Primula elatior (Primulaceae), naturally spanning entire elevation range in multiple mountain regions of central Europe, to test for the origin of its scattered subalpine populations. Nuclear microsatellite variation revealed three genetic groups corresponding with the distinct study regions. We found that genetic differentiation between foothill and subalpine populations within each region was relatively low, suggesting that the colonization of subalpine habitats occurred independently within each mountain range. Furthermore, the strongest differentiation was usually found between the subalpine populations suggesting that mountain ridges may act as migration barriers that can reduce gene flow more strongly than elevational differences between foothill and subalpine populations. Finally, we found that subalpine colonization did not result in a loss of genetic diversity relative to foothill populations in agreement with the high migration rates that we document here between the subalpine and the foothill populations. In summary, our study shows subalpine Primula elatior populations are genetically diverse and distinct results of parallel colonization events from multiple foothill gene pools.The island-like distribution of subalpine habitats across mountain ranges can trigger the parallel evolution of locally adapted ecotypes. Such naturally replicated scenarios allow testing hypotheses on how elevational differentiation structures genetic diversity within species. Nevertheless, the parallel colonization of subalpine habitats across different mountain ranges has only rarely been documented with molecular data. We chose  Local adaptation to these environmental stresses often results in the evolution of specific morphological and/or physiological traits that confer increased fitness in these challenging environments3. Populations characterized by these adaptations are often called “ecotypes” and their level of overall genetic differentiation from ancestral populations may be still very low4. In cases where reproductive barriers arise between ecotypes and their ancestral populations, either as a consequence of natural selection or simply due to spatial isolation, the ecotype may represent the first step towards the founding of a new species6. In such cases, we can observe speciation as a continuum of divergence along an elevational gradient leading to textbook examples of ecotypic differentiation8. The restriction of gene flow between lower and higher elevations as well as the reduction or absence of gene flow among mountain ridges can lead to the accumulation of reproductive barriers and/or hybrid incompatibilities9. This process is but one of several potential explanations for relatively high species diversity in alpine/subalpine habitats throughout the world10.Alpine and subalpine habitats represent challenging and often unpredictable environments for plants. Plants have to face a complex set of stresses in such environments including freezing, fluctuating temperatures, increased UV radiation, and terrain disturbances implying various selection pressures11. It can also lead to phenotypic differentiation potentially resulting in the convergent evolution of traits that confer local adaptation (i.e. ecotypes)2. Although the mountains provide an ideal model system for studying the evolution of recurrently adapted ecotypes, the parallel colonization of alpine/subalpine habitats has only been rarely documented with molecular data. Rare exceptions to this are represented by studies in Arabidopsis halleri12, Zea mays13, and Populus trichocarpa14. The alternative scenario of a single origin of alpine/subalpine ecotype followed by dispersal has also been documented, for example in Senecio halleri whose alpine populations represent a single lineage that colonized the Alps in a stepwise manner15.The island-like distribution of alpine/subalpine habitats can trigger allopatric differentiation and parallel colonization from lower elevations independently in each mountain range18. It is poorly known how the colonization of alpine/subalpine h abitats has shaped the genetic diversity of populations, but it is hypothesized that alpine/subalpine populations would be genetically depauperate relative to lowland populations due to the effects of genetic bottleneck, genetic drift, reduced gene flow, and habitat fragmentation19, all of which can promote the genetic isolation of alpine/subalpine populations from their foothill relatives20. This has indeed been observed in alpine populations of Arabidopsis thaliana from the Italian Alps, which exhibit reduced genetic diversity relative to foothill populations21, but in contrast, alpine A. thaliana populations from the Swiss Alps show no evidence of reduced genetic diversity22. In Primula merrilliana from eastern China, alpine populations exhibit even higher genetic diversity, moreover, they are larger and more inter-connected with gene flow than foothill populations23. This pattern implies that the foothill populations may have been colonized by alpine populations. In summary, studies of recurrently originated ecotypes of a single species may provide valuable replicates to test the generality of how alpine conditions shape population genetic diversity.The influence of habitat differentiation on the genetic diversity of populations has been previously studied mainly at lower elevationsPrimula elatior (Primulaceae), a species with broad ecological preferences including a large elevational range in several mountain ranges in Europe24. The wide ecological breadth is linked to high morphological variation primarily in leaf and calyx shape25. Individuals originally from subalpine populations tended to have urceolate calyxes , compared to individuals from foothill populations mainly with tubular calyxes 26. The morphological traits are plastic, except the shape of calyx, which remained stable after cultivation of subalpine population under uniform foothill conditions in a common garden experiment in one of the mountain ranges . In contrast to morphological investigations, the genetic structure of P. elatior remains unknown except studies at fine-scale in Belgium29. We focus on three mountain ranges in central Europe, where P. elatior grows along an elevational gradient from foothill meadows, river banks, and forest edges, up to subalpine meadows, snowbeds, and rocky outcrops in the glacial cirques. In the Krkonoše and the Jeseníky mountains, subalpine populations are restricted to glacial cirques. In the Tatry mountains, subalpine populations grow in valley meadows and snowbeds. Generally, the difference between foothill and subalpine populations across mountain ranges is defined by treeline, foothill populations occur below the treeline in contrast to subalpine populations, which occur above the treeline. We assume colonization of subalpine habitats from foothill habitats during warmer periods of the postglacial Holocene. Our assumption of upslope colonization is likely because temperate species are highly unlikely to have survived past glaciations that affected these subalpine habitats30.In this study, we address the genetic consequences of elevational differentiation in P. elatior as a suitable system, we examined how elevation shapes genetic structure within a species, and by comparing these results across three distinct mountain ranges , we evaluated how generally applicable these patterns of differentiation might be in P. elatior. First, we tested whether the subalpine populations in three mountain ranges represent parallel colonization events of subalpine habitats occurring independently in each mountain range or if the subalpine ecotype evolved once and later spread across the different mountain ranges. Second, we tested if the elevation acts as a barrier to gene flow between foothill and subalpine populations, and whether gene flow is asymmetric; e.g. are migration events from high to low elevations following the downslope transport of seeds and pollen more common than upslope migration events. Finally, we tested for a reduction of genetic diversity associated with the colonization of subalpine habitats relative to lowland habitats.Using By genotyping 12 nuclear microsatellite loci in 202 individuals from 16 populations we detected a total of 120 alleles with maximum of 24 and minimum of three alleles per locus.P. elatior populations across the three target mountain ranges, where it occupies both foothill and subalpine habitats, using Bayesian clustering (structure), distance networks (Neighbor-joining networks), and ordinations . The results of the structure analyses showed that populations from each mountain range  formed a separate cluster, regardless of their foothill-subalpine differentiation, under the corresponding partition of K = 3 , ranged from 0.4 to 0.6 in the whole dataset and foothill populations from the Tatry exhibited the highest values (0.52–0.6). Populations also varied in gene diversity (HS) ranging from 0.43 to 0.65 with the highest values (0.65–0.61) in one foothill and two subalpine populations from the Tatry. The proportion of rare alleles (DW index)31 exhibited a similar trend as well, with the range from 82.44 in the Jeseníky subalpine population to 411.79 in the Tatry foothill population. Despite this variation, we did not detect consistent differences between the group of foothill and subalpine populations across three mountain ranges in diversity index (non-significant ANOVA with region as a random factor)  being present in foothill populations in the Tatry Table . Similarr) Table .Table 1GST) was low, but varied considerably from 0.04 to 0.28, with an average of 0.13  compared to those between subalpine populations (on average 0.14 for six comparisons in the Tatry and 0.13 for one comparison in the Krkonoše). In contrast, two subalpine populations in the Jeseníky, that occupied different parts of one glacial cirque, were less differentiated from each other (0.04) than were their two foothill counterparts (0.06). Between the foothill and subalpine populations, we observed at most a moderate differentiation, with the highest mean pairwise FST values in the Krkonoše (0.10) followed by the Tatry (0.09), and the lowest values in the Jeseníky (0.08).Among-population differentiation  and the Tatry (two populations), respectively. The lowest variation (2.76%), although still significant, was found between subalpine populations from the Jeseníky. Finally, hierarchical AMOVAs within each mountain range showed that the foothill-subalpine differentiation was non-significant and accounted for little variation  confirmed high intrapopulation variability