Hippocampus capensis, is the world’s most endangered seahorse species, and it presently survives in only three estuaries on the South African south coast. Factors that contributed to the species becoming endangered are unclear; additionally, the lack of information on whether the three populations should be managed separately because of potential long-term isolation hampers effective management efforts. In the present study, we reconstructed the seahorses’ demographic history using a suite of microsatellite loci. We found that the largest population (Knysna Estuary) has colonised the other estuaries relatively recently (< 450 years ago), and that its population size is comparatively large and stable. Neither of the other two populations shows signs of long-term reductions in population size. The high conservation status of the species is thus a result of its limited range rather than historical population declines. Our findings indicate that the long-term survival of H. capensis depends primarily on the successful management of the Knysna population, although the other estuaries may serve as reservoirs of genetic diversity.Historical demographic events shape genetic diversity that remains evident in the genomes of contemporary populations. In the case of species that are of conservation concern, this information helps to unravel evolutionary histories that can be critical in guiding conservation efforts. The Knysna seahorse,  Through reproduction, these imprints become perpetuated from ancestral to contemporary generations3. Demographic signatures can be of value in understanding the dynamics of contemporary populations4, and can be used to address questions concerning the causes of contemporary low diversity in species of conservation concern6. A species’ effective population size (Ne) is an integral parameter in population ecology as it dictates, to a large extent, its level of genetic variation7. Specifically, population genetic theory predicts a positive correlation between neutral genetic diversity and effective population size, all else being equal9. Thus, when departures from this assumption are evident, a number of factors may be invoked to explain discrepancies, including an elevated evolutionary rate, selection, gene flow and changes in effective population size, including bottlenecks and founder events11.Demographic events, both historical and more recent, leave long-lasting and tractable footprints in the genomes of natural populations12. However, particularly in low-dispersal species, levels of population structure can be very high, and differing levels of neutral genetic diversity between uniquely identifiable populations are not uncommon14. In populations of endangered species, low levels of genetic diversity can have a detrimental impact on long-term species viability. In essence, genetic diversity is an indicator whether populations and/or species can adapt and persist under the influence of environmental perturbation15, and the maintenance of high intra-population genetic diversity is thus always beneficial16. Identifying potential factors that have driven the observed neutral genetic differentiation between populations is imperative and can help to guide the formulation of conservation policies.Traditionally it was believed that the absence of impermeable dispersal barriers in most marine systems would automatically result in genetic homogeneity19. Despite such positive outcomes, there still exists considerable reluctance and uncertainty among conservationists towards the adoption of translocations as a conservation tool23. Indeed, when faced with severe population decline in low dispersal species, populations are often allowed to become extinct instead of facilitating the mixing of individuals from populations with unique genetic identities24. This reluctance stems from the fact that the mixing of populations with supposedly long histories of isolation may result in outbreeding depression and the loss of unique adaptations22. In addition, there is a potential risk of introducing parasites or disease-causing vectors into the recipient population25.Translocation, re-introduction or augmentation of threatened populations has been applied in a number of endangered species, with tangible successHippocampus capensis Boulenger, 1900 is the world’s most endangered seahorse26. The species is endemic to South Africa and occurs in only three estuaries along the warm-temperate south coast: Keurbooms, Knysna and Swartvlei28  is preferable to augmenting its genetic diversity.The Knysna seahorse, In the present study, we used microsatellite data generated from a total of six polymorphic microsatellite loci to (a) estimate the time at which the three contemporary populations diverged from their common ancestor, (b) determine whether migration occurred subsequent to population divergence, and (c) reconstruct trends in effective population size for the three populations. The finding that the population in the Knysna Estuary recently colonised the other two estuaries provides valuable new information to help guide the process of establishing translocation programmes aimed at safeguarding the future viability of the Knysna seahorse.The genetic characteristics of the six microsatellite loci are provided in the Supplementary Information Table . Briefly31  genetic divergence between populations occurred during historical times, and it is possible that each population now represents a distinct evolutionary lineage; translocations should therefore be discouraged; or (b) divergence was recent, and either occurred naturally or as a result of human-mediated translocations during the recent past (decades or subsequent to European settlement); in this case, there would be no reason to discourage augmentation initiatives. The average divergence times between Knysna vs. Keurbooms, Knysna vs. Swartvlei, and Keurbooms vs. Swartvlei were estimated at 171, 174 and 83 years ago, respectively Table . In all Ne) for the three populations using IMa2 indicated that the Knysna population has the largest size , with the size of the Swartvlei population (Ne = 1780) being more than an order of magnitude smaller  aimed at increasing both population size and genetic diversity should be considered37. This is particularly true when there is little or no contemporary gene flow between geographically isolated populations.Endangered species typically have small population sizesH. capensis. To this end, we determined the sequence of events that resulted in the formation of the populations, as well the times when they formed and how (or whether) their effective population sizes subsequently changed. Although only six microsatellite loci were incorporated in our present analyses, these were carefully selected from a larger number of published markers on the basis of being highly informative and free from laboratory artefacts. Several recent studies indicated that this number is sufficient to examine the demographic history of populations of species with limited migratory potential37.In the present study, we reconstructed near-contemporary demographic trends to address the question whether augmentation could be considered as a practical conservation tool for the three populations of H. capensis and served as the source population for the other two estuaries. Secondly, these colonisation events occurred comparatively recently (between a few decades and ~ 448 years ago) and for that reason, it cannot be ruled out that they are the result of human-mediated translocations. Thirdly, there was subsequent gene flow between the three populations, in the form of either natural dispersal or the unauthorised translocation of some seahorses by humans. Lastly, the Knysna population has the largest long-term effective population size, and the Swartvlei population the smallest.Four findings of conservation relevance stand out. First, the Knysna population is the ancestral population of 38, underlines the exceptional importance of this estuary for the region’s unique biodiversity. The Knysna Estuary is the only estuarine bay on the South African south coast and has the highest biodiversity of any of South Africa’s estuaries40. In addition to housing the region’s only temperate seahorse, it is inhabited by several other rare species, including the Knysna goby, Pandaka silvana, which is endemic to this system41 and the limpet Patella compressa, which occurs in only one other estuarine bay42.The finding that the Knysna seahorse evolved in the Knysna Estuary, most likely from a tropical marine ancestor43. Even though the census population sizes in the Keurbooms and Swartvlei Estuaries can at times far exceed those in the Knysna Estuary28, these two smaller systems occasionally experience mass mortalities44, and at times, few or no seahorses are found during diving surveys over a number of years28. The fact that the Knysna population does not experience such boom-and-bust cycles28 highlights the importance of the Knysna Estuary for the long-term survival of H. capensis.The large long-term effective population size of the Knysna population can be explained by the fact that this estuary has a large, permanently open mouth that can buffer the negative effects of freshwater floods45, with the majority of migrants originating from the Knysna Estuary. However, given the young ages of the smaller populations, the possibility that these are merely the product of human-mediated rather than natural colonisation events cannot be ruled out conclusively.We identified post-divergence gene flow between the different populations that may have been facilitated by alongshore coastal currents29. Although significant differentiation between the three systems was found using both mitochondrial and nuclear DNA markers, there was also evidence for ongoing admixture29. The finding of the present study, namely that both divergence and admixture occurred so recently that the possibility of human-mediated transport as the cause of both types of demographic events cannot be rejected, strongly argues against the idea that the different seahorse populations were isolated for prolonged periods of time and as such, must be kept isolated to prevent outbreeding depression. Our genetic data agree with anecdotal reports that unauthorised translocations have occurred repeatedly over the past decades, and possibly even earlier.The question whether translocation should be considered as a conservation tool in the management plan of the endangered Knysna seahorse has remained controversial ever since the first genetic study on this species was conducted22. In the present case, the different populations likely diverged no more than ~ 450 years ago, and there is clear evidence for subsequent gene flow14. The fact that divergence times between both population pairs are significantly different from zero indicate that despite the comparatively low number of microsatellite markers available for H. capensis, the present data are sufficiently informative to differentiate between divergence followed by secondary contact, as opposed to incomplete lineage sorting with alleles shared due to ancestral polymorphism46. Based on the present findings, we recommend the adoption of augmentation as a means of improving the population size, as well as boost genetic diversity, particularly for the population in the Swartvlei Estuary.The available literature suggests that outbreeding depression can only be expected between populations with specific characteristics that include a long history of independent evolution , fixed chromosomal differences, or where populations have remained in isolation for the past 500 years or more with no gene flow during this period47, other factors typically associated with increasing human activities around estuaries, including pollution, siltation, habitat degradation, spread of aliens and diseases48 nonetheless constitute significant threats to the long-term survival of the Knysna population. The population size estimates of the two smaller estuaries inferred using the EBSPs are likely inflated because of post-divergence gene flow from the Knysna Estuary, since EBSP assumes no gene flow after isolation, while the IM estimates are more accurate because this method can account for post-divergence gene flow49. During the sampling period, seahorses were concentrated only in small areas in the two smaller estuaries, but previously published data nonetheless show that these estuaries are at least temporarily suitable to provide habitat for seahorses28. This suggests that human-mediated enhancement of their population sizes, and the repeated release of seahorses originating from the Knysna Estuary, should be considered as a management tool to preserve as much genetic diversity as possible in the event that the Knysna population experiences a significant and prolonged decline. Despite lack of evidence for boom-and-bust cycles in the Knysna Estuary, careful monitoring of the population size is required to ensure that translocations take place during periods when this population has a particularly large size. In addition to translocations, conservation efforts in the estuaries should also consider protecting the available suitable habitats of the seahorse to improve the species’ chances of survival. Overall, the ecological health of the Knysna Estuary is deteriorating50 and the smaller estuaries experience mass mortalities, suggesting that translocations will be most successful when environmental conditions in all three estuaries are ideal. Without a secure habitat, translocation will not benefit the species in the long term.To this end, the Knysna population with its highest effective population size and its minimal demographic stochasticity clearly represents the most suitable donor population. Although some aspects of marina development in this estuary have had a positive impact on its seahorse population14. The microsatellite data used was for a total of six variable loci which were selected from a total of 15 which were originally amplified , poor amplifications where only a limited number of samples of the species were able to amplify (Hgut4) or the second