Harmonia axyridis (Coleoptera: Coccinellidae), native to Asia, has been introduced to other major continents where it has caused serious negative impacts on local biodiversity. Though notable advances to understand its invasion success have been made during the past decade, especially with then newer molecular tools, the conclusions reached remain to be confirmed with more advanced genomic analyses and especially using more samples from larger geographical regions across the native range. Furthermore, although H. axyridis is one of the best studied invasive insect species with respect to life history traits (often comparing invasive and native populations), the traits responsible for its colonization success in non-native areas warrant more research.The harlequin ladybird Our analyses of genome-wide nuclear population structure indicated that an eastern Chinese population could be the source of all non-native populations and revealed several putatively adaptive candidate genomic loci involved in body color variation, visual perception, and hemolymph synthesis. Our estimates of evolutionary history indicate (1) asymmetric migration with varying population sizes across its native and non-native range, (2) a recent admixture between eastern Chinese and American populations in Europe, (3) signatures of a large progressive, historical bottleneck in the common ancestors of both populations and smaller effective sizes of the non-native population, and (4) the southwest origin and subsequent dispersal routes within its native range in China. In addition, we found that while two mitochondrial haplotypes-Hap1 and Hap2 were dominant in the native range, Hap1 was the only dominant haplotype in the non-native range. Our laboratory observations in both China and USA found statistical yet slight differences between Hap1 and Hap2 in some of life history traits.H. axyridis provides new insights into its invasion processes into other major continents from its native Asian range, reconstructs a geographic range evolution across its native region China, and tentatively suggests that its invasiveness may differ between mitochondrial haplotypes.Our study on The online version contains supplementary material available at 10.1186/s12915-023-01638-7. Invasive species have caused severe, mostly negative impacts on biodiversity and ecosystems globally and led to immeasurable losses to economies and ecosystems , 2. WithThe field of invasion biology has long been seeking an understanding of traits that make a species invasive and thus predicable. These traits encompass a suite of interacting phenotypes, such as growth, reproduction, dispersal, and defense against predators, collectively termed the “invasion syndrome” –10. An uHarmonia axyridis (Coleoptera: Coccinellidae). It is widely distributed in its native eastern Asian range and generally appreciated as an effective natural enemy suppressing populations of aphids and other agricultural pests  as an outgroup. Pairwise differentiation (FST) between sampling locations was estimated with the Arlequin v.3.5.1.2 program [mtCOI genes was inferred using the median-joining algorithm in the software Network v.4.6.1.0  [All MEGA 6.0 . Gaps annaSP 5.0 . The connaSP 5.0 . The nei program  using th program . The hapEngland) .H. axyridis in both China  and USA . To control for the potential effects of confounding factors, the environments for rearing H. axyridis cohorts in the two insectaries were identical . In addition, due to a lack of same prey species available for rearing the ladybird in different insectaries, two different but related aphid species were employed as diets: M. japonica in China and A. pisum in the USA. These two aphid species are in the same tribe of aphids (Macrosiphini), both feed on plants in the family Fabaceae, and both aphid species were reared on the same host plant species the broad bean V. faba for the experiments in China and the USA. Moreover, all the observations and insect-rearing procedures in both China and USA were conducted by the same individual (the first author H. L.). Neonate ladybird larvae were individually transferred into vials where aphids were supplied ad libitum as prey. They were reared at the temperature of 25 ± 1 °C, RH 65 ± 5%, and 16:8 light to dark photoperiod. Larval development was observed daily until pupation, and pupae were individually weighed . Emerged adults were paired in Petri dishes where aphids were provided ad libitum as food. After oviposition started, eggs were counted each day for 20 days, during which female ladybirds lay most of their eggs; thus, the total number of eggs laid during this period was used as a measure of fecundity.Observations of life history traits were made in the laboratory on two mitochondrial haplotypes, Hap1 and Hap2, of emmeans [A generalized linear model was fit to fecundity and larval duration (count variable), using quasi-Poisson error distribution when data were overdispersed , and a gemmeans . Statistemmeans .ustacks (v.1.3.4) program with the H. axyridis reference genome (http://v2.insect-genome.com/Organism/418) [http://broadinstitute.github.io/picard/; v1.118) was used to estimate, sort, remove PCR duplicates, and build BAM indices. Ambiguously mapped reads were removed from further analyses. Alignment files were then converted to BAM files using SAMtools v0.1.18 [Approximately 200 ng of whole genomic DNA from 159 representative individuals were digested using Type IIB BsaXI restriction endonuclease, followed by the addition of five unique adapter sequences with T4 DNA ligases. The adapter ligated restriction products were PCR amplified, barcoded, and pooled prior to paired-end sequencing on an Illumina NovaSeq at OE Biotech  . Raw reaism/418)  using thism/418) , 84, all v0.1.18 . Variant v0.1.18 : (a) min v0.1.18 . The RADH. axyridis geographical populations was constructed based on the neighbor-joining algorithm with 1000 bootstrap replicates using the Treebest (v1.9.2) program [K = 2–15 subpopulation clusters [K value with the lowest cross-validation error. Heterozygosity (Ho), nucleotide diversity (π), genetic differentiation (Fst) across the four global superpopulations  as estimated by our ADMIXTURE analyses (at K = 3) were calculated using VCFtools (v0.1.16) in 10-kb nonoverlapping sliding windows along each chromosome [The phylogenetic tree of all  program . A princ program . Additioclusters , terminaromosome . We utilromosome , based oH. axyridis populations from China, we filtered SNPs obtained by GATK with the following criteria: the max missing rate was 90%, collating a total of 4429 high-quality genotyped SNPs. Then, we used the Treemix 1.13 to investigate the admixture between populations, with migration edges ranging from 1 to 20 [For the Chinese populations, we conducted the population genetic analysis including PCA, phylogenetic tree, and population structure analysis using identical SNP dataset (7824 SNPs). Notably, to further infer population-level splits and mixtures for  1 to 20 .P < 10−4) for analyses of population structure, genetic diversity, and estimates of contemporary and historic migration and (b) the sites in linkage disequilibrium (R2 > 0.6 in 1000 sites) that were filtered for FST outlier analyses. All population genomics analyses were performed with the PPP pipeline [https://samtools.github.io/bcftools/bcftools.html).Prior to population genomic analyses, further filtering was applied using BCFtools v.1.11 to remove (a) the sites that were not in Hardy–Weinberg equilibrium . Convergence and mixing of the MCMC runs were assessed using the Tracer v.1.7.1 program [Estimates of contemporary migration rates were estimated using the Bayesian MCMC method of the BayesAss3-SNPs (BA3-SNPs) , 96 by u program , and posK = 2, with the European population comprising of admixed Asian-American beetles: Asia, Europe, and America, and generated two-dimensional folded  site frequency spectra (SFS) from the complete 2b-RAD dataset. With three super-populations, we considered four possible population tree topologies, shown in the NEWICK format: (1) t1: , MRCA1), America), MRCA2; (2) t2: , MRCA1), Europe), MRCA2; (3) t3: , MRCA1), Asia), MRCA2; and (4) t4: , MRCA. We considered four possible models under each topology: (1) no migration between sampled populations (isolation with no migration), (2) asymmetric migration between sampled populations , (3) asymmetric migration between sampled populations (IM) with exponential growth of each population since divergence, and (4) no migration between sampled populations with exponential growth of each population since divergence  . We comb−9 per site per generation for Drosophila melanogaster [Subsequently, we then performed a series of analyses in the FSC26 to obtain estimates of demographic history: (1) 100 replicate runs of 