Dendrolimus kikuchii Matsumura, 1927 is a serious forest pest causing great damage to coniferous trees in China. Despite its economic importance, the population genetics of this pest are poorly known. We used three mitochondrial genes  to investigate the genetic diversity and genetic differentiation of 15 populations collected from the main distribution regions of D. kikuchii in China. Populations show high haplotype and nucleotide diversity. Haplotype network and phylogenetic analysis divides the populations into three major clades, the central and southeastern China (CC+SEC) clade, the eastern China (EC) clade, and the southwestern China (SWC) clade. Populations collected from adjacent localities share the same clade, which is consistent with the strong relationship of isolation by distance . AMOVA analysis indicated that the major portion of this molecular genetic variation is found among the three groups of CC+SEC, EC and SWC (61.26%). Of 105 pairwise FST comparisons, 93 show high genetic differentiation. Populations of Puer (PE), Yangshuo (YS) and Leishan (LS) are separated from other populations by a larger genetic distance. Distributions of pairwise differences obtained with single and combined gene data from the overall populations are multimodal, suggesting these populations had no prior population expansion in southern China. The nonsignificant neutral test on the basis of Tajima’ D and Fu’s Fs, and the lack of a star-shaped haplotype network together with the multiple haplotypes support this hypothesis. Pleistocene climatic fluctuations, combined with the host specificity to Pinus species, made these regions of south China into a refuge for D. kikuchii. The high level of population genetic structuring is related to their weak flight capacity, their variations of life history and the geographic distance among populations. Dendrolimus kikuchii Matsumura, 1927, is a destructive forest pest with an extensive range across southern China. Larvae attack various coniferous trees and regular outbreaks occur. These outbreaks are mainly on Pinus langhianensis Chev, P. yunnanensis Franch, P. massoniana Lambert, P. armandi Franch and Keteleeria evelyniana Mast. [D. kikuchii larvae consume, on average, 7486.6 mm of P. langhianensis pine needles to complete their development [D. kikuchii can reduce the yield of timber, resin and cones, and affect the growth rate of pines, even resulting in tree death [D. kikuchii occurs in areas with short periods of optimal environment, such as in the middle region of Yunnan and Guizhou provinces. In Zhejiang, Fujian, and the southwest region of Yunnan provinces, two generations occur. In different counties of Yunnan province, such as Jingdong and Anning, the life histories of D. kikuchii also show significant variation [Simao pine moth, elopment . During D. kikuchii to pine trees in the southern parts of China and the need to establish control strategies for this pest, it has been unclear whether analogous associations between these factors and genotype patterns may be present among D. kikuchii populations.Genetic diversity and genetic structure in insects can be affected by many factors, such as host plant species, chemical controls, geographic distance and geographic barriers –11. For Dendrolimus punctatus, a sympatric species of D. kikuchii, based on these geological events. They found that D. punctatus settled down in south China with the spread of masson pine during the Pleistocene. However, to our knowledge, there is no published report on the population history of D. kikuchii, and also no studies on the population history of Dendrolimus species based on molecular data.Pleistocene climatic fluctuations are thought to have a great effect on this species’ distribution and historical demography . South CD. kikuchii in southern China, and infer the demographic history of this pest, and (ii) to test the hypothesis that geographical isolation and biological characters, such as life history, are significant factors underlying genetic variation in Chinese D. kikuchii.In this study, we use three mitochondrial genes to (i) investigate the genetic diversity and genetic differentiation of 15 populations collected from the main distribution regions of There is no endangered or protected species involved in this study. No specific permissions were required for the described field studies for this widespread forest pest. We confirm that the locations are not privately owned or otherwise protected.D. kikuchii China distribution range . Extraction was performed according to the bench protocol for animal tissues. Mitochondrial COI , COII   . All hapPhylogenetic reconstruction using the Kimura-2-parameter resulted in a consensus NJ tree with comparatively higher bootstrap values. The populations were divided into three major clusters, CC+SEC, EC and SWC, which comply well with the results from the haplotype network .The values of pairwise FST range from 0 to 0.98921. Of 105 comparisons, 93 showed high genetic differentiation. The pairwise FST values between JA and JJ populations were less than 0.15, indicating moderate genetic differentiation. The pairwise FST values between the populations from Anhui and Zhejiang provinces, ranged from 0 to 0.06363, indicating low genetic differentiation. FST values among populations from Anhui Province range from 0 to 0.11845, suggesting a relatively low genetic differentiation . This isThe values of pairwise genetic distance between PE population and other populations range from 0.032 to 0.037, indicating high genetic differentiation.Similar high values were observed in comparisons of the LS population with other populations, which range from 0.016 to 0.032. Comparisons of the YS population with other populations range from 0.015 to 0.032. The values of genetic distance among the remaining populations are less than 0.01 .AMOVA results indicate that the major portion of the molecular genetic variation is found among groups (61.26%). Exact tests showed a significant genetic variance on all three levels (P<0.001) .The Mantel test for the 15 populations revealed a positive correlation between genetic distances and geographic distances , suggesting that isolation by distance had a limiting effect on gene flow.D. kikuchii in southern China did not experience population expansion . Fu’s F statistic was significantly negative with a value of -5.132 (P>0.1) . At the xpansion . A nonsiD. kikuchii populations sampled throughout their main areas of distribution in China. The results show a high genetic diversity and high level of genetic structuring of D. kikuchii in the sampled areas.Using three mitochondrial genes, we investigated the genetic diversity and structure of 182 individuals of 15 D. kikuchii could be a species native to China. In a previous study, Zheng et al. [Grapholita molesta Busck (Lepidoptera: Tortricidae) and Chilo suppressalis  . Although the origin of D. kikuchii remains uncertain, the higher genetic diversity we observed in the present study supports our assumption that China is part of the original range of D. kikuchii.All populations displayed large numbers of mitochondrial haplotypes, with a total 31 haplotypes obtained for COI, 40 haplotypes for COII, and 23 haplotypes for Cytb, of which seven, six and four were common haplotypes shared respectively. Of 73 examined haplotypes based on combined genes, 68 were unique and did not share the same ancestral haplotype. High numbers of private haplotypes and lack of ancestral haplotype suggest that g et al.  indicateD. kikuchii populations were geographically structured in three regions: eastern China, southwestern China, and central China along with southeastern China. The sampling localities of southwestern China are isolated by the Wumeng, Leigong, and Fanjingshan Mountains, while sampling localities of central China are isolated by the Hengshan and Dabieshan Mountains, which have acted as substantial barriers to gene flow. Populations from central China and southeastern China shared common haplotypes; this is in accordance with the results of phylogenetic analysis. Although there is a geographical barrier formed by Wuyishan Mountain between these two regions, a transportation network increases the gene flow among D. kikuchii populations. In Lepidoptera species, dispersal patterns also influence genetic variation [D. kikuchii is generally regarded as a sedentary species based upon previous studies of its flight capacity, with a sphere of activity extending over 10~20 km [D. kikuchii can reduce gene flow among populations. The IBD relationship  in the present study supports this hypothesis. Similar trends have been identified for many sedentary species, such as Chilo suppressalis  [Carposina sasakii Matsumura [D. punctatus populations that ranged over five adjacent regions in Zhejiang province were low using the ISSR-PCR marker.Based on the results of three mitochondrial genes, including AMOVA analysis, haplotype network and phylogenetic analysis, we conclude that ariation ,38. D. k  and Carpatsumura . The samatsumura . They foD. kikuchii caterpillars in Puer and Yangshuo populations turn into adults one moth earlier than those of other populations. And in Guizhou, only one generation occurs, while two generations occur in the rest of the provinces [D. kikuchii in China occur across very complex topography , different climates , different agricultural landscapes and forest types. A high level of population differentiation presents a high potential for adaptation to different environmental conditions [Chilo suppressalis [Reticulitermes chinensis [Analysis of genetic distance indicate that populations of Puer (PE), Yangshuo (YS) and Leishan (LS) are separated from other populations. In fact, rovinces . These vnditions  and highnditions , which aressalis  and Retihinensis .D. kikuchii did not experience population expansion. The lack of a star shape for the haplotype network together with the existence of multiple haplotypes support such a hypothesis. This also explains why there are so many private haplotypes in different geographical populations. The times when population histories stabilized are estimated to be 26500 (COI), 21600 (COII), and 11800 (Cytb) year ago. This range is within the late Pleistocene, which was characterized by climatic oscillations between warm and cold periods [Pinus massoniana, the favorite host plant for D. kikuchii, gradually spread into the warmer south China, a condition they would prefer [D. kikuchii, with these regions of south China gradually becoming the refuge for D. kikuchii. During the late Pleistocene, some regions of south China were not covered by large ice sheets [D. kikuchii in south China has arisen in separate refuges and experienced parallel evolutions. Similar results have been found for grasshoppers [Distributions of pairwise differences (mismatch distributions) obtained with COI, COII, Cytb, and combined gene data from the overall populations are multimodal, suggesting that the populations of  periods . Over 42d prefer . These ee sheets . In addie sheets . Consideshoppers , and theshoppers .D. kikuchii in China should cover a larger area and larger number of sampled individuals, as well as use nuclear genes as markers to provide a greater understanding of genetic structure. Additional research needs to be done to detail the geographic origins of D. kikuchii in China and its spread through China.Future population genetic research on D. kikuchii sampled throughout its main distribution areas in China. The results show high genetic diversity and a high level of genetic structuring of D. kikuchii in sampled areas. The high level of population genetic structuring is related to the weak flight capacity of the D. kikuchii, variations in its life history and the geographic distance among populations. Distributions of pairwise differences (mismatch distributions) obtained with COI, COII, Cytb, and combined gene data indicate the populations of D. kikuchii in southern China did not experience population expansion. These genetic data not only provide us with an understanding of population genetics for such a secondary species, but also provide guidance for pest management strategies.Using three mitochondrial genes, we investigated the genetic diversity and structure of 182 individuals of S1 Text(TXT)Click here for additional data file.S2 Text(TXT)Click here for additional data file.S3 Text(TXT)Click here for additional data file.


