Convolvulus tragacanthoides and Convolvulus gortschakovii are closely related and have similar morphology. Through our field investigation, we found that the annual precipitation of the two species distribution areas is significantly different. Thus, C. tragacanthoides and C. gortschakovii provide an ideal comparative template to investigate the evolutionary processes of closely related species, which have adapted to different niches in response to changes in paleogeography and paleoclimate in northwest China. In this study, we employed phylogeographical approaches (two cpDNA spacers: rpl14–rpl36 and trnT–trnY) and species distribution models to trace the demographic history of C. tragacanthoides and C. gortschakovii, two common subshrubs and small shrubs in northwest China. The results showed the following: (1) Populations of C. tragacanthoides in northwest China were divided into three groups: Tianshan Mountains—Ili Valley, west Yin Mountains—Helan Mountains‐Qinglian Mountains, and Qinling Mountains—east Yin Mountains. There was a strong correlation between the distribution of haplotypes and the floristic subkingdom. The three groups corresponded to the Eurasian forest subkingdom, Asian desert flora subkingdom, and Sino‐Japanese floristic regions, respectively. Thus, environmental differences among different flora may lead to the genetic differentiation of C. tragacanthoides in China. (2) The west Yin Mountains—Helan Mountains‐Qinglian Mountains, and Qinling Mountains—east Yin Mountains were thought to form the ancestral distribution range of C. tragacanthoides. (3) C. tragacanthoides and C. gortschakovii adopted different strategies to cope with the Pleistocene glacial cycle. Convolvulus tragacanthoides contracted to the south during the glacial period and expanded to the north during the interglacial period; and there was no obvious north–south expansion or contraction of C. gortschakovii during the glacial cycle. (4) The interspecific variation of C. tragacanthoides and C. gortschakovii was related to the orogeny in northwest China caused by the uplift of the Tibetan Plateau during Miocene. (5) The 200 mm precipitation line formed the dividing line between the niches occupied by C. tragacanthoides and C. gortschakovii, respectively. In this study, from the perspective of precipitation, the impact of the formation of the summer monsoon limit line on species divergence and speciation is reported, which provides a new perspective for studying the response mechanism of species to the formation of the summer monsoon line, and also provides a clue for predicting how desert plants respond to future environmental changes.Desert ecosystems are one of the most fragile ecosystems on Earth. The study of the effects of paleoclimatic and geological changes on genetic diversity, genetic structure, and species differentiation of desert plants is not only helpful in understanding the strategies of adaptation of plants to arid habitats, but can also provide reference for the protection and restoration of vegetation in desert ecosystem. Northwest China is an important part of arid regions in the northern hemisphere.  Convolvulus tragacanthoides and Convolvulus gortschakovii, two subshrubs adapted to arid and semi‐arid climate, respectively. Our results show that the long‐term climatic differences between the arid and semi‐arid areas might increase species divergence and local adaptation.In this study, we employed phylogeographical approaches and species distribution models (SDMs) to trace the demographic history of  Populations of C. gortschakovii were divided into three groups: the Junggar Basin, the Alatau Mountains—central Tianshan Mountains—Tarim Basin, and the Alxa Desert. The Tianshan Mountains are considered to pose as a substantial geographical barrier to gene flow between the Junggar Basin and the Tarim Basin, while the former is considered the ancestral distribution area of C. gortschakovii. Further, it has been inferred that the Eurasian forest subkingdom plays an important role in the promotion of genetic diversity of xerophytes in the Asian desert flora subkingdom.Jia and Zhang  used tworpl14–rpl36 and trnT–trnY intron) and a species distribution model to trace the demographic history of C. tragacanthoides and C. gortschakovii and to discuss any potential phylogeographical events corresponding to paleoclimatic and paleogeographic changes, which may have affected these species. Specifically, we aimed to: (1) compare the genetic diversity, genetic structure, and phylogeographic pattern of two related species and; (2) compare the responses of plants adapted to different degrees of drought to paleoclimate change. These data will provide new insight into historical vegetation dynamics and plant species evolution in northwest China.In the present study, we conducted a phylogeographical investigation using two plastid regions  unit . Sequences were edited in Seqman , aligned using CLUSTAL_W  were obtained from Jia and Zhang (The data of GPS coordinates and sequence for nd Zhang .2.2h) and nucleotide diversity (π) of each population were calculated using Arlequin v.3.11  and total genetic diversity (HT) were calculated using PERMUT v.1.0  was set from 2 to 10. The DeltaK method was used in the Structure Harvester online tool to determine the optimal number of groups. A molecular variance analysis (AMOVA) was performed by Arlequin v. 3.11 . In this case, three fossil calibration points were used according to Eserman et al.   Urb., Merremia hederacea (Burm. f.) Hall. f.) between 41.2 and 47.8 Ma . The outgroups were similar to that used by Eserman et al.  with a resolution of 2.5 m. The periods involved include the contemporary mid‐Holocene (~6 ka) (ka: thousand years ago) and the Last Glacial Maximum , where the climate simulation models for the last two historical periods included the Community Climate System Model 4 (CCSM4). Considering that there may be a strong correlation between these climatic factors, the Pearson correlation coefficient (r) was used to remove climatic factors with a correlation r > 0.8  and a high total genetic diversity (HT = 0.833). In populations with more than three individuals, the highest diversity of haplotypes was observed in population S1 (h = 0.8333), and the highest nucleotide diversity was observed in population G6 (π = 1.499 × 10−3).There was a large number of populations with only one haplotype, and the geographical distribution pattern of haplotypes resulted in a low average genetic diversity (NST (0.882) value of C. tragacanthoides was significantly higher than that of the GST (0.607) value, indicative of significant phylogeographic structure.The PERMUT analysis showed that the FCT values from K = 3 to K = 4. When K ≥ 4, the grouping structure begins to disappear. Thus, the grouping scheme corresponding to K = 3 is as follows: (group 1) populations X1‐11, belonging to the Tianshan Mountains and Ili River Valleys (TI); (group 2) populations G1‐2, G4, G8‐9, S1, N1‐5, N7, M1‐4, Q1, belonging to the west Yin Mountains, Helan Mountains, and Qingling Mountains (YHQ); (group 3) populations G3, G5‐7, N6, HB1‐2, belonging to the Qinling Mountains and east Yin Mountains (QY). A Bayesian cluster analysis showed that the optimal grouping number (k) of C. tragacanthoides populations was four , and the differentiation level was high, with 83.61% of the genetic variation distributed among the three geographic groups ; group  populatiThe MDA analysis was used to estimate population dynamics. The observed multiple distribution for all samples and the group level rejected a recent history of population expansion Figure . In contIn the ancestral area reconstruction analysis, the ancestral area was estimated as AC and BC by S‐DIVA, B and C by BBM, AC and BC by DEC, and AC and BC by S‐DEC. As regions B  and C (Qinling Mountain) always occur as ancestral area, they were identified as two ancestral areas. The order of dispersal events supported by all four models was from BC to A , B and C; B to C; and C to D (east Yin Mountains). The results showed that the haplotypes in region A are closely related to the haplotypes in region C, the haplotypes in region D come from region C, and there is gene exchange between the regions B and C. Vicariance events occurred between B and C, A and C, C and D Figure .3.2C. tragacanthoides and C. gortschakovii was 0.43. For C. tragacanthoides data, the network supported the division of haplotypes into three geographic groups, TI, YHQ, and QY, and four haplotypes were star‐like in the network. The western group included haplotypes of TI, where H2 was found at the center of the network; and H10, H11, and H17 were in the center of the network for the eastern groups. H10 and H17 and their connected haplotypes were mainly distributed in YHQ, while H11 and its connected haplotypes were mainly distributed in the Qinling Mountains, and H22 was distributed in the east Yin Mountains. The network suggested that the two species were resolved as monophyletic. For C. tragacanthoides, haplotype H7 had the closest relationship with haplotype GH3 in C. gortschakovii. The results showed that the haplotypes of the two species distributed in the western region are closely related . The five main groups  mainly diverged during the Miocene. The nearest common ancestor of all 22 haplotypes of C. tragacanthoides could be traced back to the Miocene at 15.7652 Ma. The most recent divergence occurred during the Pliocene and Pleistocene periods.In the phylogenetic trees, d Figure . The phy3.3C. tragacanthoides and C. gortschakovii  and precipitation of driest month (17.8432%) explained more than half of all variation (56.1489%) observed in the distribution of C. tragacanthoides. During the LGM, the potential geographic distribution was significantly smaller than that during the present period. For C. gortschakovii, precipitation of coldest quarter (25.4588%), mean temperature coldest quarter (21.0369%), and annual precipitation (12.9671%) explained more than half of the variation (59.46%) in the distribution of the species. Compared with the present geographical distribution range, the potential distribution area in the LGM is smaller. During the mid‐Holocene period, the potential geographic distributions of the two species were closer to the present geographic distribution , whereby the Taihang Mountain‐Yinshan Mountain‐Helan Mountain‐Qilian Mountain form the boundary. There are great differences in altitude on both sides of the summer monsoon limit line, and ecological differences caused by complex terrain and climatic factors promote species differentiation ; data curation ; formal analysis ; investigation ; methodology ; project administration ; resources ; software ; supervision ; validation ; visualization ; writing – original draft ; writing – review and editing . Lina Xu: Formal analysis ; investigation ; writing – review and editing . Xiaoxiao Geng: Software . Hong‐Xiang Zhang: Funding acquisition ; resources ; software ; visualization ; writing – review and editing .The authors declare no competing interests.Appendix S1Click here for additional data file.


