Fratercula arctica) by assembling its reference genome and analyzing genome-wide resequencing data of 72 individuals from 12 colonies. We identify four large, genetically distinct clusters, observe isolation-by-distance between colonies within these clusters, and obtain evidence for a secondary contact zone. These observations disagree with the current taxonomy, and show that a complex set of contemporary biotic factors impede gene flow over different spatial scales. Our results highlight the power of whole genome data to reveal unexpected population structure in vagile marine seabirds and its value for seabird taxonomy, evolution and conservation.The factors underlying gene flow and genomic population structure in vagile seabirds are notoriously difficult to understand due to their complex ecology with diverse dispersal barriers and extensive periods at sea. Yet, such understanding is vital for conservation management of seabirds that are globally declining at alarming rates. Here, we elucidate the population structure of the Atlantic puffin ( Kersten et al. sequence a draft genome for the Atlantic puffin and report its population structure, genetic diversity and gene flow among four main clusters of populations across the northern Atlantic. These results identify a secondary contact zone between the puffins from the High Arctic and other colonies and proposes a new population structure from the currently recognized three subspecies. Nevertheless, global seabird numbers have deteriorated by an alarming 70% since the mid-20th century8. These declines pose a serious threat to marine ecosystems, human society, and culture10, highlighting the importance of seabird conservation management. Within such management, the identification of distinct population units, i.e., demographically independent populations with restricted gene flow among them12, is a fundamental first step towards optimized conservation14. Defining such units is, however, difficult for many seabirds because of their complex ecology15. Detailed genomic data including thousands of loci provide new possibilities to assess levels of connectivity and gene flow between distinct breeding populations and, thus, help identify relevant conservation units for seabirds16. Indeed, a few recent publications using reduced genomic representation approaches  have reported fine-scale structure over various spatial scales21. These studies highlight the great potential of genomic data to disentangle barriers to gene flow that would otherwise remain undetected, but remain nonetheless limited due to incomplete sampling of the genome22.Seabirds are important ecosystem indicators and driversFratercula arctica, Linnaeus, 1789, hereafter “puffin”) is an iconic seabird species, prevalent in popular culture23, important for tourism25, and inherently valuable for the marine ecosystem1. Puffins were historically widely harvested for their meat and down27 and exploitation remains an important cultural tradition in Iceland and the Faroe Islands24. Its breeding range stretches from the Arctic coast and islands of European Russia, Norway, Greenland, and Canada, southward to France and the USA28  has experienced complete fledging failure during nine of the last 13 seasons and has lost nearly 80% of its breeding pairs during the last 40 years31. Similarly, Icelandic and Faroese puffins have experienced low productivity and negative population growth since 200332.The Atlantic puffin , intermediate sized puffins around Norway, Iceland, and Canada (F. a. arctica) and the largest puffins found in the High Arctic, e.g. Spitsbergen33, Greenland34, and northeastern Canada35 (F. a. naumanni)36 36 Fig. . NeverthHere, we present the, to the best of our knowledge, first whole-genome analysis of structure, gene flow, and taxonomy of a pelagic, North Atlantic seabird. We generated a de novo draft assembly for the puffin and resequenced 72 individuals across 12 colonies representing the majority of the species’ breeding range Fig. . Our worAlca torda), a total of 13,328 puffin scaffolds were placed into 26 pseudo-chromosomes, leaving 17.06 Mbp (1.4%) unplaced and yielding an assembly of 1.294 Gbp , whereas at K = 4, ngsAdmix reflects the structure of three additional distinct clusters representing Spitsbergen, Canada, the Isle of May, and a central group with more shared ancestry  .Genomic variation across 71 puffin mitogenomes defines 66 polymorphic haplotypes that indicate a recent global population expansion and show no significant population structure Fig. , S4. In = 4 Fig. , as detetry Fig. . The shatry Fig. , S7. We Phylogenetic reconstructions using individual-based Neighbor-Joining (NJ) and maximum likelihood (ML) methods Fig. , Fig. S8π) of puffins is significantly different between colonies, with the Spitsbergen population having significantly lower nucleotide diversity than the global median  was approximated based on runs of homozygosity (RoH)42. Again, the Spitsbergen colony has significantly lower levels of heterozygosity (0.00220–0.00223) and significantly higher levels of FRoH values (0.161–0.172), compared to the Faroese and Icelandic colonies . The Faroese and Icelandic colonies contain the highest levels of heterozygosity and lowest FRoH values . Moreover, Spitsbergen harbors the most  and longest RoHs with eight being ≥2.3 Mbp long (4.21 ± 3.02% of respective chromosome), whereas none of the RoHs in the remaining colonies are >2.15 Mbp long , while intermediate between the Norwegian mainland colonies . In contrast, the Spitsbergen colony is split from the remaining colonies by migration rates up to 100× lower than the global average  analysesage Fig. , while aage Fig. . Geograps Tables . Neverths Tables , S6. Speces Fig. , the fitces Fig. . Similarces Fig. . In all f3 statistics (Z score < −3) are found for all unique combinations of the phylogeny   Table , indicata) Table . The cloa) Table .ST between the four genomic clusters in 50 kb sliding windows. These analyses show that the differentiation between the clusters is driven by increased FST in windows across the entire genome, including the presence of several smaller regions with elevated FST  Spitsbergen (High Arctic), (2) Canada, (3) Isle of May, and (4) multiple colonies in Iceland, the Faroe Islands, and Norway. Second, we find that within such clusters, genetic differentiation is driven by IBD. Finally, we find evidence for secondary contact between two clusters. These observations show that a complex set of drivers impacts gene flow over different spatial scales (100–1000s of km) between these clusters and the colonies within. In particular, the interplay between overwintering grounds, philopatry, natal dispersal, geographic distance, and potentially ocean regimes appears to explain the genomic differentiation between puffin colonies45.Barriers to gene flow leading to population structure are notoriously difficult to identify and remain largely unknown for most seabirds28. Immatures, however, have been observed to visit other nearby colonies during the summer and may breed in non-natal colonies46. Nevertheless, data on natal philopatry remain scarce, but existing evidence shows rates vary greatly (38–92%) between colonies46. If either breeding or natal philopatry alone drive the puffin population structure, each colony should constitute its own distinct genomic entity and substantial genomic differentiation across the puffin’s entire breeding range would be observed. Yet, philopatry alone cannot explain the presence of the four large-scale population clusters we observe here. Additional factors must therefore promote the distinctiveness of the four clusters. For instance, the Isle of May birds have a largely separate overwintering distribution mainly in the North Sea 21 and black‐browed albatross 48. The presence of a Canadian cluster can also be largely explained by their winter distribution around Newfoundland49. There is, however, some fragmentary overlap in the overwintering distribution of the Canadian and Icelandic colonies off southwestern Greenland49, suggesting that barriers to dispersal of immatures and gene flow in the western Atlantic may be further enforced by the large geographic distance. In contrast, the winter distribution from the colonies in Iceland, Norway, and the Faroe Islands overlaps off the coast of southern Greenland  colonies during the summer, appears to drive a pattern of IBD among colonies 50 and band-rumped storm-petrel (Oceanodroma castro)51. While these illustrated mechanisms provide reasonable explanations for the observed dispersal barriers and population structure based on our current knowledge, validation requires additional evidence, specifically on the winter distribution of immature puffins and natal dispersal rates across colonies covering the entirety of the puffin’s breeding range.Mature puffins rarely, if ever, change their colonies, resulting in very high colony fidelity once they start breedingSea Fig. 28,38,47.and Fig. 47. This ies Fig. . Indeed,28. The High Arctic puffins exclusively inhabit harsh, cold-current environments year-round, as they likely stay in an area bounded by the East Greenland ice edge, a latitudinal border at 70° N, and the front between the Barents and Greenland Sea during winter , midway between Spitsbergen and mainland Norway, represents an area of secondary contact between the puffins from the High Arctic and other puffin colonies. Based on D- and the f3-statistics, the most likely southern sources are Iceland, the Faroe Islands, Norway, or a combination thereof. Thus, the barriers to gene flow that keep the Spitsbergen colonies distinct do not prevent the formation of a hybrid colony where individuals from the High Arctic and the cluster composed of mainland Norwegian, Icelandic and Faroese colonies meet.High Arctic puffins from Spitsbergen are genetically the most divergent group within our dataset harboring the highest genome-wide differentiation. They are also characterized by significantly lower levels of genetic diversity, greater inbreeding coefficients, and longer and more abundant RoHs compared to other colonies. These observations may either result from a historical bottleneck followed by isolation , local adaptation to their extreme environment, or generally lower effective population sizes. Population abundance estimates of <10,000 breeding pairs on Spitsbergen compared to 500,000 in the West Atlantic, two million on Iceland and more than two million in the boreal East Atlantic potentially indicate a lower effective population sizeter Fig. . They ar44, and a lack of mitogenomic population structure has been observed in many marine birds with high philopatry58. In contrast to the mitogenomes, the structure in the nuclear data therefore likely originated after the last glacial period and reflects the influence of relatively recent barriers to gene flow in a context of historical demography44. Such results are relevant for understanding the “seabird paradox”, which contrasts the life-history traits of high philopatry and restricted dispersal in otherwise highly mobile species59.The distinct population structure in the nuclear data is not observed in the mitochondrial genomes, which reveal an abundance of rare alleles and lack of significant population differentiation. The mitogenomic variation suggests that puffins experienced a recent population expansion, possibly out of a refugium after the Last Glacial Maximum. Indeed, it has been shown that mitogenomic variation in seabirds is dominated by historical factors rather than representing contemporary gene flowF. a. naumanni, F. a. arctica, F. a. grabae)36. Although