Garra orientalis, a cyprinid widely distributed in South China, using sequences from the mitochondrial DNA control region and cytochrome b gene (1887 bp) and polymorphisms of thirteen microsatellite loci.The South China landmass has been characterized by a complex geological history, including mountain lifting, climate changes, and river capture/reversal events. To determine how this complexity has influenced the landmass’s phylogeography, our study examined the phylogeography of G. orientalis were distributed south of the Yunkai Mountains, including on Hainan Island. The mtDNA data revealed a strong relationship between phylogeny and geography. In the microsatellite analysis, a total of 339 alleles with an average of 26 alleles per locus were observed across thirteen microsatellite loci. A clustering algorithm for microsatellite data revealed an admixture-like genetic structure. Although the mtDNA and microsatellite data sets displayed a discordant population structure, the results of an approximate Bayesian computation approach showed that these two markers revealed congruent historical signals. The population history of G. orientalis reflects vicariance events and dispersal related to the complex geological history of South China.In total, 157 specimens were collected from eight populations. All 88 mtDNA haplotypes were identified as belonging to three major lineages, and these lineages were almost allopatric in their distributions. The results of a statistical dispersal-vicariance analysis suggested that the ancestral populations of Our results (i) found that the discordances between mtDNA and microsatellite markers were accounted for by admixtures; (ii) showed that the Wuzhishan and Yinggeling mountain ranges and Qiongzhou Strait were important barriers limiting gene exchange between populations on both sides; (iii) indicated that during glaciation and inter-glacial periods, the strait and continental shelves were exposed and sank, which contributed with the dispersion and differentiation of populations; and (iv) displayed that the admixtures between lineages took place in coastal populations and then colonized the tributaries of the Pearl River. China, a vast geographical area with complex geology, is divided into five major geographical regions according to the essential geo-historical events and ichthyofauna . These rCobitis sinensis  supported this hypothesis  and their analogue FST(nuc) for nuclear genes were expected. Furthermore, Brito [ST(nuc) = FST(mt)/[4–3 FST(mt)], to correct FST differentiation between mtDNA and nDNA. In our study, we obtain a corrected FST(mt) equal to 0.32, which is still four times larger than FST value calculated with microsatellite data (0.07). Accordingly, the differences in effective population size between mtDNA and microsatellite markers cannot account for the discordant patterns present. Thus, we suggested that incongruent genetic structures between these two genetic markers might have resulted from recent admixture events.In this study, the microsatellite loci examined were cloned from total DNA . Our stuowclough  proposedowclough  suggestee, Brito  proposedG. orientalis and test whether the mtDNA and microsatellites showed the same signals of population history, our study included approximate Bayesian computations (ABC) on our mtDNA and microsatellite data sets using the software DIYABC. Moreover, our study displayed admixture-like genetic structures of G. orientalis , Pearl River (lineage I + II), northern Hainan Island (lineage II), and southern Hainan Island (lineage III). Based on this grouping scheme, our study proposed three barriers: the central mountainous area of Hainan Island (WY Ranges), Qiongzhou Strait, and Hanjiang River (boundary between Zhejiang-Fujian and Pearl River subregions)  Fig. . Moreovens) Fig. , haplotyon Table . Based oon Table  and 3, oBased on the three breaks described above, the AMOVA analysis Table  indicateG. orientalis. Moreover, estimating the time of evolutionary events is an important process for understanding the evolutionary forces that influence phylogeographic patterns [G. orientalis. The TMRCA estimated based on the mtDNA cyt b and D-loop sequences suggest that the origin of G. orientalis can be traced back to the late Pleistocene (0.462 mya). Moreover, G. orientalis is distributed in South China, south of the Nanling Mountains and east of the Wuyi Mountains. According to geological history [In conclusion, our study showed that the patterns of genetic diversity differed between mtDNA and microsatellite sequences, but these two data sets revealed congruent signals of population history based on a DIYABC analysis. Our results suggested the mtDNA S-DIVA model Fig.  can explpatterns . This stpatterns  and Bermpatterns  to calibcene 0.46 mya. Mor history , the WuyG. orientalis were distributed widely in the southern Yunkai Mountains, including the Leizhou Peninsula and Hainan Island, in the late Pleistocene . The landforms reflect the fact that the rivers on Hainan Island originate from the central mountainous area (including the WY Ranges) and flow outwards. The rivers in the northern region of the WY Ranges flow northward, and the rivers in the southern region of the WY Ranges flow southward. In our study, the directions of water flow in populations BS and QH are northward into Qingzhou Strait and that in population LD is southward into the Gulf of Tonkin . The discordances between mtDNA and microsatellite markers accounted for admixture events. The ancestral populations were distributed widely in the southern Yunkai Mountains, and the exposure and sinking of the strait and continental shelves contributed to the dispersion and differentiation of populations. Finally, The animal experiments were performed under an animal ethics approval granted by the Shanghai Ocean University. Our sampling procedures did not affect the survival of studies species.G. orientalis were collected  with primers B-F (5‘-GCTCAGACTTTAACCGAGACCA AT-3’)  and H159π and θω) [π) and historical (θω) genetic diversity provides insight into population dynamics over recent evolutionary history [ST hierarchically with DnaSP. The existence of phylogeographic structure was examined following Pons and Petit [ST and NST, in DnaSP. The substitution model used for the phylogenetic reconstructions was generated in jmodeltest 2 [Garra imberba (KM255666) [Nucleotide sequences were aligned with CLUSTALX 1.81 . Levels  and θω)  in DnaSP and θω) . Compari history –58. Pattnd Petit  by calcueltest 2  based oneltest 2  was seleM255666)  was usedM255666)  and MEGAM255666) ; the NJ M255666) , similarST values and AMOVA  were used to partition variation among samples into within-population (FST), within-group (FSC) and among-group (FCT) components. For the hierarchical analysis, populations were grouped according to several scenarios. Furthermore, to determine the possible diversification scenarios of G. orientalis, a statistical dispersal-vicariance analysis (S-DIVA), a program which complements DIVA, was employed to determine statistical support for ancestral range reconstructions [7 MCMC steps and the first 10 % as burn-in. Each sampling population was defined by a range. The analysis was performed using the ‘maxareas = 4’ option. Additionally, our study attempted to estimate the time to the most recent common ancestor (TMRCA) based on the mtDNA data. However, the evolutionary rate of the mtDNA could not be estimated for G. orientalis because of a lack of a calibration point. A molecular clock was calibrated using the divergence rate of 2 % per million years, as previously postulated by Brown et al. [MRCA values of the different lineages. The output was visualized in Tracer v1.6 [Several analyses in Arlequin version 3.5  were useructions , 69. Theructions  with 107n et al.  and Bermn et al. . A stickcer v1.6  to deterA, θ1 and θ2, are population mutation rates for the ancestral population and two daughter populations since divergence, where θ = Neμ and Ne is the effective population size; the time since population splitting (t), where t = Tμ and T is time in years; and the asymmetric migration rates between two populations (m12 and m21). IM analyses revealed unambiguous marginal posterior probability distributions of the parameters for all comparisons. The IM produced similar estimates for each run; parameter estimates fell within the 95 % highest posterior densities (HPD) for the parameter estimates from the other runs. The mode and 95 % HPD for estimates were used from the run producing the largest ESS for the estimates (ESS > 200).The coalescent-based program IM (isolation with migration)  was usedG. orientalis (Gar 1–13) [E) and observed (HO) heterozygosity and the allelic richness (AR). GENEPOP web version 4.0.10 [ST values and the significance of genotypic differentiation between population pairs. This study also used a Bayesian procedure to infer population structure and estimate the number of genetically distinct populations using STRUCTURE v.2.2.3 [K) was completed using 20 independent runs with K = 1–9 (assuming no prior population delineation information) at 100,000 MCMC repetitions combined with a 10,000 burn-in period. The analysis was performed with a burn-in of 2 × 106 and 2 × 107 iterations following the admixture model with prior population information. To obtain the most appropriate number of genetic groups in our dataset, we used the ad hoc statistic DK described by Evanno et al. [K best fitting our data using the log posterior probability of the data for a given K, LnPr(X|K) [. Finally, this study additionally estimated population level admixture by calculating the mean of the individual admixture coefficients. To explore relationships among the populations, Nei’s DA distance [http://bioinformatics.org/∼tryphon/populations/).A total of 13 polymorphic microsatellite loci in ar 1–13)  were anaar 1–13)  was usedn 4.0.10 , 75 was  v.2.2.3 . Estimato et al. . This stnPr(X|K) . Finallydistance  between distance  with booTo reconstruct the unknown history of divergences among these eight populations, we performed approximated Bayesian computations (ABC) on our mtDNA and microsatellite data set with the software DIYABC v.2.0 . The DIYIn the first scenario (null hypothesis, Fig. Our study used three data sets, mtDNA, microsatellites and mtDNA + microsatellites, to examine these five scenarios Fig. . The ABCThe sequence dataset generated herein is available in the GenBank repository with Accession numbers KR698422-KR698486 and KR698391-KR 698421.


