Climatic fluctuations and geological events since the LGM are believed to have significantly impacted the population size, distribution, and mobility of many species that we observe today. In this paper, we determined the processes driving the phylogeographic structure of the Korean endemic white forsythia by combining the use of genome‐wide SNPs and predicting paleoclimatic habitats during the LGM (21 kya), Early Holocene (10 kya), Mid‐Holocene (6 kya), and Late Holocene (3 kya). Using a maximum of 1897 SNPs retrieved from 124 samples across nine wild populations, five environmental predictors, and the species' natural occurrence records, we aimed to infer the species' demographic history and reconstruct its possible paleodistributions with the use of approximate Bayesian computation and ecological niche models, respectively. Under this integrated framework, we found strong evidence for patterns of range shift and expansion, and population divergence events from the onset of the Holocene, resulting in the formation of its five distinct genetic units. The most highly supported model inferred that after the split of an ancestral population into the southern group and a larger central metapopulation lineage, the latter gave rise to the eastern and northern clusters, before finally dividing into two sub‐central groups. While the use of molecular data allowed us to identify and refine the (phylo)genetic relationships of the species' lineages and populations, the use of ecological data helped us infer a past LGM refugium and the directions of post‐glacial range dynamics. The time frames of these demographic events were shown to be congruent with climatic and geological events that affected the central Korean Peninsula during these periods. These findings gave us a better understanding of the consequences of past spatiotemporal factors that may have resulted in the current fragmented population distribution of this endangered plant. In this paper, we determined the processes driving the phylogeographic structure of the Korean endemic white forsythia by combining the use of genome‐wide SNPs and hindcasting of paleoclimatic habitats during the LGM (21 kya), Early Holocene (10 kya), Mid‐Holocene (6 kya), and Late Holocene (3 kya). Using a maximum of 1897 SNPs retrieved from 124 samples across nine wild populations, and five environmental predictors and the species' natural occurrence records, we aimed to infer the species' demographic history and reconstruct its possible paleodistributions with the use of approximate Bayesian computation and ecological niche models, respectively. Under this integrated framework, we found strong evidence for patterns of range shift, and population expansion/divergence events from the onset of the Holocene, resulting in the formation of its five distinct genetic units. The most recent events that have made significant impacts on extant species and their distribution are perhaps those which occurred in the Quaternary, specifically the environmental changes since the last glacial maximum (LGM) ca. 21,000–18,000 years ago , and South Japan  served as a refuge and at the same time a dispersal corridor to the ancestral populations of widely distributed East Asian plant species  to elucidate the relationship between its genetic subdivisions and current fragmented population distribution. More commonly known as white forsythia, this KP endemic and natural monument species is considered rare and is only found in the central provinces of South Korea, although some (unconfirmed) natural occurrences have been also reported from neighboring North Korea , hence, the common name. Abeliophyllum and Forsythia are the only genera in the tribe Forsythieae whose divergence was reported to have occurred in East China during the Miocene ca. 5–33.6 million years ago , and sequenced in a single lane on an Illumina HiSeq X  using 150 bp paired‐end sequencing runs.We analyzed 124 white forsythia individuals across nine collection sites Figure . More de2.2The sequenced data were demultiplexed using Cutadapt v.1.8.3 Martin,  and qualp ≤ .01) and FST (>0.1)  were filtered out using the software Plink v1.07  of >5%. Each locus was tested for Hardy–Weinberg (H‐W) equilibrium  by retaining 120 principal components (PCs). The optimal K was determined by selecting the Bayesian Information Criterion (BIC) with the lowest value. Before the final DAPCrun, we selected the optimal number of PCs to use by running the optim.a.score command and using all 120 PCs and 100 discriminant functions (DAs) to maximize information content. All the above analyses were done in R  length of 100,000 generations following a burn‐in of 50,000 generations. We selected the admixture and correlated allele frequencies options and set all other parameters to default. We calculated the ΔK  and reverse (v) mutation rates by selecting the “Calc mutation rates” option and set the MCMC algorithm to 1 million generations and a burn‐in of 200,000 generations. Trees were sampled every 1000 steps, while other options were set to default. The coalescence tree was further processed and visualized using TreeAnnotator , respectively. The resulting tree topology, along with the results of population structure analyses, was referred to in designing demographic history models to be tested using ABC.To elucidate the phylogenetic relationship among the sampled populations, we constructed a coalescence‐based tree using SNAPP , 10 to 103 for the estimation of divergence times (t) with t1 < t2 < t3 < t4, and 10−3 to 0.999 for admixture rate (ra) computation. See Table We designed our models based on our earlier findings on white forsythia distribution, specifically by following the signals supporting the central‐marginal hypothesis . The selected sumstats were as follows: mean of complete distribution for single sample statistics  among the eight candidate models, posterior probabilities (PPs) were computed via the direct approach based on 500 datasets, and the logistic regression based on the 1% of simulated datasets closest to the observed data after replacing the original sumstats by discriminant scores  for the scenario of choice were computed by selecting the logit transformation on the 1% of the closest simulated datasets. The mean posterior distribution for t was then recalibrated to the absolute divergence time  by multiplying it with the generation time of the species, which was set to 8 , we ran the DIYABC model checking option using the above‐described (applicable) sumstats. Another set of sumstats was also used for a second, less biased model checking, a method that is expected to reduce the risk of overestimating the quality of the fit by avoiding the use of the same sumstats , were taken from our sampled sites and herbarium collections deposited in major herbaria in South Korea , and the Herbarium of Hallym University (HHU). To control the quality of occurrence data, we removed duplicate records, limited our collections from sites where the species only naturally occurs, and filtered them at ca. 1 km radius using the raster  and considered the removal of variables with very high variance inflation factor or VIF (>10). We also conducted an initial screening of all 19 environmental variables in MAXENT v3.4.4  of the receiver operating characteristic (ROC). AUC values determine the relative suitability of habitats with values ranging from 0 to 1.In MAXENT v3.4.4, we selected the complementary log–log (cloglog) output to estimate the probability of presence, as recommended by Phillips et al. . Our ENMThe model that was calibrated onto the Present (1970–2000) conditions was projected to the past climatic conditions using the same five environmental predictors downloaded from CHELSA‐TraCE21k v1.0, which provides high‐resolution climatic data from 21 kya to 1990 in 100‐year time steps , which remained genetically distinct as K increased both at K = 4 and K = 5, and all the remaining populations (blue genetic assignment). At K = 4, the northernmost population YJ and the easternmost population AD are shown to have diverged from the “blue” genetically assigned cluster, which now consisted only of populations from the core central range. At K = 5, this core central cluster can be seen divided into a lower‐central geographic group comprising OC, YD‐NM364, and YD, and an upper‐central geographic group composed of GS‐NM147 and GS‐NM221. While members of the upper‐central group maintained a fairly uniform genetic identity until K = 5, the genetic assignment of populations into the lower‐central group appears to be due to admixture  and is attributable to their geographic proximity.A more delineated genetic grouping can be visualized when samples are classified into optimal s Figure . ΔK estiK = 5 as the most probable number of genetic clusters  for each genetic group, as well as that of the mean divergence times (t) during each event. After considering the generation time of 8 (6–10) years, it can be interpreted that the ANCESTRAL POPULATION  was split into a basal SOUTHERN group (N1 = 3440) and a large central metapopulation lineage  at t4 ca. 10 kya. All the remaining populations then subsequently diverged from the latter lineage. For instance, the EASTERN group (N4 = 681) and the NORTHERN group (N5 = 365) were formed from this lineage after their split at t3 ca. 5 kya and at t2 ca. 3 kya, respectively. Lastly, the split of the core central lineage into the LOWER‐CENTRAL group (N2 = 2710) and the UPPER‐CENTRAL group (N3 = 2600) appears to have only occurred at t1 ca. 2 kya. See Appendix Table ION NA = 98,000 wa3.4Based on 22 natural occurrence records, five layers of environmental variables, and 22 replicate runs with optimized settings, MAXENT inferred the species' Present (1970–2000) potentially suitable habitats with a predictive power of AUC = 0.878 (SD = 0.125), as displayed in the ROC curve Appendix . This vaWhen the five environmental variables were tested, bio14 46.7%), bio5 34.5%), and bio7 (15.1%) were revealed to have the highest combined percent contribution to the model . The same three variables also showed the highest permutation importance amounting to ca. 97%  habitat suitability was projected onto the four paleoclimatic and geographic conditions, binary output maps chronologically revealed a pattern of directional shifting and size changes in the species' past suitable habitats Appendix . Among t44.1Miscanthus across the region. In our LGM model , the demographic history of white forsythia was inferred to be a post‐LGM sequential population divergence of the EASTERN, NORTHERN, and LOWER‐CENTRAL genetic groups from the UPPER‐CENTRAL lineage, following the latter's split with the SOUTHERN group that commenced at the onset of the Holocene Figure . Althougl Figure , howevert4 ca. 10 (7–12) kya when the SOUTHERN group and the larger central metapopulation lineage  diverged from the ANCESTRAL POPULATION NA. The north‐eastward expansion of suitable habitats was most likely due to the submergence of the western side of the KP during deglaciation events. Severe post‐glacial sea‐level rise, which started ca. 18–19 kya, resulted in rapid marine transgression of the exposed Yellow Sea continental shelf due to high tidal effects and the low seafloor gradient , Figure Alnus‐ and Quercus‐dominated forests prevailed in the western regions, suggesting a wetter environment in the area, while the prevalence of Pinus forests in the east still implied a relatively drier and cooler eastern region.Although the increased sea level may have limited the species’ distribution on the western side of the peninsula during the Early Holocene, the geological events and warmer climates may also have paved the way to the extension of preferable environments inward the KP. Around this period (ca. 9 kya), according to Kutzbach , the strt3 ca. 5 (4–6) kya, most likely via migration. During this time (ca. 5–7 kya), according to Yi et al. (By Mid‐Holocene (6 kya), only a very slight reduction in areas of suitability is shown Figure . Small pi et al. , the Easi et al. . Recoveri et al. . The furt2 ca. 3 (2–3.5) kya was likely a result of poleward migration of the core central lineage. It is important to note, however, that there is a narrow chronological margin between t2 and the preceding t3  kya). This discrepancy may have resulted in the tight selection between the top two competing demographic models and on whether eastern divergence occurred first followed by northern divergence after migration , or otherwise . The further poleward migration of the NORTHERN group during the Late Holocene, however, may have been hindered by the hilly geography approaching north, and/or by the cooler temperatures that came along with the increase in elevation and latitude. We also think that the eventual separation from the core central population of the NORTHERN group  may have been associated with another major climatic transition: the shift from Mid‐Holocene hypsithermal conditions to a cooler and drier Late Holocene climate. This climatic change is supported by the evidence of the abrupt decline of pollen from Alnus‐ and Quercus‐dominated forests and the increased amount of pollen from Pinus and other cool‐adapted taxa like Picea, Abies, and Betula in the region , potential paleodistribution further decreased but suitable environments between the core central population and the NORTHERN group (yellow cluster) appears to have been maintained, as shown in Figure t1 ca. 2 (1.5–2.5) kya, delineating the UPPER‐CENTRAL and LOWER‐CENTRAL groups   but here, we present novel findings by more accurately elucidating the (phylo)genetic relationships of these population groups and lineages. Using multiple independent SNPs, our SNAPP and DIYABC analyses corroborated our STRUCTURE (K = 5) results and further characterized the distinctiveness of these population subdivisions in the context of phylogeography genetic relationships of infraspecies lineages and populations, the use of ENM helped us discover a possible past LGM refugium and the directions of post‐glacial habitat expansion and reduction events. The time frames of the succeeding Holocene population distribution dynamics were shown to be congruent with at least several climatic and geological events  that affected central KP during the period.In comparison with other studies that employed the same maximum entropy algorithm on plants with (overlapping) distribution on the KP ; formal analysis (lead); investigation (lead); methodology (lead); resources ; software (lead); visualization (lead); writing – original draft (lead); writing – review and editing (lead). Yong‐In Kim: Investigation ; methodology ; resources . Jung‐Hoon Lee: Data curation ; investigation ; resources . Bo‐Yun Kim: Investigation (supporting); methodology (supporting); resources . Dae‐Hyun Kang: Investigation (supporting); resources (supporting). Eui‐Kwon Jung: Investigation (supporting); resources (supporting). Jae‐Seo Shin: Investigation (supporting); resources (supporting). Young‐Dong Kim: Conceptualization (lead); funding acquisition (lead); project administration (lead); supervision (lead); validation (lead).The authors declare no conflict of interest.Appendix S1.Click here for additional data file.