the genetically distinct Spitsbergen cluster coincides with the classification of morphologically large puffins in the High Arctic (F. a. naumanni)28, we observe gene flow from Spitsbergen into Bjørnøya, which has been considered F. a. arctica28. Furthermore, the geographic divide between F. a. grabae and F. a. arctica lies farther south than previously thought, with the Faroese puffins being genetically closer to F. a. arctica than to F. a. grabae. Nonetheless, F. a. grabae is currently represented by a single colony (Isle of May) in our study and the geographical extent of this genomic cluster needs to be refined by additional sampling, particularly in the western UK, Ireland, and France. Finally, puffins from the Western Atlantic region  form their own distinct genetic cluster that is not recognized within the current classification. Our results do not only warrant a revision of Salomonsen’s taxonomic classification of three subspecies36, but also highlight the need to acknowledge the four identified clusters as distinct units within the conservation management of puffins14. Although puffin colonies within clusters are not genetically distinct entities, ecological independence illustrated by contrasting population dynamics across relatively small spatial scales  suggests that higher resolution local management units based on ecological differences should be considered. Nonetheless, the genetically distinct clusters at the outer edges of the puffin’s distribution with putative local adaptations that will not be easily replenished indicate that conservation of these distinct clusters must be a first priority. Finally, our sampling does not cover several outskirts of the puffin’s distribution, such as the U.S., northern Canada, Greenland, Ireland, western UK, France or Russia, and we may therefore still underestimate the true biological and genetic complexity of this species.Our results have major implications for the conservation management of the Atlantic puffin. The genetic structure we identify in puffins disagrees with the suggestion of three subspecies —FOTS ID #15602 and #15603 from the Norwegian Food Safety Authority for SEATRACK and SEAPOP; Permit 2018/607 from Miljødirektoratet (Norwegian Environment Agency), dated 4 May 2018.Gannet and Gull Island (Canada)—Canadian Wildlife Service Migratory Bird Banding Permit 10559 G, approved Animal Use Protocol (AUP) by Eastern Wildlife Animal Care Committee , Newfoundland and Labrador Wilderness and Ecological Reserves Permit—Scientific Research (DOC/2017/02003), Canadian Wildlife Service Scientific Permit ST2785 (to M.L.M.), Canadian Wildlife Service Banding Permit 10694, and Acadia University Animal Care Committee Permits ACC 02-15 and 06-15 (to M.L.M.).Isle of May (Scotland)—Scottish Natural Heritage licence 2014/MON/RP/156 and Ringing Permit A400 (to MPH).Vestmannaeyjar, Papey, Breiðafjörður, Grímsey (Iceland)—Icelandic puffins were legally hunted during the hunting period of 1 July–15 August.Faroe—Feathers came from predated birds collected in the field after the predator was finished with them.Feather and blood samples of puffins included in this study were collected and handled under the following permits.62 after subsampling to 0.8 billion and 1 billion reads to maximize performance and remain within the computational capacity of the assembler. We refined the two assemblies through several steps, including merging of ‘haplotigs’, removal of contaminant sequences, misassembly correction, re-scaffolding using mapping coverage and linkage information, and gap filling  and applying 200 N’s as padding between each scaffold. We combined unmapped scaffolds into an “unplaced” pseudo-chromosome. We assessed the order and placement of scaffolds by investigating synteny in coverage and length between the puffin and razorbill chromosomes  63 Fig. . The remes Table . Detailshttp://www.seapop.no/en), SEATRACK (http://www.seapop.no/en/seatrack) and ARCTOX (http://www.arctox.cnrs.fr/en/home—Canadian colonies). These samples had been collected between 2012 and 2018 and consisted of blood preserved in EtOH or lysis buffer, or feathers , the Crested Auklet , the Ancient Murrelet  and the Japanese Murrelet , to the alignment. To construct a maximum-likelihood phylogenetic tree, we split the alignment into seven partitions, i.e., one partition for a concatenated alignment of each of the three codon positions of the protein-coding genes, one partition for the concatenated alignment of the rRNA regions, one partition for the concatenated alignment of the tRNAs, one partition for the alignment of the control region, and one partition for the concatenated alignment of the “intergenic” regions. The best-fitting evolutionary model for each partition was found by ModelFinder68 and the tree was built with IQTree v1.6.1269 using 1000 ultrafast bootstrap replicates. We used the resulting tree to draw a haplotype genealogy graph with Fitchi70. Using Arlequin v.3.571, we calculated haplotype (h), nucleotide diversity (π), and Tajima’s D72 for each colony, for each genomic cluster defined by the nuclear analysis, and globally. In addition, an Ewens–Watterson test73, Chakraborty’s test of population amalgamation74, and Fu’s Fs test75 were conducted for each of those groups. To further identify population differentiation, the proportion of sequence variation (ΦST) was estimated for all pairs of populations and genomic clusters. Hierarchical AMOVA tests subsequently determined the significance of a priori subdivisions into colonies and genomic clusters. Calculation of ΦST and AMOVA tests were also conducted in Arlequin. Additional details on the mitochondrial analyses are given in the Supplementary File.Genotypes across the mitochondrial genome were jointly called with GATK v4.1.476. After assessing the quality of the mapped sequencing data in an ANGSD pre-run, we removed an individual from the Isle of May from the dataset. Genotype likelihoods for nuclear SNPs covered in all individuals were calculated and filtered in ANGSD. Accounting for linkage disequilibrium, we further pruned the dataset by only selecting the most central site within blocks of linked sites (R2 > 0.2) as in Orlando and Librado77. Subsequently, all variants located on the Z-pseudo-chromosome and “unplaced scaffolds” were excluded from the analyses yielding a final genotype likelihood panel consisting of 1,093,765 sites. We investigated genomic population structure with a PCA of the genotype likelihood panel using PCAngsd v0.98278. Individual ancestry proportions were estimated using a maximum likelihood (ML) approach implemented in ngsAdmix v3279, with the number of ancestral populations (K) set from 1 to 10 and conducting 50 replicate runs for each K. The runs were clustered after similarity for each K and ancestry proportions were averaged within the major cluster using Clumpak80 with default settings. Additional “hierarchical” PCA and admixture analyses were conducted for genomic sub-cluster(s) using identical methods.The majority of population genomic analyses were based on nuclear genotype likelihoods as implemented in ANGSD v.0.93181 and Treemix v1.1382, respectively. For both trees, 100 bootstrap replicates were generated. To infer patterns of population splitting and mixing, we produced population-based ML trees including up to ten migration edges. The optimal number of migrations was selected using a quantitative approach by evaluating the distribution of explained variance, the log likelihoods, the covariance with an increase in migration edges, and by applying the method of Evanno41 and several different linear threshold models. The topology for m0 and mBEST was evaluated by generating 100 bootstrap replicates. Additional details on the cluster and phylogenetic analyses are given in the Supplementary File.After adding the razorbill genome as an outgroup to the genotype likelihood panel by mapping unpublished, raw 10x Genomics sequencing data used for the assembly of the embargoed razorbill genome to the puffin draft assembly, we built a neighbor-joining (NJ) tree based on pairwise genetic distance matrices (p-distance) and a sample-based ML phylogenetic tree in FastMe v2.1.5We calculated a set of neutrality tests and population statistics in ANGSD using colony-based one-dimensional (1D) folded site frequency spectra (SFS). For each population, genomic cluster, and globally, Tajima’s D and nucleotide diversity (π) were computed utilizing the per-site θ estimates. Individual genome-wide heterozygosity was calculated in ANGSD using individual, folded, 1D SFS. We calculated heterozygosity by dividing the number of polymorphic sites by the number of total sites present in the SFS.42. We defined the 10% quantile of the average local heterozygosity across all samples as the cutoff for a “low heterozygosity region”  following the approach in Sánchez-Barreiro et al.on” Fig. . RoHs we43 was used to model the association between genetic and geographic data by visualizing the existing population structure and highlighting regions of higher-than-average and lower-than-average historic gene flow. We calculated a pairwise genetic distance matrix in ANGSD by sampling the consensus base (-doIBS 2 -makeMatrix 1) at the sites included in the genotype likelihood set  for each sample. The matrix was fed into 10 independent runs of EEMS, each consisting of one MCMC chain of six million iterations with a two million iteration burn-in, 9999 thinning iterations, and 1000 underlying demes.Assessing potential patterns of IBD within the breeding range of the puffin, the program EEMS83 and a multiple regression on distance matrix (MRM)84 analysis. FST was used as a proxy for genetic distance and computed for each population pair in ANGSD by applying two-dimensional (2D), folded SFS. We converted pairwise FST values to Slatkin’s linearized FST85. Least Cost Path distances (paths over water only) between colony coordinates (latitude/longitude) were calculated using the R package marmap86 and used as geographic distances. We performed the Mantel test (999 permutations) and MRM analysis with the R package ecodist87. All analyses for IBD were re-run on subsets of colonies by progressively removing the colony from the geographic and genetic distance matrices, whose removal led to the highest increase in the proportion of variance in genetic distance explained by geographic distance in the resulting regression model .Supplementing the results of the EEMS analysis, we conducted a traditional IBD analysis by determining geographical and genetic distances between the 12 colonies and assessing the significance of the correlation between the two distance matrices with a Mantel test88 was conducted to corroborate the results of the MRM analyses and Mantel tests and to estimate the relative contribution of IBD and IBE to the observed Atlantic puffin population structure. The dbRDA was run between the genetic distance matrix versus geographic and environmental parameters88. A global dbRDA was performed with all geographic and environmental variables, and for statistically significant global dbRDA models, the most significant variables  were selected via a stepwise regression89. Those served as input for a reduced dbRDA to calculate the marginal effect of each variable and for a partial dbRDA with variance partitioning to estimate the separate effects of IBD and IBE. Similar to the MRM analyses and Mantel tests, these analyses were repeated on subsets of colonies by progressively removing the colony from the geographic, environmental, and genetic distance matrices, whose removal led to the highest increase in variance explained