Lonicera caerulea L.) is a promising berry crop producing edible early-ripening berries with a valuable chemical composition. We evaluated the genetic diversity of native L. caerulea populations from the western  and eastern (the Russian Far East and Japan) edges of the Eurasian range using inter-simple sequence repeat (ISSR) and chloroplast DNA (psbA-trnH and trnL-trnF) markers. The genetic relationships of populations and genotypes were analyzed using principal coordinate and cluster analyses (neighbor joining and Bayesian clustering). Sampling was carried out in two disjunct areas of this circumpolar species and the analyses showed clustering of individuals and populations according to geographic origin. The analysis of genetic structure based on ISSR markers showed that the studied populations of L. caerulea were highly differentiated. However, sequence analysis of two chloroplast DNA (cpDNA) regions revealed no phylogeographic structure among the populations. We also found that the eastern populations of blue honeysuckle had significantly greater genetic diversity parameters than the populations from the Baltic region. This finding correlates with the endangered status of blue honeysuckle in the Baltic states.Blue honeysuckle ( Lonicera caerulea L. s.l. , is a highly polymorphic species native to the Northern Hemisphere that exhibits a large range from eastern Scandinavia to Kamchatka in Eurasia and is widespread in North America [Blue honeysuckle,  America ,2. It is America ,3. Curre America ,3,4,5. T America ,7,8. The America .L. caerulea [An understanding of the genetic structure of natural and cultivated populations of this widespread polymorphic species would help identify the optimal selective direction for obtaining new stable productive forms and varieties . On the caerulea ,4 or percaerulea .Lonicera, is native to Asia, where species diversification has taken place. Since diversification, there have apparently been multiple dispersal events to Europe and North America [Lonicera) are thought to have originated and evolved in present-day China, as plants from China are characterized by all the characteristics of plants from Central Asia, Transbaikalia, and Japan [L. caerulea are rare and small. In the Baltics, as a result of anthropogenic activities related to the development of forests and drainage of bogs, this species is threatened with extinction [L. caerulea grows in the wet forests of Latvia  and Estonia . In the central part of Russia, populations of L. caerulea are also rare, and the species is considered endangered [Phytogeographic studies based on chloroplast DNA (cpDNA) region and internal transcribed spacer (ITS) sequencing suggest that the clade of Caprifoliaceae, which includes the genus  America . The honnd Japan . Blue hond Japan , the rictinction ,15. At tVarious studies conducted in the past suggest that populations at the edge (periphery) of the species range often exist under suboptimal conditions ,17. A laNatural polymorphisms of blue honeysuckle are characterized by a high level of individual (intrapopulation) variability, which is shown by a number of morphological features, such as shoot color and length, leaf shape, tube, and pedicel lengths, corolla color, and fruit shape . A compaL. caerulea assessed using molecular markers. Holubec et al. [Although there have been many taxonomic, biological, and biochemical studies on honeysuckle, rather few reports have been published on the wild population genetic structure of c et al.  studied c et al. , 24 accec et al. ,20,21,22psbA-trnH and trnL-trnF) markers and tested the hypothesis that the genetic diversity of western populations experiencing suboptimal conditions is lower than that of populations in the areas of origin and that are widespread.In this study, we evaluated the polymorphism of ISSR and cpDNA markers in blue honeysuckle populations. ISSRs are universal DNA markers that, if carefully optimized, can be used for population studies in any species ,24. MarkA total of 140 individuals of blue honeysuckle were sampled, representing 9 native populations: Japan (Hokkaido and Honshu Islands)—23, Kemeri (Latvia)—12, Kandava (Latvia)—12, Mikeltornis (Latvia)—11, Ventspils (Latvia)—11, Kalli (Estonia)—14, Elizovo —22, Petropalovsk-Kamchatsky 1 —17, and Petropalovsk-Kamchatsky 2 —19 .Samples were taken at random from plants separated by a distance of at least 10 m, except for the Japanese honeysuckle population, where samples were collected from different parts of Hokkaido (19 samples) and Honshu (4 samples) (see ).2, 200 μM dNTPs, 20 ng of template DNA, each primer at 0.4 μM and 1 U of Taq polymerase . ISSR-PCR was carried out in a Mastercycler ep gradient  as follows: 94 °C for 7 min; 32 cycles of 94 °C for 30 s, the temperature for each primer  for 45 s, and 72 °C for 2 min; and ending with 72 °C for 8 min. Of the 23 tested ISSR primers, 10 were chosen for further study, all reactions were carried out at least twice in separate experiments, and reliable bands were scored. A negative control (water instead of DNA) was included in each experiment. Amplified DNA fragments were run in 1.5% agarose gels and 0.5 × TBE buffer at a constant voltage of 3.0 V/cm for 5 h. The gels were stained with ethidium bromide and examined under UV light using a BioDocAnalyse System . DNA fragment size adjustment among specimens from different populations and the evaluation of the genotyping error rate were performed as described by Patamsytė et al. [TM DNA Ladder Mix  was used as the DNA fragment size standard.Fresh and healthy leaves were collected from plants growing in natural habitats. The leaves were dried in bags with silica gel. DNA was extracted using a modified CTAB method  and adjupsbA-trnH and trnL-trnF) were analyzed by choosing 3–10 randomly selected individuals from each population   . Amplifiw et al. . DNA seqa) and the number of effective alleles (Ne) were calculated using GenAlEx v 6.5 [PT) and geographic distance matrices were examined with a Mantel test using GenAlEx v 6.5 [Scored ISSR-PCR products (DNA bands) were recorded and used to construct a 0/1 (binary) data matrix, which was used for further data analyses. Because blue honeysuckle from the studied populations is tetraploid (2n = 4x = 36), only genotype 0000 was scored as 0, whereas all other genotypes at a particular locus  were indistinguishable on the gel due to the dominant nature of ISSR markers and were considered as 1. The number of alleles per locus  were generated for L. caerulea using ten ISSR primers. The percentage of polymorphic bands ranged from 20.4%  to 53.6% .ISSR analysis was carried out on 140 individuals from nine populations of a) and effective alleles (Ne) varied between 0.983 and 1.425 (LV3-JP) and 1.123 and 1.348 (LV2-JP), respectively  between genetic and geographic distances among populations.The average polymorphism per population was 27.62 ± 3.42 (mean ± SE). When the number of individuals per population was rarefied to N = 11, the proportion of polymorphic loci (PLP 5%) varied from 0.204 (LV4) to 0.558 (JP) (mean PLP 5% = 0.283 ± 0.037). The band richness (Br) was also adjusted according to the smallest population size  and ranged from 1.204 (LV4) to 1.512 (JP). The Shannon index (I) ranged from 0.108 (LV2) to 0.3 (JP), with an average of 0.153 ± 0.02. The expected heterozygosity (Hj) varied from 0.076 (LV2) to 0.199 (JP), with an average of 0.104 ± 0.026. Only five population-specific bands were identified. One private band was detected in the RU2 population, and four private bands were identified in the JP population. A Mantel test showed a correlation . The third cluster contained honeysuckles from Japan. Samples collected in mainland Japan  were grouped within the Hokkaido Island samples in the dendrogram (not specified).In the PCoA, the first three axes explained most of the variation (84.54%). PC1 explained 48.44%, PC2 explained 26.7% and PC3 explained 9.4% of the variability. The results of the analysis revealed three clusters of individuals and populations grouped according to geographic origin .The two-dimensional scatter plot showed the most compact distribution pattern and overlap for Baltic region populations. The graph also revealed the grouping of Russian Far East populations with evident overlap between the RU1 and RU3 populations.p = 0.001), among populations within regions  and within populations . The pairwise divergence between the studied L. caerulea populations and the variation within populations is visualized in The genetic differentiation between the studied honeysuckle populations was high (Fst = 0.648 ± 0.022). We also evaluated population structure with hierarchical AMOVA to assess how genetic variability is partitioned among regions and populations and within populations. The results revealed similar proportions of variability among regions  possessed the highest within-population diversity.In assessing population genetic structure extrapolated using STRUCTURE software, we observed a major peak of real structure at K = 2 .In general, the most likely pattern of clustering separated the populations according to geographic origin, with the exception of the Japanese population .q < 0.8) than other genotypes of the same population.All western populations and the population from Japan were grouped into the red cluster, while populations from Kamchatka  were assigned to the green cluster A. Some itrnH and trnL-trnF sequences from 63 samples of different populations. The lengths of the psbA-trnH and trnL-trnF regions of L. caerulea were 559 nt and 1015 nt, respectively.No polymorphism was detected in the psbA-U = 1.00, Z = −2.205, p < 0.05), Br , and PLP 5%  values than populations from the Baltic region. L. caerulea is a fairly widespread species on the Kamchatka Peninsula and usually forms large populations [Different levels of DNA polymorphism were revealed in the nine studied blue honeysuckle populations. The highest polymorphism (53.6%) was detected in plants from the JP population, which included samples from Hokkaido Island and a few samples from Tochigi Prefecture (Honshu Island). Populations from Kamchatka  showed a moderate frequency of polymorphic loci 49.7%). The lowest level of polymorphism was observed in populations from Latvia (37.6%). The lowest values of Na, Ne, I, Hj, and Br were also observed for LV populations. Although the EE population showed greater genetic diversity, in general, the populations in the western part of the range were less genetically diverse than those in the eastern part. According to the Mann–Whitney test, populations from the eastern part of the blue honeysuckle range had significantly greater I (9.7%. TheST = 0.19) between native blue honeysuckle populations from Sakhalin and Kamchatka. Polymorphism of AFLP markers allowed these populations to be separated into different clusters in a PCoA plot and an NJ dendrogram. In general, the genetic structure of plant populations indicates the interplay among various evolutionary factors  in the context of many years of species evolution [L. caerulea across the very large circumpolar range of the Northern Hemisphere implies such complex evolutionary processes. Our results indicate that most of the genetic diversity occurs among populations. High genetic differentiation among populations is usually not characteristic of perennial outcrossing plant species [Acacia raddiana by genetic drift and local adaptation. Vashishtha et al. [Butea monosperma independent of the marker assay used, i.e., RAPD, ISSR, or sequence-related amplified polymorphism (SRAP). The authors of this study noticed that substantial population divergence may be determined by a low level of gene flow among the studied populations due to geographic distance, heterogeneity of ecological conditions, and possibly mixed breeding systems. The strong population differentiation of Juniperus communis in Lithuania (GST = 0.491) was dependent on habitat fragmentation and heterogeneity [L. caerulea populations may be the causative factors of low gene flow among them. The western and eastern populations included in our study are separated by large distances. Moreover, honeysuckle populations in Japan (Hokkaido and Honshu Islands) are separated from continental populations located on the Kamchatka Peninsula. The Mantel test showed that geographic distance is important for population divergence. Our results indicate significant IBD (p = 0.01). In addition, differences in the ecological environments