Aedes albopictus) arrived in the USA in the 1980’s and rapidly spread throughout eastern USA within a decade. The predicted northern edge of its overwintering distribution on the East Coast of the USA roughly falls across New York, Connecticut, and Massachusetts, where the species has been recorded as early as 2000. It is unclear whether Ae. albopictus populations have become established and survive the cold winters in these areas or are recolonized every year. We genotyped and analyzed populations of Ae. albopictus from the northeast USA using 15 microsatellite markers and compared them with other populations across the country and to representatives of the major global genetic clades to investigate their connectivity and stability. Founder effects or bottlenecks were rare at the northern range of the Ae. albopictus distribution in the northeastern USA, with populations displaying high levels of genetic diversity and connectivity along the East Coast. There is no evidence of population turnover in Connecticut during the course of three consecutive years, with consistent genetic structure throughout this period. Overall, these results support the presence of established populations of Ae. albopictus in New York, Connecticut, and Massachusetts, successfully overwintering and migrating in large numbers. Given the stability and interconnectedness of these populations, Ae. albopictus has the potential to continue to proliferate and expand its range northward under mean warming conditions of climate change. Efforts to control Ae. albopictus in these areas should thus focus on vector suppression rather than eradication strategies, as local populations have become firmly established and are expected to reemerge every summer.The Asian tiger mosquito ( Aedes albopictus) is a highly invasive species that spread from its native range in East Asia to more than 50 countries on every continent, except Antarctica, during the last 40 years  in 2000, in New York City and neighboring Long Island counties in 2003, and is currently spreading north into the Hudson Valley , and compared them to established populations from other USA states and countries to better understand the process of mosquito colonization at the northern expansion front. Collections include mosquitoes sampled from 23 locations along the USA eastern seaboard from Florida to MA, one population from California and temporal collections at four locations in CT spanning three consecutive years. In addition, we include collections from Thailand, Japan, and Brazil as representatives of the major global genetic clusters identified in this species  underwent 6 generations. Vero Beach samples came from field-collected larvae subsequently reared to adulthood. Samples were received as adults in ethanol and silica gel, with the exception of those of Thailand, Japan, and Brazil which were obtained as DNA aliquots. The samples included 24 locations within the USA  protocol for purifying total DNA from insects with the Qiagen DNeasy Blood and Tissue Kit , with an additional RNAse A step. Samples were stored at −20 °C until further use. Mosquitoes from Connecticut, which had previously been homogenized in 1 ml of PBS-G media , were processed following the manufacturers protocol for electrically homogenized samples.Ae. albopictus genomic data from Mosquitoes were genotyped at 15 microsatellite loci, including locus A9 from http://www.geneious.com) using the bins and panels in The resulting products were processed for fragment analysis at the DNA Analysis Facility at Science Hill at Yale University, using GS 500 Liz internal size standard . Microsatellite alleles were scored using Geneious 11.1.4 (Biomatters Ltd) microsatellite plugin (www.vectorbase.org), Population Biology Project ID: VBP0000814.Raw allele frequencies are available at VectorBase (Ho) and expected (He) heterozygosities, and inbreeding coefficients (Gis) were estimated for each population in GenoDive 3.04 (AR) was calculated in HP-RARE  using the ive 3.04 . Allelic HP-RARE , which u HP-RARE  was usedChanges in recent population size were evaluated using Bottleneck v. 1.2.02  under thF [Fe  with the bias-corrected version of the LD method from Ne was estimated using arithmetic and harmonic mean to account for the effect of outliers. Two-sample Ne estimates are known to be robust to overlapping generations and can deal with lower levels of polymorphisms (Effective population size (Ne) was calculated for the temporal collections in CT using NeEstimator  with theF [Fe ; Fk  was determined by conducting 20 independent runs from each K = 1 to 8 for the complete dataset, K = 1 to 11 for Japan + America, K = 1 to 10 for the states at the northeastern invasion front , and K = 1 to 11 for the CT temporal dataset. Each run assumed an admixture model and correlated allele frequencies using a burn-in value of 100,000 iterations followed by 500,000 repetitions. The optimal number of K clusters was determined following the guidelines of Prichard et al.  were calculated in the same software. A geographic distance matrix was produced from geographic coordinates in the Geographic distance matrix generator v. 1.2.3.  across populations of AR = 5.13 ± 0.61  is 0.54 ± 0.42, with a lowest value of 0.46 and a highest of 0.66 observed in Brazil and Florida, respectively (Gis) across populations is 0.17 ± 0.04, with a maximum value of 0.26 in Brazil and a minimum of 0.04 in Florida , p = 0.03; Ho increasing with latitude rather than decreasing. Latitude does not correlate with changes in AR , p = 0.15; Ho: t1.02 = − 1.0411, p = 0.4843; AR: t1.01 = − 0.9218, p = 0.5248).There is an average of 13.8 ± 6.46 alleles per locus, ranging from 8 to 31, with a mean allele richness , Mercer County (NJ), and Norwalk (CT), under both the Infinite Allele Model (IAM)  and the Ne = 121.21 (arithmetic mean), ranging from 37.70 to 317.10  = 0, p = 1; tGis(4) = 0, p = 1; tAR(3.98) = −0.2964, p = 0.7817), inference of bottlenecks, or the population structure analysis (data not shown).Analysis of kinship determined that, on average, 1.97% of the pairwise relationships within a population involved first degree pairs  on the complete dataset indicates that most of the variation can be explained at the individual level, with a lower contribution from the population level .Fst) was correlated with geographic distance (Km) and found no correlation  throughout the northeastern USA  to determine whether genetic distance (Time_Points p = 0.901). When DAPC was used to infer genetic clusters without population priors, three genetic clusters were inferred  at the Ae. albopictus northern invasion front, equivalent to that in the native range: Japan and Thailand. Furthermore, evidence of recent bottlenecks (founder effects) was restricted to the two collections from New York that spent 6 generations in the laboratory , and Mercer County (NJ), and Norwalk (CT). Since bottlenecks are common after laboratory colonization , indicating that either a Wahlund effect or inbreeding (or both), are most likely responsible for the observed heterozygote deficits rather than null alleles  = 0.328, p = 0.577). A Wahlund effect arises when genotype proportions are calculated from samples that include individuals belonging to genetically differentiated groups in time or space, for example, subpopulations or cohorts , which is overall lower than those estimated from wild Ae. aegypti using 12 microsatellite markers by The heterozygosity values observed in  aegypti . High Aellozymes  and in ptellites , and maytellites . Alternatellites . We detetellites , and in bopictus . Studiesbopictus . In 2017 cohorts . It is tAe. albopictus in the northeastern USA is genetically homogeneous. This lack of population structure is congruent with the findings of Ae. aegypti from California just two years after breeding populations were first detected (Ae. albopictus may be better explained by the invasion history of Ae. albopictus, spreading faster than Ae. aegypti in North America due to its biology and propagule size, and the high connectivity within the region. Consistent with this hypothesis, we detect isolation by distance along CT and NY that does not extend to MA or the rest of the East Coast. This is probably a consequence of the proximity of CT and NY, with gene-flow predominantly occurring via neighboring populations through natural and human-aided dispersal (At the regional scale, detected . The absispersal . GeograpAe. albopictus has been recorded every year since 2010 (Ae. aegypti, Ae. albopictus is capable of diapausing at the egg stage (In Connecticut, nce 2010  but it hgg stage  and overgg stage . If CT wAe. albopictus populations in the northeastern USA may already be established as overwintering populations. Furthermore, the high levels of genetic diversity, signatures of inbreeding and small neighborhood sizes suggest that Ae. albopictus populations in the northeast USA experience high propagule pressure, probably as the result of multiple, diverse, and frequent invasion sources from southeastern USA populations and possibly from abroad. We suggest that Ae. albopictus in eastern USA behave as a metapopulation, in which genetic variation is consistently introduced to the area via human-aided dispersal, and where local genetic drift and selection lead to differentiated small breeding units interconnected across space and time, with admixture through secondary contact further increasing variability.The overall absence of bottlenecks, lack of genetic structure, patterns of isolation by distance, and temporal stability at the northeastern invasive front suggest that Supplementary material 01Supplementary material 02Supplementary material 03Supplementary material 04Supplementary material 06Supplementary material 05Supplementary material 07Supplementary material 08Supplementary material 09Supplementary material 10Supplementary material 12Supplementary material 11Supplementary material 15Supplementary material 14Supplementary material 131