200,000 coalescent simulations in each cycle, with 40 optimization cycles, discarding monomorphic sites, and sites with < 10 SNP’s in the SFS for estimating demographic parameters; (2) under each such model , computing AIC from the “best” likelihood scenario; (3) picking the “best” most likely model based on the AIC estimate, and simulating a total of 100 parametric bootstrap datasets of 200,000 non-recombining 100 bp DNA segments each; and (4) re-running estimation of demographic parameters, similar to step (1) from all 100 of these parametric bootstrap datasets to obtain confidence intervals around the estimates. All these estimates were scaled using an estimated mutation rate of 3.5 × 10nogaster . GoodnesF-statistics implemented in Arlequin v.3.5.2.2 [F-statistics under a hierarchical island model. Subsequently, FST outlier loci in our empirical distribution of 7824 SNPs were identified as loci in the top 1 percentile tail of the estimated distribution after the FDR (Benjamini and Hochberg) correction for multiple tests. Additionally, after thinning the dataset for LD using BCFtools, we performed analyses of FST outlier loci using OutFLANK [FST estimates were computed across all sampled populations , and uncorrected FST estimates were used to ensure that loci were not deviating from the corrected FST estimates. The OutFLANK function was then used to compute deviating loci from the expected neutral FST distribution, and outliers were estimated at a P-value cutoff of 0.05 using the FDR (Benjamini and Hochberg) correction for multiple testing. Outlier loci were mapped back to the raw reads from our 2b-RAD sequencing, and NCBI BLAST-n was used with publicly available Coleoptera: Coccinellidae genomes to obtain highly similar “hits.”We used the outlier detection .3.5.2.2  by groupOutFLANK . Weir anH. axyridis in its native range China versus in its non-native range Europe using two published resequenced whole genomes  [−9 per site per generation, an upper limit of 5 for TMRCA, using 34 free atomic intervals [We inferred historical variation in effective population sizes of ire, UK) , 44. Briire, UK) , 101 andire, UK) . The conire, UK)  with a gntervals . ResultsAdditional file 1: Table S1. Polymorphic nucleotide position of mtCOI gene defining the 22 haplotypes identified in native and non-native Harmonia axyridis populations. Table S2. Genetic diversity indices of native and non-native Harmonia axyridis populations based on 620-bp mtCOI fragment. Table S3. Pairwise FSTand migration rateof Harmonia axyridis populations from native and non-native regions. Table S4. Individual estimates the number of observed and expected homozygote sites, observed heterozygosity, and a method of moments estimate of the individual inbreeding coefficient. Table S5. Estimates of Maximum likelihood and Akaike’s information criterionacross different 3-population evolutionary demographic models. Table S6. Parameter estimates of genetic diversity, migration rate, divergence time, and population size change under the most likely 3-population topologyunder the IM model with population size changes across three major groups of Harmonia axyridis populations worldwide. Table S7. Estimates of contemporary migration between three major groups of Harmonia axyridis populations as estimated withBA3-SNPs. Table S8. Estimates of Maximum likelihood and Akaike’s information criterionacross different 4-population evolutionary demographic models. Table S9. Parameter estimates of genetic diversity, migration rate, divergence time, and population size change under the most likely 4-population topologyunder the IM model with population size changes across four major groups of Harmonia axyridis populations worldwide. Table S10. Collection information of Harmonia axyridis samples and their haplotypes from native and non-native ranges.Additional file 2: Fig. S1. Cross validation errors computed with ADMIXTURE v.1.3.0 by varying the number of superpopulations between K = 1 and 15. Fig. S2. Differentiation measured as Fstversus all chromosomal locations from OutFLANK analyses. Fig. S3. The workflow analysis for population genomics of the harlequin ladybird, Harmonia axyridis in the present study. Fig. S4. Demographic models of global invasion history of H. axyridis from an ancestral Asian source population tested in FSC26 between 3 superpopulations. Fig. S5. Demographic models of global invasion history of H. axyridis from an ancestral Eastern China source population tested in FSC26 between 4 superpopulations. Fig. S6. Goodness of fit estimates of the observed and estimated two dimensional site frequency spectra from our best fitting four-population model comprising asymmetric migration and exponential population size change. Fig. S7. Goodness of fit estimates of the observed and estimated two dimensional site frequency spectra from our best fitting three-population modelcomprising asymmetric migration and exponential population size change.


Alectoris chukar) is an important bird species with significant economic and ecological value. Every year, thousands of partridges raised in Ministry of Agriculture and Forestry breeding farms are released into nature for hunting purposes. Our investigation of the gene pools of different A. chukar populations using mitochondrial and microsatellite markers from across Türkiye revealed that the Eastern, Western, and Thracian genetic structures have been shaped by Türkiye’s geography and topography, past climatic fluctuations, and human-mediated artificial hybridizations. There were no signs of congeneric genetic contamination in native chukar populations, but there were contaminations with the China clade-B, which were detected as reported in the previous studies.The chukar partridge (Alectoris chukar), since it is both a potential ancestral area and a diversification center for the species. Using 2 mitochondrial (Cty-b and D-loop) and 13 polymorphic microsatellite markers, we investigated the geographic genetic structure of A. chukar populations to determine how past climatic fluctuations and human activities have shaped the gene pool of this species in Türkiye. Our results indicate, firstly, that only A. chukar of the genus Alectoris is present in Türkiye (Anatolia and Thrace), with no natural or artificial gene flow from congenerics. Secondly, the geographic genetic structure of the species in Türkiye has been shaped by topographic heterogeneity, Pleistocene climatic fluctuations, and artificial transport by humans. Third, there appears to be three genetic clusters: Thracian, Eastern, and Western. Fourth, the post-LGM demographic expansion of the Eastern and Western populations has formed a hybrid zone in Central Anatolia (~8 kyBP). Fifth, the rate of China clade-B contamination in Türkiye is about 8% in mtDNA and about 12% in nuDNA, with the Southeastern Anatolian population having the highest contamination. Sixth, the Thracian population was the most genetically distinct, with the lowest genetic diversity and highest level of inbreeding and no China clad-B contamination. These results can contribute to the conservation regarding A. chukar populations, especially the Thracian population.Türkiye is considered an important evolutionary area for Chukar partridge ( Quaternary climate fluctuations are an important evolutionary mechanism that have shaped the present population structure and species/lineage diversity ,2,3,4. DThe Mediterranean Basin is considered an important refugial area for many Western Palearctic lineages ,16. FiveTürkiye (Anatolia and Thrace) is one of the world’s rare regions in that it hosts three different biodiversity hotspot regions   and is aTürkiye’s geographic features , heterogeneous topography (mountain chains and elevation), and the areas of intersection of different climatic zones serve as possible soft and hard barriers restricting the dispersal capability of populations . DependiTürkiye’s topography rises gradually from west to east, creating a clinal climate zone throughout the landmass ,61. DuriA. chukar is a good model organism for studying potential cryptic contact zones in Türkiye. Both natural and artificial/human-mediated hybridizations are common in the genus Alectoris. Several natural hybridizations have been reported: A. chukar × A. magna on Liupan Mountain in China (Gansu Province)  and M [1–800]. Asymmetric migration rate analyses were run with an automatically adjusted heating temperature using four initial chains and two multiple replicates of 10.000 iterations with a burn-in of 1.000. The convergence likelihood for the MCMC chains was verified using TRACER.adegenet [poppr [The primers of 13 polymorphic microsatellite markers and their amplification conditions are summarized in adegenet  package t [poppr  package A. chukar populations in Türkiye, two different