Juglans regia subsp. sigillata) is a deciduous tree species endemic to this region of China and cultivated there for its edible nuts. We sampled 36 iron walnut populations from locations throughout the species' range in SW China and genotyped a total of 765 individuals at five chloroplast DNA regions and 22 nuclear microsatellite loci. Species distribution models were produced to predict the evolution and historical biogeography of iron walnut and to estimate the impacts of climate oscillations and orographic environments on the species' demography. Our results indicated that J. regia subsp. sigillata had relatively low genetic diversity, high interpopulation genetic differentiation, and asymmetric interpopulation gene flow. Based on DIYABC analysis, we identified two lineages of J. sigillata in southwestern China. The lineages (subpopulations) diverge during the last glacial period (~1.34 Ma). Southwestern China was a glacial refuge during the last glacial period, but increasingly colder and arid climates might have fostered the fragmentation of J. regia subsp. sigillata within this refugium. Finally, we found that recent habitat fragmentation has led to a reduction in population connectivity and increased genetic differentiation by genetic drift in isolated populations. Our results support a conclusion that geological and climatic factors since the Miocene triggered the differentiation, evolutionary origin, and range shifts of J. sigillata in the studied region.Southwestern (SW) China is an area of active tectonism and erosion, yielding a dynamic, deeply eroded landscape that influences the genetic structure of the resident populations of plants and animals. Iron walnut ( Juglans sigillata Dode) is an endemic, deciduous tree species native to this region of China and cultivated there for its edible nuts. We sampled 36 wild iron walnut populations from locations throughout the species' range in SW China and genotyped a total of 765 individuals at five chloroplast DNA regions and 22 nuclear microsatellite loci. The data were used to infer levels of genetic differentiation, genetic structure, and contemporary/historical levels of gene flow. Species distribution models were produced to predict the evolution and historical biogeography of iron walnut, and to estimate the impacts of climate oscillations and orographic environments on the species' demography.Southwestern (SW) China is an area of active tectonism and erosion, yielding a dynamic, deeply eroded landscape that influences the genetic structure of the resident populations of plants and animals. Iron walnut ( It is considered as a hot spot for vascular plant biodiversity  estimate the degree of population differentiation and gene flow of J. sigillata populations from the different regions; and (c) determine divergence times of two populations within J. sigillata and of J. sigillata from J. regia; (d) deduce current and past population and range dynamics [during the last interglacial  and the last glacial maximum ], and understand their possible underlying environmental causes.Biogeographical studies of angiosperms frequently combine nuclear markers, such as SSRs, which are inherited biparentally, with chloroplast markers that are inherited matrilineally. By comparing results from both types of markers across the same sampled populations, complementary views of a species' evolution, genetic structure, differentiation, and gene flow can be obtained . Genotypes of all DNA samples were detected using 22 pairs of microsatellite primers developed for Juglans  individuals (used as an out‐group) were sequenced at five chloroplast DNA regions  were evaluated at 22 EST‐SSRs. We calculated FST to identify outlier EST‐SSR loci as potentially under selection , expected heterozygosity (HE), allelic richness (RS), genetic differentiation (FST), and private allele richness (PAR) for 36 populations; then, we displayed the geographic patterns of the haplotypes based on cpSSR markers . Haplotype diversity (Hd) and nucleotide diversity (π) were estimated by DnaSP 5.0  was conducted after examining genetic distance with GenALEx 6.5  by BEAST v1.8.3 . Gene pool I consisted of 377 individuals from 22 populations, and gene pool II consisted of 388 individuals from 14 populations. The J. regia population was considered gene pool III   was 0.2026 and nucleotide diversity (π) was 0.0008. Most populations (74.2%) shared haplotype H3, while only eight populations out of 31 (25.8%) had two, three or four Cp haplotypes. Higher diversity was found in western  populations  versus eastern populations  , which differed by 236 mutation steps, haplotypes H1, H7, H8, and H9 were more ancient than H3, although H3 was the most widespread and most common. All individuals of J. regia showed haplotype H3 or H9  Figure . Based o3.2J. sigillata, the number of alleles (NA) and the number of effective alleles (Ne) varied from 1.77 to 3.18 and from 1.44 to 2.07, respectively. The observed heterozygosity (HO) ranged from 0.16 to 0.41. Allelic richness (RS) ranged from 1.23 to 1.41 with average of 1.31. Screening all 765 J. sigillata individuals at the 22 EST‐SSR loci identified a total of 99 alleles, the number of alleles (NA) per locus for the 22EST‐SSR ranged from 2 to 12 with a mean of 4.85 per locus (Table Hs) ranged from 0.018 to 0.596  ranged from 1.23 (MD and ST populations) to 1.41 (TZ). High values of allelic richness (RS) were observed in populations from the northwestern and northeastern margins of the species distribution and from some populations in eastern Yunnan. Private allelic richness (PAR) was highest in population (JL) from eastern Yunnan province (PAR = 0.04). High values of the number of alleles (NA) were observed in areas near the Hengduan Mountains, where there were also some populations with high values of expected heterozygosity. High values of genetic differentiation (FST) were observed in areas near the Wuling Mountains and Da Luo Mountains in Guizhou province.For each population of us Table . The gen96 Table . The IDW3.3FST outliers  from Guizhou province clustered into population I  based on s Figure . ApplyinI Figure . The sec3 Figure .3.4GST) was 0.194, and NST was 0.222 , indicating the nonexistence of a phylogeographic pattern. Neutrality tests  on cpDNA did not detect evidence of recent range expansion. The multimodal mismatch distributions indicated that the J. sigillata populations had a stable range , with the average FST equal to 0.262 (range from 0.088 to 0.800). The pairwise FST values ranged from 0.021 (TR vs. HZM) to 0.265 (MD vs. HC). Population MD showed the highest mean pairwise FST with other populations (0.166). A Mantel test indicated no significant correlation between the genetic distance and geographic distance among populations based on nSSR data .The AMOVA on cpDNA and EST‐SSR data provided evidence for a relatively high level of genetic differentiation among sampled sites; a considerable percentage  of the genetic variation was attributed to differences among sampled location  from population II to population III was 5.64  and the out‐group  about 44.99 Ma  , which diverged from each other during the Pliocene–Pleistocene , we evaluated 13 scenarios related to the phylogeny and demography of J. sigillata and the relationship of J. sigillata and J. regia in China  at t2  and a more recent separation of two subpopulations of J. sigillata at t1  . The projection of the model fit to current climate conditions indicated suitable habitats in southwest China (current) between about 22°N and 32.5°N and most of Yunnan province  in a slightly more southern range (around 15°N–30°N) during the LIG   was found in trees across the entire range of J. sigillata [with high SSR genetic diversity that could be gene flow from J. regia , loss of genetic diversity, population fragmentation, and local extirpation . The two most likely causes for this result are first, recent sporophytic gene exchange between populations that had, until recently, been isolated, or second, recent fragmentation of a large population with recurrent sporophytic gene flow. If J. sigillata populations had a history of long‐term isolation, then we might expect each population would have independently experienced genetic drift, resulting in a high frequency of rare haplotypes in each population , providing a potential explanation for why a Mantel Test comparing the matrix of genetic and geographic distance was not significant .Iron walnut is a wind‐pollinated tree, the seeds or pollen dispersed by winds, born at 1,300–3,300 m above sea level on hillsides or valleys. In Yunnan–Guizhou plateau region, especially Yunnan province, most of the areas are characterized with highly dense mountain streams and lush vegetation. It maybe has served as effective barriers to seed and pollen dispersal and reduced the potential for long‐distance seed dispersal. The genetic barrier analysis revealed three mains statistically barrier exist among populations, including the barrier separated population DL from four adjacent populations . The other two barriers also effect gene flow between populations of a Figure . This maa Figure . Values a Figure , so geog4.4J. regia and J. sigillata was 3.13 Ma, and it showed two species separated in the Pliocene–Pleistocene. Using a Bayesian dating method, we estimate the divergence times between clade A and clade B to be 2.65 Ma . These putative refugia have been recognized as centers of plant diversity. However, the refuge of the northeastern species distribution has not appeared in the SDM model, and this might be because populations along this potential route may have gone extinct, but it will occur again in the simulation of future , which requires further research. The genetic analyses and SDMs indicated different phylogeographical patterns follow the species routes during the interglacial–glacial climatic oscillations of the Quaternary. Our study provides an additional case to help understand the origin and spread routes of the extraordinary biodiversity of the SW China.In short, we integrated molecular phylogeography, SDMs, and phylogenetic approaches to understand intraspecific divergence, evolutionary history, and range dynamics of None declared.PZ and YWS involved in conceptualization. PZ, YWS, NH, and KW curated the data. PZ, YWS, NH, CZ, and XY performed formal analysis. PZ funded the acquisition. PZ, YWS, NH, CZ, and XY investigated the study. PZ, YWS, NH, CZ, and XY wrote the original draft. PZ, YWS, NH, and KW revised the manuscript. Click here for additional data file. Click here for additional data file.


