkic speakers residing in Volga-Ural region.in lieu of the formal test of admixture, three-population test [f3 test of admixture, to find out whether a target population is admixed between two source populations. The combinations of source and target populations and the Z score in the f3 test are reported in Additional file ADMIXTURE-guided ancestry clustering suggests that, in most of the populations studied, the genetic background is complex, as it includes at least three hypothetical ancestral components: European, East Asian and North Asian (Siberian). Because the ADMIXTURE-guided ancestry clustering cannot be used ion test  was condAdmixture scenarios were tested in sampled populations geographically grouped as Northern Europe, Volga-Ural region and Central Asia and, while assuming two-way mixture between European and East Asian, or between European and Central Siberian surrogate populations. Significant Z scores (Z < −3) for admixture between East European  and either East Asian  or Central Siberian  populations were obtained for all populations of Northern Europe and Volga-Ural region.f3 (and the corresponding Z score) is negative if X is a mixture of A and B. Among all pairs compared, the most negative f3 values were obtained for populations of Volga-Ural region using East Slavs and Siberians as source groups .Populations from Central Asia also received significant negative To identify shared identical-by-descent (IBD) blocks, we used the fastIBD algorithm implemented in the Beagle package . We firsThe Volga-Ural region is populated by three major language and cultural groups: Uralic, Turkic and Slavic speakers. Bashkir and Tatar are major Turkic groups in the region. Although both ethnic groups live in the same region and their languages are mutually intelligible, we surprisingly detected only a limited amount of ancient IBD blocks shared between them, and their overall IBD sharing pattern is different: Tatar share moderate amount of IBD (3.55–7.35 cM per pair) with all neighbouring populations, while Bashkir share most of their ancient blocks (on average 8.62 cM per pair) with Khanty, a group of Uralic speakers from Western Siberia. We speculate that this disparity between cultural and genetic affinities of Tatar and Bashkir can be attributed to a phenomenon of cultural dominance: the population ancestral to Bashkir adopted the Turkic language during Turkic expansion from the east (language replacement event)..European and Siberian Uralic speakers are separated by the Ural Mountain range. This separation affects sharing of recent IBD blocks: Komi and Udmurt share in the interval of 4–10 cM almost double the amount of IBD they share with Khanty. Interestingly, the situation is different when we look at sharing of ancient IBD blocks: the amount of ancient IBD blocks that Udmurt and Komi share with Khanty  is comparable to the amount of ancient IBD shared between them (11.38 cM per pair). These data agree well with the ethnic history of European and Asian Uralic speakers . Udmurt log10IBD = 1.772 − 0.0005975 × (Distance in kilometers), adjusted R2 = 0.4923, p-value <10−16.Following B Yunusbayev, M Metspalu, E Metspalu, A Valeev, S Litvinov, R Valiev, V Akhmetova, E Balanovska, O Balanovsky, S Turdikulova, et al. , who obsAlthough Russian Starovers appeared in Siberia only about 300 years ago, details of their geographic origin are not clear. Starovers share roughly twice as much of ancient blocks with Komi and Karelian than between themselves, or with other Slavic speaking groups , adjusted R2 = 0.2774, p–value <10−16. Using this equation, we have identified pairs of populations (using the “core” dataset) that were more than two standard deviations away from the predicted log10IBD . These samples came from two groups: residents of Novosibirsk district  and the Siberian Starover . The Novosibirsk residents were thoroughly surveyed about their ancestors and selected only if reporting at least three preceding Russian generations, while members of the Starover cohort were all assumed to be “authentic” Russians. Since their resettlement from the European part of Russia in the seventeenth century, Russian Starovers deliberately adhered to a strict religious routine and avoided contact with neighbouring Native Siberian populations. Both the Starovers and most Novosibirsk residents are informally considered as “canonical Russians”. Nevertheless, only the Novosibirsk group represents a uniform sample from the modern Russian gene pool.In our dataset, ethnic Russians constituted the largest population  . Both SNp-value <0.009). The provenances of the samples were inferred by two algorithms, GPS [In the following analysis, we used admixture vectors obtained by ADMIXTURE software run with reference dataset from E Elhaik, T Tatarinova, D Chebotarev, IS Piras, C Maria Calò, A De Montis, M Atzori, M Marini, S Tofanelli, P Francalacci, et al.  in superhms, GPS  and reAdhms, GPS . For eacTo analyze modern Russians from Novosibirsk and Starover Russians, we used Russian diversity panel data genotyped on Geno 2.0 chip . Nearly 37% of self-identified Russians from Novosibirsk were mapped to various Russian populations from European part of Russia: 13% to Tver region, 13% to Arkhangelsk region, 5% to Ryazan region and 3% to Don Cossacks and Vologda region each. Not surprisingly, as many as 27% of Novosibirsk residents were identified as Mordva: 24% as Erzya and 3% as Moksha Fig. . These tStarover Russians appear to be more closely related to European Russians than Russians in Novosibirsk, with 58% identified as descendants of the migrants from various cities and villages in European part of Russia: for 45% of them, the provenance was traced to Arkhangelsk region, for 7% to Vologda region, for 2% to Yaroslavl region, and for 2% each to Tver region and Don Cossacks. Other notable ethnic component groups include 23% of Erzya and Moksha Mordva collectively, 12% of Karelian, and 5% of Veps.Altogether, GPS analysis of Starovers suggests that most of them came from northern areas of European Russia. This agrees with the slightly higher value of the Northern European component in Starovers as compared to Novosibirsk Russians.In addition to the proposed population and geographic location, the GPS algorithm also