allele being inadequately amplified, a signal for presence of large allele dropout (Hhip2), and some loci being invariable where one or two alleles were present 14. The remaining six loci were found to be free from the above problems and were thus retained for subsequent analyses ed Table . The PCRes Table . The genes Table  were obtSamples were collected with permission from the CapeNature (Keurbooms Estuary) and the South African National Parks (SANParks) (Knysna and Swartvlei estuaries). Animal handling procedures were approved by the Ethics Committee of the University of Johannesburg, South Africa.31. This programme was also used to investigate whether migrations between populations occurred post-divergence. The analysis was performed firstly by trying to identify the maximum possible number of migration events (m) between populations, starting with m = 1 to a maximum of m = 6 . The final analysis was then performed with the maximum migration events available in the dataset specified at m = 3, with default settings used for all other parameters.We investigated the sequence in which the three populations were formed (colonisation history) by using the graph-based program TreeMix version 1.1249. IMa2 implements the isolation with migration (IM) model51, and estimates populations splitting times while taking into account changes in effective population size and post-divergence gene flow. The IMa2 analyses were performed on population pairs  to obtain a posterior estimate of the splitting time (t0) since the two populations split from their common ancestor, including the ancestral and contemporary estimates of effective population sizes (Ne). The pairwise approach is recommended when limited data are available49. The mutational splitting time t is given as t = T/u, where T is the time since common ancestry in generations and u is the geometric mean of the loci’s mutation rates per year. Knysna seahorses have a generation time of approximately one year52. Prior parameters for the population size, spitting times and migrations and two heating parameters (ha = 0.99 and hb = 0.7) were identified after a series of test runs. A burnin period of 2 × 106 generations with a total of 150 heated chains (hn) and a geometric heating scheme (hfg) were specified. A teleost-specific mutation rate of 5.56 × 10–4 locus-1 year-1 was used, with a 95% confidence range of 1.52 × 10–4 to 1.63 × 10–353. The analysis was repeated three times, with each run using a different starting seed. Convergence of the analyses was confirmed by an effective sample size (ESS) greater than 200.The times at which populations diverged from each other were estimated using the coalescent based, Markov Chain Monte Carlo (MCMC) program IMa2Ne for each of the three populations. Point estimates were generated using both the coalescent method implemented in the IMa2 programme (with programme settings as described above) and the linkage disequilibrium approach54 implemented in NeESTIMATOR version 255. To run NeESTIMATOR, we selected a monogamic mating system with a critical value of 0.02 to exclude rare alleles. The long-term trends in Ne for each of the three populations were assessed by reconstructing Extended Bayesian Skyline Plots (EBSP)56, using BEAST version 1.7.557. GenAlEx version 6.558 and PGDSpider version 2.0.7.259 were used for infile preparation. For each population, we linked the substitution models, but unlinked the clock models. Under the substitution sites model, we used equal rates, linear mutation bias and the two-phase* model options. Under the strict clock model, an average mutation rate with 95% confidence intervals was used54. The analyses were run by specifying a chain length of 1.8 × 108, with parameters logged every 2 × 103 iterations. The log files were analysed in Tracer v1.660 to ensure that the program was run for sufficiently long for the Markov chains to reach convergence.We estimated both point estimates and long-term trends in Supplementary Information.


Genome-wide association studies focused on searching genes responsible forseveral diseases. Admixture mapping studies proposed a more efficientalternative capable of detecting polymorphisms contributing with a small effecton the disease risk. This method focuses on the higher values of linkagedisequilibrium in admixed populations. To test this, we analyzed 10 genomicregions previously defined as related with colorectal cancer among ninepopulations and studied the variation pattern of haplotypic structures andheterozygosity values on seven categories of SNPs. Both analyses showeddifferences among chromosomal regions and studied populations. AdmixedLatin-American samples generally show intermediate values. Heterozygosity of theSNPs grouped in categories varies more in each gene than in each population.African related populations have more blocks per chromosomal region, coherentlywith their antiquity. In sum, some similarities were found among Latin Americanpopulations, but each chromosomal region showed a particular behavior, despitethe fact that the study refers to genes and regions related with one particularcomplex disease. This study strongly suggests the necessity of developingstatistical methods to deal with di- or tri-hybrid populations, as well as tocarefully analyze the different historic and demographic scenarios, and thedifferent characteristics of particular chromosomal regions and evolutionaryforces. One of the greatest challenges in genetic epidemiology is the development andapplication of methodological strategies allowing identification of genetic riskloci in order to achieve a more thorough understanding of the genetic basis ofcomplex diseases, as they are the result of interactions between multiple geneticand/or environmental factors, each with modest effects. It is likely that differentcombinations produce the same clinical symptoms . Also, mLinkage and association studies are the two main approaches applied to identify thegenetic basis of these types of diseases . HoweverAdmixture mapping studies (AMSs) constitute an alternative approach. This methodologywas first proposed by The process of admixture in the Americas can be seen as a natural experiment forgenetic epidemiology and anthropology, in which polymorphic marker loci are used toinfer a genetic basis for traits of interest . NowadayThe grade of contribution of each parental population will be reflected not only inthe amount of chromosomes from each ancestral origin, but in the quantity of blocksfrom these origins inside chromosomes, and depend on the admixture process  inadmixed populations, because past studies were mainly based on populations ofEuropean descent. CRC is common in both sexes and has no major avoidable riskfactor. By determining the ancestral proportions, as well as the heterozygosity andsize of fragments in five admixed American populations and several populations fromEurope, Africa and Asia in associated regions, we intend to help in theunderstanding of genetic CRC causes.We used data available in 1000 Genomes Project for eight populations and anunpublished set of genetically admixed Mexican samples. Regarding the 1000Genome Project samples , five arWe are particularly interested in another Mexican sample because it is formed by healthy controls of a GWAS study of CRC . The individuals wererecruited in different blood banks, three in Mexico City , three in Monterrey and three in Torreon , from 2010 to 2012. All subjects gave informed consent forinclusion before they participated in the study. The protocol was approved bythe ethics committees of each participating institution , the Federal Commission for Protection againstHealth Risks (COFEPRIS), code CMN2012-001, and the Ethics Committee of CHIBCHAproject number: 223 678.Samples were genotyped using two complementary arrays: Axiom Genome-Wide LAT 1(Latino) Array and a Custom-designed Array, both from Affymetrix AxiomGenotyping Solutions. The former was designed to maximize coverage of common andrare disease-associated alleles in Latin American populations that have geneticcontributions from European, Native American and African ancestries. The latterwas specifically designed for this study, being the SNPs selection based onregions previously detected as associated with CRC. SNP calling in both arrayswas done following Affymetrix best practice workflow, which includes theGenotyping Console Software in combination with SNPolisher. A total of 1,169,944SNPs  wasobtained. These samples were included because its large number of individualsand the high coverage of SNPs in the considered regions represent an opportunityto compare the performance of another admixed population.Genotypes of Native American (NAM) samples were used in order to estimate theglobal individual ancestry. These genotypes included individuals from fiveethnic groups: Zapotecs from Oaxaca, Mexico (N=21), Tepehuans from Durango inNorthern Mexico (N=23), Nahuas from Central Mexico (N=14), Mayas from Campeche,Mexico (N=25), Quechuas from Cerro de Pasco, Perú (N=24) and Aymaras from LaPaz, Bolivia (N=25). We consider a panel of AIMs developed and optimized for thestudy of Latin American populations by the LACE Consortium compared to other k values .In order to understand the structure of the MEX sample, we performed a globalindividual admixture analysis using the AIMs panel described above. Estimationof individual admixture fractions were calculated with ADMIXTURE softwareversion 1.3.1  heterozygosity related to categories of SNPs vary in differentregions; b) some chromosomal regions do not show heterozygosity in somecategories of SNPs; c) heterozygosity varies when considering differentpopulations, but its behavior is relatively coherent in the differentcategories: Africans and Afro-descendants, European and Asian, and LatinAmerican admixed ones.APC gene, relative differences inheterozygosity values associated to the categories of SNPs remain constant inall populations being 3’UTR the one with greatest values of heterozygosityfollowed by synonymous variants, except in the EU and CLM samples, wheresynonymous variants are greater  have highervalues of heterozygosity in intronic and synonymous categories, while 3’UTRregions are more homogeneous. Puerto Rico has an intermediate place betweenAfrican related and other considered populations  show heterozygosity values for all genes that tend to beintermediate among the values of the parental samples . Although,the ASW, also admixed, shows a pattern closer to the AF than to any othersample, in concordances with the high contributions of African genes (76%); insome cases, also PUR approximates more to those samples.BRAF, PUR that shows more blocks inMSH6, and MEX that shows more blocks atMSH2 and PMS2 genes and an unexpectedbehavior related to the other Mexican sample (MXL).The quantity of phased haplotype blocks per gene was analyzed for each population. The AfrThe variability of the size of the blocks shows diversity among populations. It variThe results obtained using the selected AIMs supports the use of these markers fordetecting admixture in Latin American populations, as demonstrated in severalstudies performed before . A comprMLH1 andMSH6), in the 16q22.1 region the European contributionprevails. However, in most of the regions, the predominant ancestry is not the samefor all samples. In MSH2, the European contribution is predominantexcept in the ASW in which the African is the greatest. This exposes a differentsituation for each population and for each genomic region and outlines theimportance of considering the local ancestry complementary to the global ancestrywhen performing association analysis in order to avoid spurious associations.The ancestry analysis by region evidenced a different result in each one of the 10regions. While in some genes the Asian contribution (as a proxy of Native American)predominates in all the admixed samples (MSH6), being in most of the cases the highest mean value foundin the African samples . And finally, in four cases , the lowest mean values were found intwo admixed populations (PEL and MXL).A similar conclusion can be drawn by taking into account the genetic variationanalyses. The heterozygosity values showed very dissimilar ancestral contribution bypopulation and by region. Only in one of the regions considered the highest andlowest mean values of heterozygosity were detected in one parental population sample was recruited in Los Angeles,California, and consequently, it can better be compared with Mexican Americans.Both, in ancestry and in genetic variation analyses, the Native American contributionin Peruvian and Mexican samples is the highest, and consequently, it is possible topresuppose that the genetic variation patterns could be more closely related toNative Americans than in other Latin American populations. This is reflected in theversus 831, respectively). This fact is not minor, as biggersamples may uncover heterogeneities due to subestructuration. Then, variation indifferent parameters can be explained because of that, as the apparent presence ofvariation in heterozygosity at 3’ and synonymous not found in MXL but in MEX inMSH2 gene  several evolutionary forces- not only genetic flow-act on genetic variability; and 2) each region analyzed has special behavior whengenetic variation is analyzed, despite all genes and chromosomal regionsanalyzed.Related to the first, our data suggest that the patterns of ancestry and variabilityappear in certain genomic regions and under certain circumstances, but not inothers. Different microevolutive forces such as selection, genetic drift, andeventually recombination, conversion and hitchhiking are probably present . MoreoveRelated to the second, the 10 analyzed regions were detected as associated with CRCin European populations (Several authors (among others, Finally, this study strongly suggests the necessity of developing statistical methodsto deal with di or tri-hybrid populations. It is also necessary to carefully analyzethe different historical and demographic scenarios of each particular population toavoid generalizations, since, considering Latin America as a whole, is moretheoretical than real.


