Acipenser oxyrinchus oxyrinchus, Acipenseridae) populations are currently at severely depleted levels due to historic overfishing, habitat loss, and pollution. The importance of biologically correct stock structure for effective conservation and management efforts is well known. Recent improvements in our understanding of Atlantic sturgeon migrations, movement, and the occurrence of putative dual spawning groups leads to questions regarding the true stock structure of this endangered species. In the James River, VA specifically, captures of spawning Atlantic sturgeon and accompanying telemetry data suggest there are two discrete spawning groups of Atlantic sturgeon. The two putative spawning groups were genetically evaluated using a powerful microsatellite marker suite to determine if they are genetically distinct. Specifically, this study evaluates the genetic structure, characterizes the genetic diversity, estimates effective population size, and measures inbreeding of Atlantic sturgeon in the James River. The results indicate that fall and spring spawning James River Atlantic sturgeon groups are genetically distinct  with little admixture between the groups. The observed levels of genetic diversity and effective population sizes along with the lack of detected inbreeding all indicated that the James River has two genetically healthy populations of Atlantic sturgeon. The study also demonstrates that samples from adult Atlantic sturgeon, with proper sample selection criteria, can be informative when creating reference population databases. The presence of two genetically-distinct spawning groups of Atlantic sturgeon within the James River raises concerns about the current genetic assignment used by managers. Other nearby rivers may also have dual spawning groups that either are not accounted for or are pooled in reference databases. Our results represent the second documentation of genetically distinct dual spawning groups of Atlantic sturgeon in river systems along the U.S. Atlantic coast, suggesting that current reference population database should be updated to incorporate both new samples and our increased understanding of Atlantic sturgeon life history.Atlantic sturgeon ( Acipenser oxyrinchus oxyrinchus, Acipenseridae) is an anadromous fish that currently inhabits the east coast of North America from Labrador, CA to Florida, USA [th century most of the “inexhaustible” AS populations were decimated and most fisheries collapsed [th century when a moratorium was imposed by the Atlantic States Marine Fisheries Commission [The Atlantic sturgeon . The federal navigation channel maintained by the US Army Corps of Engineers runs from the river mouth at Hampton Roads, VA to rkm 150 and is maintained to a minimum depth of 7.6 m and a minimum width of 91.4 m. AS in the James River were exploited by Europeans since the founding of Jamestown Colony in 1607 [The James River, river mouth located at N 36.98889, W 76.30365, is the southern-most major tributary of the Chesapeake Bay, is 696 km long and drains 26,164 km in 1607 .Since October 2007, gill net sampling targeting adult AS has been conducted in the James River during the spring and fall season. Sampling protocols and tagging techniques are described in previous publications , 18, 22.From the 507 fall collections, 116 males were selected for genetic analysis. The 116  males were picked because each had a telemetry tag surgically implanted. All fish were captured close to spawning habitat and expelled sperm during capture , 22. ResIsolation methods, microsatellite loci, PCR conditions, visualization/scoring, and locus verification methods all followed those from Farrae et al. .Structure because having many closely related individuals can result in an overestimation of the number of clusters. All fish were analyzed together first with three runs using the full likelihood and pairwise-likelihood combined method (FPLS) with a polygamous breeding system, weak priors, updating allele frequencies, and no genotyping errors assumed. One run was of medium length with medium precision, one was of medium length with high precision, and one was of long length with high precision. Each spawning group was then analyzed independently with a long run length and high precision with all other parameters the same. The independent COLONY runs by spawning season were also used to estimate effective population size (Ne) for a single time point based on sibling relationships among individuals.COLONY 2.0.6.2 was used to identify full siblings prior to analysis with Structure 2.3.4 [K varied 1–5). Run parameters were set at 10,000 burn-in repetitions followed by 10,000 Markov chain Monte-Carlo repetitions, with and without location information included as a prior, and with five independent runs per K. Each season also was evaluated independently under the same conditions to identify any underlying structure. Structure Harvester [The Bayesian clustering program re 2.3.4  was usedarvester  was usedarvester .FST, a measure of genetic distance, as calculated in GenAlEx 6.502 [F’ST, a measure of genetic distance that is adjusted to the maximum value of FST for a given suite of markers [Genetic population structure within the James River  was evaluated using pairwise Ex 6.502 , 27. F’S markers , 29, wasNa), allelic size range, and heterozygosity (HE) [FIS) [Molecular diversity indices for the James River were calculated using GenAlEx, including number of alleles per locus (ity (HE) . Rarifieity (HE) ; inbreedE) [FIS)  were calE) [FIS) .LS39 was dropped from all analyses due to substantial problems with its amplification throughout the sample set. In fall-spawning AS dataset, three loci were observed to be out of Hardy-Weinberg equilibrium (HWE): LS-68, AoxD241, and Aox12; in spring-spawning AS dataset, one locus was observed to be out of HWE: AoxD170. Given the lack of consistency in deviations from HWE and the prior use of this marker set in multiple AS studies, these loci were retained. All genotypes were checked for individual recaptures; two were identified and removed from further analysis. Three other samples could not be genotyped successfully and were excluded.Locus Structure analyses.Each COLONY run produced minor variations in relatedness with 21, 22, and 18 full-sibling pairs identified in each of the three runs of the combined dataset. Individual full-sibling pairs were generally consistent between runs with the combined dataset and runs with fall and spring datasets separately. Based on the long run length and high precision , 17 full-sibling pairs were found in the fall-spawning dataset and 1 full-sibling pair was found in the spring-spawning dataset. Given the low number of siblings to the total sample size, these fish were not removed prior to the Structure and Structure Harvester (ΔK = 31.6) indicated that there were two population clusters in the James River, with distinct signatures assigned to AS from fall and spring spawning events ; pairwise F’ST = 0.181. Heterozygosity was moderate overall for both fall  and spring  spawning groups, and no inbreeding was detected during either period . Effective population size was similar for both spawning AS groups in the James River , Spring: Ne = 44 (95% CI: 26–79)).Pairwise Structure Click here for additional data file.


Senegalia senegal (L.) Britton, an economically important species distributed in the Sudano-Sahelian savannah belt of West Africa, to investigate the impact of climate change on intraspecific genetic diversity and distribution. We used ten nuclear and two plastid microsatellite markers to assess genetic variation, population structure and differentiation across thirteen sites in West Africa. We projected suitable range, and potential impact of climate change on genetic diversity using a maximum entropy approach, under four different climate change scenarios. We found higher genetic and haplotype diversity at both nuclear and plastid markers than previously reported. Genetic differentiation was strong for chloroplast and moderate for the nuclear genome. Both genomes indicated three spatially structured genetic groups. The distribution of Senegalia senegal is strongly correlated with extractable nitrogen, coarse fragments, soil organic carbon stock, precipitation of warmest and coldest quarter and mean temperature of driest quarter. We predicted 40.96 to 6.34 per cent of the current distribution to favourably support the species’ ecological requirements under future climate scenarios. Our results suggest that climate change is going to affect the population genetic structure of Senegalia senegal, and that patterns of genetic diversity are going to influence the species’ adaptive response to climate change. Our study contributes to the growing evidence predicting the loss of economically relevant plants in West Africa in the next decades due to climate change.Climate change is predicted to impact species’ genetic diversity and distribution. We used  At the same time, biodiversity is under threat by human population growth, increased land use, and COe change –3. One oe change , 5. Thise change . This ree change .The effect of environmental and climate change has been studied for a few African plant species , 8, 9. MSenegalia senegal (L.) Britton. (Fabaceae), syn. Acacia senegal (L.) Willd. [S. senegal naturally occurs either as a dominant extensive pure stand or in co-habitation with other species in a variety of vegetation types, including semi-desert grassland, Anogeissus woodland and rocky hill slopes [S. senegal has been observed to grow in areas that receive 100 to 950 mm annual rainfall, it thrives best in areas with 300 to 400 mm annual rainfall. The species can tolerate five to 11 months of drought and can survive temperatures high as 43°C, dry wind and sandstorms, but is highly sensitive to frost. The altitude ranges from 100 to 1700 metres above sea level within the African ASAL. The species is insect-pollinated, and predominantly out-crossing [S. senegal has been reported to be resilient to drought and grazing making it an important species in agroforestry systems [in situ conservation and future utility of S. senegal could be jeopardized [S. senegal have been previously investigated. In S. senegal, intraspecific genetic variation has been studied both on regional levels [In the sub-Saharan zone, one important species is ) Willd. . The spe) Willd. , 20. In l slopes . The specrossing . Seeds acrossing . The astcrossing –27. In a systems . The cur systems . In addipardized , with obpardized , ecologypardized , phylogepardized , and poppardized , 35 in Sl levels , 35 and l levels . The latl levels . HoweverSenegalia senegal (L.) Britton. in the SSZ of West Africa. We used both nuclear and maternally inherited plastid microsatellite markers. Furthermore, we used species distribution modelling to forecast the potential range loss and loss of intra-specific genetic diversity under climate change and promote strategies towards effective management and conservation of S. senegal in the West African SSZ. Specifically, we address the following questions: 1) Is the genetic diversity in the West African SSZ as low as previously stated for the region? 