in the resulting global dbRDA model. Methods and R code for the dbRDA were found at https://github.com/laurabenestan/db-RDA-and-db-MEM90.A distance-based Redundancy Analysis (dbRDA)f3-statistics and multi-population D-statistics (aka ABBA BABA test)91. We calculated f3-statistics in Treemix for each unique combination of ,C)) of the 12 puffin populations. D-statistics were calculated in ANGSD (-doAbbababa2) for each combination of ,C),Outgroup) using the 12 puffin colonies. The outgroup was generated in ANGSD using the 10xGenomics sequencing data of the razorbill mapped to the puffin reference genome .Additional assessments of gene flow and admixture were conducted by calculating ST values between the Norway/Iceland/Faroe cluster and the Spitsbergen, Isle of May, Canada colonies (three comparisons) were calculated in sliding windows of 50 kb with 12.5 kb steps across the 25 pseudo-chromsomes by applying 2D, folded SFS. The window size of 50 kb was chosen for sliding window analyses because LD decays to ca. 10% (R < 0.025) within this distance  across 12 colonies located in Svalbard, northern mainland Norway, Iceland, the Faroe Islands, Scotland, and Canada. The sample included six individuals per colony (12 colonies), including an equal sex ratio . All statistical tests were conducted using publicly available programs and packages as described in the methodological sections above. Reproducibility can be accomplished by following the sample collection and laboratory methods outlined above and by following the author’s GitHub (https://github.com/OKersten/PuffPopGen) using the specified parameters mentioned in in the code and methodological sections above.The research sample included 72 adult Atlantic puffins (Further information on research design is available in the Supplementary InformationReporting SummarySupplementary Data 1Supplementary Data 2Supplementary Data 3Description of Additional Supplementary Files


The hill tribes of northern Thailand comprise nine officially recognized groups: the Austroasiatic-speaking (AA) Khmu, Htin and Lawa; the Hmong-Mien-speaking (HM) IuMien and Hmong; and the Sino-Tibetan-speaking (ST) Akha, Karen, Lahu and Lisu. Except the Lawa, the rest of the hill tribes migrated into their present habitats only very recently. The Thai hill tribes were of much interest to research groups focusing on study of cultural and genetic variation because of their unique languages and cultures. So far, there have been several genetic studies of the Thai hill tribes. However, complete forensic microsatellite database of the Thai hill tribes is still lacking. To construct such database, we newly generated 654 genotypes of 15 microsatellites commonly used in forensic investigation that belong to all the nine hill tribes and also non-hill tribe highlanders from northern Thailand. We also combined 329 genotypes from previous studies of northern Thai populations bringing to a total of 983 genotypes, which were then subjected to genetic structure and population relationships analyses. Our overall results indicated homogenous genetic structure within the HM- and Tai-Kadai (TK)-speaking groups, large genetic divergence of the HM-speaking Hmong but not IuMien from the other Thai groups, and genetic heterogeneity within the ST- and AA-speaking groups, reflecting different population interactions and admixtures. In addition to establishing genetic relationships within and among these populations, our finding, which provides a more complete picture of the forensic microsatellite database of the multiple Thai highland dwellers, would not only serve to expand and strengthen forensic investigation in Thailand, but would also benefit its neighboring countries of Laos and Myanmar, from which many of the Thai hill tribes originated and where large populations of these ethnic groups still reside.  Northern and western Thailand are geographically characterized by several small river plains separated by steep mountainous ranges. These areas share borders with Myanmar to the west and with Laos to the east . The manIn socio-economic perspective, after settlements in Thailand, the hill tribes have continued to be a disadvantaged and vulnerable group in Thai society, being largely dependent on agriculture for income and employment. In the past, there were reports of high level of opium addiction  and opiuIn anthropological perspective, as a result of their living in a small, remote and isolated area, linguistic and cultural variations of the Thai hill tribes are of interest to several scholars conducting anthropological and ecological studies ,9. DiffeDespite a number of intensive studies on the Thai hill tribes, none had reported their complete genetic data. To build a genetic database for these populations, i.e., Hmong (Hmong Daw and Hmong Njua), IuMien, Akha, Lahu (Lahu Black and Lahu Red), Lisu, Karen (Karen Skaw and Karen Kayah), Lawa (Lawa Eastern and Lawa Western), Khmu and Htin  , we geneD8S1179, D21S11, D7S820, CSF1PO, D3S1358, TH01, D13S317, D16S539, vWA, TPOX, D18S51, D5S818, FGA, D19S433 and D2S1338 of a total of 654 samples were amplified using the commercial AmpFLSTR Identifiler kit, according to the manufacturer’s protocol. The amplicons were genotyped by multi-capillary electrophoresis on an ABI3130 DNA sequencer (Applied Biosystems) and the allele calling was performed by the Gene Mapper software Version 3.2.1 (Applied Biosystems). For genetic comparison analyses, we also retrieved 329 genotypic data of the Karen Kayah, Lawa Western 2, Lawa Eastern, Shan 1, Yuan and Yong from the previous studies [Most genomic DNA samples of the Hmong Daw, Hmong Njua, IuMien, Lahu Black, Lahu Red, Lisu, Karen Skaw, Lawa Western 1, Khmu, Htin Mal, Htin Pray 1, Htin Pray 2, Mlabri, Palaung, Blang and Shan 2 were from previous studies ,14,20, w studies ,21. GeneOH), expected heterozygosity (EH), average EH, number of alleles, gene diversity (GD) and standard deviation (SD), allele frequency and Hardy-Weinberg p-value (HWE) using Arlequin v.3.5.3.1 [We calculated genetic diversity indices for each locus and population, i.e., the observed heterozygosity  matrix between pairs of populations based on the number of different alleles. The STF matrix was plotted in three dimensions by means of multidimensional scaling (MDS) using Statistica v.10 demo . The R software was used to construct heat plots of the Φst distance matrix and MDS [Arlequin was also used to perform analysis of molecular variance (AMOVA)  for the  and MDS .K) was predefined from 2 to 10; ten replications were run for each K with burn-in length of 100,000 iterations followed by 200,000 iterations. In order to identify the optimal K value in the data, the STRUCTURE outputs were combined to compute a second-order rate of change logarithmic probability between subsequent K values (△K) [K, CLUMPAK [To identify genetic structure and population clustering, the model-based method as implemented in STRUCTURE version 2.3.4 was used with the following three main parameters: correlated allele frequencies, admixture and assistance of sampling locations (LOCPRIOR model) ,27,28. Tues (△K)  by STRUCues (△K) . To vali CLUMPAK  was used CLUMPAK .stF computation by allele frequency from 13 STRs of the FBI Laboratory’s Combined DNA Index System (CODIS) was carried out using POPTREE v.2 [To obtain a broader picture of population relationships within Southeast Asia, we included publicly available STR frequency from relevant populations ,55,56,57TREE v.2 .TPOX (EH = 0) and only 51 alleles in total.A total of 654 individual raw genotypes are provided in EH, total alleles, GD and forensic parameters  of the 27 individual populations are shown in TH01 to 21 alleles at FGA (EH is observed at TPOX (0.624), while the highest EH is the FGA (0.884), in agreement with other Thai [TPOX) to 0.867 (FGA) and from 0.027 (FGA) to 0.191 (TPOX), respectively. The PD ranges from 0.809 (TPOX) to 0.973 (FGA), with a value of 0.99999999 for the combined PD. The PE ranges from 0.268 (TPOX) to 0.633 (FGA), with a combined PE value of 0.999967. The allelic frequency table of the combined highlander data shows a total of 191 alleles, varying from 8 alleles at TH01 and D13S317 to 21 alleles at FGA (EH is observed at TPOX (0.611), while the highest EH at the FGA (0.886). The PIC and MP range from 0.559 (TPOX) to 0.871 (FGA) and from 0.026 (FGA) to 0.197 (TPOX), respectively. The PD ranges from 0.803 (TPOX) to 0.974 (FGA), with a value of 0.99999999 for the combined PD. The PE ranges from 0.248 (TPOX) to 0.626 (FGA), with a combined PE value of 0.999953.For forensic purpose, loci departure from the HWE, average s at FGA ; their aher Thai ,50,59 anher Thai ,40,55,56s at FGA . The lowp > 0.05)  with statistical differences and eight pairs without significant differences ( > 0.05) . The AA-A groups . We furt 1 and 2 A–C, and  1 and 2 D–F, wher 1 and 2 D,E. The  1 and 2 D–F. Inte 1 and 2 D–F. The  1 and 2 G. In genK) were run from 2 to 10  appears in the AA-speaking Mlabri and Htin, while the third cluster (light blue) is predominant in the remaining populations. At K = 6, the Mlabri split from the Htin and occupy their own light purple component, while the Htin Mal and Pray share the dark purple component but the former has another major light blue source; the ST-speaking populations are separated into two groups, i.e., a group of Black Lahu, Red Lahu and Karen Skaw who show dark green as their major component and the other group of Akha, Lisu and Karen Kayah who predominantly show light blue, which is similar to the profile of the TK- and AA-speaking Palaungic populations ; and the IuMien show the minor pink component that also occurs in the AA-speaking Palaungic populations. Notably, although the Khmu, Mlabri and Htin are ethnolinguistically closely related, the Khmu genetic component is light blue which is distinct from their ethnolinguistic relative. Although increasing K values are associated with lower delta K values, further new components and additional cryptic population structure could emerge. At K = 10, the Lahu, Hmong, IuMien, Htin Mal, Htin Pray, Mlabri, Lawa Western and Palaung exhibit their own genetic structures, while the other groups share the common component  (p < 0.05) than the variation among populations assigned to each group (3.11%) (p < 0.05), indicating that language families do not correspond to the genetic structures of these populations. The AA group shows the greatest genetic heterogeneity among populations , followed by the ST  and HM groups , while the TK group shows the lowest among-population variation . Although the variation within group among the AA populations is lower when the Mlabri is excluded , the AA group remains showing the greatest genetic heterogeneity.The AMOVA results indicate that the variation among populations accounts for 3.91% ( < 0.05) . The genGenetic variation between pairs of the linguistic groups shows significant differences among the groups in almost all comparisons, except in the pairs of AA vs. TK and ST vs. TK, further supporting a close relationship between the TK and other groups. High variations observed between the HM and other groups indicate their genetic distinction from the other linguistic groups. However, variation between the groups is lower than that among the populations within the same groups.stF computation by allele frequency of 13 CODIS STR loci  tree based on STR loci . With grSTR loci , the MlaAccording to previous maternal and paternal genetic studies of the hill tribes in Thailand, postmarital residence pattern has been shown to influence genetic variation in the Thai hill tribes ,10,11,12K = 2  based on lexical