Phocoena phocoena) form an ecotype adapted to local upwelling conditions and distinct from other ecotypes further north on the NE Atlantic continental shelf and in the Black Sea. By analyzing the evolution of mitochondrial genetic variation in the Iberian population between two temporal cohorts (1990–2002 vs. 2012–2015), we report a substantial decrease in genetic diversity. Phylogenetic analyses including neighboring populations identified two porpoises in southern Iberia carrying a divergent haplotype closely related to those from the Mauritanian population, yet forming a distinct lineage. This suggests that Iberian porpoises may not be as isolated as previously thought, indicating possible dispersion from Mauritania or an unknown population in between, but none from the northern ecotype. Demo‐genetic scenario testing by approximate Bayesian computation showed that the rapid decline in the Iberian mitochondrial diversity was not simply due to the genetic drift of a small population, but models support instead a substantial decline in effective population size, possibly resulting from environmental stochasticity, prey depletion, or acute fishery bycatches. These results illustrate the value of genetics time series to inform demographic trends and emphasize the urgent need for conservation measures to ensure the viability of this small harbor porpoise population in Iberian waters.Impact of climate change is expected to be especially noticeable at the edges of a species' distribution, where they meet suboptimal habitat conditions. In Mauritania and Iberia, two genetically differentiated populations of harbor porpoises ( The Iberian harbor porpoises form a small and semi‐isolated population belonging to a distinctive ecotype adapted to the upwelling system in the southern European Atlantic waters. Here, we analyzed the mitochondrial genetic variation for a sampling spanning over the past 30 years and reported a dramatic decline in diversity. Simulation‐based statistical scenario testing demonstrates that this loss of diversity corresponds to an ancient expansion, followed by a dramatic decline in the Iberian population size over the last three generations. These results lineup with field observations and call for urgent conservation actions. Marginal populations of temperate species at the rear (warm) distribution edge are often considered more susceptible to warming than central populations because of the warmer ambient temperatures they experience, but this overlooks the potential for local adaptation  are likely to show a poleward shift , the IBMA porpoise feeding ecology is still poorly known. It is hypothesized that they exhibit similar foraging behaviors as their counterparts on the continental shelf to the north, but with a greater emphasis on pelagic fish  population is at the border of a biogeographical transition zone between temperate and subtropical waters where species with different affinities co‐occur and reach their respective ecological limits Bowen, . Becausen Figure . In factn Figure , 2017. IUnlike previous studies, which inferred population structure between the Iberian population and other populations  for genetic polymorphisms using Sanger sequencing. Target regions were PCR amplified, quality controlled, and purified following the protocol described in Fontaine et al. . These samples form the 2010s cohort of the Iberian population  Figure . Tissue e et al. . Sanger a.k.a. the “old” Iberian cohort or IB_O), 14 from Mauritania (MA); and 12 from the Black Sea (BS). The old Iberian cohort from Fontaine et al. (n = 52) versus the old . The temporal coverage of this serial sampling of the Iberian population is thus at most 25 years, which corresponds to 2.5 generations, assuming a generation time of 10 years  also generated by Fontaine et al. (n = 135) were aligned with MUSCLE .In order to place the Iberian porpoises into a global phylogeographic context, we first reconstructed a maximum‐likelihood (ML) phylogeny with all the 135 mtDNA sequences (including the outgroup). Unique mtDNA haplotypes were identified using  and Nei 's  and the R package pegas v.1.2 , haplotype diversity (Hd), nucleotide diversity (π), and Watterson's theta (θW). Differences in sample size can significantly impact the values of some genetic diversity estimators . At each sample size increment k, we estimated the mean and standard error values per statistic and per group. Subsequently, genetic differentiation between the two cohorts was assessed using Hudson's estimator of FST . Finally, we investigated evidence of changes in mtDNA effective population size (Ne) between the two cohorts (old vs. new) by calculating values of Tajima's D  were estimated using the C library Paradis,   procedure for the model selection . In addition, we computed the confusion matrix, which provides a more global assessment of the performance of our RF procedure  and of a principal component analysis (PCA), following the recommendation of Pudlo et al. . We visu3In total, we successfully sequenced 52 Iberian harbor porpoises from the new cohort 2012–2015) for fragments of five mitochondrial genes including Cytochrome b , and ATP‐6 and ‐8 (842 bp), NADH dehydrogenase 5 (ND5: 707 bp), and the Cytochrome c oxidase subunit I (COI: 1515 bp), When aligned with the 82 previous sequences of harbor porpoises from Fontaine et al.  and an a2–2015 foP. p. relicta in the Black Sea, (2) P. p. phocoena composed of the porpoises from the North Sea to the waters of Norway, and (3) the Afro‐Iberian (IBMA) porpoises , which comprises two distinct monophyletic sub‐lineages in the upwelling waters of Iberia and Mauritania, respectively.Consistent with previous studies, the phylogenetic relationships among mtDNA haplotypes depicted by the ML tree Figure  and by tEvidence of migration from the Iberian to the Mauritanian populations has been previously documented  observed from 19 sampled sequences in each cohort decreases from 10 to 6 when comparing the old versus new cohorts. This suggests that the proportion of rare haplotypes has dropped substantially over the 25 years sampling period of this study. Consistent with this observation is the rapid loss in nucleotide genetic diversity as measured by π and θW  of the Iberian population expanded at least ~150 generations ago, thus ~1500 years ago, assuming a rough generation time of 10 years . Thus, this ABC analysis, together with the rarefaction results, adds an additional line of evidence suggesting a rapid loss of genetic variation over the last three generations due to a very recent decline in effective population size. The reader should note, however, that the effective population size (Ne) is a population genetic concept that differs from and is usually smaller than the census population size. It does not represent the number of individuals actually alive at any given time point. Following Frankham  to explore statistically six plausible demographic scenarios Figure  describiFrankham , Ne is tP. p. meridionalis) and P. p. phocoena, has been reported   . Therefore, future studies with additional samples and data from the nuclear genome are highly needed to confirm the trends highlighted here. Indeed, genomewide data can provide further insights into the temporal dynamics of the Iberian population. For example, the reconstruction of historical demography based on genomewide data  with large interannual fluctuations. The highest annual abundance estimated for that period was 3207 individuals (CV = 38.14%) in 2013, followed by a sharp decrease in 2014, when only 1653 individuals (CV = 43.27%) were estimated  in Iberian waters and the Bay of Biscay  a better assessment for the Iberian porpoises in term of their life history traits, ecology, behavior, genetics and genomics, and the threats to the viability for this unique group. This knowledge will help design better conservation measures and recommendations. It is indeed paramount to ensure that all measures are taken to gain additional knowledge and to apply effective conservation efforts to protect the Iberian population, as recently underlined by the OSPAR Marine Mammal Expert Group (OMMEG)  lineage of harbor porpoises was previously recognized as a unique Evolutionarily Significant Unit ; investigation (lead); methodology ; software (lead); validation ; visualization (lead); writing – original draft (lead); writing – review and editing . Tjibbe Stelwagen: Data curation (supporting); formal analysis (supporting); investigation (supporting); methodology (supporting); resources (supporting); visualization (supporting); writing – original draft (supporting). Jeroen P. A. Hoekendijk: Data curation (supporting); investigation (supporting); methodology (supporting); resources (supporting); writing – original draft (supporting). Marisa Ferreira: Resources ; writing – original draft (supporting). Catarina Eira: Resources ; writing – original draft (supporting). Andreia Torres‐Pereira: Resources ; writing – original draft (supporting). Lidia Nicolau: Resources (supporting); writing – original draft (supporting). Julie Thumloup: Data curation ; investigation ; methodology ; resources ; validation ; writing – original draft . Michael C. Fontaine: Conceptualization (lead); data curation (lead); funding acquisition (lead); investigation (lead); methodology ; project administration (lead); resources ; supervision (lead); validation (lead); writing – original draft (lead); writing – review and editing (lead).The authors declare that they have no conflict of interest.https://dataverse.ird.fr/dataverse/porpoise_genomics and https://doi.org/10.23708/GOPRGV.This article has earned Open Data and Open Materials badges. Data and materials are available at Data S1:Click here for additional data file.