Northern China harbored the world’s earliest complex societies based on millet farming, in two major centers in the Yellow (YR) and West Liao (WLR) River basins. Until now, their genetic histories have remained largely unknown. Here we present 55 ancient genomes dating to 7500-1700 BP from the YR, WLR, and Amur River (AR) regions. Contrary to the genetic stability in the AR, the YR and WLR genetic profiles substantially changed over time. The YR populations show a monotonic increase over time in their genetic affinity with present-day southern Chinese and Southeast Asians. In the WLR, intensification of farming in the Late Neolithic is correlated with increased YR affinity while the inclusion of a pastoral economy in the Bronze Age was correlated with increased AR affinity. Our results suggest a link between changes in subsistence strategy and human migration, and fuel the debate about archaeolinguistic signatures of past human migration. Northern China contains some of the world’s earliest farming societies. Here, authors use 55 ancient genomes to trace the genetic history of human migrations across northern China for the last 7500 years, and document genetic changes mirroring shifts in subsistence strategy. Northern China represents a large geographic region that encompasses the Central Plain in the middle-to-lower Yellow River (YR) basin, the birthplace of the well-known YR civilization since the Neolithic period. However, northern China extends far beyond the Central Plain and includes several other major river systems in distinct ecoregions  and broomcorn millets (Panicum miliaceum) were first cultivated in the WLR and lower reaches of the YR basins since at least 6000 BCE6. In the ensuing five millennia, millets domesticated in northern China spread across east Eurasia and beyond. Millets had served as one of the main staple foods in northeast Asia, particularly until the introduction of maize and sweet potato in the 16–17th centuries7.China is one of the earliest independent centers in the world for the domestication of cereal crops, second only to the Near East, with the rainfed rice agriculture in the Yangtze River Basin in southern Chinaons Fig. . Especiaons Fig.  played a9. By the Middle Neolithic (roughly 4000 BCE), complex societies with a substantial reliance on millet farming had developed in the WLR 11 and in the YR  basins11. For example, excavations of Hongshan societies in the WLR yielded public ceremonial platforms with substantial offerings including numerous jade ornaments, among which the “Goddess Temple” at the Niuheliang site is the most famous12. The establishment of the Middle Neolithic complex societies appears to have been associated with rapid population growth and cultural innovation, and may have been linked to the dispersal of two major language families, Sino-Tibetan from the YR14 and Transeurasian from the WLR15, although some scholars debate the genealogical unity of the latter17.Both the YR and the WLR are known for rich archeological cultures that relied substantially on millet farming18, but was partially replaced by nomadic pastoralism in the subsequent Upper Xiajiadian culture (1000–600 BCE). Although many archeologists associated this subsistence switch with a response to the climate change20, it remains to be investigated whether substantial human migrations mediated these changes. The WLR region adjoins the Amur River (AR) region to the northeast, in which people continued to rely on hunting, fishing, and animal husbandry combined with some cultivation of millet, barley, and legumes into the historic era22. Little is known to what extent contacts and interaction between YR and WLR societies affected the dispersal of millet farming over northern China and surrounding regions. More generally, given the limited availability of ancient human genomes so far, prehistoric human migrations and contacts as well as their impact on present-day populations are still poorly understood in this region.Compared with the YR region where crop cultivation already took the status of the dominant subsistence strategy by the Middle Neolithic, the level of reliance on crops in the WLR region has changed frequently in association with changes in climate and archeological culture. For example, paleobotanical and isotopic evidence suggest that the contribution of millets to the diet of the WLR people steadily increased from the Xinglongwa to Hongshan to Lower Xiajiadian (2200–1600 BCE) culturesHere, we present the genetic analysis of 55 ancient human genomes from various archeological sites representative of major archeological cultures across northern China since the Middle Neolithic. By the spatiotemporal comparison of their genetic profiles, we provide an overview of past human migration and admixture events in this region and compare them with changes in subsistence strategy.We initially screened a total of 107 ancient individuals across northern China by shallow shotgun sequencing of one Illumina sequencing library per individual , Tibetans, and lowland East Asian populations (e.g. Han and Tujia)  of 2077 present-day Eurasian individuals in the “HumanOrigins” datasetia) Fig. . Here fi31 analysis  statistics are nonsignificant (Z < 3) for most outgroup populations (X’s) except for the two present-day Siberian populations  and three Iron Age individuals  from the Upper AR, and one Bronze Age WLR individual from a nomadic pastoralist context (“WLR_BA_o”) form a tight cluster that falls within the range of present-day AR populations, who are mostly Tungusic speakers Fig. . One indsis Fig. . Between32 rejects the hypothesis that the ancient AR populations in this study are the direct ancestor of the present-day ones   . Tibeto-Burman-speaking Naxi from southwest China show much reduced but still significant differences from ancient YR populations  and Late Neolithic Shanxi province (“Shimao_LN”), both located between the YR and WLR, are genetically similar to each other and to ancient YR populations Fig.  and 17. 33. More broadly, recent linguistic studies favor a northern origin of Sino-Tibetan languages, suggesting the Yangshao culture as their likely origin14. We explored genetic connections between present-day Sino-Tibetan and ancient YR populations using admixture modeling. Tibetans are modeled as a mixture of Sherpa and Upper_YR_LN, although other sources also work  are closer to the YR cluster while one from a nearby site (“HMMH_MN”) falls closer to the AR cluster Fig. . F4 stat20. Our results highlight the other side of the process: climate change made a pastoral economy more favorable and may have led to an influx of people already practicing it.In addition to this genetic heterogeneity around the Middle Neolithic WLR, a temporal comparison within the WLR also shows an interesting pattern of genetic changes. First, Late Neolithic genomes associated with the Lower Xiajiadian culture (“WLR_LN”) overlap with the ancient YR cluster in the PCA and show less affinity to Siberian populations compared with WLR_MN Fig. . QpAdm mIn this study we present a large-scale survey of ancient genomes from northern China that covers many, although not all, major archeological cultures in the region. Especially, our study provides the first genomic look into people who lived in the earliest complex societies of northern China, i.e., Yangshao and Hongshan cultures in the YR and WLR, respectively. By providing genomic time series in these regions, we could detect genetic changes in each region over time and associate them with external genetic sources and with sociocultural and environmental changes.In contrast to the long-term stability of the genetic profile of the AR populations who practiced limited food production, we observe frequent genetic changes in the two centers of complex millet-farming societies in northern China, the YR and WLR, over the last six millennia. The WLR genetic profile changes over time in close association with changes in subsistence strategy. More specifically, an increase in the reliance on millet farming between the Middle-to-Late Neolithic is associated with higher YR genetic affinity in the Late Neolithic WLR, while a partial switch to pastoralism in the Bronze Age Upper Xiajiadian culture is associated with lower YR affinity. In the Middle Neolithic, we observe a sharp transition from YR- to AR-related genetic profiles around the WLR. Such a spatial genetic heterogeneity may have persisted in the WLR during the Bronze and Iron Ages although our current data are not sufficient to test such a hypothesis. The Middle-to-Late Neolithic genetic change in the YR also coincides with the intensification of rice farming in the Central Plain, which may provide another case of change in subsistence strategy via demic diffusion. We acknowledge that our current data set lacks ancient genomes from candidate source populations which may have brought rice farming into the Central Plain and call for archaeogenetic studies for them, especially Neolithic people from the Shandong and Lower Yangtze River regions. Future studies of ancient genomes across China, particularly the genomes of the first farmers will be critical to test the representativeness of the genomes reported in this study, to understand the genetic changes we detected at finer genetic, archeological and geographic scales, and to test the evolutionary correlation between archeological cultures, languages, and genes.37. Prior to sampling, we wiped all skeletal elements with 5% bleach and irradiated with UV-light for 30 min from each side. We drilled teeth to obtain fine powder using a dental drill . We sampled the dense part of petrous bones around the cochlea by first removing the outer part using the sandblaster , and then grinding the clean inner part into fine powder with the mixer mill . We digested the powder (50–100 mg) in 900 μl 0.5 M EDTA (Sigma-Aldrich), 16.7 μl of Proteinase K (Sigma-Aldrich), and 83.3 μl ddH2O  at 37 °C for 18 h. Then we transferred the supernatant to a MinElute silica spin column  after fully mixed with the 13 ml custom binding buffer  followed by two washes with PE buffer (80% ethanol). Then we eluted the DNA with 100 μl TET buffer .We initially screened 107 ancient samples  and T4 DNA Polymerase , and incubating at 25 °C for 15 min. We retrieved the repaired DNA fragments using a standard MinElute purification step , and then by eluting the samples in 18 μl TET . We ligated Illumina adapters (0.25 μM adapter mix) onto the blunt-ends using 1X Quick Ligase  in a total reaction volume of 40 μl, followed by another MinElute purification step. We performed the final fill-in step by adding 1X isothermal buffer, 0.4 U/μl Bst-polymerase (NEB) and 250 μM dNTP Mix (Thermo Fisher), followed by incubating at 37 °C for 30 min and then at 80 °C for 20 min. We then indexed the libraries with uniquely combined double indices. We purified indexed products using AMPure XP bead (Beckman Coulter Ltd). We qualified the clean-up libraries by Qubit 2.0 (Thermo Fisher). We then sequenced the libraries on an Illumina HiSeq X10 instrument at the Annoroad Company, China in the 150-bp paired-end sequencing design.38. We mapped merged reads to the human reference genome  using BWA v0.7.1239. We removed PCR duplicates using DeDup