by assigning the highest proportion of variation always among individuals within populations (from 97.24% to 73.27%). In congruence with pairwise Fmigrate-n. Within each region, we analysed pairs of representatively sampled  subalpine-subalpine, foothill-subalpine, and foothill-foothill populations; for each pair, we modelled past evolutionary history under Bayesian inference of five migration models differing in presence and directionality of migration. The models with unidirectional migration gained the best support in all population pairs analysed (Table Nm) was overall high (>1) (ranging from 5 to 551 with an average of 160), which suggests that migration via ongoing gene flow has probably a greater effect on the extent of differentiation between populations than genetic drift 32. However, besides migration, the large estimates for the number of immigrants could also at least partly reflect the recent shared ancestry of populations33.We further addressed the role of migration in foothill-subalpine population differentiation by testing for the presence of gene flow and estimating the strength of gene flow between populations in a coalescent framework using ed Table . In the Nm = 5), where subalpine populations are separated by a mountain ridge. In contrast, in the Jeseníky, a relatively weak barrier to gene flow appears to exist between the two populations residing within one glacial cirque based on the high migration rate estimated (Nm = 266). The highest number of immigrants in the entire dataset was also found between two foothill populations   and alpine populations has been documented in Dianthus callizonus37, Primula merrilliana23, and Solidago virgaurea38. The parallel origin of ecotypes has been also documented previously with genetic data in different environments such as sand dune vs. non-dune in Helianthus petiolaris39, sand dune vs. rock in Senecio lautus40, serpentine vs. non-serpentine soils in Solidago virgaurea41, wave vs. crab predation in the mollusc Littorina saxatilis42, and freshwater vs. saltwater in the fish Gasterosteus aculeatus34. The absence of similar studies in subalpine plants might simply reflect less attention to this phenomenon of differentiation in subalpine environment.The example of the parallel origin of ecotypes along an elevational gradient, although with the opposite direction of colonization than in our study, was recently documented for ST = 0.13), foothill and subalpine populations within each region were less differentiated (pairwise FST ranging from 0.08 to 0.10) and accounted for a negligible and non-significant proportion of variation in AMOVA tests (from 2.32% to 6.11% variation). The variation in these values across mountain ranges likely reflects different spatial and elevational distances between subalpine and foothill populations in each region as well as heterogeneity of habitats. In the Jeseníky and northern part of the Tatry  we observed a nearly continuous occurrence of P. elatior along an elevational gradient 44. In contrast, in the Krkonoše and the Nízké Tatry, there was a gap in the occurrence of several hundred meters, which can restrict gene flow , as was also revealed in Knautia in the Krkonoše45. Available population genetic studies have documented that the levels of differentiation among foothill and alpine/subalpine populations may vary considerably among species, from high (mean pairwise FST = 0.19 in Primula merrilliana23) to low .In contrast to differentiation among the three distinct mountain ranges 36. The extent of differentiation likely indicates if populations have an older or younger origin/divergence. Besides elevational differences, lower or comparable differentiation to our study was also observed in two Primula species between grassland and forest habitats in lowland habitats at a fine-scale of several kilometres (mean FST = 0.02 in P. elatior16 and 0.08 in P. veris)47. Our observation in P. elatior thus suggests that elevation is generally a weak isolating barrier whose strength may further vary across independent colonization events in different mountains.Our study shows that genetic differentiation along an elevational gradient may vary within a species, from one mountain range to another, although this pattern appears to be less extreme in P. elatior is likely due to high levels of retained ancestral polymorphism and/or persisting gene flow along the elevational gradient. This was suggested by coalescent modelling in which we found consistent support for models with unidirectional migration from subalpine to foothill populations. The unidirectional migration likely reflected gene flow after the colonization of subalpine habitats in postglacial times. This was the best supported model in all three mountain ranges, however, with varying estimated strength across the regions. Such unidirectional downslope migration has been observed also in P. merrilliana23, in which the seed mobility is generally restricted in a similar way to P. elatior due to the lack of any long-distance seed dispersal adaptation48. In the mountains, seed flow may be further enhanced by washing downslope by mountain streams, but pollen flow may be restricted along an elevational gradient due to differences in flowering phenology between populations at different elevations4. In the closely related species P. veris, differences in the flowering phenology affect gene flow even among distinct habitats in the same (lowland) elevation47.The relatively low foothill-subalpine differentiation that we observed in Gentiana pannonica (14.15%)49. The Jeseníky was the only exception, in which both subalpine populations were restricted to a single glacial cirque with likely high opportunities for gene flow and this is likely responsible for the low among-population differentiation and FST values. These results suggest that mountain ridges may act as relatively strong migration barriers, which can affect gene flow more strongly than elevational differences between subalpine and foothill populations. Although higher differentiation among subalpine populations may also reflect past bottleneck events during their origin from their foothill counterparts, we do not consider this likely. We have not observed significantly reduced levels of diversity in these populations . This relatively low differentiation is may be the product of a higher density and large population size of foothill populations. Pollination is more efficient in large populations and the larger population size may provide more opportunities for gene flow among populations52, which may have experienced the long-term isolation of populations in this area serving as a glacial refugium for temperate species53. Generally, subalpine populations of P. elatior appear to be able to maintain genetic diversity equal to foothill populations despite smaller population sizes and the spatial isolation of the glacial cirques in which they occur. This could be due to a large size of the initial colonizing population (ruling out founder effect) and the relative stability of the habitat in postglacial time providing good conditions for the persistence of sufficiently large populations. An additional non-exclusive explanation might be a gradual and relatively slow pace of the shifts of the treeline during the Holocene, which might have maintained sufficient population sizes without bottlenecks, and thus preserve genetic diversity during the colonization process23.Our results show that subalpine colonization did not appear to lead to a loss of genetic diversity relative to foothill populations. The differences in genetic diversity of subalpine vs. foothill populations were non-significant over the replicated mountain ranges Table . MoreoveIn summary, we describe a case of parallel colonization of subalpine habitats from multiple foothill gene pools. Our results imply that there is a distinct mountain diversity in the subalpine habitats – the subalpine populations are genetically differentiated from their foothill counterparts and from each other. In addition, subalpine populations regularly preserve genetic diversity at similar levels relative to their foothill counterparts. However, ongoing gene flow, in particular from subalpine to foothill habitats together with low levels of differentiation, likely linked to a recent  origin of the subalpine populations, seems to prevent any stronger differentiation along the elevational gradient that may lead to speciation.Primula elatior is an outcrossing perennial plant with a basal rosette of leaves. Flowers are produced in an umbel, characterized by distyly and self-incompatibility25. The main pollinators are Hymenoptera (mostly bumblebees) and Diptera55. Seed mobility in this species is generally restricted due to lack of any adaptations for long-distance dispersal, for instance, compared to closely related species P. vulgaris48. Therefore, seeds are dispersed autonomously for short distances, and occasionally they can be washed downslope from streamside habitats.Plant material was collected in 2015–2016 in the three mountain ranges in central Europe: the Jeseníky, the Krkonoše, and the Tatry. In the Jeseníky and the Krkonoše, we sampled all known populations in subalpine glacial cirques and representative set from distinct valleys in the foothills. In the Tatry, we included populations from two distinct subalpine parts: the southern mountain range of the Nízké Tatry, the northern-eastern mountain range of the Vysoké/Belanské Tatry, and representative foothill populations in the basins between these two mountain ranges . Our dataset comprises in total 202 individuals from 16 populations Table . In each56. We employed 12 microsatellite loci developed by Van Geert et al.57, Bickler et al.58, and Seino et al.59  have been already cross-amplified in P. elatior by Seino et al.59 and Van Geert et al.57. Our cross-amplification of the 12 loci was successful, meaning that we consistently amplified variable loci across the sample set, contrary to three additional loci Paca 40459, PV 888058, and PV 2672058, which were not consistently amplified, and therefore not employed in the study. The fluorescently labelled primers  were designed into two multiplexes based on the results from a complementary threshold analysis in Multiplex manager 1.2. Microsatellite loci were amplified using the QIAGEN Multiplex PCR Kit, with a total reaction volume of 5 μl of QIAGEN mix. The mix contained 0.25 μl of forward primer and 0.25 μl of reverse primer, 1 μl of ddH2O, 2.5 μl of Master Mix, and we added 1 μl (10 ng) of DNA. The PCR amplification was conducted in a thermocycler (Eppendorf Mastercycler