Arabidopsis lyrata ssp. petraea from the northernmost parts of its distribution range on Iceland to that previously documented in Scandinavia. Leaf tissue and soil were sampled from ten Icelandic populations of A. lyrata. Seedlings were grown from soil samples, and tissue from above-ground and seed bank individuals were genotyped with 21 microsatellite markers. Seed bank density in Icelandic populations was low but not significantly different from that observed in Norwegian populations. While within-population genetic diversity was relatively high on Iceland (HE = 0.35), among-population differentiation was low (FST = 0.10) compared to Norwegian and Swedish populations. Population differentiation was positively associated with geographical distance in both Iceland and Scandinavia, but the strength of this relationship varied between regions. Although topography and a larger distribution range may explain the higher differentiation between mountainous Norwegian relative to lowland populations in Sweden, these factors cannot explain the lower differentiation in Icelandic compared to Swedish populations. We propose that low genetic differentiation among Icelandic populations is not caused by differences in connectivity, but is rather due to large historical effective population sizes. Thus, rather than contemporary processes, historical factors such as survival of Icelandic lineages in northern refugia during the last glacial period may have contributed to the observed pattern.Level and partitioning of genetic diversity is expected to vary between contrasting habitats, reflecting differences in strength of ecological and evolutionary processes. Therefore, it is necessary to consider processes acting on different time scales when trying to explain diversity patterns in different parts of species' distributions. To explore how historical and contemporary factors jointly may influence patterns of genetic diversity and population differentiation, we compared genetic composition in the perennial herb  Genetic diversity and structuring of natural populations are shaped by both historical and contemporary processes within a given region in situPopulation connectivity and genetic structure will, however, not only be shaped by physical features of landscapes and biological factors affecting the dispersal kernel Arabidopsis lyrata is frequently used as a model for studies on genetic structuring, evolution of self-incompatibility and distribution in plants A. lyrata in Scandinavia, and hypothesized that variability in landscape structures may explain observed differences. In the Norwegian distribution range, the topography is rugged and dominated by valleys, whereas Swedish populations occur in a more open landscape along the coast. Mountains are expected to restrict dispersal of propagules in space, which can explain the higher between-population differentiation and lower genetic diversity in Norway compared to Sweden. Thus, if landscape structure has a significant impact on processes such as gene flow, a comparable pattern of genetic diversity and structuring might be expected for regions with similar landscape structures.A. lyrata is distributed evenly throughout Iceland, and as opposed to Norway, there are fewer pronounced topographic barriers such as mountains, fjords and deep valleys to disrupt genetic connectivity among populations. The wide distribution of lava fields on Iceland, where A. lyrata act as a pioneer species A. lyrata using DNA sequence data, possibly subject to strong natural selection, show contrasting patterns of genetic differentiation on various geographical scales A. lyrataA. lyrata has been attributed to glacial survival on the continent during the last glacial period It has been shown that life-history characteristics, such as presence of seed banks, may impact effective population sizes in Scandinavian populations of this A. lyrata differ from that observed in Scandinavia. We want to quantify the relative impact of historical and contemporary processes in order to understand what factors explain the observed patterns of genetic diversity in northern Europe. In particular, we want to investigate to what extent observed patterns of genetic structuring are attributed to historical factors, like long-term survival and population stability through evolutionary time, and contemporary processes, involving dormancy, dispersal abilities and/or topographic features.In the present study we investigate whether patterns of genetic diversity within and among Icelandic populations of No specific permissions were required for the field activities on Iceland presented in this study. None of the study populations occurred within protected areas, and the study species itself is not endangered or protected. The GPS coordinates for the study populations are provided in Arabidopsis lyrata ssp. petraea (L.) O'Kane and Al-Shebaz (Brassicaceae) is a perennial, self-incompatible herb with a highly disjunctive distribution in Europe, consisting of a narrow central European distribution area in Austria, Germany and the Czech republic, and a wide but fragmented northern European distribution area covering Iceland, Faeroe Islands, northern parts of United Kingdom, southwestern parts of Norway and the eastern coast of central Sweden A. lyrata is widespread throughout the island (http://floraislands.is) and found in diverse habitats differing in several aspects from those in central Europe and the Scandinavian Peninsula. The island is dominated by few mountainous areas and extensive lowland lava plains, with the species growing under a range of conditions, from volcanic sands to rich soils in agricultural areas  on the coast, covering a smaller geographical area  A. lyrata populations can form extensive seed banks A. lyrata.To examine whether Icelandic populations form a soil seed bank, ten soil samples of 10 cm×10 cm, with a depth of 5 cm, were taken throughout each of the studied populations before the annual seed rain. However, only five samples could be collected from the spatially very restricted population 22. Previous germination trials of field collected seeds from Norway have shown that no stratification is required for successful germination . However, to ensure germination of as many seeds as possible, soil samples were stored at 4°C for at least one month. Soil samples were spread out in a thin layer (1 cm) on top of commercial potting soil in the greenhouse and germinated at 16 h day light, 20°C and 65% air humidity. Seedling emergence from the soil was monitored over a period of ten months, and all seedlings germinated within two months after sowing. At each monthly census, new seedlings were counted and removed. Leaf tissue from each seedling was collected and dried at 45°C overnight. Seed bank density was determined by dividing the total number of seedlings emerging from the soil by total volume of soil for each population, following Falahati-Anbaran et al. Genomic DNA was extracted from leaf tissue using E-Z 96 Plant DNA Kit . Twenty-one primer pairs labeled with different fluorescent dyes were used in 3 separate multiplex PCR reactions (Table S1 in A. lyrataρ) was used to evaluate the association between soil and above-ground density. In total, 308 samples were obtained from above-ground and seed-bank cohorts and genotyped using 21 microsatellite markers. Because we did not find significant differences in seed bank density between Iceland and Norway (see below), further analysis of Icelandic samples was limited to the 263 above-ground individuals. Identical multilocus genotypes within populations were removed, i.e., only one individual from each multilocus genotype (genet) was included in the genetic analyses per population in order to avoid estimation bias due to clonality i2)2 -∑Pi4i is the frequency of the ith allele at a given locus and multiplied over all loci. The calculated PI in the study population was less than 1.6×10−8.Soil seed banks contribute to preservation of genetic variation in natural populations of HO) and expected heterozygosity (HE) over all loci was estimated using Genepop version 4.1 P-value using a Markov Chain algorithm P-value for multiple comparisons was corrected with the sequential Bonferroni correction FIS) and the statistical significance of FIS was determined by 10,000 random permutations of alleles between individuals using Arlequin 3.5 RS), the number of alleles adjusted for variation in sample size, and private allelic richness were estimated over all loci for each Icelandic population using a rarefaction method implemented in HP-RARE 1.0 md2) was calculated for each individual md2 across individuals was reported for each population. Neutral alleles at a locus will become progressively more differentiated from one another over evolutionary time due to stepwise mutations in SSR (simple sequence repeat) markers, and a positive relationship between 2md and time since divergence is expected HE, FIS, RS, PR and 2md for Norwegian and Swedish populations based on previously published data HE, FIS, RS, PR and 2md. Multiple comparisons between regions were performed using a post hoc Bonferroni test. All parameters were tested for normality and homogeneity of variance prior to analysis, and 2md was square root transformed prior to analysis in order to meet assumptions of the test. The average frequency of null alleles across populations for all loci and between regions was compared using a paired t-test.The 248 individuals were subsequently used to compare patterns of genetic variability with the Scandinavian populations included by Gaudeul et al. P-values was performed to assess genetic diversity excess under mutation–drift equilibrium relative to that expected from the number of alleles in a sample. The two-phase mutation model (TPM) was used for probability estimation based on 10,000 replications as implemented in Bottleneck version 1.2 Heq) from the number of alleles for each locus and population, and tests the deviation of Heq from observed gene diversity (HE). The bottlenecked populations should show a decline in both the number of alleles and Heq under mutation drift equilibrium, and thus Heq might be smaller than HETo detect recent declines in effective population sizes due to bottlenecks, a one-tailed Wilcoxon signed rank (WSR) test with sequential Bonferroni correction of FST among populations. The magnitude of regional FST can reflect the strength of the homogenizing effect of gene flow among populations in each region. To investigate regional differences in population differentiation, pairwise FST between Icelandic populations was compared to that in Scandinavian populations. Due to non-independence of among-population FST values, we applied a randomization test following a one-way analysis of variance (ANOVA) to test the null hypothesis of no difference between regions using EcoSim FST measures accounting for null alleles were computed using FreeNA FST for each region and one-way ANOVA was used to test for differences. For each comparison, a pseudo F ratio similar to a standard F ratio was computed and compared to the simulated F ratio calculated using a randomization test with 1,000 iterations to determine the probability that the observed F ratio was greater or less than that expected by chance. To test whether variation in size of distribution area inflates genetic differentiation levels, we limited the analysis to populations from areas of similar sizes and population numbers across regions. For this, five populations from each area were selected; Iceland , Sweden  and Norway . In order to test for isolation by distance, the relationship between log10 [FST/(1-FST)] and geographical (log10 transformed) distances was studied using a Mantel test implemented in GenAlEx 6.4 Genetic structure of Icelandic populations was assessed by computing pairwise https://lifeportal.uio.no/. A model of admixture together with correlated allele frequencies was applied for parameter estimation using 2×105 Markov Chain Monte Carlo (MCMC) iterations, following a burn-in period of 1×105 iterations. The likely number of clusters was obtained by plotting the average ln