Opistognathus aurifrons. Using the power of genome-wide variation, we estimated that the maximum effective dispersal is 8.3 km. We found that MPAs exchange migrants likely via intermediate unprotected habitats through stepping stone dispersal. At scales >50 km such connectivity is decreased, particularly across the Mona Passage. The MPA network studied would be unable to maintain connectivity of these small benthic fishes if habitat in between them is extirpated. Our study highlights the power of SNPs to derive effective dispersal distance and the ability of SNPs to make inferences from single individuals. Given that overall reef fish diversity is driven by species with life histories similar to that of the yellowhead jawfish, managers face a challenge to develop strategies that allow connectivity and avoid isolation of populations and their possible extinction.The oceans are deteriorating at a fast pace. Conservation measures, such as Marine Protected Areas, are being implemented to relieve areas from local stressors and allow populations to restore to natural levels. Successful networks of MPAs operate if the space among MPAs is smaller than the dispersal capacity of the species under protection. We studied connectivity patterns across populations in a series of MPAs in the common yellowhead Jawfish,  One conservation initiative to restore marine populations is the designation of networks of Marine Protected Areas (MPAs), which in their most restrictive form are no-take. No–take MPAs are fishing-free spaces that, when properly managed can restore populations if no other stressors are present. These MPAs act in multiple ways to increase the spawning potential of overexploited species, but they also have associated direct benefits to ecosystem as a whole2. Thus, while MPAs were originally focused on the restoration of fished species, they are now used to protect biodiversity on reefs3. Networks of MPAs are conceptually based on the idea that most marine populations are ecologically connected by the dispersal of planktonic larvae4. The spatial scale of larval dispersal determine the degree of connectivity among marine populations, providing information on the ideal reserve size and the minimum spacing among reserves to achieve both self-recruitment and connectivity5. Determining the scale of this effective movement of larvae is critical to designing successful MPA networks.Tropical ecosystems have degraded in the last decades as a result of human activities. This decline is marked on coral reefs, where species commonly seen in the 1970s are rarely observed today, especially apex predators4. Reproduction in marine systems usually results in planktonic larvae that disperse from days to months in the ocean, potentially connecting populations over hundreds of kilometers. Early biophysical models of larval dispersal in the surface ocean have often found marine connectivity across populations6, and genetic inferences have provided evidence of gene flow over thousands of kilometers8. However, recent studies have challenged previous assumptions of high connectivity among marine populations over large spatial scales13.In theory, most marine systems lack obvious barriers to dispersal and MPAs should be connected and work as networks over large spatial scales16. Understanding whether populations are connected and the spatial scale at which this occurs is central to designing effective MPA networks. Estimating larval dispersal is challenging, as common mark-recapture studies do not work efficiently in populations that produce millions of planktonic larvae that quickly dilute in the sea and almost all of which die. Fortunately, genetic surveys provide a powerful indirect estimate of population connectivity13. If populations are connected, genetic variation across populations should be minor, but if genetic exchange is restricted and migrants are unable to reproduce in the new population17, genetic differentiation among populations develops18. Now, easy to gather genomic data can capture subtle spatial genetic variation, revealing the effective movement of migrants and their per generation dispersal19.Recent physical oceanographic models suggest that populations are locally maintained and connectivity among populations restricted20. Conversely, inferences based on linkage disequilibrium21 can capture more contemporary connectivity.Connectivity inferred from genetic data often reflects the product of evolutionary processes across multiple time scales in the history of a species, including those at ecological scales, which are the most useful for designing MPA networks. Fortunately, we can measure connectivity at different time scales by applying different techniques. Gene genealogies within a coalescent framework integrate migration rates over time (e. g. whole history of a population) and provide long-term estimates of migrationOpistognathus aurifrons). In theory, populations of O. aurifrons should readily connect thanks to its three-week pelagic larval duration 23. We studied genetic variation across a longitudinal gradient of seven MPAs that span the Mona Passage, a well-known biogeographic barrier between Puerto Rico and Dominican Republic24. We first inferred genetic diversity and estimated effective population size in the common reef fish, O. aurifrons. We then quantified variation across geography and estimated the per generation effective dispersal distance using in-situ fish densities. Finally, we tested for differences between contemporary and historical connectivity across these MPAs.We used genome-wide Single Nucleotide Polymorphisms (SNPs) and microsatellites to test whether existing MPAs in the northeast Caribbean are genetically connected through migration among them, and large enough to sustain marine populations of the yellowhead Jawfish  side of Puerto Rico and the Dominican Republic Fig. . On aver5 Tables  and S2 a5 Tables  and S4.Genetic variation among populations across the MPA network is partitioned by geography Fig. . GeograpFSTs were highest when comparing populations from West  and East of the Mona Passage (p < 0.01). The Mona population also showed genetic differentiation when compared to all other populations (p < 0.01), except when compared with Tres Palmas . The change in allele frequencies detected by pairwise FSTs suggests high geographic structure even at scales smaller than 100 km. Our comparison of FSTs between microsatellites and SNPs suggests that SNP data provide higher resolution  with a mean of 5,119,320 breeding individuals across the whole Caribbean , with a density of 263.4 fish per km2. We considered a microsatellite mutation rate of 5 × 10−4 per locus per year a value commonly used in the literature31 and one year per generation. Applying these density estimates to Rousset’s equation, the effective dispersal distance of the yellowhead jawfish ranges from 0.1 to 3.4 km. To avoid the biased increase in slope due to the genetic break between the Dominican Republic and Puerto Rico, we also inferred isolation-by-distance (IBD) patterns only within Puerto Rico  The yellowhead jawfish has a large amount of genetic variation with an effective population size on the order of millions of individuals. (ii) Effective migration distance per generation is less than 8.3 kilometers generating a pattern of genetic segregation by geography. (iii) Contemporary gene flow is restricted across the Mona Passage, segregating areas into two populations: those west of Mona, and all others. (iv) Contemporary genetic structure across the Mona Passage contrasts the historically high gene flow in both eastern and western directions.The benefits of creating networks of MPAs are evident when the spacing among protected areas is small enough to allow larvae of neighboring populations to achieve genetic connectivity32. Across the Mona Passage, the effective dispersal distance was substantially lower: 4 km. Below we present a perspective on the current spacing and connectivity among these already designated MPAs, the critical role of the Mona Island Reserve to preserve connectivity across the Passage and the contrasting patterns of historical and contemporary connectivity across this network of MPAs.In the broadest sense our genetic analyses suggest populations within the studied MPA network are connected in a stepping-stone fashion via intermediate unprotected reef habitats. Effective dispersal within Puerto Rico of 10 km allows only neighboring populations to exchange migrants, and such limited connectivity results in a positive correlation between geographical and genetic distances as predicted from theoryStegastes partitus (9 km), Hypoplectrus nigricans (10 km) and Hypoplectrus puella (2–14 km)34 and the anemone fish Amphiprion clarkii (4–20 km)35, but lower than in Thalassoma bifasciatum (27 km), Haemulon flavolinueatum (46 km) Chaetodon capistratus (52 km)34.Our data suggest a pattern of genetic isolation with geography, with some populations separated only by <100 km showing significant genetic differentiation. Our effective dispersal distance estimates also suggest the fish would move less than 10 km per generation, providing an upper bound for the largest spacing among, MPA networks if constant and full connectivity are necessary design criteria. Our dispersal estimates fall within previous estimates of fish dispersal such as for Dispersal estimates contrast with the mean distance within the network of MPAs sampled (90 km) and all Management Areas (37 km) in the studied area, regardless if they are enforced or not. This is 8X  the ideal space among MPAs to ensure connectivity and a high level of protection. Our study suggests that the sustainability of MPAs separated by >20 km may exist only because of the existence of intermediate unprotected habitats. Continued decline in reef health, however, may result in the disappearance of those unprotected habitats, resulting in fragmented isolated populations prone to demographic stochasticity and extinctions.Our results also highlight the power of SNP data to derive estimates from single individuals. Slopes calculated from population level data and single individuals were almost identical. Finding similar genetic patterns from fewer individuals diminishes harm to wild fauna in genetic surveys and thus preferable for conservation biology.16. Such seasonal patterns of the currents are believed to limit larval dispersal across this narrow stretch during certain times of the year14. The population differentiation in the yellowhead jawfish highlights the importance of Mona Passage as shown in genetic studies of Acropora corals36 in a damselfish and a wrasse37, goby fishes38, and also with morphometric and color pattern variations in populations of fishes40. Our detailed sampling, however, revealed the biogeographic boundary in O. aurifrons occurs between La Caleta and PNNE within the Dominican Republic.We found a subtle genetic break west from the Mona Passage, dividing populations within the Dominican Republic, with Mona Island being part of the eastern cluster with unequivocal western genetic traces in some individuals. Historically, however, gene flow has occurred between the two populations, and Mona Island is a critical link that has allowed long-term migration in both east and west directions Fig. . Our resAmphiprion mccullochi in southeastern Australia41. One caveat from this study is the use of the program Migrate-n42 on microsatellite data for contemporary gene flow. Migrate-n uses the harmonic mean to integrate variation in gene flow over evolutionary times scales and provides an estimate of the long-term migration rate43. In contrast, we calculated long-term migration using Treemix 1.1244 as a measure of historical gene flow and STRUCTURE 2.3.4 and Admixture 1.346 to estimate recent genetic structure.Our historical estimates of genetic variation in the yellowhead jawfish suggest that the current genetic break in the Dominican Republic is a recent barrier and connectivity could be restored as has happened in the past. Variation between contemporary genetic structure and historical gene flow has also been found in the