of habitats among populations may also be important for population differentiation [L. caerulea populations . In such situations, populations may be impacted by genetic drift, which increases population differences and decreases variation within populations. The divergence between the studied populations is caused mainly by differences in allele frequencies. Only two populations (JP and RU1) possessed a few private bands. Three western populations  also did not show bands of low frequency (≤25%). The remaining populations had only one or two bands in this frequency range. Another interesting aspect of the obtained results is that the Japanese population grouped separately from the eastern populations . For example, PCoA revealed a divergence between remote populations and reliably subdivided all individuals into three groups. NJ analysis also showed three main clusters. Additionally, AMOVA showed a strong genetic structure in blue honeysuckle. Based on molecular and morphological data analyses, Holubec et al.  revealedvolution . The dis species ,45. Howe species ,46,47,48 species  explaine GST = 0.1 was depogeneity . The geontiation ,52. Anthferences ,65,66. Tferences . Differeequency ≤%. The references . Recentlferences  revealedL. cerulea continues to increase, studies on the genetic structure of blue honeysuckle will provide information about processes occurring in geographically isolated populations that are potential sources of genetic diversity and may contribute to the correct assessment of population divergence. More extensive studies of genomic polymorphisms are needed to develop a complete picture of the evolution of the species.Our study of DNA polymorphisms in populations confirmed the opinion of other authors that geographically separated blue honeysuckle populations are genetically divergent. In this study, this was determined at the level of noncoding DNA (ISSRs). The revealed high population differentiation reflects the complex intraspecific taxonomic structure of this honeysuckle species. On the other hand, the lack of polymorphism at the cpDNA level indicates the integrity of diverged populations or subspecies of one species. Populations on the western edge of the species range  are genetically less diverse than blue honeysuckle populations on the opposite edge of Eurasia. This correlates with their current endangered status in the Baltic states. As economic interest in


Ulmus laevis Pall.) located in Poland maintained slightly higher level of genetic diversity compared to the peripheral populations of this species.The core populations of the European white elm . Previous studies have indicated a low level of genetic variation in U. laevis in Poland were identified and characterized. Additionally, tests were performed to identify potential bottleneck signatures and effective population sizes of the examined populations.The genetic resources of Polymorphism was analyzed using a set of six nuclear microsatellite markers (nSSRs) for 1672 individuals from 41 populations throughout the species range in Poland.(1) A moderate level of genetic variation was found. (2) A low genetic differentiation and lack of population structuring were identified. (3) Evidence of reduction in population size was found as a consequence of severe, past bottlenecks.U. laevis probably occurred in their refugia or shortly after the postglacial recolonization. This loss may have been affected by past DED pandemics similar to those seen at present.The loss of genetic diversity of  Ulmus laevis Pall.) is a broadleaved tree whose occupied natural distribution in Europe extends from central France to the Urals. In the northern part of its range, white elm covers only the southern part of Finland; it reaches the southern end of its range in Albania and Bulgaria and grows in several isolated stands in Turkey , wych elm (U. glabra Huds.), and field elm (U. minor Mill.). The area occupied by elms covers 17,654 ha, i.e., 0.24% of the total forested area. There are only about 1000 ha dominated by elms  Nannf. and O. novo-ulmi Brasier, which are spread by bark beetles of Scolytus Geoffroy  Is the level of genetic diversity and genetic differentiation within and among examined populations comparable to other populations from entire natural distribution? (2) Is the observed genetic diversity the result of recent population decline or severe, past population bottlenecks? (3) Is the genetic structure of examined populations as a consequence of postglacial history?Microsatellite markers have been developed and successfully used for many elm species . Suitable markers originally described for o), and unbiased expected heterozygosity (He). The number of private alleles (Pa) and effective number of alleles (Ae) were calculated using GenAlEx 6  correction method . The RST values were compared after the allele sizes were permuted using the within loci pRst . The statistical significance of the alternative hypothesis of Rst > pRst, which suggests that allele size mutations contributed to the population differentiation, was estimated by a permutation (10 000 permutations) test implemented in the SpaGeDi 1.3d program , was tested using 9 999 permutations implemented in GenAlEx 6.The genetic structure of the white elm populations was evaluated using the Bayesian clustering method implemented in STRUCTURE ver. 2.3.4  of each population was estimated via the linkage disequilibrium method (Waples and Do Pcirt = 0.02). The 95% confidence intervals (CINe) were determined with the jackknife method described by Waples and Do  to r  and Wilcoxon test for heterozygosity excess  method, implemented in DIYABC v 2.0.1  was found in the Ulm6 locus, and the highest number (15) was found in both the Ulm3 and Ulm9 loci. Low frequencies of null alleles were found, with an average frequency of 0.014. As the frequency of null alleles did not exceed the threshold (0.2) over which null alleles can result in a significant underestimation of He) values was much lower, ranging from 2.1 in the LAM population to 2.6 in the WIR population, with an average value of 2.4. Due to the unequal sizes of the studied populations, the allelic richness (AR) values were calculated by reducing the sizes of all populations to 11 individuals (the size of the KLC population). The mean AR value was 4.0, and the populations were quite homogeneous, with AR values ranging from 3.0 in ZRB and BAB to 3.9 in KLE and PRZ. Private alleles (Pa) were found in nine populations at very low frequencies (below 10%), with an average frequency of 5%. The largest Pa number was detected in the BOB population (Pa = 4), and the POD and KLE populations each had two private alleles. The observed (Ho) and expected (He) heterozygosity values ranged from 0.595 (KLE) to 0.675 (BIE) and from 0.529 (LAM) to 0.585 (BIE), respectively. The mean Ho value (0.641) was higher than the mean He value (0.553), indicating an excess of heterozygotes (Fis = −0.149). The deviation of genotypic frequencies from Hardy–Weinberg equilibrium (HWE) in all studied populations was not statistically significant in all cases. The pairwise Fst values ranged from −0.0033 to 0.227, with an overall Fst of 0.076 . The differentiation value was somewhat lower when the null alleles were included, with an FstENA value of 0.074 at a 95% confidence interval (CI95% = 0.055–0.090). This result suggests that null alleles have a nonsignificant influence on the differentiation pattern among populations.In general, the studied populations were characterized by a moderate level of genetic variation Table . The meaRst = 0.061; CI95% = 0.046–0.124) was not significantly different from the differentiation that accounted for allele identities , indicating the absence of a geographic structure and that gene flow is high compared with the mutation rate.The global genetic differentiation based on allele size (K) is difficult because the current genetic structure of natural species populations is multifaceted and complex, as a consequence of the demographic, environmental, and historical processes influence (Meirmans K) did not lead to a biological interpretation for our dataset, we used alternative measures (MedMean and MaxMean) proposed by Puechmaille (K for our populations. Puechmaille methods were found to be more accurate than delta K or mean Ln P(K) with unevenly sampled populations, which is our case. The optimal number of clusters was K = 5 for all 41 U. laevis populations .Grouping and thus finding the optimal number of clusters  varied widely among the white elm populations and ranged from 2.7 (STU) to 360.9 (LEM), an overall harmonic mean of 16.8  and their interpretation was done with caution. The M-ratios (MRs) were significantly reduced according to the mean MRs derived under mutation-drift equilibrium (Mreq) for all examined white elm populations; this result is strong evidence of a past bottleneck. On the other hand, Wilcoxon’s test for heterozygosity excess (He), performed under the TPM model, indicated recent population reductions in only ten of the analyzed populations  over the scenario 1 “stable population” (posterior probability = 0.325 [CI95 % = 0.2717 - 0.3790]) and the scenario 3 “bottleneck population” (posterior probability = 0.1935 [CI95 % = 0.1449 - 0.2421]). This result revealed that the Ne population of U. laevis has undergone population size reduction in the postglacial refugia or shortly after the postglacial recolonization. The DIYABC result was in accordance with the result from M-ratio which revealed an ancestral and extended declines of population size.The effective population size based on linkage disequilibrium , were slightly higher than those in Spanish populations , Danish populations , and Norwegian group of trees   were also analyzed in Danish . Similarly, Whiteley  was lower than that of the pRst analog to Fst, indicating that random genetic drift was more important than mutation in causing the observed differences among the studied U. laevis populations in Poland.In our study, a low to moderate level of genetic differentiation was found among the studied Whiteley  found loK = 5) most likely provided representations of the overall genetic structure of the analyzed U. laevis populations. Generally, the gene pools of the analyzed populations were genetically homogeneous. The studied populations had comparable gene pool compositions except for a few populations, where one of five clusters was dominant. The non-geographically structured gene pool of Polish U. laevis populations was confirmed by the non-significant Mantel test . The current genetic divergence pattern of U. laevis in Europe, including in Poland, is the result of Quaternary climate change leading to a reduction in population sizes and the long-term isolation of populations during glacial-interglacial cycles and postglacial migration  identified three cpDNA haplotypes  which are characteristic for potential glacial refugia for white elm  , whereas Ne ≥ 500 is appropriate for securing long-term viability because the population can maintain a balance between genetic drift and mutation, thereby retaining its evolutionary potential. The Polish U. laevis populations studied herein are characterized by low population sizes with a mean Ne value of 16.8. Moreover, only eight out of forty-one (20%) examined populations exhibited Ne values over 50. Based on these assumptions, the studied populations probably maintain a low evolutionary potential. However, field observations of Polish U. laevis populations indicated that they are in good condition despite their moderate level of genetic diversity and low effective population size.The genetic bottleneck phenomenon is largely related to the Frankham . The assFrankham  has becoUlmus in the Holocene started at approximately 6000 B.P. from the southeast direction, before the advent of the Neolithic people, and the proportion of Ulmus in the overall forest stand composition was higher than 10%  in the European information system on forest genetic resources  conservation network and subjected to continuous monitoring.Despite the pandemic and moderate genetic diversity, white elm individuals are still quite numerous in Poland. However, in white elm populations, very significant reductions in the numbers of individuals have taken place, and most populations have low effective population sizes. In Poland, a twofold increase has occurred in the area of forest stands dominated by elms in the last 50 years (Napierala-Filipiak et al.