Pro) under the following conditions for both multiplexes: 5 min of denaturation at 95 °C, followed by 35 cycles of 95 °C at 30 s, 57 °C for 90 s, 72 °C for 40 s, and a final extension of 68 °C for 30 min. Amplification products were separated using 3130xl Genetic Analyser  with GeneScan 500 LIZ (Applied Biosystems) as an internal standard.Genomic DNA was extracted from dry tissue using a modified NaCl/CTAB protocolgenemarker 2.6 (SoftGenetics). We checked possible presence of null alleles, stuttering, large allele dropout by the program micro-checker 2.2.360. None of the loci showed presence of null alleles in more than half of the populations (maximum seven for Priv 4 and six for Paca 78), and we thus retained all 12 microsatellite loci in analyses. We tested if loci significantly deviated from HWE in each population in R, package pegas61. None of the populations had significant deviation from HWE in more than half of the loci (maximum seven in S5) . Analysis for each K was performed with 20 replicates, the initial length of burn-in period 100,000 and 1,000,000 of Markov chain Monte Carlo (MCMC) replicates after burn-in. Similarity coefficients among runs of the same K63 were calculated using Structure-sum-2009 script31 in R 3.3.264. For an optimal number of clusters (K), we considered the partition, where the rising likelihood of K values started to flatten and which also exhibited high similarity among replicated runs for that particular K  calculated in R, package adegenethttps://github.com/MarekLipan/Population-subsampling/blob/master/Genind_subsampling_func.R). We calculated observed heterozygosity (HO) and expected heterozygosity (HE) by subsampling in R, packages adegenet67 and hierfstat70. Further, we calculated the numbers of alleles, allelic richness , and Nei’s unbiased estimator for gene diversity (HS)71, which is corrected for small sample size, in Microsatellite analyser (MSA) 4.0572. Furthermore, we quantified the proportion of rare alleles using the frequency of down-weighted marker index (DW index), calculated as a ratio of means from the presence-absence matrix of alleles, which makes the measure less sensitive to different number of individuals per population31. DW index was calculated using R-script AFLPdat31. Finally, a number of private alleles was counted in R, package PopGenKit73. Differences in allelic richness, HE, HS, and in DW index between foothill and subalpine group were tested by hierarchical ANOVA with region as a random effect factor in R, package stats. Further, hierarchical structuring of genetic variation among populations within foothill vs. subalpine group in each region was revealed by analysis of molecular variance (AMOVA) in Arlequin 3.174. AMOVAs were calculated by the method of the number of different alleles (FST-like) with 1,000 permutations. Finally, the structuring of variation among foothill and subalpine populations in each region was also explored by hierarchical AMOVAs.In order to test differences in population genetic properties, we calculated descriptive statistics of all populations with respect to small number of individuals and imbalance sampling. Due to varying number of individuals samples per populations  we employed following subsampling strategy to rule out the effect of varying sample size: five individuals per population were randomly selected, we calculated the corresponding statistics and repeated the process 100 times and we further presented mean value with standard deviation from 100 replicates , focusing on populations with the maximum number of genotyped individuals (n ≥ 10). To keep the simulation scenarios feasible, we worked with two-population models that were iterated among all possible population combinations within each mountain range: we analysed pairs of subalpine-subalpine, foothill-subalpine, and foothill-foothill populations. For each pair, we modelled past evolutionary history under Bayesian inference of five migration models differing in presence and directionality of migration. We allowed bidirectional migration between two populations (model 1), unidirectional migration (models 2 and 3), panmixia (model 4 assuming that two populations belong to one panmictic population), and zero migration between two separate populations (model 5). In case of foothill-subalpine comparisons, the model 2 allowed migration from foothill to subalpine while the model 3 assumed only migration from subalpine to foothill populations.To identify the direction and intensity of migration among the foothill and subalpine populations, we searched for optimal models of migrations between populations within each target mountain range in a coalescent framework using migrate-n estimated two parameters Theta - θ  and M . The starting values of θ and M were calculated from Wright’s FST using prior values . Prior distributions of population sizes and migration rates were set based on the personal knowledge of the populations from the field with a broader range of both parameters with the aim to achieve the best searching of the space. To reach the stable states we ran each model for each pair multiple times (at least five). We have also checked the effective sample size, which was well over 500 (approximately 3000–5000). After burn-in of 20,000, we sampled 500,000 states from a single Markov chain, one every 5,000 steps. Four chains were run in parallel with heating static scheme . According to Hodel et al.32, based on θ and M parameters we calculated a number of immigrants per generation following the formula: Nm = [(θx ∗ My→x)/4] (for nuclear loci). For calculating Nm values, we used median values for both θ and M parameters.We used microsatellite data type with Brownian motion microsatellite model. The mutation rate was set constant over all loci. 77. We calculated natural log Bayes factors following the formula: LBF = ln [mL (model1)] − ln [mL (model2)], in which model1 is a model with the highest marginal likelihood and model2 is each of the other models. We used “Bezier” approximated marginal likelihood calculated using the thermodynamic integration with the heating scheme described above). Marginal likelihood is the integral of the likelihood function over the complete parameter range. Afterwards, we calculated the probability of each model following the formula:mLmodeli is the marginal likelihood of modeli and To select the most likely model among the five models for each population pair, we used Bayes factors comparison. Bayes factor allows comparing nested and non-nested models, without assuming normality, or large samplesSupplementary information file


Ciconia episcopus), leading to a serious risk of extinction in Thailand. Programs of active captive breeding, together with careful genetic monitoring, can play an important role in facilitating the creation of source populations with genetic variability to aid the recovery of endangered species. Here, the genetic diversity and population structure of 86 Asian woolly-necked storks from three captive breeding programs  were analyzed using 13 microsatellite loci, to aid effective conservation management. Inbreeding and an extremely low effective population size (Ne) were found in the KKOZ population, suggesting that deleterious genetic issues had resulted from multiple generations held in captivity. By contrast, a recent demographic bottleneck was observed in the population at NRZ, where the ratio of Ne to abundance (N) was greater than 1. Clustering analysis also showed that one subdivision of the KKOZ population shared allelic variability with the NRZ population. This suggests that genetic drift, with a possible recent and mixed origin, occurred in the initial NRZ population, indicating historical transfer between captivities. These captive stork populations require improved genetic variability and a greater population size, which could be achieved by choosing low-related individuals for future transfers to increase the adaptive potential of reintroduced populations. Forward-in-time simulations such as those described herein constitute the first step in establishing an appropriate source population using a scientifically managed perspective for an in situ and ex situ conservation program in Thailand.The fragmentation of habitats and hunting have impacted the Asian woolly-necked stork ( Ciconia episcopus) is a large endangered species on the IUCN Red List [in situ/ex situ management are thus necessary to recover endangered populations of the Asian woolly-necked stork, processes entailing decisions at a national policy level. Zoos have played an important role in the conservation of endangered species through scientific research and public education, preservation of genetic diversity, management policies that support increasing population sizes, and the reintroduction of captive-bred populations into their natural habitat [The Asian woolly-necked stork  as one of the most important parameters with which to estimate currently captive populations. It is necessary to first take one-step backward before moving forward to assess the management of captive breeding programs. We conclude by making recommendations for the long-term genetic management of captive populations to maintain/increase adaptive potential. This study represents the first genetic assessment of an ongoing captive breeding and reintroduction program for the Asian woolly-necked stork. This bird is under serious threat of extinction in Thailand. Previous studies indicated a relatively high level of genetic diversity in both wild and captive populations of Oriental white stork (C. boyciana) in East Asia and Russia [Captive breeding programs consist of the fundamental processes of the management of mating, and offer viability for minimal long-term ulations ,12. Bothulations –16. Lossulations . Thus, iulations ,17. Thisulations ,19. Havid Russia –23.Eighty-six Asian woolly-necked storks were captured from three captive breeding programs in Thailand . This population numbers all individuals remaining in Thailand. The DSZ population comprised just two storks, and human-mediated rotation of mating pairs was not implemented in the KKOZ and NRZ populations. A blood sample was collected and all birds were released immediately in each captive breeding area. Blood samples were collected from the ventral tail vein using a 24-gauge needle attached to a 3-ml disposable syringe containing 10 mM ethylenediaminetetraacetic acid (EDTA). Whole genomic DNA was extracted following the standard salting-out protocol as described previously by Supikamolseni et al. ,26. The C. ciconia) and oriental white stork (C. boyciana) , and 25 ng of