v0.12.240. To minimize the impact of postmortem DNA damage on genotyping, we prepared additional “trimmed” BAM files by soft masking up to 10 bp on both ends of each read using the trimbam function on bamUtils v1.0.1341 based on the DNA misincorporation pattern of each library. For the SNPs in the “1240k” panel43, we randomly sampled a single high-quality base  as pseudodiploid genotypes using the pileupCaller program (https://github.com/stschiff/sequenceTools). For C/T and G/A SNPs, we used trimmed BAM files. For the remaining SNPs, we used untrimmed BAM files.Sequence reads were demultiplexed by allowing one mismatch in each of the two 8-bp index sequences. We clipped the Illumina sequencing adapters by AdapterRemoval v2.2.023 and the other intersecting over multiple Illumina array platforms 27. The latter include more Tibetan/Sherpa genomes (30 Tibetans and 45 Sherpa) for an in-depth analysis of Sino-Tibetan-speaking populations. We augmented both data sets by adding the Simons Genome Diversity Panel44 and published ancient genomes 45. Second, we estimated mitochondrial contamination rates for all individuals using Schmutzi v1.5.146. Third, we measured the nuclear genome contamination rate in males based on X chromosome data as implemented in ANGSD v0.91047. Since males have only a single copy of the X chromosome, mismatches between bases, aligned to the same polymorphic position, beyond the level of sequencing error are considered as evidence of contamination.We used multiple methods to assess the quality of the ancient genomes from northern China. First, we tabulated patterns of postmortem chemical modifications expected for ancient DNA using mapDamage v2.0.648. We generated the mtDNA consensus sequences of our ancient individuals using the Geneious v11.1.3 software49, and then determined their mtDNA haplogroups using HaploGrep250. We determined the male Y chromosome haplogroup by examining a set of positions on the 25,660 diagnostic positions on the ISOGG database, and assigned the final haplogroups by the most downstream derived SNPs f3 statistics56 to obtain a measurement of genetic relationship between two populations since their divergence from an African outgroup. We calculated f4 statistics with the “f4mode: YES” function in the admixtools56. F3 and f4 statistics were calculated using qp3Pop v435 and qpDstat v755 in the admixtools package.We used outgroup-43. We used the following nine populations in both “HumanOrigins” and “1240k-Illumina” datasets as outgroup (“OG1”): Mbuti, Natufian, Onge (Onge.DG in “1240k-Illumina” panel), Iran_N, Villabruna, Mixe (Mixe.DG in “1240k-Illumina” panel), Ami (Ami.DG in “1240k-Illumina” panel), Nganasan and Itelmen (Itelmen.DG in “1240k-Illumina” panel). This set includes an African outgroup (Mbuti), early Holocene Levantine hunter-gatherers (Natufian), Andamanese islanders (Onge), early Neolithic Iranians from the Tepe Ganj Dareh site (Iran_N), late Pleistocene European hunter-gatherers (Villabruna), Central Native Americans (Mixe), an indigenous group native to Taiwan (Ami), indigenous Samoyedic people inhabiting the Taymyr Peninsula in north Siberia (Nganasan), and an ethnic group native to the Kamchatka Peninsula in Russia (Itelmen). Given that some AR populations show increased genetic affinity with Nganasan and Itelmen 32, downloaded from https://github.com/Schraiber/continuity. Specifically, we prepared read count data of ancient individuals from the pileup tables used for haploid genotyping, and allele count data of five present-day AR populations  from the eigenstrat format genotype data. Starting from 593,124 autosomal SNPs in the HumanOrigins panel, we excluded SNPs fixed in each present-day population and applied a default coverage filter for ancient individuals . For the remaining SNPs used for the analysis, we fitted beta prior for the allele count distribution of present-day populations to account for the relatively small sample size of the present-day populations. Then we calculated likelihoods of the models that assumes the continuity  and that does not . Likelihood ratio test provides p value for testing genetic continuity.For pairs of ancient and present-day AR populations, we tested if the ancient individuals can be placed into the direct ancestral population of the present-day ones using a published methodFurther information on research design is available in the Supplementary InformationPeer Review FileReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3


Equus asinus) populations, using polymorphism of 17 microsatellite markers. The results revealed a highly mixed genotype of all the examined donkeys, suggesting that two different group of breeds can be distinguished from each other on the basis of microsatellite markers.This study was conducted to evaluate the genetic variability of Turkish native donkey  was calculated for each locus and ranged from 0.36 (locus ASB02) to 0.98 (locus AHT05), which has the highest number of alleles per locus in the present study. The average PIC in our populations was 0.696. The average coefficient of gene differentiation (GST) over the 17 loci was 0.020 ± 0.037 (p < 0.01). The GST values for single loci ranged from −0.004 for LEX54 to 0.162 for COR082. Nei’s gene diversity index (Ht) for loci ranged from 0.445 (ASB02) to 0.890 (AHT05), with an average of 0.696. A Bayesian clustering method, the Structure software, was used for clustering algorithms of multi-locus genotypes to identify the population structure and the pattern of admixture within the populations. When the number of ancestral populations varied from K = 1 to 20, the largest change in the log of the likelihood function (ΔK) was when K = 2. The results for K = 2 indicate a clear separation between Clade I  and Clade II  populations.This study presents the first insights to the genetic diversity and structure of the Turkish donkey populations. The primary objectives were to detect the main structural features of Turkish donkeys by microsatellite markers. A panel of 17 microsatellite markers was applied for genotyping 314 donkeys from 16 locations of Turkey. One hundred and forty-two alleles were identified and the number of alleles per locus ranged from 4 to 12. The highest number of alleles was observed in AHT05 (12) and the lowest in ASB02 and HTG06 (4), while ASB17 was monomorphic. The mean  Turkey is one of the important bio-geographical countries which encompass three important biodiversity hotspots such as the Caucasus, Iran-Anatolian, and Mediterranean basin . Also TuNumerous factors such as technological improvement, population explosion, industrialization are responsible for the disturbance of natural balance permanently. For this reason, in order to conserve the plant and animal biodiversity, especially against the threat of endanger or disappearance, protection and conservation strategies have to be formed. The morphological and genetic identification of the species for the establishment of these strategies become crucial issues for the conservation of biodiversity. Without these protective strategies, significant changes are likely to occur in the ecosystem balance and biodiversity level.Turkey is an agricultural country where plants and animals have been raised since ancient times. For this reason, it is considered that many local animal breeds were first bred here and spread to other geographical regions of the world. Turkey has rich animal genetic resources and donkeys are one of these resources, but yet so little information is verified for the donkey breeds of Turkey. Donkey breeds that helped the breeders under severe natural conditions, and have assisted in transportation for centuries, and are still used in some rural areas in Turkey, are under threat of extinction due to the impact of industrialization ,4,5. In To date, little information has been found about the donkey breeds of Turkey; even some of the donkey breeds of Turkey had been extinct without clear identification. According to the Food and Agriculture Organization of the United Nations (FAO), Domestic Animal Diversity Information System (DAD-IS), Turkey has three native donkey breeds: Anatolian, Merzifon and Karakaçan donkey breeds. But on the other hand, in other unpublished records, Mardin White donkey (close to Mardin province), Toros donkey , Urfa Rahvan donkey (close to Şanlıurfa province) and Kars Yorga donkey (close to Kars Province that is located in extremely north east part of Turkey) breeds are also found in Turkey .So the aim of this study has threefold: (i) to provide a detailed sampling across the entire country for molecular characterization to check if the above mentioned breeds are still found in Turkey or not, (ii) to analyze the genetic diversity of donkeys raised in Turkey using a set of 17 microsatellite markers, (iii) and to determine the genetic relationship and characterize geographical and genetic differentiation between different donkey breeds at different sites in Turkey. This research is the first application of molecular markers to characterize the donkey breeds raised in Turkey.According to the FAO database ; the estimated number of donkeys in Turkey is about 150,000. In this study, a total of 314 blood samples from different individuals were collected from 16 different locations . These lBlood samples were taken to Ethylenediaminetetra-acetic acid (EDTA)  coated vacutainer tubes and stored at +4 °C until DNA extraction. Approximately 10 mL of blood per animal was collected and genomic DNA was extracted from whole blood using the standard phenol-chloroform-isoamyl alcohol (25:24:1) extraction method. Extracted DNA was diluted in TE buffer (10:1) and stored +4 °C till analysis.In this study, twenty microsatellite markers  which have been widely used and recommended for individual identification and parentage verification of equines were selected according to FAO’s guidelines [TM, VIC®, NEDTM, PET®) at the 5’ end. Four multiplex PCRs using fluorescently labeled primers were developed . The reactions were performed in a total 10 µL volume. PCRs were comprised with approximately 50 ng DNA, 1X PCR buffer 1.5 mM MgCl2, 0.2 mM dNTPs, 0.050 pmol of each primers, 1 U Taq DNA Polymerase (Invitrogen Taq DNA Polymerase).All the microsatellite loci were amplified by Polymerase Chain Reaction (PCR) and each forward primer was labeled with fluorescent dyes .PCR products were checked in 2% agarose gel stained with SYBR™ Safe DNA Gel Stain. The samples were mixed with formamide and LIZ® 500-bp internal size standard (Applied Biosystems™) and detected by capillary electrophoresis using a 3500 XL Genetic Analyzer® (Applied Biosystems™) sequencer. Allele sizes were determined with the O), expected heterozygosity (HE), private alleles (NP) Hardy–Weinberg equilibrium and null allele frequencies using Genetix v4.05 [ST, FIS and FIT) as proposed by Weir and Cockerham [T), diversity between breeds (DST) and coefficient of gene differentiation (GST) values were calculated with FSTAT v2.9.4 [A genetic distances were calculated using the Populations v 1.2.30 software. Neighbour-net dendrogram constructed from Reynold’s genetic distances by using SplitsTree v4.16.0 [Genetic diversity parameters were estimated for each microsatellite locus and across all loci for each