Equus caballus breeds is found, as well as a shallow level of genetic differentiation between the examined populations.Traditional horse breeding has long developed several kinds and lineages of horse breeds in Kazakhstan. Among them, Kushum and Mugalzhar are the breeds most prolific and resistant to harsh climatic conditions. Microsatellite analysis is employed to examine present genetic variability and population structure. The subpopulation structure shows three regional groups indicating (I) purebred Kushum populations from western Kazakhstan, (II) the Kozhamberdy type of Mugalzhar interbreed in populations from central Kazakhstan, and (III) admixed Kushum–Mugalzhar populations from western and southwestern Kazakhstan. The majority of microsatellite markers utilized are informative, with seven of them being extremely variable. A high level of genetic admixture among Kazakhstani Equus caballus) populations is critical for long-term herd management and breeding programs. This study examines 435 horses from Kazakhstan, covering seven groups in three geographic areas using 11 STR markers. Identified are 136 alleles, with the mean number of alleles per locus ranging from 9 to 19. VHL20 is the most variable locus across groups, while loci HTG4, AHT4, AHT5, HTG7, and HMS3 are variable in most populations. The locus AHT5 in the Emba population shows the highest frequency of rare alleles, while the lowest frequency, 0.005, is observed in the Kulandy population. All loci were highly informative for the Kazakhstani populations of E. caballus, with PIC values higher than 0.5. Pairwise variations in Wright’s FST distances show that the examined varieties have little genetic differentiation (0.05%), indicating a high degree of admixture and a continuing lineage sorting process. Phylogenetic and population structure analyses reveal three major clusters of Kazakh horses, representing (I) the Uralsk population of the Kushum breed and the monophyly of two groups: (II) the Kozhamberdy population of the Mugalzhar breed, and (III) the Mugalzhar–Kushum breed populations. Kazakhstani horse populations, while being regionally isolated, were recently in contact with each other.Understanding the genetic diversity and structure of domesticated horse ( The horse was the last domesticated animal which had a long-term significant and enduring impact on human civilizations, leading to advancements in transportation and trade, influencing people’s lifestyle, and revolutionizing the nature of warfare. In turn, human selection changed genetic diversity in horse populations, resulting in the variation seen among current horse characteristics and types. Domestic horses were subject to constant genetic restocking from the wild, mostly from females ,2,3,4.Kazakhstan, the largest Central Asian country, combines several climatic and geomorphologic features that offer an optimal environment for agricultural growth due to its geographical location at the intersection of the western and eastern Palearctic. The FAO database , identifies Kazakhstan as having eight registered domesticated horse breeds  . The oveThe Kushum horse was bred by the herd method of Ural and Aktobe farms, named after the Kushum River, flowing across western Kazakhstan. Initial approval as a separate breed came in 1976. The breed was established by a complex reproductive crossover procedure. Trotting, Thoroughbred riding, and Don breeds were crossed with local native mares . The farMugalzhar horses are an upgraded dual-purpose form of the Kazakh horse breed Jabe type, developed by Kazakhstani scientists between 1969 and 1998 . They arMany studies have been conducted on a global scale in the horse breeding sector involving research of mtDNA analysis of modern horses ,14,15,16Genetic distinctiveness can be clarified by population genetic structure, leading to breed preservation, including future breeding methods and management plans. Microsatellites have been a widely utilized genetic marker and effectively applied to studies of inter- and intrabreed variability in domestic and feral horse populations ,30,31,32Here we determine the genetic diversity of the Kushum and Mugalzhar horse breeds within populations and between them. Applied are several different approaches to evaluate the distribution of molecular indices, genetic distances, and population structural variation, which can elucidate lineage sorting processes, and be helpful for organizing individual genetic feature panels of the type or line as well as to improve the inbreeding management strategy of Kazakh horse-breed populations.DNA isolation of 435 samples from blood cohorts were taken from 7 populations across Kazakhstan, representing Mugalzhar and Kushum horse breeds .A panel of 11 highly polymorphic microsatellite loci  were chosen for typing using methods described by Zabek and Fornal (2009) .DNA extractions were conducted using the “DNA-sorb-B” set . Biomaterial was processed by the solid-phase sorption method, which consists in adding a lysing solution, DNA sorption on a sorbent, repeated washing and resorption of DNA with a buffer solution, as a result of which a purified solution containing DNA was obtained. Furthermore, the spectrometric quantification was performed as well as amplification reactions carried out using the StockMarks Equine Kit  . SeparatIT, FIS, and FST) of Wright’s F-statistics were obtained using GenAlex 6.5 and Excel microsatellite toolkit (version 3.1) [Allele frequencies and polymorphic information content (PIC) were calculated using Cervus 3.0 software ,36. Geneion 3.1) . Neighboion 3.1)  was usedion 3.1) . Factoriion 3.1) . Bayesiaion 3.1)  without ion 3.1) . In addiion 3.1)  was applion 3.1)  and the ion 3.1) .p ≥ 0.05)  to 0.093 (AHT4) with an average value of 0.211 for all loci. FIT presented a mean value of 0.157 ranging from −0.010 for HTG7 to 0.439 for LEX3. The calculation of FIS was between 0.030 (AHT5) and 0.074 (ASB2) with a mean value of 0.041 in the total population.A total of 136 alleles at 11 STR loci from 435 genotyped individuals of two Kazakh horse breeds from seven populations were identified. All markers were found to be polymorphic ( ≥ 0.05) . The mea ≥ 0.05) . Furtherp ≥ 0.001) indicating a reduction of heterozygosity, panmixia, and inbreeding processes which occurred in Kazakhstani populations of E. caballus.AMOVA analysis performed on seven populations, suggests that the majority of the variation occurred within individuals—70% . FixatioAcross Kazakhstan, horse breeds are identified with rare alleles that are typical for each population: VHL20, HTG4, HMS3, HMS6, HMS7, AHT4, AHT5, ASB2, ASB23, and LEX3. Among them, five unique alleles were observed in the Uralsk (Population 1) and Kulandy (Population 7). For the Uralsk population unique alleles were found in the HTG locus at 121 bp and 125 bp lengths, at HMS6 at 155 bp length, and HMS7 169 bp length. For the Kulandy population a unique allele was found in the locus LEX3 143 bp length. ST) distance over all loci between populations of the Kushum breed  was 4.5%, whereas between populations of the Mugalzhar breed it varied in the range of 0.008–2.8%. Genetic variability between the Uralsk population and Mugalzhar populations was from 3.3% to 4.7%, which indicates that 4.7% of the variability could be attributed to differences between breeds (p ≤ 0.05) results at all loci, rejecting the null hypothesis of random mating  . The samST findings were considerably higher than those in Bhutan horse breeds (0.003–0.008) [The subpopulation inbreeding coefficient relative to the whole population showed that genetic differentiation is higher between breeds, compared to subpopulations . Compara3–0.008) , Turkish3–0.008) , and Gui3–0.008) . PhylogeCluster analysis results  reveal tFurther, Kulandy-type horses (IIIc) revealed a different genetic background in all K, while almost the same ancestral component was distinguished in the three lines of Kozhamberdy horses (II). At all values of K, a more similar pattern was observed between Aktobe (IIIa) and Emba (IIIb) populations. In the present study, the horse breeds, types and lines are also phenotypically diverse, sampled from different locations and various pasture areas. For instance, depending on the natural, climatic, and forage conditions of breeding, the Mugalzhar breed is divided into three types: Kozhamberdy, Emba, and Kulandy. Kozhamberdy forms the type of horses established in the Karagandy region of central Kazakhstan, with Emba from the Aktobe region of western Kazakhstan, and the Kulandy type of horses produced in the Kyzylorda region of the southwest part of the country. Moreover, this type of horse has high meat and milk productivity, and is well adapted to harsh environmental conditions, showing characteristic features of main breeding traits and steadfastly passing them on to their offspring.Taking into account that the biological unit in domesticated animals is usually breed, differences of genetic traits among individuals characterize the endurance and adaptability of an organism. The STR loci are a universal tool for the examination of genetic variability among horse breeds ,55,56. FIT, FIS, and FST reflected a reduction of heterozygosity and weak divergency (FST ≤ 0.05) in the populations [In the present study among identified rare alleles, private alleles were detected in loci HTG4, HMS6, and HMS7 for the Uralsk (Population 1), and in the locus LEX3 for the Kulandy (Population 7), which is highlighting their favor in genetic differentiation from each other ,58,59. Hulations . ConfirmConsidering the fact that the SNP (single nucleotide polymorphisms) genotyping approach has been widely applied to the investigations of Equines’ population structure ,65, yet Equus caballus was detected. STR loci were shown to be particularly helpful for characterizing the allele pool and assessing the extent of genetic differentiation of studied populations. Assessment of genetic variation and F-statistics presented a reduction of heterozygosity in all populations. Conducting a comparative microsatellite typing is critical for monitoring genetic variability and tracing patterns in the selection process. Furthermore, data on genetic diversity computed within and across breed variants might give insight into future conservation initiatives and better horse breeding management.In the present study, microsatellite analysis allowed us to identify a division of the Uralsk population from populations of Aktobe (Kushum breed) and Kulandy , the latter of each differentiated by 4.5% and 4.7%, respectively. Moreover, a significant degree of population admixture between breeds and within types and lines of Kazakhstani