probability of data [ln P(D)] over 10 independent runs for K = K based on an ad hoc method to determine the conservative minimum number of clusters 5 permutations in CLUMPP version 1.1.2 K using DISTRUCT version 1.1 Genetic structure of Icelandic populations was also investigated using a Bayesian clustering analysis as implemented in Structure 2.3.3 Ne) for each population. The rate of contemporary immigration into populations was estimated as the probability that individuals belong to their respective populations, and computed with a Monte-Carlo resampling of 1×104 individuals using a Bayesian approach implemented in GeneClass2 θ) and immigration rate (M) were simultaneously estimated using a coalescence-based maximum likelihood approach implemented in Migrate 3.2 M = m/u, where m and u are immigration rate and mutation rate per generation, respectively), reflects the average rate of immigration relative to mutation rate through historical time. Migrate assumes constant population size over time, a symmetric migration rate between populations and that all populations exchanging genes have been sampled M = m/u, but can bias the effective population size estimates upward 4 trees) following one long chain (sampling at 5×105 trees). The simulation was repeated five times to improve the accuracy of results as suggested by Beerli and Felsenstein θ and M. In addition, θ and M were estimated based on the same markers for Scandinavian populations. The historical Ne demonstrates the long term effect of evolutionary processes such as mutations and random genetic changes on genetic variation. Under mutation-drift equilibrium the amount of genetic variation is expected to be θ = 4 Neu in a diploid population, where θ, is four times the product of the effective population size (Ne) and mutation rate per generation (u) for microsatellite loci. Historical Ne can be estimated by calculating θ and dividing by estimates of u, or compared between regions by dividing θ estimates, thereby canceling out the mutation rate, i.e., assuming that neutral mutation rates do not vary across the distribution range. Analysis of variance was conducted to compare θ and M between regions, and multiple post hoc comparisons based on the Bonferroni method was used to test for differences between regions. All statistical tests were conducted using SPSS version 16.0.To investigate the importance of evolutionary processes on patterns of genetic structuring, we estimated immigration rates and effective population sizes . The percentage of populations either lacking a seed bank entirely or with <10 seedlings/m2, were 21% and 70% for Norway and Iceland, respectively (Table S2 in 2 ± SE) was 1.38±0.58, , not significantly different from that observed in Norwegian populations  or Norwegian  populations.The seed banks of the Icelandic populations contained on average (± SE) 26.8±16.6 seedlings/mELF3, ICE3, MSAT2.22 and nga112; Table S1 in nga162 were polymorphic in at least one population, and the average proportion of polymorphic loci (PPL) over all Icelandic populations was 0.76±0.02 (± SE). Estimates of HO, HE, FIS, PPL, RS and RP for each population are presented in HE) varied considerably among loci (range 0.008–0.643), but less between Icelandic populations, with an average of 0.35 (range 0.32–0.39), with no populations showed significant heterozygote deficiency after Bonferroni corrections (P>0.005). None of the FIS values were significantly different from zero  showed heterozygote deficiency after sequential Bonferroni correction (P<0.0035), probably due to presence of null alleles. Additionally, the average (± SE) frequency of null alleles across populations over all loci was 0.02±0.004, and only three loci  exhibited a null allele frequency higher than 0.05. Average (± SE) allelic richness and private allelic richness over all populations was 2.28±0.05 (range 2.03–2.55) and 0.13±0.03 (range 0.01–0.26), respectively. The mean d (md2) (± SE) over all Icelandic populations was 32±1.7. Bottleneck tests revealed a signature of recent reduction in effective size in three populations  after sequential Bonferroni correction on the P-value, with significant heterozygosity excess compared to expectations under mutation-drift equilibrium based on the two-phased model (TPM).In total, 134 alleles were detected across 21 microsatellite loci in Icelandic populations, with the average (± SD) number of alleles per locus being 6.38±5.92 (range 1–26). Four loci , allelic richness , private allelic richness , and md2 , but no difference in FIS  in Icelandic  was similar to Swedish  populations and both were significantly higher than Norwegian populations based on post hoc Bonferroni test . Pairwise comparison revealed that allelic richness, RS, was significantly higher in Icelandic populations  than in Norwegian  populations, and similar to Swedish populations . Private allelic richness was significantly higher in Icelandic (average 0.13±0.030) compared to Norwegian  and Swedish  populations, whereas Norwegian and Swedish populations did not differ from each other (P>0.1). Md2 in Icelandic populations was significantly higher than that in Norwegian  and Swedish  populations based on post hoc Bonferroni tests. The average (± SE) frequency of null alleles for all loci across Icelandic populations (0.02±0.004) was similar to that for Norwegian  and Swedish populations . No significant difference was observed between Norway and Sweden Analysis of variance revealed a significant difference among regions for genetic variation (FST (± SE) among Icelandic populations was 0.10±0.01 (range 0.010–0.23). One-way ANOVA revealed a significant difference among regions in levels of genetic differentiation. The observed F ratio for Norway-Sweden, Norway-Iceland and Sweden-Iceland comparisons were 46.60, 133.80, and 44.28, respectively . The unbiased FST corrected for null alleles was similar to uncorrected FST for Icelandic , Norwegian  and Swedish  populations. The average FST (± SE) was higher in Norwegian  than Swedish populations , which in turn was higher than in Icelandic populations (P<0.001). Geographical and genetic distances were significantly correlated in the samples from Iceland , Norway , and Sweden . The regression slope of Swedish populations (b = 0.15) differed significantly from Norwegian  and Icelandic  populations, whereas the two latter regions did not differ . We also investigated genetic differentiation by including five populations at similar geographic scales in Icelandic , Norwegian , and Swedish populations . This approach resulted in average FST estimates being reduced to 0.07, 0.17 and 0.20 for Icelandic, Swedish and Norwegian populations, respectively, but regions were still significantly differentiated . Accordingly, a post hoc Bonferroni test revealed that the population differentiation in Iceland was significantly lower than that in Norway (P = 0.003) and Sweden (P = 0.019), but the latter regions did not differ significantly from each other (P = 0.932).The average K at K = 2 based on the method K = 7  was 1.06±0.03, 1.05±0.05 and 0.95±0.03 per generation for populations from Iceland, Sweden and Norway, respectively, implying that migration and mutation rates are similar. Furthermore, no significant difference was found among the regions for historical migration rates . The average (± SD) historical effective population size (estimated as θ) was 0.46±0.03 in Icelandic populations, 0.29±0.05 in Norway and 0.32±0.07 in Sweden .Bayesian analyses showed that none of the populations from the different regions had experienced a recent immigration event . Moreover, above-ground plant density do not differ between the countries Old and stable populations may contain more allelic variants than short-lived, unstable populations. Furthermore, ecological features like soil seed banks may contribute to maintaining genetic diversity by preserving alleles descended from multiple generations in the past, resulting in increased effective sizes of plant populations HE = 0.35) is similar to what is found in Swedish populations . This is consistent with Muller et al. A. lyrata is partitioned within populations, with a low but significant level of among-population differentiation (average FST = 0.10). The level of genetic differentiation among Icelandic populations (mean distance  = 93 km) is ∼2- and 3-fold lower than that in Swedish (mean distance  = 40 km) and Norwegian (mean distance  = 165 km) populations, respectively. Furthermore, the level of differentiation in Icelandic populations remains significantly lower than Norwegian and Swedish populations after adjusting for geographical scale and population number across regions. This suggests that the wider distribution area on Iceland cannot explain differences in population differentiation between the studied regions.The average genetic diversity in Icelandic populations (HE = 0.32–0.39) on Iceland compared to Norwegian (HE = 0.–0.34) and Swedish (HE = 0.21–0.38) populations. This suggests that increasing the sampling efforts to more than ten studied populations will likely not change our estimates.Our sampling regime has covered only the western parts of Iceland, but this has probably not had much or any impact on our estimates of genetic structure in Icelandic populations. It is worth noting that the Icelandic populations used for between region comparisons accounting for similar sized distribution area , exhibit similar levels of genetic diversity within populations compared to all Icelandic populations sampled in our study. Thus, the range of within-population diversity is narrow  in the Icelandic region is considerably larger than in Swedish populations while levels of genetic diversity are similar. On the other hand, Icelandic populations exhibit higher allelic richness and genetic diversity, and has larger effective population size compared to the Norwegian region. The larger effective population size of Icelandic than Swedish populations may be related to differences in private allelic richness between regions, possibly explained by older age of the Icelandic compared to the Swedish metapopulation. Interestingly, we find that the average mutation-scaled migration rate (M) do not differ between regions, suggesting similar rates of immigration over time in populations of different regions. In addition, we find no evidence of contemporary migration into populations in different regions using an assignment test, suggesting limited gene flow on both ecological and evolutionary time-scales.We observe that the historical effective population size (2 (2md) has been used as measure of divergence time between lineages within populations over time 2md in Icelandic populations compared to Norwegian and Swedish populations suggests longer coalescence times for Icelandic allelic variants, and hence possibly older populations on Iceland than in Scandinavia. Even though there is a significant difference between regions for 2md, the presence of null alleles may negatively bias 2md by increasing the observed frequency of homozygotes. However, since the frequency of null alleles is low and similar between regions, this may not be influencing the patterns observed in the present study. Additionally, 2md is sensitive to inbreeding patterns FIS equal to zero. Finally, the extent of allele length differences could be due to admixture of divergent lineages, for instance central European and Icelandic lineages in relatively recent time , effective population size (eN), allelic richness (RS) and private allelic richness (RP) in Icelandic, Norwegian and Swedish populations. Figure S2. Genetic structure of Icelandic populations of Arabidopsis lyrata detected by Structure, with optimal number of clusters determined by the highest peak for mean ln P(D). Figure S3. Probability of assignment of individuals to their respective population for different regions.(DOCX)Click here for additional data file.Supporting Information S2Microsatellite data from the ten Icelandic populations of Arabidopsis lyrata used in this study. Data is provided in GenAlEx format, with allele sizes in base pairs.(XLSX)Click here for additional data file.