clustering-based analyses were performed. First, a discriminant analysis of principal components (DAPC) was performed using adegenet, which uses sequential K-means and model selection to infer genetic clusters so as to identify and describe the clusters of genetically related individuals. Second, Bayesian MCMC clustering analysis was carried out using STRUCTURE v. 2.3.4 [A. chukar in Türkiye. STRUCTURE was run assuming correlated allele frequencies and admixture, without using prior population information [http://clumpak.tau.ac.il (accessed on 17 August 2022)” [A. chukar populations, Nei distance dendrograms [To determine the genetic structure of v. 2.3.4  to detecormation . Ten indormation  and defiormation  and CLUMt 2022)”  online st 2022)”  was usedt 2022)”  was useddrograms  were conA. chukar populations in Türkiye, approximate Bayesian computation (ABC) analyses were performed using the microsatellite dataset of the 16 populations [To test the competing alternative hypotheses regarding the divergence time and admixing events among ulations . This tyulations ,121.A. chukar populations  and East , West , and Central Türkiye .Considering the analysis results of the mtDNA and microsatellite markers, three hypothetical evolutionary hypotheses were built regarding the divergence/mixing time and gene flow direction/rate of ulations . For theA. chukar populations at time t3 before the East and West populations diverged from each other at time t2. The second hypothesis predicts the simultaneous divergence of all populations at time t3. The third hypothesis predicts that the East and West populations first diverged at time t3, when the West and Thrace populations were still in contact. At time t2, the Thrace population was split from the West population. All three hypotheses also predict that an admixture event occurred between the East and West populations at time t1, which produced a new mixed population in Central Anatolia. The admixture rate parameter (ra) represents the gene flow from the West population to the Central Anatolian population , and uniform priors with default values for all the parameters. The analyses were performed with 13 parameters and 42 summary statistics. For the time estimation, the number of generations was taken to be 3.9 years/generation [The priors required for the ABC analysis were estimated from the observed data . The ananeration .To assess the posterior probability of each hypothesis, a weighted polychotomous  logistic regression analysis was performed. The analysis was conducted using 1% of the simulated datasets closest to the observed data ,123,124.A. chukar populations in Türkiye was shaped in response to Pleistocene climatic fluctuations. The ENM analyses were performed in MAXENT v.3.3.3k [A. chukar, the analyses were run with 19 bio-climate variables from WorldClim during two periods: the Present (1960–1990 AD) and the LGM (0.021–0.018 Ma) “http://www.worldclim.org (accessed on 2 September 2022)” [Ecological niche modelling (ENM) analysis was performed to determine whether the current geographic distribution pattern of v.3.3.3k ,129 baser 2022)” ,131. Forr 2022)”  and the r 2022)” . The Genr 2022)” . Before A. chukar and other congenerics in Türkiye. Phylogenetic analyses of the 90 haplotypes supported the ML, MP, and BI trees, with similar topologies and branch support . All analyses identified A. melanocephala and A. barabara as the most ancestral lineages, whereas A. rufa, A. graeca, A. philbyi + A. magna, and A. chukar were identified as different species. The Cyt-b tree supported the presence of A and B clades within A. chukar. Although the monophyly of clade-A was supported by the low bootstrap/posterior probability, China clade-B appeared to be paraphyletic. Only three subspecies’ sequences from China clade-B  exhibited a monophyletic pattern. Six haplotypes representing 22 individuals were in China clade-B  but collected from Türkiye. Although this result supports the presence of China clade–B contamination in Türkiye, no congeneric or other species-related contaminations were detected. Similar results were obtained from the Cyt-b trees constructed using only the A. chukar haplotypes  is more recent than that of the Western populations (average 17 kyBP) . The demographic expansion time range indicates that the population growth of the A. chukar population in Western Anatolia (14–23 kyBP) began at the end of the LGM (~22 kyBP), while the population growth in Eastern Anatolia (12.5–16 kyBP) began during the post-LGM period, particularly at the end of the short ice age or Younger Dryas (~12.8–~11.6 kyBP) (The past demographic expansion time estimations were conducted using tau (τ) values for those populations that showed a strong sudden expansion model. The estimations were performed using Cyt-b haplotypes by removing the clade-B haplotypes. The time estimations for populations 2–5, 9, 13–15, and 16 indicate that the demographic expansion of these populations began in Anatolia at approximately 8–22.7 kyBP . Conside17 kyBP) . The dem.6 kyBP) .IS values were significant for all 14 populations, with the highest value in population 1 (0.3439) and the lowest in population 16 (0.0023). Among the 14 populations, population 1 had the lowest values for almost all the parameters, which suggests that population 1 may have undergone a recent bottleneck. The pairwise FST result indicated that population 1 is the most distinct population. In addition to population 1, populations 3, 4, and 5, which represent the Western populations, showed prominent differences from the other populations  performed on 347  Türkiye . Of thesA. chukar populations, implying that the Marmara water body is a strong geographical barrier for the species in the West Palearctic. In K = 2, Thrace and the Anatolian mainland are represented by two different genetic clusters. In K = 3, the analysis suggests three genetic clusters  and a hybrid area throughout Anatolia, especially Central Anatolia. In K = 3, populations 2, 3, 4, and 5 represent the West genetic cluster, while populations 6–16 represent the East cluster, with the contact zone located in the area of populations 6, 13, and 15. However, in K = 4, in addition to the Thrace, East, and West genetic clusters, the China clade-B cluster appears within the East and West clusters. The microsatellite results indicate that clade-B contamination in Anatolia is approximately 12% (ranging from 2% to 54%). Intense contamination with China clade-B individuals was found in populations 4 (54%), 2 (20%), 6 (17%), and 11 (15%), whereas no contamination was detected in populations 1 and 8. The Nei distance dendrogram indicated that the Thrace, East, and West populations form three distinct genetic clusters  and K = 2–10. The Delta K test infers the population structure using the Bayesian analysis results, showing the maximum likelihood of the data when K = 4, the number of populations assumed . The estclusters a. In add6 simulations, indicated that hypothesis 2 was the most likely supported scenario , where the direct approach considered the closest 1000 points: 0.6260 ) ) . Hypothe0.9259)) . DivergeA. chukar in Türkiye was compatible with the results indicated by the two genetic markers (mtDNA and microsatellites). Considering the results of the ENMTOOLS analyses, we removed one of the bioclimatic layers that showed a high Pearson’s correlation coefficient value (≥80%) with the other layers. After removing the correlated bioclimatic layers, eight layers from CCSM and nine from MIROCH out of the 19 bioclimatic layers were retained as input data for the LGM, and nine were retained for the Present  was used to determine whether the LGM distribution pattern of  Present . The mea the LGM a,b showeAnatolia . A. chukar distribution, whereas Central Anatolia, the Black Sea, and northern part of East Anatolia are not. The LGM-CCSM model also indicated that there was a suitable corridor for gene flow between the East and West populations via the Taurus Mountain belt. The LGM-MIROC model produced a result similar to that of the CCSM model, with some differences in the detailed distribution pattern. The model suggested that the distribution in the West and Mediterranean regions were concentrated in lowland areas, while low latitude was the major characteristic of the areas of A. chukar distribution in the East. However, the model also suggested some lowland micro-refugia in the northern part of the East Anatolia area, unlike the CCSM model. The MIROC model also showed several suitable areas for A. chukar distribution in Central Anatolia, although it did not support any connection between the East and West populations by way of the Mediterranean or Central Anatolian highlands  was not related to ecological conditions; rather, it resulted from fluctuations in the water levels