Alviniconcha  that are ecologically dominant taxa at Western Pacific hydrothermal vents. In contrast to predictions from dispersal models, among‐basin migration in A. boucheti occurred predominantly in an eastward direction, while populations within the North Fiji Basin were clearly structured despite the absence of oceanographic barriers. Dispersal models and genetic data were largely in agreement for the other Alviniconcha species, suggesting limited between‐basin migration for A. kojimai, lack of genetic structure in A. strummeri within the Lau Basin and restricted gene flow between northern and southern A. hessleri populations in the Mariana back‐arc as a result of oceanic current conditions. Our findings show that gene flow patterns in ecologically similar congeneric species can be remarkably different and surprisingly limited depending on environmental and evolutionary contexts. These results are relevant to regional conservation planning and to considerations of similar integrated analyses for any vent metapopulations under threat from seabed mining.Hydrothermal ecosystems face threats from planned deep‐seabed mining activities, despite the fact that patterns of realized connectivity among vent‐associated populations and communities are still poorly understood. Since populations of vent endemic species depend on larval dispersal to maintain connectivity and resilience to habitat changes, effective conservation strategies for hydrothermal ecosystems should include assessments of metapopulation dynamics. In this study, we combined population genetic methods with biophysical models to assess strength and direction of gene flow within four species of the genus  Lastly, in the Northwest Pacific, A. hessleri inhabits vents along 600 km of the Mariana back‐arc spreading centre. The larval biology of all Alviniconcha species is largely unknown, although a planktotrophic development has been suggested based on examinations of protoconch characteristics  .As a key foundation taxon mostly associated with active sulphide chimneys, deep‐sea snails of the genus A. boucheti, A. kojimai, A. strummeri and A. hessleri. We then compared strengths and directions of gene flow with dispersal probabilities from biophysical modelling to test whether these models are good proxies for connectivity in this important genus.We used population genetic analyses and isolation with migration models to determine patterns of intraspecific gene flow within and among back‐arc basins in the western Pacific species 22.1c‐oxidase subunit I, mtCOI) and three nuclear genes  were used to investigate gene flow among populations of A. boucheti, A. kojimai, A. strummeri and A. hessleri , haplotype diversity (H) and number of segregating sites (S), were computed with the R packages ape  and the multilocus HKA test . Significance of the DHEW test was assessed through 50,000 coalescent simulations at a p value of 0.05. The MLHKA test was run with 500,000 MCMC iterations and repeated three times with different random number seeds. A random sequence of A. boucheti for each gene was used as outgroup for analyses in A. kojimai and A. strummeri, while A. kojimai and A. strummeri were used as outgroups for analyses in A. boucheti and A. hessleri respectively. Significance was assessed through log likelihood ratio tests at a p value threshold of 0.05. Both the DHEW and MLHKA tests are relatively robust to demographic history and should thus be more powerful in detecting signatures of selection than traditional methods , as biophysical models predict that this locality acts as an important stepping stone and source site for dispersal . Gene regions that showed evidence for intralocus recombination based on the four‐gamete test  of 10–100, a maximum population size (–q) of 100–250 and a maximum time of population splitting (–t) of 5–50. All analyses were run for at least 107 generations after a burn‐in of 104 generations. All loci were used for analyses, although neutrality tests indicated signatures of selection in some of the markers  and the Lau Basin  (Table FST: 0.0838–0.1490). Among the Lau–North Fiji Basin comparisons, the only significant difference occurred between Kilo Moana and White Rhino (FST: 0.3073). Both North Fiji Basin and Kilo Moana samples were also significantly differentiated from Niua South (FST: 0.1867–0.3147). Within basins, no significant differentiation was observed among Manus or Lau Basin samples. By contrast, samples within the North Fiji Basin were significantly different from each other: both White Lady and Mussel Hill were strongly divergent from White Rhino despite the close proximity of these localities (FST: 0.3897–0.4144).In A. kojimai, significant differences were evident between the Manus and Lau Basin samples (FST: 0.0342–0.1095), but no other significant pairwise comparisons between or within basins were observed , while less variation occurred between individuals within populations (0%–2.98%) or among broader geographic regions (0%–0.76%) Table . Similar3.2COI gene than for the nuclear genes , while various patterns were observed for haplotype diversity .DHEW compound tests suggested positive selection on the mitochondrial ri Table . Selecti3.3A. boucheti was eastward from the Manus Basin into Niua South, Vanuatu and the Lau and North Fiji basins  were very similar to each other, but contrasted to those from Niua South Figure . ParticlFor the Mariana back‐arc, modelled dispersal patterns in the northern and central basin  were distinct from those in the southern end of the basin  Figure . ParticlParticles from the northernmost sampling site Alice Springs did not reach Forecast, Snail or Pika (and vice versa). Even when accounting for stepping stone transport, connections among these sites are unlikely, that is, orders of magnitude smaller than Hafa Adai and Perseverance Figure . Interes4A. boucheti, the species with the widest distribution in the Western Pacific, were genetically distinct among basins and gene flow occurred predominantly in an eastward direction. The observed genetic subdivision across basins is in agreement with previous genetic studies on other vent species  despite lower genetic differentiation. This discrepancy might be partly explained by unsampled ghost populations, low sample sizes for some localities or limitations of the applied genetic markers, which highlights the importance of closing sampling gaps for accurate inference of population connectivity  and the ATPSb genes , although other processes such as sex‐biased dispersal might contribute to these patterns  The recent discovery of intermediate vent sites on the Mariana back‐arc has contributed to our understanding of gene flow dynamics, demonstrating the importance of exploration efforts for effective conservation.Overall, the results from this study reveal several aspects that are relevant to regional, but also global conservation efforts: (a) Our data demonstrate that modelling of larval transport is a very useful tool to set hypotheses that should be tested for species of interest. Dispersal models can help guide field sampling for larvae and examination of the role of larval behaviour in affecting differential species responses to transport (Yearsley et al., Considering these aspects will be helpful to provide comprehensive guidelines for environmental management plans aimed at conserving biodiversity at hydrothermal ecosystems.The authors declare no conflict of interest.Fig S1Click here for additional data file.Tables S1–S6Click here for additional data file.


Plantago major, collected from 50 populations worldwide to investigate how patterns of genomic diversity are distributed among populations of this global weed. Although genomic differentiation among populations is found to be low, we identify six unique genotype groups showing very little sign of admixture and low degree of outcrossing among them. We show that genotype groups are latitudinally restricted, and that more than one successful genotype colonized and spread into the introduced ranges. With the exception of New Zealand, only one genotype group is present in the Southern Hemisphere. Three of the most prevalent genotypes present in the native Eurasian range gave rise to introduced populations in the Americas, Africa, Australia, and New Zealand, which could lend support to the hypothesis that P. major was unknowlingly dispersed by early European colonists. Dispersal of multiple successful genotypes is a likely reason for success. Genomic signatures and phylogeographic methods can provide new perspectives on the drivers behind the historic introductions and the successful colonization of introduced species, contributing to our understanding of the role of genomic variation for successful establishment of introduced taxa.Retracing pathways of historical species introductions is fundamental to understanding the factors involved in the successful colonization and spread, centuries after a species’ establishment in an introduced range. Numerous plants have been introduced to regions outside their native ranges both intentionally and accidentally by European voyagers and early colonists making transoceanic journeys; however, records are scarce to document this. We use genotyping-by-sequencing and genotype-likelihood methods on the selfing, global weed,  Retracing pathways of species introductions to new lands is a fundamental part of understanding what ecological and evolutionary factors are involved in the successful establishment and spread of species into new ranges . The gloThe human-mediated dispersal of plants from Europe to other continents became particularly prevalent around the year 1500, a time coinciding with European exploration, colonialism, and the start of wide-scale changes in human demography, land use, trade and industrial development . In the Molecular methods have helped to disentangle the origins of a number of introduced plants, by inferring source populations and retracing putative pathways of invasion, and comparing genetic variation between native and introduced ranges . HoweverPlantago major L. (Plantaginaceae), also known as common, greater or broadleaf plantain, as a case for understanding human-mediated plant dispersals