Thailand is a country where over 60 languages from five language families  are spoken. The Kra-Dai language family is the most prevalent, and Thai, the official language of the country, belongs to it. Previous genome-wide studies on Thailand populations revealed a complex population structure and put some hypotheses forward concerning the population history of the country. However, many published populations have not been co-analyzed, and some aspects of population history were not explored adequately. In this study, we employ new methods to re-analyze published genome-wide genetic data on Thailand populations, with a focus on 14 Kra-Dai-speaking groups. Our analyses reveal South Asian ancestry in Kra-Dai-speaking Lao Isan and Khonmueang, and in Austroasiatic-speaking Palaung, in contrast to a previous study in which the data were generated. We support the admixture scenario for the formation of Kra-Dai-speaking groups from Thailand who harbor both Austroasiatic-related ancestry and Kra-Dai-related ancestry from outside of Thailand. We also provide evidence of bidirectional admixture between Southern Thai and Nayu, an Austronesian-speaking group from Southern Thailand. Challenging some previously reported genetic analyses, we reveal a close genetic relationship between Nayu and Austronesian-speaking groups from Island Southeast Asia (ISEA). It is believed based on evidence from historical linguistics that this language family could have been spreading from Southern China to Southeast Asia since the eighth century CE2. The fact that hundreds of languages from five language families  are spoken in Mainland Southeast Asia (MSEA) reflects its ethnolinguistic diversity and complex population history. Limited genome-wide archaeogenetic studies previously suggested that some present-day Austroasiatic-speaking groups in MSEA are genetically similar to the first farmers who migrated to the region about four millennia before present4. Bronze-age individuals (dated to about two millennia before present) genetically similar to present-day Kra-Dai groups were found in Northern Vietnam4. This finding was interpreted as evidence of a migration wave of Kra-Dai speakers to Southeast Asia4. Thailand is located in the central part of MSEA. Thai, a language of the Kra-Dai family, is the official language of the country. Many other Kra-Dai languages and languages from the other four families mentioned above are also spoken in Thailand. Recent genome-wide studies6 on Thailand populations have shed light on the genetic population history of the country. We merged the data from both studies6 with other relevant published data, and re-analyzed them using new methods to scrutinize unexplored aspects of population history and re-examine previously proposed hypotheses. We focus on 14 Kra-Dai speaking groups from Thailand , Nyaw, Phuan, Phutai, Saek, Shan, Southern Thai, and Yuan) and five non-Kra-Dai-speaking control groups  along with other groups that exhibit genetic interactions with Kra-Dai-speaking populations, such as Nayu, a Malay(Austronesian)-speaking group from Southern Thailand. Our objectives are to investigate: (1) South Asian ancestry in the target groups; (2) their genetic structure and (3) genetic interactions with other populations in the region, which may be informative about routes of the migration of Kra-Dai speakers to the present-day Thailand territory; (4) test selected hypotheses on population history proposed in a previous study5.Kra-Dai is a language family uniting about 90 languages spoken mainly in Southern China, Laos, Thailand, Vietnam, and Myanmar7. The earliest known Southeast Asian individual with South Asian ancestry is a child  from the Vat Komnou cemetery, Angkor Borei, Cambodia8, and various present-day Southeast Asian populations harbor a detectable amount of South Asian genetic ancestry9. Previous studies6 reported South Asian admixture in present-day populations from Thailand, namely Khmer, Kuy, Nyahkur, Mon, Central Thai, Southern Thai, and Nayu . Among these groups, only two populations, namely Central and Southern Thai, are Kra-Dai speakers.Indian (South Asian) influence has played a substantial role in shaping MSEA culture and history. Several ancient Indian-influenced states were located in various countries across Southeast AsiafastGLOBETROTTER10), linkage disequilibrium (ALDER11), and allele frequencies (“admixture” f3-statistics11). Three Austroasiatic-speaking , one Hmong-Mien-speaking (Hmong Daw), and one Sino-Tibetan-speaking group (Karen Padaung) were chosen as controls. Our findings are in line with those of Kutanan et al.5 in that we detected South Asian admixture in Central and Southern Thai using all three methods  and in one Austroasiatic-speaking group    as best-fitting for Central Thai, Southern Thai, and Lao Isan, while a one-date multiple-way admixture model was best-fitting for Khonmueang and Palaung  estimated for Lao Isan, Palaung, and Khonmeaung are 691–721, 603–664, and 359–386 years before present, respectively (Suppl. Table The ALDER method (based on plausible models with consistent linkage disequilibrium decay rates) are younger than those estimated by fastGLOBETROTTER for Central Thai and Palaung, but older in the case of Lao Isan  estimated by ALDER . This tool implements a mixture model distinct from that used in GlobeTrotter and demonstrated better performance on simulated data12. For this reason, we decided to reanalyze the data reported by Kutanan et al.5 with SOURCEFIND. In this analysis we focused on the same set of populations analyzed by fastGLOBETROTTER above  and Southern Thai (11%) groups  among the source proxies6.A surprising result of our inference of recent ancestry with  Central 4% and Soern Thai 1% groupsSOURCEFIND and fastGLOBETROTTER results suggest that the genetic profile of most Kra-Dai-speaking populations in Thailand is a result of admixture between Kra-Dai-speaking populations (from outside of Thailand) and Austroasiatic-speaking sources. The Lao groups from Laos were previously shown to harbor Austroasiatic-related ancestry5. Haplotype-sharing analyses in the latter study5 demonstrated a high level of genetic interaction between Khmu and Lao. This finding is consistent with our SOURCEFIND analysis in this study which estimated Lao-related ancestry in Khmu at 41%  indicate that Lao is significantly closer to Kra-Dai-speaking groups from China than to Austroasiatic-speaking populations across Southeast Asia, including Kinh from Vietnam  and in a group originating from that region (Yuan), suggests that an alternative migration route of Kra-Dai speakers to the present-day territory of Thailand might have existed. Strikingly, a scheme for the spread of Tai languages previously proposed by Baker and Phongpaichit17 places Lue, Khuen, and Yuan on the same route of southward migration that is distinct from other routes (see Map 1.3 in Baker and Phongpaichit17 for further details).Kra-Dai languages have roots in Southern China and were pushed gradually southward and then westward in the last few centuries BCE17, while the remainder were settled in Northeastern Thailand20.An alternative explanation for the omnipresence of the Lao-related genetic component in the Kra-Dai speakers from Thailand is an influx of Lao people to Thailand in the past. Indeed, there were several large-scale forced migrations triggered by wars between ancient Thailand and Laos. During the reign of King Taksin (1767–1782 CE), thousands of Lao people were relocated to Central Thailand. Later, in the beginning of the nineteenth century, following a Lao rebellion led by King Anouvong, about 150,000 Lao people were transported to Thailand, with about 50,000 moved to the Chao Phraya basin21, focused on the male-specific region of the Y chromosome (MSY), proposed an idea that the arrival of Kra-Dai languages in Thailand was an overwhelmingly cultural (not a demographic) process, due to a striking similarity of the Mon  and Central Thai populations. A recent genome-wide study by Kutanan et al.5 suggested a close genetic relationship between Mon, Central and Southern Thai, and Nayu (labelled as “SouthernThai_AN”). It was concluded in that study5 that the genetic makeup of Nayu is distinct from Austronesian-speaking groups from Taiwan and Island Southeast Asia (ISEA) and that Nayu is genetically continuous with local Austroasiatic-speaking groups . As these conclusions were partly based on admixture graph results5, we revisited their admixture graph models using a new method, findGraphs22, which was not available at the time of their study.An earlier study5 presented two admixture graph models relevant for the conclusion above. The simpler model  fitting the data well  included Southern Thai and Nayu as groups cladal with Mon, and Central Thai got 22% of their ancestry from another East Asian source according to that model. The more complex model  fitted the data poorly  and was interpreted as largely supporting the simpler model. According to the complex model, Southern Thai, Central Thai, and Nayu are essentially cladal with two Austroasiatic-speaking groups (Mon and Cambodians) but differ from them slightly in the proportion of South Asian ancestry, or of Atayal-related ancestry in the case of Nayu. In other words, the sources of East Asian ancestry in Mon, Cambodians, and Thai are the same according to that model  that were used by Kutanan et al.5 and explored these spaces of graph topologies with an automated tool, findGraphs22. In other words, we attempted to find alternative well-fitting models for the simple and complex graphs presented by Kutanan et al.5, based on a very similar set of SNPs (Human Origins) and having the same population composition, the same complexity, and the same outgroup populations as in the original study. The findGraphs algorithm is seeded by a random graph of a given complexity and satisfying given constraints, and applies various graph-modification procedures iteratively, attempting to find a local optimum in the graph topology space . For each graph complexity class, the algorithm was started 500 times from random graphs and, for simplicity, only one inferred graph (best-fitting according to the log-likelihood score) was taken from each such run (see Methods for details). Fits of the resulting sets of distinct alternative topologies to the genetic data are visualized in Fig. We argue that using admixture graph models to support very specific statements about demographic history is an exercise that is fraught with problems on many levels. These conceptual problems are discussed in detail by Maier et al..22). Two metrics are used in the literature for estimating fits of admixture graphs to f-statistic data, and those are worst f-statistic residuals (WR), also referred to as Z-scores and measured in standard errors (SE), or log-likelihood scores (LL) that take all f-statistics into account. Here we placed the newly inferred and the published models in the space of both metrics that are relatively well correlated  and comparing the resulting distributions of LL scores . Unlike previous methods for comparing fits of admixture graph models25, this method takes stochasticity in evolution of unlinked SNPs into account and makes no assumptions about the number of independent model parameters 22. Relying on this bootstrap-based model comparison approach, we found that hundreds of alternative models (matching the simple or complex graphs in complexity) have fits to the data that are not significantly different from that of the published model  is guaranteed to deliver further and further models of this kind22.Comparing fits of alternative admixture graph models in a statistically rigorous way is needed for any large-scale model exploration, and an algorithm for this purpose was introduced by Maier et al.22 when revisiting admixture graphs from eight