Pinus uliginosa) and two other closely related European pine species (Pinus mugo and Pinus uncinata) using a set of 15 newly developed maternally inherited and seed-mediated mitochondrial DNA (mtDNA) markers and two previously known polymorphic mtDNA regions . Three main groups, corresponding in general to three investigated species were revealed in the haplotype network analysis. However, only P. uncinata was clearly distinct at all levels of analysis, whereas great genetic similarity and haplotype sharing was observed between P. uliginosa and P. mugo. Strong phylogeographic structure was found in P. uliginosa that showed high differentiation at relatively short geographical distance among populations and the existence of mitochondrial lineages of different evolutionary history. Hybridization with other pine species has likely contributed to genetic differentiation of P. uliginosa as indicated by contemporary distribution of mtDNA haplotypes. The research emphasizes the importance of accurate assessments of genetic structure of endangered species with complex evolutionary history for development of efficient conservation strategies.Estimates of genetic differentiation at intra- and interspecific level are often hindered by the lack of suitable molecular markers. Low phylogeographic resolution limits development of appropriate conservation strategies especially in case of endangered forest tree species with small and disjunct distribution. In this study, we assessed fine-scale genetic structure of relict and endangered peat bog pine ( Pinus uliginosa—a peat bog pine uniquely adapted to the nutrient-sparse bog environment that nowadays is highly endangered due to environmental changes and habitat loss. Surprisingly, we found evidence of strong differentiation between the neighbouring populations of this species and also signatures of hybridization events, shaping its contemporary gene pool. Our results improve current taxonomic knowledge of the studied Pinus mugo complex and could also serve as a basis for the development of successful conservation strategies for peat bog pine.Using newly developed mitochondrial DNA markers we looked at the relationship between three closely related European pine species. We were especially interested in assessing the genetic structure of  Assessments of eco-evolutionary mechanisms that shape genetic structure of populations are of key importance to understand the influence of past and ongoing environmental changes on plant ecosystems. In recent years, molecular markers greatly improved our ability to assess genetic differentiation at within and among species level. However, due to genome complexity and limited access to suitable genomic resources, phylogenetic investigations remain still challenging especially in many non-model plant species (Pinus mugo complex (Pinus uliginosa). It is a single-stemmed tree up to 20 m in height, inhabiting humid and nutrient-sparse bog environments in lowlands. Originally it has been described from two sites in Central Sudetes, Poland  from mountain regions of Central and Western Europe and mountain pine (Pinus uncinata) from Iberian Peninsula  markers, maternally inherited and dispersed by seeds on short distances, are especially valuable as they best reflect past demographic changes and longer retain patterns of demographic structure  looked at the population structure of the remaining stands of the peat bog pine, (ii) assessed levels of mtDNA variation in P. uliginosa populations to infer past population history processes, (iii) examined genetic relationship of P. uliginosa as compared to other pine species in reference to earlier hypothesis. Based on our findings we suggest potential conservation strategies for preservation of genetic resources of the endangered peat bog pine.Here, we present the results of first large-scale study on genetic structure of relict and endangered peat bog pine with the application of newly developed P. uliginosa were sampled together with 13 reference populations including 7 P. mugo and 6 P. uncinata stands sampled across the European ranges of the taxa. There are no other pines closely related to the studied taxa that occur in the sympatry of the analysed populations. Sample size ranged from 8 to 40 trees per population, resulting in a total of 384 individuals analysed . Standard amplification procedures were used with initial denaturation at 94 °C for 3 min followed by 35 cycles with 30 s denaturation at 94 °C, 30 s annealing at 60 °C for most loci and 1 min 30 s extension at 72 °C, and a final 5 min extension at 72 °C. The genotyping was done in all but one case using respective restriction enzyme and electrophoresis of restriction products in 2 % agarose gel. List of all PCR primer pairs and restriction enzymes used in this study is given in mtDNA markers including nad7 and nad1 were genotyped according to methods described in Five populations of analysed ; Table 1nad1 and nad7 were coded as single mutation events for analyses. The number of haplotypes (Hn) and haplotype diversity (Hd) were computed at species and population level using DnaSP v.5 .Multilocus genotypes were assessed for each individual using all 17 markers. All except one marker (PR29) were found to be polymorphic in investigated species and thus 16 markers were used thereafter. Individuals with level of missing data ≥ 10 % were excluded from further analysis. Phylogenetically informative gaps (indels) in PR34, naSP v.5 . A mediamtDNA sites was calculated in MEGA 7  implemented in Arlequin v.3.5.22  were calculated and compared to each other using a permutation test with 10 000 replicates in PermutCpSSR v.1.2.1 software  and geographic distance matrices.Additional measures of population differentiation , where an excess of rare haplotypes, with 13 singletons and highest value of haplotype diversity (Hd = 0.96), was observed (Hn = 40) and average haplotype diversity (Hd = 0.91) were also detected in this species (P. mugo (Hd = 0.87) but substantially lower for P. uncinata (Hd = 0.53). The three most common haplotypes were H50, H6 and H21 , H6 was almost fixed in P. mugo from Carnic Alps and occurred at low frequency in other dwarf mountain pine populations but was detected also in three peat bog pine populations  . The pattern of median-joining haplotype network revealed three main groups which coincide in general with three investigated species . Within species, population structure was observed in P. uliginosa and P. mugo, but not in P. uncinata .Presence of strong and significant phylogeographic structure was inferred from considerable genetic differentiation among populations ; (ii) P. uncinata populations; (iii) P. uliginosa. However, two outlier populations including UL_POL_BAT and M_POL_SK showed distinct patterns of genetic variation and were isolated from other clusters. Similar relationships between the populations were observed in the UPGMA tree [see].The evidence of population structure was further supported by results of the PCoA . The majK = 2–17 is shown in CT was the largest, was four. The resulting SAMOVA groups did not exactly coincide with the taxa delineations but were similar to the pattern of genetic clusters indicated by the PCoA. The results show distinct character of P. uncinata populations (SAMOVA group II), similarity of two P. uliginosa and majority of P. mugo populations (SAMOVA group I), and unique character of the remaining P. uliginosa populations (SAMOVA groups III and IV)  suggesting presence of isolation by distance among populations. Nevertheless, when the three species were analysed separately, no statistically significant relationship was observed in any taxa (P > 0.05).The result of SAMOVA at  and IV) . In the mtDNA, have been severe in phylogeography of plants, especially non-model species with limited genomic resources  markers, inherited in pines in paternal line and distributed at large geographical distances by pollen, was limited for closely related pine species  that differentiate P. uncinata from other taxa in the complex. Low haplotype diversity and presence of species-specific haplotypes show clear genetic differentiation of P. uncinata supporting earlier suggestions of limited interspecific gene flow and its ongoing divergence  lower levels of genetic diversity are expected for species with smaller distribution ranges; (ii) mountain populations tend to have lower haplotype diversity due to their peripheral location along an increasingly harsh elevation gradient  and reached its peak around 9 ky ago  are widespread and common in both taxa, and thus may represent ancestral haplotypes acquired in distant past and retained in both lineages. We also detected less frequent haplotypes shared locally between neighbouring populations, for example H14 (UL_GER_MI and M_AUT_K) and H40 (UL_UKR_MS and M_UKR_MS). Considering weak reproductive barriers, hybridization in contact zones with mitochondrial capture between those two species seems possible. The observed pattern of haplotype distribution may thus reflect different influences of past (haplotypes shared in many populations and over large distance) and more recent  hybridization events on contemporary haplotype variation in P. uliginosa. However, we cannot exclude retention of ancestral polymorphism in those taxa and therefore nuclear markers would be needed to fully test this hypothesis.High within-species divergence of conifers . The resP. uliginosa population from Zieleniec reserve. This population is particularly interesting as it represents a contact zone of three pine species  in a diverse habitat of the peat bog complex and it contains viable hybrid trees , this process alone can hardly explain mitochondrial variation observed in this population. Alternatively, mtDNA recombination mediated by hybridization events seems possible. Hypothesis of homologous recombination promoted by occasional parental leakage and heteroplasmy of mtDNA was previously proposed to explain high mtDNA variation in hybrid zone of spruce species   .The research was financially supported by the Polish National Science Centre (UMO-2015/19/B/NZ9/00024).B.Ł. and W.W. conceived the study; B.Ł. and J.Z. obtained and analysed genetic data; B.Ł. led the writing with support of W.W. and J.Z., who read and contributed to the final version of the manuscript.None declared.The following additional information is available in the online version of this article—Table S1. Analysed loci and genotyping method.Table S2. Major haplotypes and their frequency in the analysed taxa.Table S3. Distribution of major haplotypes detected in studied pine taxa and populations.Figure S1. Spatial analysis of molecular variance (SAMOVA).Figure S2. Median-joining network of haplotypes detected at 16 mitochondrial DNA (mtDNA) regions in the taxa from the Pinus mugo complex.Figure S3. Unweighted pair group method with arithmetic mean (UPGMA) phylogenetic tree of 18 studied pine populations.Supplementary MaterialClick here for additional data file.