Ex situ management strategies play an important role in the conservation of threatened species when the wild survival of the species cannot be ensured. Molecular markers have become an outstanding tool for the evaluation and management of captive breeding programs. Two main genetic objectives should be prioritized when planning breeding programs: the maintenance of maximum neutral genetic diversity, and to obtain “self-sustaining” captive populations. In this study, we use 24 microsatellite loci to analyze and evaluate the genetic representativity of the initial phases of the captive breeding program of the Montseny brook newt, Calotriton arnoldi, an Iberian endemic listed as Critically Endangered. The results show that the initial captive stock has 74–78% of the alleles present in the wild populations, and captures roughly 93–95% of their total genetic diversity as observed in a previous study on wild newts, although it does not reach the desired 97.5%. Moreover, the percentage of unrelatedness among individuals does not exceed 95%. Therefore, we conclude that the genetic diversity of the captive stock should be improved by incorporating genetic material from unrelated wild newts. In recognition of the previously described significant genetic and morphological differentiation between eastern and western wild populations of C. arnoldi, we suggest maintaining two distinct breeding lines, and we do not recommend outbreeding between these lines. Our comparisons of genetic diversity estimates between real and distinct sample-sized simulated populations corroborated that a minimum of 20 individuals are needed for each captive population, in order to match the level of genetic diversity present in the wild populations. Thus, the current initial stock should be reinforced by adding wild specimens. The captive stock and subsequent cohorts should be monitored in order to preserve genetic variation. In order to avoid genetic adaptation to captivity, occasionally incorporating previously genotyped individuals from the wild into the captive populations is recommended. Ex situ measures, such as captive breeding, should be viewed as a last resort in species recovery  is one of the most threatened vertebrates in Europe, and is classified as Critically Endangered by the International Union for Conservation of Nature (IUCN) ; and the western sector—four brooks (B1–B4)  . Its cur (B1–B4) . With an (B1–B4) , the maj (B1–B4) . Emergenmandrids .in situ management measures have been developed by the Barcelona Provincial Council, including habitat restoration and field monitoring  and use this data to design the definitive captive stock in order to maximize its potential benefits to conservation. To do this, we evaluated whether the levels of genetic diversity  observed in the initial captive stock are comparable to the genetic diversity observed in the wild populations, and if this diversity is sufficient to be maintained over time. We also estimated the optimal numbers of individuals needed to ensure that the captive stocks accurately match the overall genetic diversity of the wild populations over time.Tissue samples were taken from 42 individuals (19 founders and 23 descendants) from the captive breeding facility , and comTissue samples consisted of tail tips or fingers preserved in absolute ethanol. Genomic DNA was extracted using QiagenTM  DNeasy Blood and Tissue Kit following the manufacturer’s protocol. For consistency, all 42 individuals were genotyped for the same 24 polymorphic microsatellite loci used to examine the wild populations . DetailsHO), and expected heterozygosity (HE) using FSTAT 2.9.3.2 (Ar) was obtained with HP-RARE , defined here as alleles found in a single population throughout the study region, for each locus and each grouping was calculated with GDA (FIS) for all markers and groupings were calculated using FSTAT 2.9.3.2, which calculates the estimator f of FST and RST  were calculated using two estimators: the rqg89 estimator  for each breeding line based on one-sample Ne estimator method was calculated with ONeSAMP  M-ratio values are indicative of populations that have gone through a genetic bottleneck at some time in the past.The effective population size ( ONeSAMP . ONeSAMPn 1.2.02 . This aperations . The steerations  M-ratio HE) and the mean percentage of alleles—including all alleles, common alleles (frequency >0.05) and rare alleles (frequency <0.05)—detected at each sample size (mean of the 100 random replicates per size) were then compared to each cluster . The mean pairwise FST between the 100 random replicates at each sample size and the empirical dataset was also calculated for each dataset. In order to further examine variations between the simulated and empirical estimations, ANOVA with Tukey HSD post hoc tests were performed separately on data from each cluster using Statistica v.7.In order to estimate how many captive individuals would be needed for each population to accurately match the level of the genetic diversity present in wild populations, comparisons of genetic diversity estimates between real and distinct sample-sized simulated populations were conducted following the methodology from A total of 140 alleles were identified in the 42 captive individuals analyzed, compared to the 170 alleles identified in the 160 individuals analyzed from the wild populations by p > 0.00104). Linkage disequilibrium was only found between one pair of loci  after applying a Bonferroni correction (p > 0.00018).All 24 microsatellites were polymorphic for the eastern captive breeding line, but Us3 and Us7 were monomorphic throughout the western captive line . Only Cap = 0.5135), while captive individuals from the western sector showed slightly higher values of A than wild specimens (p = 0.0012). Regarding differences among groups whilst taking into account sample size, rarefied allelic richness was lower in the captive individuals than in the wild ones. The difference was not statistically significant in the eastern sector (p = 0.0675), though it was in the western sector (p = 0.0007). These differences are almost certainly biologically significant, as it is impossible to retain genetic diversity that did not permeate into the captive population. Also, similar values of allelic richness were found among founders and descendants. The number of alleles per locus ranged from one to seven (PA) in only one sector. A total of 57 PA were found in the eastern captive line (two of them not found in the wild populations) compared to 75 present in the wild populations . In contrast, 81.5% PA representation was found in the western captive line (31of 38 PAs previously found in the wild). Regarding F1, 11 and two new PAs were found in the eastern and western captive breeding lines, respectively.Generally, higher allelic richness values were observed in both the eastern captive line and wild populations, in comparison to their western counterparts . When coto seven . Each brHE) were 0.519 and 0.418 in the eastern and western captive breeding lines, respectively. On a locus by locus basis, HE of the captive eastern line was similar to the wild populations (p = 0.4750). No differences in HE were observed between captive and wild individuals of the western populations (p = 0.8074), although a tendency towards smaller average heterozygosity was observed in the wild populations. Overall, FIS did not show values significantly different from zero for each breeding line after applying Bonferroni correction. Pairwise FST and RST values revealed significant divergences among captive and wild counterparts. Values of FST and RST between the captive and wild populations was 0.061 and 0.036 ( p < 0.001) for the eastern sector, and 0.044 and 0.032 (p < 0.01) for the western sector.The mean expected heterozygosities , and Ne = 17.62  for the western captive breeding line  or SMM models . In contrast, M-ratio tests suggest that both breeding lines experienced bottlenecks, with a Garza-Williamson M-ratio of 0.27 and 0.30 for the eastern and western breeding lines, substantially lower than the threshold of 0.68 considered indicative of a bottleneck.The estimated ing line . FounderP < 0.001, in all four datasets) when considering all alleles, rare alleles, and common alleles , much of the increase occurring when the sample size grew from five to 20 individuals , and to continue gathering knowledge on the biology of this species. The success of the program will depend largely on the genetic variability of the captive stock  do we have a good genetic representation of neutral diversity from the wild populations? If not, (Q2) how large should the captive population be to reach this target? It is crucial to detect the point at which the genetic benefits gained by adding additional individuals to the captive populations is outweighed by the increased costs of sampling and maintaining those extra founders.Here we discuss our two initial questions which we considered when establishing the Calotriton arnoldi captures roughly 93–95% of the total genetic diversity previously observed in the wild. Moreover, the percentage of unrelatedness among dyads from both breeding lines does not exceed 95%. Therefore, in order to increase genetic diversity, new genetic material should be incorporated by introducing new unrelated individuals or their sperm. Since this species present sperm storage  when compared to the eastern wild populations  compared to the western wild populations (Ne = 80). Although it has been shown that this species is able to survive in the wild with very low values of Ne, the possibility of inbreeding depression in captivity should be considered, since the strategies used by this species to maintain high levels of genetic diversity in the wild despite the relatively low number of individuals are not known, or may not be possible in captivity. Genetic drift erodes genetic variation as Ne decreases  of the eastern captive stock is significantly lower than that of the wild populations (p = 0.0082), indicating that the eastern captive stock only partially represents the gene pool of the eastern wild populations. Captive individuals of this breeding line originated from a single population (A1). As suggested by previous analyses of both mitochondrial and nuclear data, the eastern populations are grouped into two clusters, population A3 being the most differentiated and genetically diverse, and populations A1 and A2 clustering together in the microsatellite analysis  detected in this latter population biases the mean HE value of the western wild populations. Currently, the western captive line comprises no founders from B3, but as a result of its low genetic diversity, it is unadvisable to include breeders from this site. Therefore, if additional founders are incorporated into this breeding line, individuals from B1, B2 and B4 should be prioritized.In the western breeding line, in order to reduce the relatedness detected , and to C. arnoldi  suggested that a past bottleneck occurred in both breeding lines. Thus, it is crucial to keep monitoring for population bottlenecks in managed species since a reduction in Ne may enhance the rate of inbreeding, loss of genetic variation, and fixation of deleterious alleles considerably; thereby increasing the risk of population extinction (Although no signs of a recent bottleneck are detected by BOTTLENECK (more effective at detecting very recent bottlenecks of low magnitude), tinction . As the The current breeding protocol widely used by many captive breeding programs to minimize loss of genetic diversity involves pairing individuals according to the mean kinship value (MK) , which iFinally, a further problem associated with small founder populations is their significant rapid adaptation to captivity . A possi10.7717/peerj.3447/supp-1Table S1Sample sizes at each locus are also provided. Data from wild specimens were obtained from Click here for additional data file.10.7717/peerj.3447/supp-2Table S2HO, observed heterozygosity; HE, expected heterozygosity; FIS, inbreeding coefficient. Values in bold indicate statistical significance after Bonferroni correction.N, sample size; A, number of alleles per locus; Ar, allelic richness; PA, number of private alleles; Click here for additional data file.10.7717/peerj.3447/supp-3Table S3rqg89) and rlr99) based on the midpoint between the distributions of relatedness values calculated from 10,000 simulated dyads of each relatedness category using the population allele frequencies estimated for each sample, as described in Rate of misclassification of unrelated (un) to half-siblings (hs) and first-order relatives  for the related for the relatedness indices of Click here for additional data file.10.7717/peerj.3447/supp-4Table S4rlr99 value was and then determined for each individual-pair.Un, unrelated; hs, half-sibling; fs, full-siblings; po, parent-offsprings; rel, related, including hs, fs and po. The relationship category compatible with the observed Click here for additional data file.10.7717/peerj.3447/supp-5Table S5HE) and the mean pairwise FST by each captive dataset .ANOVA and Tukey HSD posthoc comparisons among the simulated (mean of the 100 random replicates per each sample size) and empirical estimations of mean percentage of common alleles (frequency >0.05), mean expected heterozygosity (Click here for additional data file.10.7717/peerj.3447/supp-6Table S6Click here for additional data file.