Picconia azorica is an endangered endemic species of the Azores whose hard and high density wood is very appreciated for the production of toys, agricultural tools, furniture and religious statuary. Its renewed economic interest represents a good opportunity for establishing conservation programmes. To contribute with information useful for the decision making we performed the genetic analysis of 230 samples from 11 populations collected in three Azorean islands. The majority of the genetic variability was found within populations and no genetic structure was detected between populations and between islands, indicating that the oceanic barriers do not greatly affect gene flow. Picconia azorica is a valuable Azorean endemic species recently classified as endangered. To contribute with information useful for the establishment of conservation programmes, the genetic variability and differentiation among 230 samples from 11 populations collected in three Azorean islands was accessed with eight inter-simple sequence repeat markers. A total of 64 polymorphic loci were detected. The majority of genetic variability was found within populations and no genetic structure was detected between populations and between islands. Also the coefficient of genetic differentiation and the level of gene flow indicate that geographical distances do not act as barriers for gene flow. In order to ensure the survival of populations in situ and ex situ management practices should be considered, including artificial propagation through the use of plant tissue culture techniques, not only for the restoration of habitat but also for the sustainable use of its valuable wood.Knowledge of the levels and distribution of genetic diversity is important for designing conservation strategies for threatened and endangered species so as to guarantee sustainable survival of populations and to preserve their evolutionary potential.  Knowledge of genetic variability, together with information about demography, reproductive biology and dynamics, is very important when establishing any conservation and management programme .The development of molecular genetic methods has been a significant advance providing tools for answering questions on the diversity among flora and fauna and to help define strategies for conservation purposes. Inter-simple sequence repeat (ISSR) markers offer the possibility of randomly scanning the whole genome , making The archipelago of the Azores is located in the North Atlantic Ocean between latitudes 36°55′N and 39°43′N, and longitudes 24°46′W and 31°16′W. It is composed of nine volcanic islands divided into three groups: the eastern (São Miguel and Santa Maria), the central  and the western group (Flores and Corvo). From the about 200 species native to the Azorean flora only 70 have been described as endemic for the Azores , 2005, rPicconia azorica (Oleaceae Family), locally named pau-branco, is a xerophytic, evergreen shrub or a small tree, endemic to the Azores archipelago. This species grows up to 8 m tall and has simple, lanceolate to ovate, opposite leaves with entire margins; it flowers from March to July, producing small white flowers in axillary clusters and its fleshy fruits are dark blue drupes  or were very small (CL), in which case sampling was limited. Leaf samples were weighed and stored at −80 °C.DNA was extracted by downscaling the protocol described by Picconia excelsa, from Madeira island, were also genotyped to be used as an outgroup for the phylogenetic analysis of individuals.A total of 19 anchored ISSR primers from the University of British Columbia  were firstly tested in a batch of 48 samples. From these, 8 that produced clear, polymorphic, reproducible bands across two repetitions of each ISSR assay were selected Table  and usedPolymerase chain reactions were carried out in a total volume of 25 μL containing 20 ng DNA, 0.2 μM of primer, 0.2 mM of each dNTPs and 1 U of DreamTaq DNA polymerase (Fermentas) in reaction buffer. Amplification was performed in a UNO II Biometra thermocycler with 5 min denaturation at 94 °C, followed by 35 cycles of 30 s denaturation at 96 °C, 45 s annealing at the appropriate melting temperature  or absent (0) and compiled into a data matrix. As we were studying an Azorean endemic species monomorphic loci were considered for the analysis of genetic parameters as suggested by Genetic diversity was measured at individual, population and island levels. At the individual level, a cladogram of all the 233 samples based on the similarity analysis of fingerprint patterns was drawn using the unweighted pair group method arithmetic average (UPGMA) algorithm using Dice's coefficient as implemented in NTSYSpc  and was h) (I) (GST) (mN) (I) and Nei's gene diversity (h) were compared by Pearson's rank coefficient correlation  , Shannonh) (I) , unbiaseh) (I) , coefficI) (GST)  and geneST) (Nm)  in the srelation  using IBNa) and the number of effective alleles (Ne)   were alsTo learn about the partitioning of genetic variabilty among islands, among populations and among individuals  by resamφPT) based on the Euclidean distances (φPT) and geographical distances of each population (Genetic differentiation among populations (istances  and the K) was estimated by using the maximum value of L(K) and by calculating ΔK  which are necessary to represent 99.99 % of the total genetic diversity among populations (P) was calculated according to the modified equation of P = 1−(φPT)n.The number of populations of I) was 0.2456 and by computing Nei's genetic diversity (h) was 0.1503. While the population CL showed the lowest genetic diversity , the population PB showed the highest   within populations.From 230 Azorean individuals distributed across 11 populations, the eight primers used yielded 79 clearly scorable bands, ranging from four at primer UBC836 to 15 at primers UBC807 and UBC842, with a mean value of 10. Among them 64 were polymorphic (81.0 %)  and for populations CL from São Miguel and PB from Terceira. While population CL showed genetic distances >0.058 for all pairwise comparisons with populations from Pico and Terceira, population PB showed values >0.044 for all pairwise comparisons with populations from São Miguel and Pico.φPT) (Table φPT among islands showed levels of genetic differentiation of 0.118 (Pico–Terceira), 0.131 (São Miguel–Terceira) and 0.128 (Pico–São Miguel). The UPGMA dendrogram obtained with φPT pairwise values  Table  between φPT.In the PCoA based on the matrix of individual genotypes, only 44.16 % of the total variance was explained by the first two axes   was 0.0940 and the level of gene flow (mN) calculated based on GST was estimated to be 4.818, suggesting a high gene flow between P. azorica populations and islands. The number of migrants calculated as described by The coefficient of genetic differentiation between populations  was shown by the Mantel test. For pairwise φPT and geographical distances a slightly positive correlation was obtained .Insignificant correlation between the genetic distance of individuals and geographical distances (P(K) did not show any substantial increases when K varied from 1 to 14 see Supporting Information]; (B) ln Pr(G|K) values presented as a function of the number of clusters; (C) graphical presentations of different samples. Each sample is represented by a single vertical line broken into K colour segments, with lengths proportional to the estimated membership of the inferred cluster. Individuals are grouped into populations. For populations names see


Pan troglodytes) can be divided into four subspecies. Substantial phylogenetic evidence suggests that these subspecies can be grouped into two distinct lineages: a western African group that includes P. t. verus and P. t. ellioti and a central/eastern African group that includes P. t. troglodytes and P. t. schweinfurthii. The geographic division of these two lineages occurs in Cameroon, where the rages of P. t. ellioti and P. t. troglodytes appear to converge at the Sanaga River. Remarkably, few population genetic studies have included wild chimpanzees from this region.Chimpanzees (P. t. troglodytes in southern Cameroon from P. t. ellioti north and west of the Sanaga River. These two populations split ~200-250 thousand years ago (kya), but have exchanged one migrant per generation since separating. In addition, P. t. ellioti consists of two populations that split from one another ~4 kya. One population is located in the rainforests of western Cameroon and eastern Nigeria, whereas the second population appears to be confined to a savannah-woodland mosaic in central Cameroon.We analyzed microsatellite genotypes of 187 wild, unrelated chimpanzees, and mitochondrial control region sequencing data from 604 chimpanzees. We found that chimpanzees in Cameroon and eastern Nigeria comprise at least two, and likely three populations. Both the mtDNA and microsatellite data suggest that there is a primary separation of P. t. troglodytes in southern Cameroon comprises one population that is separated from two populations of P. t. ellioti in western and central Cameroon, respectively. P. t. ellioti and P. t. troglodytes appear to be characterized by a pattern of isolation-with-migration, and thus, we propose that neutral processes alone can not explain the differentiation of P. t. ellioti and P. t. troglodytes.Our findings suggest that there are as many as three genetically distinct populations of chimpanzees in Cameroon and eastern Nigeria. The online version of this article (doi:10.1186/s12862-014-0276-y) contains supplementary material, which is available to authorized users. Pan troglodytes) are still widely distributed across sub-Saharan Africa, and the species exploits a wide range of habitats including rainforests, ecotones and savannas [P. t. verus and P. t. ellioti and a central/eastern African group that includes P. t. troglodytes and P. t. schweinfurthii [P. t. ellioti [Chimpanzees  panmixia, (ii) isolation-by-distance, (iii) population structure with complete isolation, and (iv) population structure with ongoing migration; (2) test the specific hypothesis that the Sanaga River has been important in delimiting the range of P. t. ellioti from neighboring P. t. troglodytes; and (3) compare the demographic histories of these two subspecies by inferring the time to their most recent common ancestor (TMRCA), their historical and current effective population sizes, and finally, the rates and directions of migration between them.This study uses DNA extracted from fecal and hair samples to examine the population structure and genetic history of chimpanzees from Cameroon and eastern Nigeria at a fine geographic scale across this ecologically diverse region Figure . The oveWe calculated pairwise estimates of relatedness for all pairs of genotyped individuals using the software program COANCESTRY  and remoFST, and all 21 loci fell within the acceptable range of neutrality . The second haplogroup  within populations (ΦST); (ii) among populations in groups (ΦSC); and (iii) among groups north or south of the Sanaga River (ΦCT), the major population partition indicated in previous studies [P. t. schweinfurthii [P. t. schweinfurthii groups that were separated from one another by more than a 600 km straight-line distance (Table k = 2) were present across the study area confirmed the geographic grouping of sampling locations north and south of the Sanaga River. The division of haplotypes according to geographically homogeneous groups, accounted for 49.37%, and 45.56% of the variation within sample locations within these groups  Table  for mtDN studies ,10. Divinfurthii  where lece Table , whereasce Table . SimilarWe generated two sets of mismatch distributions to examine whether the sampled populations had experienced recent botttlenecks, population expansions or demographic stability  and overall accounts for for less than 2% of the total genetic variation present across the study area. Because isolation-by-distance only weakly explains the population structure of chimpanzees, we next examined the number and distribution of populations across the study area.Mantel tests of 21 microsatellite loci Figure  revealedP. t. ellioti and P. t. troglodytes), and accounted for 71% of the total extracted variation in the dataset. PC 2 accounted for 21% of the total extracted variation and separated P. t. ellioti chimpanzees into two groups: one cluster of individuals mostly from the forests of western Cameron (further designated as: P. t. ellioti (Rainforest), purple) and another cluster of individuals from the ecotone in central Cameroon (further designated as: P. t. ellioti (Ecotone), green). PC 3 and PC4 accounted for 