reports prediction uncertainty calculated from the distance to the nearest reference population. One of the Starover individuals was identified by GPS as a Khakas, a Turkic ethnicity living in the Republic of Khakassia located in southern Siberia, Russia. The same individual had the largest prediction uncertainty (7%) as compared to the average 3% prediction uncertainty for other Starovers samples. Typically, the prediction uncertainties which exceed 4% indicate mixed origin of an individual. For these cases, GPS algorithm should not be used.Therefore, for further analysis of Starovers and Novosibirsk individuals, we used reAdmix , which rSince the strict religious rules prevented Russian Starovers from marrying members of other ethnic groups, they are commonly believed to be less admixed with native Siberians than other Russian communities in the region. However, our ADMIXTURE analysis showed that the admixture profiles of Russian Starovers and Russians from Novosibirsk are similar Fig.  and thatThis genetic input can be attributed to multiple known and unknown events in the history of Starovers. We can summarize our observations as follows. According to both GPS and reAdmix analyses, Starover Russians have more significant input from Northern Russians and Finno-Ugric populations than from the South of Russia. Novosibirsk Russians represent a typical mixed Russian population of the early twenty-first century; lesser degree of admixture in the genomes of Starovers point at an increase in the rates of admixture of Russian populations with neighboring ethnicities that occurred in the last 300–400 years.f3 outgroup statistics [f3 is positive if and only if the branch supporting the population tree is longer than the two branches discordant with the population tree [f3 show that the two tested populations had a large amount of shared population drift.Earlier comparative studies of ancient and modern human DNA have helped to delineate human migration routes around the world –60. We uatistics  to test ion tree . TherefoAll populations from the “Extended” dataset were used as test populations, with Mal’ta , EasternThis analysis confirms local inheritance of genetic structure between ancient and modern populations, as evident from consideration of aDNA samples from the Caucasus and Europe. We did not find the “source” population for our Eastern Siberian samples (Yakut and Buryat). We also confirmed that modern European population is an amalgamation of ancient European Hunter-Gatherers with Neolithic Farmers. , 63–65. Since the pioneering effort by the HapMap Consortium made in 2003 , multiplOne of these blind spots is in Russia, where only a handful of genome-wide human variation studies were conducted to date , 19, 23.The relationship between genetics and geography was analysed by a combination of ADMIXTURE-based and IBD sharing approaches. We showed that Russian populations of diverse demographic histories and geographic localization share many genetic features, as reflected in their relatively tight ADMIXTURE groupings and outputs of GPS and reAdmix. The apparent positioning of some Russian samples in the genetic space of Caucasus and Siberian populations may reflect either traces of historical assimilation of these groups during the expansion of the Russians, or a recent contribution from neighbouring ethnic groups to the genomes of specific individuals. When we compared Starover and Novosibirsk Russians, representing snapshots of historical (as old as the seventeenth century) and modern (twenty-first century) Russian population, respectively, an apparent recent increase in the rates of admixture with various neighbouring population was evident. Admixture profiles of Modern Novosibirsk Russians have a lower percentage of Northern European components compared to Starovers Russians. In addition, various analyses including GPS, IBD and reAdmix suggest that Starover Russians were genetically influenced by Finno-Ugric people; this hypothesis agrees with the historical record concerning the patterns of Starover migrations within Russian Empire.One of most curious findings involved the Bashkir, an ethnicity with an extremely complex historical background. There are three main theories describing Bashkir origins: “Turkic”, “Finno-Ugric”, and “Iranian” , 77. AccSpeaking generally, our findings add weight to “Finno-Ugric” theory of the origin of Bashkir. A majority of Bashkir IBD fragments were shared with Khanty, an ethnicity related to Magyar. Interestingly, some works point out that before the thirteenth century the Hungarians were commonly called Bashkir  pointed to proximity of Bashkir to Turkic-speaking Tatar and Chuvash as well as to Finno-Ugric Udmurt and Khanty. In addition, results of It is noteworthy that the genomes of closest linguistic relatives of Bashkir, Volga Tatar, bears very little traces of East Asian or Central Siberian ancestry. Volga Tatar are a mix between Bulgar who carried a large Finno-Ugric component, Pecheneg, Kuman, Khazar, local Finno-Ugric tribes, and even Alan. Therefore, Volga Tatars are predominantly European ethnicity with a tiny contribution of East-Asian component. As most Tatar’ IBD is shared with various Turkic and Uralic populations from Volga-Ural region, an amalgamation of various cultures is evident. When the original Finno-Ugric speaking people were conquered by Turkic tribes, both Tatar and Chuvash are likely to have experience language replacement, while retaining their genetic core. Most likely, these events took place sometime around VIII century AD, after the relocation of Bulgar tribes to Volga and Kama river basins, and expansion of Turkic people.We speculate that Bashkir, Tatar, Chuvash and Finno-Ugric speakers from Volga basin has a common Turkic component, which could have been acquired as a result of Turkic expansion to Volga-Urals region. However, the original Finno-Ugric substrate was not homogeneous: Tatar and Chuvash genomes carry mainly “Finno-Permic” component, while Bashkir carry the “Magyar” one. The fraction of the Turkic component in Bashkir is, undoubtedly, quite significant, and larger than that in Tatar and Chuvash. This component reflects the South Siberian influence on Bashkir, which makes them related to Altai, Kyrgyz, Tuvinian, and