2) How is genetic diversity structured within the West African SSZ? 3) How would future climate impact the distribution of S. senegal in the West African SSZ? We discuss the potential impact of climate driven range changes of S. senegal in the SSZ of West Africa on extant genetic variation.This study aims to fill existing sampling gaps and identify genetic diversity and population structure of Senegalia senegal is threatened or under protection in any of the sampled locations. According to IUCN, the species has “least concern” conservation status.No specific permissions were required for sampling of leaf material. There is no previous report stating that Senegalia senegal were collected from thirteen localities along the fringes of the gum arabic belt, situated between latitude 11°N and 13°N and longitude -0.1°W and 13.2°E between 2012 and 2016. The study area is a mixture of tropical woodland, grassland savannah and semi-desert steppe that transverses the SSZ and extends eastwards from north-eastern Ghana to north-eastern Nigeria . The touchdown cycling program was used on a Mastercycler 2050 model thermal cycler . PCR products were sequenced on an ABI 3730 automated sequencer (Applied Biosystems) using LIZ 500 or LIZ 600. Allelic binning and scoring of genotypes were performed using GeneMapper v.5.0 (Applied Biosystems). Raw data matrices containing allelic information were double-checked for scoring errors. Samples with more than two missing loci were excluded from further analysis. The final data set consisted of 287 samples for nSSR and 303 samples for cpSSR analyses.Total genomic DNA was extracted from 30 mg of silica-dried leaves using a Nucleo-spin plant II kit . We used ten nuclear microsatellites (hereafter nSSR) specifically designed for e et al. , and two [et al. : ccmp5 aavr), the unbiased estimate of expected (HE) and observed (HO) heterozygosity [PRIV) was estimated using GDA v.1.0 [R), thus correcting for different sample sizes, and inbreeding coefficient (FIS). The package genepop v.1.2 [We used the program MicroChecker v.2.2.3  to estimzygosity  within pzygosity . The numDA v.1.0 . FSTAT vDA v.1.0  was usedop v.1.2  was usedop v.1.2 . Populatop v.1.2 . Individop v.1.2 . The “Evop v.1.2  was usedop v.1.2 , as implop v.1.2 , but we op v.1.2 . We testop v.1.2 .acpSSR), number of haplotypes detected in each population (Nb), effective number of haplotypes (Ne), genetic diversity (Dv), haplotype richness (Hr) and the number of private haplotypes (Prv). A parsimony network illustrating genetic relationships between haplotypes was inferred using the software PopArt [For the cpSSR data, HAPLOTYPE v.1.05  was usede PopArt , assumine PopArt . This toe PopArt .The sampled point locations (n = 316) were initially increased with 30 records from the GBIF database . The datSenegalia senegal was assessed using WorldClim’s future climate change scenarios. We selected two future climate change projections (2050 and 2070) from two global climate models  not present, low probability of occurrence in current and future predictions (<75%); 2) area loss, high probability in the current prediction and low in the future; 3) area gain, low probability of occurrence (<75%) in the current situation and high in future predictions (>75%) and 4) remain, high probability of occurrence (>75%) as previously done by [The impact of climate change on the current distribution of GISS-ER2 , 59 and GISS-ER2 , hereaftapproach  as impleapproach . Model papproach , 64. Varapproach  using th package , reducin package , resulti package . We then done by .In total, 110 alleles were scored across ten nSSR loci for the 287 genotyped individuals . None ofR) between 0 and 3.25 . A hierarchical AMOVA revealed that 18% of the variation resided among clusters at K = 2, while 5% and 77% resided among and within populations . Considering three clusters, differences among the clusters accounted for 13.5% while variation among and within populations accounted for 4% and 82.5%  respectively. By contrast, cpSSR non-hierarchical data showed 85% variation among and 15% of genetic variation residing within populations . A hierarchical AMOVA (K = 2) found 51% of the total genetic variance between populations from different clusters (PhiPT = 0.86). Among populations within clusters contributed 39% of the total genetic variance (PhiPR), and 10% of the genetic variation was obtained from within individual populations (PhiPT = 0.89). All three levels contributed significantly to the overall genetic variation as determined through the permutation analyses  were generally higher than those between populations from the same cluster  and oth = 0.81, ), Koompa = 0.85, ), and Vi = 0.73, ). Conver = 0.73, , 77.IS values were found in one population, indicating a heterozygote deficit. Such a reduction can either be explained by the presence of null alleles [IS = 0.12) to inbreeding and limited gene-flow consistent with isolation and small population size. On the other hand, heterozygote excess, which was found in six sites, could potentially be due to selection for heterozygosity (i.e. heterozygote advantage), or self-incompatibility and obligate outcrossing [IS estimates (mean = 0.06) obtained from our study is in agreement with earlier reports [S. senegal is almost exclusively outcrossed and self-incompatible. Studies on morphology and genetic variation of some tropical species showed that outcrossed genotypes also grew faster and had lower mortality than progeny from selfing, resulting in a greater heterozygosity in the populations due to the selective loss of homozygous individuals [Half of the investigated sites significantly deviated from HWE. Moderate F alleles , the Wah alleles . As nullcrossing . This fi reports , 32 suggividuals , 79.Senegalia senegal had experienced [S. senegal populations , which limits inter-population gene flow [S. senegal, as shown for several tropical species [et al. [S. senegal has been shown to be an outcrossing species [Furthermore, Wang et al.,  reportederienced , the comerienced , and itserienced . Asynchrene flow . Perenniene flow , 84, 85. species , 86 and  [et al. . The imp species .ST = 0.14 for nSSRs and FST = 0.86 for cpSSRs), indicating a much stronger differentiation of the chloroplast genome compared to the nuclear genome. Such differences are commonly observed and are to be expected due to the different types of inheritance and mode of dispersal [Senegalia senegalia and were attributed to likely intense gene flow [ST values only mirror the effects of past events, weak genetic differentiation among populations could also be observed if populations were young, shared a common ancestry and were only weakly differentiated despite lack of current gene flow. The high FST values in the chloroplast data set suggest that gene flow via seeds is negligible, thus highlighting the importance of pollen vs. seed dispersal for the connectivity of population cohesion in the species [There was a strong difference in the extent of genetic structuring revealed by the two types of markers , representing a southern and a northern group, may be the most parsimonious explanation for the structure present in the data, yet we consider it not to offer the most biologically meaningful explanation. This is because three clusters (K = 3) better reflect the structure in the data, showing groups of populations that are geographically coherent. Moreover, the three genetic clusters, as revealed by nSSRs were further fully supported by the analysis of cpDNA . Here, tistances , human-menturies . Various the SSZ . ConsequOverall, there is isolation by distance which is in line with gene-flow drift equilibrium and seed dispersal by animals because with geographical clustering, effects of environment cannot be totally excluded. Therefore, isolation-by-environment  may haveet al. [Senegalia senegal using sequences of ITS and psbA. They illustrated that West African S. senegal was represented by a single genetic group (nuclear), or two haplotypes (chloroplast), respectively. However, the data set in the study was characterized by a low resolution for intraspecific differentiation and contained a sampling gap in the SSZ of West Africa. Our study unveiled more diversity and population structure in S. senegal , with at least six and nine mutations between the two closest dominant relatives (H2 and H4). Although this study does not account for the origin of haplotypes, a colonization event probably from MDG to YUS, or somewhere farther from the east, cannot be neglected.Senegalia senegal in the SSZ, as reported by Traore et al. [S. senegal within the study area. This shows that S. senegal may be under threat due to environmental change in the study area and the species might be facing an increased risk of local extinction. However, considering the high allele diversity found in this study, populations may show a predisposition to maintain genetic diversity which may ultimately be a favorable potential for adaptability and persistence should they disperse to track a new suitable range. Consequently, the ability of S. senegal to persist in an increasingly marginal range will depend on factors such as ecological fitness, genetic make-up and dispersal ability. However, there is no certainty that the dispersal rates of plant species could keep pace with fast rates of environmental change [The MaxEnt model confirms the role of soil and climate properties in shaping the distribution of e et al. . Given tl change .Senegalia senegal in contrast to previous report that found this part of the range genetically homogenous. We suggest that the species’ colonization history might have played an important role in the distribution of genetic diversity and structuring of populations in the study area. Distribution modeling indicates that depending on the climate scenario, only 41% (RCP 2.6), 35% (RCP 4.5) or down to only 6.3 (RCP 8.5) of suitable area will be available. Consequently, a large part of the geographically structured genetic variation is threatened. In particular the genotypes distributed to the West of the study area, microsatellite cluster 1 and chloroplast haplotype 2, may face strong declines, as hardly any surviving S. senegal are predicted in this region. Our findings therefore offer insights into how to manage threats or predict responses to disturbances in relation to environmental changes. Finally, the findings of this study will provide a valuable base to reinforce the information available to conservationists and policy makers.Our study unveiled a high genetic and haplotypic variation in the West African Sudano-Sahelian range of S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 TablePopulation YUS is characterized by 2 two unique haplotypes that are not present in any other population. Private haplotypes are highlighted in grey/bold.(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 TableP ≤ 0.001). For abbreviations of populations see Most of the values estimated for both marker types were significant ((DOCX)Click here for additional data file.S1 Fig(A) Delta K and (B) Plot of mean likelihood L (K) and variance per K value from STRUCTURE on a dataset containing 287 individuals genotyped for ten nSSR.(TIF)Click here for additional data file.S2 FigArea under the curve (AUC) value of 0.958 indicates the accuracy of the model prediction.(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.