Aesculus hippocastanum L.) is an endemic and relict species from the Mediterranean biodiversity hotspot and a popular ornamental tree. Knowledge about the evolutionary history of this species remains scarce. Here, we ask what historical and ecological factors shaped the pattern of genetic diversity and differentiation of this species. We genotyped 717 individuals from nine natural populations using microsatellite markers. The influence of distance, topography and habitat variables on spatial genetic structure was tested within the approaches of isolation-by-distance and isolation-by-ecology. Species niche modeling was used to project the species theoretical range through time and space. The species showed high genetic diversity and moderate differentiation for which topography, progressive range contraction through the species’ history and long-term persistence in stable climatic refugia are likely responsible. A strong geographic component was revealed among five genetic clusters that are connected with very limited gene flow. The environmental variables were a significant factor in the spatial genetic structure. Modeling results indicated that future reduction of the species range may affect its survival. The possible impact of climate changes and high need of in situ conservation are discussed.Horse-chestnut ( During the Cenozoic 66–23.03 Ma) period, Arcto-Tertiary flora covered a large part of the Northern Hemisphere and formed a unique type of forest ecosystem –3. Speci.03 Ma peAccumulation of endemic taxa drives the formation of biodiversity hotspots and most conservation effort is expended on these endemic species due to their high extinction risk , 7. The Firstly, the persistence of Tertiary relicts depends on the conservation of the habitats to which they are frequently highly specialized , 14. In Two basic sets of factors drive population genetic structure and divergence. One of them is the historical biogeography including historical climatic change and tectonic movements that have modified genetic connectivity among populations resulting in significant isolation-by-distance (IBD) , 26. In Aesculus hippocastanum L.) is a Tertiary relict and naturally occurs only in the Balkan Peninsula [Aesculus was broadly distributed in Europe and paleaobotanical records prove its presence in Africa and the Caucasus [th century, undoubtedly because of its dispersed and low-density occurrence in the high mountains [th century [Horse-chestnut  spatial genetic structure of horse-chestnut is mostly determined by historical factors, and the species natural populations are characterized by low genetic variability within and high genetic diversity among populations as expected for a palaeoendemic; (2) the range of horse-chestnut will be reduced in the future due to climate change.Aesculus turbinata Blume) travel up to 14.5 m from the parent tree with a mean distance 12.2–44.7 m [Horse-chestnut is native to mountainous regions of the Balkan Peninsula, particularly Greece, where it occurs at altitudes between 228 m and 1485 m a.s.l. . The spe2–44.7 m . These dDue to fragmentary information of species distribution in Balkans, collection of the material in this study was limited to the core range of the species in Greece. Sites with the highest number of mature individuals were selected for investigation using data from Avtzis et al. . Since mAesculus during the field study, but a full determination of the connections between individuals was not possible. We used a random pattern of sampling but estimated the level of clonality in each population using a clonal diversity parameter (genotypic richness) R = (G—1) / (N—1), in which G denotes the number of distinct genotypes scored in a population (genets) and N is the total number of trees sampled (ramets) [R range from 1, where each genotype is unique, to 0, when all sampled individuals have the same genotype (We noticed root suckers of (ramets) . The valgenotype .A. turbinata were tested [2, 2 μM of dNTP mix, 0.5 U of VivaTaq polymerase , 0.4 μM of each starters and 100 ng of DNA. Reactions were conducted using the following thermal protocol: initial denaturation at 94°C/12 min., followed by 35 cycles of denaturation at 94°C/30 s, annealing at 52°C or 55°C (specific to locus) for 30 s, elongation at 72°C/60 s and final elongation at 72°C for 5 min. Products of amplification were analyzed using a 3130 Genetic Analyzer  with internal size standard GeneScan LIZ-500 and genotypes were scored using GENEMAPPER vs. 4.0 .Genomic DNA was extracted from leaf tissue using protocols described by Dumolin et al. . To detee tested . PrelimiA), effective number of alleles (Ae) and number of private alleles (Ap). INEST v. 2.0 [Ho) and expected (He) heterozygosity while FSTAT v 2.9.3. [Ar). A Bayesian approach implemented in INEST software was applied to estimate the inbreeding coefficient (FIS) including a ‘null alleles’ correction according to the individual inbreeding model (IIM). The estimation was run with 500,000 MCMC cycles with every 200th updated and a burn-in of 50,000. The Deviance Information Criterion (DIC) was used to compare the full model  with the random mating model  to assess the determinants of levels of homozygosity. The significance of the heterozygote deficiency in a sampled population was tested by U test [p-values were obtained with the Markov chain algorithm using default settings. Frequency of the null alleles (Null) and Wright’s fixation index (FST) were estimated in FREENA with ENA (Excluding Null Alleles) correction [M-ratio approach implemented in INEST.All clones were removed before analysis of genetic data, identified using the “Find Clones” procedure in GENEALEX 6.4 . This soT v. 2.0  was usedv 2.9.3.  was usedy U test  in GENEPy U test , and p-vrrection . This me4 of burn-in and 105 MCMC iterations with the maximum number of clusters set to K = 10. The STRUCTURE model assumed correlated allele frequencies within populations and allowed for mixed ancestry of individuals. To estimate the best-supported number of clusters . Function ‘xvalDapc’ with 30 replications was used to perform cross-validation on varying numbers of principle components. DAPC was conducted with the function ‘dapc’. Fourteen PCs and an optimal number of clusters (10) were applied in estimations. Output was visualized using library ‘ggplot2’ in R [Dc) genetic distances was used to obtain a neighbor-joining (NJ) tree.In addition to the model-based approach, a discriminant analysis of principal components (DAPC)  was perffinition . DAPC ist2’ in R . The prot2’ in R . A Caval7 MCMC iterations, 106 of burn-in and sapling every 1000th iteration. To check consistency, three independent runs with different random seeds were carried out. Finally, a script of Meirmans [BAYESASS 3.0 (BA3) was used to infer recent migration rates among studied populations . BA3 is Meirmans  applied Meirmans  was usedΘ) and migration rates (M) using a coalescence approach. All runs used the Bayesian inference method and the Brownian motion mutation model with identical priors and parameter values, and identical mutation rates were assumed among loci. Testing runs were conducted to set uniform priors  for Θ and M  for all subsequent runs. Each run finally consisted of 50,000 recorded steps at an increment of 100 steps, after a burn-in of 20,000 steps, and a static heating scheme . We performed three independent runs with different initial seed numbers to verify consistency and used the Bezier approximation for the marginal likelihood to test which run best fit the data [Historical gene flow among populations was investigated with MIGRATE-n v. 3.2.6 , 83. Thic. 22 000 years ago and mid-Holocene c. 6 000 years ago) and for future conditions. Three scenarios of climate changes were used for the future projection: RCP 2.6 (Representative Concentration Pathway)—increase of average temperature by 1°C before the year 2065; RCP 4.5: + 1.4°C before the year 2065; and RCP 8.5: + 2°C before the year 2065 [The program MAXENT version 3.3.2 , 85 was ear 2065 . The Comear 2065 . Analyseear 2065 , 94. Modear 2065 .A connectivity-resistance approach implemented in the software CIRCUITSCAPE v.4.0.5  was appli.e. IBE), past and present layers of the niche suitability previously obtained with MAXENT were used [FST and FSTNA values) with a multiple matrix regression approach implemented in R (function ‘mantel.rtest’).Three different resistance surfaces were developed as matrices. A matrix for isolation-by-distance model (IBD) was created using completely flat landscapes in the program QGIS 2.14.21 ; this soere used , 85. MatR = 1) or only a few individuals (1–3) were of vegetative origin (R = 0.82) was noted in Karitsa I, where 94 unique genotypes were detected among 114 trees sampled.Out of 717 individuals genotyped, 686 unique genotypes were identified . In moste origin . The higAesculus turbinata [He) was at locus AT7D1 in population Dasos Nanitsa (0.915) while the lowest (0.000) at AT6D11 in populations Kalampaka and Ondria, because this locus was monomorphic in these populations. The highest frequency of null alleles was observed in the AT6D2 locus in population Perivoli–mature (23.7%). The effective number of alleles was high at locus AT7D1 in Dasos Nanitsa and Vathirrevma , while in the Karitsa I population the highest values were at the AT6D12 (11.50) and AT7D8 (10.44) loci.Screening the sampled individuals with eight nSSR gave 124 different alleles in total and number of alleles varied from 48 in Kalampaka to 88 in Karitsa I—mature . The numurbinata . The higA) ranged from 6.00 in Kalampaka to 11.00 in Karitsa I. Average effective number of alleles (Ae) is 4.19, which is much smaller value than average number of alleles (8.73). The highest value of allelic richness (Ar) was observed in Vathirrevma (8.44), whereas the lowest was in Kalampaka (5.93). Observed heterozygosity (Ho) ranged from 0.442 (Mariolata) to 0.636 (Vathirrevma), while gene diversity (He) varied from 0.533 (Mariolata) to 0.746 (Vathirrevma). Significant positive FIS values, suggesting an excess of homozygotes, were observed for all analyzed populations. In Kalampaka, the inbreeding coefficient was highest, reaching 0.1520. According to DIC criterion, in the majority of the populations inbreeding was the likely reason for excess homozygotes. In three populations , null alleles are probably responsible for increased homozygosity level. Generally, null alleles were present in all populations with an average frequency of 6.3%. Private alleles were detected in almost all studied stands, and the highest number (4) was noted for Ondria, located at the northernmost part of the Pindos Mts.  for the set of analyzed populations. Statistically significant global FST values with and without the ENA correction attained similarly moderate values , but the pairwise estimations showed a wider range of variation (FST with ENA correction