whitesnout anemonefish 22, which could potentially allow the fish larvae to travel 400–500 km in a single generation15. While contemporary gene flow is of most use for conservation, our results stress the role of historical migration in shaping contemporary patterns. In the context of MPA theory, this suggests that present-day barriers to gene flow may hide the historical potential of dispersion of the species. Critically for MPA design, the PNNE and Mona Island represent mosaics of the genetic variation from the east and the west and have historically function as stepping-stones to connect those populations. Preserving Mona is a large step forward towards keeping a condensed amount of the Caribbean genetic diversity and the likelihood of connectivity between the east and the west, if oceanographic conditions allow and conditions in the existing MPAs do not degrade further.The genetic barrier around the Dominican Republic and Mona is a temporary barrier where over longer time scales the major westward current does not impede the flux of migrants going eastward. The high gene flow across evolutionary scales is not surprising because the yellowhead jawfish has a pelagic larval duration of more than two weeksOur results suggest MPAs along the Mona Passage only exchange migrants only through intermediate populations in unprotected habitats. Our findings stress the importance of creating intermediate MPAs to generate a network that ensures connectivity. Our contrasting pattern of contemporary genetic differentiation and historical gene flow highlights the importance of preserving populations, such as the Mona population, at the edges of current genetic breaks. Mona Island is a reservoir of genetic variation from both the eastern and western population and a critical link to allow pulses of gene flow over longer time scale.Species diversity is driven by small benthic taxa such as the yellowhead jawfish Fig. . Small bOpistognathus aurifrons), a common Caribbean species47. The yellowhead jawfish is a sedentary benthic fish that lives in decorated burrows on calcareous sand at depths between 3–50 m. Spawning occurs in the burrow and the male orally incubates the eggs48. Density estimates for the species are known and available in Puerto Rico26 and Panama27. Given our goal of capturing subtle genetic changes in a fish with pelagic larvae across a densely sampled geographical area, the benthic jawfish is ideal. Small egg-brooding fishes such as the yellowhead jawfish represent a large portion of the fish biodiversity on coral reefs. MPAs while originally developed to restore fished species, are now used to protect biodiversity on reefs3. All experimental procedures followed the regulations of the government of Puerto Rico. Our experimental procedure was approved by the University of Puerto Rico Animal Care and Use Committee, the Puerto Rico Department of Natural and Environmental Resources (permit number: 2011-IC-007) and Secretaría de Estado y Medio Ambiente y Recursos Naturales at Dominican Republic (permit number: 0611). In all instances we followed their guidelines and recommendations.To estimate effective dispersal among MPAs, we chose the benthic yellowhead jawfish . An internal size standard (ROX-400) was used to estimate microsatellite sizes. We analyzed chromatograms with size standards and scored fragments in Geneious R8 8.1.450. For spurious scoring peaks, we repeated the PCRs and ran them individually on the sequencer as single samples. Individuals were repeated at least five times before deeming them as failed.To genotype individuals at microsatellite loci, we used our described two-step protocol51. All loci were under Hardy-Weinberg equilibrium, except loci 1588 and 7983. Genetic differentiation and gene flow were estimated including and excluding these two loci, and results were similar. Analyses for all loci are presented.To evaluate deviations from Hardy–Weinberg equilibrium in microsatellite data, we used MICROCHECKER 2.2.352. The 54 individuals are also a subset of 95 individuals from a larger Caribbean wide study. GBS was performed at the Cornell Institute of Genomic Diversity. GBS is a well-standardized approach to collect massive amounts of loci. In brief, DNA samples are barcoded and adapter pairs ligated. Restriction enzymes are then used to fractionate the genomic DNA. An optimization procedure determined that the PstI (recognition site: CTGCAG) was the best for the yellowhead jawfish. Once fragments are generated, adapters are ligated to the end of the fragments. Samples are then pooled and size selected to exclude unreacted adapters. Libraries were then amplified using an 18-cycle PCR with long primers complementary to the barcode and common adapters52. Samples were run on a 100-bp single-end Illumina HiSeq 2000 lane.In addition, we obtained SNP data across all seven populations from a subset of 54 individuals using Genotyping-by-Sequencing (GBS)53. UNEAK retains all reads with a barcode, cut site, and no missing data in the first 64 bp after the barcode. Reads are then grouped into tags by 100% identity, tags are aligned pairwise, and any tag pairs differing by one bp are identified as potential SNPs. We identified 1,528,449 total tags after merging, with 639,274 total tag networks identified. To remove sequencing errors, and individuals and SNPs with a high amount of missing data, any alleles represented by a frequency of less than 3% were filtered out.Raw reads were processed using UNEAK, an addition to TASSEL 3.054 (12 loci) and consider only SNP per tag marker. To overcome any bias associated with having missing SNPs, we first generated a file with no missing information. This file has 90 individuals genotyped at 525 SNPs. In the second dataset we have individuals that have at least 90% of the SNPs and SNPs present in at least 70% of the samples. This last dataset has 54 individuals and 10,047 SNPs.To further remove data artifacts, we filtered out SNPs that deviated from Hardy–Weinberg equilibrium (p > 0.01) in at least five populations. We initially had over 58,756 SNPs; after removing individuals with <10% of the markers and SNPs with <5% frequency we retained 18,616 SNPs. We also excluded SNPs suspected under selection as identified by BayesScan 2.0155 and estimated heterozygosity and number of alleles per population across all 12 microsatellites and 10,047 SNPs. We also estimated genetic variation across geography by calculating pairwise FSTs among locations. We then estimated pairwise linear distance between locations in Google Earth 6.2 using the shortest nautical distance among populations. We then plotted genetic and geographical distances and estimated isolation-by-distance (IBD), assuming a linear regression in R56.We quantified genetic diversity across markers in GENODIVE 2.057 to measure differences between pairs of individuals.An added feature of SNP data is that one can sample thousands of semi-independent markers and estimate more precisely the average genome-wide genetic distance between individuals. This improved power allows estimating IBD patterns using single individuals per population. To test whether single individuals would give similar results than patterns derived from populations, we randomly selected an individual from each population and iterated the process twenty times. We used the ê distanceDe is the effective density and m is the slope derived from our analysis of IBD. To estimate De we used both a direct density estimate of fish per kilometer and one derived from genetic data by estimating effective population size.To estimate effective dispersal, we used Rosset’s (1997) approach:27, and two locations in Puerto Rico: Rincón and La Parguera26. We estimated differences in habitat areas by measuring habitat patch sizes from GIS maps58. These estimates are likely biased upwardly and represent the upper bound of the highest fish density.Field densities were compiled from studies in PanamaΘ = 4 Neμ; where Θ is genetic variation in the population; Ne is the effective population size and μ is the mutation rate). We ran IMa2 (version 8/27/12)28 on 12 microsatellites using as populations La Caleta and Puerto Rico . We run IMa2 for 3,000,000 steps after an initial burnin of 1,000,000 steps. We repeated the process three times and check for convergence on similar results. All ESS values for all runs were above 50. We estimated effective population size as the mean across runs. We used a mutation rate of 5 × 10−4 31 and one year generation time to estimate population sizes in demographic units. Once we had an estimate of the effective population size, we obtained fish density per km2 by dividing by the total coral reef area across the Caribbean 59. Our density from genetic data is biased towards the lowest possible density, as long-term effective sizes are lower compared to census size  in our dataset. In addition, we run Admixture 1.346 with cross-validation, and 1000 bootstrapping replicates for K values 1, 2, 3, 4 and 5  as the outgroup, as a larger study across the Caribbean suggested it to be less related to all other populations25. To increase our confidence in the Treemix estimates we ran the analysis with 500 bootstrap replicates and drew trees in R.To quantify historical connectivity across the Mona Passage, we used Treemix 1.1264 and Treemix66 do come with some limitations and assumptions. Among others, that the data are presumably derived from “neutrally” evolving markers and there is independence (not in significant LD) among the markers. We have excluded loci suspected to be under selection as inferred from Bayescan 2.0154 (12 outlier loci) and only consider one SNP per tag marker.Both contemporary and historical levels of migration inferred from BayesAssSupplementary Information


Carposina sasakii (Lepidoptera: Carposinidae) in China, which shows distinct biological differences when characterized from different host plants. Genetic diversity and genetic structure were assessed among populations from seven plant hosts and nine regions using 19 microsatellite loci and a mitochondrial sequence.Populations of herbivorous insects may become genetically differentiated because of local adaptation to different hosts and climates as well as historical processes, and further genetic divergence may occur following the development of reproductive isolation among populations. Here we investigate the population genetic structure of the orchard pest peach fruit moth (PFM) Strong genetic differentiation was found among geographical populations representing distinct geographical regions, but not in host-associated populations collected from the same area. Mantel tests based on microsatellite loci indicated an association between genetic differentiation and geographical distance, and to a lesser extent environmental differentiation. Approximate Bayesian Computation analyses supported the scenario that PFM likely originated from a southern area and dispersed northwards before the last glacial maximum during the Quaternary.Our analyses suggested a strong impact of geographical barriers and historical events rather than host plants on the genetic structure of the PFM; however, uncharacterized environmental factors and host plants may also play a role. Studies on adaptive shifts in this moth should take into account geographical and historical factors.The online version of this article (10.1186/s12862-017-1116-7) contains supplementary material, which is available to authorized users. Genetic variation among natural populations can develop due to a number of factors that include geographical isolation, ecological isolation and historical processes. Geographical barriers that limit dispersal and consequently lead to isolation by distance (IBD) appear to be particularly important in population divergence in diverse taxa . HoweverThe relative contribution of these factors on population differentiation can be difficult to determine . In empiHerbivorous insects represent a diverse group of species with a wide range of distributions and adaptive potential –13. PopuCarposina sasakii Matsumura (Lepidoptera: Carposinidae), is a major phytophagous orchard