5% and 3% respectively, and separated small clusters of individuals from the same sampling locations from the rest of the dataset. These final PCs were sample location specific and are possibly the result of sampling bias .A Principal Components Analysis (PCA) was carried out to examine how many different gene pools were present across the study region. The PCA shown in Figure K, represented by this dataset. Deviance Information Criteria (DIC) [KMAX is 2  is found north and west of the river, whereas P. t. troglodytes (orange) occurs south of the Sanaga , purple) and another population located in the ecotone in central Cameroon (P. t. ellioti (Ecotone), green)   values sstic, ΔK , suggesta Figure a. There ) Figure b.Figure P. t. ellioti, purple and P. t. troglodytes, orange) corresponds to banks of the Sanaga River  and lowest between populations of chimpanzee north of the Sanaga River (P. t. ellioti (Rainforest) versus P. t. ellioti (Ecotone)). Additional file m) as calculated using in the Arlequin version 3.5 software package [Values for three measures of genetic differentiation, D2 , RST [35RST [δμ2 , are sho package . BetweenP. t. troglodytes displaying almost twice as many distinct alleles per locus compared to P. t. ellioti (Rainforest) and P. t. ellioti (Ecotone). However, the mean number of private alleles per locus did not vary considerably between regions, although we observed a similar pattern, with P. t. troglodytes exhibiting the greatest number of private alleles, when corrected for unequal sample size. Additional file P. t. ellioti (Rainforest) and P. t. troglodytes, with the other two population pairs exhibiting similar numbers of shared alleles.Allele richness and number of private alleles by population, corrected for unequal population size, is shown in Additional files 1 and N2) and ancestral (NA) population sizes, time of divergence and migration rates between each of the three populations recovered by the cluster analysis  and P. t. ellioti (Ecotone) last shared a common ancestor with P. t. troglodytes approximately 200 to 250 kya, which is consistent with other studies using similar data [P. t. ellioti populations last shared a common ancestor with each other much more recently, about 4 kya. The results of these analyses also included estimates of ancestral and descendant effective population sizes for all pairs of populations. Estimates of effective population size for the present P. t. ellioti (Rainforest) population were consistent across runs, showing an effective population size of approximately 2,000 individuals. There was considerably more variation detected between comparisons for effective population sizes of P. t. ellioti (Ecotone) and P. t. troglodytes. In P. t. ellioti (Ecotone), effective population size was estimated to be between approximately 1,000 and 2,500 individuals. The current effective population size of P. t. troglodytes in southern Cameroon, may range from 3,000 to 10,000 individuals. The estimates for ancestral population size show that P. t. ellioti north of the Sanaga River were approximately 6,000 individuals, and that the ancestral populations of chimpanzees likely ranged between approximately 3,000 and 7,000 individuals. The number of migrants per generation was also estimated for all three pairs of populations. Between 0.79 and 1.58 migrants per generation have been exchanged between populations across the Sanaga River, since the initial separation of P. t. troglodytes and P. t. ellioti. Higher levels of migration were found between the P. t. ellioti populations  between populations should prevent divergence, under a model that assumes that drift occupies the dominant role in population differentiation [P. t. elioti and P. t. troglodytes (2 Nm ~ 1) is a minimal threshold [Overall the results of the IMa suggest very clearly that the gene pools of  Figures , 5 and 6ntiation . Populatntiation -43. Howehreshold , making P. t. ellioti and P. t. troglodytes predate the LGM, but do coincide with a pronounced glacial episode approximately 250kya [Demographic histories can be influenced by climatic histories. The climate of Africa has been affected by oscillations in global temperature and species assemblages in tropical Africa are often influenced by these changes, particularly the Last Glacial Maximum (LGM) . It is ay 250kya , with evIt may be possible that these fluctuations in local climate may have influenced the Sanaga River, but little is known regarding its historical course and size. Rivers are known to dramatically change in course and size over time, thus affecting their ability to act as dispersal barriers . Thus, ii) panmixia, (ii) isolation-by-distance, (iii) population structure with complete isolation, and (iv) population structure with ongoing migration. Overall, the results suggest that P. t. ellioti and P. t. troglodytes represent genetically distinct populations in Cameroon, confirming results from previous studies of captive chimpanzee with inferred origins [P. t. ellioti consists of two genetically- and geographically-distinct populations. One population is located in forested regions of eastern Nigeria and western Cameroon and the second is located in central Cameroon, in a savanna-woodland mosaic that occurs between the Mbam and Sanaga Rivers. The results of the IMa analysis show that the P. t. ellioti populations and P. t. troglodytes diverged from one another 200–250 kya, and have experience moderately high (2Nm ~ 1) gene flow since  [Several riverine barriers have been proposed as biogeographic boundaries for primates in the region ,14-18,45p. grayi ,18. Thesp. grayi . This obunclear) ,44. Otheunclear) ,46-49. Tand under selection.Cameroon lies at the intersection of two major rainforest biomes. The Congolian Rainforest extends northward into southern Cameroon from central Africa, and the Guinean Rainforest extends southward into eastern Nigeria and western Cameroon from western Africa Figure a. These P. t. ellioti and P. t. troglodytes (2Nm ~ 1) is a minimal threshold to propose that selection has driven the separation of these populations, there is mounting evidence that allopatric speciation alone cannot account for the divergence of these populations. A complimentary study of the ecological niche differentiation in these populations shows that the demes of chimpanzees found in this study also occupy significantly different niches [Even though the migration rate between t niches , suggestt niches ,55. Furtt niches , which lt niches , a relatA broader understanding about the role of local adaptation in chimpanzees and other taxa may provide important clues regarding why this region of Africa contains such a high proportion of the Earth’s biodiversity. Future studies that more closely examine the history of the Sanaga River and the role of historic climatic variation in shaping chimpanzee genetic diversity are crucial for building this understanding. Additionally, a better understanding of how chimpanzee social stuctures and dispersal patterns are shaped by habitat variation, and how this may contribute to regional genetic diversity, is of vital importance to unravelling the forces that have helped make this region such an incredibly diverse place on Earth.later® . Hair samples were collected directly from abandoned sleeping nests and a minimum of three hairs per nest were stored into glassine envelopes, and kept dry in silica gel. Fecal samples were inspected to estimate their likely time of deposition, whereas night nests age was estimated according to a leaf decay index [We gathered fecal n = 247) and hair samples (n = 223) from wild-living, non-habituated chimpanzee populations from 36 remote regions spanning eastern Nigeria through southern Cameroon  following well-established protocols . We brieprotocol  followedprotocol . We analprotocol .We used primers L15997 (5′-CACCATTAGCACCCAAAGCT-3') and H16498 (5′-CCTGAAGTAGGAACCAGATG-3′) to amplify a 460 – 500-bp mtDNA fragment spanning the hypervariable D-loop region (HVRI) in all samples that were newly collected for this study, using methods described in previous studies ,13,59. WWe used twenty-one autosomal microsatellite loci to produce genotype profiles from both hair and fecal samples. Additional file FST outlier methods [i) assuming a stepwise mutation model, (ii) assuming three populations, (iii) forcing a correct mean FST, and (iv) calculating a “neutral” mean FST. We also subjected the 21 autosomal microsatellites, in addition to the mtDNA HVRI sequences, to an exact test of Hardy-Weinberg equilibrium [We subjected all 21 autosomal microsatellite loci to outlier tests using LOSITAN . LOSITAN methods . We ran ilibrium  using thilibrium . We testCOANCESTRY implements three inbreeding estimators and seven relatedness estimators [We calculated pairwise estimates of relatedness for all samples that produced reliable microsatellite genotypes using the software program COANCESTRY . COANCEStimators ,70-76 totimators  above 0.http://www.fluxus-engineering.com). Because it allows for reticulation, the median-joining approach for inferring haplotype relationships is appropriate for the analyses of mtDNA control region sequences, which exhibits high levels of homoplasy in humans [We generated haplotype networks for HVRI mtDNA sequences via the median-joining algorithm of Network 4.5  from each inferred ancestral population cluster. It also produces posterior predictive values of Q for geographic areas located in between and around the individual data points.We examined population structure and individual ancestry using a Bayesian clustering approach implemented in the TESS (version 2.3) software package . This pri) with a model that allows individuals to have ancestry in multiple populations (CAR model [ii) with correlated allele frequencies; and (iii) blinded to a priori population labels. Runs were performed with a burn-in step of 500,000 Markov Chain Monte Carlo (MCMC) iterations and 1,000,000 MCMC iterations. Fifty runs each for K = 2 through K = 8 were performed for all datasets. We processed TESS outputs with CLUMPP [KMAX) including, (i) the K value that had the highest average DIC [ii) high stability of clustering patterns between runs, (iii) the KMAX value at which KMAX + 1 no longer split the cluster distinguished by KMAX [iv) correspondence between maximum PPD values from TESS runs and significant eigenvectors recovered by PCA, and (v) calculating an ad hoc statistic, ΔK [TESS runs were performed: (AR model ); (ii) wh CLUMPP  and a G-h CLUMPP . We usedrage DIC , (ii) hi by KMAX , (iv) costic, ΔK .Q matrix using the option in TESS to generate posterior predictive maps of admixture proportions. We created a template map of the extent of probable chimpanzee ranges in Nigeria and Cameroon in ArcMap Version 10  in the ASCII-raster format and input into TESS in order to generate posterior predictive maps of admixture proportions. Using these predictive maps, we generated spatial interpolations of the Q matrices by implementing a matrix based vector algorithm in the program TESS Ad-Mixer [We created spatial interpolations of the Ad-Mixer . These sWe calculated allele size range, observed and expected heterozygosity, and an M-Ratio  for eachD2, RST and δμ2. The D2 [RST [δμ2 [D2, but both assume a stepwise mutation model (SMM). Consequently, RST and δμ2 are more likely to capture whether differences in the mutation processes are important in driving population differentiation and enable assessment of sensitivity to mutation model assumptions [RST is based on the fraction of the total variance in allele size between populations and is analogous to FST [δμ2 is based on differences in the means of microsatellite allele sizes [D2 calculations were completed on untransformed allele size calls. Since RST and δμ2 assume the SMM, we transformed allele sizes to repeat size units prior to analysis in Arlequin version 3.5 [We calculated three measures of population genetic differentiation using the Arlequin version 3.5 software package : D2, RST. The D2  genetic  D2 [RST  and δμ2 RST [δμ2  are simiumptions . These ls to FST , whereasle sizes . Recent le sizes ,85. D2 csion 3.5 . We transion 3.5 . We detei) the mean number of distinct alleles per locus, per population; (ii) the mean number of private alleles per locus, per population; and (iii) the mean number of uniquely shared private alleles per locus, between populations.We calculated distributions of alleles within and between populations using ADZE . ADZE isi) the population mutation parameter, θ, for both descendant populations (θ1 and θ2) and an ancestral population (θA); (ii) rates of migration from population 1 to population 2(m1) and from population 2 to population 1 (m2); (iii) and time of population divergence (t) for all possible pairs of the three assumed populations: P. t. ellioti in western Cameroon & Nigeria, P. t. ellioti in central Cameroon, and P. t. troglodytes in southern Cameroon. We used IMa instead of the more recently released IMa2 [We used the program IMa  to estimsed IMa2  for sevesed IMa2 ,64,87. A1, θ2, θA, m1m2, and t). Each run was performed with heated chains using the two-step scheme [We ran multiple iterations of IMa in parallel, for each paired population, using a multi-processor computer cluster located at the SUNY College of Nanoscale Science and Engineering. First, we ran the IMa analysis using “M-Mode” (MCMC Mode) with a full complement of model parameters, and a broad range of priors for all parameters  using the equation Nm = (θ*m)/2. We converted the demographic parameter (t) into years by scaling by mutation rate (t/μ). We scaled the t parameter for mtDNA assuming a mutation rate (μ) of 1.64 × 10−7 [t parameter for microsatellite loci using several microsatellite mutation rates, given the uncertainty in microsatellite mutation rates in the literature [−5 [−5  using θ and a per generation μ using the equation NE = θ/(4*μ*20).We converted the migration parameters, 4 × 10−7 . We scalterature ,39,64,87fier [−4 ,39. We sfier [−4 . We scal