Dongxiang group, as an important minority, resides in Gansu province which is located at the northwest China, forensic detection system with more loci needed to be studied to improve the application efficiency of forensic case investigation in this group.A 60-plex system including 57 autosomal deletion/insertion polymorphisms (A-DIPs), 2 Y chromosome DIPs (Y-DIPs) and the sex determination locus (Amelogenin) was explored to evaluate the forensic application efficiencies of individual discrimination, kinship analysis and biogeographic origin prediction in Gansu Dongxiang group based on the 60-plex genotype results of 233 unrelated Dongxiang individuals. The 60-plex genotype results of 4582 unrelated individuals from 33 reference populations in five different continents were also collected to analyze the genetic background of Dongxiang group and its genetic relationships with other continental populations.− 24, respectively. The system could distinguish 98.12%, 93.78%, 82.18%, 62.35% and 39.32% of full sibling pairs from unrelated individual pairs, when the likelihood ratio (LR) limits were set as 1, 10, 100, 1000 and 10,000 based on the simulated family samples, respectively. Additionally, Dongxiang group had the close genetic distances with populations in East Asia, especially showed the intimate genetic relationships with Chinese Han populations, which were concluded from the genetic affinities and genetic background analyses of Dongxiang group and 33 reference populations. In terms of the effectiveness of biogeographic origin inference, different artificial intelligent algorithms possessed different efficacies. Among them, the random forest (RF) and extreme gradient boosting (XGBoost) algorithm models could accurately predict the biogeographic origins of 99.7% and 90.59% of three and five continental individuals, respectively.The system showed high individual discrimination power, as the cumulative power of discrimination (CPD), cumulative power of exclusion (CPE) for trio and cumulative match probability (CMP) values were 0.99999999999999999999997297, 0.999980 and 2.7029EThis 60-plex system had good performance for individual discrimination, kinship analysis and biogeographic origin prediction in Dongxiang group, which could be used as a powerful tool for case investigation.The online version contains supplementary material available at 10.1186/s41065-023-00271-2. Deletion/insertion polymorphism (DIP) is a fragment length polymorphism genetic marker with short insertion or deletion sequence in the nuclear genome. Compared with the traditionally used mini short tandem repeat (miniSTR), the DIP has some advantages of no amplification slippage peak and convenient detection on capillary electrophoresis platform, which suggests good application potentials in forensic individual identification, kinship testing, biogeographic origin inference, genetic background evaluation and other research fields.In recent years, some researches have constructed several multiplex DIP systems –4, microhttp://www.stats.gov.cn/tjsj/pcsj/rkpc/7rp/indexch.htm). In previous population genetic studies, researchers evaluated the forensic performances of different panels with 9-STRs [Dongxiang ethnic group that we are interested in is an important minority nationality in Chinese Gansu province. Its language belongs to the Mongolian language family of Altai language family. According to the China Statistical Yearbook–2021 released by the National Bureau of Statistics, there are 774,947 Dongxiang individuals, mainly living in the foothills of Chinese Gansu province, China  value, DA value and the informativeness for assignment (In). The pairwise FST value is a measure of population differentiation, as the greater the differentiation index, the greater the difference. In is also an important value to assign the informative of each population, in combination with DA genetic distance value, they are important parameters to evaluate whether populations are closely related with each other [In this study, we used the 60-plex panel to genotype 233 unrelated individuals of Dongxiang group, explored the genetic polymorphisms of 57 A-DIPs in the group, and evaluated the individual identification efficiency of this panel by calculating the various forensic parameters and cumulative indexes. Based on the obtained allelic frequency data of 57 A-DIP loci in Dongxiang group, the simulated families were used to evaluate the identification efficiency of the full sibling relationship by the method of LR. What’s more, the genetic similarities and differences among Dongxiang group and other 33 reference populations were fully explored through three quantitative genetic distance indicators i.e. pairwise fixation index  was used to extract genomic DNA from bloodstain card samples. The extracted genomic DNA was quantified by Qubit™ 4.0  with Qubit™ dsDNA HS and BR Assay kit (Invitrogen).All the 60 loci including 57 A-DIPs, 2 Y-DIPs and Amelogenin for sex determination were co-amplified using the AGCU InDel 60 panel  on the GeneAmp PCR system 9700 Thermal Cycler  according to the manufacturer’s instruction. The PCR products were separated on the 3500xL Genetic Analyzer (Thermo Fisher Scientific) and analyzed with the GeneMapper ID-X v1.5 software (Thermo Fisher Scientific) according to the manufacturer’s recommendation. The peak threshold of the allele calling was set as 100 relative fluorescence units (RFUs).Allele frequencies and forensic parameters including the expected heterozygosity (He), observed heterozygosity (Ho), match probability (MP), polymorphism information content (PIC), power of discrimination (PD), power of exclusion (PE) for trio and typical paternity index (TPI) for 57 A-DIPs were calculated with the online tool STR analysis for forensics (STRAF 1.0.5) . The tesR v4.2.1 (https://www.r-project.org).The 57 A-DIPs genotype data of 33 reference populations were obtained from the previous studies, of which 26 were from the 1000 Genomes Phase III , and ChiFST values among the Dongxiang group and 33 reference populations were computed with the Arlequin v3.5. Pairwise DA genetic distances among 34 populations were conducted with DISPAN program [R v4.2.1 and MDS using SPSS v19.0 software [In values of 57 A-DIPs were conducted with the INFOCALC v1.1 [R package pophelper (http://pophelper.com). The predefined ancestral components were set from two to five. Based on four AI algorithms including SVM, RF, DT and XGBoost, four biogeographic origin inference models were constructed with R v4.2.1 to predict biogeographic origins of individuals in three continents  or five continents , respectively.The analyses of molecular variance (AMOVA) and pairwise  program . The gensoftware . The NJ software  and Treesoftware . To furtALC v1.1 . PopulatALC v1.1  and visup values of HWE tests for 57 A-DIPs in Dongxiang group and 33 reference populations were shown in Supplementary Table p < 0.0009). Within 13 East Asian and five South Asian populations, these 57 A-DIPs were all in line with HWE. However, it was worth noting that several loci such as rs59841142, rs113011930, rs146875868, rs34076006, rs10590825, rs145191158, rs60867863, rs57981446, rs76158822, rs77635204, rs145010051, rs77206391 and rs538690481 were observed to be significant deviations from HWE in the non-Asian populations we explored, and most of the deviation loci were found in African populations. The results of pairwise LD tests for 57 A-DIPs in Dongxiang group were showed in Supplementary Table p < 3.1328 × 10− 5), there were no LDs among 57 A-DIPs in Dongxiang group.The − 24, respectively. The CPD, CMP and CPE values of 13 East Asian populations were calculated to evaluate the joint application efficiencies of the 57 A-DIPs among East Asian populations and were shown in Table − 22).The allelic frequencies of 57 A-DIPs in Dongxiang group were shown in Supplementary Table The LR method was used to evaluate the forensic efficacy of the 57 A-DIPs multiplex panel used in this study for full sibling identification in Dongxiang group and 12 reference East Asian populations. And the probability density curves of simulated full siblings and unrelated individuals of Dongxiang group and the line chats of accuracy distributions with different LR limits  of 13 East Asia populations were shown in Fig.To evaluate the role of geographical factors in the formation of genetic differentiations among different populations, we performed AMOVA analysis based on population allelic frequency data. The 12.41% among-group variance could be observed when the 34 populations were divided into five different geographical populations . And 1.55% variance among populations within continents was found. Furthermore, among 13 East Asia populations, only 2.08% variance among populations and 0.03% variance among individuals within populations could be observed. In the following sections, the PCA, MDS and phylogenetic analyses were also performed to further investigate the genetic relationships between Dongxiang group and 33 reference populations.FST values and DA genetic distances among Dongxiang group and 33 reference populations, and displayed in Supplemental Fig. FST values and DA genetic distances, the distribution patterns of the Dongxiang group and 33 reference populations were consistent with the results of PCA analyses.The PCAs of individual-level and population-level were performed based on genotypes, and allelic frequencies of 57 A-DIPs, respectively. And the results mentioned-above were shown in Fig. DA genetic distances and pairwise FST values were shown in Supplementary Table DA genetic distances between Dongxiang group and 33 reference populations ranged from 0.0011 (CHB) to 0.0886 . The average DA values between Dongxiang group and African, European, American, South Asian, East Asian populations were 0.0780, 0.0379, 0.0235, 0.0178 and 0.0030, respectively. Among the Dongxiang group and 33 reference populations, the remotest genetic relationship with the Dongxiang group was YRI with the biggest pairwise FST value (0.1917), whereas the closest genetic relationship was Beijing Han Chinese with the smallest FST value (0.0010). The average FST value was 0.0088 between Dongxiang group and other 12 East Asian populations. Between Dongxiang group and African, European, American and South Asian populations, the average pairwise FST values gradually decreased, which were 0.1772, 0.1116, 0.0694 and 0.0572, respectively.The FST values between Dongxiang group and 33 reference populations were shown in Fig. FST values also showed an increasing trend with the increase of clade distances, which might indicate that the genetic distances between Dongxiang group and the corresponding populations might be larger, and the phylogenetic relationship might be farther. The ML phylogenetic tree and residual fit from the tree were shown in Fig. FST and DA values with the Dongxiang group was set as the root of the ML tree. The results of ML phylogenetic tree showed that no matter the genetic relationships between Dongxiang group and reference populations, or among different continental populations were similar with the NJ results.The NJ phylogenetic tree and histogram of pairwise In values of 57 A-DIPs among the Dongxiang group and five continental populations were shown in Supplementary Table In value of 57 A-DIPs between African populations and Dongxiang group was 0.2080 of rs145010051 locus, as the minimum value was 0.001 of rs60564093 locus. And there were 18 loci which showed the average In values larger than 0.1, namely rs34287950, rs3067397, rs66739142, rs71852971, rs146875868, rs538690481, rs79225518, rs76158822, rs59841142, rs113011930, rs145191158, rs57981446, rs72085595, rs34076006, rs77206391, rs60867863, rs77635204, and rs145010051. The average In values of 57 A-DIPs varied from 0.0007 to 0.0155 in Dongxiang group and the reference populations in East Asia. The average In values of 57 A-DIPs in European populations and Dongxiang group ranged from 0.0008 (rs67487831) to 0.2037 (rs145010051). And four loci  were the average In values larger than 0.1 in European populations and Dongxiang group. Locus rs145010051 was the only one with the average In values larger than 0.1 between Dongxiang group and American, South Asian populations.The K) were set from two to five, and the genetic structure result of individual-level was shown in Fig. K was two, there were two ancestral components (the blue and red components), which were roughly derived from the African and non-African populations. When K was three, the third ancestral component (the green one) appeared in the American, South Asian and European populations. When K were four or five, no other significant ancestral components appeared in five continental populations. However, the proportions of ancestral information components among the American, South Asian and European populations were still different with each other. Among 13 East Asian populations, the genetic structure of SP group was different from other 12 populations, while the Dongxiang group shared the similar pattern with other East Asian populations.Model-based admixture analysis was performed to further investigate the ancestral information component of the Dongxiang group. The assumed numbers of ancestral components  were used to construct the biogeographic origin inference models based on the genotype data of 57 A-DIPs of 4815 unrelated individuals of Dongxiang group and 33 reference populations. The confusion matrixes and associated statistics of the biogeographic origin inference models for three continental , and five continental populations  were shown in Table FST, DA genetic distances and In value assessments, NJ and ML phylogenetic tree constructions, PCA, MDS and structure analyses, respectively. Finally, based on the genotypes of 57 A-DIPs, four AI algorithms were constructed to predict the biogeographical origins of unknown individuals.In this study, a multiplex panel including 57 A-DIPs, 2 Y-DIPs and Amelogenin was used to genotype 233 unrelated healthy individuals of Dongxiang ethnic group in Chinese Gansu province. We fully assessed the forensic parameters and genetic polymorphisms of 57 A-DIPs; and estimated the forensic efficiencies of individual discrimination and full sibling identification of the 60-plex panel in Chinese Dongxiang group. In addition, the 57 A-DIPs genotype data of 4815 individuals from 33 reference populations were collected to explore the genetic differentiations and relationships between Dongxiang group and other continental populations through the pairwise All 57 A-DIP loci were confirmed to meet HWE in Dongxiang group. And there were no LDs among pairwise DIP loci in the group, which indicated that this system had good applicability in the target group. Additionally, several loci  in this system did not conform to HWE in non-Asian populations , which also suggested that the system should conduct sufficient population genetic analyses before putting into forensic applications, so as to ensure the reliability of interpretation of subsequent evidence result.