Dama damadama) are thought to have been restricted to refugia in Anatolia and possiblySicily and the Balkans. However, the distribution of this species was also greatlyinfluenced by human-mediated translocations. We focus on fallow deer to betterunderstand the relative influence of these natural and anthropogenic processes. Wecompared modern fallow deer putative populations across a broad geographic rangeusing microsatellite and mitochondrial DNA loci. The results revealed highly insularpopulations, depauperate of genetic variation and significantly differentiated fromeach other. This is consistent with the expectations of drift acting on populationsfounded by small numbers of individuals, and reflects known founder populations inthe north. However, there was also evidence for differentiation among (but notwithin) physically isolated regions in the south, including Iberia. In those regionswe find evidence for a stronger influence from natural processes than may be expectedfor a species with such strong, known anthropogenic influence.Species that have been translocated and otherwise manipulated by humans may showpatterns of population structure that reflect those interactions. At the same time,natural processes shape populations, including behavioural characteristics likedispersal potential and breeding system. In Europe, a key factor is the geography andhistory of climate change through the Pleistocene. During glacial maxima throughoutthat period, species in Europe with temperate distributions were forced south,becoming distributed among the isolated peninsulas represented by Anatolia, Italy andIberia. Understanding modern patterns of diversity depends on understanding thesehistorical population dynamics. Traditionally, European fallow deer ( Dama dama dama), a species that has been integrated into human culturesince the Neolithic , fallow deer became extinctacross north-west Europe during the last glacial period, surviving only in refugia insouthern Europe  sequence data and 10 microsatellite DNA loci, we test thefollowing hypotheses: (1) that a signature reflecting postglacial refugia can stillbe detected; (2) that the timing and pattern of population divisions largely reflectthe historical data on known patterns of translocation; and (3) that populations inthe north reflect an amalgamation of founder events reflecting human translocationsfrom the southern refugial species range.3), were stored in 20% dimethylsulphoxidesaturated with NaCl (M EDTAand 1 M Tris) supplemented with proteinase K(20 mg ml−1). DNA was extracted from thedigested solution using a Qiagen QIAquick PCR purification kit  and stored at −20 °C. DNA was isolated from bloodsamples using a salting-out method  were genotyped using 10 previously publishedmicrosatellites shown to be polymorphic in fallow deer  with cycle conditions: 95 °C for5 min; 35 cycles at 94 °C for 45 s, 51 °C for45 s and 72 °C for 45 s; and 72 °C for5 min. PCR products were purified using Qiagen PCR purification columns and directly sequenced using an ABI 377 automated sequencer . A representative subset of the available samples(N=203) were sequenced.A mtDNA control region fragment of 683 bp was amplified using two primersdeveloped by hd; k).Locations with <10 samples were excluded from diversity andFST analyses, as was Germany where only samplesrepresenting each of the unique haplotypes from the former study of FST . The program DnaSP v. 10.4.9 hd; , nucleotST after was calcST after. SignifiFIS were calculated using the program FSTAT v. 2.9.3(K) based on microsatellite data. This was assessed usingΔK, a measure of the second-order rate of change in thelikelihood of K (K with thehighest Ln P(X|K). Five independent runs wereperformed for each value of K (1–15) at 106Markov chain Monte Carlo repetitions and 105 burn-in using noprior information on sampling population and applying the correlated allelefrequency and admixture ancestry models. Population structure not detected at thehigher hierarchical level, but apparent in other analyses, was explored further byre-running the program with subsets of population samples.For microsatellite DNA data, allelic richness for each locus and population andv. 2.9.3. The seqv. 2.9.3.The proPatterns of microsatellite differentiation were visualised using a factorialcorrespondence analysis implemented in GENETIX v. 4.0 . IsolatiLong-term average gene flow between southern populations , across 10 microsatellite loci, was investigated usingcoalescent analyses in Migrate-n v. 3.6.11 ,2009. T7 followed by 108 Markov chain Monte Carlo iterationswith a sampling interval of 1000 iterations . Mixing parameters were adjusted to improve mixing for thesouthern populations data set  and the data setthat included all populations . Trace files wereviewed in Tracer v.1.6 (circlize (BayesAss v.1.3  was useder v.1.6  from theN=24), Italian (N=21) and Iberian(N=26) populations, corresponding to the geographically isolatedregions that supported refugial populations for various other species  and 1 000 000 data sets. Parameterestimates were assessed for the 6000 simulated data sets closest to the observeddata using direct and logistic regressions. The ‘direct’ estimate isthe number of times the scenario is found among data sets closest to the observeddata set. The logistic regression compares the proportion of the scenario(dependent variable) with the differences between observed and simulated data setsummary statistics  analysis was run using the software packageDIYABC (cies see. We drawα) is 2 years, adult survival of females is 16 years(λ=1), then calculated generation time would be~1  values increased gradually with a maximum atK=10  meant that auseful value for delta K could not be determined  show greater complexity as K increases , and some possible indication of admixture.Differentiation among populations was strong, with all pairwiseowersee . For assined see . The thranalysis  and prinanalysis  analysesNe is largerin Italy than for Iberia or Turkey, though confidence limits are broad and all arebounded by zero (We generated long-term (Migrate-n) and short-term (BayesAss) estimates of geneflow, both among the southern populations on their own  and (with BayesAss) among allpopulations. The estimates of directional, long-term gene flow among these threesouthern populations (based on Migrate-n) indicate low levels of migration and noclear pattern of directionality . Theta e by zero . BayesAs by zero . When al by zero .FST values based on mtDNA sequence data reflect thepattern of haplotype clustering seen in the network, with highly significantdifferentiation between all but the northern populations that cluster together inthe network  for microsatellite DNA loci and 2.93 ×10−7 (±s.d. 1.49 × 10−7) for themtDNA sequence (per site per year). The Ne estimates from ABCagain suggest that the Italian population is larger than the other two and theputative ancestral population . These regions represent putative refugial populations in a widerange of species see . We usedpulation .t’), theposterior distribution for this scenario gives similar, overlapping posteriordistributions as the older (time ‘Tb’) division, each near the startof the Holocene . It is possible for a relatively simple model(such as the ‘trifurcation’ model in scenario 1) to be supported overmore complex models through a lack of power. However, the result from scenario 2suggests that this may not the case for these data, as there is little support forresolving distinct time points for the t and Tb divisions in thisscenario  and between England and Ireland(FST=0.053), all other putative populations werehighly differentiated for microsatellite DNA loci, with FSTranging from 0.278 to 0.815. This is much higher than seen among populations ofred deer in Poland  in that study(FST=0.072). The magnitude of diversity among ourstudy populations (FST=0.278 to 0.815) might suggestprolonged or multiple bottleneck events, possibly linked to the tendency forfallow deer to slowly disperse from points of release  recommendations ; howeverAll analyses based on the subset of data representing the traditional threerefugial areas identified for a broad range of taxa see , the IbeVarious studies suggest that an Anatolian refuge contributed significantly towardsthe recolonisation of many other European species  are known to be heavily affected by anthropogenicbarriers such as roads (The persistent signal for differentiated regional populations in the southern partof the range is of interest, and consistent with some other postglacialdistributions in Europe. For example, although European hedgehogs(as roads ,the widCollectively, the data presented have implications for the wider conservation of thisspecies. Fallow deer are currently classified as least concern by the IUCN, withspecial consideration given to Turkish and Rhodian populations on the basis that theyrepresent ancient indigenous stock see . Clearlyhttp://dx.doi.org/10.5061/dryad.d2g8v.DNA sequences: Genbank accessions KY564399–KY564432; Microsatellite DNAgenotypes: Dryad Digital Repository


Cypripedium japonicum and suggest three hypotheses: 1) that genetic drift has been a primary evolutionary force; 2) that populations in central and western China harbor higher levels of genetic variation relative to those from eastern China; and 3) that C. japonicum in China maintains the highest genetic variation among the three countries. Using ISSR and SCoT markers, we investigated genetic diversity in 17 populations to test the three hypotheses. As anticipated, we found low levels of genetic diversity at the species level with substantially high degree of genetic divergence, which can be mainly attributed to random genetic drift. Chinese populations harbor the highest within-population genetic variation, which tends to increase from east to west. We also found a close relationship between Korean populations and central/western Chinese populations. Historical rarity coupled with limited gene flow seems to be important factors for shaping genetic diversity and structure of C. japonicum. Our results indicate that the mountain areas in central and western China were likely refugia at the Last Glacial Maximum.Little is known about levels and patterns of genetic diversity for the entire range of endangered orchids native to China, Korea, and Japan. In this study, we focus on  Cypripedium L., comprising ca. 50 species of terrestrial herbs, is distributed throughout subtropical to temperate regions of the Northern Hemisphere with the exception of northern Africa1. Although East Asia and North America represent the two major centers of diversity for this genus, the former region harbors the majority of diversity with 32 species2. Cypripedium japonicum Thunb. belongs to section Flabellinervia2 and is distributed across East Asia in China, Korea, and Japan3. In China, it is the only slipper orchid that is distributed from western to eastern regions. Possessing two large fan-shaped leaves and highly appealing flowers, C. japonicum is of high horticultural value. In addition, its roots and rhizomes, and even the whole plant, are used in Traditional Chinese Medicine (TCM) to promote blood circulation for regulating menstruation, to dispel “wind”  and to alleviate pain6. Although C. japonicum is widely distributed in East Asia, the size and number of populations have recently declined because of the destruction of their habitats7. It is found only on humus-rich soil under mature and successional deciduous forests on mountainous slopes partly as a result of narrow mycorrhizal specificity8. Although the species is not listed as threatened in the Chinese Red List, it will be included in the second batch of “National Key Protected Wild Plants” as a “Class I protected species”9. In Japan, it is categorized as “Vulnerable” (VU)10, whereas in Korea, where it is known to be extremely rare, is listed as “Critically Endangered” (CR)11. At the global level, C. japonicum is listed as “Endangered” (EN)5 and in need of protection and conservation. Under this situation, sound long-term scientific conservation strategies for protecting and conserving the wild populations of C. japonicum