published studies. For instance, it was found that there are at least several models fitting the data significantly better than the admixture graph for East Asians used to support a key conclusion by Wang et al.26, and the alternative models do not support the conclusion. These alternative models were found even after applying multiple topological constraints  that Wang et al.26 relied on when constructing their graph model manually22.A question arises: is it justified to put a lot of weight on a particular graph and derive historical interpretations from its topology if hundreds of diverse topologies fit the data equally well? This is a key point discussed by Maier et al.22? First, there are inherent limitations of f-statistics related to directionality of gene flow: distinct graph topologies are known to yield identical f-statistics . Second, overfitting becomes a problem if too many admixture events are allowed. Overfitting was shown to be a common problem of admixture graphs reported in the literature22. Third, diversity of well-fitting topologies may result from a lack of reference populations that are differentially related to populations of interest and are needed for constraining the models22.What are the reasons for this high topological diversity among well-fitting models that was shown to be ubiquitous for admixture graph spaces explored in the literature5 We note that all the MSEA groups included in the published graphs  are separated by very short genetic drift edges: the lengths of these edges are very close to 0 in the case of the simple  and complex  in both the simple and complex graphs from Kutanan et al.5 probably makes these admixture graph systems unconstrained. Therefore, it is not surprising that hundreds of simple topologies fit the data well in absolute terms (WR < 3 SE) and fit the data as good as the published model . One such topology is shown as an example in Fig. p-value = 0.002). As discussed in Maier et al.22, inference of demographic history in the admixture graph framework has important limitations even if best-practice protocols introduced in that study are adhered to. For instance, it is unknown what parsimony level (the number of admixture events) is optimal for inferring true history, and changing graph complexity can dramatically change the pattern of topologies that fit the data, and hence their historical interpretation As discussed above, the outcomes of a model inference protocol also depend a lot on the choice of groups included in the model22. For these reasons we did not believe that any well-fitting model found by us is accurate; on the contrary, we believe that all of them are wrong in one way or another. However, the model shown in Fig. 29; (2) there is a fraction of Atayal (Austronesian)-related ancestry in Nayu from Southern Thailand, who are Austronesian speakers6; (3) there is South Asian (Indian) ancestry in nearly all MSEA groups included in the model6.Since the complex graphs are more constrained than the simple ones (likely due to the inclusion of Kra-Dai-speaking Dai from Southern China), the alternative models we found are more differentiated according to their fits to the data Fig. b. The puSOURCEFIND results  are genetically continuous with local Austroasiatic-speaking groups in the MSEA region. To gain further insight into the genetic relationship between Nayu and other Austronesian-speaking groups (from Taiwan and ISEA), we first tested whether the Nayu group is genetically closer to Austroasiatic-speaking groups from MSEA or Austronesian-speaking populations from ISEA. f4-statistics of the form of f4 , where “ISEA” is an Austronesian-speaking group from ISEA and “AA” is an Austroasiatic-speaking group from Thailand and Cambodia, show significantly positive values , which probably have a minimal level of Austroasiatic-related ancestry33. Most Nayu and Southern Thai individuals indeed exhibit a higher level of haplotype sharing with Atayal and Kankanaey than all the other individuals from Thailand   as the most prominent surrogates , with high levels of Cambodian- and Bamar-related ancestry     demonstrate haplotype sharing with Mon that is unusually high among all the other populations from Thailand we re-analyzed  and a group which migrated recently from that region (Yuan) points to an alternative route for the spread of some Kra-Dai languages. These two routes agree with a historical linguistic scenario proposed by Baker and Phongpaichit17 (see Map 1.3 in that study). Our study suggests a rich genetic history of Thailand, with several ancestry components correlated with geography and/or linguistic affiliation. We demonstrated bidirectional admixture between Southern Thai and Nayu, an Austronesian-speaking group from Southern Thailand, and a close genetic relationship between Nayu and Austronesian-speaking groups from Island Southeast Asia, which suggests a migration of Austronesian speakers from Island Southeast Asia to Mainland Southeast Asia. The Austronesian-speaking migrants then admixed with local Mainland Southeast Asians, probably Austroasiatic and Kra-Dai speakers. Our results do not support genetic continuity or particularly close genetic connection between Mon and Central Thai, except for two Central Thai individuals from the Potharam district of Ratchaburi province who show a level of haplotype sharing with Mon that is unusually high for other populations from Thailand. In contrast, such an ancestry profile is not observed for the other Central Thai individuals from the same and different locations.In this study, we report new insights into the genetic history of Kra-Dai-speaking and other human populations from Thailand. We revealed South Asian admixture in Kra-Dai-speaking Lao Isan and Khonmueang, and Austroasiatic-speaking Palaung; South Asian admixture in these populations was not observed in a previous study38 were provided through the curtesy of Kenneth Kidd and Judith Kidd. No human subject was directly involved in this study. The samples were made anonymous before being sent to the Kidd laboratory37. The samples were collected with informed consent under a protocol approved by the IRB at Yale University38 which was also reviewed and approved by the NIGMS  and by CEPH (Center for the Study of Human Polymorphisms in Paris)37. Each of the subjects provided consent for the collection of samples and their utilization in general population studies37. All experiments were performed in accordance with relevant guidelines and regulations. The DNA samples were genotyped on the Affymetrix Human Origins SNP array39 at the Reich Lab , and David Reich provided the new genotype data for this study as a courtesy. We merged the newly generated data for Lao with published diploid genotyping data generated on the Affymetrix Human Origins SNP array9 mainly in the following studies: Kutanan et al.5, Liu et al.40, Wang et al.26, Changmai et al.6, Lazaridis et al.41, Nakatsuka et al.42 For a list of individuals, groups, their linguistic affiliations, and data sources see Suppl. Table 6.Previously collected DNA samples for 10 present-day Lao individualsf-statistics and methods relying on them was done using the ADMIXTOOLS 2 package22 (https://uqrmaie1.github.io/admixtools/). To calculate f-statistics needed for fitting admixture graph models, we first used the “extract_f2” function with the “maxmiss” argument set at 0, which corresponds to the “useallsnps: NO” setting in the classic ADMIXTOOLS39. It means that no missing data are allowed (at the level of populations) in the specified set of populations for which pairwise f2-statistics are calculated. The “blgsize” argument sets the SNP block size in Morgans, and we used the default value of 0.05 (5 cM). Since all groups involved in the admixture graph modelling in this study included more than one individual, and diploid variant calls were available for all individuals, the “adjust_pseudohaploid” and “minac2” arguments were set to “FALSE”22. The “extract_f2” function calculates f2-statistics for all pairs of groups per each SNP block, and those are used by the “find_graphs” and “qpgraph” functions for calculating f3- and f4-statistics as linear sums of f2-statistics39. In the absence of missing data, the linear sums should be unbiased22.All our work with find_graphs” function from the ADMIXTOOLS 2 package for finding alternative well-fitting admixture graph topologies. We worked on the sets of groups and individuals that were identical to those used by Kutanan et al.5 for constructing their admixture graphs presented in Fig. 6C5 (8 groups and 5 admixture events) and Suppl. Fig. 195 (11 groups and 9 admixture events). We did not modify the graph complexity (the number of admixture events) either and kept the outgroups used by Kutanan et al.5: French for the simpler graph and Mbuti for the complex graph. Characteristics of the datasets used for admixture graph fitting in our study are as follows: (1) graphs of the “simple” complexity class were based on 8 groups, 177 individuals  and a specified outgroup (French or Mbuti). Random graphs were generated using the “random_admixturegraph” function. The settings of the findGraphs algorithm were identical to those presented in Maier et al.22 , and French or Mbuti were specified as outgroups at this topology optimization step too. From each findGraphs run, one best-fitting topology  was extracted, and a set of non-redundant topologies was constructed from all the 500 runs. Fits of these topologies to the data  were plotted, and best-fitting topologies were inspected manually for features that are important for historical interpretations. The published admixture graph topologies  were fitted to the same per-block f2-statistic data (that were used for inference of new fitting admixture graphs) using the “qpgraph” function with the following settings: “numstart = 100, diag = 0.0001, return_fstats = TRUE”.For each graph complexity class, the 22 Five hundred bootstrap replicates of the two SNP block datasets, corresponding to the simple and complex published graphs, were generated (with the 5 cM block size). The algorithm reports empirical two-tailed p-values; 0.05 was used as a statistical significance threshold; and the settings of the algorithm were identical to those used by Maier et al.22.To find out if newly found admixture graph models fit the data significantly better or worse than the published ones, we used a bootstrap-based model comparison algorithm developed by Maier et al.SHAPEIT v.2 (r900)43 with a 1000 Genomes Phase 3 genetic map44. We then ran ChromoPainter v.214 to generate inputs for SOURCEFIND v.212 and fastGLOBETROTTER10. We selected 75 surrogates and 20 target populations ; see a list of populations involved in Suppl. Table ChromoPainter v.2 assigning all the surrogates as donors and recipients, but the target populations were assigned as recipients only. This means that target populations receive haplotypes only from surrogates, but not from their own population nor other target populations. The other details of the ChromoPainter v.2 protocol exactly followed those presented by Changmai et al.6.We phased a world-wide dataset of 3945 individuals (compiled from published sources) using SOURCEFIND algorithm used for inferring complex mixture models for the same set of 20 target groups were identical to those used by Changmai et al.6, and all the 75 surrogates were used as a panel of potential sources from which the algorithm constructed mixture models for each target. The settings of the fastGLOBETROTTER algorithm were also identical to those used by Changmai et al.6, and all the 75 surrogates were used for inferring best-fitting admixture models and estimating admixture dates.The settings of the SOURCEFIND results in Suppl. Table ChromoPainter v.2  from the list of surrogates, but assigned all the other populations as surrogates. Here