pest widely distributed in Northeast Asia [The peach fruit moth (PFM), ast Asia . Larvae ast Asia , and larast Asia . Phenoloast Asia  and randast Asia , PFMs onast Asia .cox1 gene, there is a correlation between genetic differentiation and geographical distance [Geographical isolation may also contribute to genetic differentiation of PFM. Based on variation in the mtDNA distance . Using 3distance . Both hoIn this study, we simultaneously characterized genetic variation of PFM from both host-associated and geographical populations across China, using microsatellite markers and mtDNA. We hypothesized that populations from different host plants would differ genetically when the influence of geographical influence was removed, and also that populations from different geographical locations would show IBD given the wide distribution range of this species. We therefore estimated the degree of genetic differentiation of PFM associated with different hosts versus geographic distance and also considered historical factors. Our study sheds light on understanding ecological and evolutionary processes that drive divergence of PFM and the possibility of host-associated reproductive isolation in this species.In total 410 PFM larvae were sampled from damaged fruits of host plants in 16 populations with permissions from the orchard owners (Table cox1 gene (507 bp) was amplified using primer pair LCO1490 and HCO2198 [For the nuclear markers, we genotyped 19 polymorphic microsatellite loci from each individual, developed with methods used in our previous study  (Table S HCO2198 . PolymerHO) and expected (HE) heterozygosity, were estimated by macros in Microsatellite Tools [Prior to population genetic analysis, microsatellites genotyped by GENEMAPPER version 4.0  were checked for stuttering, scoring error, large allele dropout and presence of null alleles by MICRO-CHECKER . Allele te Tools . Null alte Tools  with 10,te Tools  implemente Tools .S), total number of mutations (η), number of haplotypes (H), haplotype diversity (Hd), nucleotide diversity (Pi), nucleotide diversity with Jukes and Cantor correction Pi (JC), Tajima’s D and average number of nucleotide differences (K) were calculated with DnaSP version 5.0 [Sequencing results of mtDNA from both strands were assembled. Amino acid sequences were aligned by codons using CLUSTALW  implemension 5.0 .ST calculated in FREENA version 4.0 with ENA [For microsatellites loci, genetic differentiation among 14 populations of PFM was measured by pairwise Fwith ENA . For mtDwith ENA .K) ranged from 1 to 20 with 20 iterations per K value, while for mtDNA, 20 runs  were performed to ensure convergence and consistency of the results.In order to incorporate spatial information into clustering of individuals, the BAPS  model implemented in software BAPS version 6.0  was usedWe performed a Discriminant Analysis of Principal Components (DAPC) analysis using adegenet 1.4–2 implemented in R  based onST/(1-FST)) and geographic distance was undertaken using ade4 version 1.7–4 implemented in R  with 999 replicates. The values of FST were calculated in FREENA version 4.0 with ENA [In order to evaluate the effect of geographic distance on genetic differentiation of host-associated populations in Beijing region and assess the level of isolation by distance (IBD) within geographical populations, a Mantel test correlating genetic distance (Fwith ENA  for micrhttp://www.worldclim.org/) using the getData function implemented in R package RASTER. Subsequently, we extracted corresponding bioclimatic values of each location using the getData function. Three vegetation variables  were downloaded from MODIS landcover database (https://modis-land.gsfc.nasa.gov/) and then extracted with ArcGIS multiple version 10.2 . Then we extracted bioclimatic values and vegetation values of each location using cbind function in R package and ARCGISmultiple. Finally, a principal component analysis was performed to analyze the 22 environmental variables for each locality using prcomp function in R. The first two principal components were used to estimate environmental distances between locations. Environmental distances were compared with genetic distances (FST/(1-FST)) based on microsatellite or mtDNA by Mantel tests in R package ade4 version 1.7–4 with 999 replicates.To check the influence of environmental factors on population genetic differentiation, the presence of isolation by environment (IBE) was tested. Firstly, 19 bioclimatic variables were downloaded from WorldClim database  method implemented in R with 10,000 permutations was used .Carposina fernadana and Carposina hyperlopha were used as outgroups.Haplotype relationships were constructed through the software SPLITSTREE version 4.13.1 , while tThe approximate Bayesian computation (ABC) method implemented in DIYABC version 2.1.0  was follHO was similar to HE in each population. The host-associated and geographically separated populations showed similar values for genetic diversity parameters (Table S3). There was no obvious LD among the 19 microsatellite loci; no loci were significantly linked or departed from HWE across all populations, and no population departed from HWE across all loci.All microsatellite loci used in the study proved to be polymorphic. The mean number of alleles for each population was high, and the In total 35 haplotypes were observed (GenBank accession numbers: KY492475-KY492509), among which 14 haplotypes were uniquely represented by one individual, 10 shared among individuals but not across populations, and 11 shared among different populations. Tajima’s D was not significantly different from 0 in all populations (Table S4) after Holm’s correction .ST values between the geographically separated Chinese quince population and other populations were mostly higher than those between the other population . For the microsatellite data, Mantel tests indicated the presence of both IBD and IBE when considering the geographically separated populations of PFM. However, a significant correlation between ecological and geographical distance was found . The standardized regression coefficient for geographic distance onto genetic distance based on all populations  was similar to the equivalent regression coefficient for environmental distance , suggesting that IBD was stronger than IBE. For the mtDNA, there was no evidence of either IBD or IBE.Both microsatellite and mitochondrial data showed a lack of any association between host-associated population differentiation and distance in the Beijing region  with a 95% highest posterior density (HPD) of 0.43–1.83  as the most likely based on microsatellite data  came from the pair of populations collected from adjacent apple and apricot orchards , pointing to an influence of climatic vacillation during the Quaternary on PFM. This suggests that PFM may be useful for testing hypotheses about the historical effects of the Quaternary on phylogeographical patterns in China, with patterns found so far contrasting with those of well-studied areas of Europe and North America . During Grapholita molesta [We explored all possible hypotheses on the origin and dispersal of PFM based on the identified genetic groups using the ABC method. This method is suitable to test complex scenarios in population genetics , and has molesta .G. molesta in China [Molecular dating showed the divergence time of major lineages before 0.39 Ma, indicating colonization of northern China by PFM before LGM. This is congruent with the pattern for in China .Apart from IBD, habitats can contribute to genetic divergence by creating barriers to gene flow , resultiIt is unclear why there was an apparent hierarchical structure of mtDNA variation in PFM, which did not appear connected to IBD or IBE. Incongruent population structure between mitochondrial and nuclear genes has been noted in many studies , 80, andBased on microsatellite loci and mtDNA, we found strong genetic differentiation in populations of PFM, but no obvious evidence for host-associated differentiation in PFM involving its common plant hosts, even though these plants alter PFM phenology and life history. Our study suggests that the geographical isolation and historical events in the Quaternary had a strong impact on current genetic differentiation of PFM in China. These strong effects may conceal other factors such as host-associated adaption and the impact of local environmental conditions. While host-associated adaptation of PFM might be present, it is not sufficient to generate separate gene pools of PFM that might reflect incipient speciation. Our study also suggests that geographical and historical factors need to be considered in experimental designs when attempting to assess adaptive divergence in PFM. Taking advantage of genomic tools, there are opportunities to investigate these processes further by incorporating a high density of markers across the genome that might include markers linked to loci under selection , 83.


Ne) and number of breeders (Nb). STRUCTURE analysis suggested that there were 11 genetic clusters among the coastwide collections and that from the Connecticut River was distinct from those in all other rivers. This was supported by UPGMA analyses of the microsatellite data. In AMOVA analyses, among region variation was maximized, and among population within regions variation minimized when the Connecticut River collection was separate from the other two populations in the NYB DPS indicating the dissimilarity between the Connecticut River collection and the other two populations in the NYB DPS. Use of mixed stock analysis indicated that the Connecticut River juvenile collection was comprised of specimens primarily of South Atlantic and Chesapeake Bay DPS origins. The most parsimonious explanation for these results is that the Connecticut River hosted successful natural reproduction in 2013 and that its offspring were descendants of a small number of colonizers from populations south of the NYB DPS, most notably the South Atlantic DPS. Our results run contrary to the belief that re-colonizers of extirpated populations primarily originate in proximal populations.Atlantic Sturgeon is listed under the U.S. Endangered Species Act as five Distinct Population Segments (DPS). The “endangered” New York Bight (NYB) DPS is thought to only harbor two populations; one in the Hudson River and a second smaller one in the Delaware River. Historically, the Connecticut River probably supported a spawning population of Atlantic Sturgeon that was believed extirpated many decades ago. In 2014, we successfully collected pre-migratory juvenile specimens from the lower Connecticut River which were subjected to mitochondrial DNA (mtDNA) control region sequence and microsatellite analyses to determine their genetic relatedness to other populations coastwide. Haplotype and allelic frequencies differed significantly between the Connecticut River collection and all other populations coastwide. Sibship analyses of the microsatellite data indicated that the Connecticut River collection was comprised of a small number of families that were likely the offspring of a limited number of breeders. This was supported by analysis of effective population size ( Acipenser oxyrinchus is a large, long-lived, anadromous species that is widely distributed along the Atlantic coast of North America [Atlantic sturgeon  America . Spawnin America . Atlanti America , but tha America , 5.th century, overexploitation had caused most riverine fisheries and populations to collapse to less than 10% of their historic highs [th century, smaller fisheries were re-established throughout the Atlantic coast, but these also collapsed within a few years [At one time, Atlantic Sturgeon spawning runs supported large riverine fisheries in the U.S. These fisheries primarily targeted spawning adults, particularly in the Delaware River  which haic highs . In the ew years . All harew years .A perceived failure of populations to rapidly rebuild following imposition of the U.S. moratorium resulted in a federal listing of Atlantic Sturgeon under the U.S. Endangered Species Act (ESA) in 