are badly needed; such strategies should be based on thorough knowledge of the species’ biology, especially its genetic diversity and structuring within the whole distribution area12.The lady’s slipper orchid genus et al.7 tried to test this prediction in six Korean populations of C. japonicum but, unfortunately, found no allozyme-based genetic diversity within and among populations. Qian et al.13, using amplified fragment length polymorphisms (AFLP), investigated six populations of C. japonicum in Tianmu Mts. of Zhejiang Province (eastern China) and found low polymorphism (21.0%) and high genetic differentiation (GST = 0.518). Qian et al.4, using inter-simple sequence repeats (ISSR), also found extremely low levels of within-population genetic variation (HeP = 0.042) and substantially high among-population genetic differentiation (GST = 0.671) in six populations from eastern and central China. Biological and ecological traits such as small population size and founder effects, coupled with limited pollen and seed dispersal across isolated fragmented landscapes, have been suggested as factors for shaping genetic diversity and structure of C. japonicum7. According to population genetic theory, RGD should affect levels and the distribution of genetic diversity within and among populations, but with a relatively little effect on the level of genetic diversity within the species as a whole. If RGD has operated in populations of C. japonicum as a major evolutionary force for a long period of time, we further expect extremely low levels of genetic variation at the population level but low to moderate levels of genetic diversity at the species level, and substantially high genetic divergence between populations recovered from the full geographic range of this orchid.Population genetic theory predicts that small size and spatial isolation of populations will lead to the random fixation of alleles , promoting allele frequency divergence and inbreeding as well as a reduction in heterozygosity within populations. Overall, RGD reduces genetic diversity within populations and enhances genetic differentiation between populations. Chung 15. High levels of genetic diversity are expected for those areas that are thought to be refugial21 (but see22), as plants would have endured the most adverse periods maintaining relatively large populations, and also because populations having persisted in refugia would have had a longer demographic history than those that are the result of post-glacial colonization. Mountains anywhere played a key role as refugia for biota during the Neogene and Quaternary global cooling25; reasons include their relative eco-environmental stability and the need of mountain species for moving considerably shorter distances to track climatic oscillations as compared with lowland species21.The current geographical distribution of plant taxa is assumed to be the result of both present and historical factors , being the latter important drivers in shaping the genetic structure and phylogeographical patterns of plants26. In China, several mountainous areas served as important East Asian Pleistocene refugia at the LGM for boreal and temperate species; such areas are mainly located in the western and the central parts of the country . If this scenario is also true for C. japonicum in China, we expect relatively higher levels of genetic variation in western populations compared to eastern ones. In addition, we can also anticipate that Chinese populations would harbor higher within-population genetic variation than Japanese and Korean ones, given that the main East Asian Quaternary refugia were located in China26; Chinese mountains usually reach higher altitudes , which allowed plants to track glacial-interglacial climate oscillations by means of vertical shifts17.Our study area in East Asia, the so-called “Sino-Japanese Floristic region” was not glaciated during the Last Glacial Maximum  except for a few high-elevation mountainsC. japonicum based on small regional scale sampling have generated considerable background population genetics knowledge of this species13, a comprehensive study based on a large number of populations across its whole range in East Asia is necessary for interpreting the dynamics of variation over the range of the species, which would allow to design more efficient conservation strategies at both regional and global scales. Therefore, a thorough understanding of population genetic diversity and structuring of C. japonicum across its full range is desirable.Despite several previous population genetic studies of 27, and they can be combined to reach more comprehensive conclusions. Neutral ISSR markers have been widely used for detection of DNA polymorphism in rare species28 due to their advantage of being fast, simple, and cost effective, with good reproducibility29. In addition, the start codon targeted (SCoT) polymorphisms, which were developed30 based on the translation start codon, are simple, highly polymorphic, and abundant in the genome27. As the SCoT loci are gene-based markers and are likely to be subject to selection 31, their use in population genetic studies may be compromised. However, many studies have shown that they are highly correlated with genuinely neutral markers such as ISSR, and are usually more informative and effective than the latter34.The genetic variation and relationships among wild populations can be efficiently determined by using different molecular markersC. japonicum sampled from China, Korea, and Japan to test the three hypotheses presented above . With the resulting genetic information, along with available demographic information, we tried to provide recommendations for efficient in situ conservation and ex situ preservation of this rare and endangered orchid.Using ISSR and SCoT markers, in the present study we estimated the levels and distribution of genetic diversity of PPBP was 9.7%, ranging from 1.3%  to 24.4% (FP), whereas the mean NaP, PA, NeP, HEP, and SI were 1.10, 1.88, 1.04, 0.027, and 0.042, respectively , while population BT did the lowest HEP (0.004). At the species level, all these parameters exhibited higher mean values, with PPBS of 57.7%, NaS of 1.58, NeS of 1.16, HES of 0.104, and SI of 0.169  to 24.7% (FP), and the mean NaP, PA, NeP, HEP, and SI were 1.11, 1.69, 1.05, 0.033, and 0.051, respectively  again, but Ka had the lowest HEP (0.007). At the species level, like the ISSR, all those parameters exhibited higher mean values: PPBS = 61.0%, NaS = 1.61, NeS = 1.24, HES = 0.145, and SI = 0.224  from SCoT were higher than those from ISSR . On the other hand, levels of genetic diversity of the six parameters tended to increase from Korea to China .In China, levels of genetic diversity with the exception of st Table ; populatGST showed very small differences between the total set of populations and the set of populations excluding those with small sample sizes (n < 10), and this was applicable for the whole species range  as well as within each country separately  and with all possible combinations of country pairs . We consider, therefore, that including populations with small sample size would not significantly alter results and conclusions, and we conducted further data analyses with the total set of populations.Values of HT) was 0.106, while the mean heterozygosity within populations (HS) and between populations (DS) were 0.027 and 0.080, respectively. The degree of genetic differentiation between populations (GST) was 0.749. Similar results were found with the SCoT dataset: HT = 0.152, HS = 0.033, DS = 0.119, and GST = 0.779. Chinese populations showed higher GST (0.636 for ISSR and 0.697 for SCoT) than those from Japan (0.321 for ISSR and 0.406 for SCoT) and Korea (0.104 for ISSR and 0.145 for SCoT) , followed by “China vs. Japan” , and “China vs. Korea”  , followed by China vs. Japan  and among populations within regions contributed 48.1% and 30.9% to the total genetic variance, respectively Table . Similarr = 0.713, P = 0.001 for ISSR; r = 0.515, P = 0.001 for SCoT) when all populations were used , results of the hierarchical test for IBD showed significant correlations within countries , and also significant correlations were observed when countries were combined  (Supplementary Table We found a significant correlation between pairwise genetic distances and linear geographic distances (km) of populations , whereas they clustered only with WX (a western Chinese population) and then with the remaining Chinese populations with SCoT.The UPGMA dendrograms constructed from ISSR Fig.  and SCoTPrincipal coordinate analysis (PCoA) results were in good agreement with those from the UPGMA dendrograms Fig. . In the K statistics indicated K = 2 as the best grouping scheme  populations from China and Korea have a close relationship, and 2) Korean populations are genetically more related to central and western Chinese populations than to eastern Chinese populations.In the combined datasets (ISSR and SCoT), the Δo 5 Fig. . Thus, wool Fig. . Howeverols Fig. . Interes= 4 Fig. . The finC. japonicum in East Asia. The amplification products generated from the SCoT markers may be correlated to functional genes and their corresponding traits, while the ISSR markers detect polymorphisms in microsatellite regions that do not necessarily represent functional genes35. Each marker system targets different regions of the genome, which leads to different sources of the detected diversity. In the present study, both of them indicated low genetic diversity both at population and species levels, and showed a similar clustering of C. japonicum populations (see below for details). Although the values for the genetic parameters were significantly higher for SCoT compared to ISSR, overall a significant correlation was found between these two datasets. To sum up, using independent data from two different methods  can represent a more efficient and accurate way to survey the genetic variability of a given species.In this study, two dominant genetic markers, ISSR and SCoT, were employed to assess the levels and distribution of genetic diversity of C. japonicum exhibits extremely low levels of within-population genetic variation  when compared with other orchids that have been screened with ISSR  compared to means found in orchids . We found considerably high GST values within China  and Japan , which are likely to be caused primarily by RGD (see below) and limited gene flow. A very similar GST estimate was reported in the epiphytic or lithophytic orchid Dendrobium fimbriatum from Xishuangbanna ; Ma and Yin36, using 12 ISSR markers, estimated high species-level genetic variation  and high degree of population differentiation GST (0.744) from five populations, and, accordingly, low population-level variation . We believe that, like C. japonicum, RGD coupled with restricted pollen and seed dispersal would have shaped the genetic structure of D. fimbriatum in Xishuangbanna.As expected, et al.7 found no allozyme variation in six Korean populations of C. japonicum and argued that the species was historically rare compared to its congener C. macranthos on the Korean Peninsula, suggesting that RGD would be a major evolutionary force in the former. Consistent with this and more recently, Son and Son37 found low levels of microsatellite-based genetic variation in four Korean populations of C. japonicum  but a higher among-population divergence (ΦST = 0.810 including one Japanese population). Our finding of much lower levels of within-population genetic variation when compared to those at the species level, indirectly indicates the importance of RGD for C. japonicum. A recent meta-analysis of allozyme-based population genetic studies in orchids also revealed that “rare” taxa harbor significantly lower levels of genetic diversity than their “common” congeners 38. One of the exceptions was C. macranthos var. rebunense, endemic to the small Rebun Island close to Hokkaido (Japan), which harbors comparable levels of genetic diversity with its common North American congeners. This unexpected genetic result is probably due to the fact that the decline in habitat range and population