was noted between Mariolata and Karitsa II (0.227), while the lowest was between the two closest stands, Karitsa I and Karitsa II (0.035). M-ratio test indicated a significant bottleneck effect in all studied populations except for Dasos Nanitsa (P = 0.125).CIRCUITSCAPE indicated significant IBD  was 133.74. The highest migration was noted from Karitsa I to Karitsa II (1202.21), which can be readily explained by the short distance between these stands. Generally, the gene flow was asymmetrical and more intensive from west (Pindos) to east (Ossa) than in the opposite direction (θ = 13.56) and the highest in Karitsa II (θ = 41.65).Analysis of recent migration rates among nine studied populations with BAYESASS showed that migration is very low . The aveirection . The excThe Bayesian analysis of spatial genetic structure made with STRUCTURE defined five homogenic genetic clusters among nine analyzed mature populations . Three oINSTRUCT results for K = 5 are very similar to STRUCTURE genetic groups . Lowest = 10. Among those ten clusters, five main clusters can be readily distinguished, which corresponds to results obtained with STRUCTURE , which partially proves IBE. Habitat suitability was not a significant factor of differentiation . The theoretical model of gene flow from CIRCUITSCAPE showed generally favorable conditions for gene flow in the core range, and pointed to impeded genetic connectivity with the most isolated population from Mariolata . This indicates that our sampling for genetic analysis covered the core of the species potential range. There was some overprediction of the model in an eastern direction where the species does not currently occur. Specifically, the Pontic Mts. in Anatolia were highly supported as a possible area of the species distribution , S9A Figc. 22,000 BP), the theoretical range of the species was likely much larger, and a westward expansion is evident , the theoretical range of horse-chestnut decreased, but it was still wider than the current range , a bottleneck was inferred. However, this estimation may be rather weak because it used only eight loci, where seven is considered a minimum in this type of analysis . ConsideFST = 0.109 indicates a moderate level of genetic differentiation, comparable to several Mediterranean conifer species that do not have such a fragmented and narrow distribution as horse-chestnut [Taxus baccata L. that has a much wider distribution was reported to attain a very large among-population divergence [Frangula alnus Mill. subsp. baetica (E. Rev. and Willk.) [Buxus balearica Lam. [Olea europaea L. subsp. laperrinei (Batt. and Trabut) Cif. [In principle, becoming a paleoendemic involves the shrinking and disintegration of the once widespread ancestral range , 123. Dichestnut , 128. Anvergence –131. Howvergence –135. Poo Willk.) , Buxus bica Lam.  and Oleaut) Cif. . In comput) Cif. .FST in pairwise comparisons being found between the most distant and marginally located populations, Mariolata and Karitsa, and the remaining populations; the lowest differentiation was noted between Karitsa I and Karitsa II separated with only c. 6 km.Detection of significant IBD indicates that geographical separation among horse-chestnut populations is responsible for the differentiation detected. This means that historical factors, such as range contraction induced by climatic transformations, are important drivers of spatial genetic structure in horse-chestnut. Low admixture among populations from distinct geographic regions and generally a very low rate of recent gene flow both argue for limitations in current genetic connectivity among populations, which is a consequence of range disintegration that started even before the last glaciation. This fits with the highest values of Both, DAPC and STUCTURE indicated strong divergence of marginal populations  and the very distinct character at Perivoli from the northern Pindos compared to other populations from this part of the mountain range . PopulatFST value between Mariolata and the remaining populations  particularly well exemplifies the issue. Mariolata is located at the southern edge of the species distribution in the Parnassus Range, which is in the southwestern spur of the Pindos Mts. This part, unlike to the main Pindos range which runs along a north-south axis, is more latitudinally-oriented and divided into many smaller mountain ranges [Phylogeographic studies underline the major role of southern European mountains as the centers of refugial areas during the last glacial cycle . The phyn ranges . Such spn ranges , 142.T. baccata, Mayol et al. [T. baccata, survival in spatially isolated refugia under divergent temperature regimes were significant determinants of current genetic differentiation and consequently lead to evolution of two evolutionary lineages adapted to different temperature ranges.Contrary to our initial assumptions, habitat characteristics were not an important factor in differentiation. We expected to find at least a weak signal of adaptive divergence since some populations inhabit ecologically very dissimilar habitats. Additionally, limited gene flow would be a factor enhancing such divergence. One of the reasons that we failed to detect local adaptation may stem from the limited number of populations included in our investigations or too small an area covered (only the Greek part of the range); only two populations out of nine analyzed showed a distinct edaphic character, being located on steep and dry slopes. For l et al.  confirmeA. turbinata may reach on average of c. 180 m with maximum distance reported of over 700 m. Similar distance may be attained in horse-chestnut, and it may account for effective within-population gene flow resulting in high within-population diversity. However, this distance is too short to maintain among-population connectivity in so disjunct a range and such a complex environment. Generally, in animal-pollinated tree species, highly mobile pollinators such as birds can work effectively in fragmented landscapes but in the case of insect-pollinated trees, pollen movement is mostly local [Clusters inferred by STRUCTURE and INSTRUCT were related to specific mountain ranges . Such stly local . Pollen ly local .Frangula alnus subsp. beatica and other Tertiary endemics growing by mountain rivers in southern Spain, winter flooding events were shown to play an important role in the secondary dispersal of seeds already spread by birds [A. turbinata is dispersed for distance of up to 114.5 m [Two populations from the easternmost margins of the species range, Karitsa I and II (Ossa Massif), are separated from the core range although genetic admixture from the northern Pindos was revealed by STRUCTURE. CIRCUITSCAPE. This indicates, at least theoretically, that there are no impenetrable barriers preventing gene exchange between the Pindos Mts. and the Ossa Massif , and genby birds . Seeds oby birds , 146. Co 114.5 m . Similar 114.5 m  showed tvia gene flow and longevity would synergistically buffer horse-chestnut from the negative effects of range fragmentation for a long time. Secondly, modeling of theoretical range clearly shows a possibility of wider distribution in the past. Accordingly, during LGM (ca. 22 ka BP) Aesculus sp. populations might have occupied extensive areas on the west coast of the Mediterranean Sea  [Our results indicate increased homozygosity in natural populations, implying a risk of inbreeding depression and loss of diversity in the future . In mostedlings) , 151. Hoedlings) , 59. TheDavidia involucrata Baill., is expected to be reduced to less than 30% of its current theoretical range [Many species are currently at high risk from the negative impact of climate change. In the face of changing environmental conditions, endemic species may not be able to keep pace with these changes and therefore may be first to disappear . For exaal range . In the al range , 155 andal range . Future al range . Accordiin situ conservation should be a priority and a conservation program should be immediately launched for Greek populations, which are the largest [The results obtained in this work are less optimistic about the future persistence of horse-chestnut than previous studies . The mos largest . Unfortu largest . So far  largest , but it S1 Fig(TIFF)Click here for additional data file.S2 Fig(TIFF)Click here for additional data file.S3 Fig(TIFF)Click here for additional data file.S4 Fig(TIFF)Click here for additional data file.S5 FigA—comparison between genetic clusters determined by STRUCTURE  and substructure of each population . B—comparison between STRUCTURE and INSTRUCT results.(TIFF)Click here for additional data file.S6 Fig(TIFF)Click here for additional data file.S7 Fig(TIFF)Click here for additional data file.S8 FigA—period of the maximum glaciation ; dark, bold line indicate a coastline during the maximum glaciation period; B—Mid-Holocene .(TIFF)Click here for additional data file.S9 FigA—theoretical range in current conditions; B—future theoretical range estimated for RCP 2.6 scenario of the climate changes; C—future theoretical range estimated for RCP 4.5 scenario of the climate changes; D—future theoretical range, estimated for RCP 8.5 scenario of the climate changes.(TIFF)Click here for additional data file.S10 FigLighter colors means best ways for gene flow (higher conductance); darker colors—barriers for gene flow (lower conductance). Populations are shown as red cylinders. Blue boxes show cities.(TIFF)Click here for additional data file.S1 TableCoordinates for populations from Peçi et al. (2012) are approximate.(XLSX)Click here for additional data file.S2 TableVariables used in the analysis of the theoretical range are bolded.(DOCX)Click here for additional data file.S3 Tablee−effective number of alleles, Null–frequency of null alleles, Ho−observed heterozygosity, He−expected heterozygosity.A–number of alleles, A(XLSX)Click here for additional data file.S4 TableST with ENA correction above diagonal, FST without ENA correction below diagonal. Populations: 1 –Ondria, 2 –Kalampaka, 3 –Dasos Nanitsa, 4 –Vaeni, 5 –Mariolata, 6 –Karitsa I, 7 –Karitsa II, 8 –Vathirrevma, 9 –Perivoli. FST is not significant (p > 0.05).F(DOCX)Click here for additional data file.S5 TableIn rows are the source populations, in columns—the sink populations. Proportion of migrants above 2% are bolded. Populations: 1 –Ondria, 2 –Kalampaka, 3 –Dasos Nanitsa, 4 –Vaeni, 5 –Mariolata, 6 –Karitsa I, 7 –Karitsa II, 8 –Vathirrevma, 9 –Perivoli.(DOCX)Click here for additional data file.S6 TableIn rows are the source populations, in columns—the populations into which individuals immigrate. Number of migrants above 150 are bolded. Populations: 1 –Ondria, 2 –Kalampaka, 3 –Dasos Nanitsa, 4 –Vaeni, 5 –Mariolata, 6 –Karitsa I, 7 –Karitsa II, 8 –Vathirrevma, 9 –Perivoli.(DOCX)Click here for additional data file.S7 Table(DOCX)Click here for additional data file.