Nasutitermes ephratae, which is widespread in the neotropics. To date, only one study has been published about phylogeography in neotropical termites (N. corniger). Here, we explored the population genetic patterns of N. ephratae and also evaluated the phylogeographical processes involved in the evolutionary history of the species. We used the mitochondrial genes 16S rRNA and COII as molecular markers: these were sequenced for 128 samples of N. ephratae. We estimated the genetic diversity and divergence time as well as the demography and genetic structure. We also performed an ancestral area reconstruction and a haplotype network. The results showed high genetic variability, recent demographic expansion, and strong genetic structure. A dispersal route for the species, that occurred in both directions between South and Central America, was inferred. The results emphasize a temporary separation between the South and Central America populations that affected the origin of the current Central America populations. These populations were formed from different phylogeographic histories.The neotropical region ranks third in the number of termites and includes five different families. Of these, Termitidae is the most diverse and includes the species  Evolutionary phenomena such as vicariance, dispersion, and extinction shaped the geographical distribution patterns of the species found in this regionNasutitermes—corresponding to almost 40% of the number of species of the subfamily2.The neotropical termite fauna ranks third in the number of species. Of the five neotropical termite families, Termitidae is the most diverse and includes the subfamily Nasutitermitinae with 171 species described in the neotropical region, including 67 in the genus 3.Like the other Nasutitermitinae, the soldiers of this genus are characterized by a conic-shaped frontal projection with the opening of an exocrine gland located at the top. This gland produces substances used for defense against predatorsNasutitermes ephratae 7.tae Fig.  was descNasutitermes ephratae is reconstructed close to N. corniger in various phylogenetic studies11. Both species are very common in the neotropics including most of Central and South America, e.g., Brazil, Bolivia, Colombia, Ecuador, French Guiana, Guyana, Peru, Suriname, and Venezuela15.N. corniger15. The results showed high variability and strong genetic structure for the populations sampled. These were divided in haplogroups along its occurrence area associated with South American biomes. The authors also proposed a dispersal route for N. corniger, which would have left Central America towards South America, where the populations dispersed toward the eastern regions.To date, only one phylogeographic study addressed neotropical termites, focusing on 16. Thus, the association of various phylogeographic data can help us to understand the evolutionary history of neotropical biota.Phylogeographic and biogeographic studies help to explain how the species responded to geological and climatic changes over time. This fact highlights the importance of phylogeographic studies that present a variety of taxonomic groups, including insects, that have population and evolutionary dynamics different from other taxaN. ephratae in the neotropics. For this purpose, we leveraged the mitochondrial molecular markers 16S rRNA and the subunit II of the code gene to cytochrome oxidase (COII). It is important to highlight that the mitochondrial DNA (mtDNA) have been extensively used for the investigation of phylogeographic issues as well as other evolutionary questions of a large variety of species, including termites23.The main aim of this study was to explain the phylogeographical and historical processes that gave rise to population patterns of We performed demographic analyses, estimates of variability and genetic structure, analyses of divergence time, and ancestral area reconstruction. We also performed a haplotype network and proposed a dispersal route for the species.24, as follows (considering the areas that were sampled): Antillean subregion (ANT) and Mesoamerican dominion (MES) in Central America; Pacific dominion (PAC) in Central and South America; Boreal Brazilian dominion (BOR), Chacoan dominion (CHA), Parana dominion (PAR), and south Brazilian dominion (SOU) in South America.The biogeographic units defined for the analyses are according to the neotropical regionalization in dominions proposed by MorroneN. ephratae (Table Museu de Zoologia da Universidade de São Paulo (MZUSP) and the University of Florida (UF). The samples were collected in different campaigns and stored in 96% ethanol (MZUSP) and in 85% ethanol (UF) to better preservation of the DNA. Extracted DNA are stored in the Molecular Collection of the Laboratório de Biologia Evolutiva (LaBE) of the Faculdade de Ciências Agrárias e Veterinárias, UNESP . In addition to these samples, 12 nucleotide sequences of N. ephratae obtained in GenBank (public access) were included in the analyses, totaling 128 samples analyzed.The samples of ae Table ; Fig. 2a25 protocol, which includes phenol–chloroform for the extraction and 100% and 70% ethanol for the DNA washing. The amplification of the 16S rRNA and COII (gene regions of mtDNA) was performed by PCR following the conditions described in Supplementary Table 28, PCR Master Mix (Promega) 1x, nuclease free water, and 2.4 ng/μL of target DNA. The PCR product was purified using the Wizard® SV Gel and PCR Clean-Up System (Promega) according to the manufacturer’s instructions, followed by Sanger sequencing in ABI 3730 XL DNA Analyzer (Applied Biosystems) automatic sequencer.The total DNA was extracted from the head of an individual per colony following Liu and Beckenbach30 and the sequences of COII were aligned using Geneious v. 7.1.9 (https://www.geneious.com) by ClustalW, both followed by inspection by eye. Geneious also was used to concatenate the 16S and COII sequences of the samples in which both regions could by sequenced.The nucleotide sequences were read in Chromas Lite v. 2.6.5 . The sequences of 16S were aligned using Mafft v. 731: number of polymorphic sites (S), nucleotide diversity (π), average number of nucleotide differences (k), and haplotype diversity (Hd). In addition to these parameters, the value of θ-W per sequence was estimated using Arlequin v. 3.5.1.232. This software also was used to perform the Fu’s Fs33 and Tajima’s D34 neutrality tests—these tests were performed for the entire sample set and, separately, for each neotropical dominion sampled. The other neutrality tests Fu and Li’s F* and D* and Achaz Y*35 were performed using DnaSP v. 631.To quantify the genetic diversity, the following parameters were estimated using DnaSP v. 636 (the model Kimura 2-parameter37 was used to estimate the genetic distances). The genetic distances and the neutrality tests were estimated for both the concatenated sequences (16S + COII) and the single genes (16S or COII). Only the single genes were considered to estimate the genetic diversity indexes.The nucleotide composition of the sequences and the pairwise genetic distances  were estimated using MEGA-X38, that uses parsimony method to establish the relationship among the haplotypes, with 95% of connection limit. The haplogroups were defined based on the shape of the network, considering the distance among the haplotypes and among the tips and central haplotypes of the group. The network was colored using tcsBU39 considering the frequency of the haplotype in each neotropical dominion sampled.The haplotype network was generated with the concatenated sequences (16S + COII) using TCS v. 1.2140 based on the datum WGS 1984 and on the shapefile of the neotropical region developed by Löwenberg-Neto41, considering the regionalization proposed by Morrone24.The map showing the distribution of the samples, colored according to the observed haplogroups, was performed using QGIS v. 3.6.332 to assess the possibility of genetic structure among the sampled populations. Using the concatenated sequences, tree constructions were defined for this analysis: (i) an AMOVA was performed considering the haplogroups of the haplotype network; (ii) the second AMOVA was performed separating the samples in seven groups according to their neotropical dominion ; (iii) the third AMOVA was also performed considering the dominions as groups, but regrouping them into two major groups: Central America  and South America . The F indexes, calculated by AMOVA, range from 0 to 1 and indicate high differentiation above 0.25 and moderately high differentiation between 0.15 and 0.2542.The Analysis of Molecular Variance (AMOVA) was performed using Arlequin v. 3.5.1.244 implemented in R v. 4.0.145. The results were obtained for both the concatenated sequences and the single genes. The graphs showing the geographical distribution of each genetic cluster (considering the concatenated sequences results) were obtained using Microsoft Excel (2008).Even to evaluate the inference of genetic structure, we performed an analysis of DNA clustering using the package rhierBAPS46, implemented in R v. 4.0.1, considering the Pearson’s correlation as method with a number of permutation equal to 10,000. We inputted the concatenated sequences and the geographical coordinates of each sample to perform this analysis.The Mantel test was performed using the package veganN. ephratae relying on Bayesian inference. The tree was generated in BEAST v. 2.6.347 using strict clock and chain length equal to 90 million. Two partitions were included in the analysis, the first containing the 16S sequences and the second containing the COII sequences. The tree models (Fossilized Birth Death Model)48 and the clock models for both partitions were linked, and the substitution model TrN + I + G were applied for 16S and COII49. The selection of the best-fit model of nucleotide substitution was done based on the results of jModelTest50 considering the lower values of BIC.We estimated the divergence time among the samples of 48: Valditermes brenanae51, 136.4 to 130 