size of this island’s endemic variety has been very recent (since 1950s)39; there have not been sufficient generations for the initial diversity to be substantially eroded by RGD40.Chung 42. The three bumblebees Bombus remotus, B. imitator and B. picipes are the most effective primary pollinators of C. japonicum in China43, while B. ardens and B. diversus are the effective pollinators in Japan44. It is known that bumblebees have a flight range of less than 15 km45. Pollen dispersal is, therefore, likely to be relatively restricted among the highly isolated populations of C. japonicum. Moreover, non-rewarding C. japonicum flowers bloom in May43, when precipitation is high in most of its distribution range in China, which could dramatically reduce the foraging activity of bumblebees and indirectly limit pollen dispersal46. Pollination also suffers from visits by illegitimate visitors as these insects destroy the flowers47. All these factors might contribute to the low pollen dispersal observed in the species44.Other life-history or ecological traits associated with pollen and seed dispersal have a highly significant impact on genetic variability and are strong predictors for the levels and distribution of genetic diversity in plant species48. However, direct and indirect (genetic) studies have documented that the majority of seeds are transported to very short distances, in the close vicinity of maternal plants, with events of long-distance dispersal being sporadic51. In addition, like other terrestrial orchids, the seeds of C. japonicum require mycorrhizal fungi for germination and seedling nutrition53. The limitation of pollen and seed flow across highly isolated populations would lead to low gene flow, increasing the genetic differentiation between populations of C. japonicum (see below for further details).Since orchids produce tiny, “dust-like” numerous seeds, they have the potential for long-distance seed movement once seeds are released and enter the air column54. As hypothesized, levels of genetic diversity (with the exception of PA and NeP) tend to increase from east to west in China. The finding that the central and western populations harbor relatively higher levels of genetic variation compared to eastern ones are consistent with the hypothesis that the former areas served as large LGM refugia for many species, mainly boreal and temperate plants, in China26. In particular, population FP, the highest in levels of genetic diversity among all Chinese populations examined, is located in the south Qinling Mts. . All the studied populations of C. japonicum occurring in these areas (with the only exception of population BT) are among the most genetically variable  compared to western ones  roughly correspond to the “second” and “third” steps of the “three-step ladder” of Chinese geographic features58, respectively  was a glacial refugium for a large assemblage of boreal and temperate plants during the LGM59. Plants from the BDDG often harbor moderate to high levels of within-population genetic diversity , because these mountains might provide relatively stable habitats and enabled plants to track the climate changes by altitudinal movements, ensuring relatively large population sizes. For example, C. macranthos (a congener of C. japonicum), largely occurring along the BDDG ridge, harbors moderate levels of within-population genetic variation7. Populations of C. japonicum, however, occur on hillsides with elevations of less than 600 m on the Korean Peninsula. Likewise, in Japan, C. japonicum largely occurs on lower hillsides, and our samples were collected from hillsides at altitudes below 400 m. Like in Korea, lowlands and low-elevation hills in Japan hardly harbored large refugia (“macrorefugia”). Instead, contemporary populations would be descendants of populations that endured the LGM in microrefugia, where they suffered the effects of RGD.As also hypothesized, the levels of genetic diversity of cf. dotted lines for the reconstructed LGM coastlines; Fig. C. japonicum occurs in middle-low altitude mountains in China, the most straightforward migration routes were probably through the mountains of north China  to see how much these small populations may be influencing our statistics. To test for the influence of individuals within populations, populations within regions (countries), and regions on the observed genetic variation, we conducted a hierarchical analysis of molecular variance (AMOVA) using GenAlEx 6.569. To determine the relative importance of gene flow and RGD at regional scale70, we conducted a correlation analysis between pairwise genetic distances71 and linear geographic distances (km) and ran a Mantel test for IBD (with 999 permutations) using GenAlEx. A positively significant linear relation suggests that populations are at regional equilibrium between gene flow and RGD70. As reproductive barriers would exist between Chinese and Japanese populations due to different chromosome number  and geographical isolation, we could be dealing with two cryptic species or subspecies. In order to avoid such potential “phylogenetic” biases in our genetic results, we performed further analyses of GST and IBD in a hierarchical fashion: within each country separately and then with all possible combinations of country pairs .To estimate the degree of population genetic differentiation, we further calculated total genetic diversity  with GenAlEx69, on the basis of Nei’s genetic distances74, to investigate the relationships among the populations, and the two principal coordinates were used to visualize the dispersion of accessions in a two-dimensional array of eigenvectors. To deeply explore the population structure and unravel genetic admixture, we used Structure 2.3.475 to analyze the combined dataset (16 populations with ZP exclusion due to lack of SCoT data) of two markers with a Bayesian approach. Posterior probabilities of the data for each K were obtained for K = 1 to K = 18 clusters using the Admixture Model. Fifteen runs were completed for each K, with a Markov Chain Monte Carlo (MCMC) of 200,000 iterations, following a burn-in period of 1,000,000 iterations. We inferred the most likely value of K by the ΔK statistics76, with the aid of Structure Harvester77. Since the ΔK method tends to identify K = 2 as the top level of hierarchical structure78, we combined it with the method of selecting the smallest K after the log probability of data [ln Pr(X|K)] values reached a plateau79. Programs Clumpp 1.1.280 and Distruct 1.181 were used to combine the results of the 15 replicates of the best K and to visualize the results produced by Clumpp, respectively.We conducted unweighted pair-group method with arithmetic means (UPGMA) based on Nei’s unbiased genetic distancesSupplementary material


Gymnopleurus mopsus (family Scarabaeidae), a dung beetle that displays dung-rolling behavior , have recently experienced sharp declines, and many populations are now at high risk of local extinction. However, Mongolia, which constitutes a major portion of the species’ distribution, still sustains a relatively large population. Here, we used mitochondrial COI sequences to investigate the within-population genetic diversity and both the genetic and phylogeographic structures of 24 G. mopsus populations across the species’ main distribution in Mongolia. Several lines of evidence indicated that the phylogeographic structure of G. mopsus had been influenced by a recent and sudden demographic expansion. Interestingly, the expansion of Mongolia’s G. mopsus population corresponded to the advent of livestock domestication in the region, and the species’ genetic structure coincided with road networks, which presumably serve as migration routes for livestock that might mediate the beetle’s dispersal. In addition, we also found that G. mopsus possesses high levels of haplotype diversity, which is generally indicative of large effective population sizes (Ne). Overall, the present study contributes to the current understanding of G. mopsus’ demographic history and dispersal patterns and also provides valuable information for the species’ conservation and management.Populations of  These dung beetles are also thought to play important ecological roles, such as nutrient recycling, bioturbation, plant growth promotion, seed dispersal, and parasite control, in terrestrial ecosystems3 and, as a result, have become a focus of both biodiversity and conservation research, especially owing to their broad range of responses to environmental changes and strong ecological interactions with a diverse array of organisms, including plants, vertebrates, and invertebrates4.Copronecrophagous dung beetles (subfamily Scarabaeinae) possess specialized ecological ability and behavior, in that they utilize feces as food, habitat, and breeding ground, and this extraordinary case of ecological specialization has likely contributed to their evolutionary success6. It has been suggested that the diversity and abundance of dung beetles are strongly affected by slight changes in land use and mammalian fauna, at even local scales. Overhunting of forest-dwelling mammals, for example, has led to the extirpation of dung beetle species that rely on the dung of particular mammals7. In addition, dung beetle species with rolling behavior  are especially vulnerable to anthropogenic activities, and historical records suggest that the relative frequencies of telecoprid species have been more severely affected than other scarab dung beetles during the 20th century8. Furthermore, the use of antibiotics and parasite treatments  in commercial livestock management and agriculture can also accelerate the decline and extinction of dung beetle species10, thereby profoundly affecting community- and ecosystem-level changes.Unfortunately, however, both the decreased of population size and local extinction of dung beetle species (extinction-prone) have been reported to occur as a result of natural and anthropogenic disturbancesGymnopleurus mopsus (Scarabaeidae), is also under threat of local extinction throughout its distribution in Palearctic regions, including in Asia11 and Europe8. Previous studies have reported that both G. mopsus and other roller species are threatened in Italy8 and that populations of G. mopsus on the Iberian Peninsula have declined significantly6. In addition, G. mopsus has also been declared as regionally extinct (last documented in the 1960s) on the Korean Peninsula, which constitutes the southern margin of the species’ range in Northeast Asia. As a result, the National Institute of Biological Resources in South Korea launched a restoration program in 2012, which re-introduced G. mopsus from Mongolia to South Korea, based on the high genetic diversity of the Mongolia population and its genetic similarity to populations in South Korea12.Similar to other telecoprid species, G. mopsus populations are currently maintained in the north-eastern Palearctic region11, it has the value as the species’ last “refuge” and, as such, is considered important for future conservation and restoration efforts. However, the present-day population genetic structure shaped by contemporary processes  as well as demographic (population) history  of G. mopsus in Mongolia are yet to be investigated.Because Mongolia is the only region where large G. mopsus populations would allow an understanding of the various ecological and evolutionary aspects of G. mopsus. The dispersal and migration patterns of insect species have been reported to play essential roles in determining the genetic structure of insect populations13, and the ecological associations of dung beetles and mammals have likely contributed to the evolution of the Scarabaeinae and community structuring of extant dung beetles over the last 40 million years14. Therefore, analysis of both population genetic and phylogeographic structures could elucidate the historic migration, dispersal, and demography, and the G. mopsus populations in Mongolia present a promising model system for understanding the effects of mammal  abundance and distribution on the animal-mediated dispersal patterns of dung beetles.Such investigations of