is a brief explanation of the dataset compositions for these analyses:ChromoPainter v.2 analysis. Using this set of populations, we aimed to test a hypothesis proposed in a previous study5, which suggested that the Nayu group (Austronesian-speaking) is genetically continuous with Austroasiatic-speaking groups. In other words, we examined if Nayu individuals’ genomes can be modelled as derived exclusively (or mostly) from Austroasiatic-speaking groups.List 1, all 95 groups (75 surrogates + 20 targets from the previous setup) were assigned as both donors and recipients for a ChromoPainter v.2 analysis. As we demonstrated bidirectional admixture between Nayu and Southern Thai, the large proportion of Nayu genomes derived from a Southern Thai-related group may obscure signals of other ancestry components in Nayu. Therefore, we performed a ChromoPainter v.2 analysis again, excluding Southern Thai from the dataset, which allows more haplotype sharing between Nayu and the other groups.List 2, 94 groups from list 1  were assigned as both donors and recipients for a ChromoPainter v.2 analysis. Due to the bidirectional admixture between Southern Thai and Nayu, the high level of Nayu-related ancestry in the Southern Thai group may mask signals of other ancestry component in Southern Thai. Thus, we performed a ChromoPainter v.2 analysis again excluding Nayu from the dataset, which allows more haplotype sharing between Southern Thai and the other groups.List 3, 94 groups from list 1  were assigned as both donors and recipients for a For generating the ChromoPainter v.2 analysis on 95 populations from list 1 with the “-a” argument, which instructs the software to paint each recipient individual using all the other individuals as donors. We followed a method for computing relative haplotype-sharing statistics described by Flegontov et al.25. In brief, we calculated the average haplotype-sharing statistic between individual A and population B (aHSSAB) by (1) summing up the genetic length of the DNA that individual A copied from an individual in population B  as well as the DNA length copied in the opposite direction (from Bj to A), which yields the haplotype-sharing statistic between individuals A and Bj (HSSABj); (2) averaging HSSABj across all individuals in population B (aHSSAB). We then normalized aHSSAB by the average haplotype-sharing statistic between individual A and a Japanese group (aHSSAJapanese) to estimate relative haplotype sharing between individual A and population B. In short, the relative haplotype-sharing statistic between individual A and population B = aHSSAB/aHSSAJapanese. For example, relative haplotype-sharing between individual CT201  and the Khmer group = aHSSCT201Khmer/aHSSCT201Japanese.For calculating relative haplotype-sharing statistics, we first performed a ALDER tool11 with the default settings for fitting two-way admixture models of the type “East or Southeast Asian group + South Asian group” for the set of 20 target groups. For a list of groups involved see Suppl. Table We used the f3-statistics and f4-statistics were calculated using the “qp3pop” or “qpdstat” functions of the ADMIXTOOLS 2 package22. Both f3-statistics and f4-statistics were calculated directly from genotype data, without f2-statistics as an intermediate. For each triplet or quadruplet of groups, no missing data were allowed at the group level (the default setting).Supplementary Figure 1.Supplementary Figure 2.Supplementary Figure 3.Supplementary Figure 4.Supplementary Figure 5.Supplementary Table 1.Supplementary Table 2.Supplementary Table 3.Supplementary Table 4.Supplementary Table 5.Supplementary Table 6.


Picea koraiensis is major silvicultural and timber species in northeast China, and its distribution area is an important transition zone for genus spruce migration. The degree of intraspecific differentiation of P. koraiensis is high, but population structure and differentiation mechanisms are not clear. In this study, 523,761 single nucleotide polymorphisms (SNPs) were identified in 113 individuals from 9 populations of P. koraiensis by genotyping-by-sequencing (GBS). Population genomic analysis showed that P. koraiensis was divided into three geoclimatic regions: Great Khingan Mountains climatic region, Lesser Khingan Mountains climatic region, and Changbai Mountain climatic region. Mengkeshan (MKS) population on the northern edge of the distribution area and Wuyiling (WYL) population located in the mining area are two highly differentiated groups. Selective sweep analysis showed that MKS and WYL populations had 645 and 1126 selected genes, respectively. Genes selected in the MKS population were associated with flowering and photomorphogenesis, cellular response to water deficit, and glycerophospholipid metabolism; genes selected in the WYL population were associated with metal ion transport, biosynthesis of macromolecules, and DNA repair. Climatic factors and heavy metal stress drives divergence in MKS and WYL populations, respectively. Our findings provide insights into adaptive divergence mechanisms in Picea and will contribute to molecular breeding studies. Picea is the third-largest genus in Pinaceae and a major component of coniferous forests in the northern hemisphere. Most conifers have the characteristics of long generations, large population size [ion size ,2,3, andion size . Spruce ion size . The conion size ,7. Geogrion size ,9. In coion size . UnderstPicea koraiensis, belonging to the genus Picea, is distributed in the northeast of the Great Khingan Mountains, Lesser Khingan Mountains, Zhang Guangcai Mountains, Wanda Mountains, and Changbai Mountains in China, with elevations ranging from 300 m to 1600 m. As an independent species, P. koraiensis has generally been accepted by the academic community, but its population genetic structure and geographical migration and spread history are not clear. There are different hypotheses about the origin of the genus spruce, the “North American Origin Theory” [P. koraiensis, the Chinese northeast plain, is an important transition zone for spruce migration. Many fossils of the genus Picea from the Jurassic to Early Cretaceous, which is the earliest fossil of the genus Picea found thus far, have been found in the distribution area of P. koraiensis [P. koraiensis. For example, Zhang et al. [P. koraiensis into five climatic zones. The genetic diversity of the P. koraiensis population was analyzed by phenotypic biosystematics, seedling morphology, growth, biomass, allozyme variation [P. koraiensis have been found. The variation within populations was greater than the variation among populations, and the genetic distance of P. koraiensis was positively correlated with geographical location to a certain extent. The level of genetic differentiation among different geographical populations of P. koraiensis is very high, but the population genetic structure, driving factors, and molecular mechanism of differentiation are not clear, and the phylogeography of P. koraiensis at the whole genome level has not been reported. Theory” ,12,13 an Theory” ,15,16, wraiensis ,18,19. Sg et al.  divided ariation , mineralariation , and ranariation . VariatiP. koraiensis was used as the research object to study the genetic structure of the natural population and the adaptive differentiation mechanism of different geographical populations to environmental stress by genotyping-by-sequencing. Based on the above research, we put forward the following questions: (i) What is the genetic relationship, genetic diversity, and differentiation of P. koraiensis in the natural distribution area? (ii) Is there any obvious differentiation among populations in different geographical distribution areas of P. koraiensis? (iii) If there is obvious differentiation, what is the molecular mechanism of differentiation motivation and adaptability?In this study, P. abies, the average comparison rate was 95.33%. The number of SNP (single nucleotide polymorphism) bits detected was 10,375,590. Phylogenetic analysis was conducted with 127 samples, and a total of 523,761 SNPs with missing ratio ≤20% minor allele frequency (maf) ≥0.01 were identified.The genotyping-by-sequencing (GBS) of 127 samples produced the original reads of 563.64 Gb. After quality filtering, the clean reads of 532.685 Gb were retained and the average value of each individual was 4.19 Gb. The average efficiency was 94.5%. The sequencing results showed that the average Q20 value, Q30 value, and GC content were 97.38%, 91.63%, and 40.21%, respectively. After comparing the filtered data with P. jezoensis, P. likiangensis, and P. pungens as outgroups, 112 individuals of P. koraiensis were divided into 3 groups: Great Khingan Mountains group (Group I), Changbai Mountain group (Group II), and Lesser Khingan Mountains group (Group III). Among these groups, Group I is composed of the Mengkeshan (MKS) population in the northern Great Khingan Mountains, which is located at the base. Group II is composed of four populations: Hailin (HL), Muling (ML), Linjiang (LJ), and Tianqiaoling (TQL) populations, and Group III is composed of the Wuyiling (WYL), Aihui (AH), Gaofeng (GF) and Zhanhe (ZH) populations. Group II and Group III are sister clades. The results of the phylogenetic tree showed that the genetic relationship of different populations of P. koraiensis was related to geographical distribution. The population genetic structure was further analyzed by ADMIXTURE v1.23 software [P. koraiensis and the outgroup, and the MKS population of P. koraiensis had a small amount of outgroup gene infiltration. When K = 3, P. koraiensis was divided into 2 groups, the WYL population and other populations. When K = 4, other populations had extensive gene flow except for the WYL and GF populations. The results showed that gene introgression occurred in the MKS population and outgroups, and the WYL population had greater differentiation from other populations. To further analyze the population genetic structure, 112 individuals of P. koraiensis were analyzed by principal component analysis (PCA)  and others.According to the phylogenetic tree constructed by the neighbor-joining (NJ) method , with P.is (PCA) . The resP. koraiensis population was 0.00194 to 0.00231. Among the populations, the π value of the WYL population was the lowest (0.001943), while the π value of LJ was the highest (0.00235). The stF (fixation index) among P. koraiensis was 0.0254 to 0.1569, among which the mean stF between the WYL population and other populations was the highest (0.1211), followed by the MKS population (0.0983), and the mean stF between the TQL population and other populations was the lowest (0.0538). This result is consistent with the phylogenetic analysis, indicating that the WYL and MKS populations have a high degree of differentiation from other populations, and these two populations may be isolated from other populations and have less gene flow, resulting in a significant decrease in nucleotide diversity. That is, these two populations experienced a high degree of selection in the process of evolution. The degree of differentiation of the LJ, TQL, and ZH populations was low, and the three populations had frequent gene flow with other populations and high nucleotide diversity. The Tajima’s D values of the nine populations of P. koraiensis were all positive (mean 0.1554), indicating that these nine populations may have experienced balanced selection.The data are shown in stF) and nucleotide diversity ratios (π ratio) were used in combination to identify regions where selective sweeps occurred during species differentiation. A total of 645 and 1126 selected genes were identified in the MKS and WYL populations, respectively  ≥ 0.19 and π ratio ≥ 1.57 (The results showed that the selected regions of the MKS population were o ≥ 1.57 a. Based o ≥ 1.57 . As showstF (WYL) ≥ 0.32 