and potential reasons for successful establishment in new ranges. Native to Eurasia, the species grows in a wide range of disturbed habitats, and although it is widespread in both the native and introduced ranges, is only rarely considered a problematic invasive species  generated by genotyping-by-sequencing (GBS) to investigate: (i) the genomic variation among populations of P. major subsp. major at a global scale (ii) the ancestry of native vs. introduced populations, and (iii) what the genomic patterns we observe indicate about the pathways of introduction from native Eurasia to introduced ranges around the world. We discuss what might be the role of colonial history in explaining current genetic variation among populations worldwide. This knowledge can help us understand the importance of landscape-scale genomic variation in the dispersal and establishment of new potential plant introductions, and provides a starting point for future research in understanding how introduced plants cope under changing environmental conditions when genomic variation is low due to high levels of inbreeding.Globally distributed selfing species such as ariation . Highly ariation . We sampPlantago major has a complex taxonomic history with over 50 taxonomic divisions and synonyms having been proposed and a number of ecotypes being recognized (P. major subsp. major and P. major subsp. intermedia (Gilib.) Lange, have been widely studied in Europe and even recognized as separate species based on genetic structure and differentiation  and 22 populations from the introduced range  , United States (North Carolina), and Bolivia]. Six to ten individuals were sampled from each population (only three for Chicago), amounting to 385 individuals in total, and dried and stored in silica gel. A herbarium voucher was collected from each population to confirm species identity and deposited in Herbarium C at the Natural History Museum of Denmark, University of Copenhagen, in Copenhagen, Denmark. Herbarium vouchers were not obtainable for Peru; however, digital photos were taken in lieu of vouchers.Fifty populations of naturally occurring  Africa) . Populat® Mini Plant Kit  or a CTAB protocol . DNEasy Mini Plant Kit extractions were modified by adding 50 μL of proteinase K to the cell lysis solution after 10 min incubation at 65°C, followed by 2 h of incubation at 45°C. Double extractions were made for each sample, then pooled, to ensure the quantity and quality of genomic DNA was sufficient for genotyping-by-sequencing (GBS).Dried leaf tissue was pulverized with ceramic beads in a Qiagen TissueLyser  and DNA extractions were prepared using a modified Qiagen DNEasyPstI (CTGCA^G), and both a sample-specific barcoded adapter and a common adapter were ligated to the sticky ends of fragments. Samples were pooled and fragment libraries cleaned using a QIA-quick PCR purification kit . Libraries were amplified using an 18-cycle PCR with long primers complementary to the barcoded and common adapters, purified again using QIAquick, and quantified using an intercalating dye  following In total, DNA extracts from 385 individuals were sequenced . GBS libSTACKS, a software pipeline designed for restriction-enzyme based data for organisms without a reference genome, was used to generate a catalog or pseudo-reference genome. The sequencing reads for 392 samples (385 samples plus a blank from each of the seven flow batches of samples) were demultiplexed using the process_radtags function in STACKS V.1.45 , while retaining unused reads (-r option) and requiring a minimum of three reads to build a stack (-m 3). The resulting output was used to run CSTACKS with four mismatches required between different loci (-n 4) when building the catalog. We also allowed gapped alignment between the loci, with a maximum gap length of four . This results in a minimum Hamming distance of 4 between any pair of loci in the reference catalog. From the loci identified in the reference catalog, we created a pseudo-reference genome by collapsing these loci into 10 chromosomes, with a string of 150 Ns inserted between consecutive loci.We selected 20 samples with the maximum number of reads, while maximizing the number of sampling locations spanned, to build a reference catalog using 1.45 see . These sGBSX v.1.3 , a minimum of 50% of the samples must have at least one read covering the site (-minInd NumberOfSamples*0.5), a minimum SNP p-value of 10–6 , and a penalty for poorly aligning reads that have a lot of mismatches with the reference locus (-C 50). 7594 SNPs were retained for analyses. The genotype likelihoods were calculated using the model described in SAMTOOLS , PLINK v. 1.90b4.4  was run PCAANGSD  with a mNGSADMIX . For K = 2 to K = 8, analyses were replicated 200 times, and for K = 9 to K = 12 the analyses were replicated 500 times to ensure convergence to the global maximum. The replicate with the highest log-likelihood among replicates was chosen for each K. A test statistic for determining the optimal K-value is not an available option in NGSADMIX. Therefore, results for each value of K were reviewed and compared with results from the PCA plots to select a K-value that had the most biological relevance based on the genomics of the species being studied . Base quality around indels was adjusted  and notTREEMIX v. 1.13 , France (1 and 2), Turkey, Iran1, and Japan as the root (-root). Weeds that are successful of achieving global distributions are the most difficult to place origins , bootstrap (-bootstrap) replicates were run sampling blocks of 1,000 SNPs (-k), and standard errors (-se) and were used to evaluate the confidence of the inferred tree topologies. The variance explained by each ML tree was calculated using the get_f R function supplied in the R plotting scripts in the TREEMIX suite and ln(likelihood) values were reviewed for each tree. The best trees resulting from each of the different roots we modeled were compared. The analyses resulted in trees of similar topologies in which relationships between populations and the amount of drift were unchanged regardless of the root selected; however, trees rooted with Spain1 explained the highest variance of our data and had the highest non-positive ln(likelihood) value (seeds) were carried out to examine the consistency of the migration events. The TREEMIX plotting script was used to visualize the trees and the proportion and direction of gene flow events between the 50 sampled populations. v. 1.13  was usedn events . A scripiles see . Before iles see . In the  origins . The pop origins , and bas Eurasia . One hund) value . The besPCAANGSD using the LD pruned SNP data reveal differentiation between global samples, especially along the first, second and third coordinate axes  between two and 12, and results for value K = 3 and K = 6 are illustrated as a structure graph in K. Each population comprised 6–10 individuals for which genotyping was performed (three for Chicago). Results for additional values of K are shown in K = 3 and K = 6, despite wide geographic distances between populations. At a K-value of 3, populations from eastern Asia , Yukon, Alaska (in part), California, and Iran1 are comprised of similar ancestry components. Populations from North America (except for Florida), Iceland, Greenland, and New Zealand share ancestry with north-central and eastern Europe and western Asia . Populations from South America, Florida, Hawai‘i, Africa, and Australia share ancestry with southern European populations , shows admixture between populations from Northern Europe/North America (group II) with lineages from Southern Europe, South America, Hawai‘i, Africa, and Australia (groups V and VI).Shared ancestry was estimated on genotype likelihoods in ulations . These tK-value of 6, the groupings based on shared ancestry resemble the groupings seen in the PCA plots taking the first three PCA axes into account and therefore are presented here as a likely ancestry scenario on which to base discussions. Populations belonging to groups V and VI in the PCA plots  of the variance in the relatedness between populations  migration events; however, subsequent migration events (m = 6–15) produced inconsistent results from iteration to iteration, despite all migrations being statistically significant in each iteration. The variance explained by each level of m (1–15) is shown in m = 6 and greater, we present and discuss results for the first five migration events only. The direction, weight and significance of each inferred migration event is presented in m = 1, p-value ≤ 2.2 e–308). In addition, gene flow is also inferred from Greece to North Dakota , from Yukon to Russia , Iran2 to France1 , and from Russia to Iceland .The best ML tree resulting from the ulations . The topoccurred . Adding occurred . We founPlantago major subsp. major so extensively across its global range and to provide an overview of the global distribution of this prolific weed’s genotypic variation. One of our main results was that individuals from across the global range cluster into several distinct genotype groups, based on genetic covariance, shared ancestry, and ML phylogenetic analyses. Genotypes from a given population were found to be homogenous, such that one can predict the genotype group of an individual from those of the other individuals in a given population, based on where in the world it is growing. Patterns in genotype distribution we observed in the native range may be determined by environmental factors. In the recent worldwide study of Plantago lanceolata, populations in the native European range had strong spatial genetic structure associated with geographic distance and precipitation seasonality, while non-native populations had weaker spatial genetic structure that was not associated with environmental gradients but with higher within-population genetic diversity . This includes only one of the populations where admixture was modeled . A study including the phenological assessment of each population or genotype group would help inform whether overlaps in flowering time could facilitate outcrossing.We anticipated finding lower genetic variation in introduced populations compared to native populations of  species . In factdmixture . Our datTREEMIX provided insight into gene flow from and to specific populations, including those shown to admix in our analyses. For example, admixture observed in New Zealand was best explained by a gene flow event from Greece to New Zealand. Geneflow was also modeled from Yukon to Russia; however, the opposite direction of gene flow was expected (from the native range in Eurasia to Northern Canada). TREEMIX modeling errors are most often in directionality of gene flow  to western North America , two introduction events to the Australasian continent , and at least one introduction event to each of South America, South Africa, Canary Islands, Hawai‘i, Greenland, and Iceland.P. major genotype group and the absence of divergence time estimates for our data make it difficult to infer precise origins for the introduced populations. Moreover, one would expect that multiple introduction events of the same genotype occurred historically, particularly given the extraordinary dispersal abilities and commensal nature of P. major and other species in the genus Plantago  likely gave rise to the majority of populations in North America, as well as Greenland, Iceland, and New Zealand. As previously hypothesized, plants could have traveled with early colonial explorers/settlers from France and England in the sixteenth and seventeenth centuries where these countries had colonial power . DespiteAtlantic , our datPlantago major do not show links to European colonial patterns. For example, although populations from both Melbourne and Perth  share ancestry with the southern European populations, there is no evidence of direct voyages between the Spanish or Portuguese during early colonial times, although Spanish and Portuguese were both in southern Africa and southeast Asia, and voyagers from the United Kingdom made stops in South Africa en route to Australia. Plants could have been introduced to southern Africa and then further dispersed to Australia. Alternatively, plants could have arrived with the Portuguese to Timor in the sixteenth century , around the same period the English made their first voyages there, which indicates that plant may have arrived with earlier explorers such as the Dutch, who arrived in Australia  in the seventeenth century . At least two ecotypes are recognized in Japan—one restricted to sandy shores and brackish waters (which is represented in our sampling and considered native), the other considered introduced and weedy . Interestingly, populations sampled elsewhere in eastern and Southeast Asia were identified as other Plantago species with strong morphological resemblance to P. major. Reports of the worldwide distribution of P. major may be obscured by lookalikes and mistaken identity, even for trained field botanists.The genotypes found in eastern Asia (South Korea and Japan) were not found within any of the introduced ranges we sampled other than in Yukon, Canada, and gene flow originating from these Asian populations was not inferred in our models. This could be an artifact of poor sampling across Asia, or could represent a true biological scenario in which P. major that predate the arrival of early European colonists  can be found below: NIA, NR, and NZ conceptualized the study, and NIA designed the global sampling and performed the DNA extractions. NIA, HMM, SDC, CJR, KMS, CM, GH, AS, DB, EG, MX, AG, MA, OMG, JSP, MB, VM, HC, SB, DZ, HKC, YY, BB, SV, HES, PS, ZL, DB, MH, LJC, AM, CHSL, MG, and NZ performed sampling of populations in different localities. NIA, SG, and VCB processed and analyzed the sequence data and made the figures. NIA wrote the manuscript with SG, VCB, and NR. All authors read and commented on the manuscript and approved the final version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Parantechinus apicalis) whose remnant range has been restricted to Western Australia at just one mainland site and two small offshore islands—Whitlock Island (5 ha) and Boullanger Island (35 ha). Here, we used 14 microsatellite markers to quantify genetic variation in the remaining island populations from 2013 to 2018 and incorporated these data into population viability analysis (PVA) models, used to assess factors important to dibbler survival and to provide guidance for translocations. Remnant population genetic diversity was low (< 0.3), and populations were highly divergent from each other (pairwise FSTs 0.29–0.52). Comparison of empirical data to an earlier study is consistent with recent declines in genetic diversity and models projected increasing extinction risk and declining genetic variation in the next century. Optimal translocation scenarios recommend 80 founders for new dibbler populations—provided by captive breeding—and determined the proportion of founders from parental populations to maximise genetic diversity and minimise harvesting impact. The goal of our approach is long-term survival of genetically diverse, self-sustaining populations and our methods are transferable. We consider mixing island with mainland dibblers to reinforce genetic variation.In the face of the current global extinction crisis, it is critical we give conservation management strategies the best chance of success. Australia is not exempt from global trends with currently the world’s greatest mammal extinction rate (~ 1 per 8 years). Many more are threatened including the dibbler ( Maintaining genetic diversity is a key component to successful translocation programs6, and IUCN/SSC guidelines7 recommend that individuals selected for translocations should provide adequate genetic diversity, and come from geographically close origins and comparable habitats to the intended destination. Further, it has been suggested that founders should capture 90–95% of genetic diversity of source populations9, and should be genetically compatible if they are mixed from multiple sources10.In Australia, translocations are widely used to improve the status of threatened mammal species by increasing the number of self-sustaining populations12. Island populations are often isolated with no immigration and constrained effective population sizes lead to low genetic diversity. Using island sources as founder populations in translocations may compound effects such as population bottlenecks in the translocated population. Depending on the number of individuals harvested (which determines the narrowness of the bottleneck), there can be increased inbreeding, loss of evolutionary potential and rapid genetic divergences from the source population(s)14. There can also be a negative impact on the source population(s)15, which is particularly relevant for species whose remnant ranges are restricted to just a few locations. Harvesting too many individuals from a population can alter population subdivision, reduce genetic variation, induce selective genetic changes and lead to irreversible population decline16.Gaining access to ideal founder populations may not be possible if a species is restricted to offshore islands17 and the western swamp tortoise18). While captive breeding programs provide relatively benign environments that allow populations to thrive and persist over time, issues associated with captive breeding include high cost, the possibility of individuals adapting to captivity and being unsuited to release, and the potential for disease outbreaks20. Nevertheless, captive breeding programs play an important role in conservation management of many species, but their implementation should be carefully designed19.Captive breeding has played critical roles in conservation management of threatened species when remnant source populations are of limited or uncertain size, and mitigates the inherent risk of extinction due to over-harvesting for wild-to-wild translocations  and post-zygotic isolation  which can result in a lack of interbreeding or fitness decline of admixed progeny24. For example, crossing populations that are adapted to local habitats can disrupt important gametic associations and dilute adapted alleles, resulting in hybrids with lowered fitness, referred to as outbreeding depression28. All these factors can reduce the effective population size of a newly established population, and subsequently lead to a detrimental loss of genetic variation14.One way to counterbalance the loss of genetic diversity when establishing new populations is to use multiple source populations. Genetic mixing of source populations can increase heterozygosity, increase adaptive potential and mask deleterious inbreeding effects in translocated populationsParantechinus apicalis) is a small (40–125 g) dasyurid marsupial once widely distributed in Australia  and feral cats (Felis catus), inappropriate fire regimes, habitat degradation due to dieback (Phytophthora cinnamomi) disease, and competition with house mice (Mus musculus)29. Dibblers are now restricted to the southwest of Australia33 occurring naturally on mainland Australia in the Fitzgerald River National Park (~ 3000 km2), and on two small islands, Boullanger and Whitlock Islands off the mid-west coast of Western Australia . However, despite these efforts, fewer than 1000 individuals, including those in translocated populations, remain in the wild34.To prevent further population declines, five translocations have been implemented since 1998 using captive bred dibblers sourced from island and mainland populationsReturn to 1616’ led by the Western Australian Department of Biodiversity, Conservation and Attractions (DBCA)38. Following eradication of feral cats and goats, 11 mammal and one bird species are being translocated to Dirk Hartog Island between 2018 and 2030, with the aim of restoring former faunal biodiversity and ecosystem processes. Dibblers are being sourced from the three Jurien Bay islands but are first being captivity-bred at Perth Zoo to enhance founder numbers. However, as the Jurien Bay island populations are small (10–30 adults) and subject to large fluctuations, they are vulnerable to extirpation , especially if harvesting pressure is too high. Further, it is currently unknown how many founders are required to establish a population on Dirk Hartog Island.The reintroduction of dibblers to Dirk Hartog Island, a large island  off the coast