data . The putBased on the historical evidence, the Akha Lisu and Lahu migrated from southern China through Myanmar to northern Thailand about 100–200 years ago , as a reTPOX (EH = 0) and only 51 alleles in total (The languages of the Htin, Khmu and Mlabri belong to the Khmuic branch of the AA family. Composed of two subgroups of Mal and Pray, the Htin migrated from their homeland in Laos to northern Thailand at the turn of the 20th century A.D. ,3. With in total , possiblin total , while uin total  and sharin total . AlthougK = 10, the Lawa Western stand out from the Lawa Eastern who still share a component with the TK groups, supported by historical record that indicated contact of the Lawa Eastern with the northern Thai TK group [Unlike the hill tribe Lawa who are regarded as native to northern Thailand, the migrant Palaung and Blang are highlanders and minority groups in northern Thailand who are not officially recognized as hill tribes. However, despite the difference in official recognition, all of them speak the same language branch of Palaungic, which is the most northerly spoken AA language, spanning northern Thailand, northern Laos, northern Myanmar and southern China. The first group of the Palaung recently moved from the Shan State of Myanmar to northern Thailand about 40 years ago, while the Blang migrated from southern China through Myanmar to Thailand about 60 years ago . GeneticTK group .Previous investigations of forensic STRs in Thailand were conducted on major lowland groups from all regions but complete forensic database of the hill tribes were lacking. In this work, we generated autosomal forensic STRs data of all hill tribes and some non-hill tribe groups from several villages in northern Thailand, constructed an allelic frequency table and explored genetic relationship among them. In general, we found genetic divergence of the Hmong from other populations, genetic difference between the Hmong and IuMien, and genetic heterogeneity of the AA and ST groups mirroring various population interactions that were possibly driven by geographic proximity and previous admixture events. The complete forensic STRs data for the hill tribes here provide advantages for further forensic investigation in Thailand and would also benefit Laos and Myanmar wherein large populations of the many ethnicities investigated in this study still reside. In addition, we also explored the genetic diversity, migration and demographic history of the northern Thai highlanders.


Objective: The purpose of this study was to discover the population structure and genetic diversity of Inner Mongolia White Cashmere goats (IMCGs) and demonstrate the effect of inbreeding on the live body weight (LBW), cashmere yield (CY), fiber length (FL), and fiber diameter (FD) of IMCGs.Materials and Methods: All data were collected from pedigree information and production performance records of IMCGs from 1983 to 2019. The population structure and genetic diversity were analyzed by Endog 4.8 software. Inbreeding coefficients were obtained by the pedigree package in R. Then, a linear regression model was used to analyze how inbreeding influences economic traits in IMCGs. Four levels of inbreeding coefficients (Fi) were classified in this study, including Fi = 0, 0< Fi ≤ 6.25, 6.25< Fi ≤ 12.5 and Fi≥12.5. Variance analysis was performed to determine whether inbreeding levels had a significant effect on economic traits in IMCGs.Results: The proportions of rams and dams in IMCGs for breeding were relatively small, with values of 0.8 and 20.5%, respectively. The proportion of inbred animals in the entire population was high, with values up to 68.6%; however, the average inbreeding coefficient and relatedness coefficient were 4.50 and 8.48%, respectively. To date, the population has experienced 12 generations. The average generation interval obtained in the present study was 4.11 ± 0.01 years. The ram-to-son pathway was lowest (3.97 years), and the ewe-to-daughter pathway was highest (4.24 years). It was discovered that the LBW, CY, and FL increased by 3.88 kg, 208.7 g, and 1.151 cm, respectively, with every 1% increase in the inbreeding coefficient, and the FD decreased by 0.819 μm with every 1% increase in the inbreeding coefficient. Additionally, multiple comparison analysis indicated that when the inbreeding coefficient was higher than 6.25%, the LBW showed an obvious decreasing trend. The threshold value of inbreeding depression in the CY is 12.5%. However, inbreeding depression has not been observed in the FL and FD.Conclusion: Pedigree completeness needs to be further strengthened. The degree of inbreeding in this flock should be properly controlled when designing breeding programs. The genetic resources of cashmere goats are very rich throughout the world and are mainly distributed in Asia, including Mongolia, Iran, Afghanistan, Kazakhstan, Kyrgyzstan, and Tajikistan. Inner Mongolia White Cashmere goats (IMCGs) and Liaoning Cashmere goats (LNCGs) are key breeds in China  reflects the minimum number of animals required to estimate the genetic diversity of the population under study, and it is a useful measure to determine the bottlenecks in the population that are the primary reason for genetic erosion in captive and domestic populations. It is estimated as:where qj is the marginal contribution of ancestor j, which demonstrates the genetic contribution of an ancestor that is not explained by an earlier ancestor. In general, the effective number of ancestors should be smaller than the effective number of founders due to bottlenecks that reduce genetic variability.where F) and the average relatedness (AR) coefficient were estimated by Meuwissen and Luo  is estimated for each generation using the formulae suggested by Lacy  was pointed out as the average age of the birth of selected offspring. The estimate of GI for all of the pathways was estimated for the reference population, as this subpopulation is the most recent one that could accrue at least one generation on the farm.The genetic conservation index (GCI) for each of the individuals of the analyzed population was provided by Alderson . The indervation . The folF-statistics are obtained as Wright's  F-statisancestry , 31.fg) can be defined as the number of founders that would be expected to produce the same genetic diversity as in the population under study if the founders were equally represented and no loss of alleles occurred. Parameter fg was obtained by the inverse of twice the average coancestry of the individuals included in a predefined reference population  is continuously viable; however, other factors are discrete variables. Therefore, these discrete variables need to be centered first. Then, regression analysis was carried out by using the lm function of R language  obtained by pedigree information were classified into four levels . Variance analysis of inbreeding levels on each trait was performed by the aov function of R language.To assess how inbreeding influences the important economic traits of IMCGs, it is helpful to design a proper breeding scheme. Referring to corresponding studies , inbreedThe main demographic characteristics derived from genealogical data of IMCG flocks are summarized in The number of registered and inbred animals across birth years is shown in F-statistics were used to assess genetic differentiation in subdivided populations, and the value was 5.7 × 10−5.The probabilities of gene origin parameters in the studied breed are presented in The distribution of the inbreeding coefficient among IMCGs is presented in This population has gone through 12 generations by pedigree tracing . With thP < 0.01). The birth status had no significant effect on the FL and FD (P > 0.05), but the other three traits were significantly affected by the birth status (P < 0.01). Excluding the FL, the inbreeding coefficient had a significant effect on the other three traits. The results demonstrated that the LBW, CY, and FL increased by 3.88 kg, 208.7 g, and 1.151 cm, respectively, with every 1% increase in the inbreeding coefficient, and the FD decreased by 0.819 μm with every 1% increase in the inbreeding coefficient.A linear regression model was established to assess the relationship between important economic traits and the inbreeding coefficients of IMCGs, and the results are shown in P < 0.01). Additionally, the multiple comparison analysis indicated that when the inbreeding coefficient was more than 6.25%, the LBW had an obvious decreasing trend. The threshold value of inbreeding depression in the CY is 12.5%. However, inbreeding depression has been observed in the FL and FD.Variance analysis was performed to determine the threshold value of the inbreeding value for each trait. The results are shown in In this population, 0.8 and 20.5% are presented as sires and dams, respectively. The fraction of males and females selected in IMCGs was relatively low. Individuals with no progeny accounted for 78.7% of all animals. All of these animals were sold as stud stocks to other flocks to improve performance or eliminated at the market for goat meat. Artificial insemination is used in this flock, so very few sires were selected. Out of 53,381 animals, the number of founders (with one or more unknown parents) was 5,455, accounting for ~10.22%. The results suggested a good depth in the pedigree in terms of completeness. Therefore, the accuracy of the estimated founders is reliable. The timeline trend of all animals with both known parents across the years indicated that the number of individuals with both unknown parents was low from 1990 to 2012; however, this value increased from 2013 to 2019. This may be related to incorrect pedigree information. After 1998, the population size remained stable. Our research team began to design a breeding plan for this flock in 1998, and thus, it had a reasonable population structure from then on.F-statistic in this population is very low, which is far lower than that reported in other studies (This farm is representative of IMCGs. It has always undergone closed breeding and never introduced other breeds to cross. Therefore, the proportion of inbred animals among all registered individuals was more than 68.6%. A certain degree of inbreeding can improve production performance. Increasing trends of average inbreeding coefficients were observed in all registered animals. However, there was no regular trend for average inbreeding coefficients in the inbred animals. To maximize production performance, a reasonable mating plan is created every year. Most inbred animals showed inbreeding coefficients lower than 6.25%, and inbred animals with inbreeding coefficients > 25% accounted for ~4%. This may be related to our strict implementation of the proper mating scheme. A small fraction of high inbreeding individuals may be caused by natural mating or poor management. The average inbreeding value in the entire studied population was 4.50%, which is lower than that in the cashmere goat breed of the South Khorasan and Iranian Adani goat breeds reported by Joezy-Shekalgorabi et al. , 35. How studies , 40. ThiShortening the generation interval is one of the methods to increase genetic progress. It also may result in better economic returns. This outcome is the best choice for production enterprises. However, decreasing the ability of individuals to stay on the farm will intensify genetic variability losses, especially rams. The genetic contribution of those animals will be lower because of their short lifespan. The population in this study has gone through 12 generations by pedigree tracing. The average generation length obtained in the