Ne) and inferring whether population sizes increased or decreased over time. Furthermore, analyses of genotypic, allelic frequency, and phylogenetic information can potentially be used to separate historical from recent demographic changes. For 15 populations of Galápagos giant tortoises (Chelonoidis sp.), we used 12 microsatellite loci and DNA sequences from the mitochondrial control region and a nuclear intron, to reconstruct demographic history on shallow  and deep  timescales. At the deep timescale, three populations showed strong signals of growth, but with different magnitudes and timing, indicating different underlying causes. Furthermore, estimated historical Ne of populations across the archipelago showed no correlation with island age or size, underscoring the complexity of predicting demographic history a priori. At the shallow timescale, all populations carried some signature of a genetic bottleneck, and for 12 populations, point estimates of contemporary Ne were very small . On the basis of the comparison of these genetic estimates with published census size data, Ne generally represented ∽0.16 of the census size. However, the variance in this ratio across populations was considerable. Overall, our data suggest that idiosyncratic and geographically localized forces shaped the demographic history of tortoise populations. Furthermore, from a conservation perspective, the separation of demographic events occurring on shallow versus deep timescales permits the identification of naturally rare versus newly rare populations; this distinction should facilitate prioritization of management action.Long-term population history can influence the genetic effects of recent bottlenecks. Therefore, for threatened or endangered species, an understanding of the past is relevant when formulating conservation strategies. Levels of variation at neutral markers have been useful for estimating local effective population sizes ( Many threatened or endangered species have experienced recent, severe, and sustained reductions in population size, often causing genetic bottlenecks. In these cases, the most concerning genetic outcomes include inbreeding depression, a process whereby increased homozygosity at diploid autosomal loci leads to expression of deleterious recessive alleles causing lowered individual and population fitness, and loss of genetic variation leading to reduced capacity for adaptation to changing environments  timescales. This is because genotypes are reshuffled each generation via gametic recombination in sexually reproducing species; population allele frequencies may also be quite labile in genetically small populations owing to the effects of drift. Conversely, analyses of gene genealogies  rely on mutational processes that typically operate more slowly and therefore have the ability to generate inferences on deeper (“evolutionary”) timescales  and deep  timescales. Despite the potential for some temporal overlap, this distinction serves as a useful conceptual framework.A series of analytical developments have improved our ability to understand the historical context of recent population bottlenecks. Whereas previous methods relied primarily on nuclear genotypic data  small? and (2) Does the population show the genetic hallmarks of a recent decline? To understand the deeper time historical (pre-Holocene) context of any recent human-induced bottlenecks, we used DNA sequence data to address two additional questions: (1) Was the population's Ne historically small? and (2) Was the population growing, declining, or stable in size prior to human arrival?Here, we reconstructed the demographic histories of 15 Galápagos giant tortoise populations representing all 10 of the surviving species Fig.. This waPAX1P1) intron. PAX1P1 genes are involved in early development of animals, and their introns have previously been useful for shallow-level phylogenetics and species delimitation in turtles . In addition, we increased by 36 individuals our previously published mitochondrial DNA control region  and microsatellite (12 loci) database . For the nuclear intron, length-variable heterozygotes caused by a 1-bp insertion–deletion (indel) were resolved with CHAMPURU v1.0 . Global permutation P-values (1.0 × 104 replicates), based on BLAST-like global scores and Bonferroni-corrected scores for multiple comparisons, were considered indicative of recombination when P < 0.05.DNA sequences were edited and aligned using CLC DNA WORKBENCH v5  of individuals in each of K reference clusters, using STRUCTURE v2.3.3 . For each microsatellite locus, we calculated allelic richness (AR) with rarefaction correction  and expected heterozygosity (HE), in HP-RARE v1.0  generation. The software was run using the random-mating model, with 95% confidence limits determined by jackknifing over loci. Alleles with frequencies < 0.02 were excluded when the number of sampled individuals (N) was > 25. With N ≤ 25, the criterion for excluding rare alleles (Pcrit) was set at the smallest value consistent with 1/2N < Pcrit < 1/N. Where possible, Ne values were compared to temporally and spatially corresponding estimates of population census size (Nc) from extensive habitat survey data reported by MacFarland et al.  Pcrit values, whereas populations that have a history of gene flow and/or possess first generation migrants contain “foreign” alleles, which can lead to considerable variation. For each tortoise population, we re-estimated Ne for Pcrit values spanning the range 0.02 to 0.07 and plotted the point estimates and their associated confidence intervals.Following Waples and England , we examNe was assessed using microsatellite data analyzed in BOTTLENECK v1.2.02 . To generate distributions of the difference between expected heterozygosity (HE) and expected heterozygosity under mutation-drift equilibrium (HEQ), we used 1000 simulation iterations  were calculated using the programs M P VAL and CRITICAL M, respectively , pg (the proportion of mutations greater than a single step), and Δg (the mean size of multistep mutations). We used pg = 0.2 and Δg = 3.5 following Garza and Williamson , which are consistent with values reported for herpetofauna and long-lived vertebrates (Zhang and Hewitt F) and observed heterozygosity (HO) using COANCESTRY v1.0.0.1  for which multiple local populations exist. Accordingly, we explored evidence for past intraspecific gene flow and assessed its potential impact on estimates of Ne, with DNA sequence data analyzed using MIGRATE. We first addressed the question of whether there has been non-negligible historical gene flow, as measured by M . This was done by comparing parameter values estimated within a full migration matrix to those estimated within a constraint matrix representing the null hypothesis of no gene flow , via a likelihood ratio test (LRT). We also addressed the question of whether the inferred amount of past gene flow is likely to have a non-negligible impact on estimates of historical Ne . The null hypothesis of no meaningful effect of gene flow on estimated Ne  was again evaluated via LRT (see Supplementary Methods).This study includes three tortoise species , and g . All runs employed the following search strategy: 10 short Monte Carlo chains  and five long chains  sampling every 20th step, random starting trees, empirical base frequencies, transition/transversion ratio = 2, and initial value of Θ set using Watterson's . Runs were repeated five times, and then, the mean and standard deviation (SD) of maximum-likelihood parameter estimates were calculated.To obtain estimates of the long-term harmonic mean of terson's  estimateNe, we assumed a generation time of 25 years for Galápagos giant tortoises . We note, however, that generation times are likely to vary, and overlapping (cf. discrete) generations contribute an additional source of error  > 0  < 0). FLUCTUATE requires a minimum of three different haplotypes in a dataset; as this level of variation was not available for all mtCR and PAX1P1 datasets , with significance assessed at the 0.05 level (lower tail). For comparison, we also calculated mismatch distributions . The PAX1P1 clock.rate parameter was estimated from mtCR  tree prior, linear skyline model, UPGMA-generated starting trees, auto-optimized tuning, with other priors as default. Final searches were performed using 1.0 × 108 MCMC generations sampling parameters every 2000th step (10% discarded as burn-in). Convergence of chains was assessed via effective sample size values for key demographic parameters  calculated using TRACER v1.5 , empiriReplicate runs were pooled, and a frequency distribution was constructed to establish a 90% credible set of values for the “demographic population size changes” parameter Heled . Within a priori null expectation and (2) overturned this in favor of past size change only when at least two different analyses supported the same alternative inference . Given that not all DNA sequence loci will mark the same demographic events owing to differences in mutation rates and coalescent stochasticity . The low-confidence haplotype pair was retained, given that exclusion of novel heterozygotes from a population genetic dataset can bias downstream analyses , indicating that the sequenced region of PAX1P1 is largely free of intragenic recombination, and thus satisfies assumptions of subsequent demographic analyses.Alignment of mtC. becki from northern Isabela Island, which formed two distinct clusters, plus a lack of strong genetic structure across central and southern Isabela Island, which led to pooling of two or more species into the same cluster . Recent migrants or their immediate descendants were readily identified and removed. For the majority (80%) of individuals, the decision to include or exclude was based jointly on microsatellite plus mtCR data.The archipelago-wide STRUCTURE analysis identified 12 genetic clusters, with most named species represented by a single cluster Table ; Fig. S1er Table , 2014. OC. becki tortoises from Piedras Blancas (where two loci showed homozygote excess). However, we note that lack of deviation from HWE must be considered within the constraints of our sample sizes and associated power of the statistical tests performed . In the few cases where two or more local populations (or species) were collapsed into the same cluster in the STRUCTURE analysis . Compared to other introns that have been assayed for variation in Galápagos giant tortoises . Overall, the number of different haplotypes per population (Nhap), and haplotypic diversity (Hd), were larger for mtCR than for PAX1P1 , as did expected heterozygosity  had point estimates of Ne < 50. The as-yet undescribed species from Santa Cruz Island . The mean ratio of Ne:Nc was 0.16; however, there was considerable among-population variance  and Volcano Alcedo (C. vandenburghi) on Isabela Island, and La Reserva (C. porteri) on Santa Cruz . Nonetheless, changes in either direction can be indicative of past or ongoing migration and gene flow  to 27.8 × 103 . With the exception of Cabo Rosa, tortoise populations from southern Isabela Island (C. guntheri and C. vicina) had the lowest Ne values, whereas those from the middle-aged islands  calculated from mtCR sequences supported inferences of past growth of Pinzón and Volcano Alcedo populations  at the beginning of the Holocene (10 