Salmo salar) populations have declined range-wide, but factors responsible are poorly understood. Here, we reconstruct changes in effective population size (Ne) in recent decades for 172 range-wide populations using a linkage-based method. Across the North Atlantic, Ne has significantly declined in >60% of populations and declines are consistently temperature-associated. We identify significant polygenic associations with decline, involving genomic regions related to metabolic, developmental, and physiological processes. These regions exhibit changes in presumably adaptive diversity in declining populations consistent with contemporary shifts in body size and phenology. Genomic signatures of widespread population decline and associated risk scores allow direct and potentially predictive links between population fitness and genotype, highlighting the power of genomic resources to assess population vulnerability.Global losses of biodiversity are occurring at an unprecedented rate, but causes are often unidentified. Genomic data provide an opportunity to isolate drivers of change and even predict future vulnerabilities. Atlantic salmon ( The Atlantic salmon has suffered widespread population declines over the last century. Here, Lehnert et al. reconstruct changes in effective population size of 172 populations based on genomic linkage information revealing mostly temperature-associated population declines with over 60% of populations in decline since 1975. Identifying causal factors of contemporary population decline is necessary for forecasting population changes and designing appropriate conservation actions. Genomic data provide a novel opportunity to investigate how populations have responded to change, identify mechanisms underlying these changes, and evaluate the adaptive potential and vulnerability of populations in the future4.Losses of biodiversity are rapidly occurring across the globe. Within the last century at least 200 vertebrate species have become extinct, and across taxa, numerous species are facing extinction riskNe), which is the evolutionary analogue of census size, is being considered as a relevant parameter in conservation genetics and management6. Ne represents the size of an ‘ideal’ population that would be expected to experience the same levels of inbreeding, genetic drift, and loss of genetic diversity as the population of interest6. Ne estimates can help assess the vulnerability of a population because decreases in Ne result in a greater influence of genetic drift relative to selection making small populations less capable of adapting genetically to environmental change8. Temporal genetic monitoring of Ne can provide powerful means to track populations changes over time9. However, with advances in analytical methods, temporal changes in contemporary Ne (~20–25 generations) can now be reconstructed from one sampling point using genomic data and genetic linkage information11, allowing characterisation of population trends without intensive long-term sampling. Quantifying changes in Ne can help identify factors which have shaped population abundance over time, with recent studies focused on environmental drivers of contemporary and future population changes4. However, the incorporation of high-density genomic data also provides an opportunity to circumvent identifying drivers of decline a priori and can instead characterise genomic regions key to population persistence.Increasingly, effective population size  is an ecologically, culturally and economically significant species that has experienced widespread population declines and extirpations throughout its North Atlantic range over the last century12. Current threats to Atlantic salmon populations include salmon aquaculture14 , habitat alteration, pathogens, and climate change15. Though numerous factors can contribute to population losses, historical data necessary to quantify the role and interactions of multiple threats across broad scales are lacking16. Here, we use genomic data to reconstruct trends in population size across the range of Atlantic salmon and identify populations that have experienced significant declines in recent decades, allowing the investigation of environmental, anthropogenic, and genomic correlates of decline. We find population declines are temperature-associated across the range, and genomic regions associated with these declines relate to metabolic, developmental and physiological processes. The direct associations between genotype and population fitness found here can inform population vulnerabilities and enable conservation of adaptive diversity necessary for persistence of wild populations.Atlantic salmon (Ne) was reconstructed for 172 populations . LinkNe10 bins loci based on linkage information where pairs of loci with similar recombination rates are binned together to estimate Ne at different generations in the past. For each bin of loci, the mean recombination rate (c) is used to estimate the number of generations (t) in the past (t = 1/2c). Assuming a generation time of 5 years for Atlantic salmon and a starting year of 2010 (see Methods section), estimates of Ne were calculated at four time points over approximately 25 generations. Focusing on recent declines (~6–7 generations), we found that 60% and 61% of populations in North America and Europe, respectively, showed significant declines in Ne since 1975 . Using random forest classification, top environmental and anthropogenic variables important for explaining variation in declines within each continent were identified  significantly explained variation in population declines . In Europe, population declines were explained by temperature, including greater variability  and warmer winters  . The influence of warmer temperatures at this broad-spatial scale is consistent with historical extirpations primarily occurring in southern regions, where other anthropogenic impacts are also stronger12 and these associations highlight the future threats of climate change. In other salmonids, findings of genetic constraints on upper thermal tolerance indicate that the adaptive potential of salmon might be limited under future climate scenarios18, suggesting that range shifts and additional local extirpations are likely in Atlantic salmon.Next, we investigated relationships between decline and environmental variables  and major anthropogenic threatsied Fig. . Top envied Fig.  and usedied Fig. , where w19 or a natural  or anthropogenic  catastrophic event that could lead to sudden changes within a river20. Furthermore, salmon populations are often considered to be locally adapted at fine spatial scales21 consistent with their strong natal philopatry and low straying rates22. Therefore, local adaptation can drive fine-scale differences between populations in important life history variation21  that could influence the susceptibility of populations to losses over time23. Variation in the census size of populations may also influence our ability to detect significant changes10 and may further explain some fine-scale differences in decline patterns.The importance of climate suggests that our results reflect broad-scale range-wide or continent-scale influences. Nonetheless, fine-scale factors influencing localised declines that are important at the individual river scale may be difficult to resolve. The observed geographic heterogeneity in decline may support a role for such small-scale local influences. For example, local factors could lead to changes within a single population that have no bearing on nearby populations, such as a new development project that alters suitable habitatn = 25 North American; n = 45 European) clearly separated significantly declining and non-declining populations  gene on Ssa13. The gene plays a role in head formation24, and in Arctic charr , a QTL in this region is associated with head shape25. In both continents decline-associated SNPs were also located near (<10 Kbp) three of the same genes, including ST3GAL1-like on Ssa03, snd1 on Ssa17 and GRID1-like on Ssa01. ST3GAL1-like has been associated with mucus secretion (protection from pathogens) and migratory behaviour in salmonids26 and snd1 is involved in various cellular functions including immune pathways27. GRID1-like belongs to a group of glutamate receptors which are involved in neurotransmission in the central nervous system and can play a role in learning and memory28. Functional enrichment of 15 and 11 gene ontology (GO) biological processes  from a subset of populations  that has been associated with ecotypes of Arctic charr30. In Europe, one region was associated with both differences in sweeps and population decline 31. Functional enrichment of GO biological processes associated with differences in selective sweeps were found for both continents  were related to cardiac development and regulation of cardiac muscle contractions, morphology, metabolism, and muscle adaptation. In Europe, highly over-represented biological processes 34. The second highest loading SNP in the window was located near a thiamine receptor (SLC19A3-l on Ssa20). Thiamine deficiency has been suggested to play a role in widespread wildlife declines35 and has been implicated as a possible mechanism for historical losses of Atlantic salmon populations36.In Europe, 25 genomic regions showed an opposing pattern of selective sweeps, with signals of sweeps present only in declining populations Fig. . Sweeps 38. These genomic signatures of decline are present across large latitudinal scales spanning heterogeneous environments, and thus the broad-scale nature of our analyses likely improves our ability to detect real decline-associated genomic variation rather than variation associated with geographic differences in selection. Although the majority