Modern Latin American populations were formed via genetic admixture among ancestral source populations from Africa, the Americas and Europe. We are interested in studying how combinations of genetic ancestry in admixed Latin American populations may impact genomic determinants of health and disease. For this study, we characterized the impact of ancestry and admixture on genetic variants that underlie health- and disease-related phenotypes in population genomic samples from Colombia, Mexico, Peru, and Puerto Rico.We analyzed a total of 347 admixed Latin American genomes along with 1102 putative ancestral source genomes from Africans, Europeans, and Native Americans. We characterized the genetic ancestry, relatedness, and admixture patterns for each of the admixed Latin American genomes, finding a spectrum of ancestry proportions within and between populations. We then identified single nucleotide polymorphisms (SNPs) with anomalous ancestry-enrichment patterns, i.e. SNPs that exist in any given Latin American population at a higher frequency than expected based on the population’s genetic ancestry profile. For this set of ancestry-enriched SNPs, we inspected their phenotypic impact on disease, metabolism, and the immune system. All four of the Latin American populations show ancestry-enrichment for a number of shared pathways, yielding evidence of similar selection pressures on these populations during their evolution. For example, all four populations show ancestry-enriched SNPs in multiple genes from immune system pathways, such as the cytokine receptor interaction, T cell receptor signaling, and antigen presentation pathways. We also found SNPs with excess African or European ancestry that are associated with ancestry-specific gene expression patterns and play crucial roles in the immune system and infectious disease responses. Genes from both the innate and adaptive immune system were found to be regulated by ancestry-enriched SNPs with population-specific regulatory effects.Ancestry-enriched SNPs in Latin American populations have a substantial effect on health- and disease-related phenotypes. The concordant impact observed for same phenotypes across populations points to a process of adaptive introgression, whereby ancestry-enriched SNPs with specific functional utility appear to have been retained in modern populations by virtue of their effects on health and fitness.The online version of this article (10.1186/s12864-018-5195-7) contains supplementary material, which is available to authorized users. Homo sapiens sapiens – originated in sub-Saharan Africa ~ 200,000 years ago and began to migrate out of Africa and spread throughout the world starting ~ 70,000 years ago [The modern human species – ears ago , 2. Afteears ago , 4. Distears ago , wherebyThis pattern of human evolution was turned upside down upon the arrival of Columbus in the New World a mere 500 years ago, which is less than 1% of the elapsed time since humans emerged from Africa. Columbus’ voyages precipitated the so-called ‘Columbian Exchange’ – a massive transfer of life forms, which had evolved separately for millennia, between the Old and New World hemispheres , 7. The Latin American genomes can thus be considered to represent a recent innovation in human evolution. Indeed, genomes from modern Latin American populations are evolutionarily novel in the sense that they contain combinations of genetic variants (haplotypes) that never previously existed together on the same genetic background. Our group is interested in trying to understand the implications of the recent advent of novel Latin American genomes, particularly as it relates to the genetic determinants of health-related phenotypes. In other words, we are asking what it means when genomes that were separated for many thousands of years are suddenly brought back together and what the implications of this process are for human health and fitness.Our group and others have employed an approach that we call ancestry-enrichment analysis to address these kinds of questions via population-level studies of admixed American genomes . This apA number of recent studies have used ancestry-enrichment analysis to show evidence of adaptive introgression in admixed American genomes. The first study of this kind showed an excess of African ancestry at the major histocompatibility locus (MHC) in a sample of Puerto Rican genomes , and a fAll of the previous ancestry-enrichment studies were distinguished by their interrogation of the ancestral origins of individual haplotypes, i.e. physically linked sets of genetic variants that are inherited together. For this study, we developed and applied a novel method based on individual genetic variants – single nucleotide polymorphisms (SNPs) – in an effort to expand our view of the relationship between genetic ancestry, admixture and health in Latin American populations. Our approach entails the detection of SNPs that are found at anomalously high frequencies in admixed populations compared to what is expected based on their frequencies in the ancestral source populations, i.e. ancestry-enriched SNPs. To find such ancestry-enriched SNPs, we consider the proportional contributions of ancestral source populations to admixed Latin American populations, together with SNP frequencies in the ancestral populations, to derive expected SNP frequencies for the Latin American populations. These expected frequencies are compared to observed frequencies in order to identify statistically significant ancestry-enriched SNPs; the connection between ancestry-enriched SNPs and health-related phenotypes is then explored via analysis of the functional annotations of the SNPs and their linked genes. In particular, we interrogated the impact of ancestry-enriched SNPs on disease, metabolism and immune system pathways. This approach uncovered signals of ancestry-enrichment in health- and disease-related traits shared among all four of the Latin American populations that we analyzed, raising the possibility of shared selective pressures among them.n = 94), Mexico (n = 64), Peru (n = 85), and Puerto Rico (n = 104) – were taken from the 1000 Genomes Project (1KGP) phase 3 data release [http://www.internationalgenome.org/data/. Genome sequences from the four Latin American populations were compared to whole genome sequences and whole genome genotypes of global reference populations from African, European, and Native American continental population groups to characterize their patterns of genetic ancestry and admixture Exp can be calculated as:Ancestry-enriched SNPs were characterized as SNPs found in higher frequencies in admixed Latin American populations compared to what is expected based on (1) their frequencies in the ancestral source populations, and (2) the proportion of ancestry derived from each ancestral source population. For any given SNP, in any given Latin American population, the expected frequency of the SNP f(SNP)Pop is the frequency of the SNP in a specific ancestral source population and P(Anc)Pop is the proportion of ancestry in the modern Latin American population derived from that same ancestral population. Ancestry proportions were computed using the reduced, merged set of SNPs described above, with African and European reference populations from the 1KGP and Native American reference populations from HGDP. SNP frequencies were computed using whole genome sequences, in order to provide more complete coverage of variants genome-wide, using the YRI African and IBS European reference populations with the most closely related East Asian population CHB taken as a surrogate for Native American ancestry.Obs) to expected (Exp) frequencies of the reference (Ref) and alternate (Alt) alleles for any given SNP as shown here:The statistical significance of SNP ancestry-enrichment calculated this way was determined by comparing the observed  q-values were used to account for multiple statistical tests. The SNPs that had significant FDR values (q < 0.05) were considered to be ancestry-enriched in the Latin American population, i.e. present at a higher frequency than expected based on the population ancestry profile.The Anc) that gave rise to the pattern of enrichment were also determined by jointly minimizing the frequency difference between the SNP in the Latin American population and a single ancestral source population while maximizing the distance between that single source population and the other two ancestral populations:For ancestry-enriched SNPs, the individual ancestry components (f(SNP)Popx is the frequency of the SNP in each of the ancestral source populations and f(SNP)Obs is the frequency of the ancestry-enriched SNP in the Latin American population.where P-values were computed for each gene set using the Wilcoxon signed-rank test.Ancestry-enriched SNPs were mapped to genes if they mapped within the NCBI RefSeq  gene modftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/GEUV/E-GEUV-1/analysis_results/. A total of 445 RNA-seq samples were used in the analysis, including 87 African and 358 European individuals. The RNA-seq data correspond to gene expression levels for the same lymphoblastoid cell lines, i.e. Epstein–Barr virus (EBV) transformed B-lymphocytes, which were used for the 1KGP DNA-seq characterization. RNA-seq sample preparation, sequencing experiments and read-to-genome mapping were performed as previously described [RNA-seq data were taken from the GUEVADIS RNA sequencing (RNA-seq) project for 1KGP samples escribed . The reaescribed . Gene exescribed , 30.Matched whole genome sequencing based SNP genotype calls were obtained for the same 445 individuals from the phase 3 release of the 1KGP . Only SNMeta-analysis was used to evaluate the overall ancestry-enrichment for sets of SNPs that are implicated in specific health- or disease-related pathways. For any given ancestry-enriched SNP that mapped to a gene found in an overrepresented pathway, a log odds ratio was calculated as:Obs) versus expected (Exp) frequencies (f) are compared for the SNP reference (Ref) versus alternate (Alt) alleles. Then for each pathway, the set of individual SNP log odds ratios was combined to yield pathway-specific log odds ratio values along with 95% confidence intervals using the fixed-effect model with moderators via linear (mixed-effect) models implemented in the metafor package in R [where the observed (age in R .We developed and applied a single nucleotide polymorphism (SNP)-based approach to relate genome ancestry to genetic determinants of health in admixed Latin American populations Fig. . First, Genome sequences from four Latin American populations – Colombia, Mexico, Peru, and Puerto Rico – were compared to whole genome sequences and whole genome genotypes of global reference populations from African, European, and Native American continental population groups in order to characterize their patterns of genetic ancestry and admixture. Each Latin American population has a distinct pattern of three-way continental genetic admixture characterized by population-specific proportions of African, European and Native American ancestry Fig. . Puerto Our approach to relating genetic ancestry to determinants of health and disease in modern Latin American populations relies on the detection of SNPs that are found at anomalously high frequencies in admixed populations compared to what is expected based on their frequencies in the ancestral source populations, i.e. ancestry-enriched SNPs. We reasoned that such ancestry-enriched SNPs are likely to have an outsized effect on health and disease in modern Latin American populations, perhaps related to an initial increase in population frequency via adaptive introgression.We developed and applied a quantitative method to identify individual SNPs that are enriched in admixed Latin American populations with respect to ancestry from one of the three ancestral source populations: Africa, Europe, and the Americas. To do so, the expected frequencies for each SNP were calculated using the frequency of the given SNP in each of the three ancestral source