and π ratio ≥ 2.5  in the Changbai Mountain climatic zone. Among other species, there are also examples of temperature and moisture influencing population differentiation, such as Liaodong oak [Climatic factors such as temperature and precipitation are likely to be among the important factors driving the differentiation of perature . Zhang eperature  divided dong oak , eucalypdong oak , and alpdong oak .P. koraiensis. On the one hand, the size of gene flow between different groups within the species and the degree of differentiation were inversely proportional. These two groups, WYL and MKS, have little gene exchange with other groups, low nucleotide diversity, and greater differentiation; the ZH, TQL, and LJ populations have extensive gene flow with other groups, high nucleotide diversity, and low differentiation. On the other hand, gene introgression between species promotes the differentiation of different populations within P. koraiensis. The results of phylogenetic and population differentiation analysis showed that the MKS population was more differentiated from other populations and had a small genetic component of outgroups. Because of the large overlap in distribution between the MKS population of P. koraiensis and its sympatric species P. jezoensis, we speculate that there is a small amount of gene introgression from P. jezoensis to the MKS population. Many closely related species of P. koraiensis have been successfully crossed with closely related species of P. jezoensis in hybridization experiments [P. jezoensis and its extinct relatives were widespread in northeastern Russia [P. koraiensis and its relatives may have appeared in east Asia during the same period [P. jezoensis and P. koraiensis underwent infiltration before P. koraiensis and its relatives’ migration to Eurasia [P. glauca, P. engelmannii, and P. sitchensis in western North America [P. likiangensis, P. purpurea, and P. wilsonii in the Qinghai–Tibet Plateau [Gene flow and introgression are also important causes of intraspecific differentiation in eriments . Fossil n Russia , that the period , and tha Eurasia . Gene in America ,33,34,35 Plateau ,36,37,38P. koraiensis. At the northern edge of the P. koraiensis distribution area, the MKS population has the characteristics of geographical and genetic isolation because of spatial isolation and limited gene communication; that is, the differentiation characteristics of the MKS population accord with the central–marginal hypothesis, in which marginal populations show lower genetic variation and higher differentiation than central populations. There are similar examples in other species, such as Abies sachalinensis in Hokkaido and Pinus cembra L. in the Alps [P. koraiensis. Furthermore, there is geographical isolation. There are similar examples of other species distributed in mining areas, such as Caryophyllaceae [Scopelophila cataractae [Colocasia esculenta [Geographical isolation is an important reason for the intraspecific differentiation of the Alps ,40. The yllaceae ,42, Scoptaractae , and Colsculenta . In summsculenta ,46.P. koraiensis and explore the differentiation mechanism, we performed selection sweep analysis to identify candidate genes based on KEGG and GO enrichment analyses involving various biological functions, using populations of the Changbai Mountain group as background populations and the more differentiated MKS and WYL populations as control populations. The key functional genes for which differentiation between different populations of P. koraiensis was subject to selection were to improve the resistance of the population, to promote flowering and fruiting ability for adaptation to different habitats, and to continuously reproduce offspring.To further detect natural selection signals in different populations of The MKS population is distributed in the northern part of the Great Khingan Mountains and is geographically distant from other populations, with large climatic differences. Climatic heterogeneity drives significant differentiation of the MKS Mountain population, and the function of genes shows the molecular adaptation mechanism of the MKS population to its habitat. The northern Great Khingan Mountains have long sunshine hours (approximately 2600 h per year and 2300 h per year in the Changbai Mountain region), and light affects the photoperiod, circadian rhythm, and photomorphic building of plants. The results of our selection sweep analysis showed that 15 genes in the MKS population were enriched in response to radiation, and 14 genes were enriched in response to light stimulus. Among these genes, CUL4 in Arabidopsis can regulate the flowering time in a short period of time during the day and is involved in photomorphogenesis ,48; GATAArabidopsis thaliana, the CSN4 and CSN8 participate in the development of seedlings in the dark and promote the development of light morphology in the dark [Among the Lesser Khingan Mountains group, the WYL population is distributed in the gold mining area, and there are also large amounts of iron ore and agate in its distribution area. The WYL population is distributed at a low altitude (average elevation approximately 350 m), and due to tailings pollution caused by mining, monsoon effects, and rainfall leaching, many heavy metals, such as Cd, Cr, Cu, Ni, Pb, and Zn, in the soil exceed background values, especially Pb, Cd, Zn, and Cu ,61. Heavthe dark ,105; ABCthe dark ,107; andthe dark ,109,110.P. koraiensis and identified a total of 9 populations of 10 to 15 individuals each, for a total of 112 individuals (P. jezoensis (sympatric species of P. koraiensis), P. likiangensis, and P. pungens, were used as outgroups, with five individuals per species. Plants from each population were separated by at least 100 m at the time of sampling, and needles were dried directly using silica gel and kept at low temperature. DNA extraction from plant needles was performed using a modified bromocetyltrimethylamine (CTAB) method, followed by a Nanodrop-1000 spectrophotometer  to detect nucleic acid concentration and DNA purity on 1% agarose gel electrophoresis.We collected samples according to the 4 climatic zones of ividuals , Table 2DNA samples were qualified, sequenced, and constructed in GBS libraries according to the methods reported by Poland et al. . Each saUsing the comparison software Burrows-Wheeler Aligner (BWA) ver.0.7.12  , the filThe phylogenetic tree of 127 individuals was constructed by the Neighbor-Joining method and the P-diatance model  based onhttp://www.r-project.org/, accessed on 12 January 2023). The K value range was set from 1 to 100, and the default settings were used for the other parameters. The optimal clustering number was determined according to the minimum value of the cross-validation (CV) error, and the R language package was used to draw the results.To carry out the cluster analysis of the population structure, the parameters of the ancestry coefficient matrix Q and the population allele frequency matrix F and K were calculated by admixture software  and finahttp://cnsgenomics.com/software/gcta/pca.html, accessed on 12 January 2023) to reduce the dimension of the selected SNP loci and calculate the eigenvectors and eigenvalues. Three principal components were obtained by using GCTA, and the PCA distribution map was drawn by using the R language package. The values in the axis label parentheses represent the percentage of the principal component that explains the overall variance.The genetic structure of the spruce population was evaluated by principal component analysis (PCA). We used GCTA software (stF) were analyzed by the PopGenome module in the R software package. According to the phylogenetic results, P. koraiensis was divided into three groups: the Changbai Mountain group, the Great Khingan Mountains group, and the Lesser Khingan Mountains group. Because of the moderate climate and frequent gene communication with other populations in the Changbai Mountain group, we took four populations  in this group as background groups. Combined with the results of stF, the MKS population of the Great Khingan Mountains group and WYL populations of the Lesser Khingan Mountains group were used as control groups to carry out selective sweep analysis. We scanned the genome in 100 kb sliding windows with a step size of 10 kb to identify significant regions based on a fixed index (stF) and nucleotide diversity ratio (π ratio). We respectively screened the significant regions according to the top 5%, and the intersection region is the selected region. Genes in the selected region were defined as the selected gene, namely, the candidate gene. After obtaining the candidate genes in the selected region, the genes were analyzed by GO (gene ontology)  and KEGG enrichment analysis. Then, the variations in SNPs in the coding region of the candidate genes were annotated, and the core functional mutations were speculated to provide clues for further verification.Nucleotide diversity (π) , genetic diversity analysis (Tajima’s D), and genetic differentiation among populations  drive the differentiation of MKS; genes selected in the WYL population are related mainly to metal ion transport and macromolecule biosynthesis, such as flavonoids, phytohormone signaling, and DNA repair, and the WYL population may be subject to heavy metal stress to produce differentiation. Our results provide insights into adaptive evolution molecular mechanisms of genus spruce and contribute to spruce resistance improvement via genomic-assisted breeding.In our study, phylogenetic, population structure and selective sweep analyses of different populations of


Diopatra cuprea is an ecosystem engineer of intertidal beds of high-salinity estuaries of the Atlantic and Gulf of Mexico shorelines. Here, we sequenced mitochondrial cytochrome oxidase I (COI) in D. cuprea populations and discover evidence for several deep mitochondrial lineages, suggesting the presence of cryptic diversity.Molecular tools continue to reveal cryptic biodiversity within common and ecologically important species. The decorator worm Diopatra genus, which has recently witnessed dozens of new species descriptions rooted in genetic analyses. In the northwestern Atlantic, the name D. cuprea (Bosc 1802) has been applied to populations from Cape Cod through the Gulf of Mexico, Central America, and Brazil. Here, we sequenced mitochondrial cytochrome oxidase I (COI) in D. cuprea populations from the Gulf of Mexico to Massachusetts. We find evidence for several deep mitochondrial lineages, suggesting that cryptic diversity is present in the D. cuprea complex from this coastline.Marine annelid taxonomy is experiencing a period of rapid revision, with many previously “cosmopolitan” species being split into species with more limited geographic ranges. This is exemplified by the  Annelid taxonomy is experiencing a period of rapid revision at every level, from individual species to the entire phylum ,2,3,4. HDiopatra. Historically, the two best-known species—D. neapolitana from western Europe and D. cuprea from eastern North America—were applied to far-flung onuphids from Africa to India, Southeast Asia, and Australia [Diopatra species described in the past decade alone, and more to come [D. cuprea (Bosc 1802), has not yet been evaluated using modern phylogenetic methods.The problem of refining “cosmopolitan” taxa is especially well-illustrated in the polychaete genus ustralia ,6. Systeustralia ,8, with  to come ,18,19,20D. cuprea occurs intertidally on the US east coast from Duxbury MA southward (the southern limit is not well established) [D. cuprea provides one of the best-described examples of ecosystem engineering in estuarine sediments; the worm builds robust tubes that descend 1 m or more vertically into the sediment, with an above-sediment tube-cap emerging 2–5 cm above the sediment