Pocillopora damicornis type β is known to present a mixed reproduction mode: through sexual reproduction, new genotypes are created, while asexual reproduction insures their propagation. In order to investigate the relative proportion of each reproduction mode in P. damicornis type β populations from Reunion Island, Indian Ocean, clonal propagation along the west coast was assessed through four sampling sites with increasing geographical distance between sites. Coral colonies were sampled either exhaustively, randomly or haphazardly within each site, and genotypic diversity was assessed using 13 microsatellite loci over a total of 510 P. damicornis type β determined a posteriori from their mtDNA haplotype (a 840 bp sequenced fragment of the Open Reading Frame). Overall, 47% of all the sampled colonies presented the same multi-locus genotype (MLG), a superclone, suggesting that asexual propagation is extremely important in Reunion Island. Within each site, numerous MLGs were shared by several colonies, suggesting local clonal propagation through fragmentation. Moreover, some of these MLGs were found to be shared among several sites located 40 km apart. While asexual reproduction by fragmentation seems unlikely over long distances, our results suggest a production of parthenogenetic larvae. Despite shared MLGs, two differentiated clusters were enclosed among populations of the west coast of Reunion Island, revealing the necessity to set up appropriate managing strategies at a local scale.The scleractinian coral  Concerpagation , 30–33, er et al  were ass 200 km; ). Conver 200 km;  found vePocillopora genus and the few studies for which P. damicornis lineages were clearly identified,the aim of our study was to investigate, in Reunion Island (Western Indian Ocean), the clonal propagation and population structure in the coral P. damicornis β. For this purpose, we used 13 microsatellite loci to identify genetically sampled colonies and we chose four study sites presenting contrasted environmental conditions  along the west coast of Reunion Island to assess the relative proportions of each reproductive modes in this species and the distance of clonal propagation at the island scale.Given the new taxonomic insights in the P. damicornis β were sampled in lagoons, at four sites located along the west coast of Reunion Island  and REU2 , separated by a shallow channel, are located approximately 2 km apart in the lagoon complex of La Saline-L’Ermitage. REU3  and REU4  are located in separated lagoons, REU3 approximately 25 km south of REU1-REU2 and REU4 approximately 40 km south of REU1-REU2 .Acropora patches separated by sand strips. As the colonies distribution was not uniform, an exhaustive sampling was set up. We sampled all colonies  found in a 12 m radius circle centered on a patch and recorded the X-Y coordinates using two tape measures placed perpendicularly to materialize the axes. A third tape measure combined to a 1 m x 1 m quadrat was used to move along both axes.In REU1, colonies were distributed along with Ntotal = 297; P. damicornis β patch was selected as origin of the plan. The same plan as for REU1 was set up for recording the coordinates.In REU2, as colonies were distributed uniformly on the substrate and easily accessible without damaging the reef, colonies were sampled following a random sampling method using four nested circles of 2, 4, 8 and 12 m radius, respectively .For REU3 and REU4, a random sampling scheme was unfortunately not applicable. Indeed, in REU3, the high coral cover combined with low depth prevented us from setting up the same kind of sampling scheme without damaging the reef. Also, in REU4, All colonies were photographed and their color reported (pink or cream). A small fragment (1–3 cm) was removed from each colony, placed into a numbered zip-lock bag, fixed in 90% ethanol and stored at room temperature.Hereafter, population refers to all the colonies sampled at one sampling site.™). Colonies were genotyped using 13 microsatellite loci [corallum macromorphology, P. damicornis β lineage identity was verified by amplifying a 840 bp fragment of the mitochondrial open reading frame (ORF) region with the FATP6.1 and the RORF primers and using the same conditions as described in Flot and Tillier [a posteriori based on their MLG at microsatellite loci (see below). Amplifications were sent for sequencing to Genoscreen  and edited with Geneious R7 [From the 587 sampled colonies, DNA was extracted using DNeasy Blood & Tissue kit . Finall Tillier , for 108eious R7 .PSEX (FIS), which considers possible departure from Hardy-Weinberg equilibrium in order to obtain a less biased estimator [i.e., clones genetically close, sensu [The occurrence of identical MLGs among the sampled colonies was assessed using GenClone V2.0 . The prostimator . Coloniestimator  or scoristimator  based one, sensu ).R was estimated [G, estimated as GO/GE, where GO is the observed genotypic diversity as described by [GE is the expected genotypic diversity as described in Baums et al [G reflects the reproduction mode, varying from 1 in sexual populations to 0 in clonal ones. The average number of alleles per locus  was estimated with the truncated dataset  with FSTAT V2.9.3 [PID, [For each population, the clonal richness stimated , as wellribed by  and GE ims et al . G refleT V2.9.3 . Moreove.3 [PID, ) with GI.3 [PID, .β of the Pareto distribution and the Simpson’s evenness (ED*) were estimated with GenClone V2.0 [Ac) and edge effect (Ee) were estimated and their significance tested with 103 permutations in GenClone V2.0 [I) was further estimated with SPAGeDi V1.5 [With the aim to assess clonal heterogeneity and evenness of all our populations, the parameter one V2.0  within eone V2.0 . Moran’seDi V1.5  to assesFIS) were estimated using GENETIX software V4.05.2 [4 permutations.The genotypic disequilibrium among pairs of loci was assessed with FSTAT V2.9.3 , on the  V4.05.2  with 104FST [Dest [i.e., keeping all individuals) and on the truncated one. Indeed, as some genotypes might present a higher fitness than others, keeping all the colonies sharing these genotypes might bias population differentiation estimation, as well as keeping only one representative per genotype. Thus, fitness of each genotype is taken into account when all repetitions of each MLG are kept while the truncated dataset considers all fitnesses equally (as in [Population differentiation indices FST  and DestST [Dest  were esty (as in ). For bo6 chain length after a burn-in of 105 and 10 chains were run for each K varying from 1 to 10. The analysis was performed both on the entire and the truncated datasets for comparisons. The optimal K was chosen based on lower values of the Deviance Information Criterion  within our dataset, a Bayesian clustering analysis was performed using InStruct , which con  and theon  that two colonies sharing the same MLG were identical by chance, so that shared MLGs were considered to be the result of asexual reproduction. The use of these 13 loci therefore allows uncovering the entire genotypic diversity of the dataset.All 13 loci used were polymorphic in each population except locus PV7 in REU2. The allelic richness ranged from 2.46 for Pd04 to 6.07 for Poc40 . The comP. damicornis type β, P. damicornis type α, P. damicornis type σ, P. damicornis type ε, P. verrucosa, P. meandrina and P. eydouxi  and P. verrucosa (one from REU1 and two from REU4) and were removed from the dataset. Finally, for further analyses, were kept 510 P. damicornis β colonies , corresponding to 99 different MLGs.Over the 519 genotypes without missing data, 108 distinct MLGs were detected. For one representative of each MLG, the mitochondrial ORF was sequenced  and compared to GenBank sequences identified as X985613; , 23. OvePSEX(FIS) < 1.24×10−5), indicating that colonies with identical MLG belonged to the same clone. Moreover, the first gap in the distribution of pairwise allelic differences among the 99 MLGs was found between 0 and two mutation steps. Thus, to compensate for genotyping errors and possible somatic mutations, we chose a threshold of one mutation step to differentiate MLLs, as found in other studies . Since Over the 99 MLGs, 27 occurred more than once, being shared by several colonies within sites, as well as between sites for seven of these MLGs . InteresG was low for all populations and ranged from 0.006 in REU2 to 0.264 in REU4. Concerning Simpson’s evenness index and the parameter β, the lowest values were found for REU2 , suggesting that colonies belonging to the same clone were spatially closer than colonies belonging to different clones. However, spatial autocorrelation analysis did not show a significantly higher relatedness among geographically close MLGs as compared to distant ones . Also, no significant edge effect was detected in any site , suggesting that new MLGs were not especially detected on the edge of the sampling zone  were either significantly negative for two populations REU1 and REU2  or significantly positive for REU4 , or not significantly different from zero for REU3. When estimations were performed on the truncated dataset , FIS values increased and became significantly positive except for REU2 . Then, all loci were interpreted as independent loci. Two loci (Pd2-001 and Poc40) evidenced null alleles in all populations; they were kept for analysis of clonal diversity as they allowed discriminating different MLGs, but removed for the following population structure analyses. S values  estimateFST estimates ranging from 0.016 (P < 10−3) between REU1 and REU2, to 0.428 (P < 10−3) between REU2 and REU4  between REU1 and REU2, to 0.376 (P < 10−3) between REU2 and REU4  was even found in all sites, suggesting that long-distance dispersal (40 km) of some clones is not uncommon. Despite, this long dispersal, a small scale genetic differentiation was revealed between Northern and Southern populations.Our results revealed a high proportion of repeated MLGs per reef, on all studied sites, suggesting high rate of asexual reproduction in The use of a random sampling scheme is theoretically the most pertinent way to assess clonality  as everyP. damicornis sensu lato on a patch reef in Kane’ohe Bay, O’ahu (Hawaii) [P. damicornis type β on the Great Barrier Reef, found that clonal larvae only represented 7% of the brooded larvae. Moreover, considering that P. damicornis β presents a rather thin morphology combined with a breakable skeleton, the high dominance of one genotype in this northernmost lagoon is likely the result of fragmentation, mainly induced by the exposition to waves and touristic activities (confirmed by the significantly positive aggregation coefficient in REU1 and REU2 sites). Also, no significant edge effect was detected in any site , suggesting that new MLGs were not especially detected on the edge of