populations conditioned upon the proportion of each ancestral source population in the Latin American population of interest. Observed SNP frequencies were compared to expected SNP frequencies to identify ancestry-enriched SNPs; the details of this approach are shown in the Materials and Methods section.2 values are shown in Fig. The distributions and median values of ancestry-specific SNP χThe genes with mapped ancestry-specific SNPs were analyzed with gene set enrichment analysis (GSEA) to look for overrepresented health- or disease-related pathways and phenotypes Fig. . This apq-values) – are provided in Additional file A number of pathways and phenotypes have significantly overrepresented ancestry-enriched SNP genes in all four populations Fig. . These iWe focused on several notable examples of health- and disease-related pathways that were found to have significantly overrepresented ancestry-enriched SNP genes in all four Latin American populations Fig. . For eacWe explored the effects of ancestry-specific SNPs on gene regulation via expression quantitative trait loci (eQTL) analysis. eQTL are individual SNPs with genotype variants that are associated with gene expression levels; associations of this kind point to a role for SNP variants in gene regulation  , 34. To Using this approach, we found a number of cases of SNPs that show overrepresented African or European ancestry in modern Latin American populations and are also associated with ancestry-specific gene regulation Fig. . A numbeLatin America has a unique genetic heritage with high levels of admixture from African, European, and Native American ancestral source populations –11. As sHere, we have tried to address this issue by relating patterns of genetic ancestry and admixture to health and disease determinants in Latin American genomes. To do so, we developed and applied a novel SNP-based approach to ancestry enrichment analysis. Our approach leverages information on the genetic ancestry of the modern Latin American populations to discover SNP variants that exist in a given population at higher frequencies than expected, i.e. ancestry-enriched SNPs. We found that specific sets of ancestry-enriched genetic variants, from each of the three ancestral source populations, have been preferentially retained in modern Latin American populations based on a variety of roles that they play in health and fitness. These findings have relevance for the development of genomic approaches to healthcare, i.e. personalized or precision medicine, in Latin America.Gene set enrichment analysis uncovered a number of immunity, metabolism, and disease-related pathways that are significantly overrepresented with respect to genes that contain ancestry-enriched SNPs Figs.  and 5. TExpression quantitative trait loci (eQTL) analysis revealed ancestry-enriched SNPs in modern Latin American populations that are associated with African- or European-specific patterns of gene regulation Fig. . This inOne important caveat with respect to the interpretation of the results that we report is that they can only be taken to apply to the four specific populations analyzed here: Colombian in Medellin, Colombia (CLM), Mexican Ancestry in Los Angeles, California (MXL), Peruvian in Lima, Peru (PEL), and Puerto Rican in Puerto Rico (PUR). Given the diversity of Latin American populations, and in particular their distinct ancestry profiles, we should expect to see distinct ancestry enrichments for different countries in the region, such as Argentina, Chile, Brazil etc. This caveat not only applies to different countries but also applies to different populations within the same country. Colombia, for instance, is an extremely diverse country with populations from different regions that show very distinct ancestry profiles . The popAdditional file 1:Table S1. Lists of all SNPs that show significant ancestry-enrichment. For each admixed Latin American population, all ancestry-enriched SNPs (q < 0.05) are given along with their meta-information, observed and expected reference and alternate allele frequencies, and significance values. The allele  and enriched ancestry  are designated for each ancestry-enriched SNP. (XLSX 177 kb)Additional file 2:Table S2. Lists of pathways that show significant enrichment of genes with mapped ancestry-enriched SNPs for each admixed Latin American population. For each KEGG pathway with significant enrichment of genes with mapped ancestry-enriched SNPs in at least one of the four populations, the overlapping genes and FDR q-values are given. (XLSX 29.6 kb)


Thunnus albacares, is one of the most important seafood commodities in the world. Despite its great biological and economic importance, conflicting evidence arises from classical genetic and tagging studies concerning the yellowfin tuna population structure at local and global oceanic scales. Access to more powerful and cost effective genetic tools would represent the first step towards resolving the population structure of yellowfin tuna across its distribution range. Using a panel of 939 neutral Single Nucleotide Polymorphisms (SNPs), and the most comprehensive data set of yellowfin samples available so far, we found genetic differentiation among the Atlantic, Indian and Pacific oceans. The genetic stock structure analysis carried out with 33 outlier SNPs, putatively under selection, identified discrete populations within the Pacific Ocean and, for the first time, also within the Atlantic Ocean. Stock assessment approaches that consider genetic differences at neutral and adaptive genomic loci should be routinely implemented to check the status of the yellowfin tuna, prevent illegal trade, and develop more sustainable management measures.Yellowfin tuna,  The tuna fishing industry accounts for up to 8% of all fish and shellfish products in international seafood markets. The large demand from international markets and the overcapacity of the global tuna fleet have led to the overexploitation of many stocks. Thirty-seven percent of tuna stocks worldwide are fully exploited, while 33.5% are already overexploited2, data on genetic differentiation of tuna species were not yet fully incorporated in the assessment process for most tuna Regional Management Fisheries Organisations (tRMFOs)5. Information on tuna population structure gathered by recent more comprehensive sampling and more powerful genomic approaches offer the possibility that RFMOs may need to revise previous perceptions of stock structure. The misspecification of genetically different populations may lead to management that negatively affects productivity and long-term stability of tuna populations, as well as the capacity of populations to respond to the variability of oceanic conditions and fishing pressures6. Designing stock-specific management plans and conservation policies tailored to life history traits, genetic structure and ecological dynamics would mitigate the risk of stock decline and irreversible collapse8.Despite the growing evidence that ignoring population genetic structure in fisheries management may result in local depletion through overexploitationThunnus albacares, YFT) is among the most valued wild-caught fishes of the world9. Over the last 60 years, YFT have been caught across large areas of the Pacific, Indian and Atlantic Oceans, with the majority of catch coming from the inter-tropical zone5. YFT is patchily distributed in all oceans and seas in tropical and temperate latitudes between 45° north and south of the equator9. YFT carries out feeding migrations across immense oceanic distances to search for prey to support its high metabolic rates5.Yellowfin tuna  that make it difficult to detect fine-scale genetic differentiation among populations by analysing only a few individuals and a small number of neutral loci  representative of a limited portion of the whole genetic variability12. The recent introduction of genome-wide Single Nucleotide Polymorphism (SNP) genotyping has increased the accuracy and speed of population genetic analyses at relatively low costs. In the case of YFT, the application of SNP markers has already shown that genetically divergent populations occur at local and oceanic scales13.The high dispersal potential of YFT led to the initial expectation of a globally panmictic populationet al.3 showed that YFT samples from the Western, central and eastern Pacific are genetically differentiated at putatively adaptive loci. Furthermore, in a recent study, Barth et al.11 assessed population samples from across the globe and hypothesised that local adaptation or selection are acting on some regions of the YFT genome. Understanding how genome-environment interactions modulate the distribution of neutral and adaptive genetic diversity within and between populations and species is a major challenge of modern biology14. The importance of this knowledge comes from the paradigm of local adaptation, and how the fragmentation of the gene pool of a species is translated into a geographical and ecological structure of populations exposed to different selective regimes, resulting in co-adapted gene complexes. Maintenance of the genetic integrity of populations is also essential in a modern perspective of conservation and sustainable use of marine fisheries resources4. Understanding adaptive genetic differences between populations can enhance the decision-making process in conservation by identifying priority populations to manage, protect or use for restocking14. In addition, this information is the basis for a rational definition of management units that provide a spatially explicit framework necessary for any management plan14. The use of Next Generation Sequencing (NGS) technologies to investigate adaptive genomic population structure may reveal a fine scale population structure in tuna species worldwide and shed light on the nature and magnitude of habitat discontinuities or barriers to dispersal that reduce gene flow among populations. It may also inform a more accurate definition of stock boundaries and significantly enhance fisheries management. Finally, the availability of molecular markers that identify the geographical origin of fish landed at international ports may help curb illegal, unregulated and unreported (IUU) fishing15.The separation of SNPs that are potentially under-selection  from those that are purportedly neutral in the YFT has allowed for the investigation of genomic regions putatively influenced by selective processes. For the first time, Grewe In the present study, we utilise the enhanced genetic resolution provided by SNP genotyping to better define YFT’s existing biological units at global and oceanic scales and, subsequently, contribute to building a basis for more effective management strategies. This work deals with the generation of a novel 2b-RAD SNP dataset with ten geographical samples of juvenile YFT , originated from Atlantic, Pacific and Indian oceans  to fully understand how different micro-evolutionary forces are shaping YFT population structure and dynamics between and within ocean, and to perform a robust population genetic analysis. Our research also aims to disentangle whether selection can affect spatial structuring of YFT populations and this has been addressed by comparatively measuring genetic diversity and population differentiation on neutral and outlier SNP loci. Even if we have the most complete sampling design at the global scale for this species, additional analyses based on a more spatially dense and temporally replicated experimental design are needed to fully uncover the YFT structure within oceans. Our analyses speak in favour of a local/regional population structure within these large stocks linked to adaptive processes.Libraries using the reduced representation protocol 2b-RAD were successfully obtained for 378 out of 400 juvenile YFT sampled for this study Fig. ; Table 116 in 89.34% of cases. The Stacks pipeline returned a total of 6,896 bi-allelic SNPs shared by all 370 specimens. After further filtering , the dataset was reduced to 986 SNPs shared by 357 specimens. All 986 loci conformed