Kazakh people.As a standalone approach, an analysis of shared IBD is not sufficient to support the Finno-Ugric hypothesis of Bashkir origin as a sole source, while pointing at temporal separation of genetic components in Bashkir. Hence, we demonstrated that Bashkir genepool is a multifaceted, multicomponent system, lacking the main “core”; it is an amalgamation of Turkic, Ugric, Finnish and Indo-European contributions. In this mosaic, it is impossible to identify the leading element. Therefore, Bashkir are the most genetically diverse ethnic group of the Volga-Urals region.Many Siberian populations share an unusually high amount of IBD, which may be explained by a combination of the following factors: 1) shared origin, 2) relative isolation from outside world, 3) rapid recent population growth and strong founder effect in Yakut, Buryat, and Tuva, or 4) gene flow facilitated by some migrating population. The structure of these population also reflects the role of multiple South-North travel routes along the great waterways of Ob, Yenisei, and Lena, while the Siberian taiga, which is notoriously hard to traverse, to some degree prevented lateral access. On the other hand, Southern Siberia, where the steppes border the forests, is easier to travel. The same is true for the Northern Siberia, where the cold, flat tundra is suitable for travel by deer herders. These geographical limitations corralled the East-West migration to either “northern” or “southern” corridors and North-South migrations to the banks of great Siberian rivers. The footprints of these geographical restrictions could be seen in the patterns of IBD sharing between the Siberian populations studied. We christened it as the “Siberian genetic vortex”.High IBD between West Siberian Ket and Khanty populations may reflect their relatively recent admixture with Selkup. Close genetic relationships between Ket and Tuvan can be explained by the existence of an ancient pre-Turkic and pre-Samoyedic Yenisei substrate which constitutes the main genetic component in Ket and still present in Tuva due to assimilation of extinct Yeniseian peoples   inhabiteHigh levels of shared IBD blocks in Altaic-speaking populations from Southern Siberia , North Asia (Yakut) and Central Asia (Kyrgyz) supports their recently formed common genetic core, which is geographically related to the Altay-Sayan Mountains region in Southern Siberia. Yakut and Kyrgyz populations which are now distant from this region were resettled from Southern Siberia relatively recently. It is accepted that ancestors of Yakuts (Kurykan) migrated from the Southern Yenisei to Lake Baikal area in seventh century AD, and then travelled the Lena river North in 12th -14th centuries AD , while KThe discovery of long runs of homozygosity in native Siberian populations  supports the earlier finding of pronounced founder effects and low genetic diversity in Siberians due to genetic drift, isolation by distance and recent population expansion events, that were made using the Y-chromosome analysis –89.Comparative analysis of modern and ancient genomes suggests that Western Siberians have more Ancient North European ancestry  than other populations of the Russian Federation. Other studied populations show genetic affinity to various ancient genomes, either co-located with modern inhabitants, pointing to direct gene flow and relatively sessile population, or geographically removed, pointing to their migration to currently occupied locations.We see that the shared genetic drift associated with hunter gatherers Fig.  is correto the genetic make-up of Russian Starovers (Old Believers), and postulated the existence of a Great Siberian Vortex directing genetic exchanges in populations across the Siberian part of Asia.Our project has filled an important lacuna in the genetic map of Eurasia. We revealed the complexity of genetic structure of Northern Eurasians, the existence of East-West and North-South genetic gradients, and varying inputs of ancient populations into modern populations. In particular, we have collected evidence in support of Finno-Ugric influence on the formation of Bashkir, shed light onAdditional file 1: Figure S1.Quality control process. (PDF 25 kb)Additional file 2: Figure S2.Results of ADMIXTURE for K = 2–10. (PNG 157 kb)Additional file 3: Table S1a.Table S1b List of samples included in “Core” dataset. Table S1c List of samples included in “Ancient” dataset. Table S2 Results of ADMIXTURE for K = 9. Table S3 Results of ADMIXTURE for K = 6, 7, 8. Table S4 Results of f3 test. Table S5 Results of IBD sharing analysis in 1–3 cM and 4–10 cM bins. Table S6 Total amount of shared IBD between populations. Table S7 Standard residue of linear regression analysis of distance-IBD sharing. Table S8 Distance and shared IBD between pairs of populations. Table S9: Results of f3 outgroup test with ancient samples. (XLSX 482 kb)List of samples included in “Extended” dataset.


The Mediterranean shores stretching between Sicily, Southern Italy and the Southern Balkans witnessed a long series of migration processes and cultural exchanges. Accordingly, present-day population diversity is composed by multiple genetic layers, which make the deciphering of different ancestral and historical contributes particularly challenging. We address this issue by genotyping 511 samples from 23 populations of Sicily, Southern Italy, Greece and Albania with the Illumina GenoChip Array, also including new samples from Albanian- and Greek-speaking ethno-linguistic minorities of Southern Italy. Our results reveal a shared Mediterranean genetic continuity, extending from Sicily to Cyprus, where Southern Italian populations appear genetically closer to Greek-speaking islands than to continental Greece. Besides a predominant Neolithic background, we identify traces of Post-Neolithic Levantine- and Caucasus-related ancestries, compatible with maritime Bronze-Age migrations. We argue that these results may have important implications in the cultural history of Europe, such as in the diffusion of some Indo-European languages. Instead, recent historical expansions from North-Eastern Europe account for the observed differentiation of present-day continental Southern Balkan groups. Patterns of IBD-sharing directly reconnect