of genomic changes were not parallel between continents, trans-Atlantic differences in underlying genomic architecture have been documented for many key traits related to domestication39, age-at-maturity40, and temperature-associated adaptation41.All above functional associations highlight possible adaptive changes linked to morphology, physiology, or life history, consistent with long-term declines in body size and shifts in life history documented in wild Atlantic salmon populations42 . Using a small panel of genome-wide decline-associated loci based on RDA, our results highlight populations at greatest risk of decline, including non-declining populations that might soon be at risk, such as Mulligan River (MU) in Canada and Suldalslågen River (Suld) in Norway  and genotype identified here was used to calculate weighted polygenic risk scores12. Despite conservation efforts, our study supports existing evidence that populations continue to decline throughout the range. We found that environmental and anthropogenic factors correlated moderately with declines, and these factors likely represent drivers that influence declines in freshwater and coastal marine environments. At a broad range-wide scale examined in our study, temperature variables were more important than anthropogenic factors for explaining variation in population declines, although habitat alteration was also important for explaining variation in declines within North America. Across the range, declines were consistently associated with warmer winter temperatures which can influence early development in Atlantic salmon and may have implications for later life stages43. At finer geographic scales, specific local influences  likely play a role in local population change as well, though the effects are unlikely to be consistent at the continent or range scale examined here. In addition, unidentified factors in the ocean contribute substantially to mortality16 and likely reflect the unexplained variance in our model as data on marine migration and habitat use are currently limited.It has been over two decades since scientists first asked why there are not more Atlantic salmon in the wild27. Furthermore, a process related to wound healing in fishes32 was significantly overrepresented by genomic regions associated with changes in adaptive diversity in declining populations in both Europe and North America. Additional changes in adaptive diversity documented here in genomic regions linked to developmental, physiological and reproductive processes are consistent with the loss of large salmon and life history changes across many rivers44. The changes related to declines in Ne in our study highlight the vulnerability of >60% of salmon populations across the range. As Ne decreases, the effects of genetic drift can outweigh those of selection, and thus these declining populations may be more susceptible to loss in the future7. Overall, our work highlights the value and need for genomic resources in conservation management where these data can be utilised to understand the mechanisms driving population change and predict future vulnerabilities.Multifarious selective pressures have likely driven declines and our study shows, for the first time, genomic changes associated with declines across a species’ range. Decline-associated loci consistent between continents were located near genes that have been previously linked to head morphology, immunity, and migratory behaviour in salmonids41 (available at: https://doi.org/10.5061/dryad.cv20d) from datasets that used either a 220,000 or 6000 SNP array with overlapping markers between arrays48  and 73 North American populations  for our analyses. For our main Ne analysis, we excluded collection sites that were sampled prior to the year 2000. For 172 populations across the range, a total of 1278 SNPs were used for a linkage-based estimate of effective population size (Ne) over time10. SNPs were determined based on overlap between datasets, minor allele frequency (MAF) >0.05 across the range, and whether linkage map information was available for both continents51.Genotypes of salmon from North American and European populations were compiled from a previous study54. Therefore, prior to Ne analyses, populations located near aquaculture operations were analysed for evidence of farm introgression. Details of introgression analyses are provided in the Supplementary Note Ne analyses  were removed (n = 7).To our knowledge, the various studies from which our genomic data are compiled did not aim to avoid rivers that could be impacted by salmon farming. Many sites include regions where the impacts of aquaculture have been investigated10 was used to reconstruct Ne for each population over time using default parameters of the program  and a time bin by generation. Loci were binned based on linkage using the average of the male and female linkage maps within each continent. Linkage maps of sexes were averaged because sex-specific differences in recombination rate occur in Atlantic salmon, where recombination in females is 1.38 to 2.22 times greater than in males51. The mean recombination rate (c) for each bin was used to estimate the number of generations (t) in the past (t = 1/2c) for each estimate of Ne. The number of loci pairs used for estimates in each bin are provided in Supplementary Fig. Ne  for values between the years 1975 (~6–7 generations ago) and 2005 (~1 generation ago). Populations were classified as significantly declining if Ne decreased and CIs did not overlap between 1975 and 2005. In cases where lower CIs were negative , we considered a population to be significantly declining if CIs included infinite values in 1975 but CIs were non-infinite in 2005 . Populations that were not declining could include populations with a non-significant increase or decrease in Ne  and populations with a significant increase in Ne. We did not attempt to estimate the magnitude of decline because the true magnitude of decline cannot be confidently estimated, as historical estimates of Ne will be influenced by the effects of genetic drift in the recent samples that exhibit declines (see Hollenbeck10).To assign populations as significantly declining or not declining, we used estimates of Ne estimates. Smaller sample sizes will reduce precision and lead to wider confidence intervals (infinite) and this is especially true when true Ne is large. Therefore, we expect that significant declines in Ne may be more difficult to detect in a large population with a smaller sample size10 and thus the number of populations that have significantly declined may be underestimated here. Nonetheless, we find no evidence to suggest that different sample sizes led to bias in our temporal trends in Ne  and recent (2007 or later) samples. Here, we examine whether temporal changes in Ne found using LinkNe with recent samples are consistent with temporal changes when Ne is estimated for two time points using historic and recent samples. Ne was estimated using the linkage-disequilibrium method for the same panel of 1238 SNPs with MAF cut-off of 0.05. Ne estimates and confidence intervals determined using the Jones et al.61 jackknife method implemented in NeEstimator were compared between historical and recent samples using the same criteria as above to classify populations as significantly declining or non-declining. Classifications using NeEstimator agreed with our classifications using LinkNe for all populations . Sea surface temperature  for months when salmon smolts would be moving into the marine environment (May–July) were extracted and averaged from MARSPEC63 and long term average of maximum SST (from 2000–2014) was extracted from Bio-ORACLE64. Mean annual global temperature anomalies averaged between the years 1975–2005 (resolution 0.5 degrees) was accessed from NASA Earth Observations (NEO) (https://neo.sci.gsfc.nasa.gov). For temperature anomalies, anomaly values for each year represent the difference in temperature (colder or warmer) for the same region relative to the average temperature between 1951 and 1980.Environmental and anthropogenic variables were collected from publicly available sources .17 as we expect higher human densities to have greater anthropogenic impacts on habitat through increased human occurrence. Values were averaged from a larger grid (0.5° latitude × 0.5° longitude) around the site to capture human activity in the region surrounding the population. Data were log transformed for random forest analyses. To test the assumption that human density is an appropriate proxy for habitat disturbance, we used available data from North American populations that have been evaluated for impacts associated with habitat alterations65. Regional groups (designatable conservation units) were classified by a cumulative impact score of low (<5%), medium (5–30%) or high (>30%) based on the proportion of salmon affected by habitat alterations, such as municipal waste water, industrial effluents, dams, urbanisation, transportation infrastructure and other alterations (see ref. 65). For all regions with known cumulative impact classifications (n = 14 regions), we calculated the mean human density for salmon populations located within these regions . We found that human density was significantly different among low, medium and high impact groups , where pairwise post-hoc comparisons revealed that the low impact group had significantly lower human density relative to the high impact group (p-adjusted = 0.04).Human population density in 2000 (resolution 0.1 degrees) was also accessed from NASA NEO. Human population density was a proxy for habitat disturbanceons n = 1 regions,52) was estimated for each population within continents using the AQpress function in R52. Previous work has