to Hardy-Weinberg equilibrium (HWE) in all samples following Bonferroni correction (α < 0.05). Only 14 loci were found to be in linkage disequilibrium at r2 ≥ 0.5, which were removed from the dataset. Ultimately, the final dataset consisted of 357 specimens and 972 SNPs.Filtered reads for the remaining 370 individuals were successfully mapped against the draft genome of YFTThe initial filtering process has ensured that most genotyping artefacts were removed to avoid confounding effects on outlier analyses. Detection of outlier SNP loci (i.e. loci putatively under selection) was carried out applying two different software. A total of 85 outliers were detected by using BAYESCAN 2.1, all of which were associated with a positive α–value and q < 0.05, suggesting the presence of directional outliers only. Arlequin recognized 47 candidate outlier loci. For further analyses, outlier loci identified by both methods – i.e. a consensus panel of 33 SNP loci  is much lower than the inter-group variance . Pairwise FST values among the three clusters were also significantly different from zero  grouped YFT individuals into three well differentiated and not overlapping genetic clusters according to the ocean’s origin Fig. . The Fasro Table . All pairo Table . We thenSC = −0.00728) and maximised the variance among groups , i.e. the early life-stage individuals that exhibited limited migratory behaviour and are assumed to be caught close to their spawning areas. With the largest individual/SNP dataset reported to date at a global oceanic scale, and a level of resolving power that has only recently become possible, we detected population differentiation among samples collected from the main YFT fishing areas within the three oceans. We observed that the magnitude and patterns of differentiation varied depending on the type of markers used . Results based on neutral loci pointed out significant differences among the three oceans but lack of genetic structure within ocean. In contrast, analyses based on the outlier SNPs provided evidence for strong differentiation between eastern and western samples collected within the Atlantic and Pacific oceans. No evidence of genetic structure was detected in the Indian Ocean, possibly because samples were collected only in the western part. While genetic differentiation has already been documented within the Pacific Ocean17 based on a smaller sample size and the same SNP genotyping method. The presence of a discrete YFT population unit in each ocean was further substantiated by different multivariate and individual-based analyses (i.e. DAPC and fastSTRUCTURE). This finding agrees with recent evidence of genetic separation of Atlantic and Indo-Pacific stocks, as well as with the occurrence of a third cluster within the Indian Ocean11. Based on mtDNA, RFLP, and allozyme variability, Ward et al.18 had already hypothesised population genetic heterogeneity between and within oceans and proposed a subdivision of YFT into at least four stocks: one in the Atlantic, one in the Indian, and two in the Pacific .The panel of 939 neutral SNPs clearly resolved the existence of significant differentiation among YFT of Atlantic, Indian and Pacific origin, confirming the genetic differentiation pointed out in a preliminary study19. After that event, dispersal between the Atlantic and Indo-Pacific Oceans would only be possible by passing through the Cape of Good Hope. However, the strong Benguela Current, which transports cold upwelled water northward along the south-west coast of Africa, is known to act as a potential barrier to gene flow . The extent to which this biogeographic barrier affects dispersal of epipelagic fishes, including the YFT is unknown and may vary considerably depending on the life history traits of different taxa. The differentiation depicted in our study confirmed the results gathered by a recent study11 suggesting that the Benguela Current plays an important role as a barrier to migration for YFT.Differentiation among YFT populations from the three oceans suggests that gene flow among oceans is restricted and that dispersal may be limited. This pattern of genetic differentiation may be linked to the complete closure of the Isthmus of Panama to ca. 2.8 Myr ago3, mitochondrial DNA sequences20 and neutral microsatellites21. Using putatively adaptive loci selected from among sequences obtained by reduced-representation libraries and genotyping-by-sequencing through a proprietary Diversity Arrays Technology protocol, DArTseq™, Grewe et al.3 showed for the first time that Pacific YFT populations were genetically structured. Our results agree with this pattern of differentiation within the Pacific Ocean putatively driven by selective constraints. However, Grewe et al.3 described a more complex genetic structure in the Pacific Ocean than that which emerged from our study, with significant differences between samples collected off the west coast of Baja California in Mexico, around Tokelau in the Central Pacific, and within the Coral Sea in Australia. The approximate distance between these samples ranged from 4,000 to 10,000 km, while our western Pacific samples were separated by only 2,000 km. Furthermore, the samples collected in the eastern Pacific were separated by less than 200 km. Therefore, the lack of population genetic differentiation observed in this study comparing to the results obtained by Grewe et al.3 may simply reflect the geographic proximity of our samples and the lack of samples from the Central Pacific in our study. Our inability to identify a population differentiation at the above scales within the Pacific may also depend on the different number of outlier loci used. We selected 33 outlier loci across the entire geographical range of the species by intersecting the results of two independent detection methods (Arlequin and Bayescan) while Grewe et al.3 obtained 215 potentially-adaptive SNPs strictly using LOSITAN as a single outlier detection software22. Approaches implemented in LOSITAN and Arlequin are based on an island model that uses a coalescent FST-outlier method to assess the distribution of FST as a function of the heterozygosity. However, the outlier detection method implemented in Arlequin adds the option for hierarchical clustering. In contrast, Bayescan23 uses a logistic regression model which explains the observed pattern of diversity by dividing it into locus- and population-specific components. Using multiple methods to detect outlier loci and intersecting results limits the number of markers identified but can guarantee a more conservative approach and reduces Type I error due to false positives24. At any rate, additional analyses based on a more spatially dense and temporally replicated sampling design in the whole Pacific Ocean combined with a wider panel of outlier SNPs, are needed to accurately uncover the YFT population genetic structure that tRFMOs can confidently reference to support changes in their stock assessment and management policies, and in turn to achieve fishery sustainability. Furthermore, given the highly migratory nature of tunas, samples from locations separated by hundreds or a few thousands of kilometres should be collected within a few days or weeks to each other to prevent sampling duplication.The evidence of within-ocean population genetic structure was recently shown in three different studies that used potentially adaptive SNPs25.In this study, we were not able to obtain samples from the eastern Indian Ocean and, therefore, we cannot verify the presence of additional clusters in this area. The IOTC–2015–SC18–ES04 resolution recently recognised the need for additional studies in the Indian Ocean focusing on areas of admixture at a much finer scale than reported here. The resolution recommends employing multidisciplinary approaches, including population genomic structure assessments together with other population identification methodologies. Nevertheless, the lack of genetic heterogeneity found in this study in the western Indian ocean is not in conflict with those obtained by the largest tagging program ever realised in the Indian ocean by IOTTC from 2005 to 2009, which indicated the possible presence of a single, well-mixed YFT population in the entire basinet al.3, have demonstrated that determining the spatial distribution of adaptive genetic variation is fundamental to uncovering population structure of recently-diverged, large-sized and geographically connected demes which otherwise may appear panmictic at neutral loci. The effects of selection might indeed influence the population structure much more than genetic drift in species such as the YFT, which has a large effective population size and high dispersal potential26. The analysis of outlier loci can help to identify genomic regions associated with ecotype divergence over very recent  timescales24. For instance, some studies have reported that local adaptation in fish populations was related to the occurrence of linked alleles . These alleles, in turn, may interact to mould and define the population structure of YFT. The combination of these evolutionary and biological processes may influence YFT population structure among the three oceans, as well as at the within-ocean scale. Biogeographic and physical barriers to dispersal provide simple and effective obstacles to inter-oceanic movement of fish, but less-effective barriers such as oceanographic fronts (e.g.27) and environmental gradients may also be relevant15. However, the genetic separation detected in the present study may also be linked to a wide variety of evolutionary and biological mechanisms that promote local adaptation to specific environmental constraints.Recent studies, starting with Grewe 30. Unfortunately, such a multidisciplinary and integrated investigative approach, with massive sampling carried out over several years (as done for the Atlantic Bluefin tuna by the ICCAT-GBYP) is still lacking for YFT. Tagging studies have revealed a variety of migration patterns with different management implications. For instance, restricted movements and a high degree of fidelity were displayed by YFT within the Central and Western Pacific32 and within the Eastern Pacific34, suggesting a limited mixing over large regional areas. Meanwhile in the Atlantic, YFT juveniles are stationed along the African coast within their main spawning ground in the Gulf of Guinea until they reach the pre-adult stage35. Once in the pre-adult stage, they migrate to feeding grounds in the western part of the Atlantic Ocean and return to the Gulf of Guinea to spawn when they reach at least 110 cm fork length and about 3 years of age36. Tagging studies have also validated that YFT are able to undertake long-distance horizontal migrations in the Atlantic Ocean, with the individuals tagged in the Gulf of Guinea and in the Africa - Canary Islands region (juveniles and pre-adults) migrating relatively close to the coast, both north to south and vice versa36. Likewise, pre-adult and adult YFT tagged in the US waters displayed both trans-Atlantic migrations towards the Gulf of Guinea and towards the Caribbean Sea37. Such migratory behaviour of YFT is compatible with movements of individuals at different stages between reproductive areas (e.g. Gulf of Guinea and Caribbean/Gulf of Mexico) and foraging grounds , as already shown for other Atlantic tuna species targeted by wide tagging programs39. Therefore, it is possible that most of the sexually mature and maturing YFT individuals migrate back from the north-western Atlantic feeding grounds to the Gulf of Mexico and south-eastern Caribbean for reproduction37, inducing the central western Atlantic population to be strongly genetically differentiated from that of the eastern part, as indicated in the present study by using the OL dataset. It is likely that selective constraints and local adaptation in the eastern and western reproductive areas overcome the genetically homogenising effect at the neutral loci given by a sufficient number of migrants per generation exchanged between the populations reproducing in the