the sampling zone  and several less represented ones, with 71% of our sampled colonies presenting one of the seven most abundant MLGs (out of 99) . Such result of over-representation of few clones has already been observed in (Hawaii) , where 7(Hawaii) , 31. Nev(Hawaii) , studyining zone . Indeed,Acropora palmata in a Caribbean reef [P. damicornis sensu lato in Hawaii [Pocillopora type 1 in the Tropical Eastern Pacific [A. palmata [e.g., warming, pollution or pathogen emergence), the over-represented genotype might not be longer well-adapted to the new conditions and the population could collapse, while a population presenting a higher genotypic diversity is more likely to resist such a perturbation, as it presents a higher probability that one or some of its genotypes could cope [The expansion of one superclone might result from a combined effect of asexual propagation with high fitness related to particular environmental conditions , 70. Sucean reef  or for Pn Hawaii  and for  Pacific : in thes palmata . Moreoveuld cope .Pocillopora morphotypes explains also the misidentification with P. verrucosa and P. eydouxi that occurred during sampling.The higher genotypic diversity and clonal richness found in REU4, the southernmost site, may be due to its exposition to stronger swells and waves during the southern winter, contrasting with REU1 and REU2 sites which are located in less exposed reefs with weaker hydrodynamics . Indeed,i.e., PID) is less than 10−4 . To date, two studies found a similar result, with clones shared among populations 40 km apart  and 200 richness ), it is e bottom .P. damicornis sensu lato [An alternative hypothesis is that distant clones could be the result of parthenogenetic larvae , 31 whicnsu lato , 21, witnsu lato . Anyhow,FST = 0.209), all colonies included in this analysis shared the exact same mtDNA ORF haplotype, assigned to P. damicornis β. Pairwise FST estimates involving comparisons between northern and southern populations were consequently high, whatever the dataset used. Such estimates are in the order of magnitude of what could be expected between different species using these markers, like for example between two sympatric Pocillopora types, based on seven microsatellite loci , each composed of several genetically close MLGs . We acknowledge that keeping all individuals (entire dataset) for the genetic structuring analysis may introduce a bias, but no more than keeping one representative per MLG (truncated dataset). Nonetheless, our analyses using both datasets gave exactly the same individual assignment suggesting that the pattern we observed here is robust. Despite the large genetic divergence among the two clusters . Other s , or in  > 0.22; ), but inose MLGs .P. damicornis β ([Pocillopora type 5 therein). Additionally, at very small scale (approximately 1 km), our results are concordant with another study conducted in Hawaii [Altogether, our results showed on one side, the detection of shared clones among distant sites indicates that larvae dispersal among reefs in not uncommon, while on the other side, the significant genetic structure among the four sites suggests that gene flows appeared limited along the west coast of Reunion Island. Our result contrasts with the genetic structure to be expected from a species with a larval survivorship estimated to reach 100 days , 78. Indornis β  or between two consecutive moon cycles (28 days). Spawning events are determined by a combination of environmental conditions, including sea temperature or accumulated degree days, an empirical measure of heat requirements for growth and development, largely used in agronomy for crops or insect pests, or in aquaculture for egg incubation or gamete maturation , we found a cumulative difference of 150.1°C between both sites  is obviously needed.In Reunion Island, although the spawning period of Favites; ), asynchmussels; ). In ordth sites . AssuminP. damicornis β, here we evidenced its clonal propagation for the first time in Reunion Island, South Western Indian Ocean. Indeed, around this island, asexual propagation plays a predominant role in populations of P. damicornis β, reproducing asexually by fragmentation and likely through the production of parthenogenetic larvae for long-distance dispersal (40 km). Thus, in case of high environmental and anthropogenic disturbances or global environmental changes, P. damicornis β populations from Reunion Island exhibiting low genotypic richness, such as those of La Saline-L’Ermitage lagoon, might be extremely vulnerable, implying dramatic consequences for the reef structure functioning.While clonality has been documented for S1 AppendixÂ, the allelic richness, was estimated as the average number of alleles per locus on the basis of the smallest sample size .(PDF)Click here for additional data file.S2 Appendix(PDF)Click here for additional data file.S3 AppendixIn each case, an arrow indicates the most likely number of clusters.(PDF)Click here for additional data file.S4 Appendix(PDF)Click here for additional data file.


Analysis of molecular variance (AMOVA), principal component analysis (PCA), multidimensional scaling plots (MDS) and phylogenetic analysis consistently demonstrated that the Southwest Han population had a close genetic relationship with the geographically close population (Hunan Han) and kept a distant genetic relationship with some ethnic groups, most prominently for Gansu Yugu and Fujian She. Furthermore, no significant genetic distinction between the Northern Han and Southern Han was observed. Aforementioned results suggested that these 21 STRs are highly polymorphic and informative, which are suitable for human identification and population genetics.Short tandem repeats (STRs), with high polymorphism and complex evolution information, play a significant role in genetic association studies like population genetics, molecular anthropology and human forensics. However, human genetic diversity has only been partially sampled and available for Southwest Chinese Han population, as well as the genetic architecture of this population remains uncharacterized. In this work, 368 unrelated Han individuals from Sichuan province were firstly genotyped with 21 non-CODIS autosomal STRs, and phylogenetic relationships along administrative (Han Chinese from different regions) and ethnic divisions (minority ethnic groups) were subsequently investigated. The CMP and CPE were 6.2796 × 10 In the past decades, DNA profiling based on a set of core STRs was employed in national DNA database construction, forensic personal identification and parentage testing in the forensic community6. 13 autosome STRs were initially chosen to constitute the Combined DNA Index System (CODIS) by the Federal Bureau of Investigation Laboratory2, while a parallel process occurred in European where the European Standard Set (ESS)7 based on 12 autosome STRs. In order to increase the power of discrimination (PD), to minimize adventitious matches and to increase international compatibility, additional 7 and 4 STRs were respectively added to the CODIS and ESS recently8. China, the world’s most populous country, has the largest national law enforcement DNA database and mainly STR profiles were generated based on the CODIS core loci12. Currently, serval commercial PCR amplification kits on basis of CODIS loci, such as the Huaxia Platinum System , AGCU EX22 (AGCU ScienTech Incorporation), PowerPlex Fusion Systems  and others19, are widely used in China. However, as the volume of data under management seems to be increasing even faster, more novel STR loci with high genetic polymorphisms are required to minimize the incidence of adventitious matches.Short tandem repeats (STRs), scattered throughout the genome with high heterozygosity and evolutionary information, are attractive to genetic applications like human forensics, anthropological and population genetics studies20. This system is a 22-locus, five-dye, multiplex that allows co-amplification and fluorescent detection of the 21 non-CODIS STRs  as well as Amelogenin for gender determination. In addition to significantly improving forensic efficiency, these 21 STRs hold the potential to investigate the recent evolutionary events and population substructure within China45.The AGCU 21 + 1 System (AGCU ScienTech Incorporation) was specifically developed for forensic application in Chinese populations46. Sichuan province, the provincial capital Chengdu, is located in southwest China, occupying most of the Sichuan Basin between the Himalayas on the west, the Daba Mountains in the north, and the Yungui Plateau to the east. Sichuan is the fifth largest Chinese province, with population accounting for 6% of the total population. The majority of the province’s population is Han Chinese, who are found scattered throughout the administrative region with the exception of the far western areas. In this study, we firstly obtained the genetic polymorphism data of 21 non-CODIS STRs of 368 Han Chinese individuals to provide forensic reference databases in the Southwest Han population. Subsequently, genetic population data of these 21 STRs  were collected from public databases and reported literature45 to investigate Chinese population substructure using analysis of molecular variance (AMOVA), principal component analysis (PCA), multidimensional scaling plots (MDS) and phylogenetic analysis.Recently, human genetic diversity has only been partially sampled and was available for 56 Chinese officially recognized populations. To the best of our knowledge, the information of genetic architecture and population genetic relationship of Sichuan Han population (Southwestern Chinese Han) remains limited45 were integrated to explore the genetic diversity and the population substructures. Finally, a total of 26 populations consisted of 9444 individuals were included in population relationship comparisons  were firstly obtained. Subsequently, all previously reported 21 non-CODIS STRs data in Chinese populationso), expected heterozygosity (He), power of discrimination (PD), power of exclusion (PE), polymorphism information content (PIC) and typical paternity index (TPI) for each locus are shown in Table A total of 368 unrelated Chinese Han individuals residing in Sichuan Province were genotyped using a multiplex assay amplifying 21 non-CODIS autosomal STR loci (AGCU 21 + 1 System). Forensic parameters including observed heterozygosity (Ho and He ranged from 0.5842 to 0.8179 (0.7266 ± 0.0571), 0.6095 to 0.8082 (0.7247 ± 0.0449), respectively. The PIC and TPI spanned from 0.5516 (D1S1627) to 0.7825 (D19S433), 1.2026 (D1S1627) to 2.7463 (D19S433), respectively. The locus with the largest PD was D19S433 (0.9327), and the combined match probability (CMP) value was 6.2796 × 10−20. The highest and lowest PE loci were D19S433 (0.6327) and D1S1627 (0.2724), respectively, and