Albanian-speaking Arbereshe with a recent Balkan-source origin, while Greek-speaking communities of Southern Italy cluster with their Italian-speaking neighbours suggesting a long-term history of presence in Southern Italy. While ancient DNA (aDNA) based studies have been providing new insights into the early European heritage8, high-resolution genomic analyses focused on modern-day populations allow to explore more recent genomic layers and historical demographic events15.The Mediterranean Sea played a pivotal role in human migration processes from the Levant and the Near East into Europe during the principal phases and cultural changes associated to the peopling of the continent17 and autosomal18 markers revealed high levels of within-population variability, coupled with the lack of significant genetic sub-structures among Southern Italian groups. Importantly, age estimates for the major paternal lineages pointed to genetic links between Sicily and Southern Italy with the South-Eastern Mediterranean, tracing back to Neolithic and especially post-Neolithic time frames17, while maternal lineages provided a similar link with the East from the early Neolithic and post-glacial recolonization events19.The cross-cultural gateway linking Southern Italy with the south of the Balkans and the Aegean Greek Islands represented the theatre of multi-layered migrations of peoples and cultures both in pre-historical and historical times . Our previous investigations based on uniparental21. This is documented, for instance, by the presence of two of the largest ethno-linguistic minorities of Italy. Albanian-speaking Arbereshe represent ethno-linguistic enclaves today surviving in few municipalities of the provinces of Palermo (Sicily) and Cosenza . Their migration history is quite well documented and established in terms of both times and routes of diffusion. They originated from multiple migration waves of Albanians, coming directly from Toskeria (Southern Albania) or arrived after intermediate stopovers in Greece (particularly for Sicilian Arbereshe), occurred in the 15th-16th centuries in response of the Ottoman Empire invasion of the Balkans23. The Greek-speaking ethno-linguistic minorities instead represent Hellenic islands persisting in few municipalities of Salento  and Bovesia . Their uncertain origins28 have been related either to i) the ancient Magna Graecia foundation, ii) the subsequent Byzantine domination, or iii) the infiltration of Byzantine strata onto a pre-existing Magna Graecia matrix. Previous studies based on uniparental markers agreed with historical data in revealing signatures of a Balkan genetic heritage in the Southern Italian Arbereshe ethno-linguistic groups30. On the other hand, traces of the Greek colonization remain clearly visible in the historical and cultural heritage of Southern Italy  and particularly in the presence of Greek-speaking minorities. However, the demographic impact and genetic ancestry of the Greek source is still largely debated on both historical and population genetics viewpoints32.Additionally to long-term processes of gene flow and admixture, the genetic structure of the populations currently inhabiting the area has been impacted by recent events of cultural isolation and local differentiationOur ability to assess the genetic impact of different migration processes is challenged by the number of admixture layers involving ancestral populations; this is even harder the more genetically-related the two sources of admixture are. Consequently, exploring recent population interactions necessarily poses questions about ancient admixture strata composing the present-day genetic heritage.33, especially since ancient paleogenomic data became available for Anatolia and Northern Greece34. On the other hand, while aDNA data from Southern Italy and Sicily are still limited, genome-wide analyses of modern populations from these areas mainly consisted to wide-range surveys without specific fine-scale insights36.Recently, genome-wide analyses brought new attention on different aspects of the genetic history of Greece and the BalkansIn this study, we genotyped 511 samples belonging to 23 populations from Sicily, Southern Italy, Albania and Greece, as well as from Italian Arbereshe and Greek-speaking ethno-linguistic groups  show affinity to Eastern Europeans , whereas increasing frequencies of the European-like component are observed in Albanians and mainland Greeks as well as in the rest of the Balkan Peninsula , while the CE-Sicily one is essentially observed in the Mediterranean ‘continuum’ populations . Finally, Cypriots and Calabrian Greeks exhibit private population-specific genetic clusters  share relatively more segments with the Caucasus and the Near East, while Albania and continental Greece appear significantly more related with Central and Northern Balkans, as well as Eastern Europe. Interestingly, despite showing much lower values of sharing, some Balkan IBD-relatedness also emerges in Greek-speaking islands as well as in Apulia and Western Sicily, presumptively reproducing some forms of interaction with Greece and the Balkans in the very recent ancestry of these areas, as consistently signalled by a common sharing of individuals in the FineSTRUCTURE AW-Sicily cluster .The emerging patterns have been further explored with the Some remarkable signals of genetic differentiation are found in the Albanian- and Greek-speaking communities residing in Sicily and Southern Italy included in our sample.30, Albanian-speaking groups of Southern Italy display a recent shared ancestry traceable to their putative Balkan-source populations  and Calabria (Grecani) show no clear signs of a recent (i.e. from the late Middle Ages) continental Greek origin, instead resembling the ‘Magna Graecia) and Byzantine colonization, have been historically proposed to explain the presence of present-day Greek-speaking communities in Southern Italy. Although different extents of Hellenic and Byzantine pressures were suggested to have demographically and culturally affected Calabrian and Apulian Greeks respectively, both historical and linguistic data agree on the fact that the current extension of these groups is a remnant of a wider Greek-speaking area, originally extended to larger