2012. Under this listing, the National Marine Fisheries Service established five Distinct Population Segments (DPS)  based onRecent studies suggest that the NYB DPS currently contains one of the most robust populations remaining within U.S. waters. It includes estuarine and marine waters from Chatham, Massachusetts in Cape Cod to the Delaware-Maryland border and is known to support two naturally spawning populations in the Hudson River and a second considerably smaller one in the Delaware River. Threats that may impede stabilization and rebuilding of populations in the NYB DPS include bycatch in coastal commercial fisheries , habitatThe unusual life history characteristics of Atlantic Sturgeon are important in framing its genetic population structure. Atlantic sturgeon is anadromous with spawning occurring in natal rivers above the fall line over gravel, rubble, rocky substrate . JuvenilMany genetic studies indicate that homing fidelity of Atlantic Sturgeon to natal rivers is strong. As a result, differentiation among spawning populations is significant based on frequencies of microsatellite DNA alleles , 27 and th century, the Connecticut River probably supported successful natural reproduction of Atlantic Sturgeon as evidenced by a limited fishery for adults centered at Cromwell, CT [The Connecticut River has been known for several decades to host aggregations of subadult (> 50 cm and < 130 cm TL) Atlantic Sturgeon , 31. In well, CT  at riverwell, CT . Since 1well, CT  believinNatural reproduction in Atlantic Sturgeon populations is typically confirmed by documenting the presence of mature adults at spawning locales at spawning time  or by thWe had two objectives in the present study; 1) to determine if natural reproduction of Atlantic sturgeon has persisted or recently resumed in the Connecticut River, and 2) if so, determine the genetic relatedness of juveniles collected there to other populations coastwide, most importantly to the two known populations in the NYB DPS. Our working hypothesis was that a small naturally reproducing population of Atlantic Sturgeon has persisted undetected in the Connecticut River and that its population would exhibit genetic characteristics distinct from all populations coastwide, but most similar to those in proximal populations in the NYB DPS. This hypothesized genetic result would provide strong evidence for the long-term persistence of a naturally reproducing population in the Connecticut River. A second alternate hypothesis was that a newly found cohort would be descendants of migrants from the Hudson River, the most proximal and largest population coastwide. A third less likely hypothesis was that a newly found cohort would be offspring of migrants from other more distant populations outside of the NYB DPS. To test these hypotheses, we characterized newly found specimens from the Connecticut River at 11 microsatellite loci and the mitochondrial DNA (mtDNA) control region and compared their allelic and haplotype frequencies to those in 10 (microsatellites) and 11 (mtDNA) other coastwide populations, respectively.The Connecticut River is the largest river in New England, flowing 660 km from its source on the Canadian-Vermont border to Saybrook, Connecticut on Long Island Sound. The river’s watershed encompasses 29,163 square kilometers in four U.S. states and its 555 cubic meter/second discharge produces 70% of the freshwater flow into Long Island Sound. Historically, the main stem Connecticut River hosted 13 dams, the most downstream of which, the Enfield Dam, was constructed in 1829 at rkm 0. Because the dam was relatively low head, it was thought to be an obstruction to anadromous fishes only during low flows. A center section was removed to facilitate fish passage in 1933 and additional major breaches were noted by 1976.A skiff trawl  was fished in the lower Connecticut River between river kilometers (rkm) 6–18 in May and June, 2014. The trawl was fished against the predominant river flow for 8–15 minutes at approximately 1.5 knots groundspeed at water depths ranging from 2.4–9.7 m. Tow duration and distance covered was a function of known bottom topography, obstructions, environmental conditions and vessel traffic.Gill nets  were fished from July through October, 2014. Nets were weighted to sample bottom waters and anchored parallel to the predominant current and were set for a maximum of 2 h around slack water. Typically, four nets were fished per outing in water depths varying from 7.6 to 15.2 m from rkm 6–18.2 piece of anal fin was removed for genetic analyses and stored in 95% ethanol. Ages of juvenile Atlantic sturgeon were determined based on modal distributions in length frequency histograms as described previously (2).All sturgeon were placed into a 350-L live well with ‘StressCoat’ and flow through water. Fish were individually placed into a water-filled examination box for measurement and tagging. Specimens were measured for total length (TL) and fork length, inter-orbital distance, mouth width, and scanned for previous tags and general health. All untagged sturgeon received a Passive Integrated Transponder (PIT) tag that was injected into the left side below the dorsal fin and a 1 cmSpecimens in reference collections from other spawning populations used for comparative purposes were either juveniles < 50 cm TL or adults > 130 cm TL because it is assumed that these two life-stages are natal to the rivers in which they are collected. Specimens > 50 cm and < 130 cm TL are considered subadults and because of their wide-ranging migratory behavior are not necessarily natal to the rivers in which they are collected. Mitochondrial DNA results for reference samples  were orio C with proteinase K . DNAs were purified by phenol-chloroform extractions, alcohol precipitated, air dried and resuspended in Tris-EDTA buffer as described in [Fin clips were washed with phosphate-buffered saline, and incubated in cetyltrimethyl ammonium bromide (C-Tab) buffer  and digeribed in . Concent5'- ACATTAAACTATTCTCTGGC- 3') and G1 (5'- GAATGATATACTGTTCTACC- 3') [A 560 bp portion of the mtDNA control region was amplified with derived Atlantic Sturgeon-specific primers S1 (ACC- 3') . The samACC- 3') , 28–30.ddH20. Amplification conditions were 940 C for 5 min followed by 40 cycles at 940 C for 45 s, 560 C for 45 s, 720 C for 60 s, followed by a final extension at 72°C for 10 min in MJ Research PTC-100TM thermal cyclers. Amplicons were purified with QIAquick PCR Purification kits .Polymerase chain reactions (PCRs) were in 50 μl volumes that contained 50 ng of template DNA, 5 μl of 10 x Roche Applied Science  reaction buffer, 0.25 μl of each dNTP (25 mM stocks) , 0.07 μl of S1 primer (0.1 μM stock), 0.05 μl of G1 primer (0.1 μM stock) , 1 unit of Taq DNA Polymerase (Roche Applied Science) and 43.9 μl of 0 C for 20 s, 500 C for 20 s, and 600 C for 240 s. Sequencing products were ethanol precipitated, re-suspended in 40 μl of Beckman Coulter CEQ Sample Loading Buffer, loaded into a Beckman Coulter CEQTM 8000 automated capillary-based DNA sequencer, run using the standard long fast read method (LFR-1), and analyzed with the Sequence Analysis Module of the CEQTM 8000 Genetic Analysis System.Purified PCR products were Dye-Terminator Cycle Sequenced as recommended in GenomeLab Methods Development kits by the manufacturer . Sequencing conditions were 30 cycles at 96Eleven microsatellite loci were scored that were previously shown to be effective in distinguishing reference collections from spawning populations of Atlantic Sturgeon , 17, 27.ddH20 to volume. Initial denaturation was at 950 C for 5 min and 55 cycles were at 950 C for 15 s, 600 C  for 15 s, 720 C for 30 s, and 720 C for 7 min.Microsatellite PCRs were in 12.5 μl volumes that contained 50 ng of template DNA, 1.25 μl of 10 x Roche Applied Science  or 10 x KlenTaq1 reaction buffer , 0.1 μl of each dNTP (25 mM stocks) , 0.5 μl of both labeled  and unlabeled primers (Integrated DNA Technologies) (1.0 μM stock), 0.05 μl (1 unit) of Taq DNA Polymerase (Roche Applied Science)  or 0.025 μl of KlenTaq (25 units/μl)  and Microsatellite genotypes were determined using the Beckman Coulter sequencer. Individual PCR reactions were multi-pooled, diluted up to 1:3 with Sample Loading Solution (Beckman Coulter), 0.5–2.0 μl of reactions were loaded onto 96 well plates along with 0.5 μl of CEQ DNA Size Standard-400 and 40 μl of Sample Loading Solution (Beckman Coulter), and run with the FRAG 1 program (Beckman Coulter).Individual specimens were assigned mtDNA haplotypes based on discrete combinations of nucleotides at polymorphic sites. Mitochondrial DNA sequence diversity within reference and the Connecticut River collections was assessed in Arlequin v. 3.5.2.2  by enumeST approach implemented in Arlequin. ΦST estimates consider both the frequencies of haplotypes in collections and the genetic distances among haplotypes. Values of ΦST were used to estimate effective number of female migrants in the equation Nemf = ((1/ΦST)-1)/2) [Population structure was evaluated using the ΦT)-1)/2) .ST analysis were further evaluated using analysis of molecular variance (AMOVA) [Hypotheses of population structure as suggested by Bonferroni-corrected Φ (AMOVA)  implemenP), number of alleles per locus (NA), rarified allelic richness (AR), allelic richness (A), expected heterozygosity (HE) and observed heterozygosity (HO) and inbreeding coefficient (FIS).Microsatellite data was initially examined using MicroChecker v/2.2.3  to identFST was implemented in FSTAT [θ estimator of Weir and Cockerham [FST may not the best measure of genetic differentiation among collections because the maximum value of FST = 1 cannot be obtained even when collections have completely non-overlapping sets of alleles [F’ST, which scales FST values based on observed allelic diversity so that F’ST always equals 1 when no alleles are shared. Both indices of genetic differentiation are useful because if both indicate a similar pattern, one can be more confident in the population structure observed [FST and F’ST comparisons were assessed through 9999 permutations.The significance of allelic differentiation among these collections and between the 2014 Connecticut River juvenile collection and Connecticut River subadults reported in Waldman et al.  was origin FSTAT  using thockerham . For hig alleles . Therefoobserved . SignifiTo evaluate the possibility that the Connecticut River juvenile collection represented a recently established cohort from a limited number of founders, we determined if it consisted of a small number of families, each with many full-sibling dyads, compared to juvenile collections from other rivers in the NYB DPS. We compared the number of full sibling dyads within the Connecticut River to the number in juvenile collections from the other two populations within the NY Bight DPS, the Hudson River (one of the larger populations coastwide) and the Delaware River  using the program COLONY v. 2.0 , 60.For the Hudson River, we determined the number of full sibling dyads in juvenile collections made in 2011, 2012, and 2014. For the Delaware River, we determined the number of full-sibling dyads for juvenile collections made in 2009 and 2011. Samples were analyzed with the assumptions of no per locus genotyping error , male and female polygamy, no inbreeding, medium run length with the full likelihood analysis method, and high likelihood without assignment of individuals as candidate males or females as these data were not available to use. Although our inference of family relationships is weakened by the absence of age, sex, and relationship information, and the assumption of polygamy for both sexes, COLONY is predicted to be more accurate than pairwise estimates of relationships . As a reK) among collections, assign individuals to individual clusters, and identify individuals of mixed ancestry. STRUCTURE determines the number of genetic clusters among collections by optimizing Hardy Weinberg equilibrium and linkage equilibrium within clusters. We used the admixture model in STRUCTURE using sampling locations as a prior with allele frequencies correlated. We