of Western Australia Fig. , is a ke43, and can assist with managing finely balanced trade-offs when planning translocations44. In this study we use PVA to explore management options, and their subsequent impact on genetic diversity and survival, for the island dibbler. We first quantify changes in genetic variation in the Jurien Bay island populations from 2013 to 2018. We then incorporate these genetic data into population viability analysis (PVA) models which were developed to explore various harvesting options for a captive breeding program, provide guidance for translocations to Dirk Hartog Island and assess universal factors important to dibbler survival. Our overarching objective is to maximise genetic diversity and long-term survival in new populations while minimising impact on the source populations.Population viability analyses (PVA) are widely used in planning and evaluating conservation actions for threatened populations36. Female dibblers have an annual oestrous period, breed in autumn and carry a single brood of up to eight pouch young47. On Boullanger and Whitlock Islands, males die after the first mating season in some years31. Facultative male die-off occurs more often on Boullanger Island than Whitlock Island31, and has not been observed on Escape Island36. In mainland populations, males survive well into their second year29, and no male die-off has been observed in captivity47. While facultative male die-off appears to be a consequence of an extreme mating strategy in response to highly seasonal and limited breeding periods48, reduced availability of food and nutrients may increase its frequency on Boullanger Island49.Dibblers have a predominantly insectivorous diet, a crepuscular nature and inhabit areas of dense unburnt vegetationThis study focuses on three Jurien Bay island populations: two parental—Boullanger Island (35 ha) and Whitlock Island (5.4 ha), and one translocated—Escape Island 10.5 ha)  was completed using a QIAGEN Multiplex PCR PlusKit, with a one in ten dilution of all DNA samples and 14 microsatellite primer pairs . For validation and standardisation of genotypes, samples from Boullanger Island (n = 5), Whitlock Island (n = 5), and Escape Island (n = 6) populations from 2012 or earlier were compared to an earlier cohort51. The genetic diversity of each population was assessed, including mean number of alleles per locus (NA), allelic richness , observed (HO) and expected (HE) heterozygosity. Deviations from Hardy–Weinberg Equilibrium were assessed by calculating inbreeding coefficients (FIS) for each population. Positive FIS values represent a deficit in heterozygosity while negative FIS values represent an excess of heterozygosity. These metrics were analysed using FSTAT v2.9.3.252 and GENALEX v.6.50354. Differences in allelic richness and heterozygosity values between populations were tested with Wilcoxon’s rank sum test in R v3.5.1 statistical package55, with samples paired by locus.To assess if null alleles were present within the populations, all loci were analysed with MICROCHECKER v2.2.3ST values were calculated using FSTAT v2.9.3.252 and pairwise Jost’s D values were calculated in GENALEX v.6.50354. Pairwise FST measures genetic fixation or the amount of genetic drift between populations, whereas pairwise Jost’s D measures allelic differentiation. For both, values can range from zero (low divergence) to one (high divergence). Clustering analysis using the program STRUCTURE v2.3.456 was completed to visualise the genetic composition of the translocated population on Escape Island. As previous work has shown that Boullanger and Whitlock Islands are genetically divergent39, suggesting no gene flow between islands since rising sea levels separated them, we assumed allele frequencies were uncorrelated in our models. In contrast, Escape Island is an admixed population, and so we compared models that both included and excluded admixture. The number of clusters (K) was set from one to ten, and ten replicates were run per K tested, over 100,000 steps of the Monte Carlo Markov chain after a burn-in length of 10,000 steps. To confirm the best value for K, ΔK was estimated in STRUCTURE HARVESTER57, where the largest ΔK value indicated the K value which was the best fit58.To measure genetic distances between populations, pairwise F59. A linkage disequilibrium model with random mating was selected, and the lowest allele frequency was set to 0.0560. To detect the probability of a recent bottleneck event occurring within populations, data were analysed in BOTTLENECK v.1.2.0261, using the two-phase model (TPM) with the probability of the stepwise mutation model (SMM) set to 95% and variance set to 12, as recommended by Piry et al.62. A one-tailed Wilcoxon sign rank test was used to determine excess heterozygotes.Estimates of effective population size (Ne), defined as the size of an ideal population that will show an equal rate of genetic drift as the observed population, were generated in NeEstimator v2.154, and reported as the mean per population. To determine if pairs of individuals had alleles identical by descent (IBD), Queller and Goodnight’s63 pairwise relatedness (r) was estimated in GENALEX v.6.50354. Within-group means and 95% confidence intervals for each population were calculated using 999 permutations and 1000 bootstraps, respectively. Differences within populations across years were tested with Wilcoxon’s rank sum test in R v3.5.155.Individual heterozygosity, or the number of heterozygous loci within each individual, is a potential indicator of an individual’s fitness and was measured in GENALEX v.6.50364. VORTEX is an individual-based PVA program that utilises species life history traits and stochastic environmental factors to predict outputs such as the probability of extinction, population growth rate and genetic diversity65. A baseline PVA model for island dibblers39 was refined by consulting experts  and literature46. A summary of demographic parameters used in models is provided in Table 67. Meteorological data indicate that the Jurien Bay area has periods of low rainfall at an average frequency of approximately eight years68. Thus, catastrophes at eight-year intervals that reduced reproduction and survival by 70% were implemented , population carrying capacity , founder number (10 to 100 in increments of 10) and frequency of droughts (0% to 20% probability in increments of 2.5%).Sensitivity analyses, defined as the evaluation of how changes to life-history traits affect population growth or long-term viability39. Initial population sizes were set to 68, 29, and 26 for Boullanger, Whitlock, and Escape Islands respectively. Simulations were run for six years (500 replicates), and predictions from the model for 2018 were compared to empirical data collected in 2017/2018. This allowed for evaluation of how well the optimised baseline model captured short term  viability and genetic diversity within the island populations.After sensitivity testing, a best-performing  baseline model was used to estimate current population sizes, genetic diversity, and allele frequencies based on census data available for 2012One captive breeding population and one new population on Dirk Hartog Island were simulated, to reflect current management actions. As the Perth Zoo can accommodate ten dibbler breeding pairs  11 scenarios were simulated to determine the ideal harvest design to provide ten males and ten females for captive breeding, without detrimentally affecting the populations on Boullanger and Whitlock Islands. These scenarios ranged from harvesting ten males and ten females from one island only, to harvesting five males and females from each island. A further 11 scenarios were simulated as above, using Escape and Whitlock Islands as source populations.To model a translocated population on Dirk Hartog Island, the same parameters were used for the translocated population established on Escape Island, but with a much larger estimated carrying capacity of 10,000 based on land area. As female dibblers can produce a maximum of eight offspring per brood, and Perth Zoo plans to conduct captive breeding for two years, up to 160 dibblers could be available for translocation to Dirk Hartog Island and will be released over two consecutive years . Allowing for some captive mortality and retention of adults for a second breeding season, release groups of 30, 40, 50, 60 and 70 juveniles per year were modelled to determine the impact of varying founder size on survival probability and genetic diversity.In 2018, nine dibblers from Whitlock Island  and five dibblers from Escape Island  were captured for Perth Zoo’s latest captive breeding program. To maximise outcomes of the current captive breeding program, we modelled release groups of 20 (to account for the possibility of fewer dibblers being born in captivity than expected), 30, 40, and 50 juveniles per year at Dirk Hartog Island. While seven female dibblers can produce up to 112 dibblers over two years, we chose lower numbers to account for some mortality and animal retention for breeding. We made the same assumptions as in previous models and compared outcomes to the optimal translocation scenarios.Permission to collect samples was granted by the Department of Biodiversity Conservation and Attractions Animal Ethics Committee , and all methods were performed in accordance with relevant guidelines and regulations.All authors give consent for this publication.The percentage of polymorphic loci in each population ranged from 7.1% (1/14) in Whitlock Island, to 64.3% in Boullanger Island (9/14) and 85.7% (12/14) in Escape Island. Since 2013, two loci had become monomorphic in two populations . Analysis of markers in MICROCHECKER confirmed that no loci contained null alleles.O and HE values in the Whitlock Island population were significantly lower than both Boullanger and Escape Island populations . In contrast, there were no significant differences in allelic richness, HO, or HE values between Boullanger and Escape Island populations. Inbreeding coefficients (FIS) ranged between – 0.14 to 0.11, but no FIS value was significantly different from zero after correction for multiple comparisons. Mean values of individual