population by total number of alleles, the mean number of alleles (Na), effective number of alleles (Ne), polymorphic information content for each locus (PIC), observed heterozygosity  ,30,31. AOut of the 20 microsatellites analyzed, the following 17 showed useful data in study population of the Turkish donkey: AHT05, ASB02, ASB23, COR007, COR018, COR058, COR071, COR082, HMS02, HMS03, HMS07, HMS20, HTG06, HTG07, HTG10, LEX54 and VHL209 . The ASBa), the number of effective alleles (Ne), the number of private alleles (Np), expected (HE) and observed (HO) heterozygosity, FIS, and PIC values for each population were given in OH) and unbiased expected (E)H heterozygosities per location ranged from 0.6266 (KIR) to 0.7139 (AYD) and 0.6294 (ISP) to 0.6983 (ANT), respectively. The mean OH in the Turkish donkey was estimated to be 0.677, while mean EH was 0.675 (a was the highest in KIR (6.706) and lowest in AYD donkeys (4.529). FIS value within the populations varied between −0.0557 in KAS and 0.0923 in KIR population, wheras FIS was statistically significant only for KIR breeds due to the deficiency of heterozygosity. A total of 13 private alleles were identified in the present work, and most of the private alleles (ten) were at low frequencies of below 5%. Three alleles unique to KAH (0.115), MAL (0.050) and AYD (0.083) showed a frequency that exceeded 5%  per locus was 0.418 (COR082) to 0.857 (AHT05), 0.658 on average. The expected heterozygosity per locus was 0.438 (HMS07) to 0.884 (AHT05), 0.670 on average. Mean HO and HE were higher than 0.418 for all loci. However, the value of HO for 7 loci  was lower than the value of HE, indicating an excess of homozygosity. The highest FIS value was observed in marker COR082 (0.2959), while the lowest FIS was recorded for locus HMS07 (−0.0602). The maximum and minimum FIT values were found in markers COR082 and HMS07, respectively. FST values ranged from 0.0000 to 0.1955, and the average values of FIS, FST and FIT were 0.0194, 0.0192 and 0.0382 accordingly. Mean FST (0.0192) was moderate to low while HS (0.683) was relatively high. Obtained overall DST value describing the diversity between breeds was 0.014. The average coefficient of gene differentiation (GST) over the 17 loci was 0.020 ± 0.037 (p < 0.01). The GST values for single loci ranged from −0.004 for LEX54 to 0.162 for COR082. Nei’s gene diversity index (Ht) for loci ranged from 0.445 (ASB02) to 0.890 (AHT05), with an average of 0.696. The presence of null alleles, defined as non-amplifying alleles, due to mutations at PCR priming sites, causes overestimation of both FST and genetic distance values. The null allele frequencies ranged from 0.000  to 0.1894 (HTG06). The null allele frequencies in the studied microsatellite loci were below 20%  is a useful tool to check how the genetic diversity is distributed within and among populations, whose structure is quantified by FHypothesis (1) seven groups according to the geographical distribution, i.e., group 1: The Marmara region (MAR) populations ; group 2: The Black Sea region (BSR) populations ; group 3: The Aegean region (AER) populations ; group 4: The Central Anatolia region (CAR) population (KON); group 5: The Mediterranean region (MDR) populations , Eastern Anatolia region (EAR) population (KAR) and group 7: South East Anatolian region (SAR) populations .Hypothesis (2) four groups according to the Reynold’s genetic distances distribution, i.e., group 1: MRM region , SAR region  and EAR region (KAR) populations; group 2: AER region (KUT) population and MDR region population (ISP); group 3: AER region population (AYD) and MDR region population (KAH); group 4: MRM region population (MAL), BSR region populations , AER region population (MUG), CAR region population (KON) and MDR region population (ANT).p < 0.001) for all the studied loci (p < 0.05). The Hypothesis (2) AMOVA analyses results showed that the variation among groups, among populations within groups, among individuals within populations, and within individuals were 2.16%, 0.49%, 1.68% and 95.67%, respectively. Variance components among groups, among individuals within populations and within individuals were significant (p < 0.001) for all the studied loci demonstrating significant Reynold’s genetic distances distribution in studied donkey populations (p < 0.05).The ied loci , demonstulations . FurtherThe populations’ structure  was analIn this study, 20 microsatellite markers  were used for genetic characterizing the population genetic structure and genetic diversity of 16 donkey population that cover the 7 geographical regions in Turkey. The ASB17, COR022 and LEX73 loci were excluded from the statistical analysis. The COR022 and LEX73 loci failed to amplify in all the samples and did not provide data for reliable analysis while the ASB17 marker was monomorphic (91 bp) for all examined animals. Similar results were reported in the Balkan donkey populations . This sta), effective number of alleles (Ne), and expected and observed heterozygosities. These results are comparable to the previous values reported in Balkan donkeys [The 17 microsatellite analysis revealed relatively high level of genetic diversity between individuals, but no high significant differences in the main genetic characteristics of the geographical region’s population : number of alleles  to 0.6983 (ANT), which showed a comparable level to the previous values reported in Spanish donkeys [The results of microsatellite polymorphism revealed relatively high degree of heterozygosity in the Turkish donkey populations investigated in this study. Among Turkish donkey populations, the H donkeys , Catalon donkeys , Croatia donkeys , Balkan  donkeys , Tunisia donkeys , Chinese donkeys ,40,41,42 donkeys ,45,46,47 donkeys . This fiIS index indicates the excess of homozygosity in a subpopulation and, with reference to molecular markers, informs if a pattern of reduction in diversity owing to several causes exists. In this study, FIS ranged from a minimum of −0.0557 in KAS to a maximum of 0.0923 in KIR population, whereas FIS was statistically significant only for KIR breeds due to the deficiency of heterozygosity. The highly significant (p < 0.001) FIS value (0.0923) revealed a rather high inbreeding degree within populations. The heterozygote deficiency found in the KIR population, could be due to the higher rate of inbreeding, to the population subdivision (Wahlund effect), and to the presence of “null alleles” .FST and genetic distance values. It was reported by Dakin and Avise [Null alleles, defined as nonamplifiable alleles due to mutations in the PCR binding site, cause only a single allele to peak like a homozygote, thus cause erroneous readings. These alleles cause overestimation of both Fnd Avise  that nulPrivate alleles are alleles that are found only in a single population among a broader collection of populations. These alleles have proven to be informative for diverse types of population-genetic studies. These private alleles are present in greater numbers in differentiated donkey breeds ,38,42. TPIC is a parameter indicative of the degree of informativeness of a marker. The PIC value may range from 0 to 1. In the studied Turkish donkey population, the average PIC value was 0.696 ranging from 0.3600 (ASB02) to 0.9800 (AHT05). When PIC > 0.5, 0.5 > PIC > 0.25, and PIC < 0.25, it indicates the locus has high polymorphism, moderate polymorphic, and low polymorphism, respectively . ThirteeST), coefficient of gene diversity (GST) and Nei gene diversity (HT), were determined as 0.014, 0.020, and 0.696, respectively. The global mean of the genetic diversity value (DST) indicated that the low diversity among 16 Turkish donkey populations studied. Nei’s gene diversity values (HT) was considerably lower than Zhang et al. [ST value obtained from overall loci pointed out that 2% of total genetic variation resulted from the differences between the populations. In all other respects, it can be said that 98% genetic variation is caused by the difference between individuals. All studied loci showed a not significant deviation from the Hardy-Weinberg Equation. Null allele frequencies were lower than the reported value (20%) by Dakin and Avise [In this study, the mean value of between-population diversity value  in this study, the distribution of FST showed low genetic divergence (0.000 < FST < 0.05) among populations in general. The FST comparison values obtained were significant in 70 pairwise calculations . The highest level of differentiation was observed between SAN–MAL, KAR–TOK and KUT–TOK populations (FST > 0.05) and the lowest between CAT–KIR, KON–MAR, KAS–MER, ANT–KAS and ANT–KON breeds (FST = 0.000), respectively. Neighbor-net representing the Reynolds distance confirmed these findings; Cluster II (KUT – ISP) and Cluster II (AYD – KAH) groups clustered in an intermediate position between the Cluster I  and Cluster IV . The FST value of Turkish donkey populations detected in this study was similar to the value of American donkeys [According to all the pairwise differences , although substantially smaller than the within individual’s component.An AMOVA was carried out to investigate the relative contribution of different factors to the observed genetic variability, with each factor considered in a separate analysis, i.e., seven groups according to the geographical prevalence , four groups according to the Reynold’s genetic distances distribution , SAR region  and EAR region (KAR) populations, 2nd: AER region (KUT) population and MDR region population (ISP); 3rd: AER region population (AYD) and MDR region population (KAH); 4rd group: MRM region population (MAL), BSR region populations , AER region population (MUG), CAR region population (KON) and MDR region population (ANT)). AMOVA analysis results indicate that the majority of the observed variance is due to differences among individuals within populations. The most part of the variation is observed within the individuals (96.29% Hypothesis 1 and 95.67% Hypothesis 2) whereas the differences among groups represent only the 1.07% and 2.16% of the variation, respectively. These results are similar to a wide range of studies ,45,54. AST and genetic distance results. Our results provide a broad perspective on the extant genetic diversity and population structure of Turkish donkey populations. In this study the analysis with the STRUCTURE programme revealed that Turkish donkey populations were grouped into two lineages when K = 2 . ClusterGenetic diversity is the main component of the adaptive evolution mechanism because of its preeminent role for the long-term survival probability of all species. In summary, our results suggested the relatively high genetic diversity of 16 Turkish donkey populations and brought an insight in the structure of the analyzed populations. This study is the first attempt towards a comprehensive genetic characterization of Turkish donkey populations. Despite the decreasing population size, the genetic diversity of Turkish donkey population seems still conserved. Nevertheless, further studies should be conducted to deeply evaluate the genetic variability of the Turkish donkey breeds. Furthermore, the results of this study can be utilized for future breeding strategies and conservation.