of the Marmara water body during the LGM.In the LGM-CCSM model, southern Thrace, a large part of Western Anatolia, and the Mediterranean appear to be suitable areas for ighlands . The curA. chukar populations exhibit a genetic pattern that is well-structured in line with the geographic and topographic structure of Türkiye. The genetic structure of A. chukar in Türkiye supports three refugia or diversity areas: Thrace, East, and West. The differences between the Thracian and Anatolian mainland populations indicate that the Marmara waterway  is a barrier that prevents gene flow between A. chukar populations. Recent genetic studies also support the claim that the Marmara waterway limits the dispersal capability of many terrestrial organisms, including mammals, insects, and plants, between Anatolia and Thrace/the Balkans [Both the mtDNA and microsatellite data results showed that  Balkans ,136,137. Balkans ,139. In  Balkans . Before  Balkans ,142. The Balkans ,144. TheA. chukar individuals. In the post-LGM period, however, the melting of glaciers in the northern latitudes caused a rapid rise in water levels in the Black Sea basin (~13.800 yrBP). The resulting catastrophic water flow from the Black Sea into the Marmara Sea raised the water level of the İstanbul Strait and Marmara Sea [Stratigraphic studies of the İstanbul Strait basin have indicated the presence of a freshwater lake system in the İstanbul Strait between 26.000 and 13.800 yrBP, when global sea levels were at their lowest ,146. Thimara Sea . By ~12.mara Sea . A. greaca–A. chukar and A. chukar (Anatolia)–A. chukar (Thrace) in the Western Palearctic.Due to the euxinic climatic structure of the northern Marmara region, with high annual precipitation, the amount of water carried by the İstanbul Strait in the LGM may have been higher than previously thought. This implies that the İstanbul Strait’s barrier effect may have persisted during the LGM period, rather than the strait being a completely dry, deep valley. Depending on global sea level fluctuations throughout the Quaternary, the barrier effect of the Marmara waterway between Anatolia and Thrace/the Balkans followed a variable regime . This maA. chukar populations. Climatic conditions on the northern side of the Marmara waterway also created an ecological barrier during this period. In contrast, the ENM analysis showed that the southern part of Thrace could have been an important corridor for A. chukar distribution during the LGM. Since the Çanakkale Strait’s sea level was largely preserved during the LGM (−83 ± 2 m) [A. chukar populations in the Quaternary.The ENM analysis indicated that the climatic conditions of the northern Marmara region during the LGM were unsuitable for the distribution of 3 ± 2 m) , it coulA. chukar populations occurred ~12.600 (12.500–12.800) generations ago. Assuming a generation time for A. chukar of 3.9 years, it was dated to 49.140 yrBP (48.750–49.920 yrBP). This date falls within the Würm glacial period (115–11.7 kya), when temperature fluctuations occurred. In particular, the last period, 57–29 kyBP, is quite complex in terms of changes in global sea levels [A. chukar individuals do not fly well, the Marmara waterway and the straits system may have had an intermit barrier effect, depending on Quaternary climatic fluctuations, global sea level changes, and regional tectonics.The ABC simulation suggested that the Anatolia–Thrace/Balkans differentiation of the a levels ,150. Dura levels ,142. TheA. chukar populations in Türkiye are Türkiye’s heterogeneous topography, different climate types, and past climatic fluctuations. Although the long cooling periods experienced in the Late Pleistocene did not cause great glaciation in Türkiye, the ice ages affected the distribution and demography of many populations [A. chukar populations during the LGM period may have shifted towards lower altitudes and southern latitudes in parallel with the cooling. The ENM analysis showed that the distribution of A. chukar populations during the LGM was restricted to low latitudes in East Türkiye, with the populations largely eradicated in the northeast regions. Both the MICRO and CCSM4 climate models showed that, during the LGML, there were no suitable habitats for A. chukar in the areas of population 9 and 10 in northeast Türkiye or that of population 11 in the east . This divergence time coincided with the end of a short warming period (45–60 kyBP) in the Würm glacial period [A. chukar populations to lower altitudes or southern latitudes in Türkiye (A. chukar populations in Türkiye started at approximately 15 kyBP (8.3–22.7 kyBP) in parallel with post-LGM warming  . This asymmetrical gene flow from west to east may be due to a rapid invasion of western A. chukar individuals, given the wider distribution range of western populations in the past and assortative mating between A. chukar individuals.The ABC analysis estimated that the East–West and Thrace splits occurred simultaneously in the l period . This cl Türkiye c–f. Acco Türkiye e. The mi warming . As a reAnatolia f. The ABCA: 0.5) . The BayCA: 0.5) . That isCA: 0.5) . SimilarA. chukar populations in Türkiye have high genetic diversity, except for all the genetic markers in population 1, which represents the Thracian part of Türkiye. In addition to the low haplotype and nucleotide diversity of this population, the haplotype distances within the population are very close to each other. This result implies that population 1 is affected by high inbreeding depression because of the founder effect, limited gene flow, or both. In addition, the samples from this region were mostly collected from Gökçe Island. Since islands generally provide limited habitat and food sources, island populations are very sensitive to changing environmental conditions and anthropogenic effects [A. chukar populations, in addition to, is human activities and domination in the area. The pressure of urbanization and the conversion of natural areas into agricultural lands are destroying the species’ natural habitats. To prevent human-mediated contamination, farm-reared A. chukar of unknown origin and A. chukar from other parts of Turkey should not be released in Thrace. Special conservation measures for the Thrace population are needed immediately. In this study, both mtDNA and nuDNA (microsatellites) indicated that  effects , making  effects ,154. The effects . A few sA. chukar individuals. The microsatellite analyses indicated that the highest heterozygosity was observed in populations 16, 13, and 10. This may be due to their locations, as all three populations are situated in the intersection zones of different ecoregions [According to the Cyt-b results, the genetic diversity parameters of population 2 are higher than those of the other populations, with no China clade-B contaminations. This suggests that population 2 carries traces of old hybridization events between Anatolia and Thrace in its gene pool. Population 11 has the highest genetic diversity parameters in both mitochondrial markers. Population 11 is rooted in a special location, namely, the entrance to Anatolia from Asia. The high distance value between haplotypes in this population indicates that these areas may be another cryptic hybrid zone between Anatolian and Iranian oregions . PopulatAlectoris are located in the Mediterranean Basin. The phylogeny of the genus also indicates that the Mediterranean Basin is the ancestral and diversification area for this genus [A. chukar [A. greaca [A. chukar is one of the natural species endangered by human-mediated congeneric gene contamination. According to some studies, two species (A. greaca and A. chukar) are distributed in Anatolia [A. greaca is found in Western Anatolia, the coastal areas of Thrace and the Aegean region, and the Western Mediterranean. However, other studies concluded that only one species (A. chukar) is present in Türkiye, and that the natural distribution of A. greaca ends in the eastern part of the Balkans [A. chukar and A. greaca undermines the reliability of phenotype-based diagnosis for these species. On the other hand, the Balkans and Western Thrace are the natural distribution areas of A. greaca and the areas with a high A. chukar × A. greaca hybridization probability. Both the mtDNA (Cyt-b and D-loop) and microsatellite results showed that Türkiye hosts no species of Alectoris other than A. chukar, with no congeneric contamination.Six of the seven species in the genus is genus . As an i. chukar ,79. Ther. greaca ,158,159.Anatolia . They al Balkans ,162. TheA. chukar are distributed in Türkiye: A. c. cypriotes Hartert, 1917, in the Mediterranean part of southern Türkiye and the Levant; A. c. kleini Hartert, 1925, in most of Anatolia north of the Taurus Mountains; and A. c. kurdestanica Meinertzhagen, 1923, at elevations