heterozygosity were greatest in Escape Island, lowest in Whitlock Island and although these appeared to decline over time in all three populations, differences between the latest and earliest cohorts were not significant  (Table K = 2), and models that included admixture showed better convergence and greater absolute probabilities (mean posterior probability (± s.d.) of − 2435.8 (± 0.6) with admixture vs. − 2540.5 (± 1.9) without admixture). Despite an even number of founders used in the captive breeding program plus an additional three Whitlock island males46, the admixture seemed to bias toward the Boullanger source population  Table . The Esc1) Table . ClusterP < 0.05), suggesting that a bottleneck event has occurred recently within these populations (Table 62. Estimates of effective population size (Ne) were low for Boullanger and Escape Islands but could not be determined for most Whitlock Island populations  of the Boullanger Island population was high and comparable to that of the Escape Island population (1.68 and 1.62 respectively), while the Whitlock Island population had the lowest lambda at 1.49. The cohort generation times (Tc) were estimated to be 1.53 (Boullanger Island), 1.56 (Whitlock Island) and 1.54 (Escape Island), with a mean Tc across populations of 1.54 years.The baseline model predicted that for those populations that survive 100 years (or around 65 generations), Boullanger Island’s population size remained relatively stable, Whitlock Island’s population decreased from 33 to 26) and Escape Island’s population increased (from 21 to 33)  while carrying capacity also has a strong effect . Founder size did not have a strong impact on survival. Sensitivity analyses are summarised in Supplementary Fig. Linear regression on sensitivity testing showed that the frequency of droughts has the greatest impact on the survival of new dibbler populations  and only one polymorphic locus, increasing error in the empirical estimates. Nonetheless, both modelling data and empirical data show the 2018 trend of highest genetic diversity in Escape Island, closely followed by Boullanger Island with substantially lower genetic diversity in Whitlock Island. The six-year probability of survival was 98% in Boullanger Island, 91% in Escape Island and 90% in Whitlock Island.Estimates of population size, gene diversity and number of alleles in 2018 for all three island populations from a six-year (~ four generations) PVA model populated with 2012 empirical data were in good agreement with observed values Tables . Modellids Table  fell witThe best scenario for establishing a captive breeding program of ten breeding pairs from the parental populations was to harvest 14 dibblers  from Boullanger Island or Escape Island, and six dibblers  from Whitlock Island  should be the minimum threshold due to the survival probability of above 90% and slightly higher predicted levels of gene diversity  showed a minimum of 80 dibblers should be released from the captive breeding program over two years (40 per year) on Dirk Hartog Island Table b, as sur71. Dibblers are a prime example of these vulnerable species, as we predict that Jurien Bay island populations will decline over the next century. The use of PVA has provided important insights into the genetic and ecological management of the Jurien Bay island populations, as well as a framework for planning translocations to Dirk Hartog Island, which if successful, will provide a critical insurance population where the relictual genetic variation can be maintained.Since European settlement in the early 1800s Australia has lost much of its faunal diversity, and a further 106 mammalian species are at risk of extinction73, in line with the results of this study. Genetic drift is expected to have a large impact on the genetic diversity of island dibblers, as smaller populations are more prone to drift than larger ones74. Drift decreases heterozygosity at the rate of 74, and if population sizes remain small for multiple generations, large losses of heterozygosity are more likely6. While we were not able to show a significant reduction in the various genetic diversity measurements we used across our year cohorts for each island, which spanned a 4 or 5-year period, in almost every case the most recent estimate was less than the initial estimate  and Whitlock Island (~ 72%) from 2006 to 2017, and Escape Island (~ 12%) from 2002 to 2018. This suggests a gradual loss of genetic diversity, which is not unexpected as islands are isolated with no gene flow, however this finding requires formal testing.Low genetic variation is common in island populations of many mammal speciesentclass1pt{minima75, however, this cannot be the case for Whitlock Island given the carrying capacity is 42. As more than five polymorphic loci are required to accurately predict Ne values of 100 using the linkage disequilibrium method70, infinite estimates for Whitlock Island likely reflect the low power from the low number of polymorphic loci, for example there were only two polymorphic loci in the 2017 cohort. Overall, although there is uncertainty around estimates, the Ne values for all island populations are likely to be much smaller than Ne values recommended to avoid inbreeding depression (Ne ≥ 100)76. This suggests that these island populations are more prone to the effects of genetic drift and loss of heterozygosity, and are susceptible to inbreeding depression and the loss of adaptive potential14.Effective population size (Ne) estimates for island dibblers were small or undefined with evidence that two of the three island populations have undergone recent bottleneck events. However, facultative male die-off on Boullanger Island may cause an overestimation of recent bottlenecking events and underestimates of Ne. Therefore, the small estimated Ne for Boullanger Island across cohorts may be misleading. Infinite estimates of Ne generally suggest very large population sizes36. Like other studies79, this endorses the use of multiple source populations to found translocated populations, as translocated populations often have higher genetic variation than their respective source populations. Furthermore, the higher individual heterozygosity observed in Escape Island dibblers relative to dibblers on other islands indicate this mixed population may have higher fitness82.Escape Island has the highest genetic diversity due to the population being admixed from Boullanger and Whitlock IslandsST and Jost’s D) were also greater between Whitlock and Escape Islands than between Boullanger and Escape Islands in 2014, but interpretation of this trend is confounded by the level of fixation in Whitlock. If this representation bias in the Escape Island population towards Boullanger Island is real, it has occurred despite equal numbers from both parental populations being used to found the original captive bred source population36, and requires further investigation. Males from Boullanger Island are larger than males from Whitlock Island39, and heavier and younger males were observed to have higher mating success in captivity83. As biased mating leads to a decrease in heterozygosity, an increase in inbreeding and a reduction in Ne84, confirmation or refutation of this trend in the wild would be useful to help future translocations maximise retention of genetic diversity.Interestingly, we provide tentative support that the 2014 population on Escape Island may have greater genomic representation from Boullanger Island than Whitlock Island. Genetic distances (Fr > 0.8), implying high levels of inbreeding and a deficit in heterozygotes. In contrast, negative inbreeding coefficients (FIS) in the 2017 and 2018 Whitlock Island populations reflected an unexpectedly high heterozygosity, which could be caused by a recent population bottleneck, inbreeding avoidance or biased sampling. Ambiguities in results for Whitlock Island are likely further confounded by the low power associated with few polymorphic loci available for this population (e.g. 3 loci in 2016 and 1 locus in 2018), likely due to fixation, in conjunction with the small sample size in 2018 (n = 9). Future studies with orders of magnitude more genome-wide genetic markers, e.g. single nucleotide polymorphisms (SNPs), will provide greater statistical power to better resolve whether inbreeding is occurring85, estimate effective population sizes86 and quantify inter-population genetic distances87.Whitlock Island dibblers were found on average to be highly related to each other  will likely expire within 50 years, or around 33 generations. These observations are consistent with observed downward population trajectories  and the apparent gradual reductions in genetic diversity we see when this study is compared to Thavornkanlapachai39.Small isolated populations are susceptible to reductions in survival and gene diversity, especially in the absence of immigration (i.e. gene flow)93. If stochastic events such as drought occur more frequently, temporarily reducing reproduction and increasing mortality, and population recovery is constrained by limited carrying capacities, population sizes may fall below extinction thresholds. Concerningly, drought events will likely become increasingly severe as periods of low rainfall become more frequent in the future95. To compound matters, due to its extensive sand-based habitat, Boullanger Island is projected to erode as sea levels rise and further reduce its carrying capacity . In contrast, the carrying capacities for Whitlock and Escape Islands are not expected to change over the next century, but the small size of these islands limits population growth, and so could substantially constrain long-term viability96.Island dibbler populations appear to be particularly sensitive to carrying capacity and the frequency of drought. Although the deterministic growth rates are relatively high, so too are the estimated rates of juvenile and adult mortality. High juvenile mortality is typical of mammals that mature early and produce large numbers of offspring after a short gestationWith an estimated carrying capacity for dibblers of 10,000 on Dirk Hartog Island, the inclusion of dibblers as part of the ‘Return to 1616’ ecological restoration