present study was 4.11 ± 0.01 years, which is longer than that estimated in Beetal goats. Tomar et al.  reportedThe linear regression analysis demonstrated that the LBW, CY, and FL increased by 3.88 kg, 208.7 g, and 1.151 cm with every 1% increase in the inbreeding coefficient, and the FD decreased by 0.819 μm with every 1% increase in the inbreeding coefficient. It was illustrated that inbreeding had no negative impact on the economic traits under the current mating scheme. However, the variance of analysis of inbreeding levels indicated that when the inbreeding coefficient of most individuals was higher than 6.25 and 12.5%, the LBW and CY will produce inbreeding depression, respectively. Hence, the breeding plan should be updated appropriately. However, it is surprising to find that the increase in inbreeding resulted in a decrease in the FD, which is beneficial to the improvement of fiber quality. The results of this population are similar to those reported by Dai et al. . This stPopulation structure across years in the Inner Mongolia Cashmere goat breed was documented in this study. A small number of unknown parents was found to be the reason for complete and detailed pedigree information. Although the proportion of inbred individuals in the entire population is high, the low average inbreeding coefficient and average relatedness obtained in the studied population indicated that the current mating scheme is relatively reasonable. Generally, animals with unknown parents are assumed to have no inbreeding. However, in reality, this will lead to underestimation of the inbreeding coefficient. An effective way to solve this problem is to ensure pedigree completeness. Alternatively, utilizing paternity testing methods helps to overcome the problem of pedigree incompleteness. Although inbreeding has not resulted in an obvious decrease in economic traits, it should be controlled properly when designing mating schemes.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.TZ, XY, YY, and QL analyzed the data. RS, CD, and ZhiyW conceived of and coordinated the study. BM, RW, and JL helped in conceive of the study. ZhixW, YZhan, ZL, and YZhao help to collect data. ZhiyW and BZ wrote the manuscript. All authors read and approved the final manuscript.TZ is only employed by the company Inner Mongolia Bigvet Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Transcripts were coded by independent investigators and validated by external reviewers. Results: Major themes relating to pre-test experiences were ‘attitudes & beliefs’ (most frequently cited theme), which revealed altruism as a strong motivator for pursuing research testing and ‘information and support,’ which revealed a striking lack of pre-testing decisional support/genetic counseling. Major post-test themes included ‘return of results,’ revealing frustration with the lack of return of results and limited emotional support, and ‘family communication,’ describing challenging intrafamilial communication. Themes describing ethical concerns  were least frequently noted and related to pre- and post-test experiences. Conclusions: Patients with CHH are highly motivated by altruism when pursuing testing but have significant unmet needs for pre-test decisional support and post-test counseling. It is regarded that patient values, beliefs and experiences can inform more person-centered approaches to genetic testing for rare diseases.Most rare diseases are genetic in etiology and characterized by a ‘diagnostic odyssey’. Genomic advances have helped speed up the diagnosis for many rare disorders, opening new avenues for precision therapies. Little is known about patient attitudes, experiences, and beliefs about genetic testing for the rare disease congenital hypogonadotropic hypogonadism (CHH). Methods: We conducted six focus groups with patients with CHH ( Rare diseases are often characterized by a “diagnostic odyssey” . From inCongenital hypogonadotropic hypogonadism (CHH) is a rare endocrine disorder that is clinically characterized by absent/incomplete puberty and infertility resulting from deficient secretion (or action) of gonadotropin-releasing hormone (GnRH) . A rangeUnlike many rare diseases, effective treatments are available for inducing secondary sex characteristics , and fertility can be induced in roughly 75–80% of men with either gonadotropin injections or pulsatile GnRH . SimilarGenetic testing has the potential to surmount some of the diagnostic challenges and hasten detection, thereby helping to alleviate the patient’s psychosocial burden . In the In this study, we sought to explore patient experiences, attitudes, and beliefs regarding genetic testing for CHH/KS as well as communication with potentially at-risk blood relatives. A deeper understanding of patient perspectives on genetic testing could inform more person-centered approaches to supporting patients in navigating genetic testing services and decision-making.n = 3 in-person, 33 total participants) and online  focus group discussions. The study was conducted in accordance with the Declaration of Helsinki. The study protocol was reviewed and approved by the ethics committee of the University of Lausanne (protocol #2016_02184) and the Boston College Institutional Review Board (protocol #18.081.01). All participants provided informed consent prior to study participation.This prospective qualitative study involved both face-to-face (We employed a community-based participatory research framework  for the Semi-structured focus group discussions (90–120 min) were led by an investigator (AAD) and the patient group leader (NS). Question prompts  related www.dedoose.com (accessed on 17 February 2022)). Briefly, investigators highlighted excerpts (sections of meaningful content in the transcript) and labeled excerpts with an identifying code (representing a theme or sub-theme). Subsequently, investigators met and created the codebook by discussing a hierarchy of themes/sub-themes, collapsing similar codes, and operationalizing theme/sub-theme definitions. Investigators then used the codebook to independently code the remaining transcripts. To ensure rigor, we used external reviewers to “triangulate” and validate the independent coding results. The patient group leader (NS) and an investigator experienced in qualitative research (SD) served as external reviewers who evaluated transcripts and the list of themes/sub-themes to determine if all concepts were appropriately captured.We employed a coding reliability approach to thematic analysis . To creaT-test/Mann–Whitney U test as appropriate. A p value < 0.05 was considered statistically significant. Theme/sub-theme frequencies were calculated to determine patient-identified priority areas.Participant characteristics are reported using descriptive statistics. Comparisons between in-person and virtual groups were performed using Chi-square/Fisher’s Exact and Student’s Six focus group discussions were conducted including three in-person and three virtual. Focus group participant characteristics are reported in Two dominant themes emerged related to pre-test experiences. ‘Attitudes & beliefs’ relates to motivating factors (for/against) genetic testing). This theme includes four sub-themes: motivating factors (for testing), test type (diagnostic vs. research), uncertainty , and cost of genetic testing. ‘Information and support’ relates to where patients learned of genetic testing and experiences with decisional support for genetic testing. This theme includes three sub-themes: information source, pre-test decisional support, and genetic counseling .n = 30 mentions). Relating to the sub-theme ‘test type’ (n = 50 mentions), approximately half of participants  had undergone genetic testing for CHH/KS. Of those who had testing, the overwhelming majority  did so in a research setting. Only two participants had testing in the context of a clinical diagnostic lab. The rarity of clinical diagnostic testing among participants likely relates to the sub-theme ‘cost’ (n = 21 mentions). Indeed, a number of patients reported cost as the single biggest barrier preventing them from receiving genetic testing and expressed concerns that insurance would not cover genetic testing. The strong role that altruism plays as a motivator for pursuing genetic testing (particular in the research context) is a notable finding emerging from the focus group discussions.The ‘attitudes & beliefs’ theme was the most frequently cited in focus group discussions . The subn = 40 mentions) and lack of pre-test genetic counseling (n = 30 mentions). The dearth of pre-test decisional support is highlighted by the observation that only 4/58 (7%) participants had met with a geneticist or genetic counselor prior to genetic testing. No participants reported receiving virtual/online decisional support. Participants also expressed a desire for counseling and support to help them make better-informed decisions about genetic testing  or via online discussions in the patient support group’s private Facebook page. In total, 58 participants had undergone genetic testing. A number of participants expressed frustration that they were not offered genetic testing . Similar testing . The lacTwo dominant themes related to post-test experiences. First, ‘return of results’ includes four sub-themes: uncertainty about test results, difficulty understanding results, waiting for results, and lack of emotional support following genetic testing. Second, ‘family communication’ includes the sub-themes of perceived promoters and barriers to discussing CHH/KS with family members .n = 85 mentions) relates to patient concerns about having a negative test result that would not fully elucidate the cause of their condition (n = 67 mentions) relates to patients feeling overwhelmed by the complexity of the genetic test results and feeling unsupported and “on their own” in understanding the implications of genetic test results . Participants expressed feelings of disappointment and sadness associated with a perceived lack of emotional support following genetic testing . Several participants also expressed a desire for coaching and counseling to help cope with living with CHH/KS as well and how to discuss the diagnosis with potentially at-risk blood relatives (n = 54 mentions). Participants expressed frustration and disappointment that they had “done their part” —yet they rarely (if ever) received results (The theme ‘return of results’ was frequently cited in focus group discussions. The sub-theme ‘uncertainty’ (ondition . Particiondition . The sub results . A relatelatives . Partici results . Extenden = 109 vs. n = 65 mentions respectively). Male patients, female patients and parent/guardians alike shared stories highlighting the shame related to a rare disease diagnosis. Unaccepting family members  and personal feelings of shame were often cited as barriers to intrafamilial communication about CHH/KS  and barriers to intrafamilial communication regarding CHH/KS diagnosis. Barriers were more frequently discussed than promoters of family communication (t CHH/KS . Many pat CHH/KS . Specifin = 44 mentions). Participants expressed significant concerns about threats to their privacy and confidentiality . A number of participants claimed distrust of for-profit organizations  and worried that their information could be used in harmful ways  (n = 24 mentions) relating to concerns that their DNA would be shared and passed between laboratories without patient consent/permission (n = 20 mentions) relates to pre-test ethical concerns. Participants noted that consent was treated as a document to be signed rather than a process  related to