Mongolian G. mopsus in Mongolia would allow us to infer whether anthropogenic activities, such as moving cattle herds by pasturing, have facilitated the dispersal of dung beetles. Phylogeographic approaches can also reveal the historical and evolutionary origins and demographic processes of G. mopsus, including the genetic footprints of historic fluctuations in population demography, which could also be related to human activities15. Phylogeographic analysis might also elucidate the effects of human-mediated disturbances, such pasture reduction or habitat destruction, on the distribution and size of G. mopsus populations. Pasturing areas in Mongolia have been rapidly decreased by urbanization and changes to pasturing style. Livestock pasturing can prevent the range expansion of some dung beetle species ranges by altering vegetation density, temperature, and moisture levels16. Mongolia can be classified into two large biogeographic regions: northern humid areas (grassland and forest-steppe areas) and southern desert or semi-desert areas that have undergone drastic climate changes seasonally17. Therefore, determining both the current population genetic and phylogeographic structures of G. mopsus may help us to elucidate its historical distribution patterns in an ecological context.The current population genetic structure of a species is shaped by the effects of the population demographic history  of a species, as well as the level of contemporary gene flow among populations. Therefore, historical anthropogenic events, such as the domestication of wild animals, could have influenced the population demography of dung beetles. Studying the levels of genetic diversity and population genetics structure of G. mopsus currently maintains large populations in Mongolia, future population declines are very likely, owing to rapid urbanization and land development  now taking place in Mongolia. Here, we first investigated the population genetic and phylogeographic structures of G. mopsus in Mongolia, in order to understand the species’ evolutionary history  and distribution, as well as to determine its current population genetic status.Although COI sequences (658 bp) that were generated from G. mopsus specimens collected in Mongolia (N = 406), as well 12 specimens from Korea, China, and France, we identified 94 polymorphic sites and a total of 230 haplotypes (Table Among 418 h) for the 14 large (N > 10) populations ranged from 0.889 to 1.000 in the ST23 and ST24 populations, respectively, with an average of 0.974, whereas estimates of nucleotide diversity (π) ranged from 0.006 to 0.010, with an average of 0.008  located in the most internal position in the network , whereas most of the variance occurred within populations . The greatest between-group difference (3.06%) was observed when the four populations  in the northern humid area were compared to the 20 desert populations. However, the genetic structure (ϕCT = − 0.002) among the three South groups  was insignificant (P = 0.775), which indicated the occurrence of relatively high levels of either historical or contemporary gene flow among populations in South area.Hierarchical analyses of molecular variance (AMOVA) revealed significant spatial genetic structuring among the 24 Mongolia populations, according to three different biogeographic groupings of (1) two groups, the humid areas  and desert areas (South and East populations); (2) three groups, the North and Central area (NC), South area (S), and East area (E); or 3) three groups of South populations . These groupings were based on our Fst statistics (ϕST), which ranged from −0.019 to 0.079  were relatively small, even between distant populations , which generally indicated low levels of inter-population genetic differentiation. As a result, after sequential Bonferroni correction applied, only 18 of the 91 comparisons among the 14 large (N > 10) populations indicated significant differentiation . However, neither the ST07 nor ST10 population was significantly differentiated from any of the other populations, which suggests the occurrence of either historical or ongoing gene flow between these and the other populations. Isolation-by-distance (IBD) analysis detected a significant positive relationship between genetic distance (Fst) and geographic distance  between the 14 large Mongolia populations  and −24.64263 (P = 0.001), respectively, which suggests that a sudden historical population expansion occurred following a population bottleneck. These significant negative values also indicate that the hypotheses of population stasis, selective neutrality, and population equilibrium can be rejected.Phylogeographic analyses revealed a recent, sudden expansion of is Table , Fig. 4.lia Fig. . Moreovelia Fig. , with Ta18 , and both the non-significant SSD statistic and small raggedness index indicated sudden population expansion, again confirming the results of the neutrality tests, whereas the non-significant raggedness index indicated a good fit to a model of population expansion19 (under the demographic expansion model). Based on the pooled Mongolia populations, the mismatch distribution again fit the model of demographic expansion.A mismatch distribution was clearly smooth and unimodal for all of the 14 large populations, further supporting the model of sudden population expansionG. mopsus expansion corresponded to both the expansion of human populations and advent of large mammal domestication in Mongolia.Historical demographic reconstructions, based on Bayesian skyline plot (BSP) analysis, further supported a model of sudden population expansion Fig. . Indeed,G. mopsus spanning major distributional ranges across Mongolia using mitochondrial COI sequences. The high frequency of unique haplotypes (singletons) and limited nucleotide differences between haplotypes could reflect rapid and recent population expansion, following a population bottleneck. Otherwise, such rare mtDNA variants would be eliminated by genetic drift. The significant negative Tajima’s D and Fu’s Fs statistics of the pooled G. mopsus populations also reject the hypothesis of the constant population size assumed under mutation-drift equilibrium20. In addition, the star-shaped topology of the haplotype network is another line of evidence that suggests the sudden demographic expansion of dung beetles, as in previous studies on flies22, moths23, leaf beetles24, and marine gastropods25, as well as domestic cattle and goats27. The most common and presumably ancestral G. mopsus haplotype (H19) was located in the center of the haplotype network and was connected to a large number of infrequent haplotypes by only a few mutational steps. This pattern is generally interpreted to indicate a population that has recently expanded in size from a small number of founders following a population bottleneck28. The single bell-shaped mismatch distribution for the COI haplotypes also supported the hypothesis of population range expansion29, whereas the hypothesis of sudden population expansion was further supported by the abrupt increase in effective population sizes, as suggested by BSP analysis  of large mammals, such as cattle, horses, and camels, that provide food and breeding ground for G. mopsus and, therefore, limit its abundance and distribution. The expansion of G. mopsus populations is estimated to have occurred ~2500 years ago, based on BSP analysis with estimated substitution rate. Such estimates depend on which molecular clock rates are applied, since different lineages are often subject to different mitochondrial substitution rates32. However, the hypothesis of a recent history of expansion was supported by many other analyses, as well, and the population expansion of G. mopsus was estimated to have occurred a few thousand years ago, based on the BSP analysis that accounts for the variation in our time estimation. Interestingly, our estimated timing of G. mopsus expansion seems to correspond to the advent of livestock domestication in Mongolia. Thus, the domestication of cattle and livestock was a turning point in the histories of both human and dung beetle populations33. The earliest domestication of wild cattle in northeastern Asia, including Mongolia, North China, Korea, and Japan, has been suggested to have occurred between 4000 and 5000 years ago34. The similar population structure of livestock and G. mopsus in Mongolia might further support the hypothesis of livestock-mediated expansion of G. mopsus populations. Recent population expansion in various domesticated animals, including cattle26 and goats35, has been supported by bell-shaped mismatch distributions or star-shaped haplotype networks, much like the ones presented for G. mopsus populations in the present study. Furthermore, other population genetics studies of cattle populations have also reported unusually high genetic diversity within domesticated populations in Mongolia35, as well as domestication-driven population expansion34. Moreover, such studies have also observed that the most common and presumably most ancestral haplotypes are located at the centers of the haplotype networks and that a number of rare haplotypes originate from that haplotype with only a few mutational steps.The historical demographic expansion of domesticated animals, which has resulted from archeological or anthropogenic events , has been well documented in a number of taxaHelictopleurus species (H. neoamplicollis and H. marsyas) in Madagascar, researchers used COI sequences to document a rapid population expansion that followed a resource shift36. The resource shift from primate droppings to cattle dung resulted in extended geographic ranges and population expansion with low genetic diversity and was associated with the historic introduction of cattle ~1500 years ago36. Indeed, the two Helictopleurus species exhibited considerably lower COI haplotype diversity than more wild, forest-dwelling dung beetles with more varied diets36. Another common cattle dung-using species, H. quadripunctatus, possesses a more intermediate diet, in that it is less specific, and, compared with forest-dwelling species, exhibits more similarities to the G. mopsus populations in Mongolia, particularly in regards to its high haplotype diversity, low nucleotide diversity, and significant IBD. In fact, like H. quadripunctatus, G. mopsus feeds on a broad range of food sources . Thus, a resource shift by G. mopsus from wild animal dung to domesticated cattle droppings during the domestication of cattle in Mongolia could account for the species’ demographic expansion. The contemporary cattle population in Mongolia is known to have originated from hybridization with wild yak populations ~7300 years ago and has probably undergone rapid population expansion for the last 3600 years37.In a previous study of cattle dung-using 14. Overall, the weak genetic structuring observed among the G. mopsus populations of Mongolia, as indicated by low Fst values, suggests the occurrence of either contemporary or historical gene flow. In particular, the significant relationship between the genetic and geographic distances of population pairs (IBD) indicates that the geographic proximity of populations significantly influences the spatial genetic structure of G. mopsus, probably resulting from the fact that its dispersal ability is associated with the geographic closeness.The dispersal potential  of a species is a critical factor in characterizing its present-day population genetic structure. There is evidence that the dispersal and movement patterns of dung beetles are strongly associated with those of mammals38. Thus, the dispersal and migration distances of this small beetle are likely limited, especially when considering the long distances between certain populations . However, if the migration of G. mopsus is promoted by other factors, such as livestock dispersal , the high level of connectivity between the G. mopsus populations becomes much more conceivable. Pasturing is a popular type of livestock farming  in Mongolia, and farmers often move their cattle herds among grasslands on a seasonal basis, following road networks. Such long-distance dispersal, either