KYA), (2) at the height of the Last Glacial Maximum (20 KYA), and (3) at the end of the penultimate interglacial (100 KYA). Overall, Ne values ranged from 0.8 × 103  to 14.1 × 103  had low Ne values, whereas the middle-aged islands had larger values . Little discordance in the rank-ordering of populations was seen across the three selected EBSP time points  was indicated by EBSP analysis to have had a much smaller Ne (3.3–3.4 × 103) than suggested by FLUCTUATE , we determined that three tortoise populations have a pre-Holocene history of growth , whereas no populations were historically declining  and La Reserva (C. porteri) populations are presently <20 km apart on the same island  population may have trait distributions that are poorly suited to the current demographic condition, whereas a naturally rare population may have evolved traits that are suited to consistently low densities , which we cautiously attribute to retained ancestral polymorphism resulting from large historical Ne . Given that Pinzón tortoises are known to have been severely reduced in numbers following the arrival of humans  from Santa Cruz Island  were all associated with relatively large census sizes (Nc = 600–4000), whereas those well above average  were seen only when census sizes were very small . However, with these caveats in mind, we speculate that reproductive success of adult Galápagos giant tortoises may be partly dependent on overall population size whereby as population size increases, so too may variance in reproductive success.The average ratio of effective to census population size  generations. Generally speaking, although age structure may have limited impact on estimators of long-term d Waples . In the d Waples . Our samd Waples . YoungerNe values estimated from DNA sequence data were unexpectedly high (Table CR substitution rate could be too slow for population-level inferences, translating into overestimates of historical Ne in this study. Second, simulations have indicated that FLUCTUATE shows systematic upward biases in estimates of θ, and thus estimated Ne (Kuhner et al. Inherent error in parameter estimates may also affect inferences, particularly the ability to detect recent bottlenecks using microsatellite data (e.g., Busch et al. gh Table  and diffgh Table . Discrepgh Table  and at lgh Table . In the gh Table . If truegh Table  phylogenDespite these limitations, demographic insights from this study illustrate the value of a long-term evolutionary perspective, as well as an understanding of recent or ongoing population processes. For example, our results highlighted the idiosyncratic nature of historical population size changes in the Galápagos, prior to the arrival of humans. Such characterization of species' responses to past environmental change can provide critical baseline data for predicting future impacts (Cordellier and Pfenninger


All three populations suffered dramatic declines during the 20th century, but the bottleneck was particularly severe in Lake Saimaa. Data from 17 microsatellite loci and mitochondrial control-region sequences show that Saimaa ringed seals have lost most of the genetic diversity present in their Baltic ancestors, while the Ladoga population has experienced only minor reductions. Using Approximate Bayesian computing analyses, we show that the genetic uniformity of the Saimaa subspecies derives from an extended founder event and subsequent slow erosion, rather than from the recent bottleneck. This suggests that the population has persisted for nearly 10,000 years despite having low genetic variation. The relatively high diversity of the Ladoga population appears to result from a high number of initial colonizers and a high post-colonization population size, but possibly also by a shorter isolation period and/or occasional gene flow from the Baltic Sea.Island populations are on average smaller, genetically less diverse, and at a higher risk to go extinct than mainland populations. Low genetic diversity may elevate extinction probability, but the genetic component of the risk can be affected by the mode of diversity loss, which, in turn, is connected to the demographic history of the population. Here, we examined the history of genetic erosion in three Fennoscandian ringed seal subspecies, of which one inhabits the Baltic Sea ‘mainland’ and two the ‘aquatic islands’ composed of Lake Saimaa in Finland and Lake Ladoga in Russia. Both lakes were colonized by marine seals after their formation  Island populations have higher rates of extinction than mainland ones, mainly because they tend to be small and, hence, more vulnerable to environmental and demographic stochasticity Ricklefs . Small pPhoca hispida) that inhabit the large Fennoscandian lakes Saimaa and Ladoga  in southeastern Finland, and the Ladoga ringed seal (P. h. ladogensis) in northwestern Russia (Ukkonen P. h. botnica)  and/or the recent bottleneck. On this account, we combined new data from 17 microsatellite loci , and mtDNA sequences for 166 of these (=Nmt). For the Ladoga subspecies Nms = Nmt = 16, and for the Baltic population Nms = 21 and Nmt = 19.For Saimaa and Ladoga seals, we used samples from a tissue bank maintained by the University of Eastern Finland and the Natural Heritage Services of Metsähallitus, while Baltic individuals were provided by the Finnish Game and Fisheries Research Institute. Microsatellite variation was inferred at 17 variable loci; detailed methods and data characteristics are described in Valtonen et al. . The posA), and observed (HO) and expected (HE) heterozygosities] as well as Wright's in breeding coefficient (FIS) for each subspecies. HP-RARE v. 1.1  by rarefaction to the smallest sample size (N = 16). Deviations from Hardy–Weinberg equilibrium (HWE) in individual loci, as well as linkage disequilibrium across locus pairs, were evaluated in ARLEQUIN. The overall loss of genetic diversity over time in the landlocked populations was estimated as Fe = 1 – , assuming that the (observed) heterozygosity in the Baltic population represents the level at the start of the lacustrine isolation  in the Baltic and Ladoga samples (see Results), global and pairwise FST values were also calculated while correcting for the presence of null alleles in these loci in FREENA.To investigate the amount of genetic differentiation among the subspecies, we performed AMOVA  from 1 to 10 using a burn-in period of 100,000 followed by 500,000 MCMC iterations, with 20 replicate analyses per each value of K to ensure convergence. The analysis was repeated using the no-admixture model with otherwise similar settings. In both cases, we used the ad hoc approach of Evanno et al.  implemented in the program GENETIX v. 4.05.2  analyses in DIYABC v. 1.0.4.46 . For the hypothesized colonization bottlenecks, we set the effective population size prior as U[2–100] and the duration prior to U[1–50] generations in both lakes, reflecting the common assumption that colonization bottlenecks are relatively brief . These changes were made on the basis of exploratory runs showing that posterior distributions of mutation-rate parameters tended to fit poorly within the default prior bounds . As above, this change was based on preliminary test runs, but an elevated upper limit also conforms with control-region mutation rates estimated by Valtonen et al.  and three two-sample statistics  for microsatellites. For mitochondrial DNA sequences, we used four single-sample targets  and three two-sample statistics . Following the recommendation of Cornuet et al.  of best-fitting simulated datasets.A total of four million simulated datasets were generated using the Vuori cluster of the Center for Scientific Computing in Espoo, Finland. This corresponds to roughly one million simulations per scenario, given that the prior probability of scenarios was set to equal. Posterior support for each scenario was assessed based on (1) a direct estimate, in which 500 datasets with summary statistics closest to the target values were extracted from the simulated pool, and (2) a polychotomous logistic regression approach  with relatively high estimated null-allele frequencies in the Baltic and Ladoga samples (see Results). The overall results (not shown) and inferences resulting from the analysis of this 15-locus + mtDNA dataset did not differ from the results of the complete 17-locus + mtDNA dataset, so only the outputs of the main analyses are presented below.Because ABC inferences theoretically can be influenced by the presence of null alleles, the analyses described above were repeated with a dataset from which we excluded the two microsatellite loci  to infer the discriminatory power of the analyses , indicating absence of structural linkage of markers. Tests for presence of null alleles were significant for six loci in Lake Saimaa , but the low estimated frequencies  suggest that this result simply reflects the presence of population structure within the lake , and three in Lake Ladoga ; for Hg1.4 and Hg3.6, frequency estimates of these alleles were high at r = 0.181–0.286 (Table S3 in Appendix S1).These loci also deviated significantly from HWE due to heterozygote deficiency in all three populations (Table S4 in Appendix S1).Consistent genotyping results across all 17 microsatellite loci were obtained for 168 Saimaa, 15 Baltic, and 16 Ladoga ringed seals, while four Saimaa and six Baltic individuals lacked data for a single locus each. No consistent linkage disequilibria across pairs of loci were found in all populations . Differentiation was statistically significant (P < 0.05) across all pairwise comparisons, but markedly higher between Saimaa and the two other populations  = 0.367; FST(Saimaa–Ladoga) = 0.343) than between Ladoga and the Baltic Sea  = 0.041). For comparison, we note that corresponding indices estimated by Valtonen et al.  = 0.227; FST(Saimaa–Ladoga) = 0.233; and FST = 0.028.As expected .In the FCA plot, Saimaa individuals were tightly grouped and clearly separated from the two other subspecies, while Ladoga and Baltic seals formed loose, partially overlapping clusters Fig.. RegardlThe direct-estimate comparison of posterior probabilities favored Scenario CB–RB over the simpler alternatives Fig.. The supEstimations of confidence in scenario choice revealed a fairly high capability of the applied ABC method to discriminate among the scenarios, both when measured as Type I  and Type II  error rates .The scenario also agrees with Scenario CB–RB with respect to the relative duration and severity of the colonization bottlenecks in the two lakes. The median for effective population size subsequent to the colonization phase in the Baltic Sea is 16,900 , in Lake Saimaa 373 [109–2080], and in Lake Ladoga 52,200 .Ne estimates: Baltic Sea 10,800 ; Lake Saimaa 567 [253–1040]; Lake Ladoga 10,500 . During the recent bottleneck phase, corresponding values are 3040 [712–4900] for the Baltic Sea, 164 [65–199] for Lake Saimaa, and 1380 [584–1960] for Lake Ladoga, but the posterior distributions are weakly defined especially for the Baltic and Ladoga populations.For comparison, we note that the simple but disfavored Scenario NCB–NRB, which assumes constant sizes in all populations, gives the following median estimates of long-term effective population sizes: 12,800  for the Baltic Sea, 414 [191–736] for Saimaa, and 7810  for Ladoga. The likewise disfavored Scenario NCB–RB, which includes only the known recent bottleneck, gives the following pre-bottleneck P < 0.05) was found for both scenarios when using alternative targets (results not shown). For Scenario CB–RB, this was seen in that the clouds formed by simulated datasets were centered on the observed data in principal components (PCA) space, both when using the original summary statistics (results not shown) and when using an alternative set of target values .Model-checking analyses, in which 200 datasets per favored scenario (CB–RB and CB–NRB) were simulated with parameter values drawn from their respective posterior distributions, revealed that both ‘winning’ scenarios could produce the observed data with high probability; a single outlying summary statistic  found in the geographically more extensive survey of Martinez-Bakker et al.  safer levels  is comparable to values estimated by Valtonen et al. (Ne = 12.3–32.7) and temporal changes in allele frequencies during the last three decades (Ne = 69–113).This preferred, two-bottleneck interpretation of the history of the Saimaa population exhibits some striking similarities with long-term demographic trends in Black Sea harbor porpoises (Phocoena phocoena relicta) estimated recently by Fontaine et al.  and mitochondrial control-region sequences (Valtonen et al.