two areas. Such sufficient level of gene flow between the two Central Atlantic populations is supported by evidence of transoceanic migrations of YFT between the Caribbean and Gulf of Guinea37. Even though, there is still not enough tagging and reproductive information to confirm the nature and the rate of the YFT spawning migrations or natal homing across the Atlantic Ocean, or to independently ascertain whether populations from the Gulf of Guinea and the Gulf of Mexico/south-eastern Caribbean are two reproductively isolated units37, our results suggest a strong adaptive genetic divergence between the eastern and western stocks. Moreover, discrepancies between our results and much of the findings of previous studies may also be due to sampling design and their use of older fish that migrated to mixed feeding areas. Quite the opposite, we collected juveniles YFT that are characterised by a limited swimming ability and are assumed to be caught close to their spawning areas, which better reflect the true genetic composition of the spawning populations.Migration behaviours play an important role in shaping population structure and adaptive divergence in highly-migratory large epipelagic fish (i.e. the Atlantic bluefin tuna) with genetic differentiation among populations linked to strong fidelity to natal spawning areas and spatial learning for feeding, with population admixture occurring primarily in the large foraging grounds3 and this study). Loci putatively under selection are much more able to properly delineate and separate locally adapted subpopulations at regional and local scales, finely tuning the stock structuring in these highly migratory fish species with large populations. Our results call for a re-assessment of the within-ocean YFT stock subdivision by the International Commission for the Conservation of Atlantic Tunas (ICCAT), by using a multi-disciplinary approach that takes advantage of modern genetic technologies and incorporates the use of loci potentially under-selection to detect adaptive divergence in stocks. In fact, genetically differentiated populations that are potentially adapted to regional environmental constraints may experience different productivity regimes and levels of mortality, with direct repercussions on recruitment, growth, and general demographic patterns. The possible presence of genetically structured populations in the western and eastern Atlantic Ocean require scientific and management communities to consider the potential for multiple, independent assessment and management units within the ICCAT jurisdiction. At the least, demographic models developed for assessing the stock status of YFT should include spatial stratification in order to account for the strong signal observed in the genomic studies. More generally, genomic analyses can provide critical information about the delineation of the areas used in spatially-resolved fish population models, which have been shown to have major impacts on scientific advice (e.g.40).In the present study, we successfully used potentially adaptive and neutral loci to trace back YFT individuals sampled in different geographical areas of Atlantic, Indian and Pacific Oceans. Our results suggest a possible metapopulation structure in YFT, with different populations occupying various habitat patches that are connected to one another by the movement of individuals among them. While we were able to demonstrate more complexity in the global YFT population structure than previously assumed, we also pointed out that it is not sufficient to use a large number of neutral loci and specimens to detect genetic structure in the YFT  are required to understand the reproductive and feeding dynamics of YFT in the Western Atlantic Ocean in order to confirm our findings and to help develop more realistic population dynamics models and effective fisheries management strategies for this species.Our results underline once more the need to include fishery-independent data, such as those from population genomics, into the stock assessment models of YFT to ensure its proper and sustainable management. Further massive and deep surveys and studies as the 5-years ICCAT’s Atlantic Ocean Tropical tuna Tagging Programme  were collected in 2013 and 2014 from ten locations over the entire species distribution  were amplified and pooled into equimolar amounts according to the corresponding barcode. Pooled libraries were sequenced on an Illumina HiSeq2500 platform with a 50 bp single-read module at the Genomix4Life S.r.l. facility , which also performed data demultiplexing. The quality of demultiplexed reads was checked by using FastQC (www.bioinformatics.babraham.ac.uk/projects/fastqc/). The reads were filtered using a custom Perl script as described in17. Filtered reads were mapped against the genome of T. albacares16, retaining only uniquely mapped reads and using the program CLC Genomics Workbench v. 5.1 . Mapping results were exported in SAM format and used as input files for refmap_map.pl in the software Stacks v. 1.32 (http://creskolab.uoregon.edu/stacks/43). The most appropriate Stacks parameters were identified on the mapped technical replicates, as described in17. Quality filtering was performed with the process radtags module with default parameters. Only libraries with >500,000 retained reads were kept. 2b-RAD loci containing bi-allelic SNPs in at least 80% of the individuals within each geographic sample were retained and selected to be exported into Genepop format using the Stacks module populations. SNPs with a minimum allele frequency (MAF) smaller than 1% and genotyping rate less than 0.2 were identified with the R package Adegenet 2.0.1  and excluded from downstream analyses. Exact test for Hardy-Weinberg equilibrium (HWE) was carried using the function hw.test (R package Pegas46). SNPs out of HWE at p < 0.05 in at least nine out of 10 populations were removed. Linkage disequilibrium was tested for each pair of loci with the log likelihood ratio statistic (G-test) using the program Genepop 1.2 (Genepop on the webpage). A Bonferroni correction was applied to the global significance threshold (α = 0.05) when multiple tests were performed.A 2b-RAD library was prepared for each sample and for technical replicates following the method described in47 and Arlequin ver. 3.5.1.248. Using BAYESCAN 2.147, we conducted 20 pilot runs of 5,000 iterations each, followed by an additional burn-in of 50,000 iterations and then 5,000 samplings with a thinning interval of 10. To correct for multiple testing, the program computes q-values based on the posterior probability for each locus. Loci with an α-value significantly >0 and q-values < 0.05 were defined as “outliers” – i.e. loci putatively under directional selection. Loci with an α-value significantly <0 were considered putatively under balancing selection. Remaining loci were classified as neutral.We identified candidate outlier loci (loci that are putatively under selection and involved in adaptive divergence) by the independent approaches implemented in BAYESCAN 2.149 implemented in Arlequin ver. 3.5.1.248 as a second approach to detect outlier loci. Loci with significantly higher FCT or FST values (at the 0.05 significance threshold) were classified as outliers potentially under directional selection among groups. Loci with significantly low FCT or FST values were classified as putatively under balancing selection. Remaining loci were considered as putatively neutral. Results from the two programs were intersected to build a consensus panel of candidate loci with high probability of being under selection. Only the candidate SNPs jointly identified by both programs were categorized as outliers. All downstream analyses were run independently for the neutral and outlier loci datasets.We used a hierarchical Fdist model50, in order to assess if any detected high levels of relatedness should be taken into account when interpreting our population structure results. We used the Wang estimator that performs better than the other estimators when sample sizes are small, and many loci are used51. Genetic diversity within samples in terms of observed (Ho) and expected heterozygosity (He) and inbreeding index (FIS) was calculated using the software Arlequin ver. 3.5.1.248 and 20,000 permutations. All diversity parameters were calculated within each sample . All tests aimed at investigating population differentiation were applied to two different datasets, one containing the neutral markers and one with the outlier loci. To estimate the level of differentiation among samples, pairwise and global FST values were calculated with Arlequin ver. 3.5.1.248, using 20,000 permutations and a significance threshold of α = 0.01.We assessed the level of relatedness in fish caught in same area using COANCESTRY45 R version 3.1.2, R Development Core Team, 2014; http://www.r-project.org), for both neutral and outlier loci without any a priori population definition. The find.clusters  function was initially used to run successive numbers of (K)-means clusters of the individuals, across a range of K = 1–20. We identified the best supported number of clusters through comparison of the Bayesian Information Criterion (BIC) for the different values of K. Prior to running a Discriminant Analysis (DA), an optimum number of principle components  was identified using the optim.a.score  function. The DA was then run on the retained principal components using the dapc  function. Finally, after selecting the best number of eigenvalues for the DA analysis, the DAPC results (DAPC scatterplots) were visualized graphically with the scatter.dapc  function.To determine the number of expected genetic clusters (K) present in the dataset, a discriminant Analysis of Principal Components (DAPC) was performed with Adegenet ver. 2.0.1  using default parameters, a logistic prior, and K from 1 to 12, with 100 replicate runs for each K. To select the value of K that best explained the subdivision in clusters within our dataset, we ran the python chooseK.py script following52. To assess the strength of population subdivision in clusters based on DAPC and FastSTRUCTURE, we performed a hierarchical AMOVA with Arlequin ver. 3.5.1.248 and compared F-statistics for each best K provided by previous clustering analyses.To reconstruct the genetic ancestry of individuals in relation to their cluster membership, we ran FastSTRUCTURE  database under the accession number SRP067271.Supplementary Tables S1-S5