genomic DNA. The PCR conditions were as follows: initial denaturation at 94°C for 3 min, followed by 35 cycles of 94°C for 30 s, 50°C for 30 s, 72°C for 1 min with a final extension at 72°C for 7 min. The PCR products were firstly detected by electrophoresis on 1% agarose gels. Fluorescent DNA fragment length analysis was subsequently performed using 23 ABI 3730XLs automatic sequencer  at the DNA sequencing service of Macrogen Inc., and allelic sizes were determined using Peak Scanner version 1.0 (Applied Biosystems).All 13 microsatellite primer sets were taken from Shephard et al. , Wang etoyciana) . The 5'-A), observed heterozygosity (Ho), expected heterozygosity (He), and linkage equilibrium were calculated using Arlequin version 3.5.2.2 [Ho and He of all captive breeding programs, Bartlett’s test of homogeneity of variances was first conducted using the “bartlett.test” function in the package stats of R version 3.5.1 [Ho and He, using the “t.test” function in the package stats of R version 3.5.1 [AR) was then calculated using FSTAT version 2.9.3 [Na) was obtained using GenAlEx version 6.5 [AR among populations using the Kruskal-Wallis Test [PIC) was estimated using the Excel Microsatellite Toolkit [I) and a fixation index (F) were also calculated for each locus of each population using GenAlEx version 6.5 [Ne) was estimated as the number of breeding individuals that contributed to the population using the linkage disequilibrium method in NeEstimator version 2.01 [Allelic frequency, the number of alleles  were calculated for all pairs of storks  and mean pairwise r values, based on allelic frequencies of the population, were calculated at each captivity using GenAlEx version 6.5 [r values between all pairs from the sampled captivities was compared using a bootstrap version of the Kolmogorov-Smirnov test to provide relationships [FIS) with 95% confidence intervals were also calculated by LynchRt estimator [r values and FIS was conducted under the assumption that the averages did not differ significantly from random assortments of unrelated individuals. Parentage analysis and the probability that two individuals shared the same genotype were calculated using the COLONY program version 2.0.6.5 [To consider the possibility of sibling or parent-offspring pairs in captive populations, we determined whether the Asian woolly-necked storks were more related than random unrelated individuals. Relatedness values  using RST in FSTAT version 2.9.3. [FSTENA values with ENA correction for null alleles. To obtain a better understanding of group structure, an analysis of molecular variance (AMOVA) was performed using Arlequin 3.5.2.2 [FST, this algorithm identifies subgroup hierarchical structure and does not require a priori assumption of the Hardy-Weinberg equilibrium [M ratio test [M ratio is the mean number of alleles in a population divided by the allelic size range and indicates reductions in both recent and historical population sizes. The model-based clustering method performance in Structure version 2.3.3 was used to determine population structure [K) was varied from 1 to 25, with 25 replicates for each value of K. The most likely number of clusters was determined by plotting the log probability of the data (ln Pr (X|K) [K values tested and selecting the K value at which ln Pr (X|K) stabilized. The ΔK method was also applied using Structure Harvester [Pairwise genetic distances among populations were calculated based on the infinite allele model (IAM) using 3.5.2.2.  with corn 2.9.3. . Considen 2.9.3.  was also 3.5.2.2 . Unlike ilibrium . Nei’s gilibrium ,48. The ilibrium . The Wililibrium . This tetio test  was perftio test . The M rtructure . Run lenPr (X|K)  across tarvester .F values close to zero in NRZ and DSZ, but not in KKOZ. The PIC of all captive populations ranged from 0.000 to 0.710, and I ranged from 0.000 to 1.302  and the He values ranged from 0.000 to 0.751 (mean 0.524±0.200) . Within each population, Welch’s t-test found that Ho was significantly different from He in the KKOZ population  but not in the NRZ population . AR values of the KKOZ population were statistically higher than those of the NRZ population . Standard genetic diversity indices are summarized in A total of 86 captive individuals  were genotyped, and 68 alleles were detected among all loci, with a mean number of alleles per locus of 5.231 . Null alto 1.302 . The Ho 4±0.200)  Tables. r value of 3,665 stork pairs among the 86 sampled storks was -0.007±0.115 (KKOZ population = -0.032±0.118 and NRZ population = -0.027±0.116). No stork pairs showed r < -0.25; there were 3,578 pairs with -0.25 < r < 0.25 , and 77 pairs with 0.25 < r (KKOZ population = 52 and NRZ population = 3) (r > 0.25). Distributions of r values for the storks were skewed left, indicating pairwise r values lower than expected by chance from a null hypothesis of unrelated individuals. Relative to storks from all captivities, distributions of pairwise r values from KKOZ and NRZ populations were significantly different from each other and the mean of pairwise r values of all populations  (FIS was 0.107±0.210 (KKOZ population = 0.314±0.208 and NRZ population = -0.231±0.208), with individual FIS ranging from -0.190 to 0.761   Tables,  < 0.05) , Table 2to 0.761 .Ne that genetically contributed to the population for KKOZ comprised 2 storks (95% CI: 1.5–1.9), and the Ne for the NRZ population comprised 26 storks (95% CI: 5.7–∞) (−8 (The : 5.7–∞) . Simulta7–∞)  on the basis of Evanno’s ΔK with all storks grouped into three clusters (K) revealed one peak (K = 5), which provided evidence for five clusters , a small population is detrimental ,54. GiveHo and He did not differ significantly between the KKOZ and NRZ populations; however, He was significantly higher than Ho in the KKOZ population. This suggests the possibility of inbreeding, resulting from a large number of generations in captivity and poor breeding success [AR values of the KKOZ population were higher than those of the NRZ population, both captive populations showed low AR values. This suggests that low AR values were derived from the loss of rare alleles, caused by the initial founding size of only a few storks during the 2000s [The genetic diversity of all captive breeding programs consists of two informative forms: allelic variability and heterozygosity. Heterozygosity is sensitive to population growth rate, while allelic variability is sensitive to population size ,57. Here success ,58. Althhe 2000s . Howeverhe 2000s .FST estimates revealed that while the captive KKOZ and NRZ populations differed significantly due to small population size [FSTENA estimates were not different. AMOVA indicated that the largest proportion of variation occurred within populations (84.49%), and Nei’s pairwise genetic distance and RST comparison between the KKOZ and NRZ populations was also low when compared with the captive populations of other species [F values of the KKOZ population were also positive. These results were consistent with Bayesian structural analysis, which exhibited several subdivisions in the KKOZ population. This suggests that genetic partition is a consequence of the possible mixed origin of captive populations, with founding individuals from various historically distinct lineages in the wild. Unfortunately, the original sources of the captive populations are unknown. Only one subdivision of the KKOZ population shared alleles with the NRZ and DSZ populations, suggesting genetic connectivity between the populations. In 2012, transfer occurred between the KKOZ and NRZ/DSZ populations in the absence of genetic diversity data, and managers rely solely on ethological, demographic, and logistic information for the implementation of short-term management strategies . This suggests that shared genetic alleles among captive populations result from the movement of storks from KKOZ to other captivities. However, an additional bottleneck might have occurred during reintroduction, when the captive population was genetically sub-divided into several groups, including individuals, prior to release [ion size , the FST species . F value release . Therefo release  or matinM ratio signaled a historical reduction in all populations. A recent demographic bottleneck was not supported by the bottleneck test for the KKOZ population. Collectively, these results suggest that the KKOZ population underwent a recent expansion. An estimate of the ratio of Ne to consensus population (N) enables us to understand the population fitness including the risk arising from genetic factors [Ne and Ne/N of the KKOZ population were extremely low and have generally remained low, relative to the captive populations of other species [Ne of the NRZ population was low, but the Ne/N value was greater than one. This suggests that genetic drift, with a possible recent and mixed origin of the initial population, occurred in NRZ. The occurrence of monogynous behaviors and lack of extra-pair fertilizations were likely to cause a reduction in Ne [Ne and a management strategy including long-term gene flow between captive populations are required to mitigate genetic drift, especially when they are expected to function as sources of genetic variation for an in situ program.Bottlenecks with low genetic diversity often occur when a small number of founders are taken from a declining wild population, resulting in poor breeding success . This wa factors . The Ne  species ,65. This species ,58. By con in Ne ,67. Popuon in Ne ,69. An iNe in relation to inbreeding states in KKOZ and NRZ captive populations. The positive FIS value in the KKOZ population is indicative of inbreeding; on the other hand, a negative FIS in the NRZ population indicates outbreeding, consistent with the historical transfer. Distribution of pairwise r values in the KKOZ population was also significantly different from the NRZ population. In New Zealand, the Takahe (Porphyrio hochstetteri) are managed to maintain genetic variation and reduce inbreeding by removing individuals with high r values from the population and replacing them with unrelated individuals [r value was near zero in the NRZ population, indicating the possibility of unrelated individuals being introduced, rather than related storks, as in KKOZ. The minimization of relatedness within the source population is necessary to promote successful management action [ex situ management. These could include the frequent introduction of new individuals into gene pools, breeding protocols to maintain high levels of genetic diversity in captive populations, and selection of release groups. Ideally, the state of genetic diversity for