million of years ago (My); Nanotermes isaace52, 56 to 47.8 My; Nasutitermes electrinus53, 23.03 to 15.97 My; and Atlantitermes caribea54, 20.44 to 13.82 My. Besides the fossil records, the origin dates estimated by Bourguignon55 for Termitidae (54 My), Nasutitermitinae (26.2–19.4 My), and Nasutitermes (22.6–16.4 My) were used as an additional calibration to improve de analysis estimation , Amitermes dentatus (Termitidae: Termitinae), Atlantitermes snyderi (Termitidae: Nasutitermitinae), and Nasutitermes longinasus (Termitidae: Nasutitermitinae).The Bayesian inference was calibrated with the ages of four fossil records, following Heath, Huelsenbeck, and Stadler instructions56 to check the values of Effective Sample Size (ESS > 200). The trees were resampled using the BEAUti v. 2.6.3 application “Full To Extant Tree Combiner”47 to remove the fossil taxa of the topology, keeping only the extant species. The best tree was annotated by TreeAnnotator v. 2.6.357 with 10% burn-in, and it was viewed and draw using FigTree v. 1.4.458.The MCMC trace files generated by Bayesian inference were viewed and analyzed using Tracer v. 1.7.159 in R v. 4.0.145. We defined seven occurrence areas for this analysis, according to the neotropical dominions which were sampled . We tested the models DEC, DIVALIKE and BAYAREALIKE with and without j . The best-fit model was selected based on the lower values and higher weights of AIC and AICc.The tree topology, as well as the divergence times estimated by this analysis, were used for the ancestral area reconstruction analysis, which was performed using the package BioGeoBEARSWe obtained 123 nucleotide sequences for 16S [396 base pairs (bp)] and 108 for COII (742 bp) within the samples, totaling 103 specimens with both mtDNA genes sequenced. The nucleotide composition of the concatenated sequences corresponds to 40.27% of adenine (A), 25.35% of thymine (T), 21.20% of cytosine (C), and 13.19% of guanine (G). The mean genetic distance among the samples was 0.011 for the concatenated sequences, 0.007 for the 16S sequences, and 0.015 for COII sequences (the pairwise genetic distances are presented in Supplementary Tables We found 66 haplotypes considering the concatenated sequences: 41 haplotypes (Hd = 0.890) for 16S and 53 haplotypes (Hd = 0.932) for COII  and Central America (MES dominion). Cluster 3 includes only samples from Central America (MES and northern portion of PAC), and cluster 4 includes samples from BOR, ANT, and mainly from PAC (northern and southern portions) and MES. Cluster 5 includes samples from BOR and southern portion of PAC , thus indicating that there is a positive correlation between the genetic distance and the geographic distance, i.e., the genetic distances can increase as geographic distance between populations increase.Mantel’s test was performed to verify the existence of isolation by distance and resulted in a significantly positive N. ephratae populations were estimated from a Bayesian inference (BI) analysis performed using the haplotypes of the 16S and COII sequences  separated the Hg 4 + H25-H59 clade  from the other clades – the ancestral node of this clade was dated to 2.19 My by the analysis and the Hg 4 clade was dated to 1.74 Ma. The second event (2.98 My) separated the Hg 1 clades + Hg 3 clade from the Hg 2 clade, which ancestral node was dated to 2.57 My. The last one includes only specimens distributed in Central America (except for one sample of H19 found in BOR). The third event (2.52 My) caused the divergence among the Hg 1 main clade (formed by South American populations) from the Hg 1 + Hg 3 clade (formed by samples from northern South America and Antillean islands). The ancestral nodes of the Hg 3 and the Hg 1 main clade were dated to 1.59 My and 1.73 My, respectively.The divergence times of the ces Fig. . Accordij, presented AIC and AICc equal to 211.7 and 212.4 with weights equal to 0.85 and 0.84, respectively. The results showed that the common ancestor of all the clades was distributed in MES and PAC dominions and were dispersed posteriorly to the other areas  and particularly for each neotropical dominion sampled. This also can be inferred from the star shape of the haplogroups in the haplotype network, thus suggesting that a lot of descendent haplotypes had recently risen from the ancestor haplotypes located in the center of the haplogroups.Regarding to the demographic history of the ts Table b,c, specThe haplotype network Fig. b also shThese groups were also recovered by the clustering analysis Fig. a except 61. Despite this, large clades of this tree include haplotypes from the same haplogroup . Moreover, the early Quaternary climate changes (early Pleistocene) was characterized by temperature and dryness oscillations in the continents and was also impacted the adaptability and the migration of species and populations62.Geological and/or geographic factors could also help to explain this split. Following this approach, it is possible that the separation is related to the tertiary's tectonic and paleographical reorganization movements (in the late Pliocene), which led to the emergence of barriers and changes in dispersal routes in South America65, it is more likely that the paths taken by the populations of N. ephratae were overland, which makes the PAC dominion an obligatory passage for the dispersal of populations between South and Central America. This dominion harbor peculiarly haplotypes from all haplogroups and present a higher value of intra-dominion genetic distance (Table N. ephratae dispersions.Although dispersion by water (inside flotsam carried by ocean or river currents or ferried by vessels) helps to explain dispersion patterns for termite speciesce Table b. These N. corniger species15 including the dispersion from Central to South America, the eastward dispersion on South America lands, and the late occupation of Atlantic Forest. Nevertheless, the dispersal from South to Central America and the temporary split among the population have not yet been detected for N. corniger unlike N. ephratae. However, it is important to note that only the 16S mitochondrial marker was used for the N. corniger analyses15. The addition of other markers can lead to a more robust comparison among the population patterns of the two species.The dispersal route traced for the populations analyzed here are very similar to the dispersal route observed for 66 also studied dispersal routes and identified that South America is most probably the origin of most of the Caribbean arthropod fauna including N. ephratae, N. corniger, and other species of the genus Nasutitermes. These data contradict the inferences raised by Santos et al.15 although there are some important methodological differences among the studies. Crews and Esposito66 analyzed 18 species of Nasutitermes using the same mtDNA genes used here as molecular markers (16S + COII). The dispersal route proposed herein is according to the inferences made by Crews and Esposito66 because the N. ephratae Antillean populations arose from a South America ancestor according to the ancestral area reconstruction. Specifically, for these island populations, it is possible that the dispersion from northern South America to the Antilles occurred via flotsam or floating wood carried to the islands by oceanic currents. This kind of dispersion has already been detected for Caribbean termite species65. Most of the islands’ termite species analyzed here are compose of haplogroup 3 together with samples from French Guiana (BOR dominion) and Trinidad and Tobago (located in the transition between BOR and PAC). This data suggests high genetic similarity among populations of these areas—although a larger sampling can better clarify the relations among the two dominions.Crews and Esposito71. As a speculation, this hypothesis could help clarify the existence of haplotypes that are genetically distant from each other in Amazonian localities that are geographically close to each other. However, we point that it is necessary to include a greater sampling of this region to clearly detect the genetic population patterns and the evolutionary events involving N. ephratae in this dominion.Regarding the BOR dominion, we found that the samples from this area have haplotypes typically found in other areas, which stayed distant from each other in the haplotype network (except for the haplotypes from French Guiana). This distance was also observed in BI. Some authors argue that the Amazonian biota (composed of parts of BOR and SOU dominions and the southeastern Amazon dominion—the latter was not sampled herein) have unnatural biogeographic originsN. ephratae and/or including new molecular markers in future studies, as well as addressing phylogeographic issues of other species, can help solidify the inferences made in this work and can expand the understanding of the evolutionary history of the group and of the neotropics.In general, there are many questions to be explored about the phylogeographic processes of the South American and neotropical species especially for termites whose studies are still new. Better sampling of N. ephratae occurs in the neotropics. It was possible to make important inferences about the general panorama of the evolutionary history of the species in this region although a broader sampling, especially from central-eastern South America, could better clarify some phylogeographic patterns. Our data also showed similarities on the population and dispersal patterns among N. ephratae and N. corniger. Here, it is possible to speculate that both species responded similarly to the biogeographic processes that have occurred in the neotropics although new comparative studies may better answer this question. In summary, this study offers important contributions to the understanding of biogeographic and phylogeographic issues in the neotropics especially evolutionary studies of termites and other insects.This study considered most area where Supplementary Figure S1.Supplementary Figure S2.Supplementary Figure S3.Supplementary Tables.