parts of Apulia, Calabria and Sicily24. In the whole area, the Greek-language was well represented before the spread of Latin, and this Greek substratum has influenced the local Romance varieties in various respects. In fact, contacts between Greek and Romance speakers have been frequent and systematic27. Accordingly, historical and linguistic data suggest that this area was characterized by a pervasive multilingualism at least from the antiquity38, thus showing that both cultural transmission and genetic admixture may have played an important role in the formative process of these groups since the very beginning.Different hypotheses, either counterpoising or combining the Hellenic  and Italian neighbours  remarkably show evidences of genetic differentiation, as suggested by PCA  differentiation of continental Greek and Southern Balkan groups (cf. paragraph below). This does not exclude migrations from Aegean/Dodecanese and Crete islands, that presumptively did not  experienced - by virtue of their higher geographic marginality - the North-South Balkan gene flow that instead interested the continental part of Greece.Furthermore, we observed that both Calabrian and Apulian Greeks from Southern Italy almost completely lack the ‘f3-statistic between all possible trios of clusters, dating the significant ones with ALDER  after these recent events which interested the Balkan Peninsula in historical times. Similar contacts may be also responsible of the relatively high Balkan-IBD sharing  compared to Calabria and Eastern Sicily (CE-Sicily cluster).Significant admixture events successfully dated by ALDER reveal that all Southern Italian and Balkan groups received contributions from populations bearing a Continental European ancestry between 3.0 and 1.5 kya Fig. . This pekya Fig. . Accordi39). In most cases, these events depict populations from the ‘continuum’, and particularly the two SSI-clusters (CE-Sicily and AW-Sicily), as a mixture of Sardinian and Caucasus or Near Eastern related groups , we can hypothesize these Levantine- and Caucasus-related admixtures as introgressions interfering with the Neolithic (Sardinian-like) genetic background of our Southern Italian and Southern Balkan populations.To explore population events possibly exceeding the dating accuracy threshold of the f3/ALDER approach and to formally assess hypotheses explaining the admixture patterns outlined above, we applied four-populations D-tests by comparing available ancient samples and the detected modern European fineSTRUCTURE clusters  layer as one of the most relevant both in Southern Italy, Greece and the Southern Balkans Fig. . In factf3  appear lower in all our newly analysed Mediterranean populations .The most recent literature demonstrated significant impact of Caucasus-related ancestry in the Central European Late-Neolithic and Bronze-Age through the migrations of Yamnaya/Pontic-Steppe herdersOur results demonstrate that the genetic variability of present-day Southern Italian populations is characterized by a shared genetic continuity, extending to large portions of central and eastern Mediterranean shores. This area, which is cored in Southern Italy and the Greek-speaking islands, exceeds cross-linguistic differences, encompassing populations belonging to different Indo-European subfamilies . Noticeably, Southern Italy appear more similar to the Greek-speaking islands of the Mediterranean Sea, reaching as far east as Cyprus, than to samples from continental Greece, suggesting a possible ancestral link which might have survived in a less admixed form in the islands. Their genetic ancestry traces its heritage to complex and extensive patterns of pre- and proto-historical admixture. Besides a predominant Neolithic-like component, our analyses reveal significant impacts of Post-Neolithic Caucasus- and Levantine-related ancestries, which might be further addressed by future studies with a higher sample coverage for a precise contextualization in time and space and by integrating multiple lines of evidence from different disciplines . More recent historical expansions from Continental Europe added further admixture layers, accounting for the genetic and cultural complexity that currently differentiates present-day Southern Balkan and Southern Italian populations.genetic continuum’ ; as a consequence, their arrival in Southern Italy could at least predate the recent differentiation of mainland Greece. A possible key of interpretation would stress the Mediterranean genetic signal as the result of ancient links, which were partly modified by more recent historical movements in the Southern Balkans involving Continental Greece and Albania. In this light, the genetic similarity between Greek- and Italian-speaking groups of Southern Italy may suggest long-standing genetic and cultural exchanges originally diffused over the whole region, also outside the ethno-linguistic enclaves that survived until the present times. This would not exclude that continuous interactions between the Italian- and Greek-speaking populations of Southern Italy, especially in contexts of lower geographic isolation, contributed to their present-day genetic similarity in spite of the preserved linguistic differences. Additionally, Greeks from Calabria revealed remarkable signs of genetic drift, which are presumptively ascribable not only to cultural but also to geographic isolation. This fact led to their partial differentiation from their Italian local neighbours, despite common patterns of IBD-sharing.This complex genetic scenario opens new insights into the recent cultural transformations associated to the Greek- and Albanian-introgressions in Southern Italy that originated the Italian Arbereshe and Greek-speaking ethno-linguistic minorities. Overall, Arbereshe groups confirm the Southern Balkan genetic characterization typical of their putative source populations, whereas Italian Greeks are related to the Mediterranean ‘While more specific hypotheses could be only elucidated by the discovery of new local sources of aDNA for testing explicit models, our results hint to some important implications from both genetic and cultural viewpoints, illustrating the different and complex dynamics that accompanied the formation of present-day cultural