demonstrated that this index is significantly associated with the number of farmed escapees found in rivers, as well as the proportion of aquaculture introgression in wild populations52. Data for North America were previously compiled52 between 2005 and 2015, with data on the presence and absence of each aquaculture site for each year. In Europe, we assessed data for only two countries, which represent the greatest producers of Atlantic salmon aquaculture in the region: Norway and Scotland66. In Scotland, we used all aquaculture sites that were registered since 2006 and/or were active in the last 3 years (source: http://aquaculture.scotland.gov.uk/data/site_details.aspx). In Norway, we used all sites that were registered since 2006 as no information was available prior to this year (source: https://kart.fiskeridir.no/akva). We acknowledge that more historical aquaculture data would be beneficial for these analyses provided the time frame examined for population decline. Nonetheless, we assume that the same geographic regions that are amenable to aquaculture presently would be amenable to aquaculture in the past. For example, in Canada, the same geographic regions in NB, NS and NL would be expected to harbour aquaculture sites in the past and present, although the intensity may change over time. In North America (n = 202 aquaculture sites), aquaculture intensity index was averaged across years as presence and absence of each aquaculture operation was known in each year. In Europe, we did not have data for each year for all aquaculture sites (n = 1080 aquaculture sites), therefore aquaculture intensity index was calculated assuming all sites were active at the same time (one year). Using the AQpress function52, latitude and longitude for sites were shifted to move sites slightly offshore to allow least cost distance calculations. In Europe, the AQpress function52 was modified to incorporate European bathymetry using the marmap67 package. Data were log transformed for random forest analyses. Although this metric of aquaculture intensity has been correlated with the number of escaping salmon and introgression in some regions52, here, we use the aquaculture intensity index to represent a relative measure of aquaculture activity near a river site. River sites that are in close proximity to many aquaculture operations will have higher aquaculture intensity index indicating a greater potential for interactions with aquaculture, including interactions with escaping salmon, aquaculture pathogens, pollution, and other effects on the local environment.Atlantic salmon aquaculture site and year information were acquired from government resources, and an aquaculture intensity index  was selected using the tuneRF function69 with 10,000 trees and stratified sampling with equal sample sizes for both groups for each run. All other parameters were set to default. The relative importance of each predictor was determined using the mean decrease in accuracy (MDA) averaged across runs. MDA values represent how the accuracy of RF decreases when the variable is excluded, thus higher MDA values indicate greater importance in the model and negative MDA values indicate that the incorporation of the variable reduces model accuracy. Therefore, within each continent, the top 10 predictors that were positive (contributed to model) were used for a generalised linear model (GLM). To account for correlations among environmental variables, environmental data were collapsed into principal components (PCs) using prcomp function in R. Anthropogenic factors (n = 2) were not collapsed into PCs. For our analyses, the first 3 environmental PCs (enviro-PCs) explained 99 and 95% of the environmental variation in North America and Europe, respectively and were thus used in subsequent models. We tested all three PCs and important anthropogenic variables (if identified by RF) in a GLM with binary response (decline or no decline) within each continent. For any significant enviro-PCs in the model, the importance of environmental variables contributing to declines was determined by loadings on PC axes.All 24 environmental and anthropogenic variables and latitude were included in random forest70 or identify factors that contributed to declines at different temporal scales71. High resolution data collected during the same time frame as the declines would be ideal; however, limited resources at fine scales  restrict our ability to compile comparable and high resolution datasets at large spatial scales.Provided the different spatial and temporal scales of both environmental and anthropogenic data, we acknowledge the limitation of this data. Specifically, many of the variables are averaged data over multiple years, which could reduce our ability to identify a critical event in one year that contributed to population changes72 and Europe49, respectively. We note that in some cases different individuals were genotyped for the same population between the 6K and 220K arrays (Supplementary Data vegan73 with change in Ne (significant decline vs. no decline) as a constraining factor and conditioned on geography (latitude) to explain variation in individual genotypes. Missing individual genotypes were replaced by the highest frequency genotype for the locus. Decline-associated SNPs were identified as SNPs with scores ±3 standard deviations  from the mean on the RDA axis74.Redundancy analyses (RDAs) were used to investigate the genomic basis of population declines using high-density genomic markers. A subset of populations genotyped for a 220,000 SNP array was used for the analysis, which included 25 and 45 populations in North America75 was used to detect selective sweeps within declining and non-declining populations separately. To incorporate a larger number of markers for the analyses, we filtered the 220,000 SNP dataset for high quality SNPs within each continent and MAF of 0.01 within each continent . RAiSD calculates the µ statistic (representing signatures of sweeps) across the genome from overlapping windows. To compare differences in selective sweeps between significantly declining and non-declining groups, we calculated the change in µ (∆µ) by subtracting µ in declining populations from µ in the non-declining populations, representing the difference in adaptive diversity between these groups. We calculated ∆µ in Europe and North America separately. Higher (positive) values of ∆µ indicate sweeps present in the non-declining populations and absent in the declining populations, which may indicate a loss of adaptive diversity in declining populations or a change in adaptive diversity in the non-declining populations underlying potential adaptation enabling population persistence. Lower (negative) ∆µ values indicate sweeps present in the declining populations and absent in the non-declining populations, which may indicate adaptive diversity that has been selected in declining populations in response to population disturbance. ∆µ was calculated for 1 Mbp windows and ∆µ values ± 3 standard deviations (SD) from the mean were considered significant outlier regions. These outliers were compared against outliers from the RDA by examining decline-associated SNPs that fell within 1 Mbp of the start or end of the ∆µ window. We allowed a 1 Mbp range outside of the ∆µ window given that windows calculated in RAiSD are based on a SNP-driven, sliding-window algorithm and thus the exact window size/position is not defined by the user and may be slightly offset75.Within each continent, RAiSD76. We first identified reference sets of genes for each analysis using BEDTOOLS77 where for the RDAs we extracted genes within 10 Kb of all SNPs used in the analysis and for the sweep analysis we extracted genes within all windows tested. Genes associated with outlier SNPs and within window regions were extracted in the same way. The R package topGO78 was then used to test for over-representation of GO biological processes using a node size of 5 and the ‘weight01’ algorithm to account for structural relationships among GO terms. We used an alpha level of 0.01 to determine significance.For both decline-associated SNPs (RDA) and sweep outlier windows, we examined functional enrichment of genes in these regions. We conducted gene ontology (GO) enrichment analysis using GO annotations in the Atlantic salmon genome from SalmoBase42 was used to calculate weighted polygenic risk scores based on genotypes of RDA outliers using the riskScore function. The top 100 loci were selected based on their RDA loadings and physical distance, where the highest loaded SNP outliers in 1 Mbp windows were retained for the analysis. In total, 100 decline-associated loci were used for European analysis, but only 90 loci were used for the North American analysis after filtering based on physical distance. Model coefficients for each locus were determined by the fitLogRegModel function and weighted polygenic risk scores were calculated for all individuals to estimate risk of decline.PredictABELn = 4) showed significant differences in risk scores when the population was excluded or included in the construction of the risk model . For each population, weighted risk scores with and without the population included in risk model were compared using a Mann–Whitney test. Alpha level was adjusted for comparisons in each continent based on the number of populations . Across the range, only 6% of populations (Further information on research design is available in the Supplementary InformationSupplementary Data 1Supplementary Data 2Reporting SummaryDescription of Additional Supplementary Files