by long-distance flight or long-distance animal-mediated movement, could explain the low levels of inter-population genetic differentiation observed in the present study.The movement processes of dung beetles might be stochastic since individuals could move short distances between dung pats, as well as long distances between pastures, during foraging. Maximum flight distance of 1.5–2 kilometers per day has been reported in a previous studyG. mopsus population groups  that were clustered by the major roads that are seasonally used for moving cattle  and, possibly, high levels of historical and ongoing gene flow. Large Ne values typically indicate that populations have undergone historical demographic expansion but are now stable, thereby suggesting that G. mopsus originated in Mongolia. Our haplotype network analysis, which included individuals from multiple regions, including South Korea, China, and France, also supports the hypothesis that G. mopsus originated in Mongolia. Indeed, the haplotypes from South Korea and China were connected to those from Mongolia by only one or two mutational steps, and the haplotypes from France were not much more removed. It is possible that Mongolia is the geographic origin of G. mopsus expansion and that the species radiated (or migrated) to both the East and West during the demographic processes of population expansion.In the present study, we also identified an extraordinarily high level of genetic diversity, which could indicate extremely large effective population sizes  and southern dessert area, which were characterized by different vegetation types, i.e., grassland or forest-steppe areas and desert, respectively. A previous study39 reported that there was substantial year-to-year variation in the insect community of Mongolia. For example, in one year, the number of coleopteran species at a region near our ST23 locality was reduced by 60%39. The ST23 population, which was located at the center of a main road and was the most urbanized of our sampling localities, possessed the lowest genetic diversity of the Mongolia populations and yielded only negative Tajima’s D and Fu’s Fs values. This could indicate urbanization effects, which would indicate that the species is at a high risk of population size reduction in Mongolia. However, the widespread COI haplotypes and the pattern of star-shaped haplotype network may still indicate that the species is highly adaptive. Therefore, in order to understand the relationship between the genetic diversity and ecological adaptive potential of G. mopsus, future studies should investigate the response of G. mopsus to diverse environmental dynamics.The high genetic diversity of the remnant G. mopsus in South Korea, understanding the genetic characteristics  of potential source populations, like those of Mongolia, will be crucial40. The National Institute of Biological Resources in South Korea established a restoration program in 2012. The conservation and restoration efforts of endangered and regionally extinct species often require the introduction of individuals from source populations, whose genetic and evolutionary lineages are comparable to those of the recipient populations. Therefore, both the population genetic structure and phylogenetic relationships of source populations should be considered carefully when developing reintroduction programs41. The findings of the present study highlight the value of the Mongolia populations for G. mopsus conservation and suggest that the populations could be used as source populations for restoration efforts in South Korea. Additionally, this species has been suggested to have a very large distributional range in Palearctic region42. Therefore, the observed phylogeographic trends by molecular data in this study need to be corroborated in a wider spectrum of its geographic distribution in future research, and it will also allow for exploring the conservation values of other remnant and potentially important populations of this species.To design effective conservation and restoration management plans for G. mopsus populations across Mongolia. Additional ecological studies could further contribute to the effectiveness of dung beetle restoration in South Korea. The results of the present establish a basis for future research regarding the effective conservation and restoration of this highly cherished dung beetle species.The genetic data presented by the present study provide valuable information about the phylogeographic structure, evolutionary history, and the genetic structure of G. mopsus individuals were collected from 24 localities in Mongolia. Upon collection, the specimens were washed with distilled water to remove dirt and animal dung and then stored in absolute ethanol until subject to DNA extraction. Dried specimens that were collected from South Korea from 1931 to 1964 (N = 7), France in 1971 (N = 2) and China in 2015 (N = 3) were also included , using 20-μl reactions, universal forward (LCO1490: 5-GGT CAA CAA ATC ATA AAG ATA TTG G-3)44 and reverse (C1-N-2191: 5-CCC GGT AAA ATT AAA ATA TAA ACT TC-3)45 primers using AccuPower PCR Premix , with the following conditions: an initial denaturation at 94 °C for 1 min; followed by 35 cycles of 30 s at 94 °C, 30 s at 50 °C, and 1–2 min at 72 °C; and a final extension step of 72 °C for 7 min. The PCR products were visualized on 1.5% agarose gels using UV light, purified enzymatically using Exonuclease I and Shrimp Alkaline Phosphatase , and then sequenced by Macrogen INC Sequencing (Korea) using an ABI PRISM 3130xl Genetic Analyzer . The resulting sequences were deposited in GenBank under accession numbers MF674025–MF674381. Previously published COI sequences collected in South Korea from 1931 to 1964  and from Mongolia  and China (KX879047-879049) were also included12.Genomic DNA was isolated from a femur of each specimen using the DNeasy Blood & Tissue Kit , and polymerase chain reaction (PCR) was used to amplify a 658 bp fragment of the mitochondrial cytochrome oxidase I gene , and nucleotide diversity (π), were estimated using ARLEQUIN v3.549, and Tajima’s D and Fu’s Fs statistics were also analyzed to test for neutrality. Ten populations that had insufficient sample sizes (N < 10) were excluded from all subsequent analyses, except the AMOVA. Haplotype richness was calculated using a rarefaction method in CONTRIB v1.0250 that corrects for unequal sample sizes among populations.The total and population genetic diversity indices, including the number of haplotypes (HN), haplotype diversity (Fst)51 was calculated for population pairs using ARLEQUIN v3.5, and the geographical distance between sampling localities was measured in decimal degrees using ArcGIS Desktop 10.5 52. The correlation between genetic distance (Fst) and geographic distance , i.e., the Mantel test 53, was used to test for isolation by distance (IBD) in GenAlEx v6.50254.The fixation index  was performed using ARLEQUIN v3.5, in order to assess the spatial population genetic structure of ns Table  was assins Table , Table 2G. mopsus populations was tested using mismatch distribution analysis and both Tajima’s D55 and Fu’s Fs neutrality tests56 in ARLEQUIN v3.5. Meanwhile, the demographic history of G. mopsus in Mongolia was determined using Bayesian Skyline Plot (BSP) analysis in BEAST v2.4.557. The GTR + G + I model of evolution was selected as the best model by jModeltest58. MCMC run was performed with 300 M iterations, with sampling every 1000 iterations, the first 10% of generations discarded as “burn-in”, a constant skyline model, and Bayesian skyline tree priors. The molecular clock rate and substitution rates were estimated as a default function. The run was examined using Tracer v.1.659, and all the parameters were analyzed to confirm that the effective sample sizes were sufficient (ESS > 200).The expansion of


Although it is generally accepted that geography is a major factor shaping human genetic differentiation, it is still disputed how much of this differentiation is a result of a simple process of isolation-by-distance, and if there are factors generating distinct clusters of genetic similarity. We address this question using a geographically explicit simulation framework coupled with an Approximate Bayesian Computation approach. Based on six simple summary statistics only, we estimated the most probable demographic parameters that shaped modern human evolution under an isolation by distance scenario, and found these were the following: an initial population in East Africa spread and grew from 4000 individuals to 5.7 million in about 132 000 years. Subsequent simulations with these estimates followed by cluster analyses produced results nearly identical to those obtained in real data. Thus, a simple diffusion model from East Africa explains a large portion of the genetic diversity patterns observed in modern humans. We argue that a model of isolation by distance along the continental landmasses might be the relevant null model to use when investigating selective effects in humans and probably many other species. Departing from Africa around 100 kya (thousands year ago), modern humans colonized the globe, scattering over the continents. This slow migration process created genetic divergence as populations migrated, splitting along the way, to settle over the landmasses. The history of humans can be deciphered using genetic differences between populations, reaching further than anthropological knowledge –4.In particular, it has been recognized that geography plays a major role in structuring populations . The sigHowever, looking at populations worldwide, genetic patterns show clustering of populations into major groups  . AlthougHence, two types of patterns arise out of empirical population genetic studies, cline and cluster, which seems contradictory. Interpretations have flourished around these patterns, fueling the misplaced debate of human races ,14.2 = 0.85) between genetic distances  and geographic distances . Although agreeing with this observed global pattern, studies favoring a cluster view point to discontinuities along the decline of diversity. For these clusters to appear, serial bottleneck events associated with isolation, must have generated what one could see as steps in a staircase of genetic diversity [Favoring a clinal view, some researchers have shown that human genetic variability declines as one moves further away from East Africa ,15. Moreiversity .ST and genetic diversity, and attributed the few deviations from these trends as being caused by “admixture or extreme isolation” [As an attempt to reconcile both perspectives, Serre et al.  brought olation” . ConcretThis apparent opposition between a clinal and a cluster view of human diversity arises because current models fails to re-create both patterns. Indeed, those models tend to simplify the complexity of human demographic history  as well as genetic processes . For example, studies looking for adaptation ,18 as weFor instance, Hofer et al. , lookingTo bridge the gaps between theoretical study and the discordance in empirical genetic studies, we present a simulation-based study.Here, we investigate the distribution of neutral genetic diversity in modern humans using spatially explicit simulations to model the demographic diffusion of our species throughout the globe and to recover the genetic signature left by this process. The simulations are used to estimate, the demo-genetic parameters best fitting a large microsatellite dataset of published data , 23 usinData from this study represent a subset of the dataset originally made available by Pemberton et al. , RosenbeAlthough dense SNP datasets and full genomes are now available, we used a microsatellite dataset in this study for the following reasons: (i) The microsatellites used here have been extensively checked and shown to have equally sized repeat units, which is expected if they evolve under the stepwise mutation model; (ii) they are unlinked and essentially neutral and (iii) we could only simulate so many loci in a spatially-explicit approach with the currently available computational power. Note that being multi-allelic