used burn-in lengths of 10,000 and run lengths of 100,000 and ten replicates were done for each run of K = 1–12. The best value of K was determined from values of lnP(D) [K [Clustering among individuals within and among collections was analyzed using STRUCTURE v. 2.3.4 . STRUCTUf lnP(D)  and ΔK [nP(D) [K  using STnP(D) [K . Output nP(D) [K .The evolutionary relationship among Atlantic Sturgeon collections based on the microsatellite data was visualized through the construction of UPGMA trees in POPTREE2 . To furtNe) and effective number of breeders (Nb) of the Connecticut River 2014 juvenile cohort to those for the Hudson River and Delaware River based on 2011, 2013, 2014 juvenile collections and 2009 and 2011 juvenile collections, respectively. The Hudson River and Delaware River collections were chosen for comparison because they are also within the New York Bight DPS and because robust numbers of juveniles were available from each. We used the bias-corrected single-sample linkage disequilibrium method [Ne and Nb based on the microsatellite data.As a result of its small number of families determined with the COLONY, we compared the effective population size  and individual based assignment (IBA) testing based on the microsatellite and mtDNA data implemented in ONCOR  were useIn 2014, 64 juvenile Atlantic Sturgeon ranging from 22.5 to 71.0 cm TL were collected within the lower portion (rkm 6–18) of the Connecticut River from May through October during CT DEEP Marine Fisheries sturgeon research efforts . In totaEight of the smallest Atlantic Sturgeon ranging from 22.5–33.0 cm TL were collected with skiff trawls from May 20, 2014 to June 25, 2014. Most were collected as a single fish per tow/day except for three that were caught on June 25, 2014. Fifty-six Atlantic Sturgeon were collected in gill net sets from July through October 2014. Three specimens were collected in July, one in early August, and the remaining 52 from September 22 until October 29, 2014. Not all juvenile sturgeon collected were genetically analyzed as several fish were recaptured and tissue samples were not taken from some others.Recaptured fish served to document survival, relatively rapid growth, and assisted in determining the age of juvenile specimens. One Atlantic Sturgeon captured on June 12, 2014 increased from 27.0 cm TL to 58.5 cm TL by Oct. 6, 2014. A second recapture from first collection/tagging in September 2014 occurred in October 2014 with a similar rate of increase in length in 30 days.Sequence analysis of the mtDNA control region revealed 35 haplotypes among 1,519 specimens from 12 collections of Atlantic Sturgeon spanning the species’ coastwide distribution . The num< 1, except for the Altamaha River where it was estimated at 2.38 . Use of Microchecker found no evidence of genotyping errors due to large allele dropout or scoring of stutter peaks at any locus, however, null alleles were observed at 3 loci in 1–2 collections each. Since no single locus or population consistently departed from expectations, eliminating locus- and population-specific factors as causes for the deviations, all loci were retained for subsequent analyses.We found no consistent evidence of Hardy-Weinberg or linkage disequilibria after Bonferroni correction in any population or at any pair of loci except for in the Connecticut River collection where 8 of 11 loci exhibited Hardy-Weinberg disequilibrium and 23 of 45 locus pairs displayed linkage disequilibrium. Consistent with the mtDNA results, several measures of allelic diversity were lower in the Connecticut River collection compared to others, including number of alleles/locus (5.64) and allelic richness (4.69) . HoweverX2 = infinity, P = Highly Significant) (data not shown) except between those from the James and Savannah rivers . Similarly, both FST and F’ST analyses revealed significant differentiation between all collections coastwide  was greater than the magnitude of differentiation among all other pairwise comparisons of collections. Of particular note was the extent of allelic differentiation between the Connecticut River and the two other collections within the NY Bight DPS. F’ST and FST values between the Connecticut River and the proximal Hudson River collections were 0.497 and 0.186, respectively. Similarly, the F’ST and FST values between the Connecticut River and the Delaware River collection were even greater at 0.547 and 0.291, respectively. In summary, both the mtDNA and microsatellite data suggested a closer genetic affinity of the Connecticut River collection to others in the SA DPS than to the two other populations within the NYB DPS.Significantly different allelic frequencies were observed between the Connecticut River and all other collections coastwide. Using Exact G tests, all collections were significantly differentiated  to other cohorts of juveniles within the NYB DPS including what is thought to be the largest population coastwide in the Hudson River and one of the smallest in the Delaware River  for the Connecticut River cohort compared to 26.9  and 34.8  for the two Delaware River cohorts and 261.8 , 158.9 , and 264.9  for the three Hudson River juvenile cohorts. In fact, the Connecticut River cohort exhibited by far the smallest Ne of any population coastwide that we have characterized .Because of the extensive family structure in the Connecticut River juvenile cohort, we proceeded to compare its effective population size and number of breeders  was included in the analysis. We found 11 genetic clusters within our complete data set and these clusters usually corresponded with the collection locales of the specimens comprising them . Of partThe significant genetic distance between the Connecticut River collection and the other two populations within the NYB DPS was reflected in phylogenetic analyses using UPGMA dendrograms developed microsatellite distance data . In the AMOVA analyses of the microsatellite and mtDNA data were used to evaluate several models of population structure suggested by the various statistical analyses discussed above . The besBecause of the genetic dissimilarity between the Connecticut River collection and others in the NYB DPS and its small effective population size, we treated it as a mixed stock and quantified the likely source(s) of its potential colonizers. Mixed stock analysis implemented in ONCOR indicated that approximately 50% of Connecticut River specimens originated in the Altamaha River within in the SA DPS . AdditioNe compared to juvenile collections from other population within the NYB DPS and coastwide.Although the Connecticut River probably once hosted a spawning population of Atlantic Sturgeon, there has been no evidence of successful natural reproduction there for many decades. Our collection of a moderate number (n = 64) of specimens in 2014 of which a subset (n = 45) were juveniles indicates that successful natural reproduction occurred in the Connecticut River in 2013. Furthermore, our genetic results on a subset of the 2014 collection (n = 45) indicate that the juveniles collected there were most likely offspring of a small number of recent colonizers from spawning rivers outside of the NYB DPS, mostly from the SA, CB, and Carolinas DPS. These results are contrary to expectations that recolonizers would most likely be migrants from proximal, not distant spawning populations. The low levels of microsatellite and mtDNA diversity exhibited by the Connecticut River collection is consistent with its much smaller < 64 cm TL, even when collected in mid-fall, and based on our length frequency histogram  and which was found in 87% of Connecticut River juveniles . But, itFST analyses indicate significant mtDNA haplotype and allelic frequency differences between the 2014 Connecticut River juvenile collection and the pooled sample of its subadults collected in earlier years (data not shown). It is likely that in 2013 the Connecticut River served as a spawning river for a genetically unique adult aggregation and hosted seasonal migrants of subadults from populations in all five DPS, but mostly from the Hudson River.Further evidence of the genetic uniqueness of the Connecticut River juvenile collection is its comparison to subadult collections from the Connecticut River made between 1989 and 2011 that were reported in . Use of Because of the small number of mtDNA haplotypes in the Connecticut River collection and its reduced allelic diversity  comparedNe) and effective number of breeders Nb with those of the juvenile collections from other populations in the NYB DPS. Not unexpectedly, Ne and Nb of the Connecticut River cohort were considerably smaller than those in the three juvenile cohorts from the Hudson River, the presumed largest population coastwide. Furthermore, Ne and Nb of the Connecticut River collection were even smaller than in two juvenile collections from the Delaware River, one of the smallest populations coastwide and at one time thought to be nearly extirpated. These results provided empirical evidence that the Connecticut River juveniles spawned there in 2013 were the likely offspring of a limited number of breeders.The extensive family structure in the Connecticut River cohort prompted us to compare its effective population size  for Delaware River subadults . It is possible that their Delaware River estimates were inflated by the difficulty in accurately distinguishing between fish of Hudson River and Delaware River origins in the absence of mtDNA data.How do the estimates of termined  based onBecause of the genetic distinctiveness of the Connecticut River collection compared to the others from the NYB DPS and the likelihood that its juvenile aggregation was the offspring of a limited number of recent colonizers, we determined their likely population and DPS origin. Mixed stock analysis (MSA) and individual based assignment testing indicated that colonizers from the Altamaha River, Georgia, likely contributed the greatest percentage (50.2%) of offspring to the Connecticut River cohort, followed by the James River, Virginia (22.6%), and Albemarle Sound, North Carolina (17.4%) . Thus, tAlong its extensive U.S. distribution, Atlantic Sturgeon is managed as five DPS based on their genetic discreteness, significance to the species as a whole, and differences in features such as habitat, climate and geology of spawning rivers as reported in Federal Register , 11. BasThis is at least the second example of an Atlantic Sturgeon population at one time thought to be extirpated exhibiting recent evidence of renewed successful natural reproduction. Unlike our random sampling in the Connecticut River, a directed and systematic sampling effort in the Satilla River, Georgia, detected juveniles (young-of-the-year and yearling) and thus evidence of natural reproduction in the third and final year of a three-year effort . SimilarOur results should provide important new information and considerations for the effective federal management of Atlantic Sturgeon in two regards. First, NOAA recently proposed rules designating critical habitat for Atlantic Sturgeon in the GOM, NYB, and CB DPS in the Federal Register . While cWe report the first detection of successful natural reproduction of Atlantic Sturgeon in the Connecticut River in many decades. These were the smallest Atlantic Sturgeon collected in Connecticut waters in 28 years of sampling. Although our Connecticut River juvenile collection was genetically distinct from other collections coastwide, including the two other populations in the NYB DPS, it was surprisingly most closely genetically related to populations in geographically distant DPS. We recommend that directed sampling for juvenile Atlantic Sturgeon be conducted in the future in the Connecticut River to determine if successful spawning continues to occur, and if so, determine its genetic sources. It will also important to monitor the persistence of the 2013 cohort in the Connecticut River as they develop into subadults and to determine if additional successful spawning events reoccur.S1 Table(DOCX)Click here for additional data file.S1 File(PDF)Click here for additional data file.