Betula platyphylla Suk.) across East Asia. We found a north‐to‐south trend of declining genetic diversity and five clusters, corresponding to geographical regions. Different clusters were inferred to have diverged through Pleistocene climatic oscillations and have different expansion routes, leading to genetic admixture in some populations. Ecological niche models indicated that the distribution of B. platyphylla during the last glacial maximum still had a large latitude span with slight shifts toward southeast, and northern populations had more variable distribution ranges than those in the south during later climatic oscillations. Our results reflect the relatively stable distribution through the last glacial–interglacial cycles and recent multidirectional expansion of B. platyphylla, providing new hypotheses for the response pattern of widespread tree species to climate change. The gradual genetic pattern from northeast to southwest and alternative distribution dynamics possibly resulted from environmental differences caused by latitude and topographic heterogeneity.Widespread tree species cover large geographical areas and play important roles in various vegetation types. Understanding how these species responded to historical climatic changes is important for understanding community assembly mechanisms with evolutionary and conservation implications. However, the location of refugial areas and postglacial history of widespread trees in East Asia remain poorly known. We combined microsatellite data  and ecological niche modeling to examine the range‐wide population diversity, genetic structure, and historical demography of a pioneer tree species, Asian white birch ( It occurs primarily throughout the mountainous area extending from northeast to southwest, with an elevation span from 20 m to 4100 m  detect wide‐range genetic structure and patterns of genetic diversity of B. platyphylla, (b) infer the existence and locations of refugia for B. platyphylla during Quaternary climatic changes, and (c) reconstruct the glacial–postglacial history and migration pattern of this species. We tested the following hypotheses: If the refuge system of B. platyphylla corresponded to the southern refuge model, genetic footprints, such as a south‐to‐north decline in population genetic diversity corresponding with the direction of northward recolonization and extensive bottlenecks, should be expected; if the northern refuge model was supported, B. platyphylla presumably survived in both northern and southern refugia across East Asia and expanded from these refugia resulting in lineage admixture and secondary contact zones  markers combined with ecological niche modeling approaches to investigate the phylogeographical structure and demographic history of s Figure .22.1A total of 1756 wild white birch trees were sampled from 63 different geographical sites, including 16 from northeast China, 18 from north China, 11 from northwest China, 16 from southwest China, one from Japan, and one from Russian Far East Table . For eac2.2B. platyphylla var. japonica, Betula maximowicziana, Betula pendula  following the manufacturer's instructions. We tried 32 cpDNA primers, all of which were invariant along the species range. Ten pairs of microsatellite primers developed for 2.32.3.1P‐values were adjusted applying a sequential Bonferroni correction , the observed heterozygosity (HO), the genetic diversity within populations (HS), and the total gene diversity (HT). For each population, descriptive statistics such as the observed number of alleles (AO), the expected heterozygosity (HE), and the fixation index (FIS) were calculated across all nSSR loci. These statistics were estimated using FSTAT v. 2.9.3  and private allelic richness (PA) for each population were calculated by rarifying to 16 gene copies using HP‐RARE v. 1.1  from zero, was tested by randomization using FSTAT v. 2.9.3  and geographical ordinates (latitude) for each population were analyzed in the package SPSS v. 20.0.The presence of null alleles was checked with MICROCHECKER v. 2.2.3   with 50,000 burn‐in steps followed by 500,000 MCMC (Monte Carlo Markov chain) steps using the admixture model with correlated allele frequencies. STRUCTURE HARVESTER  was conducted in GENALEX v.6.502  and migration rates , using coalescence. The starting values for θ and M were estimated from FST, and proposal distribution was Metropolis–Hastings sampling. Under a Brownian motion mutation model, 1 long chain and three concurrent chains  were run with an initial burn‐in of 10,000 trees. We used a static heating scheme at four temperatures  to efficiently search the genealogy space.Historical rates and direction of gene flow between the five STRUCTURE groups were estimated using the Bayesian inference in MIGRATE‐N v.3.6.11 Beerli, . The sof2.4.2We used the program BOTTLENECK v. 1.2.2  was applied to pre‐evaluate the combination between observed data sets and simulated data sets and to ensure that the setting of reference tables was appropriate. Subsequently, we used 1% of the simulated data sets closest to the observed data to estimate the relative posterior probabilities for each scenario via logistic regression , in order to identify regions of environmental stability where the species may have persisted overtime (the possible refugia). By identifying historical shifts in suitable habitats, which expanded and contracted during glacial and interglacial periods, we also tried to infer the possible recolonization pattern  and the Global Biodiversity Information Facility (http://www.gbif.org) were also included. To reduce the effects of spatial autocorrelation in climate variables, we removed duplicate records within 24 km of one another (12 arc‐min), leaving a total of 155 presence records for use from throughout the species range. Considering the dispersal ability of B. platyphylla, we buffered the area around the occurrence points by 1,000 km and selected the background points within this range. The ecological layers for the current time, the mid‐Holocene , and the last glacial maximum  were downloaded at 2.5’ spatial resolution. For MIH and LGM prediction, we used both CCSM4 and MIROC‐ESM database. We downloaded 37 environmental variables from WorldClim (http://www.worldclim.org)   maximum entropy; (b) generalized boosting models; (c) classification tree analysis , and the genotypes had been adjusted. No significant genotypic linkage disequilibrium was detected between pairs of loci (p > 0.05). The 1756 sampled individuals of B. platyphylla revealed 180 alleles across the ten nSSR loci. For characteristics of individual microsatellite loci, see Table AO ranging from 23 to 88, Ar from 2.34 to 6.03, PA from 0 to 0.33, HE from 0.303 to 0.795, and FIS from −0.16 to 0.137 , allelic richness , private allelic richness , and expected heterozygosity  was found with increasing latitude  , increased progressively from K = 1 to K = 5 (except K = 4) and then plateaued . However, the tests within the NE and NC lineage were not significant , suggesting that geographical distance did not impact the genetic differentiation within these two lineages. Measures of genetic diversity  were significantly different among the five groups . The YN regional cluster of populations attained lower values of Ar, HO, HS, and FIS than the other population clusters, and the FST among populations in YN cluster was much higher than the others , with 4.58% (FCT = 0.046) of the variation partitioned among the five groups, and 2.29% (FSC = 0.024) among populations within groups . The minimum gene flow was YN to XJ, 0.198 (0–0.599), and the maximum gene flow was NE to NC, 34.578 (22.551–37.800). Gene flow between XJ and the other four regions was generally weak, consistent with the remote geographical location of Xinjiang populations. The other four regions were adjacent in turn. From south to north, the gene flow between adjacent regions increased gradually, and there was clear asymmetric gene flow between NE and NC ; NC → NE, 20.244 (17.322–22.733)), suggesting that NC obtained more pollen or seeds from NE (Table The results based on migration inference showed that there was a certain level of gene flow between the five distribution regions of Mθi = 0.98–34.5783.3.2p = 0.574 (0.544–0.603)). The median values of the effective population sizes of XJ and NE were 6,870, 7,520 respectively. According to the results of the second step, we reconstructed the evolutionary history of the three adjacent lineages NE, NC, and NW, NE and NW were separated at 22, 170 years BP , and the central lineage (NC) was generated by admixture of NE and NW at 1902 years BP  ). The estimated admixture coefficient suggested that 35.6% of the nuclear genome of the NC lineage in the initial population was derived from NE, and 64.4% came from NW. The median effective population sizes of NE, NC, and NW were 9,000, 6,400, and 4,240, respectively. When inferring the origin of populations growing in the area at southwest corner (YN and NW), divergence between YN and NW was estimated to have happened around 27, 510 years BP  as implied by scenario 1 , if assuming a generation time of 30 years, populations from XJ and NE had a common ancestor that diverged into two different lineages , is exceptions, because of limited dispersal mechanisms  than in southern groups   Table . This paN) Table .N , the north China, the northeast China, and adjacent regions (Russian Far East), and the southern refugia in northeastern (the Qilian Mountains) and southeastern (the Hengduan Mountains) edge of Qinghai–Tibetan Plateau. This scenario best explains the phylogeographical pattern of B. platyphylla and conforms with the fossil evidence that suggests that Betula had four distributional centers in eastern continental Asia . Additionally, the value of gene flow was reduced from north to south  years ago, as estimated by Tsuda et al., have very low private allelic richness values, which were close or even equal 0 Figure . The abs4.3AO, Ar, PA, and HE were high in the northeast, moderate in the middle area, and relatively low in the southwest mountainous area (Figure Our data indicate that genetic diversity in white birch populations was not evenly distributed across the range. The values of a Figure . By conta Figure . Moreovea Figure .B. pendula (Tsuda et al., There are three possible factors forming different genetic characteristics in the north and the south. Firstly, the terrain difference between the south and the north is probably the most important factor. The northern populations are located in a flatter region with no large geographical barriers. The pollen of white birch can spread far by air and be collected by tall trees at similar altitudes, leading to more frequent gene flow, which improves the possibility of hybridization, slows divergence and increases genetic diversity (Havrdova et al., The influence of topographic heterogeneity is also supported by the different distribution dynamics of the northern and southern populations from the LGM to the present. As we can tell from the ecological niche models, the species responded in different ways to past climatic changes. The northern populations extended to a larger area with the relatively continuous range and have more opportunities to expand and contract in response to climate changes. Whereas the southern populations are often restricted in distribution with only slight range change Figure , possiblB. platyphylla exhibited an uneven pattern indicated that this species has a broad capacity to grow under various conditions and the capacity facilitates its wide distribution range. However, the genetic and distributional difference among populations should be paid more attention in relation to species conservation. After the glacial periods, migrations from separate refugia would meet in contact zones forming high genetic diversity there (Havrdova et al., B. platyphylla showed high genetic diversity in north China because of admixture. In contrast, for marginal populations, such as YN in southwest China, the limited genetic admixture caused by topography would make them more vulnerable or even disappear in the face of climate changes. We should be careful when we formulate the policy of tree species conservation and forest management, and we should pay more attention to fragmented populations with low diversity in such areas. After all, studies of genetic variation within species allow us to track their population histories and to predict future range shifts induced by the climate change, making it possible to apply proper protection activities (Jadwiszczak, The findings that genetic pattern and distribution dynamics of B. platyphylla, which covers a large area of East Asia. Our study suggests that multiple LGM refugia, with different altitudes and landforms, may have allowed temperate trees to persist in both southern and northern East Asia. Postglacial range expansion occurred in different directions leading to admixture between recolonizing populations. In addition, besides historical climate change, topographic heterogeneity might also have an impact on the formation of genetic pattern and make it possible that northern and southern populations have had different expansion/contraction dynamics. These findings reveal new perspectives on our understanding of how widespread tree species responded to climatic variation.In conclusion, the use of genomic data coupled with ecological niche modeling approach allowed us to recover for the first time a detailed demographic history of A‐R.L. and T‐Y.C. planned and designed the research. T‐Y.C. performed experiments, analyzed data, and wrote the manuscript. Click here for additional data file.