extending from the southern Aras to the Zagros Mountains in the eastern part of Eastern Anatolia [Based on morphological characteristics, authors of previous studies suggested that three subspecies of Anatolia . HoweverAlectoris may also face human-mediated artificial hybridization, because their economic importance and use in hunting have led to their introduction into different areas via trade routes [Alectoris species in Europe, where human-mediated artificial hybridizations are common, mostly stem from the China clade-B of A. chukar [A. rufa and A. greaca in Europe, which appears to be related to A. chukar, previously introduced into the Mediterranean Basin via the Silk Road [A. chukar-contaminated individuals with farm-reared chukars for hunting purposes contaminated the local gene pools of A. rufa and A. greaca in Europe, with A. chukar genes at a dangerous level [Artificial hybridizations caused by human activities can endanger many natural populations ,164,165.e routes ,179,180.. chukar ,80,181. ilk Road ,79,178. us level ,184,185.A. chukar contamination with China clade-B in Türkiye. Nevertheless, our results also support the presence of China clade-B contamination in Türkiye. The ratio of China clade-B contaminations in Türkiye is approximately 8% in mtDNA and about 12% in nuDNA. The absence of China clade-B contamination in Thrace is important in terms of the preservation of the genetic specificity and autonomy of this population. Among the contaminated regions, the highest contamination was observed in population 4 (44% mtDNA and 54% nuDNA). This may be because the major city in this population’s region, Antalya, is a historically important port settlement, which has served as an important trade area since ancient times. Alternatively, these contaminants may have originated from farm-reared A. chukar stocks, as in the European cases.The presence of China clade-B contamination in Türkiye was reported in a previous study . HoweverA. chukar in Türkiye: Türkiye’s geography–topography, past climatic fluctuations, and human-mediated artificial hybridizations. Traces of the influences of these three factors are clearly seen in the gene pool of A. chukar populations in Türkiye. The genetic structure is shaped in two ways: along an east–west cline due to the Anatolian Diagonal and a pattern with a distinct population in Thrace due to the barrier effects of the Marmara waterway. A contact/hybrid zone between the Eastern and Western clades arose in Central Anatolia. This contact zone formed around an area known as the Anatolian Diagonal due to the loss of its barrier effect during post-LGM warming. Interestingly, this hybrid zone is one of the largest clearly defined hybrid zones among all the Mediterranean refugia. Pleistocene climatic fluctuations had different effects on populations in the eastern and western parts of Türkiye. In particular, the West Anatolian populations were less affected by glaciation than those in the east. These results highlight the presence of cryptic micro-refugial areas in the eastern part that played an important role in preserving the lineages in the area during glaciation periods. While our results support the presence of China clade-B contamination in Türkiye, we found no evidence of A. graeca and no congeneric genetic contamination. Regarding the conservation of the species, the genetic differences between the Thrace, Western, and Eastern Anatolian populations should be considered, and only genetically controlled A. chukar stocks should be released into nature.Three factors have played important roles in the formation of the genetic pattern of


Oenanthe aquatica populations and their soil seed banks in a kettle hole system to uncover hidden connectivity among populations in time and space. Considerable spatial genetic structure and isolation-by-distance suggest limited gene flow between sites. Spatial isolation and patch size showed minor effects on genetic diversity. Genetic similarity found among extant populations and their seed banks suggests increased local recruitment, despite some evidence of migration and recent colonization. Results indicate stepping-stone dispersal across adjacent populations. Among permanent and ephemeral demes the resulting meta-population demography could be determined by source-sink dynamics. Overall, these spatiotemporal connectivity patterns support mainland-island dynamics in our system, highlighting the importance of persistent seed banks as enduring sources of genetic diversity.Progressive habitat fragmentation threatens plant species with narrow habitat requirements. While local environmental conditions define population growth rates and recruitment success at the patch level, dispersal is critical for population viability at the landscape scale. Identifying the dynamics of plant meta-populations is often confounded by the uncertainty about soil-stored population compartments. We combined a landscape-scale assessment of an amphibious plant’s population structure with measurements of dispersal complexity in time to track dispersal and putative shifts in functional connectivity. Using 13 microsatellite markers, we analyzed the genetic structure of extant  In plants that lack long-distance dispersing propagules, landscape-scale fragmentation may limit spatial gene flow among distant patches6. Therefore, spatially isolated populations may diverge more and harbor less genetic variation, which can ultimately limit their adaptive response to selection8. However, population genetic structure may be homogenized, and local extinction risks reduced if the landscape configuration and vector availability maintain gene flow. Accordingly, effective propagule dispersal in a meta-population increases genetic connectivity which is crucial for long-term persistence. Increased immigration may particularly prevent the long-term extinction of local populations in areas subject to intensive disturbance with presumably increased mortality rates11. While the limitations of applying meta-population theory in plants have been a subject of controversy, genetic techniques have emerged as a promising tool to assess population fluctuation and migration14. However, inferring connectivity and genetic diversity from above-ground population characteristics may be misleading, as only a fraction of the actual viable population and history of local selective forces may be assessed from such single snapshot data17. Especially in systems with drastic short-term environmental shifts, dormancy may evolve if plants with delayed seed germination produce more surviving offspring than those whose seeds all germinate in their first year19.Continuous habitat has become a scarce resource—many organisms are scattered across habitat patches resulting from progressive fragmentation and degradation of natural areas20, rates of germination, seed mortality, and recruitment may widely differ among patches, further complicating the estimation of local extinction risks and colonization probabilities23. Tellier24 postulated that selection effects on plant fitness components may be amplified or mitigated by stochastic events when viable seed bank fractions are small. In the face of disturbance, increased genetic diversity can enhance the survival of subpopulations by potentially providing individuals with a wider range of adaptive capabilities. Consequently, in highly dynamic patch networks, the persistence of a plant meta-population critically depends on whether a species is equipped to maintain the exchange of genes among subdivided habitats and across successive generations25. Inferring current and past dispersal events can therefore unravel the dynamics of connectivity and cross-generational source-sink effects between spatially separated populations. Recent research emphasizes the role of persistent soil seed banks and their potential to decrease rates of genetic drift27. Moreover, under short-term environmental perturbation, seed banks enable the maintenance of potentially adaptive variation by balancing selection29. Indeed, soil-stored population compartments can prevent a loss of genetic diversity across multiple reproductive seasons, resulting in an overall increased effective population size30. This is particularly relevant in highly dynamic systems, such as arid environments, where local populations are expected to undergo strong demographic fluctuations with varying presence/absence in the above-ground population34. Numerous studies have examined the long-standing prediction of a trade-off between spatial and temporal dispersal as complementary risk spreading strategies39. While spatial dispersal is suggested to be more beneficial in spatially heterogeneous but locally stable habitats, temporal dispersal is assumed to be favored when local conditions are unpredictable.Although