The Tai–Kadai (TK) language family is thought to have originated in southern China and spread to Thailand and Laos, but it is not clear if TK languages spread by demic diffusion  or by cultural diffusion, with native Austroasiatic (AA) speakers switching to TK languages. To address this and other questions, we obtained 1234 complete mtDNA genome sequences from 51 TK and AA groups from Thailand and Laos. We find high genetic heterogeneity across the region, with 212 different haplogroups, and significant genetic differentiation among different samples from the same ethnolinguistic group. TK groups are more genetically homogeneous than AA groups, with the latter exhibiting more ancient/basal mtDNA lineages, and showing more drift effects. Modeling of demic diffusion, cultural diffusion, and admixture scenarios consistently supports the spread of TK languages by demic diffusion.The online version of this article (doi:10.1007/s00439-016-1742-y) contains supplementary material, which is available to authorized users. Thailand and Laos are regarded as the geographical heart of Mainland Southeast Asia (MSEA) Fig. . ArchaeoThere is also considerable linguistic diversity, with five language families , spoken in the area. Most people speak TK languages , while AA is the second most common language family (4.10% in Thailand and 22.70% in Laos) , analysis of molecular variance (AMOVA) and a Mantel test comparing genetic and geographic distances between populations; for the latter, we computed three types of geographic distance, i.e. great-circle distance, least cost path distance, and resistance distance. The great-circle distance matrix was generated by Geographic Distance Matrix Generator v 1.2.3  as well as correspondence analysis (CA) using haplogroup counts were constructed using STATISTICA 10.0 .Nonparametric multidimensional scaling (MDS) analysis . Contour maps are generated by Golden Software Surfer 10.0 .BEAST 1.8 was used to construct Bayesian skyline plots (BSP) by population and maximum clade credibility (MCC) trees by haplogroup, based on Bayesian Markov chain Monte Carlo (MCMC) analyses. The software jModel test 2.1.7 . A neighbor-joining (NJ) tree  was employed to choose the best-supported hypothesis about the maternal origins of the Thai and Laotian populations. Owing to the different local histories specific to each region, three different mtDNA data sets from the TK and AA as well as a priori parameters (e.g. divergence times) was used in the simulation process. As the origin time of prehistorical TK-speaking groups is unknown, we employed the existing time of the Tai in southern China of ~3 kya, similar to a previous study . The posterior probabilities for each model were calculated by the modified R scripts (http://code.google.com/p/popabc/source/browse/#svn%2Ftrunk%2Fscripts). The following summary statistics were employed: the number of haplotypes, haplotype diversity, total number of segregating sites, number of private segregating sites, Tajima’s D, and mean number of pairwise differences for each population, as well as mean number of differences between pairs of populations and pairwise Φst. The distribution of simulated data under different models with respect to the observed data was evaluated by a visual inspection of a principal component analysis (PCA) of the best 1000 (or 5000) simulations for each model, using the PCA function implemented in the R package FactoMineR . Three different sets of models were considered separately. For each model, we evaluated the proportion of cases where the true model was correctly chosen  as well as the proportion of cases where the model selection procedure assigned the highest support to one of the other two tested models , considering a posterior probability threshold of 0.5 to assign the support.h) ranges from 1.00 in the LA2  and mean number of pairwise differences (MPD) are also the lowest in the TN1 group , while the largest values are observed in the MO2 group .For the 1234 mtDNA genome sequences obtained (GenBank under accession numbers KX456435–KX457668), there are 761 distinct sequences (haplotypes) belonging to 212 haplogroups (Table S3 in Online Resource 1). Details concerning sequencing results and sequence coverage are provided in Online Resource 2. The summary statistics for the genetic diversity in each population are provided in Table S1. Haplotype diversity . However, the π values of AA groups are not significantly different from those of the TK groups .Haplotype and haplogroup diversity values as well as the number of segregating sites are significantly higher for TK than for AA groups . Thus, the genetic structure of the Thai/Laos populations is more complicated than would be predicted from either linguistics or geography.The MDS analysis Fig. a, b reveGreater genetic homogeneity among the TK populations was also reflected in the haplotype sharing analysis (Table S4 in Online Resource 1), which showed that they shared more haplotypes than the AA populations. In particular, the various KM populations shared a number of haplotypes, as did the PU populations, indicating some recent genetic exchange/ancestry among populations within the same ethnolinguistic group. The highest number of shared haplotypes is five, which are shared among the KM5–KM6 and PU2–PU4 groups. Many haplotypes in the PU are shared with almost all of the other TK populations. Among the AA populations, despite the relatively large genetic differences between the TN2 and TN3 populations, they share four haplotypes. Overall, only four populations  did not share any haplotypes with any other population.Surprisingly, we observed striking and significant genetic differences between populations classified as the same ethnolinguistically but sampled from different locations. This can be seen in the MDS analysis Fig. a, b, in The genetic relationships of 113 Asian populations , as revealed by MDS analysis, indicated, in general, population clustering by both language family and macro-geographic scale , followed by B5 (13.13%), M7 (11.02%) and B4 (6.00%) ; these haplogroups provide further evidence for genetic connections between these Mon groups and South Asia.Although many haplogroups are shared between MSEA and ISEA, there are distinct differences in the distribution of some sublineages. For example, haplogroup B4 is widespread throughout SEA; in our study, it is almost entirely restricted to TK groups Fig. . SeveralFinally, the more extensive sampling of Thai/Laos mtDNA sequences in this study has resulted in much deeper ages for some haplogroups that were poorly sampled in previous studies. For example, we estimate that haplogroups R9b and R22 both coalesce at ~39 kya Table  and ~19 Ne over time  reveal four overall trends in change in To address the role of demic vs. cultural diffusion in the origins of Thai/Lao people, we proposed and tested demographic models according to immigrant vs. indigenous hypotheses Fig. . The immAlthough these three demographic scenarios are proposed for all TK people, archaeological, linguistic and historical evidence clearly indicate the potential for differences in the local history and demography, especially for groups from northern vs. northeastern Thailand Penth . We, theIn general, the results of the ABC analyses show that in all cases, the simulated data included the observed data  and the results of the model selection are consistent among different thresholds, i.e., the different numbers of simulations retained to fit the logistic regression curve. The highest posterior probabilities in both approaches, AR (0.70–0.74) and LR 0.84–0.86), support the demic diffusion model in the northern Thai KM . The false positive rate is low in almost all of the comparisons (less than 0.05) for the selected model of the second test, and slightly higher (0.066) for the selected model of the first test (Table S5 in Online Resource 1). In sum, these results confirm the reliability of the posterior probabilities of the models.The extensive and intensive sampling of complete mtDNA genomes in 51 AA and TK groups from Thailand and Laos shows a high genetic diversification with a total of 212 haplogroups observed. The proposed autochthonous ancient lineages are B5a1d, B6a, R22, R9b and F1f; the many basal lineages detected in this study suggests that the area of present-day Thailand and Laos may have been an ancient migratory route for modern humans, in accordance with the finding that the oldest modern human remains in East Asia are from Tam Pa Ling Cave in Laos Supplementary material 2 (PDF 1752 kb)Supplementary material 3 (PDF 7072 kb)Supplementary material 4 (PDF 1093 kb)Below is the link to the electronic supplementary material.


ST analysis showed that the genetic composition of two varieties multiplied in different environments changed. At the phenotypic level, differences were observed in flowering date and leaf length. Results indicate that three years of multiplication suffice for local adaptation to occur. The spatial dynamics of genetic and phenotypic bean diversity imply that the maintenance of diversity should be considered at the scale of the network, rather than individual farms and gardens. The microevolution of bean populations within networks of gardens and farms emerges as a research perspective.Recognizing both the stakes of traditional European common bean diversity and the role farmers’ and gardeners’ networks play in maintaining this diversity, the present study examines the role that local adaptation plays for the management of common bean diversity in situ. To the purpose, four historical bean varieties and one modern control were multiplied on two organic farms for three growing seasons. The fifteen resulting populations, the initial ones and two populations of each variety obtained after the three years of multiplication, were then grown in a common garden. Twenty-two Simple Sequence Repeat (SSR) markers and 13 phenotypic traits were assessed. In total, 68.2% of tested markers were polymorphic and a total of 66 different alleles were identified. F Europe’ological . In geneological . In thisological . Neverthological . Given aological ,4,5,6,7 Recently, the demand of organic market gardeners engaged in local food systems for crop diversity is increasing ,12,13. AP. vulgaris and many SSR markers developed [Improving our knowledge about both genetic and phenotypic diversity, as well as the possible evolution of plant populations in different environments and management systems over generations, appears to be crucial in order to understand local on-farm adaptation . Since Meveloped ,19,20,21eveloped . Previoueveloped ,23 and beveloped , but alseveloped ,26. At teveloped  and wheaeveloped ,29. In collaboration with an association of small-scale organic seed companies operating in France, Belgium and Luxembourg, the present research was conducted with the objective of assessing the adaptation of historical bean varieties  to local growing environments, both at the genetic and at the phenotypic level. To the purpose, four historical bean varieties and a modern variety (used as control) were multiplied in two farm environments over three years. The fourth year, the resulting populations, as well as the original ones were grown in a common garden. Both morpho-phenological traits and genetic markers were assessed. Results confirm that most of the studied bean populations multiplied in the tested growing environments differentiate at both these levels within three years. Conclusions are drawn to support the in situ management of bean diversity in gardens and on farms.Three different populations for each one of the five studied bean varieties  were assessed by means of 22 SSR markers and 13 phenotypic traits. In particular, for each variety, the three populations were composed as follows: the initial one used to establish the multiplications ) and two populations derived from each initial population by multiplying it in two contrasting growing environments  and Luxembourg (LUX)) for three consecutive years and under organic management conditions. Hereafter, each population is designated by a code combining the abbreviation of the variety name and of the multiplication site/origin as reported in DNA was successfully extracted from 470 individuals and subsequently SSR genotyping resulted in the production of about 20,000 data points. Among the 22 tested markers AG01, BM137, BM157, BM170, BM 172 and BM201 resulted monomorphic, while BMd-43 was excluded because of problems in the interpretation of the allelic pattern. A total of 66 different alleles were identified by means of the residual 15 polymorphic