surface. This tube-cap is characteristically decorated with fragments of drift algae, shell, and other debris. The tube stabilizes sediment, alters water flow across the sediment surface, and physically excludes epibenthic predators. Together, these physical effects increase the diversity and abundance of nearby infauna, and of the epifauna living on the tube itself  ,22). D. iewed by , and thist coast ,25,26,27D. cuprea’s type locality is in South Carolina, but the name has been applied to Diopatra from Cape Cod through Brazil [Diopatra from Brazil have recently been redescribed with four new species, none of which were D. cuprea [Diopatra diversity in the northwestern Atlantic remains currently unexplored. D. cuprea is known to exhibit a curious latitudinal gradient in its tube-decorating behavior, with worms in Florida decorating far less than worms between Cape Cod and Georgia, even when offered the most commonly utilized algae in controlled conditions [D. cuprea populations from the Gulf of Mexico to Massachusetts in order to assess population genetic structure and any cryptic diversity.The h Brazil ,20. Diopnditions . This biPer site, 10–40 samples were collected by hand, placed into 95% ethanol and returned to the College of Charleston Marine Laboratory in Charleston SC in approximately 2009–2012 . Samples4Peaks software v1.8 (nucleobytes.com) and saved as a fasta-formatted file. This yielded a dataset of 539 nucleotides across 153 individuals (GenBank Accession Numbers OQ700009—OQ700161). We used ape 5.0 [D. cuprea into a wider phylogeographic context, we used MUSCLE v3.8.31 [PhyML 20120412 [MrBayes 3.2.7a [R::ape to visualize the Bayesian tree. We also used strataG 1.0 [ST and the proportion of unique haplotypes within populations. Finally, we used linear regressions to assess the relationship between latitude and nucleotide diversity, and the latitude and proportion of unique haplotypes.The sequences were trimmed and quality-checked with  ape 5.0  and a cu ape 5.0  to estim v3.8.31  with def v3.8.31  to align20120412  as imples 3.2.7a  with 10,ataG 1.0  to asses2 = 0.317; F1,9 = 4.185; p = 0.071) nor proportion of unique haplotypes with latitude .The within-population nucleotide diversity ranged from <0.001 to 0.101 and the proportion of unique haplotypes per populations ranged from 7 to 67% . There wDiopatra cuprea from the east coast of the United States (Gulf of Mexico to Massachussetts) clustered into a single monophyletic clade, although the clade had minimal statistical support . None of these samples clustered within Clades 1–3 from the work of Hektoen et al., 2022 [All the COI haplotypes sequenced from morphologically similar l., 2022 . Insteadl., 2022 ; Figure D. cuprea samples, we detected five divergent lineages that differed between 14.6 and 20.5%  and the overall FST across the four regions  was 0.826 (p < 0.001). ST among the populations and regions, respectively.The overall FDiopatra cuprea (Bosc 1802). While we found no statistical evidence for a monophyletic clade of D. cuprea in the northwestern Atlantic, all five COI clades were clustered away from the recently defined Clades 1, 2 and 3 in the work of Hektoen et al., 2022 [D. cuprea lineages and their exact placement within the wider phylogeny presented in the work of Hektoen et al., 2022  likely reflects the Gulf of Mexico vs. eastern seaboard, which now have a secondary contact zone in eastern Florida near Cape Canaveral [Spartina alterniflora [D. cuprea habitats.The phylogeography of the widespread anaveral ,41,42, aanaveral  and has anaveral . There arniflora , which lThe populations were not reciprocally monophyletic. Each population was dominated by one or two COI clades, but most populations harbored 1–2 other clades at low frequency. As examples, the Gulf of Mexico population has a low frequency of the mid-Atlantic B clade, while a North Carolina population has a low frequency of the Gulf of Mexico A1 clade and Massachusetts D clade. These may reflect low-frequency dispersal events between the regions, incomplete lineage sorting, or a mix of both.Diopatra in Europe, D. cuprea is not harvested for bait in the US  especially close to commercial aquaculture sites, but bivalve aquaculture certainly does occur throughout the eastern United States. We can tease apart the relative importance of lineage sorting and more recent dispersal by assessing the phylogeographic patterns in the nuclear genome . Transpunlikely . If human France . Galaskan France  found the . MoreovD. cuprea populations of the northwestern Atlantic. This is consistent with the explorations of other Diopatra populations in other parts of the world.Overall, our results provide preliminary evidence that cryptic diversity is indeed present in


OR) and taste (TASR) receptor genes among rainforest foragers and neighboring agriculturalists in Africa and Southeast Asia. We analyze 378 functional OR and 26 functional TASR genes in 133 individuals across populations in Uganda  and the Philippines  with differing subsistence histories. We find no evidence of relaxed selection on chemosensory genes in agricultural populations. However, we identify subsistence-related signatures of local adaptation on chemosensory genes within each geographic region. Our results highlight the importance of culture, subsistence economy, and drift in human chemosensory perception.Chemosensation  is essential for detecting and assessing foods, such that dietary shifts elicit evolutionary changes in vertebrate chemosensory genes. The transition from hunting and gathering to agriculture dramatically altered how humans acquire food. Recent genetic and linguistic studies suggest agriculture may have precipitated olfactory degeneration. Here, we explore the effects of subsistence behaviors on olfactory ( Genetic comparison of rainforest foraging and neighboring agricultural communities in Uganda and the Philippines shows no distinction in the size of olfactory receptor gene repertoires, but there is evidence for subsistence-related local adaptation. Agricultural foodways have fueled profound changes––to human environments, behavior, health, and social structure5, but also dietary ecology, resulting in diminished food diversity and shifts in the types and proportions of nutrients consumed7. Mounting evidence from studies of modern and ancient human populations point to attendant selective pressures on the sequences and diversity of genes underlying digestion, metabolism and growth13, sometimes convergently between agricultural origin centers11. But the effects of agriculturalization on the human sensorium are understudied despite the essential role of chemosensation  during food detection and assessment 20.Transitions from hunting and gathering to agricultural food production were formative events during human history, beginning 11.5 kya and occurring independently in multiple geographic regionsOR) and taste receptor (TASR) genes of some species25, which raises the possibility of similar differentiation between human populations. Transitions from forest-foraging to agriculturalism are expected to exert selective pressure on human chemosensory genes for at least two reasons. First, farming clears land in a way that reduces humidity and increases temperatures at ground-level, which increases convective air turbulence and hinders chemotaxis (detection and movement toward odorants)27. Second, agriculturalists are more sedentary and reliant on a subset of available foods, mostly domesticates6, which limits their exposure to available chemosensory stimuli in the natural environment. Farming may therefore accompany a relaxation of natural selection, leading to impoverished chemosensory gene repertoires. Conversely, rainforest foragers are expected to retain greater numbers of functional OR genes for enhanced odor detection and discrimination29. Exemplifying this pattern are language studies––the diversity of olfactory words and expressions is striking in the languages of some rainforest foragers30, as are their abilities to name odors relative to nearby agriculturalists speaking closely-related languages32. Taste sensitivities can also differ between rainforest foragers and neighboring farmers33, and there is growing evidence of diet-driven haplotype variability at local scales34. Yet when comparative studies of TASR genes have included foraging and farming peoples, the focus has been limited to single genes or a few gene regions37, rather than a wider genomic-scale approach. Further, there was little effort to compare adjacent populations of foragers and farmers.Shifts in feeding behavior appear to drive changes in the olfactory receptor  of 133 individuals across six populations. Four of the populations – the Agta, Mamanwa, Sua, and Twa – turned increasingly, but at different moments and to varying degrees, toward agricultural foodways during the last century . We found that 76 (30%) of the LOF variants are shared between populations from Uganda and the Philippines, however, 4.7 times more LOF variants are specific to the populations from Uganda (n = 142) than specific to populations in the Philippines (n = 33)   mutations. We identified 235 LOF variants  across 161 33) Fig. . In cont01) Fig. . It is ip = 0.233). We similarly found no significant difference when separately comparing the number of homozygous LOF variants between BaKiga and the Sua  or the BaKiga and the Twa . In Asia, individuals from the agricultural and traditional foraging populations did significantly differ in the number of homozygous LOF variants , with the traditional foraging populations carrying on average 12.43 homozygous LOF variants versus the agriculturalist’s 10.71 homozygous LOF variants. When compared separately, neither the Agta  nor the Mamanwa  significantly differed in the number of homozygous LOF variants relative to the Manobo. However, the difference in homozygous LOF load between continental African and Asian populations , which controls for variation in the number of genes called per individual, population, and continent of origin. While some segregating LOF variants do show differences between agriculturalist and traditional foraging populations within continents  . We further found that Ugandan populations have higher nucleotide diversity independent of their subsistence histories. These results further support the interpretation that the variation we observed in these populations are likely a product of different demographic histories, rather than population-specific adaptation.For our second approach, we calculated Tajima’s D valuesory Fig. . Negativory Fig. , suggest41 to search for signatures of recent positive selection on chemosensory genes, identifying SNPs that show unusually high allele frequency in the agriculturalist populations relative to the traditional foraging populations as well as those SNPs that show unusually high allele frequencies in the traditional foraging populations. We used empirical tests to detect chemosensory SNPs that significantly diverged from neutral expectations ons Fig. . In Philons Fig. . Many ofons Fig.  and S2. ons Fig.  and S2. ons Fig.  and S4. ons Fig. . We note2 > 0.7).We next explored the evolutionary history of the SNPs in these haploblocks using ages available through Human Genome Dating database (Table OR5T1) evolved relatively recently (126 Kyr). While OR5T1 and this specific SNP are associated with age at menarche, other genes in its associated haploblock include tropical adaptation in cattle and a honey/floral odorant (Supplementary Table We targeted six of these putative haplotype blocks with clear phenotype associations for further investigation Table . Using 5se Table . The tarOR2A5 and TAS2R3/4/5). Both are relatively ancient . At the ORA25 haploblock (chromosome 7), the ancestral haplotype is associated with a preference for “bread intake” and the