a captive population is determined by the proportion of high heterozygosity under a specific time. This also depends on the rate of species reproduction and generation length [Before moving forward with the captive breeding program and reintroducing storks, we should take one step backward and consider ividuals . In thist action . By priot action . In futut action . Howevern length . Integran length ,75,76. Tn length ,5. Our fin situ and ex situ management strategies. It is clear that the assessment of genetic diversity is an excellent means of maximizing reproductive success and promoting genetic variation in captive-bred individuals for subsequent release into the wild or to supplement captive stocks [Directed efforts  are underway in Thailand to protect and restore populations of Asian woolly-necked stork through e stocks ,77. The S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 TableC. episcopus individuals is presented in Detailed information for all (DOCX)Click here for additional data file.S4 Tablep values, with 110 permutations.The number indicates (DOCX)Click here for additional data file.S5 Tablep values, with 110 permutations.The number indicates (DOCX)Click here for additional data file.S6 Tablep values, with 110 permutations.The number indicates (DOCX)Click here for additional data file.S7 TableC. episcopus individuals is presented in Detailed information for all (DOCX)Click here for additional data file.S8 TableC. episcopus individuals is presented in Detailed information for all (DOCX)Click here for additional data file.S9 TableC. episcopus individuals is presented in Detailed information for all (DOCX)Click here for additional data file.S10 TableC. episcopus individuals is presented in Detailed information for all (DOCX)Click here for additional data file.S11 TableC. episcopus individuals is presented in Detailed information for all (DOCX)Click here for additional data file.S12 TableC. episcopus individuals is presented in Detailed information for all (DOCX)Click here for additional data file.S13 Tablep values, with 110 permutations. Detailed information for all C. episcopus individuals is presented in The number indicates (DOCX)Click here for additional data file.S14 TableC. episcopus individuals is presented in Detailed information for all (DOCX)Click here for additional data file.S15 Table(DOCX)Click here for additional data file.S16 TableC. episcopus individuals is presented in Detailed information for all (DOCX)Click here for additional data file.


ST) was measured. Caribbean populations were particularly distinguished from North, Central, and South American populations, in having higher differentiation (FST = 0.12 vs. 0.07–0.09) and lower mean numbers of alleles (MNA = 4.11 vs. 4.84–5.54). While mammalian populations had lower MNA (4.86) than anadromous fish, reptiles, amphibians, freshwater fish, and birds (5.34–7.81), mean heterozygosity was largely similar across groups (0.57–0.63). Mean FST was consistently lowest in anadromous fishes (0.06) and birds (0.05) relative to all other groups (0.09–0.11). Significant differences in Family/Genera variance among continental regions or taxonomic groups were also observed. MacroPopGen can be used in many future applications including latitudinal analyses, spatial analyses , taxonomic comparisons, regional assessments of anthropogenic impacts on biodiversity, and conservation of wild populations.Population genetic data from nuclear DNA has yet to be synthesized to allow broad scale comparisons of intraspecific diversity versus species diversity. The MacroPopGen database collates and geo-references vertebrate population genetic data across the Americas from 1,308 nuclear microsatellite DNA studies, 897 species, and 9,090 genetically distinct populations where genetic differentiation (F  However, populations, not species, are generally recognized as the appropriate scale for the management of sustainable harvesting and protection in endangered species legislation8. Nevertheless, population diversity – the number of genetically distinct populations within species – is typically excluded from most biodiversity syntheses and large-scale conservation planning (e.g.13). This has consequences when assessing biodiversity loss, as population extinction occurs at a much faster rate than species loss, and as such, a species’ vulnerability could be grossly misrepresented14.Collating large quantities of data is useful not only for assessing large-scale patterns but also for testing theories, informing conservation initiatives, and providing a valuable resource for future data comparisons. In particular, macro-ecological biodiversity assessments are becoming increasingly popular to identify hotspots of species biodiversity that can inform local management strategies15. One example is the global population diversity estimate based on allozymes and restriction fragment length polymorphisms where authors found on average 220 populations per species and estimated annual loss of 16 million populations, a coarse estimate obtained by dividing the number of sampling locations by the sampling area10. The collated data from this study was not made publicly available for future usage and is outdated following the advancement of genetic tools. No study has formally revisited these concepts since this 199710 study , indicating the need for collating population information.Molecular markers provide an increasingly effective way to differentiate populations and estimate population diversity21. Population structure studies and vulnerability assessments have used microsatellites as their molecular marker for the past two decades, yet this wealth of data has not been thoroughly collated, although a few authors have collated related information in the form of microsatellite genetic variation12, population density estimates11, and pairwise FST estimates13. Despite the great degree of data collation across these studies, no work has combined the geo-referencing of population-specific genetic variation, FST measurements, and the number of populations within a species to create a single database across a wide variety of taxa and geographic regions.Population genetic technologies have seen advances in recent years, switching from allozymes to microsatellites to single nucleotide polymorphisms (SNPs), largely due to the better resolution of within-population variation that more recent technologies provideST to be directly compared to species and genetic diversity  through mapping applications.Here we provide the first description of the release of the Macro-ecological, Population Genetics Database (MacroPopGen Database) – a database that contains geo-referenced population-specific characteristics based on nuclear DNA microsatellites. It contains information on 897 species from 1,308 studies published between 1994–2017, and 9,090 distinct populations of amphibians, birds, fish , mammals, and reptiles, totalling 561,605 genotyped individuals. Every population entry is georeferenced to permit large-scale spatial analyses, opening a variety of opportunities for overlaying microsatellite genetic data with environmental, geographic, or anthropogenic variables. It allows for population diversity and F9. We provide a baseline database for future works to build from and to compare to, particularly for comparing results to different, newer technologies. We urge future population studies using newer technologies to strive for a similar standardized repository for reporting population-specific statistics.MacroPopGen exemplifies the importance and usefulness of collating population genetic data by standardizing data from >1000 different studies, allowing for large-scale comparisons and many future applications, including latitudinal analyses, spatial or temporal analyses, taxonomic comparisons and regional assessments of genetic diversity across taxa or in relation to anthropogenic effects. Previous works focusing on older markers have already shown incredible usefulness in testing a variety of genetic and ecological theoriesST”. A full list of the 1304 key terms and combinations used can be found online22. We also cross-referenced the list of bird microsatellite papers from Willoughby et al.9.To collect population-genetic data from vertebrate populations located in the Americas, we first scanned Web of Science and Google Scholar for relevant articles using key search terms including country of occurrence, species common names, author names, and scientific names in combination with “microsatellite”, “distinct population”, and/or “Fet al.’s9 bird reference list. Once a study was selected, we extracted where possible: population locality name, latitude-longitude coordinates, average population-specific FST (Wright’s FST or Weir & Cockerham’s unbiased FST estimator θFST24), population-specific observed and expected heterozygosity averaged across loci , sample size (N), population-specific mean number of alleles per loci (MNA), study-specific corrected allelic richness (AR), and the number of microsatellite loci used in the study. For each population, we also documented the taxonomic group , family, genus, species, common name, continent, and country. We chose not to include marine species because microsatellites have typically been unable to detect fine-scale population structure in such species, in contrast to the increased power and resolution of more recent genome-scale analyses for such species25. Instead we focus on terrestrial and aquatic ecosystems.Search results with over 1000 hits would be filtered where if two consecutive pages did not yield a relevant result, further pages would not be considered (on average the first 15 pages on Google Scholar would be filtered for relevant articles). This preliminary screening limited results down to 6,297 peer-reviewed studies, technical reports, dissertations and government documents, of which only 1,308 fulfilled our criteria, including 142 of which were obtained from Willoughby ST, we consulted pairwise FST tables and averaged across values that included the focal population, or population group if there was no significance between one or more population pairs. When only a global or regional FST was reported then that value would be used for all populations within the study; such FST values are indicated in the database where applicable.All populations were georeferenced in decimal degrees; if coordinates were not provided, they were inferred from the text or maps in a study. To calculate a metric of population-specific FST as opposed to other differentiation estimators because of its wide usage. Microsatellites were favoured