SSR markers . Number of alleles per locus ranged from two  to eight  with a mean value of 4.4 alleles per polymorphic marker. flc, rdc, and rdb showed a higher mean number of alleles (from 26.0 to 31.3) when compared to cal and ses ones . The shape of mean SSR allelic richness accumulation curves showed that the highest proportion of alleles had been sampled for all populations and very few new alleles would have been discovered by increasing the number of analyzed individuals per population  to 25 (rdc) while the number of private alleles ranged from zero  to nine (rdb_BZH). Venn diagrams showing the distribution of alleles among populations are reported in Populations of the varieties pulation . NumbersMorpho-phenological characterization was effectively carried out on 540 single plants grown in the common garden experiment. Among the assessed traits, days to flowering and leaf length showed strongest evidence for local adaptation, as most varieties were concerned.flc and rdc varieties .Analysis of Molecular Variance (AMOVA) carried out on the total dataset located 76%, 20% and 4% total diversity among populations, individuals and within individuals, respectively (not reported). Results of the same analysis, carried out separately for each variety, are reported in He) was computed as a measure of genetic diversity within populations , which were used to establish the trial, ranged from 0.005 to 0.227, being the lowest for the modern control variety cal and the highest for rdc. Except for variety ses, within populations diversity showed a tendency to increase when the original seed lot was transferred to a new geographic location. By contrast, for the varieties rdb and ses diversity remained stable or decreased in the populations obtained after the three years of multiplication in the same environment from which the original seed lot had been obtained (LUX).Expected heterozygosity  was performed to provide spatial representation of the relative genetic distances among populations. In total, 77.7% of diversity is explained by the first two axes : 56.6% brdb, ses, and cal almost completely overlap, suggesting a very low differentiation among them, the LUX populations of both flc and rdc demarcate from the BZH and ORI ones, indicating a clear differentiation.Whereas the three populations of the varieties ST were also calculated. Results indicated that flc_LUX and rdc_LUX differed significantly from the respective original population (ORI) and the ones multiplied in BZH (p ≤ 0.001). In particular, among significant comparisons, a moderate differentiation was observed for flc populations while the differentiation was high among rdc populations. No significant differences were observed in any of the other pairwise comparisons  is reported in The Evanno test on STRUCURE results clearly indicated arieties . A graphses. When compared with the other populations, the presence of admixed individuals is particularly relevant in flc_LUX (15.6%) and in rdc_LUX (9.4%) while populations cal_LUX, rdb_LUX, flc_BZH and rdb_BZH showed a lower presence . The overall presence of admixed individuals in the populations obtained in LUX (6.3%) is about three fold higher than of those obtained in BZH (1.9%), suggesting that outcrossing events have been more frequent in LUX than BZH. Finally, admixed individuals were not detected in any of the original populations, according to the selected threshold [Despite conscious selection of seeds during the three multiplication cycles, which consisted of removing off-type seeds from seed lots, some admixture of the populations multiplied in BZH and LUX was observed in all tested varieties, with the only exception of hreshold .An overall effect of variety was found for all phenotypic traits assessed, reflecting that different varietal types were represented in terms of morpho-phenological and agronomical characteristics. As for the genetic level, differentiation among populations of a given variety was also observed. Considering all the assessed traits, days to flowering and leaf length showed the most relevant differentiation, i.e., among populations of most varieties.p ≤ 0.0001) and 1.6 days earlier than original populations (p ≤ 0.0001). When looking into the population effect within each variety, significant differences were found for all varieties but rdb  and rdc (p ≤ 0.0001). Overall, populations from LUX flowered 1.2 days earlier than populations from BZH ( but rdb A. Phenolp ≤ 0.0001) with the only exception of rdb. Comparisons among populations within varieties resulted in different trends as shown in For leaf length, a significant effect of population within variety was found in all the studied varieties (ses_ORI) to rather high (rdc_ORI). The control variety cal had the lowest rate of initial intra-population diversity, which is in accordance with the uniformity and stability criteria imposed for the registration of modern varieties. Regardless of initial diversity and with the exception of variety ses, diversity increased when varieties were displaced from their initial farm site. This observation is particularly relevant for rdc, as halo bacterial blight (HBB) had strongly affected this susceptible variety in LUX. In fact, despite the loss of a high number of plants of rdc in LUX due to HBB, no genetic bottleneck effect was observed. The initial diversity of the historical varieties analysed in this study ranged from low (rdb and ses were multiplied in the farm environment (LUX) which had provided the initial populations. Indeed, the two varieties had been multiplied on the participating farm in LUX for more than ten years before the experiment was set up. For both rdb and ses, diversity slightly decreased in LUX over the three years of the experiment. In our experiment, varieties In summary, according to our data diversity within populations rose when confronted with novel growing conditions in the short term, but decreased in populations multiplied in the same growing environment over more than ten years suggesting a strong role of pedo-climatic conditions on the organization and evolution of populations’ diversity. flc and rdc multiplied in the environment from which they were initially provided for the experiment. The evolution of genetic diversity within bean populations as they are transferred across growing environments within networks of gardens and farms constitutes a future research perspective. Nevertheless, the analysis of genetic structure discussed below  and from the respective populations multiplied in BZH. Both these populations were initially provided by a seed grower from Aquitaine (AQU) in the southwest of France, where weather conditions are considerably warmer and dryer than in LUX. The significant differentiation of the flc and rdc populations in LUX may thus be explained by high selection pressure on these populations in the novel growing environment. As discussed above . Indeed, practitioners recognize different lineages of a given variety by differences at the phenotypic level, rather than the genetic level.Phenotypic traits reflect the genetic differentiation of populations and concur to confirm that bean populations can adapt to local growing conditions within as little as three growing seasons. In the jargon of many farmers involved in the in situ maintenance of crop diversity in France and Belgium, a crop population adapted to a given farm environment is called a cal was provided by the breeding company with an unknown seed treatment (colorless and not declared) of which we became aware only after the experiment was established. Comparisons of this population with the cal populations multiplied in the framework of the experiment and which did not undergo any seed treatment must thus be interpreted with much care.Overall, populations multiplied in LUX had a tendency to flower earlier and develop longer leaves. Both “days to flowering” ,34,35 anPhenotypic differences among populations cannot be explained by genetics alone. Environmentally induced parental effects, or epigenetic effects, such as physical, chemical and biological seed properties can also influence plant phenotypes and can in some cases be adaptive ,40,41,42Results of this study, like that of similar studies on other crop species ,28,34,35The first conclusion concerns the commonly employed term of “in situ conservation”. This study demonstrates that both the genetic and the phenotypic properties of bean populations may be reconfigured as they circulate in a network of seed growers, which manage and use bean diversity dynamically. Thomas  has obseThe second conclusion we draw concerns the role of local adaptation in networks of seed growers managing crop diversity. Since one of the main objectives of in situ management of heterogeneous populations, like landraces and historical varieties, is to maintain diversity —be it folineages . Results from this study indicate that selection for varietal type, at least at the basis of seeds, does not prevent outcrossing events from generating and maintaining genetic variability within populations. Concomitantly, local adaptation may also be a means to maintain maximum genetic diversity of a given variety, under the condition that crop diversity management is considered at the collective scale of the farmers’ and gardeners’ network , rather than the scale of the individual farm or garden ,51. A rerefer to . In thisFinally, the dynamic character of both genetic and phenotypic bean diversity revealed by this study points to the role that moving plant populations from one location to another may play for the in situ management of crop diversity. Considering this management as a dynamic process opens new research perspectives, concerning the spatial dynamics of farmers’ and gardeners’ seed networks in particular. In the objective of assessing the short-term adaptation of bean populations to local growing environments under conditions of on-farm multiplication and maintenance of historical varieties, four historical bean varieties and a “modern” control variety were each multiplied on two organic farms with contrasting environmental conditions over three growing seasons (2012–2014). The fourth year (2015), the resulting bean populations were grown in a common garden ,59. PhenFlageolet Chevrier (flc) and Rognon de Coq (rdc). Two others were provided by a producer in Luxembourg (LUX), Roi des Belges (rdb) and St. Esprit à œil rouge (ses). The oldest (ses) was first mentioned by Vilmorin-Andrieux in 1855 [rdb) was bred in Belgium in the first half of the 20th century [Calima  was obtained from a large-scale breeding company as a modern control. Seed of this variety had been produced in East Africa.The varieties assessed in the trial are all bush beans with a determinate growth habit. Four historical varieties were selected among those produced on a regular basis by two small-scale, artisanal seed producers participating in the research project. Two varieties originating from a seed grower in the Aquitaine region in southwestern France (AQU) were included in the trial,  in 1855 . The mos century . They werdb and ses). Each year, the total plot surface per variety ranged from 8.4 to 12.0 m2, with an average of 11 m2. A description of the experimental sites (BZH and LUX), as well as the farms from which original seed lots originated (AQU and LUX), is given in Each of the five varieties was multiplied on two organic farms during three years, from 2012 to 2014 . Each yeAs our objective was to assess the dynamics and local adaptation of bean populations as they are managed by farmers’ and gardeners’ networks, conscious selection was applied to seed lots between each growing season. Seeds that were mechanically damaged, off-type or showing disease symptoms were removed. No conscious selection was applied to bean plants in the experimental plots, based on the observation that farmers involved in seed saving generally sort their seeds, whereas selection in the field is not a generalized practice.Three years of on-farm multiplication thus resulted in three populations of each variety: the original seed lot used to establish the trials (ORI), and the populations multiplied in BZH and LUX, respectively. Mid-June 2015, all populations were sown in a common garden ,59 in BZIn 2015, twelve bean plants were identified in each subplot for phenotypic observations throughout the growing season, i.e., a total of 36 plants per population. Among them, 30 plants were randomly sampled for DNA extraction in the initial populations (ORI) and 32 in the BZH and LUX populations. Leaf tissues were sampled at flowering.Genomic DNA extraction was performed