post-test ethical concerns. The sub-theme ‘privacy and data use’ arose most often (remiums) . Given trmission . Several process . Concernp = 0.79) [Herein, we report patient and parent/guardian experiences, attitudes, and beliefs regarding genetic testing for a rare endocrine disorder (CHH/KS). We observed similar rates of genetic testing in our participants compared to prior prospectively recruited patients with CHH/KS  . Of part = 0.79) . As suchp = 0.36) [In the present study, patients/families expressed frustration with the lack of services—pointing to an unmet need for decisional support. Genetic counselors are typically well-versed in common genetic conditions—i.e., tier 1 conditions including hereditary breast and ovarian cancer (HBOC), Lynch syndrome and familial hypercholesterolemia . However = 0.36) . The gen = 0.36) . Indeed, = 0.36) ,22. Curr = 0.36) . Only a  = 0.36) . In resp = 0.36)  and the  = 0.36) . As rare = 0.36) , online  = 0.36) . Such a BRCA warriors”, informing at-risk blood relatives. Importantly, feelings of guilt often accompany a genetic diagnosis [Another key finding regarding pre-test experiences relates to altruism as a strong motivator for pursuing genetic testing (particularly in the research context). The psychosocial impact of CHH/KS has been well described ,11,14,29iagnosis . The guiiagnosis ,31,33. Fiagnosis . Recentliagnosis . Such efIn relation to post-test experiences, the extended wait for the return of results and lack of communication/results was a major finding and a significant source of frustration . InteresFinally, participants voiced ethical concerns regarding privacy and secondary use of return of results. Such elements are required to be described in informed consent documents. However, it is unclear whether consent documents clearly explicate this information in easy-to-understand lay terms. Indeed, evidence suggests that many participants do not understand informed consent for research studies and biobanks . Thus, in = 58), which is quite robust for qualitative research in rare-disease populations. However, there are several study limitations. A critique of qualitative inquiry relates to data saturation . To address this potential concern, we conducted six focus-group discussions. Typically, three to five focus-group discussions are needed to reach saturation [A relative strength of the study is the sample size (turation . Moreoveturation . There ituration . It is pturation . It is pturation .Patients with CHH/KS are highly motivated by altruism in pursuing genetic testing—particularly in a research context. Many patients hope to shorten the ‘diagnostic odyssey’ and alleviate psychosocial effects of CHH/KS for future generations. Cost and providers who were unaware of genetic testing services were identified as significant barriers to genetic testing. The lack of pre-test decisional support/genetic counseling was identified as a significant unmet need as only 7% of participants had received genetic counseling. Focus group participants also expressed desire for post-test support to understand results and gain skills/confidence for initiating conversations with family members about CHH/KS. Notably, patient support groups often played an important role in patients finding information about genetic testing and for supporting intrafamilial communication about CHH/KS. Lastly, a more coherent approach to informed consent is needed to clarify ethical concerns, appropriately frame patient expectations, and delineate researcher responsibilities. Future directions may include web-based decisional support to increase access to genetic counseling, support high-quality decisions and promote intrafamilial communication regarding heritable conditions. Considering patient values, beliefs and experiences can inform more person-centered approaches to genetic testing for CHH/KS thereby fueling genetic discovery and propelling novel precision therapies for this rare condition.


Protected‐area systems should conserve intraspecific genetic diversity. Because genetic data require resources to obtain, several approaches have been proposed for generating plans for protected‐area systems (prioritizations) when genetic data are not available. Yet such surrogate‐based approaches remain poorly tested. We evaluated the effectiveness of potential surrogate‐based approaches based on microsatellite genetic data collected across the Iberian Peninsula for 7 amphibian and 3 reptilian species. Long‐term environmental suitability did not effectively represent sites containing high genetic diversity . Prioritizations based on long‐term environmental suitability had similar performance to random prioritizations. Geographic distances and resistance distances based on contemporary environmental suitability were not always effective surrogates for identification of combinations of sites that contain individuals with different genetic compositions. Our results demonstrate that population genetic data based on commonly used neutral markers can inform prioritizations, and we could not find an adequate substitute. Conservation planners need to weigh the potential benefits of genetic data against their acquisition costs. Article impact statement: Conservation plans based on supposed surrogates of genetic data can perform much worse than those based directly on genetic data. Protected areas are needed to maximize the long‐term persistence of biodiversity  between sites for each species to estimate broad‐scale genetic diversity (Jost After cleaning the genetic data (Supporting Information), we calculated genetic diversity metrics for each species (Supporting Information). Specifically, we calculated rarefied allelic richness for each site for each species to estimate site‐level genetic diversity. We also calculated pairwise genetic distances   . First, we created a grid with 10 × 10 km cells over the study area to standardize spatial analyses (UTM Zone 30N). Second, we obtained species distribution data from national atlases (10 × 10 km resolution)   . For site‐level genetic diversity, we computed the long‐term environmental suitability of each species’ sites with the harmonic mean of the contemporary (1979–2013) and historical environmental suitability  data at 10 × 10 km resolution. Harmonic means were used because they penalize sites with large variation in environmental suitability over time, and such sites would be expected to harbor less genetic diversity than temporally stable sites  compared with the total number of sites with these levels of genetic diversity. To ensure that our results were not overly sensitive to this particular threshold, we also conducted a sensitivity analysis in which we examined performance with 2 other thresholds  (Supporting Information).We generated prioritizations to examine surrogates for site‐level genetic diversity (Supporting Information). For each species, we generated a series of prioritizations by incrementing the number of selected sites from 1 to the maximum number of sites for that species. For a given number of sites, we generated a prioritization by selecting the set of sites with the top ranked scores of allelic richness. In a similar manner, we generated prioritizations with the long‐term environmental suitability scores. We also generated 1,000 random solutions for each incremental number of sites for each species to evaluate efficiency (per standard practices) , values were clamped to 0 to facilitate statistical analyses.We also generated prioritizations to examine surrogates for broad‐scale genetic diversity (Supporting Information). These prioritizations aimed to identify a set of sites that secure a representative sample of the diversity between sites based on pairwise distances . They were generated with the environmental diversity formulation of the reserve‐selection problem  between sites to examine isolation by distance and resistance processes  were used to determine whether the surrogate‐based prioritizations performed better than the randomly generated prioritizations when analyzing all species together. Analyses were completed with the lme4, merTools, multcomp, and r2glmm R packages  models were fitted to genetic distances .Prioritizations based on long‐term environmental suitability had much poorer performance than those based on genetic data Fig.  . They only secured, on average, 15.81% (SD 0.38) more sites with high genetic diversity than randomly generated prioritizations—a mere fraction of the performance improvement of genetic‐based prioritizations (mean 80.41% [SD 0.25]) (Supporting Information). These surrogate‐based prioritizations performed, on average, worse than random for 3 species . When considering only prioritizations containing a realistic fraction of sites for real‐world planning , prioritizations based on long‐term environmental suitability had, on average, 4.35% (SD 0.32) worse performance than randomly generated prioritizations.Prioritizations based on long‐term environmental suitability did not perform significantly better than randomly establishing conservation priorities when analyzing all species together (R2β mean = 0.33 [SD 0.19] and R2β mean = 0.2 [SD 0.16] respectively) (Supporting Information).Prioritizations based on geographic and resistance distance data secured less broad‐scale genetic diversity than those based on genetic data Fig.  (Support70%) than randomly generated prioritizations for 1 species (Alytes dickhilleni) and had near‐equivalent performance  for 2 species . As a consequence, they did not significantly outperform the randomly generated prioritizations when analyzing all species together . Furthermore, when considering only prioritizations containing a realistic fraction of sites , prioritizations based on geographic and resistance distances secured, on average, 81.31% (SD 1.12) and 72.59% (SD 1.08) (respectively) more broad‐scale genetic diversity than random.Prioritizations based on geographic and resistance distances secured, on average, 44.31% (SD 0.29) and 41.95% (SD 0.29) (respectively) more broad‐scale genetic diversity than randomly generated prioritizations across all species (Supporting Information). They had much better performance  and Emys orbicularis occidentalis (368 samples) have relatively low allelic richness (likely) due to rapid postglacial expansion   and broad‐scale  patterns of genetic diversity . If such data do not exist, microsatellite markers and mitochondrial DNA could be used. Additionally, value of information analyses could help optimize data collection , genetic data cleaning procedures (Appendix S2), genetic diversity metrics (Appendices S3–S6), multivariate environmental similarity surface map comparing contemporary and Last Glacial Maximum conditions (Appendix S7), contemporary environmental data (Appendix S8), historical climatic data (Appendices S9–S14), environmental niche models (Appendices S15–S20), contemporary, historic, and long‐term environmental suitability maps (Appendices S21–S29), sites with high allelic richness (Appendix S30), distance‐based reserve selection procedure (Appendix S31), relationships between the genetic diversity metrics and their supposed surrogates (Appendices S32–S34), spatial prioritizations (Appendices S35–S84), and data that underpin results for site‐scale genetic diversity (Appendices S85–S89) and broad‐scale genetic diversity (Appendices S90–S94) are available online. The authors are solely responsible for the content and functionality of these materials. Queries  should be directed to the corresponding author. Code and data  are archived in a Zenodo digital repository .Click here for additional data file.