over other molecular markers  because their polymorphic nature allows them to resolve population structure at fine scales, particularly for closely related populations27. Additionally, microsatellites have higher mutation rates than other markers28 and have been one of the most widely used genetic markers in recent decades21. Therefore, microsatellites presently provide an abundance of collectable data across taxa relative to more recent molecular developments associated with single nucleotide polymorphisms (SNPs) or barcoding. While barcoding can assess phylogenetic signals across populations and species, microsatellites allow for the comparison of genetic characteristics between populations such as heterozygosity and allelic diversity, which has been noted to indicate levels of inbreeding or adaptive potential32.A study was retained if two criteria were met: 1) microsatellites were used as molecular markers and 2) genetic differentiation was measured by Weir and Cockerham’s pairwise F33 and have relatively fast mutation rates34. Microsatellite loci are often selected based on their polymorphism due to these faster mutation rates, causing concern that microsatellites may bias measures of genetic diversity compared to whole DNA sequencing-based measures35. Polymorphism bias has also been recognized in studies using other genetic markers such as SNPs37, and will continue to present challenges in genetic studies. An inherent assumption of this database is that ascertainment bias is similar across all studies and taxa, and therefore comparable. Additionally, previous work9 has concluded that the number of loci and primer type  were not important in explaining variability in genetic diversity, an indication that ascertainment bias may not be very significant for large quantities of microsatellite data such as this database. Regardless, we tested ascertainment bias with a subset of the database, as described below.Studies were assumed to have used selectively neutral nuclear microsatellite loci unless otherwise indicated because microsatellites are located within non-coding regions of the genomeST value of 0.02. FST was used as the statistical measure of differentiation because of its standardized and common use in the literature for measuring genetic differentiation. The chosen threshold was based on a previous analytical review38, which indicated that genetic differentiation is not negligible if FST ≥ 0.05, but an FST value as low as 0.01 can also denote statistically significant differentiation38. While lower values of FST (0.02 to 0.01) are sufficient to show significant genetic differentiation, such values are more relevant for distinguishing specific taxonomic groups, such as marine fish populations which exhibit more gene flow39. Freshwater and terrestrial species tend to experience lower rates of gene flow than marine species and therefore an FST threshold above 0.01 is more appropriate39. To avoid accepting biologically insignificant population differentiation (type I error) or rejecting biologically significant differentiation (type II error) when demarcating populations, we considered the significance of FST values where available. We ensured that any pairwise comparisons >0.02 were statistically significant; we also checked significance when FST was <0.02 and significance implied two separate populations despite a lower FST. We also accounted for sample sizes with respect to significant FST. If sample size was five or less  and populations were found to be significantly different, the populations were instead grouped as one unless an adequate biological explanation was provided (n = 5). Likewise, if sample size was very large (e.g. >50) but FST was <0.02, consideration would be taken to determine if the populations were significantly different given the statistical support large sample sizes provide . Additionally, if multiple studies were conducted in the same location for the same species, data from the most recent study or the one with the most microsatellite loci was used (n = 268 populations were duplicates and removed). When FST tables were unclear , we considered results from population structure analyses  to make informed decisions about population structure.Populations were considered genetically distinct above a threshold FST between a focal population and all other populations within that study, and (ii) the number of populations included in the calculation as well as the geographic distance or breadth that they span. For example, low FST values resulting from only a few sampling locations (e.g. 5) in a small geographic region (e.g. 10 km) may have a different interpretation than low FST values across many (e.g. >10) sampling locations in a broad geographic range . To estimate the geographic breadth that sampled populations cover, we obtained coordinates for each population including locations that had been combined into one population. These data were put into a separate file that contains 10,921 sampling localities. Next, we used custom code22 utilizing the R package geosphere to calculate the maximum, minimum, and mean distances in metres between all populations of a study; distances are reported in metres in the database. We additionally note how many sampling localities make up each population in the database and how coordinates were obtained/estimated for populations that encompass multiple sampling localities.We also report (i) how differentiated each population is in relation to all other populations it was compared to by calculating the average FO and FST (R package glmmTMB v 0.2.2.0) because both these response variables and distributions are bounded between zero and one with no exact zeros or ones. Gamma distributions were used for MNA (R package lme4 v 1.1-18-1) as MNA follows a positively right skewed distribution characteristic of gamma distributions. We then used the R package and function emmeans (v 1.2.3) to calculate the mean values while accounting for model structure. For the models that used beta distributions, we used the function back.emmeans (R package RVAideMemoire v0.9-69-3) to back transform estimates.To calculate mean genetic diversity for taxonomic groups and continental regions we used generalized linear mixed models (GLMMs) that accounted for the random effect of study, species, genus, and family. Fixed effects included either the taxonomic group, or the continental region. Beta distributions were used to model HO, MNA, and FST. Mixed models using the gamma distribution and random effects of reference, genus, and family were constructed. We then used model selection to see which between taxonomic group or continental region best explained differences between groups.To compare the degree of variation in each taxonomic or continental group, we calculated the coefficient of variation grouped at the species level for H22. In addition to the number of microsatellite loci, we obtained from 230 mammalian studies the number of species used to develop those loci (ranged from 1 to 7), and whether the species were focal (n = 384), non-focal (n = 545), or mixed (n = 692), as well as information on the senior author’s country of affiliation. Using IUCN descriptions for each species, we also determined whether the species was harvested and to what extent , the species’ IUCN status , whether the species was of conservation concern , charismatic , or of economic value . Extent of harvesting was determined by the degree of harvesting described in IUCN’s “Use and Trade” category: none (“no”), subsistence or local harvesting (“low”), or substantial commercial harvesting (“high”). Conservation concern was specified to account for species that may have a lower IUCN rank  but still have populations at risk or aspects of their habitat at risk (e.g. 563 LC species were still of conservation concern and therefore considered as “low”); this was largely described in IUCN’s “Threats” and “Conservation Action” categories. Charisma of species was somewhat subjective as it was determined by how generally well-known the species was, and whether the species may be considered a nuisance which would negatively affect their charisma score (e.g. the coyote is well known but can be considered a pest and as such its score was “low”). Economic value of a species was determined by the “Use and Trade” section, where if the species was commercially harvested it would be considered to have economic value (“high”); if the harvest has declined or is relatively low, a species’ economic value was considered as “low”.We assessed trends of ascertainment bias related to microsatellite loci development using a subset focusing on North American mammalian data O (R package glmmTMB) and a gamma distribution for MNA (R package lme4). Following Zuur et al.40 guidelines for forwards and backwards model selection, we used the likelihood ratio test to find significant factors for the HO and MNA models, respectively.We tested the fixed effects and interactions among these factors for ascertainment bias as well as the random effects of reference, species, genus, and family. We used GLMMs, using a beta distribution for H22.The data and R code used for the analyses are available from FigShare22 and can be downloaded as one XLSX file. It consists of 9,098 rows (distinct populations), and 24 columns. The columns include taxonomic identifiers , population locality information, and study-specific data .Data from the MacroPopGen database is hosted at Figshare22. Most of the references were published in English, although a minority are in Spanish.An additional XLSX file containing the corresponding references for each reference ID, and the list of key terms used in searches is also available on FigshareBetween 1994 and 2017, most population microsatellite data came from species studied in North America  aside from birds (mean FST = 0.05) , and lower average F05) Fig. , consistO among taxonomic groups but not continental regions, with bird species showing the least variation  as well as a weak increase in genetic diversity with year . Additionally, we evaluated bias with respect to the number of microsatellite loci and the degree of genetic variation in HO and MNA using funnel plots , although adjusted R2 values were very small .We assessed how genetic diversity and the number of microsatellite loci employed in empirical research has changed over time using linear models Fig. . There hots Fig.  and lineO model (Table 9 but indicate that microsatellite loci and loci origin do not significantly affect genetic diversity metrics when analyzed across diverse taxa.After model selection testing for ascertainment bias with respect to loci type and origin, only the interaction between level of harvesting and conservation concern as well as the random effects of reference, family, and genus were significant for the Hel Table . For theDownload metadata file