SNP50 BeadChip developed for domestic sheep to examine population structure and evaluate genetic diversity of snow sheep (Ovis nivicola) inhabiting Verkhoyansk Range and Momsky Ridge. A total of 1,121 polymorphic SNPs were used to test 80 specimens representing five populations, including four populations of the Verkhoyansk Mountain chain: Kharaulakh Ridge–Tiksi Bay , Orulgan Ridge , the central part of Verkhoyansk Range , Suntar‐Khayata Ridge , and Momsky Ridge . We showed that the studied populations were genetically structured according to a geographic pattern. Pairwise FST values ranged from 0.044 to 0.205. Admixture analysis identified K = 2 as the most likely number of ancestral populations. A Neighbor‐Net tree showed that TIK was an isolated group related to the main network through ORU. TreeMix analysis revealed that TIK and MOM originated from two different ancestral populations and detected gene flow from MOM to ORU. This was supported by the f3 statistic, which showed that ORU is an admixed population with TIK and MOM/SKH heritage. Genetic diversity in the studied groups was increasing southward. Minimum values of observed (Ho) and expected (He) heterozygosity and allelic richness (Ar) were observed in the most northern population—TIK, and maximum values were observed in the most southern population—SKH. Thus, our results revealed clear genetic structure in the studied populations of snow sheep and showed that TIK has a different origin from MOM, SKH, and VER even though they are conventionally considered a single subspecies known as Yakut snow sheep (Ovis nivicola lydekkeri). Most likely, TIK was an isolated group during the Late Pleistocene glaciations of Verkhoyansk Range.Insights into the genetic characteristics of a species provide important information for wildlife conservation programs. Here, we used the Ovine The largest units are located in Yakutia, Kamchatka, and Okhotsk Seaboard. However, even the sheep in those areas are decreasing, especially at the periphery. A few years ago, the Putorana and Chukchi subspecies received a special conservation status—they are now listed in the Red Book of the Russian Federation  Figure . In 2016The most promising method for the investigation of genetic diversity and population structure is the analysis of single‐nucleotide polymorphisms (SNPs). SNP genotyping is based on a substitution in a single nucleotide at a specific position in the genome  using the BovineSNP50 BeadChip created for cattle (Bos taurus). With the same chip, Haynes and Latch (Odocoileus hemionus) and white‐tailed deer (Odocoileus virginianus). Kharzinova et al. ((Rangifer tarandus). Miller et al.  and thin‐horn sheep , using the OvineSNP50 BeadChip.Thus, Tokarska et al.  evaluatend Latch  differena et al.  have shor et al.  performeO. nivicola) and to obtain information that could be used in the development of conservation strategies and as a first step in understanding the taxonomy and evolution of snow sheep based on genotypes.The aim of our study was to investigate the genetic diversity and population structure of snow sheep (22.1Muscle tissue samples of snow sheep were collected under permits issued by the Department of Hunting of the Republic Sakha (Yakutia) during scientific expeditions. Some samples were taken from indigenous peoples’ representatives, who are licensed to hunt snow sheep for personal consumption according to the Federal Law of the Russian Federation.2.2n = 80) were collected in different parts of the Republic Sakha (Yakutia), Russia, covering the area between 63 and 71 degrees north latitude and between 128 and 146 east longitude during 2011–2016. They were divided into five groups according to their geographic locations. Four populations represented the Verkhoyansk Mountain chain including Kharaulakh Ridge at Tiksi Bay , Orulgan Ridge , the central part of Verkhoyansk Range , and Suntar‐Khayata Ridge . The fifth population included sheep inhabiting Momsky Ridge .The sampling sites of individual snow sheep are presented in Figure 2.3https://www.illumina.com/documents/products/datasheets/datasheet_ovinesnp50.pdf). The BeadChip contains more than 54,000 SNPs that cover the entire ovine genome and therefore enables a broad range of applications, including genomewide selection, identification of quantitative trait loci (QTL), evaluation of genetic merit, crossbreed mapping, linkage disequilibrium studies, comparative genetic studies, and breed characterization for evaluating biodiversity.Genomic DNA was extracted from muscle tissue samples using a Nexttec column , following the manufacturer's instructions. The DNA concentration was estimated by measuring the absorbance at 260 nm, and the DNA quality was checked by separation on agarose gels. SNP genotyping was performed using the OvineSNP50 BeadChip, which was developed by Illumina  in collaboration with the International Sheep Genomics Consortium (ISGC) .To select SNPs to create a set of markers suitable for snow sheep from the chip designed for domestic sheep (Illumina OvineSNP50 BeadChip), quality control was performed. During an initial quality check, GenCall (GC) and GenTrain (GT) cutoff scores of 0.5 were applied to determine the valid genotypes for each SNP. The GC score, or no‐call threshold, is a quality metric representing the accuracy of genotyping, and the GT score is a statistical measure from the Illumina clustering algorithm  and unbiased expected (He) heterozygosity , and rarified allelic richness (Ar) were calculated in the R package “diveRsity”   based on a pairwise identity‐by‐state (IBS) distance matrix was performed with PLINK 1.9  and visualized in the R package “ggplot2” Wickham, .K = 1 to K = 7 to determine the lowest cross‐validation (CV) error, which shows the optimal number of clusters. The individuals within populations were ordered according to the sampling points based on the following: TIK and ORU by latitude from north to south, VER from northwest to southeast, and SKH and MOM by longitude from west to east. The output files were visualized using the R package “pophelper” .Population structure was evaluated using Admixture 1.3 software  distance matrix (–distance 1‐ibs) and a population tree based on the pairwise To estimate and visualize the distribution of heterozygosity at the individual level, we also calculated standardized multilocus heterozygosity (sMLH) in the R package “inbreedR”  was performed in the software GenAlEx 6.5  from Kyrgyzstan were used as an out‐group  for the TreeMix analysis. The selection of the out‐group was limited to the species that could be genotyped using the same SNP BeadChip, that is, belonging to the genus Ovis. We selected O. ammon because its habitat was the closest to O. nivicola of all Eurasian wild species and because they could have a common ancestor. The divergence time between the two species was estimated to be 2.4–3.1 MYA  was plotted using the R packages maps and mapdata , which passed all the quality control procedures. The selected SNPs were distributed over all chromosomes, and their number ranged from 15 on chromosome 22–124 on chromosome 1  analysis and constructed a Neighbor‐Net tree based on an identity‐by‐state (IBS) matrix. The MDS analysis showed variances of 14.40% and 5.38% in the first and the second components C1 and C2), respectively  and ORU and MOM (0.142). Between adjacent populations, the values were lowest for TIK and ORU (0.052), ORU and VER (0.053), and VER and SKH (0.044). All the FST values were highly significant (p < 0.001).To estimate the level of genetic differentiation, pairwise FST distances depicted a network representing the phylogenetic relationships between the studied populations  between the genetic (FST) and geographic (km) distances (p = 0.009).To evaluate the isolation by distance (IBD), a Mantel test was conducted Figure . The ana3.4Ovis ammon)  as an out‐group. After filtering, we obtained a dataset with 1119 SNPs, which was used for the TreeMix analysis. We added migration edges to the tree sequentially in order to choose the most frequently found variants with significant gene flow (p < 0.05). Standard errors (−SE) and p‐values were calculated using jackknife blocks of 10 SNPs (−k 10). We did not observe significant results when more than two migration events were allowed. To estimate how well our model fit the data, we inspected residuals from the trees. Residuals above zero indicate that populations are closer to each other than they are presented in the tree, and residuals below zero indicate that populations are less close to each other than they are presented in the tree. When no migration events were allowed, it was shown that MOM was the closest population to the out‐group  and from the MOM/SKH common ancestral population to ORU (p = 0.025). This fact implies that MOM/SKH and TIK originated from two different ancestors. The residual plot corresponding to this tree  were observed for the  and  topologies, implying that ORU is derived from two ancestral populations , and allelic richness (Ar) are shown in Table SE) were 0.223 ± 0.005 and 0.243 ± 0.005, respectively. We found that the heterozygosity levels in populations of snow sheep increased southward; the minimum values were observed in the most northern population—TIK, and the maximum values were found in the most southern population—SKH. The distribution of heterozygosity at the individual level (sMLH) is presented in Supporting Information Table FIS calculated for all the populations together (OVL) revealed a significant deficiency of heterozygotes in the snow sheep (FIS 95% CI > 0), while for each group separately, the 95% CI included values of 0. The Ar values showed that the number of different heterozygous alleles in SKH was slightly lower than that in all the studied groups together. The lowest value of Ar was observed in TIK, and as in the case of heterozygosity, its level increased southward. The heterozygosity and allelic richness in the MOM