using the TissueLyser II  and the DNeasy 96 plant kit (Qiagen) on a total of 470 samples according to the procedure provided by the manufacturer. DNA quality and concentration were assessed through UV-Vis spectrophotometry using NanoDrop 2000™ .In order to explore genetic diversity among the initial and the multiplied populations, 35 SSR primer pairs were preliminary tested on a subset of samples . Sixteen®, NED™ and ROX™ . In order to reduce time and costs of the molecular analyses, seven multiplex PCR were developed and optimized. Multiplex PCRs were carried out grouping primers with similar annealing temperature and diverse predicted amplicon size. The forward primer of each combination was 5′ end-labelled with one of the following fluorescent dyes: 6-FAM™, VIC2 (Sigma Aldrich), 1.5 μL of 5 mM dNTP mix (Sigma Aldrich), 0.25 μL of 5 U·μL−1 Taq DNA Polymerase (Sigma Aldrich) and 0.4 μM of each primer (forward and reverse). The PCR cycle was adapted from Raggi and co-workers [x °C for 30 s, 72 °C for 30 s, 25 cycles of 94 °C for 10 s, x − 1 °C for 15 s, 72 °C for 20 s and 20 min at 72 °C for the final extension, where x is the annealing temperature  in a total volume of 12.5 μL composed as follows: 15 ng of template, 1.25 μL of 10× Buffer , 1.2 μL of 25 mM MgCl-workers  and consperature . ® software, version 4.0 (Applied Biosystems).Amplicons were denatured using highly deionized formamide and they were subsequently separated an sized using the automatic sequencer AB3130xl Genetic Analyzer (Applied Biosystems) according to size standard GeneScan™ LIZ-500™ (Applied Biosystems). All alleles were visualized and manually scored using the GENEMAPPEROnly data obtained from consistent and reproducible SSR were considered for any analyses. Mean allelic richness was calculated up to the number of individuals present in each population using the R package ARES from Van Loon et al.  and meanST matrix was also calculated to understand both the degree of diversity and the level of evolution [A population pairwise genetic distance matrix was also calculated and subsequently Principal Coordinates Analysis (PCoA) was performed via distance matrix with data standardization using the previously mentioned software. In order to understand the partition of molecular variance, AMOVA was carried out on the total dataset and for each variety respectively, using 1000 permutations. Population pairwise Fvolution  of the svolution .K) ranging from 1 to 20. For each tested cluster ten runs were carried out and results were based on a 30,000 burn-in period and a Markov Chain Monte Carlo (MCMC) of 60,000 iterations after burn-in. The effective number of clusters (K) was subsequently assessed using the approach of Evanno et al. [With the aim of understanding the genetic structure of the material, a Bayesian approach was used to assign membership of each individual  using both STRUCTURE  and InSto et al. , implemeo et al. .q > 0.7 was considered to assign an individual to a cluster [Accordingly, a new analysis was accomplished using InStruct software  which ha cluster .During the multiplication of populations in BZH and LUX between 2012 and 2014, plants were assessed for morpho-phenological and agronomical traits, as well as scored for plant health traits. In the common garden in 2015, the flowering date was noted for each plant as the date of the first open flower. In addition, maturity was scored at 95 das with a scale from 1  to 3 . Concerning morphology, the length of the middle leaflet of the fully developed third trifoliate leaf was measured at 43 das . The lenThroughout crop development, disease symptoms  were scored on a scale from 1 (no symptom) through 3 (medium symptom expression) to 5 . At flowering and mid seed fill growth stages, overall plant vigor was scored on a scale from 1 (very little vigor) to 5 (very vigorous). Finally, after harvest, the number of pods, empty pods and seeds produced per plant were counted.flc were all but completely lost to Necrotic Black Root Syndrome—a systemic phloem necrosis [In the common garden in 2015, all populations of variety necrosis —leading p-values below the significance level (α) of 0.05 were rejected.For phenotypic traits, statistics were computed using the programming language and software environment R version 3.3.0 . In testInterval and ratio type data  were analyzed with linear mixed effects models using the R package “nlme” ,78. NestFor ordinal variables (score data), rank-based ANOVA-type statistic ,81 was cBetween August 2014 and June 2016, a total of 21 people were interviewed in the framework of a qualitative sociological study on the management of bean diversity and plant health. The thematic guide used for the interviews is presented in As a final conclusion, this study has shown the ability of common bean historical varieties to adapt, both genetically and phenotypically, to local growing conditions within as little as three growing seasons. When considering farmers’ and gardeners’ networks engaged in the management of bean diversity at the collective scale, processes of local adaptation may contribute to maintaining genetic and phenotypic diversity at the level of the metapopulation. Concomitantly, this diversity constitutes a resource upon which to meet the needs of food systems and environmental challenges in the future. In view of supporting the efforts of actors engaged with in situ crop biodiversity management and conservation, our findings are an invitation to have a closer look at the dynamics within crop diversity networks, in particular the circulation of seed lots among different growers and the effect it may have on genetic and phenotypic diversity.


Using factorial correspondence analysis, assignment tests and Bayesian clustering algorithms we arrive at an estimate of 3.7% non‐autochthonous animals (or their descendants). Some of these animals were assigned to a nearby French population and may have immigrated into Belgium naturally, but the large majority must have been introduced by humans. Our analysis pointed to the British Isles and Germany/Poland as the potential origin of many introduced deer, regions known to have been source populations for translocations in Europe and beyond. We found evidence for recreational hunters using carcasses from farmed deer to fulfill mandatory hunting quotas. Our study is the first to quantify the extent of human‐mediated introductions in a European game species at such a large scale with large and representative sample sizes.Game species like the red deer have been subjected to anthropogenic impacts for centuries. Translocations are often carried out—sometimes illegally—not only for sporting purposes, but also to increase trophy quality, reduce inbreeding, or mitigate bottlenecks after excessive persecution. Apart from the blurring of large‐scale genetic structure, translocations without adequate quarantine measure risk introducing pathogens into potentially immunologically naïve populations. It is therefore important to understand the frequency of clandestine translocations. Identification of non‐autochthonous animals and their potential origin is often difficult and, in red deer, has been hampered by the lack of large‐scale genotypic datasets for comparison. In the present study, we make use of a recently published European‐wide microsatellite dataset to detect and quantify the presence of non‐autochthonous red deer in a large population sample ( The former include, among others, species‐specific dispersal behaviors and demographic changes in the wake of climate change, for example, during the Pleistocene when populations retreated to glacial refugia and subsequently expanded again Hewitt, . The latCervus elaphus) has arguably been one of the most important game species in Europe—and as a cervid with treasured antler trophies is known to have been impacted by humans for a long time  of all individuals using the harmonic mean method , 10 independent runs of K = 1–10 were carried out with 106 Markov chain Monte Carlo (MCMC) iterations after a burn‐in period of 105 iterations, using the model with correlated allele frequencies and assuming admixture. ALPHA, the Dirichlet parameter for the degree of admixture, was allowed to vary between clusters. After deciding on the most probable number of sub‐populations based on the log‐likelihood values (and their convergence) associated with each K, we calculated each individual's percentage of membership (q) for each cluster, averaging q over 10 runs. Individuals were assigned to the STRUCTURE cluster for which they had the highest q value. Finally, we also analyzed the data using the spatially explicit genetic clustering method that is implemented in the program BAPS v.6.0 .We analyzed the population genetic structure of the whole Belgian dataset (minus outliers) using two different (Bayesian) genetic clustering algorithms. First, we analyzed the data using the program STRUCTURE v. 2.3.1  animals that were assigned with a  score of >0.85 to one (or two) European reference population(s). Furthermore, reference animals that were assigned with a score of <0.85 to a Belgian population and with a score of >0.15 to a European reference population were considered to have at least one non‐autochthonous recent ancestor. We choose the 0.85/0.15‐threshold as a compromise between correctly estimating the number of admixed individuals (efficiency) and identifying admixed individuals (accuracy). According to simulations by Vähä and Primmer  and the degree of genetic differentiation between the reference populations , we first calculated the exclusion probability of each Walloon deer for the three STRUCTURE‐defined reference populations using a leave‐one‐out approach where each individual in turn is excluded from the populations during computation. Furthermore, for each Belgian deer (that was not an outlier), we calculated the probability of belonging to each European reference population and assigned it to its most likely Belgian or European source population. The following Belgian animals were considered to be non‐autochthonous and excluded from the reference dataset for further analysis: (1) deer that could be excluded with  Primmer , the accFinally, considering the same criteria to assess non‐native status, we identified non‐autochthonous individuals among the 89 FCA‐outliers by first calculating the exclusion probability of each animal for every reference cluster (Belgium and Europe) and then assigning them to their most likely population of origin. The harvest locations of the non‐native deer were mapped using ArcGIS 10.3.3After excluding the 89 outliers identified using an FCA Fig. , the logWhile one to two loci deviated from Hardy–Weinberg equilibrium (HWE) in each STRUCTURE‐defined cluster after correcting for multiple tests, this was the case with zero to three loci in case of the BAPS‐derived clusters Table . Locus Cp < .01) from all three STRUCTURE‐defined reference populations. Of these, seven could be excluded from all 19 European reference populations  could be excluded p < .0 from allp < .01‐level . Of these, seven animals could also be excluded from all European clusters. The remaining samples were mostly assigned to the NE Germany/Eastern Poland cluster, the deer farm, and Scotland , but our results suggest that their numbers were small compared to human‐mediated introductions  for identifying non‐autochthonous animals were appropriate to produce an accurate estimate of the number of non‐native deer. We failed to detect some non‐autochthonous animals by excluding the 5%‐FCA‐outliers from the dataset. However, our results suggest that, in principle, the approach allows the identification of introduced animals in the absence of a priori suspects  Poland can be explained by a number of documented translocations from Germany into the northeast of Poland at the end of the nineteenth and early twentieth centuries  suggests that, at smaller scales than those usually addressed in phylogeographic analyses, natural structures have been blurred to a certain extent by the introduction of non‐native individuals that subsequently reproduced successfully. This is likely to hold true not just for the Belgian red deer, but also for other European populations, including those of other ungulate species.None declared.ACF, MCF, and SB designed the study. SB and MC collected samples. MCE and MCF generated the raw data in the laboratory. ACF, FEZ, and MCF analyzed the data. All authors contributed to the writing of the manuscript.https://doi.org/10.5061/dryad.145g7.Sample locations and microsatellite genotypes: DRYAD entry  Click here for additional data file.