Homo sapiens across the globe during the last 200,000 years was accompanied by adaptation to local climatic conditions, with severe winter temperatures being probably one of the most significant selective forces. The TRPM8 gene codes for a cold-sensing ion channel, and adaptation to low temperatures is the major determinant of its molecular evolution. Here, our aim was to search for signatures of cold climate adaptation in TRPM8 gene using a combined data set of 19 populations of East Asian ancestry from the 1000 Genomes Project and Human Genome Diversity Project. As a result, out of a total of 60 markers under study, none showed significant association with the average winter temperatures at the locations of the studied populations considering the multiple testing thresholds. This might suggest that the principal mode of TRPM8 evolution may be different from widespread models, where adaptive alleles are additive, dominant or recessive, at least in populations with the predominant East Asian component. For example, evolution by means of selectively preferable epistatic interactions among amino acids may have taken place. Despite the lack of strong signals of association, however, a very promising single nucleotide polymorphism (SNP) was found. The SNP rs7577262 is considered the best candidate based on its allelic correlations with winter temperatures, signatures of selective sweep and physiological evidences. The second top SNP, rs17862920, may participate in adaptation as well. Additionally, to assist in interpreting the nominal associations, the other markers reached, we performed SNP prioritization based on functional evidences found in literature and on evolutionary conservativeness.Dispersal of  Homo sapiens across the globe during the last 200,000 years was accompanied by adaptation to local environments. Spatial variations in selective pressures have ultimately led to observable geographic distribution of many physiological and anatomical traits in present-day humans. For example, the low level of UV radiation at higher latitudes is now considered to be the major cause of evolution of depigmented skin  is the only one to use environmental data to search for signatures of cold climate adaptation in the Homo sapiens are likely to have had distinct genetic background before and during the process of climate-driven selection, it is possible that in phylogenetically distant groups, adaptation may have recruited different genes. Second, even in the case when selection is acting on the same gene, variants involved in adaptation may differ in different branches. Our supposition is supported by the example of variants associated with lactase persistence. Thus, within European populations, the activity of the lactase enzyme in adulthood is connected with the C/T-13910 variant in the enhancer region of the LCT gene, whereas in sub-Saharan Africa, this trait is mainly correlated with the presence of the G/C-14010 mutation  and 1000 Genomes (1000G) Project with the assistance of environmental correlation analysis techniques. Locations of chosen populations are characterized by a large range of average winter temperatures (−37–+27°C), implying substantial differences in selection pressures.For the above reasons, the aim of this study was to search for signatures of adaptation to low temperatures in the TRPM8 gene were obtained from NCBI dbSNP (https://www.ncbi.nlm.nih.gov/snp/), resulting in 60 polymorphic markers  being at the intersection of HGDP and 1000G sets. The missing genotypes in HGDP data were imputed using fastPHASE v.1.4.8 software  to infer the phylogenetic tree used in PGLS, to estimate the covariance matrix used in Bayenv2-BLM and Bayenv2-SRC, to correct for background levels of the population structure in LFMM and to make population inferences in BayScEnv more precise. Also, this set of SNPs was used to construct a null distribution for empirical p-value calculation. Information on latitudes and longitudes for 1000G populations was taken from https://climatecharts.net) using the corresponding coordinates. We believe the average winter temperature to be a more pertinent predictor for distribution of cold-adaptive alleles than the annual average temperature, as regions with a continental climate may have cold winters and hot summers.In this study, we used genotypic data on 656 individuals from 19 HGDP  and 1000software  with defAs our sample consisted of phylogenetically close populations, we first performed conventional Spearman’s rank correlation test not accounting for the sample structure  at the pPGLS analysis  software  and LFMMIn addition, we used Spearman’s rank correlation test from Bayenv2 (further referred to as Bayenv2-SRC) that uses allele frequencies standardized to have no covariance. It is less powerful than Bayenv2-BLM but more robust to outliers and can detect monotonic relationships.st distances for each locus averaged over populations . The strongest XP-CLR peaks within this putative sweep are mainly located near rs10929317 and rs7577262 SNP loci. The former was removed from the analysis because of high LD with rs17862920: r2 = 0.966/D’ = 0.995 in East Asian populations . Probably, this is the reason for the correlation the former demonstrates. Both SNPs are risk loci for migraine. At the same time, it has been mentioned above that rs17862920 is associated with sensing cold pain in Finnish and Norwegian individuals, with C allele carriers  being more susceptible. It can be assumed that both loci are independently involved in adaptation to low temperatures. In that case, however, the adaptive role of rs17862920-C allele is hard to explain. The possible mechanism of differential survival might be avoidance of potentially lethal hypothermia by those harboring С allele.Another SNP, rs17862920, is linked with rs7577262 .Changing model parameters drastically affects the output in BayScEnv . Furthermore, in our case, given parameters pi = 0.9/ p = 0.1, 18 out of a total of 60 SNPs reach significance level when analyzing 1,369 markers, whereas none is significant when using 60 SNPs. This discrepancy might be explained by less precise constructing a null model of population structure.Several lines of evidence point to possible involvement of rs7577262 in cold adaptation. This SNP is considered the best candidate based on its allelic correlations with winter temperatures, signatures of selective sweep and physiological evidences. The second top SNP, rs17862920, may participate in adaptation as well. As for rs6723922 and rs11682848 loci, these appear to be false positives or at least linked to some unobserved selected variant.NY and MV conceived the project. KG supervised the project. AI and KG processed and analyzed the data. AI, KG, and NY drafted the manuscript.This study was supported by budget from project No. 0324-2019-0041 of the Federal Research Center «Institute of Cytology and Genetics» SB RAS (ICG SB RAS).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Triturus cristatus and T. carnifex) in the southern part of the Czech Republic using morphological, microsatellite, and mitochondrial (mtDNA) markers. Specifically, we tested the hypothesis that the structure of the hybrid zone is maintained by species‐specific habitat preferences. Comparing the genetic structure of populations with geographical and ecological parameters, we found that the hybrid zone was structured primarily geographically, with T. cristatus‐like populations occurring in the northeast and T. carnifex‐like populations in the southwest. Despite T. cristatus tending to occur in deeper ponds and T. carnifex on localities with more shading, the effect of both ecological parameters on the structure of the zone was minimal. Next, we corroborated that T. carnifex individuals and some hybrids possess mtDNA of T. dobrogicus, whose nuclear background was not detected in the studied hybrid zone. Hybridization between T. carnifex and T. dobrogicus  had to predate subsequent formation of the hybrid zone between T. cristatus and T. carnifex. Populations of crested newts in the southern part of the Czech Republic thus represent a genetic mosaic of nuclear and mitochondrial genomes of three species.Reproductive isolation barriers maintain the integrity of species by preventing interspecific gene flow. They involve temporal, habitat or behavioral isolation acting before fertilization, and postzygotic isolation manifested as hybrid mortality or sterility. One of the approaches of how to study reproductive isolation barriers is through the analysis of hybrid zones. In this paper, we describe the structure of a hybrid zone between two crested newt species ( Triturus cristatus and T. carnifex) in the southern part of the Czech Republic using morphological, microsatellite, and mitochondrial (mtDNA) markers. Specifically, we tested the hypothesis that the structure of the hybrid zone is maintained by species‐specific habitat preferences. Contrary to our expectations, we found that the hybrid zone was structured only geographically but not ecologically, with T. cristatus‐like populations occurring in the northeast and T. carnifex‐like populations in the southwest.In this paper, we describe the structure of the hybrid zone between two crested newt species ( These transition zones are relatively narrow regions of admixed genotypes originating when two diverged populations  come into contact, mate, and produce hybrid progeny Hewitt, . They arTriturus cristatus superspecies group includes seven currently recognized closely related species widespread in Europe, Asia Minor, and the Caucasian‐–Caspian region , T. dobrogicus , and T. carnifex . While T. cristatus is widely distributed from the British Isles to the Ural Mountains, T. dobrogicus is restricted to the lowlands of the middle and lower Danube. Triturus carnifex occurs in the Apennine Peninsula and a north Adriatic part of the Balkans, from whence it spreads its range into Central Europe , but the mechanisms preventing hybridization between the species and maintaining the width of the zone are unknown.T. cristatus and T. carnifex in a hybrid zone situated in southern Moravia. We assume that if the hybrid zone were shaped by ecological preferences of the crested newt species, genetic composition of their populations should correlate with specific habitat characteristics. Specifically, we aim (a) to describe the structure of