the combined power of exclusion (CPE) value was 0.9999999. Aforementioned results suggested that the 21 non-CODIS STRs are polymorphic and informative in Sichuan Han population. This study provided the first batch of genetic diversity information of 21 non-CODIS STRs in Southwest Chinese Han population and enriched the Chinese non-CODIS STRs reference databases.A total of 177 alleles were observed with the corresponding allelic frequencies varied from 0.0014 to 0.5476. D2S441 was detected with the 13 alleles at the maximum, while D1GATA113 was only detected with 5 alleles in Sichuan Han population , one Xinjiang Kazakh (n = 114), one Hainan Li (n = 504), one Inner Mongolia Mongolian (n = 523), one Inner Mongolia Russian (n = 114), one Qinghai Salar (n = 120), one Fujian She (n = 154), one Lhasa Tibetan (n = 104), one Hubei Tujia (n = 107), one Xinjiang Uyghur (n = 502), one Xinjiang Xibe (n = 226), one Yunnan Yi (n = 110), one Gansu Yugu (n = 180) and one Yunnan Bai (n = 106) were included in the population comparison study , Nei’s genetic distance and Reynolds genetic distance) were performed to explore the genetic differentiation. The DCE genetic distances between the Sichuan Han and other 25 Chinese populations are listed in Supplementary Table CE = 0.0090), followed by Gansu Yugu (DCE = 0.0079), Yunnan Yi (DCE = 0.0078), whereas Hunan Han, Shandong Han and Henan Han 2 are the closest with our investigated group (DCE = 0.0011). The Multidimensional Scaling plots and a neighboring joining tree on the basis of the DCE pairwise genetic distance matrix were used to infer evolutionary relationships. As shown in Fig. Three distinct genetic distances  were employed to reveal the genetic affinity of 26 Chinese populations. The pairwise Nei’s genetic distances are listed in Supplementary Table 47 and Y-STRs49. Interestingly, one Uyghur population residing in Aksu kept a genetic affinity with Hunan Han, Hainan Li and Guangdong Han, which may be caused by gene flow in human large-scale migration. In order to further understand population substructure and differentiation within China, more genetic markers, such as ancestry informative SNPs, microhaplotypes and InDels should be used and analyzed in the future genetic researches.In this study, substantial allele frequency distribution difference between Han Chinese populations and ethnic groups was observed, most significantly in She, Yi, Yugu, Tibetan and Kazakh. The results of the MDS and phylogenetic analysis based on three kinds of genetic distances consistently revealed that Yi, She, Salar and Russian had relatively close genetic relationship with each other and may be having a common ancestor. In addition, Tibetan, Kazakh, Yugu, Xibe, Mongolian were clustered in one clade in the three Neighboring-joining trees and had genetic affinity. These aforementioned populations mainly residing in the west of China and showed a geographical affinity and may be descendants from one ancestor. The population genetic stratification was consistent with our previous studies based on X-STRsIn this paper, genetic polymorphism data of 368 Han individuals residing in Southwest China were firstly obtained. Results of forensic characteristics indicated that all 21 non-CODIS STRs are highly polymorphic and informative in the Sichuan Han population and can be used as a powerful tool for human identification and forensics. Phylogenetic analysis demonstrated that the Sichuan Han population had a close genetic relationship with the geographically close population (Hunan Han), and significant genetic differences existed between Han populations and ethnic groups . Furthermore, no significant genetic distinctions between Southern Han and Northern Han but the genetic affinity within North–South gradient changes were observed.Human blood samples were collected upon approval of the Ethics Committee at the Institute of Forensic Medicine, Sichuan University. Informed consent was obtained from all participants. All the experimental procedures and the methods for each procedure were carried out in accordance with the approved guidelines of Institute of Forensic Medicine, Sichuan University. This study was approved by the Ethics Committee of Sichuan University.368 peripheral blood samples were collected from unrelated healthy Han Chinese individuals  recruited from Sichuan Province. All participants were required to have ancestors who had resided in Sichuan Province at least three generations. Human genomic DNA was extracted using PureLink Genomic DNA Mini Kit (Thermo Fisher Scientific) according to the manufacturer’s instructions. The quantity of the DNA template was determined using Quantifiler Human DNA Quantification Kit on a 7500 Real-time PCR System (Thermo Fisher Scientific). Samples were then normalized to 1.0 ng/μL and stored at −20 °C until amplification.PCR amplification was performed with 30 PCR cycles following the manufacturer’s protocol on a ProFlex 96-well PCR System (Thermo Fisher Scientific). The PCR system was a 25 μL reaction volume containing 10 μL of Reaction Mix, 5 μL of Primers 21 + 1, 0.75 μL C-Taq and 1 ng of template DNA. The thermal cycling conditions consisted of an initial step at 95 °C for 2 min; followed by 30 cycles of 94 °C for 30 s, 60 °C for 60 s, and 65 °C for 90 s; and a final extension at 60 °C for 60 min.Amplification products were separated and detected on the Applied Biosystems 3130 Genetic Analyzers using POP-4 polymer and 36 cm capillary array. One microlitre of PCR products or allelic ladder was added to a mixture containing 9.5 μL of deionized Hi-Di formamide and 0.5 of μL AGCU Marker Size-500 (AGCU). The mixture was injected at 1.2 kV for 16 s and electrophoresed at 13 kV for 1550 s with a run temperature at 60 °C. Initial fragment sizing and allele calling were performed using GenoMapper ID v3.20 software with the peak amplitude threshold set at 100 RFUs for all colors.50. Allelic frequencies and forensic parameters, including the polymorphism information content (PIC), power of discrimination (PD), power of exclusion (PE) were calculated using PowerStat V12 spreadsheet (Promega)51.In order to evaluate the forensic efficiency of this non-CODIS panel for application in Southwest Han, genotype data of 368 unrelated individuals were analyzed. The observed heterozygosity (Ho), expected heterozygosity (He), the exact test of Hardy-Weinberg equilibrium (HWE) and linkage disequilibrium (LD) were estimated and performed using Arlequin 3.5.2.245. The geographic position and detailed information about these 26 Chinese populations are presented in Supplementary Table 52, Cavalli-Sforza chord distance53 and Reynolds genetic distance54) between pairwise populations were estimated in the Phylip version 3.695 (http://evolution.genetics.washington.edu/phylip.html) based on allelic frequency distribution. Three genetic distance matrixes were visualized as multidimensional scaling plots in the SPSS software. Principal component analysis (PCA) based on allele frequency distribution was also carried out in the SPSS. Phylogenetic analysis among the aforementioned Chinese populations was conducted in the Mega 6.055 according to the genetic distance matrixes. Three Neighboring-Joining trees were constructed based on different genetic distances.Additionally, genetic similarities and differences between the Sichuan Han population (Southwest China) and other Chinese populations were analyzed. A total of 25 previously investigated Chinese populations containing 9076 individuals were selected56.Control DNA 9947 A (AGCU) and ddH2O were used as positive and negative controls respectively for each batch of genotyping. All experiments were conducted at the Forensic Genetics Laboratory of Institute of Forensic Medicine, Sichuan University, which is an accredited laboratory (ISO 17025), and has been accredited by the China National Accreditation Service for Conformity Assessment (CNAS). We strictly followed the recommendations of Chinese National Standards and Scientific Working Group on DNA Analysis Methods (SWGDAM)Supplementary Figure S1 and Supplementary Tables S1-S9


Despite that genetic diversity did not differ between invasive and non‐invasive species, we observed that populations of non‐invasive species showed a higher degree of genetic differentiation. Furthermore, we found that both geographic and evolutionary distances might explain genetic differentiation in both non‐native and native ranges. This suggests that the lack of population genetic structure may facilitate the distribution of mutations that despite arising in the native range may be beneficial in invasive ranges. The fact that evolutionary distances explained genetic differentiation more often than geographic distances points toward that deep lineage divergence holds an important role in the distribution of neutral genetic diversity.Biological invasions are worldwide phenomena that have reached alarming levels among aquatic species. There are key challenges to understand the factors behind invasion propensity of non‐native populations in invasion biology. Interestingly, interpretations cannot be expanded to higher taxonomic levels due to the fact that in the same genus, there are species that are notorious invaders and those that never spread outside their native range. Such variation in invasion propensity offers the possibility to explore, at fine‐scale taxonomic level, the existence of specific characteristics that might predict the variability in invasion success. In this work, we explored this possibility from a molecular perspective. The objective was to provide a better understanding of the genetic diversity distribution in the native range of species that exhibit contrasting invasive propensities. For this purpose, we used a total of 784 sequences of the cytochrome  More specifically, we extend comparisons to species of the same genus. We focused on seven gammarid species chosen based on their variable invasive propensities: FST—will be higher among native populations of noninvasive species. Furthermore, due to the recurrent identification of deep evolutionary lineages within this genus  following manufacturer's instruction. A fragment of the mtDNA‐COI was amplified using a few different pairs of primers: LCO1490 and HCO2198 , 100 mmol/L dNTPs, 10 mmol/L of each primer, 1– 10 ng of genomic DNA, and 1 U of Taq DNA polymerase . The amplification protocol consisted of 5‐min denaturation at 94°C, followed by 33 cycles of denaturation at 94°C for 35 s, annealing at 47°C for 45 s, extension at 69° for 45 s, and a final extension step of 69°C for 10 min. PCR products were prepared for sequencing using a BigDye Terminator v3.1 cycle sequencing kit , purified with a BigDye XTerminator Purification Kit , and sequenced on an automated ABI 3130XL capillary sequencer. In order to complement our field samples,we