seed banks can create a reservoir of genetic diversity41, weak42, or significant39 negative covariation37. Both dispersal and dormancy have been suggested to evolve in response to the spatial and temporal heterogeneity of habitats47.Yet, empirical and simulation studies have discovered diverse relationships between spatial and temporal dispersal strategies, related to specific life-history traits, providing evidence for absent48.At the (meta-) population level, both seed dormancy and dispersal are predicted to increase with spatiotemporal environmental variability34. Especially strongly structured populations that primarily track favorable conditions through recruitment from local seed rains  are prone to extinction if the magnitude of environmental shift exceeds their niche breadth49. On the other hand, recurrent spatial dispersal may counteract local adaptation to conditions at the tolerance limit50. Consequently, climate change effects are likely to unbalance the forces of spatial and temporal rescue effects in the future51.Disentangling the relative prevalence of risk-spreading mechanisms in erratically fluctuating environments may also enhance the predictability of the potential for evolutionary rescue in populations under climate change. Although contributing to the resilience of fragmented populations, both dispersal strategies present distinct risks and opportunities in the context of range shifts52. Here, we use a series of isolated wetlands as a well-suited study system to assess meta-population processes by examining above- and below-ground populations in different cohorts of the early successional short-lived monocarpic perennial species Oenanthe aquatica (L.) Poir53. This landscape genetic case study allows us to track signals of dispersal and thus determine the relative contribution of spatial gene flow versus seed bank recruitment in shaping local genetic variation. The target populations are distributed across small spatially distinct temporary wetlands embedded in an area of intensive agriculture. These glacially formed "kettle holes" are densely distributed habitat islands characterized by short-term changes in local hydrologic conditions, ranging from flooded to completely dry within a season, and longer-term periods of desiccation or standing water54. As hydrochorous pioneer O. aquatica is adapted to variable water levels in the study region, colonizing emerged wetland bottom when water levels recede and tolerating temporal flooding during vegetative growth. This life history likely results in population fluctuations in response to fluctuations in water levels and vegetation succession53. Due to the absence of connecting elements like ditches, the dispersal of seeds is likely restricted to regionally abundant animal vectors, such as deer species, wild boar, raccoons, and waterbirds60. Further, the species has been shown to form long-lived seed banks, with seed longevity, rather than dormancy, promoting storage in the soil62. We, therefore, expect the extent of past gene flow between the discrete habitat islands to be reflected in the genotype composition of the soil-stored viable seed pool66. By conducting population genetic analyses on extant populations and soil seed banks, we infer the temporal dynamics of local populations and deduce the potential connectivity among habitat islands. Our target species is a pioneer strategist with a short lifespan and our target sites and their soil strata are spatially distinct53. Taking advantage of these preconditions we aim to test (i) whether O. aquatica population structure changes over time, (ii) how the spatial configuration of habitat patches affects the genetic diversity of local populations, and (iii) if meta-population theory can explain observed population genetic patterns.To date, only a few studies have addressed the spatiotemporal patterns of plant meta-populations since this requires long-term data on patch occupancy, a series of discrete habitat patches, and the assessment of potentially long-lived seed banksO. aquatica individuals were found in extant or seed bank communities. Of the remaining 18 patches, six were surveyed in a previous population genetic study conducted in 2016. In two of these six patches, O. aquatica was absent from the standing vegetation in 2016. The 2019 survey confirmed the absence of a viable O. aquatica seed bank in those particular patches  were excluded from further analyses as no 2a were ees Table . NeverthR varied between 3.743 and 5.846 (mean=4.617) with consistent values among cohorts of the same population. Overall average HE was 0.637 with values ranging from 0.554 to 0.702. HO was generally lower than HE (mean=0.571), with values ranging from 0.269 to 0.692 (Supplementary Table Pairwise comparisons of the genetic diversity between extant (cohort 2019) and soil (cohort S2) populations revealed no significant differences over time in any of the parameters examined . We found five kettle holes with no viable seed banks, but individuals still formed separate clusters specific to each kettle hole  with cohort samples grouped according to their respective population revealed that most genetic variation 87.32%) was found within cohorts, while only 1.31% was attributable to the differentiation between cohorts within populations. Genetic differentiation among populations accounted for 11.38% of the observed genetic variance  in all cohorts . The MEM analysis showed significant but weak spatial genetic structure among populations in three analyzed cohorts . We identified two MEMGENE axes for 2019 and three for each soil bank cohort (MEMGENE1-3) that explained 7–10% of the genetic variation underlying Moran’s eigenvector maps  into unique clusters. In particular, long-established populations exhibit nearly unchanged allele frequencies over the period studied here. Overlapping generations in O. aquatica could promote cross-cohort gene flow and the maintenance of local genetic variation. Conversely, the substantial genetic divergence among populations is likely the result of genetic drift due to long-lasting habitat isolation and small population sizes. With increasing population isolation, the influence of genetic drift surpasses that of gene flow, resulting in pronounced differentiation between most of the sites. The asymptotic IBD pattern suggests that dispersal is limited to a local scale, implying that landscape barriers, i.e. the agricultural matrix, are impeding gene flow across longer distances67. Some admixture across sites is however suggested by the population structure analyses and inferred migration events.Our results on spatiotemporal connectivity of 60. Individual sites harbor genetically distinct populations with repeated local recruitment and formation of persistent soil seed banks. Genetic variation partitioning confirmed a substantial divergence among local populations, despite occasional outcrossing with variants from nearby sites, as well as rare, but traceable, long-distance dispersal  colonization of patches with presumably limited or absent seed banks . Interestingly, of the limited number of recent migrants inferred, a considerable proportion were long-distance migrants. Animal vectors with large home ranges, such as waterbirds and large mammals may contribute to this spread by using kettle holes for shelter or foraging, facilitating seed deposition through ingestion or adhesional Table , underscO. aquatica, reproducing once and then dying, may account for variations in genetic diversity and population size over time. Thus, patterns of pulsed reproduction can arise causing rapid population growth followed by a sudden decline, as new generations compete for resources. Overall, many populations in the habitat network store a long-term viable, diverse seed bank and therefore are not primarily driven by recurrent extinction/recolonization dynamics as assumed by classical meta-population theory proposed by Levins68.At individual sites, genetic diversity stochastically varies among cohorts, presumably reflecting varying degrees of drift due to local environmental fluctuations  and genetically inferred dispersal across some kettle holes comprise features of meta-population dynamics. In particular, the occurrence of 69. Water regimes among individual kettle holes are typically asynchronous. However, short-term synchronization, e.g. intense spring flooding in a range of patches may occur occasionally. Kettle holes in this region were reported to overflow after snowmelt and to dry up during the summer period70. Stepping stone dispersal facilitated by mobile linkers, such as waterbirds71, could enable the repeated transport and accumulation of O. aquatica seeds among these flooded patches throughout the season. The absence of dormancy has been suggested to increase colonization