Oryza rufipogon Griff.) in China. A better understanding of the genetic structure of O. rufipogon populations will therefore be useful for the development of conservation strategies. We examined the diversity and genetic structure of natural O. rufipogon populations at the national, provincial, and local levels using simple sequence repeat (SSR) markers. Twenty representative populations from sites across China showed high levels of genetic variability, and approximately 44% of the total genetic variation was among populations. At the local level, we studied fourteen populations in Guangxi Province and four populations in Jiangxi Province. Populations from similar ecosystems showed less genetic differentiation, and local environmental conditions rather than geographic distance appeared to have influenced gene flow during population genetic evolution. We identified a triangular area, including northern Hainan, southern Guangdong, and southwestern Guangxi, as the genetic diversity center of O. rufipogon in China, and we proposed that this area should be given priority during the development of ex situ and in situ conservation strategies. Populations from less common ecosystem types should also be given priority for in situ conservation.Over the past 30 years, human disturbance and habitat fragmentation have severely endangered the survival of common wild rice ( Oryza rufipogon Griff.) is the putative progenitor of Asian cultivated rice, one of the most important food crops in the world. It is also an important source of germplasm for rice improvement3. Ding Ying found wild rice (O. rufipogon) in Guangzhou in 1926, and the wild × cultivated cross Zhong Shan No. 1 was widely planted in South China for more than 50 years. In 1970, Yuan Longping and his assistant discovered wild rice with male sterility in Hainan and used it to breed high yielding “three-line” hybrid rice varieties5. The increased yield of this hybrid rice saved thousands of lives in China and around the world. In recent years, wild rice has been used to introduce genes that confer agriculturally beneficial traits into cultivated species, and it holds great potential for future rice breeding efforts.Common wild rice  have either disappeared or decreased dramatically (unpublished data).Before the 1970s, O. rufipogon is therefore useful to guide the selection of future sites.The threatened status of wild rice has attracted increasing attention in China, and there is a desire to collect samples for ex situ conservation and to develop in situ conservation programs. The Chinese government began to invest in the construction of in situ conservation sites in 2001, and 15 such sites were established by the end of 2014. The government also established eight other in situ conservation sites using a mainstreaming approach, a new conservation strategy in which farmers are encouraged to participate in conservation activities that span physical boundaries such as hills and rivers. Most in situ conservation sites were selected based on scientific expertise or local government recommendations, although it was still necessary to justify their value and rationale. Detailed information on the population genetic structure of 8. More directly, genetic diversity studies can help us to adjust our strategies for collection, evaluation, and breeding. Although some studies have recently documented the population genetic structure of O. rufipogon in China13, few have focused on the development of conservation strategies based on this population genetic structure. Our study used SSR markers to examine the genetic diversity and population genetic structure of natural populations at three different levels: national (China), provincial (Guangxi Province), and local (Dongxiang population in Jiangxi Province).Information generated using molecular methods has direct and indirect consequences for the practical management and conservation of germplasm. Genetic diversity data can be useful for understanding the taxonomy and evolution of crop species, and this basic knowledge supports their conservation14 with polymorphisms and a uniform distribution among chromosomes were selected for use in the analysis of population genetic diversity and genetic structure , HO (the observed heterozygosity), HE (the expected heterozygosity) and I (the Shannon–Weaver information index) across all loci were 6.97, 0.58, 0.83, and 2.08, respectively. The values varied widely among loci: AE ranged from 2.80 (RM244) to 15.41 (RM336); HO ranged from 0.15 (RM244) to 0.88 (RM336); HE ranged from 0.64 (RM244) to 0.93 (RM253); and I ranged from 1.13 (RM244) to 2.85 (RM336).Twenty-four SSR primer pairs from previous studiesFST was 0.44, and it varied from 0.25 to 0.59, indicating that there was substantial genetic variation among populations  among populations also reflected larger differences between all populations from the whole country .The genetic structure of populations across China was analyzed. The mean value of ce Table . Among tP(D)) was largest when the number of populations, K, was equal to four  and construction of a UPGMA tree Fig. . In the our Fig. b, and foour Fig. c. The UPour Fig. d. Three Monmonier’s maximum difference algorithm was used to perform a genetic barrier prediction analysis with all populations included Fig. . The firO. rufipogon in China.The populations with higher genetic diversity formed a triangular area Fig. a that reHS), the among-population genetic diversity (DST), and the coefficient of genetic differentiation (GST) were 0.38, 0.36 and 0.49, respectively, suggesting that most genetic variability occurred among populations, although GST showed somewhat higher differentiation than DST. These results agreed with the tests of pairwise genetic identity among populations  Table  and smalWe also used Monmonier’s maximum difference algorithm to perform a genetic barrier prediction analysis for 14 populations from Guangxi, revealing five predicted barriers to gene flow . The mean DST was 0.06, indicating that there was little genetic variation among the populations. The genetic differentiation over loci assessed by GST (0.12) was slightly higher than DST, but it nonetheless indicated that there was minimal differentiation among the Dongxiang populations. The genetic similarity of all individuals from the four populations was 0.74, highlighting their close genetic relationship. Based on these analyses, the four Dongxiang populations can be considered a single population when collecting samples for ex situ conservation and the establishment of in situ conservation sites.Next, DXP4 was included in the genetic structure analysis, together with the other three populations. The mean O. rufipogon population differentiation at the national, provincial, and local levels using SSR markers. FST calculations showed that almost half of the total variation occurred among populations. Previously, Zhou12 used SSR markers to investigate twelve Chinese wild rice populations from four provinces and found high genetic differentiation among them (RST = 0.52). Zheng16 analyzed the sequences of seven chloroplastic and nuclear loci and found that pairwise FST values between O. rufipogon populations at the nuclear loci ranged from 0.3175 to 0.5748. The AMOVA and pairwise FST results in the present study provide further evidence for relatively high genetic differentiation and corroborate previous results. Zhou12 concluded that population isolation caused by habitat fragmentation increased genetic differentiation by increasing the frequency of inbreeding and clonal growth. Previous studies have reported indica-like and japonica-like differentiation in the O. rufipogon population17. Wang18 also suggested that spatial or physical isolation and local adaptation may contribute to population differentiation within this species. The FIS of O. granulata was 0.402, suggesting that most populations deviated from Hardy–Weinberg expectation within populations and were deficient in heterozygotes. The FST of O. granulata was 0.859, indicating that 85.9% of the total genetic variation existed among populations19. For O. officinalis, FIS was 0.899 and FST was 0.88220. Compared with O. officinalis (selfing) and O. granulata (selfing), O. rufipogon has higher heterozygosity and greater genetic distances between populations. The value of FIS in our result was varied from -0.49 to 0.44, indicating that the diversity of mating system among O. rufipogon populations was very high. The mean value of FST was 0.44, and it varied from 0.25 to 0.59, indicating that there was substantial genetic variation among populations. The diversity patterns of species in the genera Leavenworthia21, Lycopersicon22 and Miscanthus23 with different mating systems are similar. The accumulation of mutations from generation to generation as a result of asexual reproduction may also contribute to high heterozygosity in O. rufipogon. In outbreeding species, a decrease in recombination rates is observed in certain regions of the genome, especially around centromeres24. On the contrary, in species with a high level of inbreeding, the rarity of double heterozygotes results in lowered effective recombination rates in the whole genome. Therefore, it is expected that both hitch-hiking and background selection will strongly affect genetic variability in inbreeding species24.We examined O. rufipogon in China. Previously, a genetic diversity center for O. rufipogon in south China, including Guangdong and Guangxi provinces, was proposed based on random amplified polymorphic DNA analysis13, allozyme analysis9, and SSR data12. However, our samples were collected according to a more systematic sampling strategy25 in which sampled individuals were at least 12 m apart, and approximately 30 individuals were sampled from each population to encompass at least 95% of its genetic diversity. Our genetic diversity center included one population from Hainan that was not included in the previous diversity center. Gao11 found that, like Guangdong and Guangxi, Hainan also maintained higher levels of microsatellite diversity. Wang18 also found that Hainan ranked first in China with respect to its gene diversity index and gene richness. It is reasonable that the genetic diversity center includes Hainan because it has appropriate annual temperatures (16–23 °C) and precipitation , as well as higher levels of outbreeding and diversity of ecological habitats26. In addition, populations in our proposed diversity center had more private alleles than did populations from other areas. Although the diversity of phenotypes in populations within and outside the proposed diversity center should be documented28 and compared with molecular data, our SSR data strongly suggest that the proposed triangular area is the diversity center of O. rufipogon in China. Southeastern China has generally enjoyed relative tectonic stability since the late Tertiary29, with perhaps the single exception of its two large islands. The high percentage of endemics in this region30 suggests that central and south China has played a significant role both as a center of survival but also as a center of plant differentiation and evolution during the Quaternary31. This may explain why the center of O. rufipogon genetic diversity is located in Southeastern China.Here, we identified a triangular area, including northern Hainan, southern Guangdong, and southwestern Guangxi, as the genetic diversity center of O. rufipogon is the most important genetic resource for rice breeding and the most endangered wild rice species in China; its collection