heritage, especially in contexts of extensive - both geographically and temporally - admixture. The genetic patterns observed in Southern Italy integrate the picture of the genomic structure of Europe and the Mediterranean, and support different histories behind the evolution of the Southern Italian ethno-linguistic minorities, moreover emphasizing the importance of considering complementary scales of investigations and detailed population samplings to assess demographic processes involving tightly related ancestries.th, 2013). This study was performed in accordance with relevant guidelines and regulations, and according to ethical principles for research involving human subjects stated by the WMA Declaration of Helsinki.A total of 560 individuals belonging to 23 populations spanning from Sicily and Southern Italy (SSI) to Greece and the Balkans were newly-collected and analysed in the present study Fig. , includiGenomic DNA was purified from the Oragene-DNA collection kits following manufacturer’s recommendations, quantified by fluorometric methods  and checked for DNA integrity.41. Post-processing genotyping checks failed for 30 samples.DNA samples were genotyped by the Gene-by-Gene Lab  for the ~150,000 markers implemented in the GenoChip 2.0 DNA Ancestry Kit42 to include only SNPs with genotyping success rate higher than 90% and individuals showing less than 1% of missing genotypes. In addition, we estimated the degree of identity-by-descent (IBD) sharing and we excluded one individual for each pair of samples with kinship coefficient (PiHat) higher than 12.5% (3rd degree relatives). After filtering procedures, we retained 123,700 autosomal SNPs typed for 511 individuals  that were proved to have contributed appreciable impacts on the present-day European genetic heritage7. We excluded outlying and/or low-coverage (in terms of the number of usable SNPs) ancient individuals  was carried out with the 45. We thinned the dataset with the PLINK software42, by excluding SNPs in strong LD (r2 > 0.1) within a sliding window of 50 SNPs advanced by 10 SNPs at the time. We performed series of admixture runs from K = 2 through 10 and we used the cross-validation (CV) error to identify the best predictive model. For a given K, we performed ten independent runs with different random seeds, and those with the highest log-likelihood values were plotted for each K. The best number of inferred ancestral components for the modern pooled dataset was K = 4 .To estimate admixture proportions among modern populations we used the software ADMIXTURE46, as a way to improve population delineation and individual assignment in a context of generally weak genetic structuring.To explore fine-grained population structure and detect subtle levels of genetic differentiation, we exploited the haplotype-based approach implemented in CHROMOPAINTER/fineSTRUCTUREextended comparison dataset. We excluded from the analysis the Jewish groups, due to their complex recent demographic and migration history. Samples were phased jointly with the software SHAPEIT47 according to default parameters. We initially estimated the mutation/emission and switch rates using 10 steps of Expectation-Maximisation (E-M) by running the algorithm on a subset of four chromosomes {4, 10, 15, 22}. We averaged the inferred values across these chromosomes, weighting by number of SNPs and across individuals, and then re-ran CHROMOPAINTER on all individuals and all chromosomes using the above-mentioned estimated values, without any additional E-M iteration. The count of haplotype segments, combined across all the 22 autosomes, was submitted to the fineSTRUCTURE clustering algorithm. We ran fineSTRUCTURE for 3,000,000 “burn-in”, followed by another 1,000,000 iterations of MCMC where inferred clustering were sampled every 10,000. We finally used fineSTRUCTURE to perform 100,000 additional hill-climbing steps to improve the posterior probability and merge clusters in a step-wise fashion. Individuals were hierarchically assembled into genetic-based clusters until reaching the final configuration tree, represented in our study by 52 population groups  to test for evidences of each of the 14 inferred groups to derived from the admixture of any other two. Values of f3 were estimated in 997 blocks. Significant mixture events (Z-score < −2) were dated with ALDER49 by using a generation time of 28 years. We decided to apply a SNP-based dating approach to a haplotype-based reconstruction of population groups for two main reasons. On one hand, we exploited the greater precision of haplotype- than allele-based methods in the definition of admixture sources. This would reduce the noise due to the most recent admixture events in populations, that actually exhibit mixed ancestry themselves as a result of extensive gene-flow networks. On the other hand, we overcame the strict dependence (in terms of accuracy of provided estimates) of haplotype-based dating approaches on the number of SNPs available for inferring haplotypes.Starting from the final clustering pattern provided by CHROMOPAINTER/fineSTRUCTURE, we considered assignment of Euro-Mediterranean individuals to inferred genetic clusters for the level of the tree at which each cluster has at least 10 members (K = 14). Then, we used fastIBD method implemented in the BEAGLE 3.3 software50. We run fastIBD ten times for each chromosome with different random seeds. To call IBD blocks we post-processed results by using the modified ‘plus-process-fibd.py’ tool proposed by Ralph et al.9, which minimizes the number of spurious gaps or breaks introduced into long IBD blocks by low marker density. We considered only blocks longer than 1 cM, due to the low power of fastIBD to detect shorter segments, and we set the fastIBD threshold to 1e-10.Patterns of IBD sharing among populations were estimated with the WAB metric51, which is the total length of genome shared IBD by any two tested populations, averaged over the number of possible pairs of individuals. The average IBD-sharing was calculated for both the total and five different classes of length . From the original extended dataset of 68 comparisons, we removed Jewish groups and redundant populations . To identify significant differences in patterns of sharing between the detected groups, we computed the IBD-statistics for each of the 18 Southern Italian or Southern Balkan populations (excluding minorities) separately with all the other 35 considered comparisons . Then, we averaged the obtained population-vectors by groups and pairwise subtracted the averaged vectors between groups to measure differences in IBD-sharing  was considered jointly with Central Greece.Segments of identity by descent (IBD) and runs of homozygosity (ROH) were used to explore variation patterns within each of the newly analysed populations. Runs of homozygosity were calculated using the PLINK softwareSupplementary InformationSupplementary Tables S1–S7