The Himalayas show a huge variation in topography and climatic conditions across its entire range, and have experienced major climatic fluctuations in the past. However, very little is known regarding how this heterogenous landscape has moulded the distribution of Himalayan fauna. A recent study examined the effect of these historical events on the genetic diversity of the Himalayan langurs in Nepal Himalaya. However, this study did not include the samples from the Indian Himalayan region (IHR). Therefore, here we revisit the questions addressed in the previous study with a near complete sampling from the IHR, along with the samples from the Nepal Himalaya. We used the mitochondrial Cytochrome-Our dataset contained 144 sequences from the IHR as well as the Nepal Himalaya. Phylogenetic analysis showed a low divergent western clade nested within high divergent group of eastern lineages and in the network analysis we identified 22 haplotypes over the entire distribution range of the Himalayan langurs. Samples from the Nepal Himalaya showed geographically structured haplotypes corresponding to different river barriers, whereas samples from IHR showed star-like topology with no structure. Our statistical phylogeography analysis using diyABC supported the model of east to west colonisation of these langurs with founder event during colonisation. Analysis of demographic history showed that the effective population size of the Himalayan langurs decreased at the onset of last glacial maximum (LGM) and started increasing post LGM. The palaeodistribution modelling showed that the extent of suitable habitat shifted from low elevation central Nepal, and adjoining parts of north India, during LGM to the western Himalaya at present.The current genetic diversity and distribution of Himalayan langurs in the Nepal Himalaya has been shaped by river barriers, whereas the rivers in the IHR had relatively less time to act as a strong genetic barrier after the recent colonisation event. Further, the post LGM expansion could have had confounding effect on Himalayan langur population structure in both Nepal Himalaya and IHR.The online version contains supplementary material available at 10.1186/s12862-022-02054-1. Accumulation of genetic variation across different populations of a species could be attributed to changes in geography and climate via a combined effect of genetic drift, gene flow and selection –3. GeogrRivers are considered as long-term barriers to gene flow and therefore plays an important role in speciation and divergence –8. WallaSemnopithecus ajax, Semnopithecus schistaceus and Semnopithecus hector, as per the classification scheme proposed by Groves [Semnopithecus schistaceus Hodgson, 1840 [The Himalayan range is a globally recognised biodiversity hotspot with high levels of endemism and a complex geography, topography and climate . It extey Groves . Neverthy Groves , 55–60. on, 1840 .S. entellus in their paper) in the Nepal Himalayas were isolated into six major clades by the snow-fed Himalayan rivers suggesting the role of these rivers as a barrier to gene flow. Further, their demographic analysis showed that the Himalayan langur populations in Nepal started declining with the onset of the last glacial maximum (LGM) i.e., ~ 22,000 years before present (YBP). For their study, they used two mitochondrial genes—Cytochrome b (Cyt b) and hyper variable region (HVR) I. However, this study did not include any samples from the Indian Himalayan region (IHR) which constitutes a major part of the Himalayan langur distribution zone.Another study investigated the role of Himalayan rivers as well as past climate in shaping the distribution of Himalayan langur in Nepal Himalaya . They foIn this study, we revisit the questions addressed by Khanal et al.  with neaS. entellus sequence was used as an outgroup  of the My–Bg population was highest 0.015), in fact it was equivalent to all the populations combined together; the haplotype diversity (H) was also high (0.8)  which showed a significantly negative Tajima’s D value   had the highest contribution to the model (45.1%) followed by annual mean temperature  and precipitation seasonality . The response curves reveal that Bio17 value of above 200, Bio 1 value between 80 and 120 and Bio 15 value in the range of 115–121 seem to be an ideal habitat for Himalayan langurs , and a low nucleotide diversity (π = 0.015) which is comparable to R. brelichi (π = 0.014) [ST analysis indicates that the WMP and EMP have been separated from each other longer than populations within WMP and EMP.The study found that all the Himalayan langur samples, when pooled together, have a high haplotype diversity (Hd = 0.915) when compared to another temperate colobine = 0.014) . This co= 0.014) . To testOur phylogenetic tree with wider sampling is very similar to the tree in Arekar et al.  wherein The effects of past climatic events, such as Pleistocene glaciation, on the distribution and demography of a species includes range contraction, range fragmentation, local extinction and resultant bottleneck , 70. Aft2 statistic were not informative since the values for these indices were not significant in any of the population, except for the population Bg–A. However, it should be noted that these statistics by itself may not be very reliable for investigating the demographic history of a population and may depend on additional factors. So, for reliable predictions of demographic history, a combination of different indicators should be used [In our analysis, the Tajima’s D, Fu’s F, and Ramos-Onsins and Rozas’s R be used . In our  be used , 73 and The Bayesian skyline plot indicated that the Himalayan langur population size was constant for a long period of time and started decreasing about ~ 25,000 years before present (YBP) i.e., after the onset of the last glacial maximum (LGM). After the LGM ended, the effective population size started increasing and reached the present-day estimate Fig. , which wPalaeodistribution modelling results suggested that during LGM, the Himalayan langur distribution was spread to lower elevation central Nepal and adjoining parts of India, although the probability of distribution was less. Precipitation of the driest quarter, annual mean temperature, and precipitation seasonality were the main contributing factors in defining the suitable habitat for the Himalayan langurs. Given the cold and dry conditions during LGM at high altitudes in the Himalayas and in contrast, warmer conditions with high precipitation , 75 at tb gene; there might be a possibility that this gene did not capture enough variation, especially within the western population given the recent colonisation. Therefore, using markers with high substitution rates such as microsatellites and SNPs, could help us better understand the role of these river valleys in population subdivision within the Himalayan langurs.We used multiple complementary methods to assess the genetic diversity, population structure and demographic history of the Himalayan langurs. In the network analysis, two distinct population clusters within the Himalayan langur were retrieved—WMP and EMP corresponding to the western clade and eastern lineages, respectively as described by Arekar et al. . Interesb (Cyt-b) gene using the primer pairs Cytb_278F and Cytb_1052R. For details on DNA extraction, PCR amplification and sequencing see Arekar et al. [We collected 176 fecal samples from 46 different locations in the Himalayas covering the Union Territory of Jammu and Kashmir (J&K), and the states of Himachal Pradesh (HP), Uttarakhand and Sikkim (Table S1 in ). MultipThe sequence files obtained were viewed and edited manually in ChromasLite v2.01 (Technelysium Pty. Ltd.). Sequences generated in this study were combined with those generated by Khanal et al.  and alig−9 substitutions per nucleotide per year and for the third codon position, rate was set to 1.9 × 10−8 substitutions per nucleotide per year. The analysis was run for 100 million generations with sampling frequency of 10,000.For divergence time estimation we used BEAST v2.6 . The seqTo explore the role of rivers in shaping the population genetic structure of Himalayan langurs sampling locations were divided into 10 different populations, corresponding to 10 regions in the Himalayas, demarcated by different river valleys—WestSutlej (W-S): sample locations west of the river Sutlej; Sutlej_Bhagirathi (S–B): sample locations between the rivers Sutlej and Bhagirathi; Bhagirathi_Mahakali (B–M): sample locations between the rivers Bhagirathi and Mahakali; Mahakali_Karnali (M–Kr): sample locations between the rivers Mahakali and Karnali; Karnali_KaliGandaki (Kr–Kg): sample locations between the rivers Karnali and Kali Gandaki; KaliGandaki_Marsyagandi (Kg–My): sample locations between the rivers Kali Gandaki and Marsyagandi; Marsyagandi_BudhiGandaki (My–Bg): sample locations between the rivers Marsyagandi and Budhi Gandaki; BudhiGandaki_Arun (Bg–A): sample locations between the rivers Budhi Gandaki and Arun; Arun_Tamor (A–T): sample locations between the rivers Arun and Tamor; EastTamor (E-T): sample locations east of the river Tamor Fig. .http://popart.otago.ac.nz/ [ST was calculated using Arlequin v3.5.2.2 and the statistical significance was tested by performing 10,000 permutations.Genetic diversity indices including number of polymorphic sites (s), haplotype number (H), haplotype diversity (Hd) and nucleotide diversity (π) were calculated using DnaSP v6  for eacho.ac.nz/  to build2 statistic for all the populations to understand the evolutionary history under different demographic scenarios using DnaSp v6 [S. entellus and S. schistaceus. The analysis was run for 750 million generations with sampling every 1000 generations. We used Tracer v1.6 to check for stationarity by ensuring that the effective sample size (ESS) for each parameter was > 200. The BDSKY plot was visualised using a R script [To infer the demographic history, we used multiple, complimentary methods. We calculated three summary statistics: Tajima’s D, Fu’s F, and Ramos-Onsins and Rozas’s RDnaSp v6 . We alsoDnaSp v6 . We usedDnaSp v6 . The besDnaSp v6 . The cloDnaSp v6 . The treR script .To understand the westward expansion of these langurs, we carried out a model based hypothesis testing in a Bayesian framework using Approximate Bayesian computation (ABC) analysis implemented in DIY ABC v2.1.0 . ABC appwww.gbif.org). The occurrence records will be made available upon request. We used the MaxEnt algorithm [www.worldclim.org) for the current (~ 1960–2000) and last glacial maximum (LGM)  layers. Spatial resolution of LGM was resampled to 30 arcsec to match the current layers. These bioclimatic variables were clipped to the region from 68 °E to 97.4 °E and from 6.7 °N to 37 °N using ArcGIS 10.2.1. These clipped layers were then exported to ASCII format using QGIS 2.18.12. The 19 bioclimatic layers were tested for multicollinearity by calculating Pearson’s correlation coefficient (r). The layers with r ≤ |0.8| were selected for further analysis. Performance of Maxent depends on the choice of features and regularisation multiplier (RM) [For our second hypothesis, we implemented the Ecological niche modelling (ENM) approach. Here we used 217 occurrence records of the Himalayan langurs. Out of these, 104 records were from the field surveys conducted for this study, 58 occurrence records were obtained from previous studies , 91, 92 lgorithm  and 19 bier (RM) . We testier (RM) . The mod−5. Jackknife test was used to estimate the contribution of each environmental variable. The feature type was selected LQPTH with RM value 1. To overcome sampling bias, a bias file was created in R (v4.0.1) using the package ENMeval v0.3.0 [ENM analysis was performed in Maxent v3.4.1 with the following modifications; Random test percentage was set to 30%, maximum number of background points was set to 10,000 and the replicates were set to 10 with replicated run type changed to Subsample. 5000 iterations were performed with the convergence threshold set to 1 × 10l v0.3.0 . The outl v0.3.0 . AUC valAdditional file 1: Figure S1. Maximum likelihood(ML) phylogeny of Himalayan langur for mitochondrial cytochrome b (Cyt-b) gene.Numbers at the node indicate ML bootstrap (MLBS) values. Node support values of > 75 are shown. Node support values only for major clades are shown.Additional file 2: Figure S2. Divergence time tree for the mitochondrial cytochrome b (Cyt-b) dataset constructed in BEASTv2.6. The bars at the nodes indicate the 95% credible interval. Details of the collapsed IHR node can be found in Additional file Additional file 3:Figure S3. Response curves for top three variables of importance for Himalayan langur in the niche modelling analysis.Additional file 4: Table S1. Samples used for molecular phylogenetic analysis. Samples that are marked with † were sequenced in this study. Samples with the same accession numbers are identical sequences. Table S2. Sequences used for divergence dating analysis in BEAST v2.6.6. Sequences that are marked with † were sequenced in this study. Sequences from sr. no. 1–66 constitutes the collapsed clade IHR in Additional file Table S3. Model selection for Maxent analysis—the table shows AUC values for different models. AUC values in bold shows the features and RM values selected.