success as it enables the species to rapidly exploit available resources under favorable conditions19. Such processes would promote regional connectivity in the long term. Further, ephemeral local populations may emerge occasionally based on randomly occurring mass effects, based e.g. on zoochorous dispersal, given that some patches may provide less suitable conditions for seed longevity22. Such ephemeral populations with rather transient seed banks . Short-term favorable conditions and occasional seed influx may promote the sudden emergence of large O. aquatica populations, capable of forming monotypic stands from a small number of individuals53. A community study conducted in 2015 in the same study area suggests mass effect processes to be prevalent in non-flooded patches73, as these may be characterized by (1) temporarily increased mobile link movements and/or (2) prolonged dry periods associated with higher seed or seedling mortality in amphibious plant species.Such colonization would require some recent synchronization of favorable conditions among donor and recipient patches74, as observed in the most spatially isolated populations, i.e., P12 and P10  pattern. This IBD population structure suggests a stepping-stone dynamic in our study system, which is further corroborated by contemporary dispersal being predominantly inferred among adjacent sites.However, the genetic consequences of the two described dispersal scenarios will differ. High genetic diversity is expected in populations emerged from diverse migrant seed variants accumulated in short-term standing waters, e.g., at P14 and P16. Contrary, when recovering from high mortality, large populations may emerge from recent immigration of a few variants in drier environments. Here, founder effects/bottlenecks may cause reduced genetic diversity With our methodology, we cannot fully exclude that seeds in the soil remained undetected. Specifically, if a population without a detected seed bank forms a unique cluster, it could originate from scarce seeds bearing a local genotype. However, the high seed production of our studied species and our small-scale sampling of both soil and extant populations make this interpretation less probable. Therefore, we consider it more likely that those populations which form their own genetic cluster but do not hold a detectable seed bank have been colonized by specimens from a population not sampled in our study.In future studies, it would be worth monitoring the longevity of seed banks in different hydrological regimes to identify factors that limit seed viability in soil banks. Moreover, determining population demographic structure and phenology in different environments could provide information on the role of environmental synchronization. Such information could improve the reliability of predictions about how stable the observed population connectivity may be under altered environmental conditions.76, as confirmed by the inferred IBD pattern in our study system. Populations with a long-lived seed bank represent starting points for recolonization rescue, from where local variation slowly propagates across the landscape. This is in agreement with the significant latitudinal cline detected by spatially explicit analyses, separating northern and southern populations with an increased admixture at the geographical center. As precipitation is a major driver of kettle hole hydrological conditions, increased levels of connectivity between adjacent habitat patches are likely linked to local environmental synchrony78. Small-scale patch configuration effects on local diversity were already shown in plant meta-communities exhibiting a decline in species richness with increasing spatial isolation and decreasing patch area 79. We confirmed noticeable effects of isolation on local genetic diversity at the population level , an open research platform covering an area of approximately 268 km2 in Brandenburg, Northeast-Germany and provide unique habitat for a range of (semi-) aquatic communities83. Periodic changes of local water regimes vary asynchronously depending on size, steepness, and successional stage, and occur at different time scales driven by seasonal precipitation54.The study was conducted in the ’AgroscapeLab Quillow’ (Oenanthe aquatica (Apiaceae) is a diploid annual to perennial monocarpic, amphibious plant species frequently occurring in disturbed eutrophic stagnant waters and ditches on muddy sediments84. As a wetland colonizer, O. aquatica is found in habitats with strong fluctuations in water levels. In environments with regular recurrent flooding events, populations can build up dominant stands. The species can produce high numbers of long-lived seeds, ranging from a few hundred to several tens of thousands per individual, that can be dispersed through hydrochory or animal vectors and may accumulate in the soil53. As typical in Apiacean species, flowers are protandrous and pollinated by a range of insects covering different taxonomic orders85. O. aquatica is a non-clonal, predominantly outcrossing species, however, incidences of selfing have also been observed57.Fine-leaved water Dropwort, O. aquatica populations in spring 2019, including two sites without O. aquatica in the extant plant community. These kettle holes comprised eight pairs of spatially proximate sites  distributed across three spatial clusters (>5 km apart) as well as four intermediate sites selected as putative stepping stones between clusters  of each composite core sample was spread on a sandy substrate layer (2 cm ~480 ml) in a plastic tray. Using the emergence method of van der Valk & Davis (1978), a randomized flooding treatment was applied to induce germination at natural water-logged habitat conditions. This method has proven effective for assessing both persistent and transient seed banks in ephemeral wetlands87. Samples were exposed to natural light regime and day and night temperature of 23−30 and 18 °C, respectively. After morphological identification, seedlings other than O. aquatica were removed to reduce competition. Leaf tissue was sampled when reaching two-leaf-stage, yielding a total of 194 samples from viable soil populations in both layers of ten study sites, while for one further site, a viable population was found only in the upper layer. All leaf samples including soil bank seedling tissue were preserved in 96% EtOH at -20 C until further processing. Voucher specimens (species identification by Maxi Tomowski and Michael Ristow) are deposited at the Unit of Evolutionary Biology, Institute of Biochemistry and Biology, University of Potsdam, Germany under the number MT-2023-001.Integrated with a soil community study, 20 kettle holes were selected for tissue and soil core sampling of O. aquatica populations was extracted using a modified CTAB protocol as described by Inglis et al.88. Each individual was genotyped for 13 polymorphic microsatellite loci described in Favre-Bac et al.89 . For multiplexing, forward primers of each primer pair were differently fluorescent-labeled  and assembled in 6 Polymerase Chain Reactions (PCR) . Allele size data for a total of 579 individuals is provided in the supplementary Table Genomic DNA of 90 and R studio (Version 2022.7.1.554)91 for all cohorts with n ≥ 8 of each local population. For each population’s cohort and locus, we tested for significant deviation from Hardy–Weinberg-Equilibrium using the hw.test function from the pegas package92 with 1,000 permutations. To assess multilocus linkage disequilibrium (LD) we computed the standardized index of association rd93 calling the ia function from the poppr package94. Significant deviation from the expectation of linkage equilibrium among loci was tested using 1,000 permutations, followed by a Bonferroni-correction of significance levels. We calculated descriptive statistics, including observed and expected heterozygosity  calling the bootstrapHet function implemented in the popgenkit package95, and rarefied allelic richness (AR), unbiased expected heterozygosity (uHE), total allele number as well as the inbreeding coefficient FIS using the hierfstat package96. Confidence intervals for He, Ho, and Fis were determined using 1,000 bootstrap replicates. The mlg.id function implemented in the poppr package94 was called to identify putative clones. Effects of patch area, nearest-neighbor patch distance, and their interaction on diversity estimates AR, HE, HO, and FIS of cohorts (n ≥ 8) were assessed in R running generalized linear models (GLMs) with Gaussian distributions and z-transformed variables. We performed a stepwise backward selection to reveal best-fit models (minimum AIC) excluding variables iteratively from the full model. Effect size R2pseudo was calculated using the MuMin package97. Patch area and distance to the nearest-neighbor