population, which inhabits a different ridge in the east, were similar to those of ORU, with which they share the same latitude.The observed (Ho) and expected (He) heterozygosity, inbreeding coefficient  is the least genetically studied species within the Pachyceros subgenus, which also includes Dall and Stone sheep  and Rocky Mountain and desert bighorn sheep (Ovis canadensis) inhabiting North America. Due to difficult accessibility of the snow sheep habitat, sample collection is problematic, and such studies are limited. To date, Bunch et al.  and MOM (100%) showed no traces of admixture with the other populations. In TIK, only an insignificant (95% CI) component of ORU (0.6%) was found. ORU (84.3%) was significantly (95% CI) admixed with TIK (10.5%) and VER (5.1%). A higher level of admixture with TIK was observed for the most northern ORU specimens. In VER (94.9%) and SKH (96.6%), only insignificant (95% CI) components of the other populations were found. Such results show a low level of gene flow between the populations of snow sheep. This may be explained by the fact that snow sheep are nonmigratory animals.After quality control procedures, 1121 polymorphic SNPs distributed on all the autosomes were selected for subsequent analyses. Various approaches were used to evaluate the population structure of snow sheep. First, multidimensional scaling (MDS) showed that all the samples in our study were clustered according to their geographic locations Figure . Then, mFST values, which showed significant (p < 0.001) genetic differentiation among all the populations of snow sheep from low (FST < 0.05) between adjacent populations to moderate (FST < 0.15) and even high between the most distant groups TIK and MOM (FST = 0.205). In addition, the evaluation of isolation by distance (IBD), performed with a Mantel test  between the pairwise FST values and geographic distances (km).This was supported by calculations of pairwise Ovis ammon) was added to our dataset as an out‐group. It has been discovered that TIK has a different origin from MOM, SKH, and VER. ORU was characterized as an admixed population. Most likely, TIK was isolated from the other groups of snow sheep during the periods of Late Pleistocene glaciation of Verkhoyansk Range. Glaciers in this region attained their maximum extent approximately 140 kya, and further series of glaciations continued up to 50 kya. Ice masses were generally mountain‐centered and did not extend more than 270 km beyond the ridges  in a minor refugium  in each group was not significantly different from zero (95% CI), indicating that the populations were neither inbred nor outbred. However, the calculation of FIS for the entire subset of individuals altogether showed a significantly moderate level of inbreeding in snow sheep. This fact reveals that for snow sheep and other species with a clear population structure and different levels of heterozygosity between the groups, the calculation of FIS may produce misleading results if samples are taken from remote locations.The inbreeding coefficient (5Ovis nivicola lydekkeri). The studied population from Kharaulakh Ridge (TIK) represent descendants of the group that was isolated from other groups of snow sheep for a long time, most likely during the time of glaciation of northeastern Siberia. A mixture between this population and migrants from the other studied populations occurred in the Orulgan Ridge. As long as the numbers of Kharaulakh snow sheep are rather small and they inhabit a limited territory that is located in a low‐hill terrain, making them easy prey for poachers, we suggest that this group should be treated with special care. As one of the solutions, we propose to classify this group as a separate subspecies.Our research revealed that the studied populations of snow sheep are genetically structured according to a geographic pattern. Genetic diversity parameters  were increasing southward, with minimum values in the most northern population—TIK and maximum values in the most southern population—SKH. It was found that snow sheep inhabiting territories in Verkhoyansk Range and Momsky Ridge originated from two different ancestral populations even though they are conventionally considered a single subspecies, known as Yakut snow sheep (None declared.AD, NZ, and GB conceived the study design and specified the research goals; AD, TD, and GM wrote the manuscript; AD and HR provided data processing and analytical support; TD and HR performed molecular genetic studies; IO provided the samples; AD, TD, GM, IO, HR, KW, JS, GB, and NZ developed the manuscript concept and contributed to the discussion. All authors read and approved the final manuscript.https://doi.org/10.5061/dryad.ps8kr80Data available from the Dryad Digital Repository:  Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Platanus orientalis is a typical representative of Tertiary flora in southwest Eurasia; its distribution spreads from the Caucasus to the Mediterranean, with its western border in Southern Italy and Sicily. We analysed genetic diversity and differentiation in the centre and western part of its distribution range using nuclear microsatellites and compared genetic parameters between core and western populations. We found an overall decrease in genetic diversity estimates  from central towards western populations, with those from Southern Italy and Sicily showing the lowest values. The low level of genetic diversity probably results from historic range fragmentation experienced by P. orientalis in its westernmost distribution as confirmed by high level genetic isolation of these populations. Ornamental hybrids were genetically distinguished from P. orientalis as contained private alleles, indicating that gene flow with natural populations is rare. Population assignment and neighbour-joining (NJ) analysis of populations identified four groups belonging to two main phyletic groups  that seem to have different biogeographic origin and also excluded an artificial origin for southern Italian and Sicilian populations as previously suggested. These results show that quantifying the genetic variation of a Tertiary relict in a geographical context, and the potential effect of hybridization with introduced ornamental hybrids, can provide useful insights on factors influencing population genetic structure. Such information is crucial to predict how such taxa will respond to increasing anthropogenic influence on the environment.The distribution of plant species around the Mediterranean basin is a product of the influence of both geographical barriers and of climatic changes experienced during the Tertiary, with the transition from a warm to cool periods. Several species, once largely distributed across the Northern Hemisphere, retracted to refugial areas in southern Europe where they are described as Tertiary relicts.  Platanus orientalis, which has an unusual distribution in the Mediterranean with its largest populations in the east, and it is increasingly rare in the west. We found an overall decrease in genetic diversity from eastern to western populations, with populations in southern Italy containing the lowest levels of genetic diversity. As P. orientalis requires habitats with high moisture levels, these results provide crucial genetic information which may influence its potential to respond to environmental change.Quantifying the genetic diversity of species is important for understanding their evolutionary history. We investigated the genetic diversity of a Tertiary relict, the tree  Geographical barriers play a key role in determining genetic divergence and species distributions . SignatuThe current distribution of relict Tertiary plants has been constrained by their intrinsic limitation in migrating and colonizing new environments, and today is also threatened by the recent anthropogenic influence on their habitat . Plant pPlatanus orientalis, the oriental plane, is representative of the Tertiary flora in southwest Eurasia, distributed along river courses from the central Mediterranean to the Caucasus and India (Ceratocystis platani)  during Greek and Roman times , has been widely planted as an ornamental tree providing shade in cities and along main roads, in part due to its resistance to abiotic stresses  . How migan times . AccordiP. orientalis has not yet been investigated on a large geographical scale. In the present study, by using nuclear microsatellites, we investigated genetic diversity and differentiation in a large and representative portion of P. orientalis range, in order to understand whether current fragmented distribution has affected genetic variability and genetic structure in core and western peripheral populations. In particular we aimed to (i) test whether genetic diversity decreases and genetic differentiation increases in marginal populations compared with those in the core of the species range, (ii) determine genetic diversity and connectivity of core and marginal populations, (iii) identify patterns of phyletic relationships among core and marginal populations and (iv) estimate potential hybridization with introduced ornamental hybrids.To our knowledge, the genetic variation and differentiation of Platanus is the only living genus of the Platanaceae. Seven living Platanus species are distributed with the classic disjunct pattern of Tertiary relict taxa around the Northern Hemisphere . Seven loci previously developed for dentalis  and two employed . The PCRemployed , 1 min aA), allelic richness (Rs), private allelic richness (PA), expected (HE) and observed (HO) heterozygosity, and the inbreeding coefficient FIS, calculated using the programs msa v. 4.05 (HE) and longitudinal distribution using the Pearson correlation coefficient. Departures from Hardy–Weinberg equilibrium (HWE) due to within-population inbreeding were estimated using exact tests in Genepop v. 4.0  and the number of immigrants per generation  were estimated under a coalescent framework using the program Migrate-n 3.6.4  and a single long chain with 50 000 recorded steps, an increment of 50 and 20 000 steps discarded as burn-in. The number of concurrent chains (replicates) was 10. Stationarity of the Markov chain was assessed by examining the effective sample size for each parameter.The intensity of gene flow is crucial to explain current patterns of genetic structure and is of particular interest for small and/or isolated populations. Theta  and genotyped as described above to detect occurrence of putative P. hispanica alleles in the seed progeny. Additionally, we also genotyped a set of Southern Italian P. hispanica plants, both of nursery and of street origin. In particular, two specimens were collected at the closest proximity (1 km) to the natural P. orientalis Alento population.Based on this first screening, the amount of ongoing pollen flow between a natural stand of R = 0.79; P ≤ 0.0001; Clonal individuals were detected in some populations, particularly in the Alento population. Here, several individuals share an identical microsatellite profile suggesting a clonal propagation from local wild stock . In geneP. orientalis  and 0.442 (Alento). In general, the lowest values of inbreeding depression were detected at the western  and eastern  parts of the distribution of ientalis . The preP < 0.001) were found for FST (0.209) and DST (0.183). Most of the pairwise FST values were significant, and similar values were obtained by pairwise DST analysis , Balkans  and Bulgaria . Analysis of molecular variance revealed that a significant proportion of the genetic diversity was found within populations , while 21.0 % genetic diversity was found between populations. The AMOVA performed after grouping populations according to the regions of origin gave similar results, with 12.0 % of the variation distributed between groups, 25.9 % between populations within groups and 62.1 % within populations .Significant levels of nuclear genetic differentiation among populations ; the fourth cluster included the central range of the species, i.e. Bulgaria, Turkey and Crete  we detected at least one private allele of putative P. hispanica plants harbour exclusive alleles respect to P. orientalis individuals sampled in the present survey [see]. Genotyping of seed progeny in the Alento population revealed that only two out of 239 genotyped plantlets (i.e. 0.8 %) contain private alleles also detected in the neighbouring P. hispanica ornamental trees. This indicates there is putative gene exchange via pollen flow between ornamental and natural stands.As already reported in P. orientalis populations along an east-west distributional gradient. Overall, we found a progressive decrease in genetic diversity and connectivity from the core  to the marginal (western) populations.We evaluated neutral genetic variation and genetic structure of P. orientalis, gene flow is expected to homogenize the neutral genetic variation among populations, even at a very large geographical scale  was significantly lower than the expected heterozygosity (HE) in several populations indicating deviation from HWE and significant inbreeding  from core towards marginal populations, with those from Southern Italy and Sicily, at the species western edge, showing the lowest values , suggesting that fragmentation represents a strong barrier to gene flow between stands. In particular, pairwise genetic differentiation showed the highest values between the western marginal populations than between the Balkan ones (core population). FST values between marginal populations of Southern Italy and Sicily were even higher than those found between them and the Balkan populations  which has represented or a glacial temperate refugia in southern Europe for most European tree species (Svenning 2003) or a barrier to migration for other tree species  and an eastern one that groups together Crete, Bulgaria and Turkey  displayed higher genetic differentiation and reduced genetic diversity, in terms of allelic richness (Ne), private allele richness, heterozygosity  and higher FST, than the southern marginal populations of Crete . Even if we have no estimation of potential reproductive success of P. orientalis populations in the western range, the absence of natural regeneration prevents the dispersal and colonization potential of these marginal populations. Asymmetrical gene flow from the core to the marginal populations may increase frequency of unfavourable genes in peripheral populations, thus reducing their potential range expansion and local adaptation , or by genetic barriers that hinder pollination and/or fertilization. aptation . HoweverP. orientalis in proximity of Greek/Roman archaeological sites, the disappearance of Platanus pollen from the Holocene records and its reappearing since the Roman era raises the question whether P. orientalis populations from Southern Italy and Sicily have to be considered remnant of ancient introductions  regeneration found in natural marginal populations, should protect P. orientalis from introgression and potential risk of genetic erosion by the introduced ornamental hybrid.Apart for the potential contribution of ancient introductions, in more recent time, is range , P. orieP. orientalis populations have high genetic differentiation and low gene flow, particularly at western edge, which is determined mainly by geographical isolation linked to its relict distribution. These results show that quantifying the population genetic variation of geographically disjunct species can yield insight into the mechanisms underlying their distributions, and help better understand their ability to colonize novel habitats in future changing environments.In conclusion, our study highlights that An xlsx file of microsatellite genotype data set.This study was partially supported by a bilateral mobility grant CNR-Bulgarian Academy of Science.S.S. and S.F. planned and designed the project; R.R. and D.C. conducted the experiments, F.S. ran the analyses; S.S., A.C. and S.F. wrote most of the text. All authors contributed in the preparation of the study and have commented on and approved the final manuscript.None declared.Supplementary Table S1Click here for additional data file.Supplementary Table S2Click here for additional data file.Supplementary Table S3Click here for additional data file.Supplementary Table S4Click here for additional data file.Supplementary Figure S1Click here for additional data file.Supplementary Figure S2Click here for additional data file.Supporting InformationClick here for additional data file.


Spatial genetic structure (SGS) of plants mainly depends on the effective population size and gene dispersal. Maternally inherited loci are expected to have higher genetic differentiation between populations and more intensive SGS within populations than biparentally inherited loci because of smaller effective population sizes and fewer opportunities of gene dispersal in the maternally inherited loci. We investigated biparentally inherited nuclear genotypes and maternally inherited chloroplast haplotypes of microsatellites in 17 tree populations of three wild cherry species under different conditions of tree distribution and seed dispersal. As expected, interpopulation genetic differentiation was 6–9 times higher in chloroplast haplotypes than in nuclear genotypes. This difference indicated that pollen flow 4–7 times exceeded seed flow between populations. However, no difference between nuclear and chloroplast loci was detected in within‐population SGS intensity due to their substantial variation among the populations. The SGS intensity tended to increase as trees became more aggregated, suggesting that tree aggregation biased pollen and seed dispersal distances toward shorter. The loss of effective seed dispersers, Asian black bears, did not affect the SGS intensity probably because of mitigation of the bear loss by other vertebrate dispersers and too few tree generations after the bear loss to alter SGS. The findings suggest that SGS is more variable in smaller spatial scales due to various ecological factors in local populations. Spatial genetic structure (SGS) of plants may differ between biparentally and maternally inherited loci and vary among spatial scales. We found more intensive SGS in maternally inherited loci in a larger spatial scale although SGS in both loci is more variable in a smaller spatial scale due to various local factors, such as tree aggregation. Pollen dispersal transfers only a male gamete, while seed dispersal transfers an embryo derived from both male and female gametes. Thus, seed dispersal is twice as effective as pollen dispersal in gene flow although pollen is often dispersed over longer distance than seeds  within populations reflects gene dispersal in a local scale  H. Ohba, C. leveilleana (Koehne) H. Ohba, and Padus grayana (Maxim.) C. K. Schneider. Next, we evaluated whether the spatial distribution of trees and the loss of seed dispersers affect fine‐scale SGS in these populations.To study the expected higher interpopulation genetic differentiation and higher within‐population SGS intensity in chloroplast haplotypes than in nuclear genotypes, we investigated 17 tree populations of three wild cherry species, 22.1U. thibetanus distribution in 2003  of the intervals as well as the coefficient of variation (CV = SD/mean) in each population  in each population and the Wright fixation indices within populations (FIS) and among populations within species (FST) were estimated using GenePop 4.6.9. Significant deviation from the Hardy–Weinberg equilibrium (significantly positive or negative FIS) at all loci in each population was tested using GenePop 4.6.9. Statistical significance (p < .05) was adjusted in multiple tests for the loci, locus pairs, and populations in Bonferroni method using the function p.adjust in R 3.3.2.Because HE in each population and FST among populations within species were also estimated using GenePop 4.6.9.Because of the complete linkage among loci in a chloroplast haploid genome, we discriminated chloroplast haplotypes based on allelic combinations of the microsatellite loci. For the discriminated chloroplast haplotypes, 2.3FST of 6, 15, and 21 population pairs in C. jamasakura, C. leveilleana, and P. grayana, respectively, was calculated in nuclear genotypes FST(b) and chloroplast haplotypes FST(m) using GenePop 4.6.9. Spatial distance (m) of these population pairs was calculated from the longitudinal and latitudinal coordinates of the study sites. Interpopulation genetic differentiation parameters in nuclear genotypes FST(b)/(1 + FIS)(1 − FST(b)) and chloroplast haplotypes FST(m)/(1 − FST(m)) were obtained for every population pair in each species (Appendix Pairwise Appendix . RelatioAppendix .FST(m)/(1 − FST(m)) to FST(b)/(1 + FIS)(1 − FST(b)) is expected to be 3 when pollen flow is equal to seed flow and to increase as the pollen‐to‐seed‐flow ratio increases (Appendix FST(m)/(1 − FST(m)) with FST(b)/(1 + FIS)(1 − FST(b)) and three times of it, using the function wilcox.test (paired = TRUE) in R 3.3.2. To compare the genetic differentiation parameters between the genera, we performed Wilcoxon signed rank test for 18 pairs of Cerasus and Padus populations at the same combinations of sites where both populations existed.The ratio of the genetic differentiation parameters in nuclear genotypes and chloroplast haplotypes depends on the ratio of pollen flow to seed flow between populations in the assumptions of Wright island model Ennos, . The ratAppendix . To veri2.4Sp was calculated as Sp = −b/(1 − F1), where b is the regression slope, and F1 is the mean kinship coefficient in the first distance class, which was defined as a <10 percentile of spatial distance in each population  is expected to be 3 when pollen dispersal area is equal to seed dispersal area and to increase as the ratio of pollen dispersal area to seed dispersal area increases (Appendix bm with bb(1 + FIS) or three times of it, using the function wilcox.test (paired = TRUE) in R 3.3.2. We calculated the slope ratio bm/bb(1 + FIS) in each population when bb < 0.The ratio of the regression slopes in nuclear genotypes Appendix . To veriSp in nuclear genotypes, Sp in chloroplast haplotypes, and the slope ratio bm/bb(1 + FIS), among the 17 populations under different conditions of tree distribution and seed dispersal. We performed Kruskal–Wallis rank sum tests for differences in these indices between Cerasus and Padus populations and between populations at bear‐absent and bear‐present sites using the function kruskal.test in R 3.3.2. We tested their Kendall rank correlations or their Pearson correlations with the mean tree interval (m) and with the coefficient of variation (CV) in tree intervals using the function cor.test  in R 3.3.2. In addition, we selected linear models to predict the three indices, nuclear Sp, chloroplast Sp, and the slope ratio. These models included possible combinations of four effects of the genus (Cerasus = 0 and Padus = 1), the mean tree interval, CV in tree intervals, and the bear absence (present = 0 and absent = 1). We selected models with low Akaike Information Criterion (AIC), that is, delta AIC < 2, using the function glm(family = gaussian) and dredge(rank = “AIC”) in R 3.3.2.We compared the indices, 3Cerasus than in Padus  from 17 loci in four C. jamasakura populations, 15 loci in six C. leveilleana populations, and 14 loci in seven P. grayana populations. From 544 locus pairs in C. jamasakura populations, 630 locus pairs in C. leveilleana populations, and 637 locus pairs in P. grayana populations, significant linkage disequilibrium was detected only at locus pair UDP98‐024 and UDP98‐412 in three C. leveilleana populations and locus pair BPPCT012 and BPPCT041 in three P. grayana populations (p < .001). Thus, almost all of the nuclear loci were independent of each other and had rare null alleles. The expected heterozygosity (HE) was higher (0.692 ≤ HE ≤ 0.741) in Cerasus populations and lower (0.485 ≤ HE ≤ 0.538) in Padus populations (Table FIS) ranged from −0.061 to 0.063, and significantly positive FIS was detected in C. leveilleana and P. grayana populations at Okutama site  was lowest in C. jamasakura (FST = 0.024), intermediate in C. leveilleana (FST = 0.046), and highest in P. grayana (FST = 0.147).In the nuclear genotypes, significant deviation from the Hardy–Weinberg equilibrium was detected only at locus BPPCT028 in e p < .00 from 17 C. jamasakura populations, 16 in C. leveilleana populations, and 15 in P. grayana populations. These chloroplast haplotypes were polymorphic in every population, and their HE was comparable (0.187 ≤ HE ≤ 0.794) to that of nuclear genotypes , intermediate in C. leveilleana (FST = 0.168), and highest in P. grayana (FST = 0.455).The observed number of chloroplast haplotypes was 11 in FST(b)/(1 + FIS)(1 − FST(b)) and chloroplast haplotypes FST(m)/(1 − FST(m)) were obtained from pairwise FST of 42 population pairs in the three species  and in chloroplast haplotypes , indicating that pollen flow exceeded seed flow between populations (Appendix Padus (mean = 9.35) than in Cerasus  than in Cerasus (3.92).The interpopulation genetic differentiation parameters in nuclear genotypes Appendix . The lins Figure . The rega Figure . In 18 pAppendix . The ratbb and chloroplast haplotypes bm were obtained, and bb(1 + FIS) and bm were examined to evaluate pollen and seed dispersal (Appendix bb(1 + FIS) and significantly positive regression intercepts and indices F1 and Sp were detected in 10 populations  or nuclear genotypes (p = .143) although much steeper slopes were found only in Padus  (p = .459) nor significantly higher than it  in 16 populations with bb < 0 was 5.17, while the ratio varied from –3.67 to 21.88 among the populations , did not significantly differ between Cerasus and Padus populations (p > .143) although Sp tended to be higher in Padus than in Cerasus  and marginally significant in chloroplast haplotypes , indicating more intensive SGS as trees became more aggregated  indicates that pollen flow exceeds seed flow. This ratio is less than the median ratio (17) in various plant taxa (Petit et al., Maternally inherited loci are predicted to have higher interpopulation genetic differentiation and more intensive within‐population SGS than biparentally inherited loci because of smaller effective population sizes and fewer opportunities of gene dispersal in the former loci (Chybicki et al., Sp index, which was not expected from the equilibrium of spatial population dynamics (Furstenau & Cartwright, In contrast to the interpopulation genetic differentiation, the estimated within‐population SGS intensity did not significantly differ between nuclear genotypes and chloroplast haplotypes due to a large variation among populations. This variation in the SGS intensity may result from ecological variabilities among populations, such as colonization history, although small sample sizes in the within‐population SGS analyses are partially responsible for the nonsignificant results. Fine‐scale SGS is likely to differ between the stages of population development from initial colonization of open habitats to subsequent regeneration in the colonized habitats (Hamrick & Trapnell, Cerasus and Padus. In both spatial scales, more intensive SGS was observed in Padus than in Cerasus. These findings suggest lower effective population size and/or more limited gene flow in Padus than in Cerasus (Ennos, Padus than in Cerasus, suggesting that the intensive SGS mainly results from more limited seed flow. Because the breeding systems and the pollen and seed dispersal systems are similar between these genera, their differences in SGS are related to other life history traits. A candidate trait is regeneration strategy, which is associated with different patterns of spatial tree distribution. At the study sites, local tree density was higher in Padus than in Cerasus. Also in a sparse pine forest, the germination rate of sound seeds was higher in P. grayana than in C. leveilleana, resulting in higher juvenile density (Shirota, Miyauchi, Saito, Maruyama, & Okano, P. grayana occurred in only young stands, whereas C. leveilleana occurred in both young and old stands (Masaki, Padus than in Cerasus. In tropical forests, more intensive SGS was observed in tree species, of which local density was higher (Hardy et al., Cerasus and Padus.Consistent differences in SGS within and between populations were found between s Ennos, . Further Masaki, . These fIn the studied cherry populations, the SGS intensity increased as trees were more aggregated but did not depend on local tree density. As discussed above, aggregation is expected to increase the SGS intensity (Doligez et al., The loss of effective seed dispersers, Asian black bears, is expected to reduce seed dispersal distance and result in more intensive SGS in chloroplast haplotypes than in nuclear genotypes (Calvino‐Cancela et al., Comparison of SGS in multiple spatial scales using genetic markers with different modes of inheritance is a feasible indirect method to evaluate pollen and seed dispersal (Chybicki et al., None declared.SKo, TM, and TN designed this study. KS, SKi, SN, and TN conducted the field work. KS, SKi, and TN arranged the genetic data. TN analyzed the data and wrote the manuscript. Click here for additional data file.