the hybrid zone based on morphological, microsatellite, and mitochondrial markers using more extensive sampling than in a previous study  were caught on 38 sampling sites during the breeding season between the years 2010–2015 , where a contact zone between three crested newt species has been documented  genes and the light‐strand replication origin using primers  and protocol following Krupa et al. . The novel sequences were deposited in GenBank under accession numbers: MN394474–MN394541. The ND2 fragment was aligned using the Clustal W algorithm , the southern part of Slovakia , and the northern part of the Czech Republic  were analyzed to establish allele frequencies of microsatellite loci in the parental species.Seven microsatellite loci  using primers labeled with fluorochromes NED, PET, VIC, and FAM, and the Qiagen multiplex PCR kit (Qiagen). Thermal profiles for both multiplex amplifications consisted of 15 min initial denaturation at 95°C followed by 35 cycles of 30 s at 94°C, 90 s at 60°C, 60 s at 72°C, and 30 min at 60°C.Microsatellites were amplified in two multiplex PCRs  without any a priori population information. In a second procedure, a priori population information for individuals from reference populations was used. Individuals from populations in or close to a presumable contact zone were assigned to the clusters without using any a priori information. To evaluate convergence and to estimate optimal genetic clustering, five replicates were run for each K value using an admixture and uncorrelated allele model. All Structure analyses were based on runs of 106 iterations, following a burn‐in period of 104 iterations. The number of populations that best fitted the data set was defined by the ΔK method . First, we ran analyses with FIS), and observed (HO) and expected (HE) heterozygosity. These parameters were calculated for each population under study and then for three groups in the contact zone, that is “pure” populations of T. cristatus, “pure” populations of T. carnifex and hybrid populations. Assignment of populations to these groups was based on an admixture proportion of each individual  averaged for a population. Populations were considered as hybrid when they possessed more than 20% of introgressed microsatellite alleles .To estimate the level of genetic diversity, we applied the program GenAlEx 6.5 , Pp—hind limb (on both sides of the body), LiE—interlimb distance (on both sides of the body). All 12 characteristics were measured by the same caliper and same person.Morphological characteristics were obtained from anesthetized newts (0.8% solution of 2‐phenoxyethanol) using a dial‐caliper (with an accuracy of 0.5 mm). We measured the following characteristics: q values calculated in Structure based on microsatellite loci entered the analysis as response variables (log transformed), morphological characteristics as explanatory variables, weight, and sex as covariables. Two characteristics, that is, interlimb distances measured on both sides of the body, were removed from the RDA analysis because of their collinearity . The statistical significance of the model, first axis, and individual morphological factors were tested by Monte Carlo permutation (999 repetitions).We used RDA  to find out which morphological characteristics discriminate between both newt species. RDA models were constructed in Canoco 5  originating from T. cristatus or T. carnifex cluster was equal or higher than 0.8.Then, we tested if WI discriminates between 2.4q values from Structure, which were entered (after the log transformation) to the analysis as response variables.In order to test preferences of particular species to specific habitats, we compared genotypic composition of populations with environmental variables. For each population, we calculated average 2), maximum depth in three classes , fish presence or absence, origin of pond , and presence or absence of human use. We also estimated density of submerged vegetation (in 25%‐classes) and proportion of shade (in 25%‐classes). These characteristics (including latitude and longitude) were tested to collinearity and then entered into the analysis as explanatory variables.Seven habitat characteristics of ponds where crested newts reproduced, plus geographic coordinates (latitude and longitude), were recorded in April 2014. These habitat characteristics are relevant for the distribution of crested newts as was found in previous studies  revealed many missing genotypes and difficult‐to‐interpret patterns in a fragmentary analysis. Therefore, these loci were excluded from population genetic analyses. Structure Harvester using the ΔK method estimated the most likely number of clusters for K = 2 (ΔK = 960.5). Cluster 1 and cluster 2 corresponded to T. cristatus and T. carnifex, respectively. The second highest optimal cluster number, for K = 6, had much lower probability (ΔK = 32.7). Newts from reference populations were assigned to the correct Structure clusters  with an average probability q = 0.978 for T. carnifex  and q = 0.837–0.986 for T. cristatus populations  and on all axes  confirmed the statistical significance of the RDA model. Simple term effects of individual morphological variables .Comparison between morphological index (WI) and microsatellite genotypes was used for designation of reliability of WI for species determination. Correlation between the T. cristatus on the basis of the WI (WI < 64), only 40 were assigned to this species according to microsatellite markers (q ≥ 0.8). Out of 32 males assigned to T. carnifex on the basis of the WI (WI > 64), 22 were assigned to this species according to microsatellites. Similarly, out of 96 females assigned to T. cristatus on the basis of the WI (WI < 54), only 40 were assigned to this species using microsatellites. Out of 33 females assigned to T. carnifex on the basis of the WI (WI > 54), 22 were assigned to this species genetically.Nevertheless, the percentage of misclassification based on the WI was high. Out of 89 males assigned to T. cristatus males and females were misclassified using WI. In T. carnifex, the percentage of misclassification in males and females was 31.3% and 33.3%, respectively.This means that 55.1% and 58.3% of 3.4q values per population in Table Triturus cristatus tended to occur in deeper ponds, while T. carnifex occurred on localities with more shading . We also corroborated that T. carnifex and hybrid individuals in the studied zone possess the mitochondrial genome of T. dobrogicus, a species which is distributed in the lowlands of the middle and lower Danube River but has not been documented in the Znojmo region. Next, we found that the Znojmo transect is structured primarily geographically but not ecologically. Triturus cristatus and T. carnifex do not reveal marked species‐specific habitat preferences, and their hybrid zone is unlikely to be maintained by exogenous selection. Finally, we evaluated the use of morphological characteristics for species recognition and found that most of them significantly discriminated between “pure” T. cristatus and carnifex individuals. Comparison of the Wolterstorff index (WI) and microsatellite genotypes, however, revealed that this index is not reliable for species identification in the studied hybrid zone.Crested newts are widely distributed amphibians, with parapatric distributions which come into contact and hybridize in Central Europe and the Balkans  as a traditional marker used for crested newt delimitation  and extensive introgression  where T. carnifex was introduced. While T. cristatus preferred ponds containing an abundance of aquatic vegetation, T. carnifex thrived in disturbed quarries with little or no vegetation. Another study focusing on hybridization between both species reveals an expansion of the introduced T. carnifex at the expense of the native T. cristatus in the Netherlands and links this displacement (among others) to the wider ecological amplitude of T. carnifex and its greater resistance to disturbed aquatic habitats  located southerly. At the end of the Pleistocene, when ice‐sheets retreated and climatic conditions became more hospitable, species dispersed out of refugia and colonized the newly available areas further north. In the case of the studied species, T. cristatus had a glacial refugium in the Carpathians  organellar genome “surfed the wave” of a range expansion on a “borrowed (nuclear) board”  and T. carnifex‐like  populations in this study  was ca 17 km. The fact that hybridization and interspecific gene flow are restricted to a relatively narrow transect depends (besides selection against hybrids) on the limited dispersal abilities of newts (Arntzen & Wallis, The width of the k et al.  at ca 15T. cristatus and T. carnifex, which indicates that adaptation to alternative environments and exogenous selection do not play a significant role in the structure of the hybrid zone. On the contrary, the zone is structured geographically. Spatial distribution of the parental and hybrid genotypes likely reflects the postglacial colonization of Central Europe from two different directions. On this colonization “road,” the gene pool of T. carnifex was enriched by the mitochondrial genome of a third species, T. dobrogicus. Newt populations in southern Moravia thus represent a genetic mosaic of nuclear and mitochondrial genomes of three crested newt species historically or currently hybridizing.Hybrid zones are characterized by abrupt changes in species‐specific allelic frequencies along a cline, the width of which depends on the strength of selection and dispersal of parental genotypes. Loci under selection, revealing a sharp cline and limited interspecific introgression, influence the rate of neutral introgression. Hybrid zones thus might be seen as barriers preventing gene flow between the hybridizing species. The strength of such a barrier can be determined not only by endogenous selection associated with incompatibilities between parental genomes, but also by exogenous selection acting when parental genotypes, adapted to alternative habitats, are selected in the wrong environment. In the present study, we did not find marked species‐specific habitat preferences between the crested newts The authors declare that they have no competing interests.The authors have made the following declarations about their contributions: ZM, MR, and PM conceived and designed the study. ZM, AR, and LJ collected data in field. ZM, DJ, SR, and PM analyzed the data. ZM, DJ, and PM wrote the manuscript. All authors participated in the scientific discussions, revised the manuscript, and read and approved the final manuscript.