derived haplotype associated with a preference for “fruit intake”  of 133 individuals across six populations. Overall, we do not find strong evidence in support of previous claims that foraging peoples possess more diverse olfactory receptor gene repertoires. However, we do find evidence of local adaptation via differences in allele frequencies with implications for associated phenotypes. We conclude by highlighting potentially fruitful future directions.In this study, building on our collaborative relationships with foraging peoples in Uganda and the Philippines, we used a geographic-neighbors population genomic approach to investigate how subsistence history has influenced the evolution of chemosensory genes. We sequenced the chemosensory gene repertoire . Previously, researchers have suggested that hunter-gatherers have a higher number of functioning OR genes relative to agriculturalists, which was attributed to higher reliance on olfaction for finding and assessing foods in the former36. We found no evidence of this in our study. Among the Ugandan populations, there was no significant difference in the number of homozygous LOF variants carried by individuals in agricultural versus traditional foraging populations. We did detect a significant difference in the Philippine populations, however the direction was the reverse direction of our predictions, i.e. higher LOF levels in traditional foragers. Overall, we found that geographic region and not subsistence explained most of the variance in LOF alleles; the total number of LOF variants was higher in the Ugandan populations, while the highest LOF homozygosity was found in Filipino individuals. While it is possible that our study is underpowered, this pattern is consistent with other studies of LOF variants across the genome and is likely associated with serial founder effects following an Out of Africa migration44. Thus, drift alone may explain much of the overall distribution of OR and TASR LOF variants. While previous studies found higher numbers of functional ORs in forager populations36, these studies were primarily conducted on geographically disparate populations. Our approach permitted a more direct test by comparing populations that are geographical neighbors but diverged in their traditional subsistence strategies.We identified 252 loss-of-function (LOF) variants  in the chemosensory gene repertoires across six populations  found that ~95% of the variation in pleasantness rankings was associated with individual taste and odorant molecular identity, suggesting a “universal bedrock of olfactory perception” across populations47.Our findings have potential implications for understanding the plasticity and development of odor identification. A number of language studies have found that rainforest foragers have a much richer vocabulary of olfactory terms and are better at naming odors than neighboring agriculturalists52. While understanding of TASR variation is growing, in part because of successful laboratory assay systems (e.g. ref. 53), the functional consequences and specific phenotypes associated with OR variation are hard to anticipate because ligands of OR genes are poorly resolved49. OR genes represent one of the largest gene families occurring among mammals and are well known for their rapid pace of evolution24. Future developments in the in vitro expression and assay of OR genes and developments in behavioral assays of characterized individuals will hopefully contribute to phenotypic characterization of this fascinating chemosensory gene family.We also identified substantial inter-individual variation in LOF variants, consistent with previous reports of individualized chemosensory gene repertoires and a highly personalized sense of smell54. We identified genetic variation at 26 genes as potential candidates for local adaptation within each geographic region. We did not find evidence of convergent evolution on specific SNPs across continents. However, this is perhaps unsurprising, given the vastly different plant assemblages, cultivars, and regional flavors characterizing these geographic regions. Interestingly, 16 of the 26 genes (~61%) have been directly linked to food perception or feeding behavior in humans or other animals , and 5 other genes are associated with traits often linked with BMI and food intake   and one TASR haplotype (TAS2R3/4/5) to explore here in greater detail. Both haplotypes exhibited signatures of positive selection in the BaKiga agriculturalists relative to the Twa and Sua and are directly associated with food preferences relevant in an agricultural context . Specifically, we found an increased frequency of the haplotype of OR2A5 associated with fruit intake and the TAS2R3/4/5 haplotype for reduced bitter perception of ethanol in the BaKiga. The increased frequencies of these haplotypes in the BaKiga may reflect different food histories, including the important cultural role of fermentation (such as fermented drinks like obushera) to Ugandan agricultural societies62. These two haplotypes may be particularly promising candidates to explore in adaptations to agriculture in other regions. Both also tended to be higher in the Manobo compared to Philippine foraging communities, although the differences were not as clear. Additionally, both loci have been identified in positive selection scans in dogs63, which - along with other domesticated animals - can exhibit convergent adaptations to agricultural human societies living in similar environments66. However, while selection pressures on OR and TASR genes are often attributed to foods, it is important to recognize other factors potentially influencing selection on these genes. For example, humans are sensitive to socially and biologically relevant odors67. Further, olfactory and taste receptors also act as “extrasensory” nutrient/toxin detectors in other body systems, such as the gastrointestinal, respiratory, and immune systems68 (extrasensory chemosensors are discussed further in Supplementary Note Many of the genes we identified (15 of 26) were detected in previous positive selection scans across human populationse TAS2R3//5 to expOR2A5 and TAS2R3/4/5 loci is relatively ancient. For example, the age of the derived TAS2R3/4/5 haplotype (for decreased ethanol bitterness) is estimated at 619 kyr. The ages of the derived haplotype for five of the six haplotypes we explored in more detail pre-date the origin of modern humans69. These data suggest that many of the variants that exhibit allele frequency variation between agriculturalist and traditional foraging populations evolved from standing variation, and their initial emergence and maintenance were not associated with agriculture. Collectively, it is plausible that genetic variation that shapes human perception has mostly evolved prior to out-of-Africa migrations and their allele frequencies were responsive to environmental and cultural adaptations, similar to the model described for genetic variation shaping skin pigmentation71.Interestingly, our data suggest that variation at both the The study is part of a larger project that seeks to understand human adaptations to rainforest habitats, which is conducted in collaboration with Makerere University and University of the Philippines, Diliman under formal agreements or memoranda of understanding, and approved by the Research and Ethics Committee, Makerere University Faculty of Medicine (protocol 2009-137), the Uganda National Council for Science and Technology (permit HS 617), the National Commission on Indigenous Peoples (NCIP), and the Department of Environment and Natural Resources (permit 03-2010). Consent to saliva collection  and genetic analysis occurred at three levels––local government units, indigenous elder members of each settlement or community, and adult individuals––and in close coordination with the Batwa Development Programme, a nongovernmental cultural organization, or regional officers of the NCIP . Our protocol for informed consent was also approved by the Committee for the Protection of Human Subjects, Dartmouth College (protocol 22410), the Institutional Review Board, University of California, Santa Cruz (protocol HS0801367), and the Institutional Review Board, University of Chicago (protocol 16986A).Saliva samples were donated by 158 individuals 72 (https://genome.weizmann.ac.il/horde/), TASR gene coordinates from Ensembl , and 69 intergenic regions in the human genome from autosomes using the Neutral Region Explorer73. We performed target capture on genomic libraries using the MYbaits v3 protocol. In brief, we prepared our extracted DNA samples by introducing a hybridization buffer in which RNA baits were added, then introduced a “blocker” mix to allow blockers to hybridize with DNA segments of interest. We denatured our genomic DNA libraries  and added RNA baits to the libraries for hybridization (16–24 h). After RNA baits were hybridized with DNA libraries, we recovered target DNA by introducing streptadavin-coated magnetic beads to attract the targeted RNA bound to the baits. We initially washed bead-bound libraries with a solution of hybridization reagent, nuclease-free water, and a wash buffer. The libraries were then placed with washing buffer in a water bath . A magnetic particle collector was used to pull the beads to the bottom of the tubes, after which supernatant was removed. A binding buffer was introduced to the beads, beads were incubated  resulting in two more steps of “washing” the streptadavin-coated magnetic beads to remove non-targeted library material from the sample. The enriched libraries captured by the magnetic beads were suspended in 30 uL of 10 mM Tris-CI 0.05% TWEEN −20® solution. The captured DNA was amplified at 14 cycles  for sequencing. The resulting captured DNA was sent to the University of Calgary’s Core DNA Services for four lanes of sequencing on Illumina’s NextSeq platform.We designed custom RNA baits  using autosomal DNA sequence coordinates (Supplementary Data Picard Tools (http://broadinstitute.github.io/picard/) and aligned reads to the hg19 reference genome using GATK’s HaplotypeCaller74 to generate individual g.vcf files. We next generated a multi-sample g.vcf file by first using GATK’s CombineGVCF to generate a combined g.vcf for all individuals within each population, then GenotypeGVCF  to generate a single g.vcf file from the six populations. Using vcftools75, we filtered all variants by site quality (>30), genotype quality (>20), individual coverage , and site coverage . We excluded variants on sex chromosomes and variants from four OR genes that have been found to have issues mapping to the reference genome. Three of these genes  are duplicated at a separate location of the chromosome in non-reference genomes but not in the hg18 reference76, while OR8U1 is suggested to have been recently formed by a deletion event that fused parts of OR8U8 and OR8U9 in some individuals78.For each individual, we marked and removed PCR duplicates with ORs and TASRs reside in regions rich in segmental duplications. Moreover, these gene families are known to be copy number variable among human populations79. Even when there is no copy number variation, segmental duplication regions present difficulties because the probes can bind to multiple copies. These regions can also be problematic when sequence reads are aligned, and unique mapping may not be possible80. Because of these factors, we expect the read-depth of these areas to be lower than expected across all individuals. Further, it is plausible that our capture platform may be biased in the capture efficacy, potentially introducing technical noise. We would expect unusually high or low read-depth in all samples in such loci. To address these issues, we calculated read-depth using samtools-1.981 (http://www.htslib.org/doc/samtools-depth.html). First, we screened