initiative will be critical for their long-term viability. As genetic variation in island dibblers is low and declining, genetic management will be required to prevent further loss. Our PVA modelling indicates admixing island populations in a captive breeding program will maximise the genetic diversity and subsequent adaptive potential of the introduced population on Dirk Hartog Island. If the population establishes successfully, the ideal founder population (Scenario 8) will be able to capture higher genetic diversity relative to single source populations, and the predicted growth of the Dirk Hartog Island population to a census size above 6000 will facilitate the retention of this diversity.44, woylies43, golden bandicoots97, pygmy rabbits98 and frogs15. The good agreement between historical empirical data and our simulations gives confidence that our models can predict demographic and genetic changes relatively accurately. We have therefore provided a management tool that can help predict outcomes of conservation actions, even in the absence of further empirical data. Furthermore, our sensitivity analyses show that the viability of dibbler populations are strongly influenced by carrying capacity and the frequency of drought. Consequently, these ecological factors warrant inclusion in future conservation management of the island dibbler, and it is worthwhile to investigate ways in which to mitigate their impact. For example, the planting of preferred habitat vegetation may increase otherwise restricted local carrying capacity, and the provision of alternate sources of water could help traverse challenging drought periods100, which for dibblers would mean supplementing insect prey. To this end, introductions to Dirk Hartog Island will facilitate a much greater carrying capacity than exists on any of the Jurien Bay islands.The benefits of using PVA to optimise translocation designs have been demonstrated in many other threatened vertebrates, including banded hare-wallabiesThe current captive breeding program implemented at Perth Zoo should result in higher genetic diversity in the new Dirk Hartog Island population, relative to the founder populations, and reach survival probabilities of 80% if 80 individuals or more can be released over two years. Harvesting these numbers would not be feasible without a captive breeding program due to the very low current census sizes of the Jurien Bay island populations. However, the program would benefit from replacing three Whitlock Island dibblers with nine Escape Island dibblers. This would increase genetic diversity in the translocated population by 53% and reduce the threat of overharvesting on Whitlock Island. Due to downward population trajectories and the sensitivity of population viability on Whitlock Island to population crashes, we strongly advocate that harvesting is avoided during drought-driven periodic declines. Further, we recommend ongoing genetic monitoring of Jurien Bay island populations and the new Dirk Hartog Island population to detect potential reductions in genetic diversity and increases in inbreeding, as well as monitoring for signs of inbreeding depression.39, and should therefore be more robust to harvesting. Using them to supplement island dibblers could lead to a genetic rescue effect, which would increase genetic diversity, fitness and evolutionary potential, ultimately reducing the risk of extinction106. However, several studies have found that crossing genetically distinct populations or subspecies can reduce the overall fecundity and viability of species by both pre- and post-zygotic reproductive barriers109. For example, outbreeding depression may occur where hybrid offspring are maladapted to local environments and consequently suffer reductions in fitness28. The Jurien Bay Islands have been separated from the mainland for over 6500 years110, and presumably a greater time still has passed since gene flow occurred between the Jurien Bay dibbler populations and those 600 km away on the south coast of Western Australia. What’s more, the two regions have different climates and habitats. Consequently, mainland dibblers have begun to diverge from island dibblers—for example, mainland dibblers are significantly larger in body size and weight than island dibblers33 meaning non-random mating could potentially occur if mainland and island dibblers were to inter-breed46. In addition, island dibblers have a shorter breeding season and gestation period relative to mainland dibblers, as well as mate and produce offspring later in the year47. Hence, while there is an obvious rationale to mixing island and mainland dibblers to address low genetic diversity, we recommend the use of ex situ husbandry to determine the success of interbreeding between island and mainland animals before this becomes a recommended management option.To improve population viability on Boullanger, Whitlock and Escape Islands, as well as in future translocated populations, it is worth considering demographic and genetic augmentation via supplementation from dibbler populations that occur on the Western Australian mainland . Mainland population sizes are larger and more genetically diverse than those on the Jurien Bay islandsIn summary, this study shows captive breeding and translocation are critical for continuation of the declining island dibblers. We have demonstrated how using both PVA models and genetic information can generate recommendations for ongoing and future conservation actions, e.g. conservation translocations, for threatened species such as the dibbler, and maximise their chances of success. The PVA model developed in this study, complemented with greater genetic resolution from using a substantially greater number of markers (e.g. SNPs), should be used to evaluate future viability of the translocated population on Dirk Hartog Island, to better inform ongoing management of the species. After quantifying the probability and impact of outbreeding depression, expanding the modelling developed here to encompass all extant dibbler populations (i.e. island and mainland) will be useful for predicting the long-term consequences of admixture between the more distantly related source populations. With many threatened Australian species experiencing increased fragmentation and subsequently elevated extinction risk, devising strategies that maximise genetic diversity, such as admixture, is increasingly being considered for long-term management.Supplementary Information.


Megalobrama terminalis) that inhabits southern China drainage with multiple geographic populations and relatively high digestive plasticity as a candidate to explore the potential effects of genetic variation and environmental discrepancy on fish gut microbiome. Here, high-throughput 16S rRNA gene sequencing was utilized to decipher the distinct composition and diversity of the entire gut microbiota in wild M. terminalis distributed throughout southern China. The results indicated that mainland (MY and XR) populations exhibited a higher alpha diversity than that of the Hainan Island (WS) population. Moreover, a clear taxon shift influenced by water temperature, salinity (SA), and gonadosomatic index (GSI) in the course of seasonal variation was observed in the gut bacterial community. Furthermore, geographic isolation and seasonal variation significantly impacted amino acid, lipid, and carbohydrate metabolism of the fish gut microbiome. Specifically, each geographic population that displayed its own unique regulation pattern of gut microbiome was recognized as a specific digestion strategy to enhance adaptive capability in the resident environment. Consequently, this discovery suggested that long-term geographic isolation leads to variant environmental factors and genotypes, which made a synergetic effect on the diversity of the gut microbiome in wild M. terminalis. In addition, the findings provide effective information for further exploring ecological fitness countermeasures in the fish population.Gut microbiome is considered as a critical role in host digestion and metabolic homeostasis. Nevertheless, the lack of knowledge concerning how the host-associated gut microbiome underpins the host metabolic capability and regulates digestive functions hinders the exploration of gut microbiome variation in diverse geographic population. In the present study, we selected the black Amur bream ( The vertebrate gut contains trillions of microorganisms collectively known as the gut microbiome  is a migratory species that holds an important position in fishery production in southern China drainages. The river drainages located in southern China are complex and diverse. Sea-level fluctuations due to climate changes have been frequently documented to be a pivotal factor that shapes dynamic histories of drainages, particularly rivers near the sea, thereby influencing the geographic distribution of the M. terminalis population , each population presents a genetic structure related to the local geography  and flood (reproduction) seasons to gain insights into the altering environmental conditions that drive the effect on the diversity of the gut bacterial community, which is of great importance for the research of its ecology and conservation biology. In parallel, illustrating the fine-scale variation in gut bacterial community of multiple M. terminalis populations can provide useful data for further exploration of host-associated microbiomes in the wild fish populations.According to related research, at the population level, local adaptation to the landscape may be reflected in the variable gut microbiome , and pH of sampling sites were measured with an HQ30 instrument . The sampling variables of time of collection, location, water temperature, SA, pH, and DO are listed in t, to the nearest 1 g), eviscerated weight , gonad and liver weights  were measured. The gonadosomatic index (GSI = 100 × GW/EW), hepatosomatic index (HSI = 100 × LW/EW), and fatness (K = 100 × W/L3) were calculated. To evaluate the fish gut microbiome, 36 individuals  were randomly selected for sequencing from three populations: the Wanquan River (WS), Moyang River (MY), and Pearl River (XR) populations. All selected fish were