Over the past 50 years, geneticists have made great strides in understanding how our species' evolutionary history gave rise to current patterns of human genetic diversity classically summarized by Lewontin in his 1972 paper, ‘The Apportionment of Human Diversity’. One evolutionary process that requires special attention in both population genetics and statistical genetics is admixture: gene flow between two or more previously separated source populations to form a new admixed population. The admixture process introduces ancestry-based structure into patterns of genetic variation within and between populations, which in turn influences the inference of demographic histories, identification of genetic targets of selection and prediction of complex traits. In this review, we outline some challenges for admixture population genetics, including limitations of applying methods designed for populations without recent admixture to the study of admixed populations. We highlight recent studies and methodological advances that aim to overcome such challenges, leveraging genomic signatures of admixture that occurred in the past tens of generations to gain insights into human history, natural selection and complex trait architecture.This article is part of the theme issue ‘Celebrating 50 years since Lewontin's apportionment of human diversity’. Here weBeyond the allele-frequency-based summaries of variation studied by Lewontin , variatiBox 1.Discussions of genetic admixture are, implicitly or explicitly, predicated on the idea that meaningful genetic differences exist between discretized human groups. In practice, the term ‘admixed’ can vary to encompass a range of spatial and temporal processes of gene flow between previously isolated groups. In this review, as noted in §1, we have focused on recent admixture occurring within the past tens of generations. However, extensive gene flow between groups is a hallmark of recent human evolution; when examined in enough detail, nearly all populations can be described as descended from a combination of multiple ancestries. Similar to other discussions on delineating population definitions and boundaries, because there are no strict criteria that determine which populations should be considered admixed from a genetic perspective, classification of a population as admixed is often dependent on the context of the line of inquiry –14. AddiMethodological issues and patterns of genetic diversity are not the only factors shaping our understanding of genetic admixture; a long record of societies’ and scientists’ use of largely superficial characteristics to classify human groups also plays a role. Geneticists and anthropologists have long wrestled with the various field-specific and lay definitions of ‘population’, ‘ancestry’, ‘ethnicity’ and ‘race’, which interact and intersect with each other in complicated ways –14,17. IMany population genetics methods and analytical results are based on assumptions about populations that do not hold under recent admixture. Under a model of isolation, metrics of genomic diversity often have well-defined theoretical expectations with respect to fundamental parameters of the population's evolutionary history. However, many of these relationships are unclear, with admixture introducing blocks of linked ancestral haplotypes each with potentially different patterns of variation based on the history of their source populations. That is, admixture changes both linkage structure and allele frequency distributions, which is often not accounted for in traditional inference methods developed without consideration of admixture.c). These advances have enabled the use of local ancestry patterns in admixed populations to infer demographic history, adaptation and the genetic bases of complex traits.Studying the ancestry patterns of present-day admixed groups has revealed information about the demographic histories of their source populations, including those that are uncommon in unadmixed form today ,19. For figure 1et al. in this issue consider ancient admixture events with archaic humans and their consequences for human genetic variation [Here, we consider three inferential problems based on studying patterns of genetic variation produced by admixture: inference of population history, identifying adaptive mutations and complex trait associations and prediction using admixed genomes. We summarize recent progress in the field, highlight as yet unresolved issues, and outline potential avenues of future research on the genetics of admixed populations. We focus on recent admixture between modern human populations, roughly corresponding to admixed populations founded within the last tens of generations; Witt ariation .. 2Well before polymorphism data could be generated at a genome-wide scale, several methods of measuring genetic diversity had already been proposed, including heterozygosity and nucleotide diversity ,33. By c‘a collection of individuals made by pooling two populations ought always to be more diverse than the average of their separate diversities, unless the two populations are identical in composition' (p. 338).In his 1972 paper, Lewontin discusses his choices of a genetic diversity measure at some length, ultimately settling on one that is analogous to heterozygosity [FST between them and the admixture contributions [FST, which has taken the place of entropy partitioning statistics that Lewontin [In this statement, Lewontin describes expectations of diversity in a set of pooled haplotypes originating from individuals of distinct ancestries, as might result from sampling schemes that combine populations in genetic analysis. This quote also gives insight into how admixture may impact the measures of genetic variation that Lewontin considers. These ideas were revisited in a recent study that explores patterns of heterozygosity in admixed populations . The autibutions . FST, whLewontin  used, caLewontin . These swithin individuals that originate from each ancestry source [a). Second, individuals within an admixed population may vary in their genome-wide, or ‘global’, ancestry proportions (b). Third, individuals with similar source ancestry contributions and admixture histories may vary by ‘local’ ancestry across genomic loci (c). At each level, these patterns of diversity contain information about admixture and post-admixture processes.Beyond within- and between-population estimates of genetic diversity and ancestry, admixed populations introduce another class of summaries of genetic variation: tracts of the genome y source ,17,36. Iy source  from theportions b. Third,mic loci c. At eacIn practice, genetic ancestry of individuals from admixed populations is not fully known and is inferred, often using reference panels that are collated to represent the source populations ,27–30,38et al. [The quality of ancestry estimates depends on a variety of sampling and evolutionary scenarios . A recenet al.  demonstr. 3The admixture history of a population, such as the timing and source contribution levels, leaves predictable patterns of genetic variation within and between individuals from the admixed population ,37,41–43Under a simple admixture scenario, the allele frequency of a locus in the admixed population is expected to be the average of the allele frequencies in source populations weighted by their contribution levels –46. ThatPatterns of global and local ancestry within and between individuals are informative about admixture histories. For example, over time, recombination tends to break up local ancestry tracts; therefore, longer tracts generally indicate more recent contributions from source populations to an admixed population and may be used to infer the timing of admixture ,42,52–58Similarly, sociocultural practices that govern mate choice or sex-specific contributions from the source populations will leave signatures in patterns of genetic ancestry. Individual behaviours such as mating preferences or long-range migration can exhibit ancestry biases in which the ancestry patterns in the subset of the population that migrates are not representative of the whole admixed population, potentially driven by correlations between ancestry and visible traits like skin pigmentation or socioeconomic differences –69. SimpAdditionally, based on the sex-specific inheritance of the X chromosome , comparisons of X-chromosomal and autosomal ancestry proportions have been used to infer sex-biased admixture in ancient and modern human populations ,50,77–80et al. [Differences in ancestry proportion across the geographical span of a population or populations with shared ancestry components have been used to infer ancestry-biased migration patterns, which may be driven by social cues. For example, ancestry-biased migration, often combined with other mating dynamics, has been proposed as a process shaping regional variation in African ancestry proportions across the USA ,81,82. Set al.  found a Sufficiently accounting for these spatial and temporal dynamics of the admixture process presents an exciting challenge. One solution to address admixture processes that vary over space or time involves simulation-based demographic inference frameworks, such as approximate Bayesian computation and machine learning-based approaches. For example, MetHis is an approximate Bayesian computation-based approach for inference under complex two-way admixture models ,83. An aStudying the genomes of admixed populations can also provide insight into the genetic origins and demographic histories of their founding populations, particularly for source ancestries that are no longer commonly represented by an extant single-ancestry population ,19. An iet al. [Local ancestry inference can also be used to unravel source-specific historical population size dynamics. The process of admixture often involves bottlenecks at the time of founding, the timing and strength of which Browning et al.  demonstrMoving forward, combining ancestry-based inference with patterns of homozygosity and IBD may help elucidate these complex and dynamic population histories. For example, homozygosity and IBD are shaped by the relationships between mating pairs, which are in turn influenced by sociocultural processes ,86,89,90. 4Adaptation to biotic and abiotic environments leaves signatures in patterns of human genetic variation that can be used to identify adaptive loci and infer their selection history –94. HoweAs described previously, admixed populations are often considered as a linear combination of their sources such that the expected allele frequency of a locus is an average of the allele frequencies in each source population at that locus weighted by their proportional contribution to the admixed population. Loci that dramatically differ from this expectation are candidates for loci under selection  at the adaptive locus; the proportion of simulations in which the adaptive locus is an outlier increases with increasing FST between the sources. Additionally, the power of outlier approaches to localize adaptive loci depends on the length of the admixture tract containing the locus, and therefore the selection history. Finally, while useful for identifying adaptive loci, these methods must be combined with other information or simulations to infer parameters of the population's history such as the strength or timing of selection.Empirical studies have identified numerous candidate regions under selection post-admixture using ancestry outlier methods ,110–112;et al. [DARC gene in the admixed population of Cabo Verde based on long, high frequency African ancestry tracts. Hamid et al. [DARC, a known malaria susceptibility locus, in multiple admixed populations on multiple timescales [Ongoing work extends initial implementations of ancestry-outlier approaches to study post-admixture selection, and often uses simulations to improve interpretation and test power ,115,116.et al.  found sid et al.  further mescales ,117–119.While these recent studies using empirically driven summary statistics have proven informative in certain scenarios, more work is needed to develop expectations of the distributions of ancestry under models of selection with admixture. Indeed, recent work has suggested perhaps unexpected relationships between ancestry tract lengths, allele frequencies and selection history, emphasizing the need for additional theory .. 5For decades, human genetics research has aspired to make personalized medical therapies a reality by improving the prediction of traits from genetic data; while progress has been made on the genetic prediction of traits in recent years, its potential for making personalized medicine a reality may only be currently applicable to individuals of European ancestry . Genome-Admixture complicates the identification of genetic underpinnings of complex traits. For example, GWAS generally assume that there are no systematic differences in the genetic variation of the study cohort except in those variants that underlie the trait of interest. Yet patterns of ancestry vary widely across individuals within an admixed population, both at the genome-wide level and within regions of the genome, as shown in et al. [Increasingly, research suggests that by excluding individuals from admixed populations (as well as from non-admixed minority populations), geneticists are discarding a rich source of genomic information . PGS accet al. ). Furtheet al. . All of et al. ,133.In an effort to address these challenges for predicting traits in admixed populations, new frameworks are being developed to improve the performance of PGS in individuals from admixed populations, such as including local ancestry-based principal components to correct for heterogeneous patterns of population structure along the genome or subdividing the cohort by genome-wide ancestry and taking a meta-analysis approach .. 6Though it was not the focus of his paper, Lewontin  acknowleMultiple future directions in research on admixture will extend our understanding of human evolution and the distribution of human genetic variation. First, there is a need for more theory regarding how natural selection interacts with admixed population histories (but see ). Figure