our data for individuals that have consistently higher read-depth for all loci , gene type , consequence , protein position, amino acid, and known variation (RefSNP rsID). For variants where VEP provided more than one annotation due to the presence of multiple transcript isoforms, we followed four steps to select a single annotation: (1) coding region annotations (exonic/intronic/UTR) were prioritized over upstream or downstream; (2) exonic annotations were prioritized over intronic or UTR; (3) sensory genes were prioritized over RNA genes; (4) if multiple transcripts per gene were present, the longest transcript was chosen. OR segregating pseudogenes are fairly common in human populations84. These genes are annotated as a “pseudogene” by VEP. Using data from Olender et al.48 and the Human Olfactory Data Explorer (HORDE72), we documented the variants that “rescue” these genes to an active status again and included these variants in our analysis of the number of loss-of-function variants (see below). We also found one OR gene (OR52B1P) that was classified as “protein-coding” by VEP. However, multiple other sources, including Hughes et al.84, HORDE, the University of California Santa Cruz Genome Browser85, and Ensembl GRCh38 consider it a pseudogene, so we categorized it as a pseudogene.For each SNP and indel in targeted chemosensory genes and intergenic regions, we used Ensembl’s Variant Effect Predictor (VEP) web server for GRCh37chemosensory dataset, we filtered SNPs to only include variants in exons and untranslated regions of functional chemosensory genes (coding and segregating pseudogenes). For the putatively neutral dataset, we included biallelic SNPs from targeted intergenic regions and pruned for sites in linkage disequilibrium (r2 threshold = 0.5) using plink86 following Schweizer and colleagues87. We also included in this putatively neutral dataset SNPs from chemosensory regions that were classified by VEP as intergenic, upstream/downstream of functional genes/pseudogenes, introns of pseudogenes, or ancient chemosensory pseudogenes that are presumed to have experienced a long history of neutral evolution. These ancient pseudogenes include 90 OR pseudogenes with premature stop codons shared between modern humans and chimpanzees84, and 2 TASR pseudogenes  that have been psuedogenized since at least the last common ancestor of hominids 89. After these filtering steps, we were left with a chemosensory dataset of 4913 SNPs in the exons and untranslated regions of 378 ORs, 2 sweet/umami taste receptor genes , and 24 bitter taste receptor genes (TAS2Rs). The putatively neutral dataset included 1704 SNPs.To evaluate signatures of positive selection on chemosensory genes, we first filtered our SNPs into “chemosensory” and “putatively neutral” datasets. For the π) using the –site-pi option in vcftools75 for the 232 SNPs from targeted neutral intergenic regions with adequate read-depth in each of the six populations: the BaKiga, Sua, and Twa from Uganda, and the Agta, Mamanwa, and Manobo from the Philippines. We then took the π values for each site and reported the average genome-wide value for each population . Overall, we documented 235 LOF variants among 161 OR genes, and 17 LOF variants among 12 TASR loci (Supplementary Data 91 (version 1.1-26).In order to test for a relaxation in selective pressure on chemosensation with agriculture, we specifically targeted potential LOF variants  in our chemosensory dataset. We considered SNPs/indels as potential LOF variants by VEP consequence , or if they were previously identified in published lists of pseudogenizing 40 across 1 kb windows including chemosensory genes for which we have more than 3 SNPs genotyped in all individuals. We also calculated Tajima’s D across whole chromosomes for putatively neutral SNPs. This test measures deviations from the expected allele frequency distribution with negative values indicating selection. Thus, if the strength of purifying selection on chemosensory genes in traditional foraging populations has been stronger than in agriculturalist populations, we expect Tajima’s D values to be smaller in the former than the latter populations when compared to neutral expectations. We used one-tailed Wilcoxon tests to compare Tajima’s D values between chemosensory SNPs and putatively neutral intervals within each population.To further test for different levels of negative selection acting on agricultural and rainforest foraging populations, we calculated Tajima’s D41 to investigate potential signatures of positive selection on chemosensory genes that aligned with the transition to agriculture or with traditional hunting and gathering in each region. This test assumes that divergent selective pressures acting on different populations will lead to changes in allele frequencies and higher differentiation between populations. PBS measures the relative phylogenetic distances between three populations. Two are the focal populations that we compare to each other and the third is a more distantly related population. Under neutrality, the ratio of phylogenetic distance between these two populations when normalized by the outgroup population should remain constant independent of mutation rate in this locus. Thus, any significant deviation from this expectation may indicate a non-neutral increase in the allele frequency and hence a local adaptation92. We calculated PBS for exonic and putatively neutral SNPs for each pairing of agricultural (AGR) and hunter-gatherer (HG) populations in each geographic region, treating one HG population from the other continent as the outgroup population . Using another HG population as the outgroup rather than an AGR population can better identify candidate genes specifically distinguishing agriculturalists from rainforest HGs. For each population pair, we estimated Fst93 using vcftools between the AGR population and the HG population, the HG and the outgroup, and the AGR and the outgroup. We omitted SNPs that were invariant within each pair of populations. Any “undefined” FST values between one population and the outgroup  was set at 0. We then estimated PBS for the agricultural branch. We set negative PBS values as 0. We performed separate analyses for each focal pair in each region, as well as a third analysis that grouped the two HG populations in the region as a single “population.” We identified outlier SNPs for each focal pair using an empirical p-value approach. Specifically, we calculated a percentile for each exonic SNP relative to the presumed neutral SNPs for that focal pair using the ecdf function in R, then corrected for multiple testing using the false discovery rate. We identified outliers as those SNPs with an adjusted p-value < 0.05. To investigate positive selection associated with rainforest foraging, we repeated these analyses, changing the outgroup to the AGR population from the other continent . In total, we performed six analyses per region . The final number of SNPs for each focal pair was : Manobo vs. Agta ; Manobo vs. Mamanwa ; Manobo vs combined Agta/Mamanwa ; BaKiga vs. Sua ; BaKiga vs. Twa ; and BaKiga vs. combined Sua/Twa .We used population branch statistics (PBS)94 and UK Biobank datasets (http://geneatlas.roslin.ed.ac.uk/). In addition, we performed literature searches on each SNP to identify previous work that may link any of these SNPs to functional or evolutionary patterns. We noticed that these SNPs are not randomly distributed across the genome but cluster together and have similar allele frequencies, suggesting that they are in linkage disequilibrium. Specifically, we focused on six haplotype blocks that show significant (p < 10−5) association with perception-related phenotypes. We further managed to cluster the SNPs from the same genomic locations and with similar association signals into haplotype blocks by showing linkage disequilibrium to each other using vcftools with r2 values > 0.65.To understand the potential functional effects of the putatively adaptive SNPs, we conducted a focused analysis of the potential functional effects of the 43 SNPs identified as outliers in the PBS analyses by conducting a PheWAS search in GWAS Atlasrworldmap95. PheWAS associations plots were taken directly from GWAS Atlas. Phylogenetic networks were constructed from phased SNP calls from the 1000 Genomes Project96 as well as Neanderthal97 and Denisovan98 variant calls. We also included Chimpanzee haplotype from the reference genome (PanTro4). In order to integrate data from our own study and 1000 Genomes, we used Illumina HumanOmni1-Quad genotyping array data for the same Twa and BaKiga individuals that we sequenced in this study available from an earlier work56. In brief, the data from this array based study allowed us to extend our linkage disequilibrium analysis to non-coding and intergenic sequences, allowing us to better determine the extent of the haplotype blocks and directly compare with the haplotypic variation in 1000 Genomes dataset. We used plink86 and vcftools to extract our haplotype SNPs from this dataset. We used Picard to perform a liftover from hg18 to hg19 on the BaKiga/Twa vcf files. We phased our.vcf files without a reference dataset using Beagle 5.199. From there we combined the SNP data sets from 1000 Genomes and Twa/BaKiga allowing us to delineate distinct haplotype groups 101. Haploblock lengths used in tree construction included 21,974 bp  were calculated in vcftools from g.vcf files, while statistical analyses  were performed in R (version 4.0 and above). The specific details of the methods and sample sizes for target capture, variant filtering, quality control of individuals and variants, variant annotation, nucleotide diversity, statistical analyses for assessing relaxation of selective constraint and local adaptation, and PheWAS and haploblock analyses are described in the methods above.Most statistics in this study (Further information on research design is available in the Supplementary InformationReporting Summary


Lymantria dispar, is an irruptive forest pest native to Eurasia where its range extends from coast to coast and overspills into northern Africa. Accidentally introduced from Europe in Massachusetts in 1868–1869, it is now established in North America where it is considered a highly destructive invasive pest. A fine‐scale characterization of its population genetic structure would facilitate identification of source populations for specimens intercepted during ship inspections in North America and would enable mapping of introduction pathways to help prevent future incursions into novel environments. In addition, detailed knowledge of L. dispar's global population structure would provide new insight into the adequacy of its current subspecies classification system and its phylogeographic history. To address these issues, we generated >2000 genotyping‐by‐sequencing‐derived SNPs from 1445 contemporary specimens sampled at 65 locations in 25 countries/3 continents. Using multiple analytical approaches, we identified eight subpopulations that could be further partitioned into 28 groups, achieving unprecedented resolution for this species' population structure. Although reconciliation between these groupings and the three currently recognized subspecies proved to be challenging, our genetic data confirmed circumscription of the japonica subspecies to Japan. However, the genetic cline observed across continental Eurasia, from L. dispar asiatica in East Asia to L. d. dispar in Western Europe, points to the absence of a sharp geographical boundary  between these two subspecies, as suggested earlier. Importantly, moths from North America and the Caucasus/Middle East displayed high enough genetic distances from other populations to warrant their consideration as separate subspecies of L. dispar. Finally, in contrast with earlier mtDNA‐based