Microcosmus squamiger and Ciona robusta) to examine how anthropogenic transport affects our ability to reconstruct colonization pathways using genomic data. We first investigated shipping networks from the 18th century onwards, cross-referencing these with regions where the species have records to infer how each species has potentially been affected by different levels of anthropogenic transport. We then genotyped thousands of single-nucleotide polymorphisms from 280 M. squamiger and 190 C. robusta individuals collected across their extensive species' ranges and reconstructed colonization pathways. Differing levels of anthropogenic transport did not preclude the elucidation of population structure, though specific inferences of colonization pathways were difficult to discern in some of the considered scenario sets. We conclude that genomic data in combination with information of underlying introduction drivers provide key insights into the historic spread of range-shifting species.Unravelling the history of range shifts is key for understanding past, current and future species distributions. Anthropogenic transport of species alters natural dispersal patterns and directly affects population connectivity. Studies have suggested that high levels of anthropogenic transport homogenize patterns of genetic differentiation and blur colonization pathways. However, empirical evidence of these effects remains elusive. We compared two range-shifting species (This article is part of the theme issue ‘Species’ ranges in the face of changing environments (part I)’. Previous work has found a positive correlation between Ne in the introduced range and time since invasion [Ne would prevent genetic drift, slowing divergence between populations, even in the absence of ongoing gene flow via continuing introductions. Conversely, an ancient invader might be expected to develop strong population structure throughout its range if local adaptation of introduced populations has evolved and/or if reduced gene flow has led to genetic drift. Another mechanism enhancing population structure may be through multiple introductions of genotypes from genetically divergent source populations, increasing the propensity for intraspecific genetic admixture [Anthropogenic transport of species, by definition, increases population connectivity across species' ranges. The genetic composition of colonizing populations can be affected by numerous different processes and scenarios. For example, genetic bottlenecks and founder effects in recent colonizations may lead to population structure across the species range . Conversinvasion . Large Ndmixture . Changesdmixture  or a reddmixture .High-throughput sequencing (HTS) enables scientists to obtain a substantial genomic coverage and capture patterns of genome-wide variation  and thisHere we used a comparative approach to unravel the effects of different levels of anthropogenic transport on the reconstruction of introduction pathways using HTS data. For this, we studied two biologically similar sessile marine NIS that have widespread distributions but have presumably been affected by different levels of anthropogenic transport. Both species belong to the class Ascidiacea (phylum Chordata) and have limited natural dispersal capabilities with the duration of motile early life-history stages being only a few days ,35. Asci. 2 (a)Microcosmus squamiger  and Ciona robusta  for which species records suggest differing levels of anthropogenic transport . Briefly, M. squamiger is native to Australia [Ciona robusta is putatively native to the northwest Pacific [We studied two ascidian species, ustralia ,39 and wustralia . Ciona r Pacific  and has  Pacific , followe Pacific , northea Pacific , Austral Pacific ,46, New  Pacific  and Hong Pacific  througho Pacific  since th Pacific ,41,42, aWe sampled individuals from both the native and introduced ranges of the study species . At each (b)http://webs.ucm.es/info/cliwoc/) and the International Comprehensive Ocean-Atmosphere Data Set . The CLIWOC dataset draws from digitized British, Dutch, French and Spanish ships' logbooks, with a focus on ships sailing in the Atlantic and the Western Indo-Pacific. The ICOADS data derive from various sources worldwide (http://icoads.noaa.gov/). Both datasets were originally constructed to reconstruct historical ocean and atmospheric conditions, and not shipping dynamics. As a result, they do not include all shipping activity, but give a good representation of general shipping dynamics at that time.We obtained historical shipping data from global regions across the study species' ranges. These data came from two independent datasets that spanned two sequential time periods: the Climatological Database for the World's Oceans . We only considered large ports  that we could assume persisted over the past 250 years. Geographical details of ship locations were only provided once a day and no records were available when a ship stayed in the actual port. We therefore considered a port call if a ship sailed within 10 km distance from a port. We checked individual ship trajectories and used different distances to test the sensitivity of the reconstruction of shipping routes. In total, we obtained 7238 individual ship movements from the CLIWOC dataset and 210 423 ship movements from ICOADS. For both datasets, the temporal and spatial coverage was not always consistent and thus data were only analysed on coarse temporal  and spatial  scales. To visualize historical shipping data, we created chord diagrams using the R package ‘circlize' [Both datasets provided ship location dates and geographical details during their travel, enabling the reconstruction of individual ship trajectories and shipping intensities. The CLIWOC dataset provided additional information about anchor points, which can be interpreted as port calls of that ship. The ICOADS dataset did not provide information about anchor points, and it was thus necessary to infer port calls from ship trajectories. To determine actual port calls, we calculated the shortest distance of each ship coordinate to a list of 1620 ports obtained from the World Port Index 26th Edition ( (c)TM gDNA Tissue Miniprep System . DNA was sent for sequencing at Cornell Genomics Diversity Facility . The restriction enzymes PstI, EcoT221 and ApeKI were trialled to identify the one that created suitable libraries , and thus PstI was used for M. squamiger, and EcoT221 for C. robusta. Genotyping was performed using the genotyping-by-sequencing protocol (GBS) [Total genomic DNA was extracted from all tissue samples using the ReliaPrepol (GBS) , and too (d)http://ipyrad.readthedocs.io/). We then conducted read assembly, single-nucleotide polymorphism (SNP) filtering and loci selection .We processed data from each species independently using the same bioinformatics pipeline. Briefly, sequence data were first passed through FastQC  to inves (e)E), observed heterozygosity (HO), and the inbreeding coefficient (FIS)) were calculated using the ‘diversity v.1.9.90' package [dudi.pca. Secondly, we used the software ADMIXTURE v. 1.3 [K putative clusters, using a maximum-likelihood estimation. For both species, the number of tested clusters ranged from 1 to n, where n = the number of sites individuals were sampled from. The R package ‘hierfstat v. 0.5-7' [FST.Within-population indices of genetic diversity (expected heterozygosity (H package  within R package . To prov package  in R to E v. 1.3  to group. 0.5-7'  was used (f)FST values of sites among these regions, before performing a Spearman's rank correlation between shipping intensity and FST in R using the package ‘ggpubr v. 0.4.0' [For each period of shipping data available we assessed the correlation between the number of shipping events (hereafter referred to as shipping intensity) and genomic differentiation. We grouped our study sites into regions corresponding to the spatial scale of our shipping data analysis, and calculated mean . 0.4.0' . (g)We used DIYABC Random Forest v. 1.0 , which u. 3 (a)f). South Africa was also a major shipping donor/recipient particularly before 1850 and was involved in minor shipping trade with the northwest Pacific prior to the 1800s. Shipping within the M. squamiger native range , while being present at low intensity in the eighteenth century, intensified from the mid nineteenth century onwards. Mediterranean shipping steadily increased from 1750, representing 20% of shipping traffic from the 1950s onwards. These shipping data indicate that from the 1750s, shipping was prevalent among regions across the range of C. robusta . Thus, the combined used of historical shipping data and the species records  suggested a longer history of anthropogenic transport in C. robusta compared to M. squamiger.We found a clear pattern of increasing complexity and magnitude of global shipping over recent time . Indeed,figure 2 (b)M. squamiger and 214 C. robusta individuals from across their species ranges. Of these, 280 M. squamiger and 190 C. robusta successfully passed our sequencing QC . Following our filtering protocol, we retained 2115 SNPs and 3227 SNPs for M. squamiger and C. robusta, respectively. We then identified putatively non-neutral SNPs using BAYESCAN and pcadapt and removed those that were presented in either method, leaving a dataset of 1994 SNPs and 3139 SNPs for M. squamiger and C. robusta, respectively.We genotyped 365  (c)M. squamiger, expected and observed heterozygosities were consistent across the range  and the mean number of private alleles per site was greater in the native range  than the introduced range . For C. robusta, HE was higher in the putative native ranges (mean HE = 0.191) than in the introduced range ; however, for Ravenna (the Mediterranean site) in the introduced range, HE was higher than all other sites (0.241). The number of private alleles across the range showed the opposite pattern to HE, with sites within the native range having fewer private alleles (mean = 10.0) than the introduced range (mean = 35.6). All sites, for both species, exhibited positive FIS values .For  (d)M. squamiger, but low within the introduced range (a). The optimum number of clusters identified by ADMIXTURE was K = 2, with one cluster containing three native sites  and the second cluster containing the native site MEL and all introduced sites. Owing to the heuristic nature of ADMIXTURE, we also plotted K = 3–5, which recovered further potential structure within the introduced range, separating South African sites and the Eastern Pacific from those in the Atlantic and Mediterranean and blurring the initially inferred structure . The PCA identified four main clusters, each corresponding to one of the four Australian sites  with individuals from all introduced sites clustered with those individuals from Melbourne. The first axis of the PCA recovered groupings matching the ADMIXTURE result at K = 2 . This close relationship between MEL and the introduced sites was reinforced by the pairwise genetic differentiation FST values .Genomic differentiation was high among native sites of ed range a. The opC. robusta than M. squamiger, as seen in the both the higher optimal value of K in the ADMIXTURE analysis (b), and PCA . The ADMIXTURE