anesthetized with an overdose of MS 222  and stunned and quickly decapitated. The exterior surfaces of the fish body and instruments were wiped with 75% ethanol to prevent contamination from the skin surface, and instruments were sterilized before dissection. Approximately 0.4 g of gut contents were extracted for DNA extraction, and 0.2 g of gut contents were separated for use in enzymatic analysis; all contents samples were quickly put in liquid nitrogen and then transferred to an ultra-low temperature freezer and stored at −80°C until use.In the present study, three geographic populations of the black Amur bream were sampled from six different sites in the Pearl River, Moyang River, and Wanquan River during the flood (July 2019) and dry (January 2020) seasons . A totalM. terminalis was sent to Novogene Bioinformatics Technology, Co., Ltd., Beijing, China for further sequencing analysis.Approximately 0.2 g of each sample was extracted using a QIAamp DNA Stool Mini Kit . All DNA extracts were stored at −80°C until being tested. The quality and integrity of each DNA concentration and purity were monitored on 1% agarose gels. The V4 hypervariable region of 16S rRNA genes was used in the present study. PCR was performed using the specific primer (515F−806R) that contained a barcode. Total DNA from the gut of different groups of Sequencing libraries were created using the Ion Plus Fragment Library Kit 48 rxns  according to the manufacturer's instructions. The library quality was assessed on a Qubit@ 2.0 Fluorometer . After that, the library was sequenced, and single-end reads were generated. Quality filtering of the raw reads was performed under specific filtering conditions to obtain high-quality clean reads according to the Cutadapt quality-controlled process Martin, . The tagPermutation multivariate analysis of variance (PERMANOVA) was utilized to test the statistical significance of WS, MY, and XR populations  were measured using corresponding enzyme assay kits from Nanjing Jiancheng Bioengineering Institute, P.R. China . The soluble protein (mg/ml) contents were determined by the Bradford method using bovine serum albumin as the standard (0.563 g/L)  was conducted to explain the relationship between the gut microbial compositions of different M. terminalis populations and their basic environmental and biological parameters and the correlation between gut microbial diversity and the degrading enzyme activity in different populations of the black Amur bream. Redundancy analysis (RDA) was conducted to explain the relationship between the KEGG orthologies (KOs) of the gut microbiome and the degrading enzyme activity in different populations of the black Amur bream. Here, we used the R implementation of the procedure (version 3.1.14).STATISTICA 6.0  was used for statistical analysis of the data. The normality of the data and homogeneity of variance were assessed p < 0.05). No matter whether the flood or dry season, the WS population exhibited the lowest alpha diversity and richness indices. Moreover, no significant differences in alpha diversity or richness were found between the MY and XR populations. In general, our results demonstrated that the gut microbiome in mainland populations had higher alpha diversity than that in the Hainan Island population. The Good's coverage of all the groups ranged from 98.87 to 99.53% , and richness indices  were applied to evaluate the gut microbial complexity of the three populations . It can o 99.53% .p < 0.05, p = 0.249).To compare the similarity of gut bacterial community between different populations, the unweighted pair group method with arithmetic mean (UPGMA) clustering tree based on weighted UniFrac distances elaborated that MY and XR populations formed a cluster, while the WS population was a single cluster apart from the other populations. The bacteria were classified into 84 phyla, 161 classes, 351 orders, 549 families, and 1,260 genera . At the Cetobacterium could be considered as a microbiological marker to differentiate WSF groups from the other groups. Aeromonas and Clostridium in the WSD group presented higher linear discriminant analysis (LDA) scores than those in the other groups . It is evident from the result that overall, there was a decreasing trend in SA with latitude change of habitat in the dry season (p < 0.05). The results of fish basic biological information revealed that SL, Wt, GSI, and K of WS, MY, XR populations were rising in turn. Meanwhile, it was exhibited that there were significant differences in SL, Wt, GSI, K, and sex mature ratio (SMR) in three populations between flood and dry seasons. In the flood season, HSI of MY was the highest among the three populations. The gut bacterial community composition in each group had a close relationship with their habitat environment and biological index . Likewise, trypsin activity was decreased significantly while the amylase and lipase activities were clearly increased from MYF to MYD groups (p < 0.05). Lipase activity dropped off sharply, while amylase activity had a dramatic increase from the XRF to XRD groups. As shown in Gut content enzyme activities in the three geographic populations are shown in Aeromonas and Cetobacterium in the WS population are significantly higher than that in the MY and XR populations (p < 0.05), while the genera Lactobacillus and Bacteroides are lower. According to p < 0.05; MetaStat was utilized to analyze the relative abundance among the three disparate populations at the genus level. From above M. terminalis in the flood and dry seasons. Our results indicated that the XR population had higher alpha diversity than that of the WS population. Moreover, the UPGMA clustering revealed that the MY population was similar to the XR population that was aligned with a genetic divergence of three black Amur bream populations  can be found in the article/The methods involving animals in this study were conducted in accordance with the Laboratory Animal Management Principles of China. All experimental protocols were approved by the Ethics Committee of the Pearl River Fisheries Research Institute, Chinese Academy of Fishery Sciences.YLiu: conceptualization, data curation, and writing—original draft. XL: funding acquisition. JL: funding acquisition and supervision. YLi: formal analysis and writing—review and editing. QZ: formal analysis. All authors contributed to the article and approved the submitted version.This study was funded by Guangdong Basic and Applied Basic Research Foundation (Grant No. 2019B1515120064); the National Key R&D Program of China (Grant Nos. 2018YFD0900902 and 2018YFD0900903); Open Fund of Key Lab of Freshwater Biodiversity Conservation, Ministry of Agriculture and Rural Affairs of China (Grant No. LFBC1006); and Open Fund project of Fishery Resources and Environmental Science Experimental Station of The Upper-Middle Reaches of Yangtze River Ministry of Agriculture (Grant No. 0202020017).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer CS declared a shared affiliation with the authors YLiu, YLi, JL, and XL to the handling editor at the time of review.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Anthyllis vulneraria along elevational gradients in the Alps from the valleys to the elevational limit (2500 m) and along a latitudinal gradient (2500 km) from Central Europe to the range margin in northern Scandinavia. Both types of gradients corresponded to an 11.5°C difference in mean annual temperature. Genetic diversity strongly declined and differentiation increased with latitude in line with the predictions of the ACM. However, as population size did not decline with latitude and genetic diversity was not related to population size in A. vulneraria, this pattern is not likely to be due to less favorable conditions in the North, but due to serial founder effects during the post‐glacial recolonization process. Genetic diversity was not related to elevation, but we found significant isolation by distance along both gradients, although the elevational gradient was shorter by orders of magnitude. Subarctic populations differed genetically from alpine populations indicating that the northern populations did not originate from high elevational Alpine ones. Our results support the notion that postglacial latitudinal colonization over large distances resulted in a larger loss of genetic diversity than elevational range shifts. The lack of genetic diversity in subarctic populations may threaten their long‐term persistence in the face of climate change, whereas alpine populations could benefit from gene flow from low‐elevation populations.The abundant centre model (ACM) predicts that the suitability of environmental conditions for a species decreases from the centre of its distribution toward its range periphery and, consequently, its populations will become scarcer, smaller and more isolated, resulting in lower genetic diversity and increased differentiation. However, little is known about whether genetic diversity shows similar patterns along elevational and latitudinal gradients with similar changes in important environmental conditions. Using microsatellite markers, we studied the genetic diversity and structure of 20 populations each of  Anthyllis vulneraria (kidney vetch) along an elevational gradient in the Alps and a latitudinal gradient from Central Europe to northern Scandinavia. Genetic diversity strongly decreased and genetic differentiation among populations increased with latitude, suggesting that founder effects played an important role in the establishment of high‐latitude populations during the post‐glacial recolonization of formerly glaciated areas. Our results also indicate that subarctic populations differed genetically from alpine populations suggesting that the northern populations did not originate from high elevational Alpine ones.We studied the genetic diversity and population structure of 40 populations of the widespread plant species  However, there are also important differences between the changes in environmental conditions along the two types of gradients, including those in day length, irradiance, CO Körner, . Moreove Körner, . Four pa Körner, : (1) Pop Körner, . (2) Pop Körner,  because  Körner, . Finally