retrieved available mtDNA‐COI sequences for our six species and one additional  from NCBI.Specimens of five species were collected in their native areas, whereas those of ns Table . We ampl, et al. , Kelly, , et al. . PCR amp2.2The sequences of all species were treated in parallel. Downstream analyses were performed independently for each species. Alignments were performed in Muscle v3.8.31 with default conditions Edgar, . Sequenc2.3nHap), number of segregation sites (S), haplotype diversity (Hd), and nucleotide diversity (π) were calculated for each sampling location in DnaSP v5  versus those of non‐invasive species . Nucleotide substitution model was estimated independently for each species through maximum‐likelihood method by allowing a strong branch swapping. Best‐fit model was chosen according to Bayesian inference criteria for downstream analyses. Phylogenetic relationships were investigated with the Neighbor‐joining method  are statistical estimates that aim at calculating the divergence between DNA lineages  and non‐invasive species  differed in the degree of population structuring at their native ranges. To account for a possible influence of spatial distance between sampling sites on the FST estimates, we first averaged the log geographic distances obtained per matrix and then divided all FST obtained through pairwise comparisons by that value. This procedure was performed independently for each species.Geographic distances (in kilometers) were calculated by comparing the latitude and longitude coordinates of each location through java scripts implemented in FST as the response variable and evolutionary divergence plus geographic distance as predictors. Under neutral evolution, measures of genetic differentiation, such as the FST estimates among continuous populations, are expected to increase linearly with geographic distances ; , while the lowest was S = 0 observed in several populations of G. tigrinus and G. oceanicus. The highest number of haplotypes within a sampling location was detected in the G. locusta population from Falckenstein , and the lowest was the several nHap = 1 associated with the locations that had S = 0. Haplotype diversity, as a standardized measure of genetic diversity, showed much more homogeneous distribution with Hd = 1 observed in four populations of P. maeoticus and Hd = 0 than those populations constituted by a single haplotype. Lastly, nucleotide diversity had its highest value recorded in the P. maeoticus population from Astara (πAstara = 0.048) and lowest was π = 0 associated with the locations where only one haplotype was found. The complete set of diversity indices is shown in Table S: Winvasive vs. non‐invasive = 214, p = .970; Hd: Winvasive vs. non‐invasive = 271.5, p = .166; π: Winvasive vs. non‐invasive = 90.5, p = .307). Phylogenetic reconstructions revealed branch support above 75% bootstrap value in line with previous studies that revealed the existence of highly divergent evolutionary lineages  produced a wide range of values across species. Briefly, d ranged between 0.006 and 0.011 for G. locusta, 0.010 and 0.019 for G salinus, 0.000 and 0.095 for G. tigrinus, 0.003 and 0.027 for G. oceanicus, 0.008 and 0.015 for G. zaddachi, 0.001 and 0.117 for P. maeoticus, and 0.005 and 0.008 for O. crassus  . Linear models explained only 33% and 43% of FST variation for G. tigrinusNorth America  and G. oceanicus: , respectively   positively correlated with evolutionary distances  but negatively with geographic distance  in the model that explained 68% of FST variation   was not significant.The 4G. salinus and G. tigrinus in Europe did fall in line with our expectations.The distribution of neutral genetic diversity provides important clues to understand the processes and mechanisms underlying biological invasions at the molecular level. Our study showed that despite the wide variation observed in indices of genetic diversity within each species in their native ranges, no significant differences were observed at any level between populations of non‐invasive and invasive species. Population genetic structure was pervasive among pairwise comparisons within each species, but interestingly, populations of non‐invasive species produce significantly higher levels of differentiation than those of invasive in their native range. We also observed the occurrence of deep evolutionary lineages for almost all species, a feature that is commonly found among gammarids and documented in a series of related studies  and evolutionary distances estimates (d) among the Baltic Sea locations in our study suggest that (1) Travemünde is dominated by a single haplotype and is a very likely representative of clade N1, which is present in northern Europe, and (2) Pärnu and Liu are similar to populations composed by clades N1 and N4 . Travemünde population is apparently composed of descendents of G. tigrinus introduced in the Werra river in the 1960s, while the other locations suggest a stepwise introduction along the pathway North America–British Isles–Baltic Sea  sites and haplotype diversity (Hd). This is not surprising, as Kelly, MacIsaac, et al. (G. tigrinus in this study might be attributed to the introduction of highly variable populations of Pärnu and Liu. Information on evolutionary history of P. maeoticus was readily available through the work of Nahavandi, Ketmaier, Plath, and Tiedemann (FST values among the newly added populations of Bandar‐e Anzali, Jafrud, and Shafarud fell into the range of estimates of those obtained by Nahavandi et al. (Obesogammarus crassus is another Ponto‐Caspian species that is gradually extending its range northward. Diversity indices did not find evidence that suggested existence of deeply divergent lineages at least at the extent of those reported in P. maeoticus. Still, the lack of deeply divergent lineages—a common trait among amphipods—can be explained by the fact that we did not sample the Black Sea, where the phylogeographic break is usually detected in Ponto‐Caspian fauna (Cristescu et al., Of all invasive species investigated in our study, , et al. , Kelly, , et al. . Those a, et al. , Kelly, , et al.  also rep, et al. . In contiedemann . In our iedemann —suggesteiedemann  were usei et al. . Obesoga4.3FST at native ranges, further suggesting that non‐invasive species have their populations more structured (Gaither et al., We also tested a possible relationship between genetic diversity in the native range and invasion propensity. Our results indicated that there was no difference between the average diversity estimates obtained for any group. Still, we found that the degree of differentiation among the populations of non‐invasive species is higher than that of invasive species in their native region. Joint interpretation of the comparisons of diversity and differentiation suggests that genetic diversity is more segregated in non‐invasive species. Perhaps the most parsimonious justification is that this conjugation of patterns constitutes a spurious correlation between distinct evolutionary histories that shaped the variation and distribution of genetic diversity independently in each species and propensity to invade. Still, evidence obtained at molecular level suggests that the nature of genetic variation is more important in establishment and invading success than the overall quantity (Dlugosch et al. 4.4G. tigrinus from which we were able to analyze relationships both for its North American native range and European non‐native range. For those distributed in their native ranges, we found distinct patterns of differentiation–distances relationships (Figure Gammarus salinus was the only species for which we found a positive correlation between population differentiation and geographic distances (Table G. salinus, which constrains dispersal among locations and permits the evolution and maintenance of site‐specific genetic diversity (Gaston & Spicer, The high degree of divergence often reported among members of this superfamily led us to investigate relationships between population differentiation and distinct distance measures. We investigated mostly relationships in native populations; the exception was s Figure . Gammarues Table . This paG. oceanicus and G. tigrinus in its American native range revealed a positive relationship between differentiation and evolutionary distances instead, while the geographic variable attained no significant weight (Table G. tigrinus and G. oceanicus, and despite suggestive of lineages being locally adapted, no empirical evidence exists to support such claim. Mitochondrial variation shaped by selective processes other than strong purifying selection is not commonly reported in studies of natural populations, but see Silva, Lima, Martel, and Castilho (b (Silva et al., Interestingly, none of the species reported positive linear relationship with geographic distances; ht Table . This inCastilho  for an eP. maeoticus can be tentatively explained by a mix of ancient and contemporary factors. Alternatively, it is possible that Quaternary glaciation cycles have impacted the distribution of the species within and between the basins of the Caspian and Black Sea and shaped a genetic patchiness that relates neither with geographic or evolutionary distances (Hewitt, The absence of any relationship between the variables explored here and differentiation among  Hewitt, . However Hewitt, .G. tigrinus, where population differentiation negatively correlates with geographic distances (Figure G. tigrinus in Europe, because the introduction has occurred at limited spatial scales and from multiple introduction events (Kelly, Muirhead, et al., G. tigrinus could indicate an effect of natural selection in the invaded area (Herkül et al., Particularly interesting are the relationships between the three variables for the European non‐native distribution of s Figure ; Table 24.5The mtDNA‐COI was used because it is the most represented gene in public databases and allowed us to cover a high number of species and populations. The drawback is that we had to standardize fragment lengths among recorded sequences for each species, in order to avoid creating intraspecific artificial variation. However, the substantial level of polymorphism observed at a gene known to be as conservative as mtDNA‐COI suggests that rather than extending COI representation, a much higher number of genetic markers and extensive sampling are required to validate and understand the patterns brought in by our work. This would offer the possibility to obtain better estimates of population differentiation, as those are dependent on well‐characterized within‐population diversity to produce robust conclusions (Meirmans & Hedrick, Another important point is that species presented as non‐invasive in our study are assigned to be non‐invasive as they have not been reported in areas outside their native ranges. However, morphological identification of gammarids to the species level can be a challenging task and a species might also invade in new areas in the future; therefore, we acknowledge the possibility of one or more of currently assigned non‐invasive species becoming invasive in the future.None declared. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Rattus norvegicus) is a key pest species globally and responsible for seasonal outbreaks of the zoonotic bacterial disease leptospirosis in the tropics. The city of Salvador, Brazil, has seen recent and dramatic increases in human population residing in slums, where conditions foster high rat density and increasing leptospirosis infection rates. Intervention campaigns have been used to drastically reduce rat numbers. In planning these interventions, it is important to define the eradication units ‐ the spatial scale at which rats constitute continuous populations and from where rats are likely recolonizing, post‐intervention. To provide this information, we applied spatial genetic analyses to 706 rats collected across Salvador and genotyped at 16 microsatellite loci. We performed spatially explicit analyses and estimated migration levels to identify distinct genetic units and landscape features associated with genetic divergence at different spatial scales, ranging from valleys within a slum community to city‐wide analyses. Clear genetic breaks exist between rats not only across Salvador but also between valleys of slums separated by <100 m—well within the dispersal capacity of rats. The genetic data indicate that valleys may be considered separate units and identified high‐traffic roads as strong impediments to rat movement. Migration data suggest that most (71–90%) movement is contained within valleys, with no clear source population contributing to migrant rats. We use these data to recommend eradication units and discuss the importance of carrying out individual‐based analyses at different spatial scales in urban landscapes.The Norway rat ( As a result, during this period urban landscapes have been expanding while human population densities have increased. Despite this trend, urban habitat has generally been considered depauperate in biodiversity and received little attention from ecologists and population geneticists relative to its increasing scale on the landscape. However, urban landscapes can have large impacts on wildlife  is an urban exploiting pest species that has expanded to a near‐global range over the last 300 years. Originally native to forests and brushy habitat in northern China, R. norvegicus is now most commonly associated with human‐dominated landscapes and thrives in areas of high human population density , has seen a 500% increase in human population size over the last 60 years, with most new settlement occurring in urban slum, or favela, neighborhoods within the city  in areas with high leptospirosis incidence. However, rat numbers rebound quickly with population sizes reaching pre‐intervention densities within six months  distributed across large urban regions, where low sampling density limits the resolution of spatial genetic data and restricts our ability to detect patterns across complex urban landscapes. Moreover, the distribution of sampled rats tends to be clumped at a limited number of sites, reducing the spatial coverage of the study to the areas sampled. While genetic data are a valuable proxy for individual movement and dispersal, it should be noted that these data are providing insight into the subset of all dispersal events that result in breeding . We then amplified 16 microsatellite loci previously identified as polymorphic in i et al. . PCR ampi et al.  was usedAlthough most of our analyses have no requirements of Hardy–Weinberg equilibrium and the loci were selected based on chromosome location to ensure linkage equilibrium among loci, we evaluated any deviations from these two common population genetic models. Independence of loci was evaluated by testing for linkage disequilibrium in FSTAT v2.9.3 Goudet, . Hardy–W2.3Rather than arbitrarily grouping rats for population‐level genetic analyses, we took advantage of our high‐resolution sampling  to perform individual‐based analyses. Using individuals as the unit of analysis rather than populations allows for the evaluation of much finer‐scale genetic patterns because information is not lost by averaging within groups  are an effective method to create new synthetic variables that maximize the variance among genotypic data  to evaluate (A) the degree of genetic similarity among individuals in the dataset and (B) the patterns of genetic similarity among individuals from different areas of Salvador and within Pau da Lima. DAPC uses coefficients of allele loadings in linear combinations to maximize the between‐groups variance while minimizing within‐group variances in these loadings , which are often used in Mantel tests of isolation by distance /2], and to make this computationally feasible, we grouped the 706 rats into 28 geographic areas based on topography and distance from other sampling areas .We estimated the rates of recent and ongoing genetic migration between the different sampling areas using the default parameters within the program BayesAss v3.0 . At each scale, Valley 2 within Pau da Lima . One to three principal components explained the most variation at all scales, and those eigenvalues were used to plot the sPCA data , representing a surprisingly high degree of spatial genetic structure at such a small scale, given that the area is only 0.37 km2. Genetic variation at the intermediate (r2 = .051) and city‐wide scales (r2 = .06) was less strongly shaped by spatial orientation. At all scales, the first MEM variable, representing the principal coordinate eigenvector, explained more than 30% of the spatial genetic variation . Visualizing this first MEM variable score within Pau da Lima indicates that valleys 2 and 4 are strongly divergent despite their adjacent locations  had the most genetic variation that was explained by spatial patterns (adjusted r3.5p = .96), or pairwise migration rate and genetic distance . There was a significant association between geographic and genetic distance among sampling groups across the city, indicating isolation by distance in this system .As expected, most of the recent gene flow  occurred within sites, rather than among sampled areas. An average of 79% of “migration” occurred internally within each valley, satellite site, or sampling area. Valley 2 showed the greatest genetic insulation with 90% of individuals identified as originating from within Valley 2. In Valley 4, 84% of individuals were estimated to have originated within this valley. Consistent with the other spatial genetic analyses, Valley 1 showed the highest proportion of migrants from outside this valley . The source of migration estimated to have come into Pau da Lima, and other sampling areas, was distributed fairly evenly across the other sampling areas . A Mantel matrix correlation test found no significant relationship between migration rate and geographic distance among sampled areas  across all of Pau da Lima, particular valleys can be targeted individually. Moreover, continual trapping efforts can be used to limit rats migrating between valleys 1 and 2, even outside of intervention campaigns. Our data suggest that these permanent trapping efforts are not needed between valleys 2 and 4. Importantly, verifying the success of this modified intervention strategy will require evaluating rat numbers and genetic signatures before and after control campaigns, as described in the previous paragraph.4.3Rattus norvegicus is a globally distributed pest species invasive to most cities because it is so adept at exploiting urban habits and resources  captured over a much wider area across Salvador. These previous studies from Baltimore and Salvador support our findings that genetic heterogeneity exists within urban landscapes, even at very small scales. However, the two aforementioned studies were limited by the low density of samples across complex urban landscapes.Our study demonstrates the power and utility of combining dense sampling with spatially explicit analyses to characterize the genetic structure and likely movement patterns of wildlife in complex urban landscapes. These patterns are difficult to study, but Norway rats are an excellent species to investigate urban ecology and movement. es Long, . This spes Long, ; howeveres Long,  evaluatea priori for analysis, which is often arbitrary with respect to biology  within a relatively small area within the Pau da Lima neighborhood (0.37 km2), which vastly exceeds previous efforts. Such high‐resolution spatial genetic data provide newly available and powerful information for the public health initiatives to control Norway rats and advance urban population genetics more broadly.Our use of individual‐based analyses is an important advance in spatial population genetics. Individual‐based approaches treat individual samples as the unit of analysis, negating the need to group samples Very few studies have explicitly investigated how scale can influence observed spatial genetic patterns. Those that have evaluated scale found similar patterns to what we observed in Salvador rats, including different patterns of genetic divergence in simulated  define eradication units based on genetic divergence among valleys in the Pau da Lima slum community, (B) evaluate migration rates to determine that no clear source area is responsible for recolonization after intervention campaigns that reduce rat populations, and (C) advance the methods associated with urban ecology using spatially explicit and individual‐based approaches to evaluate genetic patterns at multiple spatial scales. The data from this study are particularly useful when combined with other epidemiological interventions, including reductions in the number of environmental reservoirs supporting the parasite, and implementing changes in human behavior that can reduce the prevalence of disease. These approaches are currently being employed in urban centers in Brazil and other developing countries to reduce the risk of leptospirosis, which has emerged due to the expansion of slum settlements and rat populations that populate such communities. The approaches used in this study also provide a framework for future work on urban ecology and landscape genetics, as well as the potential for microgeographic evolution in urban habitats that vary dramatically within very small spatial scales, in terms of both landscape features and genetic divergence.Data for this study are included as Genepop‐formatted genotypes in a supplemental file in the online version of this article. Click here for additional data file. Click here for additional data file.