investigations that identified the Caucasus as L. dispar's place of origin, our analyses suggest continental East Asia as its evolutionary cradle, from where it spread to Central Asia and Europe, and to Japan through Korea.The spongy moth,  Along with two closely related Lymantria species found in eastern Hokkaido (L. umbrosa) and the Japanese Ryukyu Islands  approach. Many earlier studies have assessed population differentiation in this species, using different types of molecular markers and analytical approaches, as well as a varying assortments of focal populations, sample sizes and specimen ages  sampled across  sampled 22.1L. dispar  and baited with synthetic female spongy moth sex pheromone . For moth collection, we relied on a network of colleagues and contacts at different locations across the spongy moth's range Table . Capturear Table . For somar Table .2.2PstI and MspI . Then, DNA was extracted with the DNeasy 96 Blood & Tissue Kit (Qiagen) following the manufacturer's instructions, with the exception of an additional RNase A treatment before the addition of buffer AL/ethanol . DNA concentration and purity of the extracts were assessed using a NanoDrop 8000 spectrophotometer (Thermo Scientific). Samples were diluted to 10 ng/μl prior to library construction. Libraries were prepared based on a genotyping‐by‐sequencing (GBS) protocol using the restriction enzymes 2.3L. dispar asiatica reference genome  produced over a large number of markers in a large sample of individuals. Briefly, the pipeline retains only reads displaying a perfect match to a barcode and having no missing data in the following 64 bp. Identical reads are then clustered into tags, and only tags sustained by a minimum number c ≥ 5 (user‐specified) of reads across all samples are kept for further processing. Then, the tags are aligned to the reference genome using the external software BWA and the built‐in aln algorithm . During this step, tags with multiple or unknown physical genomic positions are discarded. Tassel 3.0 GBS performs quantitative SNP calling, i.e., the minimum number of reads of the minor allele needed to call a heterozygote genotype is defined by both the sequencing error rate  and the sum of the number of reads for the two alleles.Read quality was first evaluated using the FastQC software, a quality‐control tool for raw sequence data generated by high‐throughput sequencing pipelines Andrews, . Then, S2.4FST calculations or other divergence metrics  in at least 6 populations  that were then iteratively and alternately increased to obtain a final data set with a genotype call rate ≥80% comprising individuals with ≤25% of missing data. This gradual filtering is known to result in the retention of more loci and individuals than a one‐step hard cut‐off , the “divergent set”; and (2) putatively neutral SNPs, the “neutral set”. To identify loci under putative divergent selection and to reduce the incidence of SNPs being incorrectly defined as outliers  based on the neutral dataset using the function Hs of the R package adegenet .To assess population genomic diversity, we computed the unbiased gene diversity , and on populations featuring >15 moths, the sample size that yields accurate estimates . The find.clusters function (adegenet R package) was used to run sequentially the k‐means method with an increasing number of K values (from 2 to 30), and the optimal number of groups was identified based on the Bayesian information criterion . The find.clusters function was also run 10 times with different random seeds. The missing values present in the data set were replaced with the mean allele frequency calculated on the entire dataset. For each K groups identified by the maximum‐likelihood approach and the k‐means method, the existence of lower‐level structures was explored using Discriminant Analysis of Principal Components , which allows visualization of the overall variability among individuals and populations. As PCA does not accept missing data, the latter were replaced with the mean allele frequencies calculated on the entire dataset using the R function m (migration events). For each value of m, 10 independent runs were performed with random orders of input populations  to evaluate the convergence of the tree topology and migration events  and their standard error were estimated by jackknife resampling  and a p‐value for each migration event was calculated. The optimal number of migration events was estimated using an ad hoc statistic (Δm) based on the second‐order rate of change in the log likelihood (method Evanno) and implemented in the OptM R package  among them, a population graph tree was built using the software TreeMix  and backward (υ) mutation rates, that is, the instantaneous rate of mutation from allele 0 to allele 1 and conversely, were fixed at 1. As initial state of the “coalescent rate” parameter, we used the default value of 10, but we subsequently estimated it  in the course of running the MCMC chain. The priors for the speciation rate (λ) and the ancestral population's sizes (theta) were chosen to follow broad gamma distributions, as we do not assume strong a priori knowledge about the parameters . We ran two independent MCMC chains for 3 × 106 generations, with a pre‐burning of 40,000 generation length and sampling for parameters and trees every 100 generations. Using Tracer v1.7.1  gene revealed a clinal pattern of allele frequency mirroring variation in female flight capacity observed across the geographic range of the spongy moth , indicated that the SNP and its associated scaffold (Lda_tig00005865) map to an autosome (chromosome 23), an observation that is consistent with conclusions drawn from the results of crossing experiments  were deemed to be putatively under divergent selection, while 2125 SNPs were considered neutral see Data . Blastx −6 Table . Among th Figure . Tblastx3.3D) varied notably across the spongy moth's range , which were not significantly different from one another . However, an overall decline in gene diversity was observed from the Russian Far East  to Western Europe, with values lower for Japan than for continental East Asia . Two of these pairwise comparisons involved populations from central Europe (AT02 vs. SK01 and BG01 vs. SK01), while the third one involved populations from China (CN01 vs. CN02). Relative to the three established subspecies in Eurasia  performed on the 2125 neutral SNP set revealed a west‐to‐east gradient (PC1 5.65%) in Eurasia and northern Africa, with populations from the Iberian Peninsula and Algeria seen at one end of the gradient and populations from far east Asia observed at the opposite end Figure . The seck‐mean methods implemented in the find.clusters function indicated that the minimum likely number of genetically distinct clusters was K = 8 or 9   North‐America, (2) Iberian peninsula and Algeria, (3) western and central Europe , (4) Caucasus and Middle East, (5) northern Europe  and western Russia up to the Novosibirsk region, (6) eastern Central Asia , (7) east continental Asia and (8) Japan . The DAPC and assignment analyses carried out in each of the K = 8 groups yielded 28 subgroups across the species' range  Figure . With thn Figure . The ADMn Figure . Not surs Figure  separatee Figure , in whicp‐values ≤10−9 . In view of the weaker modelling success of the Algerian population and the inferred French origin of the North American population, we interpret this apparent gene flow as an artefact associated with the genetic proximity of French and Iberian/Algerian populations, instead of a direct contribution of Algerian genes to the North American population.We conducted several iterations of the TreeMix‐based computation aimed at generating a population tree, using different East Asian spongy moth populations to root the tree, with no impact of the latter parameter on the overall inter‐population relationships depicted by the tree. The optimal population tree shown here, rooted with a population from Japan (JP02), explained 97.4% of the variance in relatedness among populations and inferred six ‘migration events’, all with 9 Figure . The tre9 Figure . However3.5L. d. asiatica and L. d. japonica, whereas the European lineage separated into L. d. dispar and the Caucasus/Middle East group. All nodes were well supported with posterior probability values ≥0.99 .4.1Choristoneura fumiferana, displays weak spatial genetic structure across its North American continental range, likely as a consequence of the long‐distance migrations its adults undertake  and population structure analyses  presented here indeed confirmed the distinct status of spongy moths from the Iberian Peninsula and North Africa, while the Italian/Slovenian populations displayed less pronounced distinctiveness. Based on these results, the Pyrenees seem to be a more important barrier than the Alps to spongy moth gene flow. In contrast with East Asia and Europe, western and central Russia , which covers >3000 km longitudinally, revealed limited spatial genetic structure. An earlier study  and population structure analyses  clearly highlighted the genetic singularity of spongy moths in this region. Previous studies based either on mitochondrial sequences  and continental Asia, respectively. The geographical limit between the latter two subspecies was considered to be the Ural Mountains in western Russia. In the present study, the spongy moths sampled in Japan (Honshu and western Hokkaido) were found to be genetically distinct from other populations . The existence of such a genetic cline was also suggested by the work of Wu et al.  among populations of the three established L. dispar subspecies can be used as genetic threshold for attribution of subspecies status to other populations. In the present study, North American and Caucasian/Middle Eastern populations displayed genetic distances with other populations similar to or greater than those observed among the three recognized L. dispar subspecies in Eurasia . Recently, Galtier  proposed4.4L. dispar. Thereafter, the European lineage gave rise to the L. d. dispar subspecies and the Caucasian/Middle‐Eastern populations, whereas the Asian lineage split into the two sister subspecies, L. dispar japonica and L. dispar asiatica. The phylogenetic relationships obtained here for the three recognized subspecies  are in agreement with previous phylogenies conducted using mitochondrial markers  as opposed to being basal to all L. dispar subspecies (mtDNA‐based studies). Thus, neither mitochondrial nor nuclear DNA phylogenies support Goldschmidt's  and Israel (https://mfa.gov.il/MFA/AboutIsrael/Maps/Pages/Israel‐Forests.aspx), where we observed some of the lowest gene diversity indices in the present study  between the dispar and asiatica subspecies in continental Eurasia. Furthermore, based on genetic distances, we suggest that the North American and Caucasian/Middle Eastern L. dispar populations potentially deserve distinct subspecies status. Regarding the phylogeography of L. dispar, while the present work suggests a continental East Asian origin for the species, we are pursuing this line of enquiry through a more detailed assessment of its evolutionary history, taking into account the spongy moth's two sister species, L. umbrosa and L. albescens, and comparing the histories revealed by nuclear and mitochondrial genomes.Spongy moth populations show a remarkable degree of spatial genetic structure across the native species' geographic range, a feature that is likely associated with the insect's limited mobility. The elevated degree of population structure reported here will certainly facilitate the development of molecular tools, in the context of genomic biosurveillance (Hamelin & Roe, The authors have no conflicts of interest to declare.Appendix S1Click here for additional data file.Appendix S2Click here for additional data file.