and conservation are therefore increasingly important. At the national level, a region in southern China whose populations have higher genetic variation and more private alleles is likely to be the genetic diversity center of O. rufipogon. More valuable genes may exist in populations from this area, and its gene pool may be more useful for future variety improvement and biotechnology applications. Therefore, attention should be focused on O. rufipogon from this area for both the construction of in situ conservation sites and the collection of ex situ samples. However, populations outside the genetic center are also important for conservation: almost half of the genetic diversity and 32 out of 76 private alleles existed in these populations. Indeed, populations with relatively low genetic diversity may contain unique alleles that are absent from the diversity center  theory of molecular evolution36, which predicts that similar environments will select for similar genetic variants, regardless of geographic distance37. Environmental factors such as historical habitat fragmentation and local adaptation can cause divergence38, and adaptation to local conditions rather than simple geographic isolation appears to have driven O. rufipogon population differentiation. Our results suggest that ex situ sampling of multiple populations from similar ecosystems should not be a priority because such populations tend to be genetically similar even when they are separated by large distances. We should therefore reduce the number of samples for ex situ conservation collection to avoid duplication, no matter how far apart they are.Smaller population sizes that result from habitat fragmentation may lead to a loss of genetic variation through genetic drift, thereby increasing population differentiationO. rufipogon accessions from 20 populations were collected at locations from 18° N to 28° N latitude. The locations of all 20 populations are shown in Fig. O. rufipogon’s natural distribution areas7 and spread across six provinces in southern China, including Hainan, Guangdong, Guangxi, Fujian, Hunan, and Jiangxi . The PCR reaction in a total volume of 20 μl consisted of 100 mmol/L Tris–HCl, 1 U Taq polymerase, 2.5 mmol/L MgCl2, 2.5 mmol/L dNTPs, 4 μmol/L forward and reverse SSR primers, and 100 ng DNA. The PCR program was 5 min at 94 °C, followed by 35 cycles of 1 min at 94 °C, 50 s at 56–61 °C, and 1 min at 72 °C, with 10 min at 72 °C for the final extension. PCR products were mixed with 6 μl of loading buffer, denatured at 95 °C for 5 min, and separated in 6% polyacrylamide denaturing gels (38 × 30 × 1 cm3). The banding patterns were visualized according to the manufacturer’s instructions .Genomic DNA was extracted using the CTAB method according to the protocol of Edwards40, which transformed SSR data to readable input files for STRUCTURE, PowerMarker, Tassel, GENEPOP and POPGENE. Micro-Checker 2.2.3 was used to check for scoring errors and null alleles41. Based on solid foundation on study of O. rufipogon diversity, we selected the markers without null alleles for analysis45.We used POPGENE 1.3146 and GENEPOP 3.447 to calculate the genetic diversity parameters: the mean number of alleles per locus (A), the effective number of alleles (AE), the Shannon–Weaver information index (I), the observed heterozygosity (HO), and the expected heterozygosity (HE)49. Deviation from Hardy–Weinberg equilibrium and population differentiation were assessed at each locus across all populations using F statistics, including the fixation index within populations (FIS), the fixation index across all populations (FIT) and the gene differentiation index (FST)50. STRUCTURE was used to infer genetic clusters (K) with the model-based clustering method51. We assessed K values from 2 to 9 by performing ten independent runs for each K value, and the model was run with a 10,000 burn-in period and 100,000 Monte Carlo Markov chain repetitions. CLUMPP version 1.152 was used to obtain the optimal clusters for each K. The relationships between populations were assessed by Nei’s53 standard genetic distance using the Unweighted Pair Group Method with Arithmetic Mean (UPGMA) in PowerMarker54. Principal component analysis (PCA) was performed using Tassel 3.0 (https://www.maizegenetics.net/tassel) to summarize the major patterns of variation. Analysis of molecular variance (AMOVA) was performed using Arlequin 3.1155, and Mantel 2.056 was used to assess whether the data fit the hypothesis of isolation by distance, which predicts a significant relationship between geographic distance and genetic distance. A genetic barrier analysis was performed to suggest historical barriers to gene flow among or between collection sites using BARRIER57  with Monmonier’s maximum difference algorithm, which takes the geographic coordinates and genetic distance (GD) of each population as inputs.The original SSR data were preprocessed using DataFormater 2.7The presence of private alleles in each population and in groups within and outside the genetic diversity center were assessed, and the richness of private SSR alleles was defined as the average number of private alleles per genotype for each population.Supplementary Information.


Calocedrus formosana using amplified fragment length polymorphism (AFLP) and scored a total of 437 AFLP fragments using 11 selective amplification primer pairs. The AFLP variation was used to assess the role of gene flow on the pattern of genetic diversity and to test environments in driving population adaptive evolution. This study found the relatively lower level of genetic diversity and the higher level of population differentiation in C. formosana compared with those estimated in previous studies of conifers including Cunninghamia konishii, Keteleeria davidiana var. formosana, and Taiwania cryptomerioides occurring in Taiwan. BAYESCAN detected 26 FST outlier loci that were found to be associated strongly with various environmental variables using multiple univariate logistic regression, latent factor mixed model, and Bayesian logistic regression. We found several environmentally dependent adaptive loci with high frequencies in low- or high-elevation populations, suggesting their involvement in local adaptation. Ecological factors, including relative humidity and sunshine hours, that are generally not altitude related could have been the most important selective drivers for population divergent evolution in C. formosana. The present study provides fundamental information in relation to adaptive evolution and can be useful for assisted migration program of C. formosana in the future conservation of this species.Population diversification can be shaped by a combination of environmental factors as well as geographic isolation interacting with gene flow. We surveyed genetic variation of 243 samples from 12 populations of  Linking ecology with evolutionary biology is important to understand how environments drive adaptive divergence among populations within a species’ distribution range. Gene dispersal is a vital process that maintains genetic diversity and is critical to population resilience and persistence  because If a species’ distribution covers only a small elevational band in a small latitudinal range, population adaptive divergence can be precluded due to the effect of gene flow and divergence between populations may be mainly result from drift-mediated process . HoweverKeteleeria davidiana var. formosana , is distributed in geographically distant areas restricted in northern and southern Taiwan , representing DNA sequence variation, for non-model organisms. AFLP can be used in testing patterns of genetic differentiation influenced by gene flow and environments , 2019. OCalocedrus formosana is distributed in a wide geographic area along an elevational band from 300 to 2200 m , montane evergreen cloud zone , and montane mixed cloud zone   . Mountai1400 m∼)  and werekonishii . Geograpnge HMR, . The HMRC. formosana may be reduced or even disappear in view of global climate change, its unique distribution from low to high elevations makes it useful for investigating the role of elevation on genetic divergence in association with environmental conditions. Information obtained in this study can be useful in the future assisted migration program of C. formosana. We surveyed AFLP variation of 243 samples from 12 populations of C. formosana to examine the role of gene flow on the pattern of population genetic diversity and to test environmentally driven local adaptation in C. formosana populations. Our goals in the present study were to (i) assess whether there is a pattern of genetic diversity along elevation across forest vegetation zones; (ii) test the effect of environment relative to geography on adaptive genetic divergence; and (iii) investigate whether there are low- and high-elevation adaptive genetic loci with high frequencies that are likely to play roles in local adaptation.High elevation populations are typically more vulnerable to environmental change than their low elevation counterparts because of range restriction and dispersal limitations . In addiC. formosana individuals from 12 populations distributed in Taiwan  and digested DNA products were added to a 10-μl reaction mixture for ligation at 22°C for 1 h. The ligation reaction mixture contains 5 μM of the EcoRI adaptor, 50 μM of the MseI adaptor, and 0.25 U T4 DNA ligase . 1 μl diluted digested samples (1:9 dilution with water) was used as a template to perform pre-selective amplification. Pre-selective amplification was performed in a 10-μl volume containing 1 × PCR buffer , 100 nM each of the EcoRI (E00: 5′-GACTGCGTACCAATTC-3′) and MseI (M00: 5′-GATGAGTCCTGAGTAA-3′) primers, 0.25 mM dNTPs, and 1 U Taq DNA polymerase . Conditions of the pre-selective amplification were initial holding at 72°C for 2 min and pre-denaturation at 94°C for 3 min, followed by 25 cycles of 30 s at 94°C, 30 s at 56°C, and 1 min at 72°C, with a final 5-min holding at 72°C. Eleven EcoRI-MseI selective primers combinations with sequences of E00 and M00 and additional bases were added at the ends of these primers (EcoRI selective primer was labeled with fluorescent dye (6-FAM or HEX). Conditions of selective amplification were a 10-μl volume containing 1 × PCR buffer, 100 nM of the EcoRI selective primer, 100 nM of the MseI selective primer, 0.25 mM dNTPs, 0.75 U Taq DNA polymerase, and 1 μl diluted pre-selective amplified product (1:9 dilution with water). The selective PCR amplification was performed with initial holding at 94°C for 3 min, followed by 13 cycles of 30 s at 94°C, 30 s at 65°C with 0.7°C touchdown per cycle, and 1 min at 72°C, then 23 cycles of 30 s at 94°C, 30 s at 56°C, and 1 min at 72°C, with a final 5-min holding at 72°C. PCR amplified fragments were separated using an ABI 3730XL DNA analyzer and scored with Peak Scanner v.1.0 . We scored AFLP fragments with a fluorescent threshold set at 150 units. Peaks of amplified fragments in the range of 150–500 bp separated by less than one nucleotide in a ±0.8 base pair window were scored as the same fragment. We removed markers scored higher than 99% or less than 1% of individuals. Locus genotyping error rate of each primer combination was calculated based on amplification replicates obtained from three samples in each population. Loci with error rate greater than 5% were removed  and unbiased expected heterozygosity (uHE) within a population  (rD) (ia function of R poppr package v.2.8.5 (f-free model of a Bayesian approach for dominant marker implemented in HICKORY (FIS). Assessment of significant difference of mean uHE per locus between populations was performed using a linear mixed model (LMM), based on reduced maximum likelihood estimation, with population as a fixed effect and locus as a random effect using the lmer function of R lme4 package v.1.1-21   and modiIA) (rD) , represe v.2.8.5  in the R v.2.8.5 , and sig HICKORY  was usedv.1.1-21 . Signifi v.3.0-6  with the v.1.4.3 .C. formosana individuals. We assessed individual assignments with K = 1–12 based on least-squares optimization using the snmf function of R LEA package v.3.0.0  and the Bayesian information criterion (BIC).Sparse non-negative factorization (sNMF) algorithm  and disc v.3.0.0 . In snmf v.2.1.2 . The bes1. Ecological factors obtained were normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) derived from moderate resolution imaging spectroradiometer (MODIS) dataset MOD13A2 (1 km resolution) and leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (fPAR) derived from a MOD15A2 dataset (500 m resolution). These MODIS datasets were acquired from Land Process Distributed Active Archive Center2 during 2001–2018, and monthly mean values computed using a maximum-value composite procedure (3 recorded in 1990–2018) at spatial resolution of 1 km using a universal spherical model of the Kriging method in ArcGIS (n = 1150) conducted in 1969–1986 obtained from the Agriculture and Food Agency of Taiwan  were downloaded from the WorldClim v.1.4 . Topograrocedure . The annn ArcGIS . Soil pHf Taiwan . Annual f Taiwan  was calccor function of R to calculate correlation coefficient (r) between environmental variables. The vif function of R package usdm v.1.1-18 (r| < 0.8) were retained. Fourteen environmental variables retained were: BIO1 , BIO7 , BIO12 , and BIO18 (precipitation of the warmest quarter) of bioclimatic category; aspect, elevation, and slope of topological category; and fPAR, NDVI, RH, RainD, soil pH, sunH, and WSmean of ecological category  using adonis function of R package vegan v.2.5-6 , Euclidean distance matrix was generated using the  v.2.5-6 . Pairwisv.0.9-74 . SignifiFST-based methods (BAYESCAN and DFDIST) were used to identify FST outliers indicating signature of selection across populations. BAYESCAN v.2.1 ; smoothing proportion = 0.04; 500,000 resamplings; critical P = 0.05, and target average θ (level of differentiation) = 0.099575 were used for running DFDIST. In DFDIST, significant P values of FST outliers were evaluated at the 99% confidence level with 5% FDR.The Beaumont and Nichols model, modified for dominant marker, implemented in DFDIST  was usedFST  versus uFST (uHE . Loci thlfmm function of R LEA package (K), as a random effect due to demographic history and IBD pattern. Genetic data was used as a fixed effect in a testing procedure based on Z-scores. The number of latent factors, K, was set to 3 based on the results of LEA and DAPC analyses (see section “Results”). Five LFMM runs for each value of K with 10,000 iterations of the Gibbs sampling algorithm and a burn-in period of 5,000 cycles were performed. Z-scores of five independent runs were combined using Fisher-Stouffer method . An FDR correction of 1% was further used in P-value adjustment using the q-Value function of R q-value package v.2.20.0 . AMOVA was used to estimate the levels of population genetic differentiation at the hierarchical levels of geographic areas north and south of the HMR, genetic clusters (see section “Results”), and sample sites using the v.1.7-13  with 9,9mantel function of R vegan package. The retained environmental variables in the three environmental categories  were analyzed separately using redundancy analysis (RDA) to assess the relative contribution of environmental variables in each category explaining the outlier AFLP variation using the varpart function of R package vegan, and significance tested using the anova.cca function with 999 permutations. Four fractions of the total outlier variation were partitioned: pure environmental variables (fraction [a]), geographically structured environmental variables (fraction [b]), pure geographic variables (fraction [c]), and residual effects (fraction [d])  and with the matrix of elevational difference using the ion [d]) . The amo2 values . Longitu2 values  of the f v.0.3-7  was useduHE was 0.146 and ranged between 0.104 (population FCH) and 0.175 (population KW). No positive correlation between population uHE and sample size was found based on Spearman’s rank correlation test . The measures of multilocus LD, IA and rD, showed significant departure from random association between AFLP loci for all populations except the population SS (FIS estimated using the f-free model of HICKORY ranged between 0.497 and 0.509.We obtained 437 AFLP (mean ± SD: 39.7 ± 12.0) loci using 11 selective amplification primer combinations . The aveation SS . MoreoveC. formosana, LMM analysis showed the level of mean uHE per locus was significantly different among populations . Significant difference was also found in many pairwise population comparisons , between populations of different genetic clusters , and between north and south of the HMR   . Howevertal data .Z-scores and q values of candidate outlier loci identified using LFMM were summarized in BAYESCAN identified 26 loci as potential selective outliers . Nine loAllele frequencies of the outlier loci across populations arranged altitudinally were depicted in ΦST = 0.3448, P < 0.001; ΦST = 0.3499, P < 0.001), and between populations north and south of the HMR .Genetic differentiation analyzed using AMOVA, based on the outlier data, showed significant outlier genetic differentiation between populations (P = 1), significant environmental differences between sample sites and between sample sites of different vegetation zones were found . Mantel test revealed significant IBD based on the total and the outlier data . Total explainable outlier genetic variation was 18.52%, 30.09%, and 19.64%, respectively, for bioclimatic, ecological, and topological variables analyzed separately  based on the outlier data  was found compared with other Taiwanese conifers with narrower geographic distributions such as K. davidiana var. formosana  populations compared with that of central populations  based on the total data in the present study  compared to T. cryptomerioides . The HMR may not act as an effective barrier to gene flow can also be supported by the grouping of population SLS located in the south of the HMR with populations WL, SS, CL, SKL, and KW located in the north of the HMR in the same genetic cluster revealed by the LEA and DAPC analyses  > 2) for outlier identification was employed in BAYESCAN as compared to the criterion of log10(PO) > 0.5 used in the other three previous studies.Evolutionary divergence can occur in the presence of gene flow  and the C. konishii, K. davidiana var. formosana, T. cryptomerioides, and C. formosana. The relationship of higher number of environmentally dependent selective outliers detected with higher levels of genetic differentiation in these conifers suggests that gene flow among environmentally distinct populations can be effective in homogenizing locally adapted alleles and dampened local adaptation driven by natural selection  and relative humidity and sunshine hours .Geography and environment can both be effective barriers to gene flow between populations . These fRhododendron oldhamii. Temperature was also found to be strongly associated with AFLP variation in K. davidiana var. formosana  and sunshine hours (adjusted R2 = 0.0780). Using a Mantel test, significant isolation by elevation was found based on the total  and the outlier  genetic data. However, elevational difference in meters cannot be the causal factor in driving population adaptive divergence (R2 = 0.0355) compared with annual mean temperature and annual temperature range in bioclimatic category, and relative humidity and sunshine hours in ecological category (C. formosana could have been invoked by the combination of environmental variables, particularly the combinatorial effect of altitude-related temperature and generally non-altitude-related relative humidity and sunshine hours. Relative humidity and sunshine hours significantly explaining the outlier genetic variation (cumulative adjusted R2 = 0.2176, C. formosana. For plant species occurring in Taiwan, EST-SSR and AFLP variations were found to be strongly associated with relative humidity and sunshine hours in R. oldhamii (K. davidiana var. formosana (Temperature can play a significant role in shaping genetic divergence and enhancing speciation rate . It is cormosana , in Saliubescens  and in Ckonishii . Associaormosana . In C. fvergence  and elevcategory . It is loldhamii . Relativormosana .C. formosana (c.f. Variation partitioning also revealed significant amounts of the outlier genetic variation explained by geographically structured environmental conditions in bioclimatic and ecological environmental categories , indicatana c.f. , 2019.C. formosana in the present study (Although aspect and slope might have only played minor roles in shaping genetic variation in nt study , these tnt study . Aspect nt study  and contnt study . Habitatnt study . A singlnt study  and it int study , and incC. formosana distribution range (The amount of genetic diversity, the strength of natural selection, and the extent of gene flow can all influence the probability of the occurrence of local adaptation . Local aon range .Biogeographical as well as ecological factors can affect the level of diversity within and among populations . Forest C. konishii, K. davidiana var. formosana, and T. cryptomerioides. Moreover, strong migration may also inflict the break-up of locally adapted gene complex. Nonetheless, increased divergence associated with environmental variables at the edge populations of plant species shifting their range altitudinally may provide insights into the adaptive evolution of species. A higher portion of genetic loci was found to be environmentally dependent in C. formosana compared with the three other Taiwanese conifers. Additionally, our results revealed the associations of high frequency outlier loci with environmental variables, including altitude-related annual mean temperature and annual temperature range and generally non-altitude-related relative humidity and sunshine hours at elevational margin populations. Relative humidity and sunshine hours generally not altitude specific could have been the major ecological drivers invoked adaptive divergence in C. formosana. Results of this study and the previous studies provide insights into the interplay of gene flow and environments in shaping population genetic divergence in Taiwanese conifers.For plants with limited dispersal capabilities, global climate change may manifest the risk of extinction because of loss of the unique genetic diversity at elevational margin populations. Here, our results showed no pattern of population genetic diversity in consistent with the abundant central hypothesis. Relatively lower level of genetic diversity and higher level of genetic divergence were found compared with other Taiwanese coniferous species such as The AFLP data used in this study is included in the article/ S-YH proposed, funded, and designed the research. W-MC, Y-CC, and S-YH collected the samples. W-MC, C-TC, and S-YH performed the research. S-YH and W-MC wrote the manuscript. All authors analyzed the data and read and approved the final manuscript.Frontiers Media SA remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.