Bagrus docmak). Samples were acquired from five geographical locations in East Africa within two major drainage basins; the Albertine Rift and Lake Victoria Basin. Individuals (N = 175) were genotyped at 12 microsatellite loci and 93 individuals sequenced at the mitochondrial DNA control region. Results suggested populations from Lakes Edward and Victoria had undergone a severe historic bottleneck resulting in very low nucleotide diversity  and negatively significant Fu values . Heterozygosity deficiencies and restricted effective population size (NeLD) suggested contemporary exposure of these populations to stress, consistent with reports of the species decline in the East African Region. High genetic structuring between drainages was detected at both historical  and contemporary  levels. Patterns of low genetic diversity and strong population structure revealed are consistent with speciation patterns that have been linked to the complex biogeography of East Africa, suggesting that these biogeographical features have operated as both macro‐ and micro‐evolutionary forces in the formation of the East African teleost fauna.The complex geological history of East Africa has been a driving factor in the rapid evolution of teleost biodiversity. While there is some understanding of how macroevolutionary drivers have shaped teleost speciation in East Africa, there is a paucity of research into how the same biogeographical factors have affected microevolutionary processes within lakes and rivers. To address this deficiency, population genetic diversity, demography, and structure were investigated in a widely distributed and migratory (potamodromous) African teleost species, Ssemutundu ( For instance, prior to the introduction of the Nile perch, Helfman, . GeologiHelfman, . PresentHelfman, . DespiteHelfman, , 2009, aHelfman,  considerHelfman, .Africa's freshwater systems are degrading at a very high rate with over 80 species listed as critically endangered, 116 species endangered and up to 103 threatened  is a freshwater catfish with a widespread distribution in African freshwater rivers including the Nile, Chad, Niger, Volta, and Senegal. It is also found in Lake Victoria as well as the Rift Valley Lakes Edward, George, Albert, Tanganyika, Malawi, and Turkana  and habitat degradation   Figure . Lakes A2.2B. docmak were obtained from commercial fishers at each of the five sampling locations . For some inhibited samples that failed to amplify during PCR, DNA was re‐extracted using a column based Bioline Isolate II Genomic DNA kit following manufacturer's protocol. Briefly the protocol involved a prelysis stage using 180 μl Lysis buffer with 25 μl of protease K incubated at 56°C. 200 μl of G‐3 lysis buffer was added and further incubated for 10 min at 70°C. 210 μl of 100% EtOH were then added to alter the buffer conditions prior to two (GW1 and GW2) buffer washes. The eluted DNA was stored at −80°C prior to downstream PCR.Total genomic DNA was extracted using a modified CTAB protocol Wilson,  and lateDNA from 175 individuals was genotyped at 12 polymorphic microsatellite loci, Bd04, Bd18, Bd01, Bd02, Bd12, Bd09, Bd06, Bd20, Bd05, Bd03, Bd14, and Bd10 Appendix . ForwardB. docmak sequences that were then used to design more robust species‐specific primer pairs. Primers specific to Bagrus spp. were designed using a free online software, primer3  and 1 μl DNA (5 ng/μl); PCR cycling conditions comprising of an initial denaturation of 94°C for 5 min, then 30 cycles of 94°C 30 s (denaturation)/50°C for 30 s /72°C for 30 s and a final extension of 72°C for 5 min. Visualization for amplification was achieved via electrophoresis using a 1.5% agarose gel. PCR product was cleaned with Sephadex G‐50  columns prior to sending approximately 5 ng of each sample for Sanger sequencing at the Australian Genome Research Facility (AGRF), in Brisbane–Australia. Sequencing was performed in both forward and reverse directions using the designed B. docmak specific primers.The Mitochondrial control region (D‐loop) was amplified in 93 individuals from the five locations. Initially, catfish oligonucleotide primers, MT16498H and L19 , inbreeding coefficient (Fis) and conformation to Hardy–Weinberg Equilibrium (HWE) at 12 microsatellites loci were performed in GenAlex , haplotype diversity (Hd), and nucleotide diversity (π). These parameters were used as a measure of genetic divergence at the mitochondrial D‐loop region within and among the sampled populations. All computations were implemented in DnaSP  were used to test for signatures of population reductions for each of the five populations using Bottleneck 1.2.02 software  and pairwise comparisons between locations were completed in Arlequin 3.5  set from 1 to 10 iterations with 10,000 burn‐ins followed by 100,000 Markov‐Chain Monte Carlo (MCMC) steps for each run. Potential clusters were determined based on an MCMC approach, both with and without a priori definition of structure, and also assuming independent frequencies of alleles. Using the web‐based Structure Harvester software , was performed in the R package adegenet v1.4.2 . The nucleotide diversity was highest in individuals from Lake Albert and the Victoria Nile river, both with π = 0.010, and least in Lakes Edward and Victoria, π = 0.004 and 0.006, respectively  and lowest in the Lake Victoria population (mean Na = 3.25 ± 0.494). Similarly observed and expected heterozygosities were highest in the Lake Albert population (mean Ho of 0.55 ± 0.034 and He 0.66 ± 0.031) and lowest in the Lake Victoria population (mean Ho = 0.199 ± 0.046 and He = 0.213 ± 0.053)  for B. docmak