-24) in Dongxiang group, which indicated that the system had high individual identification effectiveness in the group. The individual identification efficiency of 57 A-DIPs (CPD: 0.99999999999999999999997297) was higher than that of 30 A-DIPs (CPD: 0.999999999985) [The insertion and deletion alleles of 57 A-DIPs were widely distributed in the Dongxiang group, and all loci were relatively high polymorphisms. Compared with the reference populations in East Asia, the 57A-DIPs system had the lowest CPE (2.7029E9999985) , 9 A-STR9999985)  and 15 A9999985) .FST values, DA genetic distances, In values assessments, and NJ and ML phylogenetic tree constructions. From the AMOVA analysis, 12.41% of the A-DIP loci diversities could be attributed to geographical aspect. The FST and DA values between Dongxiang group and African, European, American, South Asian, East Asian populations gradually decreased, indicating that Dongxiang group had relatively remote genetic distances with African and European populations, while which had relatively closer genetic distances with East Asian populations. And it could also be verified by the presence of more loci with In values larger than 0.1, which indicated the large difference between the two populations [FST and DA genetic distances also confirmed the close genetic relationships between Dongxiang group and other East Asian populations. Previous researches based on various genetic markers also indicated that Dongxiang group had closer genetic distances with Chinese other groups, such as the Xibo and Salar groups [The genetic similarities and differences among Dongxiang group and 33 reference populations were explored by the AMOVA, PCA, MDS analyses, pairwise ulations . In the r groups , Tibetanr groups , 33, Huir groups .K values) increased from two to five, the East Asian, American and South Asian populations also showed the slight difference of ancestral components with other continental populations. Due to the genetic differences among different populations, 60-plex system had the potential to predict the unknown individuals' biogeographic origins. Based on the 57 A-DIPs genotypes, when we differentiated three continents , the highest accuracy of biogeographic origin inference model was observed in RF algorithm, the accuracy of XGBoost algorithm model was the second, and followed by SVM algorithm model and DT algorithm model. While in the more detailed divisions of five continents , XGBoost algorithm showed the most accurate prediction result of unknown individuals' biogeographic origins, which was 90.59%. The prediction accuracies of RF algorithm model and SVM algorithm model were the same, and followed by DT algorithm model. In previous study, the XGBoost model performed the best for small ranges of population  differentiations through analyzing the input data of genotypes and allelic frequencies of 15 A-DIPs, and genetic distances between populations [From the results of individual-level and population-level ancestral component evaluations by the model-based admixture analyses, the Dongxiang group and other continental reference populations could be significantly divided into three different ancestral components. When the hypothetical ancestral components . The XGBoost algorithm model could correctly predict the biogeographic origin information for 90.59% of the five continental individuals . In general, this 60-plex system had good performances of individual identification, full sibling identification and biogeographic origin prediction, which could be used as a powerful tool for forensic case investigation.Additional file 1: Supplementary Table I. The detailed information of the target Chinese Gansu Dongxiang group and 33 reference populations. Supplementary Table II. The p values of Hardy-Weinberg equilibrium (HWE) tests of the 57 A-DIPs among Dongxiang group and 33 reference populations. Supplementary Table III. The p values of the LD tests of 57 A-DIPs in Chinese Gansu Dongxiang group. Supplementary Table IV. The insertion and deletion allelic frequencies of 57 A-DIPs in Chinese Gansu Dongxiang group. Supplementary Table V. The forensic parameters  of 57 A-DIPs in Chinese Gansu Dongxiang group. Supplementary Table VI. The accuracies and false positive ratios of full sibling identifications with different LR limits  by 57 A-DIPs system among 13 East Asia populations. Supplementary Table VII. The pairwise FST values between Chinese Gansu Dongxiang group and 33 reference populations. Supplementary Table VIII. The pairwise DA values among Chinese Gansu Dongxiang group and 33 reference populations. Supplementary Table IX. The In values between Chinese Gansu Dongxiang group and 33 reference populations. Supplementary Table X. The confusion matrixes and statistics of the RF, XGBoost, SVM and DT models for the biogeographic origin predictions of the five continents.Additional file 2.


The latter view is also supported by archaeogenetic data6. Here we fill key chronological and archaeogenetic gaps for the Maghreb, from Epipalaeolithic to Middle Neolithic, by sequencing the genomes of nine individuals (to between 45.8- and 0.2-fold genome coverage). Notably, we trace 8,000 years of population continuity and isolation from the Upper Palaeolithic, via the Epipaleolithic, to some Maghrebi Neolithic farming groups. However, remains from the earliest Neolithic contexts showed mostly European Neolithic ancestry. We suggest that farming was introduced by European migrants and was then rapidly adopted by local groups. During the Middle Neolithic a new ancestry from the Levant appears in the Maghreb, coinciding with the arrival of pastoralism in the region, and all three ancestries blend together during the Late Neolithic. Our results show ancestry shifts in the Neolithization of northwestern Africa that probably mirrored a heterogeneous economic and cultural landscape, in a more multifaceted process than observed in other regions.In northwestern Africa, lifestyle transitioned from foraging to food production around 7,400 years ago but what sparked that change remains unclear. Archaeological data support conflicting views: (1) that migrant European Neolithic farmers brought the new way of life to North Africa Genome sequencing of nine individuals shows ancestry shifts in the Neolithization of northwestern Africa that probably mirrored a heterogeneous economic and cultural landscape in a more multifaceted process than observed in other regions. The fossil record suggests long-term hominid and human presence9, although continuity over the past 100,000 years cannot be deduced due to the fragmented nature of the record. In the Late Pleistocene, 15,000 years ago, the remains of foragers excavated in Morocco show a distinct genetic make-up intermediate between contemporary Levantine foragers and sub-Saharan African populations10. Current-day North Africans are largely related to Eurasian populations, which was probably caused by ‘back-to-Africa’ migrations7.North Africa’s geographic location, centred between the vast Saharan desert, the fertile Near East and Mediterranean Europe, has resulted in a complex human history in the area18. Mediterranean coastal routes have long been recognized in the archaeological record as an important part of the Neolithic expansion in Europe. In the western Mediterranean, Impressed Ware technology—and further the Cardial Horizon—spread along the European mainland coast and islands to reach the Iberian peninsula, where both phenomena are present at 7,550 calibrated years before the present  (refs. 20).Both archaeological records and archaeogenomic data show that Neolithic farmers  dispersed from the northern Levant and Anatolia to the Mediterranean islands, Italian peninsula and Iberiabp (ref. 21), the earliest evidence for pottery, domestic cereals and husbandry is found in northern Morocco approximately two centuries later at Kaf Taht el-Ghar (KTG) around 7,350 cal bp (refs. 23). Although Early Neolithic material culture and the first domestic mammals and pulses suggest a connection to Iberia3, the extent and legacy of these connections remain unclear. However, the first genomic analysis of Early Neolithic farmers from northwestern Africa (from the site Ifri n’Amr o’Moussa (IAM) in central Morocco) shows no traces of admixture with European Neolithic farmers. Instead, it shows long-term population continuity since the Upper Palaeolithic in the region6. This result aligns with the hypothesis that the Neolithic transition in northwestern Africa was initiated by local Epipalaeolithic communities adopting technological innovations5, such as those found at IAM: impressed Cardial-like ceramics, similar to those present throughout the western Mediterranean Neolithic Europe, and domestic cereals 2. This pattern implies a Neolithization process that contrasts markedly with that of Europe, where it has been established that agriculture was introduced by the west- and northward demic diffusion of Anatolian early farmers12. The local development, or acculturation, of the North African Neolithic is further supported by signs of increasingly sedentary Epipalaeolithic groups developing strategies for resource management, such as the exploitation of wild plants and pottery26. Rapid climatic changes favoured mobile herding27 and, whereas it has been hypothesized that cattle were independently domesticated in the Sahara28, radiocarbon data suggest a gradual introduction of pastoralism in the Sahara in a southwestwards direction 7,000–6,000 cal bp, possibly from the Near East30.Whereas some studies support a simultaneous appearance of the Neolithic in northwestern Africa  and Iberia around 7,550 cal 33, North Africa has been the focus of only a single study that generated human genetic data from one Early and one Late Neolithic site6, leaving substantial gaps in the chronology of events. It is evident that the site of IAM shows a Neolithic lifestyle and an absence of European Neolithic ancestry, but whether this was an independent development or the inspiration came from other groups in northwestern Africa or across the Mediterranean Sea remains unclear. Hence, the timeline and processes involved in the Neolithization of the region, the nature and dynamics of different economies in North Africa and the role they may have played in the broader European Neolithic remain understudied and controversial.Whereas palaeogenomic studies on the European Mediterranean Neolithic transition are abundant10. By sequencing the genomes of nine individuals excavated from these four archaeological sites, we can demonstrate that the Neolithic transition in northwestern Africa was ignited by migration of Neolithic farmers from Mediterranean Europe.In this study we investigate a time series of human remains from four archaeological sites spanning the Epipalaeolithic to Middle Neolithic in current-day Morocco: the Epipalaeolithic site of Ifri Ouberrid (OUB), the Early Neolithic sites of IAM and KTG and the Middle Neolithic cemetery of Skhirat-Rouazi (SKH), co-analysed with previously published genetic data from that regionn = 1), Early Neolithic (n = 5) and Middle Neolithic (n = 3). Two Early Neolithic sites were studied—KTG (n = 4) and IAM—where we co-analysed the newly generated genomic data of one individual and those previously reported6 35 and Early Neolithic