analysis recovered three distinct genomic clusters, with one group containing the native range and NEL, a second group containing the European sites (RAV and PLY) and the western South Africa sites , with the third group containing the eastern South Africa sites  (b). Interestingly Australian site MEL contained individuals composed of all three clusters (b). Unlike M. squamiger, increasing values of K did not result in blurring of inferred structure . The PCA recovered a similar picture, however it recovered four clusters . Individuals collected from the northwest Pacific (i.e. the native range) once again clustered together, individuals from South Africa were found in two clusters, corresponding to either the east  or west coast , and both the site within the English Channel (PLY) and the site within the Mediterranean Sea (RAV) clustered closely to the western South African cluster. However, the PCA recovered the site from New Zealand as a unique cluster (NEL), and genotypes from the Australian site (MEL) encompassed all clusters except the native range . Considering population differentiation , northwest Pacific sites were genetically similar (average FST = 0.01), but strongly differentiated from other sites (average FST = 0.13).A greater separation of clusters was identified in analysis b, and PC and EL) b. Intereclusters b. UnlikeFST were slightly negatively correlated with average shipping intensity between 1750 and 2000, though not significantly , for both species.Regarding the correlation between historical shipping and genomic differentiation, values of  (e)Preliminary analyses showed that 2000 simulated datasets per model were suitable for inferring model choice by computing error metrics from both the entire training set and a subset. Likewise, evaluations for each DIYABC-RF run showed that the number of RF trees produced for each model set was sufficient (i.e. error rates stabilized with increasing number of trees). We thus tested a comprehensive variety of models for each species .M. squamiger, DIYABC-RF was able to confidently identify a split between western and eastern Australian sites , followed by admixture between the western site AL and an eastern site MEL. This admixture originated the other western site BU ). Strong evidence of admixture between MEL and BU (figure 4a model 3) was also found. Though the final colonization to the introduced range was unresolved, a consensus of potentially suitable models included a split between SA and MED , with these two populations being a bridgehead for the BF and ATL populations, respectively.For C. robusta, the DIYABC-RF found that NWP split initially from an unsampled population, with MED (b 1) and ESA (b 2) also being sourced from unsampled populations. WSA was found to be sourced from the MED group (b 3), AUS was identified to be a result of admixture between the east and west coasts of South Africa (b 4), and NZ was recovered to be a secondary introduction from NWP (b 5). The most recent grouping, EC, was identified as sourced from MED (b). See electronic supplementary material, table S9 for posterior probabilities and error rates, and electronic supplementary material, table S10 for the mean number of votes and standard deviations per model.Regarding with MED b 1 and E and ESA b 2 also ED group b 3, AUS h Africa b 4, and from NWP b 5. The from MED b. See el. 4Our results provide evidence that putatively varying levels of anthropogenic transport do not preclude our ability to recover patterns of population structure across species ranges that have undergone complex introduction histories. Such patterns would not be expected if anthropogenic transport consistently eroded the geographical distribution of genotypes across the species' ranges, and effectively homogenized genomic divergence across the species’ ranges. Additionally, we showed that differing histories of anthropogenic transport can provide a suitable explanation for observed genomic differences between native and introduced ranges. (a)C. robusta was subject to anthropogenic transport earlier than M. squamiger, providing more opportunities to be redistributed from its original range and a greater time to differentiate from the source populations. For example, the putative native range of C. robusta (the northwest Pacific), was an important region for shipping throughout all the time periods studied, becoming a sizeable contributor to shipping from the mid-nineteenth century , it appeared in our initial time period (1750–1800) but was not present again until 1854–1900 . Footprints of founder effects, such as the reduction in genetic diversity observed in some C. robusta populations, could be explained by introductions of few individuals into the introduced range .A fundamental assumption made in interpreting our results was the close association between NIS’ introductions and shipping intensity. It would be unreasonable to assume every ship included in our dataset of shipping intensity would lead to an introduction, and our data cannot resolve the magnitude of ongoing, recurrent introductions. However, a higher intensity of ship traffic increases the likelihood of individuals being transported along a certain route and makes it therefore more likely that individuals colonize new sites ,63. Inde (b)M. squamiger, with the number of clusters ranging between two and three depending on the analysis. Additionally, the number of private alleles present within sites within the native range was approximately six times greater than those found in the introduced range . By contrast, the putative native range of C. robusta showed limited population structure. This could be due to high levels of gene flow within the native range [C. robusta can be found further east along the coast of Japan than the sampling conducted here [et al. [Species' native ranges are expected to show a clear population structure  as the ave range , high efve range  or insufted here . Despite [et al. , that is [et al. . (c)M. squamiger, possibly either due to increased propagule pressure owing to intense anthropogenic transport, or genetic admixture between native sites . The extensive introduced range of M. squamiger was highly homogeneous, both in terms of population structure and genomic diversity patterns. Global genomic homogeneity within the introduced range could be the result of the introduction of genotypes from a single-source population from the native range [C. robusta. Population structure within introduced ranges has been found in other ascidians [C. robusta was present at differing spatial scales. For example, geographically distant regions such as Europe and western South Africa were genomically homogeneous, supporting previous results found by Zhan et al. [C. robusta identify the ascidian as being present along the western coast of South Africa since the 1950s [Ne supressing the effects of genetic drift, remains unknown, though we recovered a drop in HE in western South Africa sites compared to those found in eastern South Africa . It is unclear whether the limited natural dispersal potential of ascidians, coupled with their affinity to inhabit artificial environments , has an effect on Ne. However previous work on the congener C. savignyi showed a large effective population size as inferred in San Francisco Bay [C. robusta has been present along the western coast of South Africa since at least the 1950s [C. robusta displays population structure in South Africa likely due to existing marine biogeographic provinces, demographic processes and/or independent introductions.While genetic bottlenecks are often mentioned in the literature of biological invasions , it is bve range  or high ve range  promotinscidians , and canscidians . The obsn et al. . Historihe 1950s . Whetherisco Bay . On a rehe 1950s , it is u (d)M. squamiger, showed limited confidence in the reconstruction of invasion routes, with only one scenario set having a prior error rate of less than 45% . In accordance with previous work using microsatellite and DNA sequence data [M. squamiger is native to Australia. Furthermore, we found evidence that the genomic homogeneity of the introduced range of M. squamiger resulted from a single-source introduction from an unsampled site comprising individuals from either Melbourne or from admixture between Melbourne and Bunbury sites, with subsequent stepping-stone dispersal. Such a signature of high homogeneity across the introduced range has been observed in other marine organisms. For example, genetic homogeneity has been identified within the introduced range of the invasive lionfish (Pterois volitans) with the conclusion that gene flow can quickly erode previous signals of genetic divergence [The species with the shorter history of anthropogenic transport, nce data , we founvergence .M. squamiger and the native range outside Melbourne, we could not discount the possibility of introduced alleles re-entering the native range. This is suggested by the discord between the clustering and the DIYABC-RF analyses, with the former indicating that Melbourne was the sole source. Further evidence for Melbourne being the source of all the introduced populations came from the fact that the lowest number of private alleles across all native sites were found in Melbourne. We know from historical data that Melbourne and Bunbury opened as ports from the 1850s onwards [M. squamiger individuals were found in California [M. squamiger colonized distant regions around the globe, demonstrating how rapidly anthropogenic transport can facilitate the establishment and spread of NIS.While we found evidence of population structure between introduced populations of  onwards ,84, and lifornia . This waC. robusta. While the prior error rates of the scenario sets were lower (i.e. higher confidence in model choice) than those for M. squamiger, they still ranged between 14 and 36% . This in part may be the reason why our DIYABC-RF analyses were unable to identify the source of the Mediterranean and eastern South Africa sites (both coming from an unsampled population). In turn, we were able to find evidence for multiple introductions and potential admixture  despite exhibiting chaotic propagule dispersion associated with trans-continental anthropogenic transport. Similarly, Lesieur et al. [Leptoglossus occidentalis) could still be tracked. Our genomic results showed that invasion routes of NIS with high historical anthropogenic transport can be studied with similar confidence as NIS with both shorter residence times in the introduced range, and lower levels of anthropogenic transport. We therefore conclude that although considering anthropogenic transport remains important, it does not preclude inference with genomic data, providing that sampling is of sufficient geographical breadth.Until recently, little has been known regarding the effects of anthropogenic transport on genetic patterns across species ranges, but a growing number of studies are unravelling invasion routes despite an intensification of anthropogenic transport in recent decades/centuries . For exaet al.  were ablr et al.  found thWith anthropogenic transport of species being a major factor dictating the distribution of many range-shifting species ,7,87, it