populations from Lake Victoria and Edward. These two populations additionally displayed an L‐shaped allelic distribution characteristic of populations undergoing expansion following contractions (data not shown). The effective population sizes, NeLD, for Lake Victoria, Lake Edward and the Nile River were finitely restricted , with the exception of Lake Victoria having the highest Fis of 0.1101 (p = .054), but still statistically insignificant . The Lake Albert population exhibited characteristics of a stable population with three loci showing heterozygosity deficiency and nine loci with heterozygosity excess. Contrary to Lake Albert, results indicated that the Lake Victoria population exhibited a normal L‐shaped curve characteristic of a young and expanding population. This is further confirmed by a significantly high heterozygosity deficiency at 10 loci (p < .001 under the SMM model). Additionally, relative to all populations, Lake Victoria displayed the highest FIS index of 0.11 (Table p > .05).Overall, all populations sampled exhibited heterozygosity deficiencies except for the Lake Albert population that did not show significant deviations from a stable population  in NeEstimator, Lake Albert showed an infinite NeLD with infinite number of breeders, followed by the Kazinga channel which also exhibited a relatively high Ne . Populations from Lake Victoria and Lake Edward had restricted effective population sizes, NeLD of 18.2 (6.7–127.4) and 31 (18.8–60), respectively . Pairwise ɸST between populations across the two regions ranged from 0.628 to 0.820 (p < .001) which were up to sevenfold higher in comparison to the within‐region population ɸST pairs (range of 0.013–0.097). Moderate‐to‐weak structuring also existed between populations within basins, especially for water systems that were geographically proximate, or directly connected with no major biogeographical barriers. For instance, pairwise ɸST values between “Kazinga Channel–Lake Albert” and “Lake Edward–Lake Albert” showed moderately low ɸST of 0.046 (p < .05) and 0.0968 (p < .001), respectively (Table p > .05), which is not surprising given their direct geographical connectivity. Similarly, there was weak and insignificant structuring (p > .05) between the Victoria Nile River samples and the Lake Victoria population.MtDNA D‐loop analysis for ST patterns. Here, population pairwise FST values were in the range of 0.014–0.549, with significant structuring between populations across the Albertine (Lakes Edward and Albert and the Kazinga Channel) and Lake Victoria basin . Bayesian STRUCTURE analysis and DAPC multivariate analyzes also showed a similar trend with varying degrees of resolution  and the second genetic cluster contained populations from the Albertine Rift Valley . However, individuals from Lake Albert comprised both genealogies from the Albertine rift and Lake Victoria basin  and 23 other smaller haplotypes that were branching out in the periphery. The most common haplotype was shared by 33 individuals restricted to the Albertine rift  and representing up to 35% of the all sampled individuals. The second largest haplotype had 16 individuals restricted to the Nile River and Lake Victoria. From these two major haplotypes span other smaller haplotypes , with two major genetic stocks identified: one within the Albertine rift and the other within the Lake Victoria basin. Mitochondrial DNA analyzes show that the separation of stocks has an ancient origin with a high degree of haplotype sorting evident, with just two major haplotypes and restricted mutations of up to four base pair changes between these two major haplotypes. The near complete haplotype sorting and reduction in diversity suggests a severe vicariant and bottlenecking event among catfish populations  is consistent with the direct connection of these lake–river systems which have no known biogeographic barriers. In addition, B. docmak being potamodromous  underwent severe contractions as a result of biogeographical influences within the East African region. Generally the mitochondrial haplotypes branches were very short showing that the divergences were fairly recent with just up to four mutations between the two major lineages. It also indicates a short evolutionary time since the common ancestor. Additional evidence of low diversity in the mitochondrial data reveals that the populations from Lakes Victoria and Edward have been restricted to a very small size over a few thousands of years. High haplotype diversity coupled with low nucleotide diversity as was observed in the Lake Victoria and Edward B. docmak populations  provides an advantage of a large habitat in which the species will survive for a while if proper management strategies are put in place. Conversely, Lake Edward which is a small  EAR lake, has been considered hydrologically and chemically sensitive to climatic changes as it lies in the intersection of the Indian and Atlantic air masses  and two management units . These findings are important in developing appropriate conservation and management strategies which depend majorly on our ability to correctly assign genetically distinct populations (Latch, Dharmarajan, Glaubitz, & Rhodes, Information provided by the current study will facilitate the comprehensive management of 5B. docmak are consistent with patterns observed in other freshwater fish species that have been linked to the complex biogeography of East Africa (Danley et al., B. docmak, both population fragmentation and reductions in population size due to the complex geological and climate variability in the EAR have resulted in significant genetic structure among populations.The low genetic diversity and strong population structure patterns revealed in Strategically, the management of aquatic fauna in the East African region should initially take into account the two major management units identified, that is the Albertine rift valley region and the Lake Victoria basin. However, further research is required for the Lake Albert population which was singled out as a discrete group at a finer scale with DAPC analysis to make a total of apparently three management units comprising of Lake Edward and Kazinga Channel cluster, Lake Victoria–Nile River cluster and Lake Albert.None declared.