individuals from IAM  via the Epipalaeolithic at OUB to the Early Neolithic at IAM, we observe the persistence of the unique genetic make-up that existed in northwestern African inhabitants 15,000 years ago  changes of oub002 follow a pattern similar to that of Eurasian populations with a relatively small effective population size reached 50,000 years ago  between 50,000 and 27,000 years ago during the peak of the Last Glaciation. Remarkably similar patterns are observed for the Mesolithic western hunter-gatherers (WHG) of Europe  northwestern African groups  . Taken together, these results suggest a European Neolithic origin of KTG farmers whose ancestors dispersed from Anatolia throughout Europe, admixing with European hunter-gatherers on their path to southwestern Europe40 before crossing the Mediterranean to North Africa. The presence of European hunter-gatherer ancestry excludes the possibility that Early Neolithic migrants exclusively followed North African Mediterranean shores from Anatolia or the Levant.The Early Neolithic site of KTG, located on the North African Mediterranean coast near the Gibraltar strait Fig. , predate2 Table . At KTG ns Figs.  and 3a. ns Figs. , consist41, confirmed by direct radiocarbon dates showing that Impressed Ware farmers expanded rapidly across the western Mediterranean42.Iberian Early Neolithic  was found to be the best source population for the European ancestry in KTG, followed by Sicily Stentinello Early Neolithic  than in later ones  . Approximately one quarter of Maghrebi ancestry in early KTG suggests that they represent at least the second generation of interbreeding between the groups. We estimated the time of admixture using two approaches based on ancestry covariance patterns and linkage disequilibrium decay, using Iberia or Sicily Early Neolithic and TAF as admixture sources. Both methods date the contact within the last six to 13 generations (Supplementary Information bp (ref. 23).Different individuals from KTG date to slightly different time periods. We find a twofold larger proportion of Maghrebi ancestry in earlier KTG individuals  Fig. . This co22. This could have limited the potential of these groups to recover from an initial founder effect.Kaf Taht el-Ghar farmers had slightly lower genetic diversity levels and greater RoH than most Early Neolithic European populations Fig. . The Mag45. However, the northwestern Africa Neolithization process involved the notable survival of genetically unadmixed local populations (represented by IAM), despite coexisting for at least 300 years with foreign farming communities (KTG), and still adopted several elements of the Neolithic ways of living from them. Whereas the archaeological findings in IAM and KTG point to the exchange of ideas between groups and support an acculturation process of foraging communities4, our genetic data show that the exchange of genes was unidirectional.Overall, the genetic patterns of local interaction between different groups in northwestern Africa are comparable to those found in Europe: farmers assimilated local foragers’ ancestry in a unidirectional admixture process. Cases of hunter-gatherer communities adopting certain elements of the Neolithic have been described in EuropeAnother, distinct, ancestry was introduced to northwestern Africa during the Middle Neolithic. All individuals from SKH show large proportions of a genetic component maximized in individuals from Neolithic and Chalcolithic Levant, Ptolemaic Egypt and modern-day Near Eastern populations Fig. . The anc46. Both in SKH and eastern African Neolithic pastoralists, Levantine ancestry is admixed with local ancestries  space  and in the Guanches of the Canary Islands , and the fact that both regions were populated by foragers for many millennia prior to the Neolithic, gene flow across the Mediterranean Sea was not established until the Early Neolithic. The newcomers brought new ways of life, farming practices, domestication and pottery traditions that were subsequently adopted by local populations. Our results show that the Neolithization process in northwestern Africa was ignited by migrant Neolithic Europeans, but that local groups  adopted some of these practices without mixing with the newcomers. Two genetically distinct groups coexisted in close proximity in the region. Interestingly, cultural and technological knowledge appear to have been transferred mainly from European Neolithic farmers to local groups  whereas genetic ancestry flowed only from local groups to the incoming farmers, such as the population of KTG. Furthermore, in the Middle Neolithic a new ancestry with an eastern origin is detected in northwestern Africa. This ancestry indicates new migrating groups, potentially associated with Sahara pastoralists, which admixed with local groups , the remaining DNA extract (for samples with over 1% proportion of human DNA) was used to build four to six additional double-stranded DNA libraries; for extracts with roughly 5% endogenous human content or more, 15–20 µl of DNA extract was treated with uracil DNA glycosylase (UDG) for double-stranded library building55. Libraries were PCR amplified using a unique 7 bp indexed primer56 in either four reactions of 25 µl or two of 50 µl, with the application of 12–20 PCR cycles depending on previous qPCR quantification cycle indication. Two extraction negative controls, two library negative controls and one PCR negative control were included per sample batch. PCR reactions were pooled and purified with AMPure XP beads . Library quality was checked by electrophoresis on Tapestation  and DNA concentration was quantified using a Qubit dsDNA HS (High Sensitivity) Assay Kit (Invitrogen). Equimolar pools of amplified and purified libraries were sequenced on Illumina HiSeq X at the SNP & SEQ Technology Platform in Uppsala. To reach higher coverage, between four and ten libraries were pooled equimolarly and sequenced to depletion.Human remains were sampled in dedicated clean-room laboratories at Uppsala University, Sweden after a series of stringent procedures aimed at minimization of bone and tooth surface contamination. Thirty to sixty milligrams of bone powder or solid pieces of bone material were used for DNA extraction either following ref. 57 or Adapter Removal v.2.1.7 (ref. 58). Forward and reverse paired-end reads were merged when an overlap of at least 11 bp was found. Mapping against the human reference genome build 37 (hs37d5) was done using Burrows–Wheeler aligner 0.7.13 (ref. 59). For each library we merged bam files resulting from all resequencing rounds using SAMtools merge v.1.5 (ref. 60). We then separately merged data from UDG-treated and untreated libraries for each individual and used data from the former for subsequent analysis, except for individuals ktg001 (for which only non-UDG data were generated) and iam004 . We used a modified version of FilterUniqSAMCons_cc.py57 to ensure random choice of bases to collapse reads with identical start and end positions into a consensus, thereby removing PCR duplicates. Reads shorter than 35 bp and more than 10% mismatches to the human reference genome were filtered out.Data were demultiplexed according to the indexed primer sequence and adaptors were trimmed with either MergeReadsFastQ_cc.py61, on the X chromosome in males62 and on nuclear data63  and mapping quality (MAPQ) scores were set to MAPQ > 30 and BQ > 30, and only sites with at least threefold coverage were used. Haplogroups were assigned using Haplogrep 2.1.16 (ref. 66) and PhyloTree mtDNA tree Build 17 (18 February 2016)67 ) from bam files using SAMtools mpileup with option -B. We extracted sites with mapping and base quality greater than 30. Insertions and deletions, and sites showing multiple alleles, were excluded 10.We ran kinship analysis with READ34 for a 2.2 million SNP panel64. Alleles were sampled from bam files by randomly drawing one read with MAPQ > 30 and BQ > 30 per SNP site for each ancient individual (using SAMtools v.1.5.0 mpileup with option -B), and that position was treated as (pseudo)haploid. For non-UDG-treated data (ktg001) or merged UDG and non-UDG data (iam004) we trimmed off 10 bp of sequence-ends to avoid integration of miscoding C-to-T and G-to-A substitutions. For the published partial UDG-treated data , 2 bp were trimmed off the sequence-ends. SNPs showing more than two alleles were excluded from the data, leaving 1,379,466 SNPs for analysis.Data from over 300 ancient Eurasian, North African and Sub-Saharan African individuals, organized according to geography and chronology (Supplementary Data 69). Principal components were calculated based on individuals from 18 Mediterranean Eurasian or North African modern-day populations from SGDP. Ancient individuals were projected onto the PCA space with options shrinkmode: YES and lsqproject: YES. An unsupervised model-based clustering algorithm, implemented in ADMIXTURE v,1.3.0 (ref. 70), was performed for K = 3–5 (30 runs) on a fully pseudohaploidized, linkage disequilibrium-pruned dataset of modern-day and ancient individuals from Mediterranean Eurasian or North African populations, leaving 812,092 SNPs for analysis. The results were parsed, aligned and plotted with pong71.Principal component analysis was performed using smartpca v.10210  were calculated with a weighted block jack-knife approach.Popstats74 using ADMIXTOOLS v.5.0, through an adapted version of qpAdm_wrapper (https://github.com/pontussk/qpAdm_wrapper) that cycles through all possible subsets of the list of source populations provided (selected based on previous results), to test one-, two-, three- and four-way admixture models. SEs were computed with 5cM block jack-knife. We used a set of 11 reference populations whose power to disentangle divergent strains of ancestry present in Europe, North Africa and the Near East has previously been described and that are differently related to the sources tested75. Distantly related sources were explored and, where possible, also more proximate groups . We tried to find the most parsimonious models consistent with the data (P > 0.05) by checking the lowest possible number of ancestry sources necessary to explain the ancestry in each test population  over all called positions for each individual and filtered for QUAL > 30 and a depth span from fivefold to 3× avg.DP per individual, using BCFtools view. This dataset was merged with data from modern-day individuals from the SGDP dataset.Diploid genotype calls for a panel of 49,791,572 SNPs were performed for northwestern African ancient individuals with at least ninefold genome coverage , as well as relevant, previously published ancient individuals with sequenced high-coverage genomes. Before genotype calling, base quality in read ends was reduced and indel realignment conducted with GATK 3.5.0. Diploid genotypes were called using dbSNP v.142 as known SNPs, with GATK’s UnifiedGenotyper80). We estimated the length and number of runs of homozygosity after filtering with the command PLINK –geno 0. MSMC81 input files were generated from VCF files. Filters for MAPQ > 30, minimum genotype quality of 50 and sequencing depth were used. Sites not passing these filters were masked out per individual. MSMC 0.1.0 was then run for each individual.Individual heterozygosity was calculated from the number of variable positions divided by that of sequenced SNPs, using the –het command in PLINK 1.9 (ref. The sampling for this study emerged from archaeology projects that involved local universities and researchers, including Y.B., whose involvement in research design included the selection of archaeological material for analyses as well as sampling supervision. The local relevance of this research is tied to the region’s history, and it is locally relevant in regard to describing the human past in northwestern Africa. The study was undertaken with the highest standards of archaeogenomic research, and relevant research by local scholars was cited.Further information on research design is available in the Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-023-06166-6.Supplementary InformationA description of the northwestern African archaeological sites studied and a detailed description of the methods used for analyses, supplementary results and supplementary discussions for these results.Reporting SummarySupplementary DataSupplementary Data 1–15.