As increasingly fragmented and isolated populations of threatened species become subjected to climate change, invasive species and other stressors, there is an urgent need to consider adaptive potential when making conservation decisions rather than focussing on past processes. In many cases, populations identified as unique and currently managed separately suffer increased risk of extinction through demographic and genetic processes. Other populations currently not at risk are likely to be on a trajectory where declines in population size and fitness soon appear inevitable.Using datasets from natural Australian mammal populations, we show that drift processes are likely to be driving uniqueness in populations of many threatened species as a result of small population size and fragmentation. Conserving and managing such remnant populations separately will therefore often decrease their adaptive potential and increase species extinction risk.These results highlight the need for a paradigm shift in conservation biology practise; strategies need to focus on the preservation of genetic diversity at the species level, rather than population, subspecies or evolutionary significant unit. The introduction of new genetic variants into populations through in situ translocation needs to be considered more broadly in conservation programs as a way of decreasing extinction risk by increasing neutral genetic diversity which may increase the adaptive potential of populations if adaptive variation is also increased.The online version of this article (doi:10.1186/s12983-016-0163-z) contains supplementary material, which is available to authorized users. Puma concolor), morphology, ecology, genetics and/or a mix of the above )pulation , but thiOther methods exist which can also provide information on the uniqueness of populations e.g., , 49). Si. Si49]).We have shown that random genetic drift effects in threatened animals are widespread and lead to the erosion of neutral genetic diversity within species. This places many populations and species at a greater risk of extinction in a changing environment even if other threats can be obviated. Conservation strategies need to focus on the preservation of genetic diversity at the species level, rather than that of the population, subspecies or evolutionary significant unit, and augmented gene flow from genetically diverse populations needs to be considered as a way of increasing fitness and the adaptive potential of populations.Ar, allelic richness; EPBC, Environment Protection and Biodiversity Conservation; ESU, evolutionary significant unit; He, expected heterozygosity; IUCN, International Union for Conservation of Nature; MU, management unit


Cronartium ribicola J.C. Fisch . This invasive alien pathogen was introduced into North America at the beginning of the 20th century on pine seedlings imported from Europe and has caused serious economic and ecological impacts. In this study, we applied a population and landscape genetics approach to understand the patterns of introduction and colonization as well as population structure and migration of C. ribicola. We characterized 1,292 samples of C. ribicola from 66 geographic locations in North America using single nucleotide polymorphisms (SNPs) and evaluated the effect of landscape features, host distribution, and colonization history on the structure of these pathogen populations. We identified eastern and western genetic populations in North America that are strongly differentiated. Genetic diversity is two to five times higher in eastern populations than in western ones, which can be explained by the repeated accidental introductions of the pathogen into northeastern North America compared with a single documented introduction into western North America. These distinct genetic populations are maintained by a barrier to gene flow that corresponds to a region where host connectivity is interrupted. Furthermore, additional cryptic spatial differentiation was identified in western populations. This differentiation corresponds to landscape features, such as mountain ranges, and also to host connectivity. We also detected genetic differentiation between the pathogen populations in natural stands and plantations, an indication that anthropogenic movement of this pathogen still takes place. These results highlight the importance of monitoring this invasive alien tree pathogen to prevent admixture of eastern and western populations where different pathogen races occur.White pine blister rust is caused by the fungal pathogen  Cronartium ribicola J.C. Fisch  causes white pine blister rust, a disease that affects all white pines (Pinus subsection Strobus). This invasive alien pathogen was introduced into North America and discovered at the beginning of the 20th century on pine seedlings imported from Europe and planted for reforestation in the USA and Canada. White pine blister rust is responsible for one of the worst forest disease epidemics recorded in the Northern Hemisphere. It can cause mortality rates approaching 100% in high hazard zones and has severely reduced survival, natural regeneration and reforestation efforts with white pines in North America . Th. Th38]. C. ribicola in Kansas in the late 1800s on currant [Ribes, outside of the distribution range of the pine host, resulting in reduced genetic diversity.One intriguing fact that could support the second hypothesis is a report of  currant . This raC. ribicola is a biotrophic fungus, a host gap constitutes an effective barrier to migration. The eastern and western genetic populations of white pine blister rust defined by our analyses overlap with the distribution ranges of white pines, the aecial hosts of the pathogen. We identified a barrier to gene flow that traverses the Canadian Prairies and extends into the Great Plains, where there are no naturally occurring white pines [Host connectivity likely plays an important role in the dispersal of the pathogen and in shaping the populations. As te pines .Melampsora larici-populina Kleb. and Melampsora medusae Thuem., two poplar rusts, were carried by wind over 3000 km, from Australia to New Zealand [Cronartium ribicola clearly has the capacity to disperse over long distances [Long-distance spore dispersal has been well documented for many fungal species, and in particular for rusts ,40. Exam Zealand ,42. Cronistances . The lowP. strobiformis and P. flexilis, have fragmented distributions separated by dry inter-montane rangeland, creating a network of habitat islands subject to island biogeographic processes [P. flexilis). This is the easternmost natural extension of limber pine and it is isolated from the nearest limber pine stand by 160 km and from the nearest known population of C. ribicola by 240 km [But clearly, the absence of host connectivity can prevent or slow long-distance spread. Some of the white pines found in western North America, such as rocesses . In Souty 240 km . It is ay 240 km , and ourC. ribicola is a heteroecious pathogen, it requires alternation between aecial and telial hosts in order to complete its life cycle. The basidiospores, the only C. ribicola spore type that can infect pines, are produced only on the telial hosts, i.e. angiosperms in the families Grossulariaceae and Orobanchaceae [Ribes nigrum, the black currant, could increase host connectivity and eventually lead the to the expansion of white pine blister rust across the eastern and western barriers that we identified.Since nchaceae ,17,18,45nchaceae . It is pWhite pine blister rust is exposed to more landscape features and climatic variations as well as to a broader range of host species in western than in eastern North America. Thus, we conducted intensive sampling in western Canada to cover the breadth of landscapes , climates  and hosts (three white pine species) to investigate whether there are additional drivers of population structure.We identified a cluster of populations in the Rocky Mountains that is genetically differentiated from the remainder of the western populations. These populations could be undergoing drift maintained by a barrier to gene flow that corresponds roughly to an area where host connectivity is discontinued. This area also has some landscape features, such as the Rocky Mountains and the Trench, which separates the Rocky Mountains on the eastern slopes from the Columbia Mountains and the Cassiar Mountains westward.C. ribicola sampled in natural stands and plantations is indicative that there is some additional dissemination of the pathogen during the regeneration process. The most likely cause of this differentiation is the additional introduction of the pathogen from nurseries. This disease is difficult to detect in seedlings since infections can take up to four years to generate visible symptoms. Since many seedlings are planted after 2 years of growing in the nurseries, it is possible that infected asymptomatic seedlings are planted, generating populations that are genetically distinct.The genetic differentiation that we observed between per se did not affect the structure of rust populations. This confirms other reports where C. ribicola isolated from different aecial and telial hosts did not have different genetic profiles or differ in ability to infect telial hosts  [C. ribicola and native North American pines is too recent for this specialization to leave a genetic footprint. We only sampled a small number of genes and it is likely that host specialization involves other genes such as effectors undergoing accelerated evolution leading to speciation [Host species association and elevation culated) . Howeverculated) . It is peciation .C. ribicola is important for the management of white pine blister rust. Extensive resources are invested in breeding programs to develop and deploy rust-resistant white pines and black currants [Ribes [C. ribicola infections. Monitoring populations of C. ribicola and testing seedlings before outplanting could prevent additional introduction of migrants with novel genetic backgrounds.Understanding the population structure of currants –51. Our s [Ribes –56 maintS1 Fig(TIFF)Click here for additional data file.S2 FigA Mantel correlation between genetic and geographic distance was generated for each pair of populations and a correlation coefficient was measured (A). A Monte-Carlo test was conducted to obtain 1000 simulations and plot the random distribution of the data (B). The observed value was added.(TIFF)Click here for additional data file.S3 Fig(TIFF)Click here for additional data file.S4 Fig(TIFF)Click here for additional data file.S1 Table(DOC)Click here for additional data file.S2 Table(DOC)Click here for additional data file.S3 Table(DOC)Click here for additional data file.S4 TableVector values were averaged over populations and used to create the map in (XLSX)Click here for additional data file.