markers, microsatellites contain more information per locus than SNPs .We estimated demographic and genetic parameters using an Approximate Bayesian Computation (ABC) framework. In brief, simulated dataset are generated over a large set of demographic parameters . The simulation outcome that best match the empirical data are selected to define a posterior probability distribution for each parameter. Genetic data were generated using a modified version of quantiNEMO  in a twoi, uniform prior distribution, from 2 to 5120), in Eastern Africa –today’s Ethiopian city of Addis Ababa, the origin of human expansion as estimated by Ray et al. [−5–10−3 mutations/locus/generation) for the same 70 populations and same number of individuals as the observed sampling scheme  for each of the 70 demes. These 2,485 summary statistics were then transformed into six “pattern” statistics, summarizing the relationships between FST, number of alleles and geographic distance as follows: The number of alleles sample was regressed on the geographic distance between the sampled location and Addis Abeba, and pairwise Fst were regressed against pairwise geographic distances. From these two regressions, we extracted six pattern statistics, namely the means, slopes, and the logarithm of the sum of residuals. The calculations of summary and pattern statistics for the observed data were carried out in R and the R-package hierfstat [In ABC, summary statistics are used to compare observations with simulations ,34. IdeaST  and ChorST  between ST , we usedierfstat . Finallyierfstat . This tei, μ, m, N, r, T) were estimated based on a comparison of the simulated and the observed summary statistics and a subsequent estimation step. The comparison of the summary statistics was obtained by assessing the Euclidean distance between simulations and the statistics from the empirical data, which can be used to rank the simulations from closest to most distant from the observations. Here, we retained the 5,000 simulations with smallest Euclidean distances from the observations. This subset of simulations was then used to estimate the parameter values using a weighted generalized linear model (GLM) [The six simulations parameters (Nel (GLM)  of the sel (GLM) .2) for the regression between pseudo-observed and estimated values, the slope of this regression, the standardized root mean squared error of the mode (SRMSE) and the proportion of estimates for which the 95% higher posterior density interval included the true value.Validation. In order to assess the quality of our estimation process, we perform a standard ABC validation. Hence, we used pseudo-observed values taken from the simulations. We quantify how well these values could be recovered when estimated through our ABC pipeline . This waST). A second comparison was based on the first two axes of a principal component analysis (PCA) computed on the individual allele frequencies in each sampled population. Since the sign of the coordinates along PCA components can differ between replicates, we compared the different sets of simulations by means of the squared correlation between observed and simulated PCA results. Each axis was considered separately. Thus, for each simulation, we estimated an R2 representing the correlation between simulated and observed populations coordinates on the PCA axes. These R2 values were compared across the three different sets of simulations .Using these estimated parameters, we generate new simulated samples with 100 loci per individual, with quantiNEMO. To investigate the effect of our estimated parameters, we ran three sets of 100 simulations each whose parameter values were sampled from the (i) prior distribution of the estimation step, (ii) posterior distribution (95%HPD) of the estimation step or (iii) taken directly from the point estimates  of the estimation step. Using the output of these simulations, we investigated how well these simulations could reproduce analyses carried out on the real data set. To check for consistency, the first comparison was based on the same six pattern statistics used for the estimations  successfully colonized all the sampled patches and were therefore used in the subsequent analyses. We obtained posterior estimates for all six demo-genetic parameters, which are presented in 2 = 0.877) for which the slope was nearly 1 (slope = 0.908), and the error rate low (SRMSE = 0.099). The proportion of the estimates that included the pseudo-observed value within their 95%HPD interval was 0.977, suggesting that our posteriors are slightly conservative. Good inference was also achieved for migration rate (m), current population size (N) and initial population size (Ni) for which the R2 values were about 0.5 and the slopes above 0.6. We had rather poor estimations for time of the onset (T) and population growth rate (r) where R2 values were below 0.3 , mimicking the empirical sampling scheme. These additional simulations were carried-out by randomly sampling parameter values from either (i) the prior posterior, (ii) the truncated posterior (at the 95%HPD level) distributions or (iii) the point estimates.As these parameters were estimated using basic genetic polymorphism summary statistics, it is essential to check whether such simple expansion can produce the empirical cline and clustering patterns.We first verified that our simulations were able to replicate the clinal pattern observed in the original genetic data. The comparison of the three simulation sets and the empirical cline emphasizes the power of the ABC inference. Indeed, as expected, parameters sampled from posterior distribution produce patterns closer to the empirical dataset than the prior distribution. The point estimates produce patterns close, on average, to the posterior distribution, with less variation around the true value . FinallyNext, we investigated whether the simulated genetic data could reproduce the clustering patterns observed in the Principal Component Analysis (PCA). In the empirical dataset, one observes clear divisions between continental groups , as prevFinally, we also looked at the partitioning pattern generated by the software STRUCTURE. Simulations and empirical data gave the same estimates of the most likely number of groups (K) within the worldwide sample either using the highest likelihood of the data as the criteria for defining K (which led to K = 7 in both observations and simulations), or using ΔK , which fWe have shown using approximate Bayesian computation that a simple model of expansion from East Africa using the world-wide landmasses leads to meaningful estimates of the past demography of our species. Furthermore, when genetic data sets generated according to this past demography are analysed with Principal component analyses or the STRUCTURE program, we obtain results that are extremely similar to those observed in the original human microsatellite dataset. We discuss these findings below.Despite the increasing use of genetic markers in anthropological reconstruction, it remains unclear how to model the observed patterns of genetic diversity around the world, largely because of the complexity of evolutionary processes of the human species. Specifically, the apparent opposition between cline and clustering patterns, as observed in empirical studies, remains a challenge as most existing model fail to reproduce both patterns. Owing to the release of new fast simulation tools, such as quantiNEMO, and the rising availability of global datasets, we reconstruct a simple expansion scenario that reproduces the clustering effect of modern populations, using large samples of published microsatellites data.−4 mut/allele/gen) matches recent estimates [Based on empirical data of 346 microsatellites in 1,484 individuals from 70 populations, this study has inferred six parameters  that defines a worldwide expansion model using the computationally intensive ABC framework. Despite the simplicity of the model, the inference works remarkably well. The estimated values are similar to other studies. The mutation rate , the simple one used here proved to be very useful for explaining the main patterns of human genetic variation. Such a model may represent a good choice for establishing a neutral background in future studies looking at more complex questions in modern human evolution such as the detection of selective events –60. SpecS1 FigABC framework shows the basic structure of an ABC analysis focused in parameter estimation. Full-dataset simulations represents the following step in which simulations were run based on the estimations above and for which complete allele frequency data was retained. In Pattern comparison, further analyses were run in order to compare simulations and observations in way they produce results for IBD regression analysis, PCA and STRUCTURE.(TIF)Click here for additional data file.S2 Fig2. The pairwise geographic distances between populations were calculated with the R package gdistance correcting for the Earth curvature and considering only on-land pathways–and between A and geographic distance from Addis Ababa (the origin of the expansion).The origin of the expansion of humans in East Africa is marked as the green dot. Map following Fuller’s Dymaxion projection, the same applied to the maps used in the simulations. The modeled map contained 20,384 square demes , each with an approximate area of 160 x 160 km(TIF)Click here for additional data file.S3 FigGray lines represent the prior distributions; black lines, the posteriors; the gray dashed vertical lines, the modes for the posteriors (point estimates). The estimations were carried out on 5,000 out of ~1 million simulations which were the closest to the observations in six pattern statistics (see (TIF)Click here for additional data file.S4 FigST comparisons between patches. CAR_CAPA stands for current population size; INI_SIZE, initial population size; MUT_RATE, mutation rate; GRW_RATE, population growth rate; EXP_TIME, time of onset of the expansion; MIG_RATE, migration rate. Below each panel, the values for the mode (point estimates) are given for every parameter.Gray lines represent the realized priors; blue dashed lines represent the distribution of the parameter values in the retained simulations; red lines represent the posterior distributions. The PLS calculation was conducted on a set of 2,485 statistics comprising number of alleles (A) and gene diversity (Hs) per patch and all pairwise F(TIF)Click here for additional data file.S5 FigA, comparison of the patterns generated for the cline in heterozigosity between observation and a simulation based on the point estimates. B, convergence of different pattern statistics related to the heterozigosity cline across different samplings from prior or posterior.(TIF)Click here for additional data file.S6 FigVertical bars represent the 70 populations as used in the simulations and the colors code for the proportion of each inferred ancestry group . One can observe that particular populations become highlighted in the observations ; while, in the simulations, many populations begin to show admixed compositions.(TIF)Click here for additional data file.S7 FigThe figure contains the results obtained both for observations (Observed) and simulations (Simulated). L(K) is the direct assessment of likelihood for each number of groups. Delta-K is the estimate based on Evanno et al.’s 2005 approach.(TIF)Click here for additional data file.S1 TablePopulations marked with “a” were merged together due to their geographical proximity (less than 160km apart) and were considered to inhabit the same deme in the simulations and also in the analyses applied to the read dataset. Populations marked with “b” were removed from the pattern statistics calculations: They were either known exceptions to the general patterns found in the continent (Aché), or were sampled in the vicinity of other populations, on the edges of their original distributions. For these, we kept the populations with the larger sample sizes and these were the Karitiana (as opposed to the Suruí) and Guarani (as opposed to the Kaingang).(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.