Körner, . Reviews Körner, , and lit Körner, .Anthyllis vulneraria (Fabaceae) as a model species to study the patterns of genetic variability and differentiation as influenced by latitude and elevation because it has an exceptionally wide geographic and elevational distribution in Europe. The large distribution of A. vulneraria allowed us to study general genetic patterns that cannot be detected in arctic‐alpine species or in rare species with fragmented and isolated populations. We studied the genetic diversity and structure of A. vulneraria along two gradients chosen to correspond to a change of 11.5°C in annual mean temperature: a latitudinal gradient of c. 2400 km from Central Europe to Iceland and northern Norway and three elevational gradients of c. 2000 m elevational difference in the European Alps  Does the genetic diversity of A. vulneraria vary similarly along gradients of elevation and latitude? (2) Are patterns of genetic differentiation similar along the two types of gradients?The effects of the two types of gradients should ideally be compared in species that have both a large latitudinal and elevation extension  is a diploid biennial to perennial herb of nutrient‐poor calcareous grasslands and screes. Its distribution is exceptionally wide as it occurs from the North of the African continent across Europe to above 70°N in Scandinavia and from sea level up to 3000 m a.s.l. . In small populations (<100), we recorded the number of plants to the nearest five, while in large populations we counted the plants in a part of the total area and extrapolated the number to the whole population area.2.3We extracted genomic DNA using a DNeasy Plant Mini Kit (QIAGEN) starting from approximately 10 mg of dried material. Samples were genotyped at 17 microsatellite loci  and 30″ at 72°C and a last step of 30′ at 68°C. Reactions were performed using a Mastercycler nexus (Eppendorf). PCR products were separated using an automated sequencer . The data were analyzed using Geneious 11.1.5 , number of private alleles (NP), number of alleles (NA), number of effective alleles (NE), and observed and unbiased expected heterozygosity  were estimated in GenAlEx 6.5  was calculated with the R‐package PopGenKit 1.0 . Genetic diversity indices including number of multi‐locus genotypes (We tested for the significance of heterozygote deficiency or excess (Hardy–Weinberg equilibrium) in the 40 populations using the Markov chain method in GENEPOP 4.7.3 Rousset,  with 10,The software Microchecker  and 999 permutations (G"ST).We used  Goudet,  and Geno Goudet, , respectThe partitioning of genetic variation within and among populations, and between the three mountain regions  was analyzed with AMOVA as implemented in GenAlEx 6.5  geographic distance differed between southern (1 to 9) and northern populations (10 to 20) using a permutational GLM with 5000 permutations with the R‐package lmPerm 2.1.0. ) and geographical distances for populations of the latitudinal gradient with a Mantel test  to test for isolation‐by‐distance (IBD). For the populations in the Alps, we analyzed the relationship between pairwise genetic distances and both geographical distances and the differences in elevation with a linear model. p‐values were derived from sequential permutation tests with 1000 permutations using lmPerm. We also tested whether the mean genetic distance between pairs of populations differed between populations north of 56°N and those further south by relating the genetic distances to the geographic distances and population type using a permutational analysis of covariance with 1000 permutations using lmPerm.We examined the relationship between genetic  based on pairwise A. vulneraria populations. To estimate the number of genetic clusters (K), we carried out ten independent runs with K = 1–20 with 106 Markov chain Monte Carlo (MCMC) iterations after a burn‐in period of 105, using the model with correlated allele frequencies and assuming admixture. We decided on the most probable number of K based on the log probability of the data and their variability associated with each K  was generally high but was lower in the subarctic populations and was particularly low for the three northernmost populations  alleles per locus. The number of multi‐locus genotypes (E) per population varied from 0.39–0.65 (mean = 0.53) for the elevational gradient and from 0–0.57 (mean = 0.32) for the latitudinal gradient (Table E) did not decrease clearly with elevation , but decreased strongly with latitude , but not with elevation . However, closer inspection showed that genetic diversity (uHE) was similarly high in populations from 46 to 56°N (no. 1 to 10) and decreased strongly for the populations further north. Genetic diversity (uHE) did not decrease with population size  indicating that there was no strong genetic drift in small populations  in the populations along the latitudinal gradient  but did not vary consistently with elevation .The inbreeding coefficient We found significant deviations from Hardy–Weinberg equilibrium (HWE) for less than 10% of the allele × population combinations after correcting for the false discovery rate. Thirteen of the 17 loci significantly deviated from HWE in at least one population and 15 populations showed significant deviation from HWE (Table p > .81). Therefore, all following analyses were performed with the original data set.Null alleles were suggested in 64 locus × population combinations (9%), of which 17 were for locus AV23. However, adjusting the allele frequencies for the null alleles did not change the unbiased expected heterozygosity in any of the analyzed populations .The first two axes of the PCoA explained 35.6% of the variation ] increased gradually and the value that also converged well across the 10 independent runs was obtained for 7 Figure . Howeverr Figure , confirms Figure , which ws Figure .3.3FST = 0.36 and G"ST = 0.63). The genetic differentiation among populations was much higher across the latitudinal (FST = 0.46 and G"ST = 0.69) than across the elevational gradient (FST = 0.19 and G"ST = 0.40), indicating that the populations in the Alps are less differentiated from each other. AMOVA indicated that only a small proportion (4.8%) of the genetic variation was among the three mountain regions, the differentiation among populations within regions was much higher (15%). Most of the genetic variance was within populations . In contrast, mean pairwise G"ST increased strongly with latitude . Mean pairwise G"ST decreased with increasing genetic diversity (uHE) for both gradients , but the slope of the relationship was less steep (interaction gradient type × uHE:p < .01).Pairwise population n Figure  and did e Figure  and was G"ST/(1 − G"ST) was not related to geographical distance . However, after removing an outlier caused by the high mean G"ST of northern populations, a pattern of isolation‐by‐distance (IBD) was detected for the latitudinal populations . The mean pairwise genetic distance between populations, adjusted for the effect of geographical distance, was much higher for the northern (lat. >56°N) than for the more southern populations .Among populations of the latitudinal gradient, genetic distance b = 0.0008, p < .001). Moreover, adjusted for the effects of geographical distance, genetic differentiation between the populations in the Alps also increased with their difference in elevation . The effects of 1 km difference in elevation on the genetic distance between populations were similar to those of a difference of 161.3 km in horizontal distance indicating that the effects of vertical were much stronger than those of horizontal distance. However, the maximum elevational distance between populations was only 2 km.Genetic and geographical distances of the populations in the Alps were also related . This limit corresponds to the southern limit of the ice‐shield during the Younger Dryas period  and a northern (no. 6–10) subgroup in the STRUCTURE and the PCoA analyses. The geographical location of this separation is reflecting another genetic signature of the last ice age as this limit corresponds to the maximum extent of continental ice sheets during the Last Glacial Maximum (LGM) some 22,000 years BP  because the elevational gradients were much shorter than the latitudinal gradient. The kinship analysis revealed that gene flow is even restricted over short distances within populations, which may be due to restricted pollinator movement and seed dispersal.Along both gradients, we found significant isolation by distance patterns, indicating that gene flow is restricted and strongest between geographically close populations. However, a vertical  distance of a certain length between populations in the Alps resulted in a much stronger genetic differentiation between populations than the same horizontal distance between populations of the latitudinal gradient. This could be due to phenological differences in flowering periods that may restrict cross‐fertilization among populations at different elevations Premoli, . HoweverA. vulneraria in northern Europe separated by a certain spatial distance were more differentiated genetically than populations in Central Europe, indicating lower gene flow between them. A possible reason are greater mean spatial distances between neighboring populations in the North. Occurrence data from GBIF.org ; data curation (supporting); formal analysis (supporting); funding acquisition (lead); investigation (lead); visualization (lead); writing – original draft (lead); writing – review and editing . Diethart Matthies: Conceptualization ; investigation ; supervision (supporting); validation ; writing – review and editing . Sylvie Hermant: Data curation (lead); formal analysis (lead). Guy Colling: Conceptualization ; investigation ; project administration (lead); supervision (lead); validation ; writing – review and editing .The authors declare that they have no conflict of interest.1https://doi.org/10.5061/dryad.ht76hdrjp.This article has earned an Open Data badge for making publicly available the digitally‐shareable data necessary to reproduce the reported results. The data is available at Figure S1Figure S2Table S1Click here for additional data file.