Plasmodium vivax populations and the complexity of infections along the China–Myanmar border.Parasite diversity and population structure influence malaria control measures. Malaria transmission at international borders affects indigenous residents and migrants, defying management efforts and resulting in malaria re-introduction. Here we aimed to determine the extent and distribution of genetic variations in P. vivax samples from local and migrant malaria patients from Laiza and Myitsone, Kachin State, Myanmar, respectively. We characterized the polymorphisms in two P. vivax merozoite surface protein markers, Pvmsp-3α and Pvmsp-3β, by PCR-restriction fragment length polymorphism (PCR–RFLP) analysis. We sought to determine whether these genetic markers could differentiate these two neighboring parasite populations.We collected clinical Pvmsp-3α and four for Pvmsp-3β among the 370 and 378 samples, respectively. PCR–RFLP resolved 26 fragment-size alleles by digesting Pvmsp-3α with Alu I and Hha I and 28 alleles by digesting Pvmsp-3β with Pst I. PCR–RFLP analysis of Pvmsp-3α found that infections in migrant laborers from Myitsone bore more alleles than did infections in residents of Laiza, while such difference was not evident from genotyping Pvmsp-3β. Infections originating from these two places contained distinct but overlapping subpopulations of P. vivax. Infections from Myitsone had a higher multiplicity of infection as judged by the size of the Pvmsp-3α amplicons and alleles after Alu I/Hha I digestion.PCR revealed three major size variants for P. vivax parasite populations. The results suggested a more diverse P. vivax population in Myitsone than in the border town of Laiza. PCR–RFLP of Pvmsp-3α offers a convenient method to determine the complexity of P. vivax infections and differentiate parasite populations.Migrant laborers from Myitsone and indigenous residents from Laiza harbored overlapping but genetically distinct The online version contains supplementary material available at 10.1186/s41182-022-00492-7. Plasmodium vivax is widespread, occurring in endemic areas of Asia, Oceania, Central and South America, the Middle East, and West Africa [Malaria has been a major threat to public health and economic development . With ant Africa . Increast Africa . In soutmsp1, msp2, and glurp in Plasmodium falciparum [msp1 and msp3α/β in P. vivax [Genetic markers have been used to determine the diversity of parasite populations, spatial and temporal transmission dynamics, and complexity of infections. Molecular markers are also used to quantify the intensity of infections and determine the number of parasites harbored by an individual . Many merozoite surface proteins, such as lciparum , and mspP. vivax , have beP. vivax .Pvmsp-3α and Pvmsp-3β genes encode merozoite surface proteins with an alanine-rich central domain, which strongly predicts coil-like tertiary structure [Pvmsp-3α length polymorphism was first identified in parasites from Asia, South America, and the South Pacific region, with four major amplified fragment types reported [Pvmsp-3β allow the differentiation of P. vivax from different geographical areas [Pvmsp-3α and Pvmsp-3β have been used to study the genetic diversity of P. vivax from Pakistan [The tructure . The centructure . The Pvmreported . Similaral areas . When coal areas . For exaPakistan  and KachPakistan .P. vivax in the border region of Myanmar and its contribution to imported malaria to China prompted us to investigate the changes in the parasite population and the spatio-temporal transmission dynamics in the border region [Pvmsp-3α and Pvmsp-3β from two P. vivax populations along the China–Myanmar border to see if they can be used to differentiate these parasite populations and trace the parasite origins. Our study identified that migrant workers, who had acquired P. vivax infection from Myitsone while constructing the Myitsone Dam [P. vivax than the local malaria patients from Laiza.The continued transmission of r region . Specifisone Dam , harboreP. vivax were analyzed from symptomatic patients in hospitals or malaria clinics in two regions of the China–Myanmar border in 2012–2015. Among them, 230 samples were collected from 13 clinics around Laiza township, Kachin State, Myanmar .Pvmsp-3α and Pvmsp-3β used previously described methods [2, 0.4 mM dNTPs, distilled H2O, and 0.2 μl of each primer (10 pM). For nested PCR, 2.0 μl of the first amplification product was used as the template and added to a total volume of 50 μl of the reaction system, including distilled H2O, primers and Premix Taq™ (TaKaRa Taco™ version 2.0 plus dye). The PCR products were resolved on 0.8% agarose gel, and their sizes were determined using a 200 bp DNA ladder .Nested PCR and genotyping of  methods  with modPvmsp-3a and Pvmsp-3β genes was assessed using established PCR–RFLP techniques. Briefly, 3 μl of each PCR product were digested with restriction enzymes for more than 5 h. Pvmsp-3a amplicons were digested in 10 μl of reactions with 1 μl of Alu I (enzyme cutting site: 5′-AGC|T-3′) or Hha I (enzyme cutting site: 5′-GCG|C-3′). Pvmsp-3β was digested with 1.5 μl of Pst I (enzyme cutting site: 5′-TTA|TAA-3′) in 11 μl reactions . Restriction fragments were separated and visualized in 1.8% agarose gels.Allelic polymorphism of the Pvmsp-3α and Pvmsp-3β genotypes.Mixed-strain infections were inferred when PCR products of more than one size appeared in the initial amplification  or when 2) test was used to compare the frequencies of RFLP alleles among the populations using the Prism GraphPad 6. Fisher’s exact test was used to compare the differences between two populations in individual alleles. T-test was used to compare the infection multiplicity between two populations. P values of < 0.05 were considered significant.The Chi-square  samples from Laiza and 178 of 192 (92.7%) Myitsone samples from Chinese migrant patients. Three different target bands  were amplified .For Pvmsp-3β was successfully amplified in 220 of 230 (95.7%) Laiza samples and 173 of 192 (90.1%) Myitsone samples. The Pvmsp-3β PCR products had four amplicon length types , corresponding to 1700–2200, 1400–1500, 1100–1300, and 600–800 bp, respectively . Notably, single-band samples were only present in two types; Type A occurred in 57.8% and type B in 34.7%. Types C and D appeared only in mixed infections .Alu I digestion of the amplified Pvmsp-3α gene. A total of 16 isoforms of Type A, 7 isoforms of Type B, and 3 isoforms of Type C were apparent in the two regions. Each region had 19 alleles, but with different allele types or frequencies. In the Laiza samples, 10 A, 6 B, and 3 C isoforms occurred, while in Myitsone samples, 12 A, 4 B, and 3 C isoforms occurred . However, the two sample populations had significantly different distributions of Type A alleles of Pvmsp-3a. The same is true for the alleles of Type B and C , compared to 23 isoforms in Myitsone  . However, as described for Alu I digestion, the frequency distribution of these alleles was significantly different between the two populations . Similarly, examining the distribution of A and B alleles separately failed to identify a significant difference between the two parasite populations . Hha I digestion yielded estimates of MOI at 1.23 ± 0.517 and 1.397 ± 0.637 (Table P < 0.01). When restricting the analysis of MOI to adults 18 and older, we found that the MOI, estimated by Hha I digestion of Pvmsp-3a, was significantly lower among the Laiza residents (1.221 ± 0.5099) than migrant workers (1.368 ± 0.6116) (P < 0.05).PCR–RFLP analyses revealed occasional occurrences of mixed genotype infections in both parasite populations. The MOI of the Laiza samples ranged from 1 to 3 and 1 to 4, as judged by Pst I digestion of Pvmsp-3β yielded statistically equivalent estimates of MOI in the two patient populations  (Table P > 0.05).P. vivax transmission along the China–Myanmar border [Pvmsp-3α and Pvmsp-3β among P. vivax populations from two adjacent areas (Laiza and Myitsone) of Kachin State, Myanmar. The results showed different patterns and degrees of genetic diversity in the two populations. The higher genetic diversity of parasites from Myitsone suggested a higher level of malaria transmission there.Parasite genetic diversity illuminates the dynamics of malaria transmission and the fate of control efforts. Malaria eradication remains a public health goal, so a better understanding of malaria transmission dynamics and population genetics is essential. Laiza and Myitsone are hot spots of r border . Despiter border . Here, wPvmsp-3α and Pvmsp-3β indicated high genetic diversity of the P. vivax population from this region, revealing large numbers of fragment-size alleles of the two polymorphic markers. Genotyping Pvmsp-3α also identified significantly different allele distributions and frequencies in the two neighboring areas, suggesting population differentiation. These two areas differed considerably in the prevalence of the Pvmsp-3α PCR size polymorphism, with the Laiza samples having more evenly distributed frequencies of the A, B, and C types as compared to the predominant A type found in the Myitsone samples .PCR–RFLP genotyping of g Tables , 3. HowePvmsp-3α Type A was the dominant infection type in central China, Thailand, Myanmar and other regions, while Type C was reportedly rare or absent [Pvmsp-3α types, with Type C being the most abundant (37%). This difference may reflect recent parasite population expansion events revealed by whole-genome sequencing analysis [A comparison of the parasite samples studied here with earlier publications suggested population changes in the China–Myanmar border, probably reflecting the impact of scaled-up control in the region. r absent . The Myir absent , this stanalysis , since tanalysis .Plasmodium species are common in many places where malaria is endemic [Pvmsp-3α (P < 0.05). Consistently, a higher MOI was estimated from Myitsone laborers than residents of Laiza using Pvmsp-3α digested by Hha I (P < 0.05). Again, Pvmsp-3α provided more power to differentiate multiple infections than Pvmsp-3β. These results indicate greater malaria transmission intensity among these laborers during their stay in Myitsone.Multiple infections of genetically distinct clones of the same  endemic . Some st endemic , whereas endemic . RegardlPvmsp-3α and Pvmsp-3β genes is a convenient method to identify the genetic diversity of the parasites from the China–Myanmar border [Pvmsp-3α, it allowed us to differentiate parasite populations and identify mixed infections. However, PCR–RFLP analysis using these markers also has lower sensitivity in distinguishing closely related alleles, and may not be suitable for tracking cross-border parasite migration [We showed that PCR–RFLP analysis of r border . Especiaigration . Deep seigration .Pvmsp-3α and Pvmsp-3β genes, we found evidence of genetic differences in P. vivax populations from two adjacent regions along the China–Myanmar border. Patients from Myitsone harbored more mixed infections and had higher MOI than those from Laiza.By PCR–RFLP genotyping Additional file 1. Supplementary Tables.Additional file 2. Supplementary Figures.