Poa secunda, a common native grass species in Intermountain West grasslands and a frequent component of restoration seed mixes, is one such species. Here, we evaluate the genetic variation of local Poa secunda collections in the context of wild populations and commercial seed releases. We evaluated AFLP markers for seven Poa secunda collections made over a 4000-hectare area and four commercial releases . We compare the genetic distance and distribution of genetic variation within and between local collections and commercial releases. The extent and patterns of genetic variation in our local collections indicate subtle site differences with most variation occurring within rather than between collections. Identical genetic matches were usually, but not always, found within 5 m2 collection sites. Our results suggest that the genetic variation in two Poa secunda releases (High Plains and MT-1) is similar to our local collections. Our results affirm that guidelines for Poa secunda seed collection should follow recommendations for selfing species, by collecting from many sites over large individual sites.The genetics of native plants influence the success of ecological restoration, yet genetic variability of local seed collections and commercial seed releases remains unclear for most taxa.  Poa secunda is a cool-season perennial bunchgrass common to grasslands of western North America. A polyploid complex, P. secunda is composed of variants that may have distinct ecological and biogeographical roles [‘Ampla’), Canby’s bluegrass (‘Canbyi’), Pacific/slender bluegrass (‘Gracillima’), Nevada bluegrass (‘Nevadensis’), alkali bluegrass (‘Junctifolia’), Sandberg bluegrass (‘Sandbergii’), and pine bluegrass (‘Scabrella’) [Poa secunda is a facultative apomict, reproducing via selfing, outcrossing, and apomixis [P. secunda [Poa secunda individuals often produce viable pollen and can contribute to selfing, outcrossing, and apomictic seed development even if they are entirely or highly apomictic [al roles , though al roles ,3 with aal roles –7. Thesebrella’) ,8. Poa sapomixis ,10. Apom secunda . Poa secP. secunda and that the species is predominantly apomictic [P. secunda is unknown. Kellogg [P. secunda at the individual (up to 40% apomictic ovules) and population (25–100% apomictic ovules) levels, as well as with site conditions. Kelley et al. [P. secunda wild-collected seed lots, suggesting high levels of apomictically generated seed. Clausen et al. [P. secunda does outcross at a fairly low rate, averaging 0.46% and 0.6%, respectively, though the former characterized hybrids as poor performers.Despite the common belief among growers and restoration practitioners that selfing and outcrossing are rare in pomictic , the rel Kellogg  demonstry et al. , howevern et al.  and KellPatterns of gene flow influence genetic variation across the landscape. Genes translocate and/or recombine by outcrossing, selfing, and asexual reproduction –16 such P. secunda [Patterns of genetic variation are difficult to predict in plants with mixed reproductive modes, such as  secunda ,23. Even secunda ,24,25,26 secunda ; to captGene flow depends on propagule dispersal that, in grasses, is mostly localized with rare long distance dispersal events ,29. PropP. secunda has a capacity for apomictically “fixed” genotypes, as in the Sherman (single genotype) and Canbar (three genotypes) releases [P. secunda are unknown.Commercial seed releases are often used in restoration plantings –35. Locareleases , the genP. secunda collections to assess small-scale genetic variation and to inform germplasm sampling strategies. We also examined four P. secunda releases to compare their genetic variability and evaluate their suitability for use as plant materials in nearby restoration sites. Our study is the first to compare genetic variation and structure in P. secunda local collections and commercial releases. We examined this common bunchgrass species, which shares an early spring phenological niche with the important invasive species Bromus tectorum with the aim of broadening native plant genetic information to inform restoration management. In particular, we predicted that: 1) local collections are moderately variable given previous studies of phenotypic variability [We evaluated local iability ,44; 2) cPoa secunda individuals (‘Sandbergii’) were collected from seven sites located on MPG ranch, a private 4,000-hectare conservation property near Florence, MT , omission of DNA purification, and the addition of a second unlabeled, selective MseI+NNN primer . We performed AFLP analysis according to Vos et al.  as modifimer see .EcoRI-HF  and the frequent cutter MseI (New England Biolabs), ligated to corresponding adapters with T4 DNA ligase (New England Biolabs), and diluted (1:10 v:v) with molecular grade dH20. The restriction-ligation mix was PCR-amplified using EcoRI and MseI pre-selective primers  using LIZ-500 (Applied Biosystems) and ROX-500 (Applied Biosystems) size standards for primer combinations 1–3 and 4–6, respectively . FragmenWe calculated polymorphism within local and commercial sources by dividing the number of polymorphic bins (markers) that occurred at a rate greater than the error threshold (5%) by the total number of reproducible bins . MonomorWe constructed Jaccard distance matrices of polymorphic AFLP profiles and performed pairwise comparisons of average Jaccard distance of releases, within collections, between collections and overall for local collections. Samples with complete pairwise similarity with respect to the 5% error threshold were identified in the dataset using pairwise Jaccard similarity indices. To calculate the total percentage of identical genotypes, we divided the number of individuals with one or more identical genotype by the total number of individuals within each collection or release. Genetic identical match rates were calculated within and among local and commercial collections. Additionally, we calculated the overall number of identical genetic matches in combined local collections and the frequency of within and between collection genetic matches.http://eros.usgs.gov) to estimate elevation on each site. To evaluate the importance of spatial distance, we constructed a Euclidean distance matrix from spatial coordinates of collection sites. Mantel tests were performed using PASSAGE software.We performed overall comparisons of releases and collections using principle coordinates analysis with a Jaccard similarity index. We evaluated the relationship between local genetic and geographic variation and elevation using Mantel tests . We usedP. secunda source identity (9999 iterations) using Canoco (ver. 5) software.Analysis of molecular variance (AMOVA) ,53 evaluWe scored 236 AFLP markers with an average error rate of 2.36%. Twenty percent of these markers were polymorphic among samples at a rate greater than the error threshold of 5%, resulting in 47 polymorphic markers, 19.9% overall polymorphism, and an error rate of 4.92%. Polymorphism rates varied between sources , with the proportion of polymorphic markers ranging from 0% (Sherman) to 30.93% (High Plains).F = 4.0; P < 0.002) among local collections , indicating that identical genetic matches explained only 2.3% of the variation among collection sites. Overall, 18.7% of local individuals had one or more identical genetic match, 16.2% of which had one or more same-collection genetic match and 4.9% had one or more genetic match among collections. AMOVA indicated that most genetic variation occurred within collections  indicated that a significant proportion of genetic variation was explained by collection site proximity  and elevation .Distance-based redundancy analysis explained 14.3% of AFLP variation . In analyses including only ‘Sandbergii’ variant releases (High Plains and MT-1), 6.6% of data variation could be explained by release identity . No identical genetic matches were found within or between MT-1 and High Plains. Thirty-three percent of Opportunity individuals had one or more within-release genetic match. All Sherman individuals were genetically identical.We analyzed releases  separately from local collections and found that 31.3% of variation was explained by release identity . However, when we pooled local collections, only 3.8% of the variance in AFLP profiles could be explained by release or local identity . Similar to local collections, this result suggested most of the variation occurs within rather than between ‘Sandbergii’ sources. Principle coordinates analysis explained 19.22% of the variation in local collections and releases in the first two axes, with 11.14% of variation explained by the first axis and 8.08% by the second axis .We compared local collections, MT-1, and High Plains AFLP profiles and found that 23.8% of AFLP variance could be explained by collection site or release identity  and found no evidence for gene shifts after two generations of seed increase. We found comparable levels of variation in local (wild) collections and the ‘Sandbergii’ releases, High Plains and MT-1. The genetic diversity of the original High Plains and MT-1 source populations is unknown and the evaluation of genetic truncation is outside the scope of this study, but our analyses do not indicate that these releases lack variability compared to our local collections.Genetic variation in populations is desirable because it functions as a pool of adaptive capacity. Despite a strong theoretical basis ,39, empiu et al.  examinedP. secunda release or collection displays this level of genetic uniformity. Collections such as Sherman, that represent a fixed genotype, might displace other genotypes through “genetic swamping,” limiting overall population diversity in restoration plantings and impacting remnant, wild populations [Facultative apomixis may confer genetic stability in diverse populations by slowing genetic equilibration to new environmental conditions , and mayulations .Bromus tectorum differed between the High Plains release and our local P. secunda collections. Herget et al. [P. secunda collections with the Mountain Home release in the context of cheatgrass die-offs and found better performance in the local collections. Johnson et al. [AFLPs generally represent neutral genetic variation, and therefore do not correspond to functional variation. Although genetic diversity is important for long-term adaptability, how genetic variation manifests as phenotype in restoration plantings determines plant establishment and persistence ,41,64. Ct et al.  also idet et al.  comparedn et al.  found hiThe closer we can get to evaluating restoration genetic concerns at their source, the better we can mitigate them. A clear understanding of how native plant species vary genetically in the landscape is essential. In the meantime, the use of commercial releases is common and widespread, so we must identify genotype and phenotype mismatches with natural populations, monitor their performance and persistence in restoration plantings, and incorporate them into the development of trait-based, plant material transfer zones.S1 Dataset(TXT)Click here for additional data file.S2 DatasetPoa secunda collection sites.Latitude, longitude, insolation (watt hrs./sq. m), aspect and elevation (m) for all local (TXT)Click here for additional data file.