Tamarix chinensis Lour. is a 3–6-meter-tall small tree with high salt- and alkali- tolerance and aggressive invasiveness, mainly distributed in the eastern part of China in warm-temperate and subtropical climate zones, yet there is little information available regarding genetic diversity and population structure.T. chinensis populations were investigated for genetic diversity and population structure using a set of 12 highly polymorphic microsatellite markers.A total of 204 individuals of nine ST.The total number of alleles detected was 162, the average number of effective allele was 4.607, the average polymorphism information content (PIC) value of the 12 loci was 0.685, and the mean observed heterozygosity (Ho) and the mean expected heterozygosity (He) was 0.653 and 0.711, respectively. Analysis of molecular variance (AMOVA) showed a 5.32% genetic variation among T. chinensis populations. Despite a low population differentiation, Bayesian clustering analysis, discriminant analysis of principal components (DAPC) and the unweighted pair group method with arithmetic mean (UPGMA) clearly identified three genetic clusters correlated to the populations’ geographic origin: the northern populations including those from Yellow River Delta, the Fangshan (FS) population from Beijing, the Changyi (CY) population from Bohai Bay, the Huanjiabu (HHJ) population from Hangzhou Bay, and the remaining two populations from Hangzhou Bay. There was a significant relationship between the genetic distance and geographical distance of the paired populations. Gene flow (Nm) was 4.254 estimated from FT. chinensis possessed high genetic diversity comparable to tree species, and although the population differentiation is shallow, our results classified the sampled populations according to sampling localities, suggesting the different origins of the study populations. Tamarix L. (Tamaricaceae) comprises 68 species native to Eurasia and Africa, typically inhabiting deserts and sub-deserts in temperate and subtropical regions (Tamarix reached the western China in Oligocene (Tamarix chinensis and its two congeners (T. austromongolica and T. ramosissima) further colonized eastward, possibly along the Yellow River corridor according to molecular evidence, and it established populations along East coast of China . Random primers analysis of populations distributed in the Yellow River Delta with Random Amplified Polymorphic DNA (RAPD)   and InteA (RAPD)  also shoT. chinensis mainly from two estuarine flats of different climate zones: Yellow River Delta of the warm-temperate zone and Hangzhou Bay of the subtropical zone. Our specific aims were to (1) evaluate the genetic diversity and genetic differentiation among nine T. chinensis populations, (2) determine whether population structure relative to sampling locations exists, and (3) compare the population genetic indices of the two different climate regions. Our study can deepen understanding of the spatial distribution pattern of genetic variation of T. chinensis and lay foundation for further exploring its adaptation to heterogeneous environments.Simple sequence repeat  markers are widely used in population diversity and structure of various trees and shrubs for their codominant characteristics, high polymorphism and stableness . In thisT. chinensis distributed from Beijing, Shandong Province and Zhejiang Province. Sampling mainly focused on two estuarine flats of different climate zones: Yellow River Delta of the warm-temperate zone, and Hangzhou Bay of the subtropical zone  from Yellow River Delta and three populations  from Hangzhou Bay were sampled. One population (CY) of the Bohai Sea coast from Changyi City and one inland population (FS) from Beijing were also included. A total of 19–29 individuals per population were collected according to the population size and a total of 204 individuals from nine populations was sampled. We collected young leaves from individual trees spaced at least 50 m apart, in order to avoid sampling related individuals. Fresh leaves were desiccated insilica gel. Geographical coordinates and altitude were acquired with GPS , and Shannon diversity index (I). The genetic differentiation coefficient (Fst) across all populations at each locus and over all loci, gene flow (Nm = (1 − FST)/4 FST), and allelic richness (Ar) were calculated using FSTAT version2.9.3 software  was performed using ARLEQUIN 3.5  to partiadegenet” package  and Euclidean geographical or environmental distance. The climatic layers of 19 bioclimatic variables under current periods were downloaded from the WorldClim database (http://www.worldclim.org/) with a resolution of 2.5 arc-minutes to evaluate the effects of environment on genetic variation. The Vifstep function of the R package usdm  . Specifip < 0.01 . At popup < 0.01 . Except p < 0.01 . Across p < 0.01 .T. chinensis populations (Ne) ranged from 2.312 (Gssr4) to 10.636 (AB298390), with an average of 4.607. The observed heterozygosity (Ho) for each locus varied from 0.3939 (Gssr4) to 0.8469, with an average of 0.6533 and the expected heterozygosity (He) varied from 0.4073 (Gssr4) to 0.9083 (AB298390), with an average of 0.7111. Shannon’s information index (I) ranged from 0.5401 (Gssr4) to 2.7643 (AB298390), with an average of 1.569. The genetic differentiation coefficient (Fst) (0.036 (Gssr5)–0.110 (Gssr3)) and the polymorphic information content (PIC) values (0.406 (Gssr4) −0.899 (AB298390)) were detected at all loci. Gene flow (Nm) ranged from 2.023 (Gssr3) to 6.694 (Gssr5), with an average of 4.245.We used 12 pairs of SSR primers to generate 162 alleles from 204 individuals of nine ulations . The resT. chinensis population of was shown in The genetic diversity analysis of nine I = 1.73125, Ho =0.680, He =0.710) for populations from Yellow River Delta were obtained than those of populations from Hang Zhou Bay  except for the number of private allele (Np =4.75 for populations from Yellow River Delta and Np =9 for populations from Hangzhou Bay)  . InbreedUnder SMM and TPM, our Wilcoxon tests detected no significant heterozygote excess for the nine populations . LikewisFST = 0.0532, p < 0.001)  to 0.110(for Gssr3) (p < 0.05), among populations within regions , and 94.68% of variation was within-populations. The pairwise FST values between populations are shown in Low population genetic differentiation ( < 0.001)  was deter Gssr3) . AMOVA tK = 2  formed two distinct clusters almost without admixture, while the individuals from the remaining populations were mostly admixed with two other clusters with approximate proportion    , but no IBE patterns in the populations studied.Mantel tests showed the studied populations conformed to the patterns of isolation by distance (IBD) (= 0.012)  and isol= 0.015)  at signi= 0.002) , partialT. chinensis mainly distributed in two coastal flats of different climate zones. Our study revealed a high genetic diversity and low differentiation level and strong population genetic structure correlated to populations’ location that previous studies did not detect. The genetic diversity level of populations from Yellow River Delta is higher than that of those from Hangzhou Bay on averageIn this study, we evaluated the genetic diversity level and population genetic structure of T. chinensis       despite that it can be reproduced by cuttings and root suckers. It has a short generation time of 1-2y and its raceme flowers produce a large number tiny hairy seeds which can easily be transported by wind and/or water. Its seed can tolerate high salt concentration (0.15 g/l)   comparab= 0.760) , Q. liao =0.801) , Q. rubre =0.78) , and Q. e =0.76) . Similare =0.76) . The hig species . T. chin.15 g/l)  for a loT. taklamakanensis  (T. chinensis studies may be due to their usage of the microsatellite markers sets which developed through biotinylated-oligonucleotide capture method from T. ramosissima Ledeb. (Our measurement of population genetic diversity showed significantly higher level than that of = 0.432) . The rela Ledeb. . These 1a Ledeb. .T. chinensis populations showed a low level of population differentiation (FST = 0.0532) (T. taklamakanensis (Fst =0.21) (T. chinensis with SSR markers: (FST = 0.0533) and FST = 0.058). The low level of population differentiation has also been revealed by using RAPD markers  for population genetic analysis. The inland population FS from Beijing was clustered together with populations of Bohai Bay, which is consistent with a study where a Beijing population is genetically closer to a population of the Bohai Sea coastal flat from Korea (via the Yellow River at different times when 150 years ago the Yellow River gorged into the Huanghai Sea instead of the Bohai Sea. Yet we did not include populations from the old estuary flat of the Yellow River in the Huanghai Sea coast in this study. 0.0532) , in compt =0.21) . The levmarkers:  , we performed partial Mantel tests to disentangle the role of geographic distance and environmental variables in shaping the genetic variation detected by microsatellite markers. Our high correlation (R2 = 0.5224 for IBD and R2 = 0.5225 for IBE) between genetic distance and geographical and environmental variables contrasts with no or low significant correlation  detecte = 0.39,  and the T. chinensis using 12 SSR primer pairs. T. chinensis was found to maintain a high level of genetic diversity. On average, populations of Yellow River Delta showed a higher level of genetic diversity than those from Hang Zhou Bay. The studied populations showed strong IBD and IBE patterns and significant population genetic structure corresponding to sampling sites was detected despite a quite low level of population genetic differentiation. Our results revealed population genetic structure related to environmental variables of two climate zones in a fast spread shrub species. Further extensive sampling and more informative variable loci analysis will enable us to determine the detailed dispersal routine and better understand genetic variation conferring local environmental adaptations of T. chinensis.In this study, we evaluated the genetic diversity and population structure of 10.7717/peerj.15882/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.15882/supp-2Supplemental Information 2yes, the presence of null alleles; no, the absence of null allelesClick here for additional data file.10.7717/peerj.15882/supp-3Supplemental Information 3∗P < 0.05, ∗∗P < 0.01Click here for additional data file.10.7717/peerj.15882/supp-4Supplemental Information 4Click here for additional data file.10.7717/peerj.15882/supp-5Supplemental Information 5Click here for additional data file.10.7717/peerj.15882/supp-6Supplemental Information 6Click here for additional data file.10.7717/peerj.15882/supp-7Supplemental Information 7Click here for additional data file.10.7717/peerj.15882/supp-8Supplemental Information 8Click here for additional data file.10.7717/peerj.15882/supp-9Supplemental Information 9Click here for additional data file.10.7717/peerj.15882/supp-10Supplemental Information 10Click here for additional data file.10.7717/peerj.15882/supp-11Supplemental Information 11Click here for additional data file.10.7717/peerj.15882/supp-12Supplemental Information 12Click here for additional data file.10.7717/peerj.15882/supp-13Supplemental Information 13Click here for additional data file.