patch were determined using Google Earth Pro 7.3.6.9345 (Supplementary Table Measures of genetic diversity were estimated in R (Version 4.2.2)poppr94 using Edwards chord distance99 for seed banks and standing cohorts separately. A Mantel test for isolation by distance (IBD) was conducted to test whether genetic distances and geographic Euclidean distances are correlated, using the R package adegenet100. The significance of correlations was assessed using the mantel.randtest function with 1,000 permutations. Additionally, linear regression models were applied for each correlation by the lm function in R using log-transformed and original geographic distance data. The best-fit model based on R2 was chosen for IBD calculation. To identify potential barriers to gene flow and spatial genetic patterns, the mgQuick function in the MEMGENE package100 was used to integrate geographic configuration with genetic distance based on Moran’s eigenvector maps (MEM)101. This stepwise analysis was performed separately for each set of cohorts using Edwards genetic distances between individuals and patch coordinates. Significant MEM eigenvectors were obtained through forward selection with 1,000 permutations followed by a redundancy analysis (RDA) to determine the proportion of explained variation by each eigenvector. Finally, MEMGENE axes were computed to assess spatial genetic neighborhoods. Moreover, we conducted a resistance analysis based on the deviation from IBD for the aboveground population 2019 to identify recent dispersal corridors and barriers, using the ResDisMapper package102. In this analysis, the residuals obtained from the isolation by distance method (see above) were considered as line segments connecting each pair of individuals across our target landscape. To calculate resistance, the landscape was divided into a grid of cells and the resistance of each cell was computed by averaging the IBD residuals of all lines that traverse the cell. The significance of resistance was obtained using 1,000 permutations. We mapped interpolated resistance scores as well as corridors and barriers based on confidence limits. This method requires no prior information about dispersal-limiting environmental characteristics and has been shown to produce accurate resistance values at small spatial scales103.Inter-individual genetic distances were calculated with the R package STRUCTURE Version 2.3.4104. Using a fused dataset comprising samples of all cohorts, the likelihood for the number of putative genetic clusters (K) was estimated to evaluate structuring among years and sites. We ran ten simulations for each K from two to 42  under the admixture model without prior population information. Parameter settings involved a burn-in period of 100,000 iterations followed by 500,000 Markov Chain Monte Carlo (MCMC) repetitions. The optimal number of clusters was determined by plotting the likelihood of K for each value of K (LnPr⌈X|K⌉) using the △K method105 in the Pophelper package106 in R. Clustering results of individual membership probabilities were visualized with the implemented pophelperShiny App.To test whether individuals can be assigned to distinct genetic groups following increased differentiation among sampled locations, Bayesian genetic clustering was performed using 107) using the same settings as in the previous analyses. Additional STRUCTURE analyses with a maximum of ten random subsamples from each site, regardless of cohorts, were performed for ten compiled sample sets based on the default model priors and the same settings. A complementary neighbor-joining tree (NJ- tree) based on Edwards genetic distance with 1,000 bootstrap replicates was constructed in ape for cohort level (n ≥ 5) and individual level analyses to assess spatial and temporal divergence simultaneously NJ-trees were visualized using ggtree108.The relative contribution of each inferred genetic cluster per population and cohort was illustrated as a pie chart on a physical map. To account for the putative effects of differences in sample size in STRUCTURE, we conducted a separate analysis using the admixture model with uncorrelated allele frequencies and an ancestry prior α = 0.026 , cohorts (n ≥ 8) were pooled for any population before the calculation of G’st. We tested for the significance of G’st values by estimating 95% confidence intervals based on 1,000 bootstraps over loci using the diveRsity package111. We performed a further nested AMOVA analysis on pooled cohort samples grouped by each site (npooled > 8) and aggregated into three regions  was performed in GENECLASS2112. First, we conducted the implemented self-assignment test to assign or exclude reference populations as possible origins for each individual. Therefore, we calculated individual assignment probabilities by the Monte Carlo resampling method proposed by Paetkau et al. (2004)113 with 10,000 simulations and assessed source population log-likelihood under the Bayesian Criterion of Rannala & Mountain (1997)114. As a baseline for individual assignments, we pooled the data from all cohorts per population115. A probability threshold of 0.01 was set to exclude individuals likely originating from unsampled sources. Additionally, we detected potential first-generation migrants under the Bayesian Criterion of Rannala & Mountain114 using the same resampling algorithm to calculate the likelihood of individuals belonging to their local gene pool or external ones. A probability threshold of α = 0.01 was set to reduce the probability of false positives  116. We further examined dispersal in time by determining the proportion of the most recent cohorts (2019) classified as descendants of earlier cohorts. For this, we created representative reference data for each local population by removing putative immigrant samples detected during the first two steps115 resulting in ten local datasets. 2019 population data and reference data from previous cohorts  were selected to perform an assignment test as earlier (α = 0.01).Recent dispersal and signals of immigration were inferred by a stepwise assignment procedure in Oenanthe aquatica were collected under a sampling permit issued by the State environmental authority LfU Brandenburg (Germany).We hereby declare that our experimental research and field studies on cultivated and wild plants, including the collection of plant material, are in accordance with relevant institutional, national, and international guidelines and legislation. Leaf samples of Supplementary Information 1.Supplementary Information 2.


Phyllopteryx taeniolatus, Syngnathidae) are an emblem of the diverse endemic fauna of Australia’s southern rocky reefs, the newly recognized “Great Southern Reef.” A lack of assessments spanning this global biodiversity hotspot in its entirety is currently hampering an understanding of the factors that have contributed to its diversity. The common seadragon has a wide range across Australia's entire temperate south and includes a geogenetic break over a former land bridge, which has called its status as a single species into question. As a popular aquarium display that sells for high prices, common seadragons are also vulnerable to illegal capture.Common seadragons  for 198 individuals and mitochondrial genomes for 140 individuals to assess species status, identify genetic units and their diversity, and trace the source of two poached individuals. Using published data of the other two seadragon species, we found that lineages of common seadragons have diverged relatively recently (< 0.63 Ma). Within common seadragons, we found pronounced genetic structure, falling into three major groups in the western, central, and eastern parts of the range. While populations across the Bassian Isthmus were divergent, there is also evidence for secondary contact since the passage opened. We found a strong cline of genetic diversity from the range center tapering symmetrically towards the range peripheries. Based on their genetic similarities, the poached individuals were inferred to have originated from around Albany in southwestern Australia.We conclude that common seadragons constitute a single species with strong geographic structure but coherence through gene flow. The low genetic diversity on the east and west coasts is concerning given that these areas are projected to face fast climate change. Our results suggest that in addition to their life history, geological events and demographic expansions have all played a role in shaping populations in the temperate south. These insights are an important step towards understanding the historical determinants of the diversity of species endemic to the Great Southern Reef.The online version contains supplementary material available at 10.1186/s12915-023-01628-9. Species are the cornerstone of biology yet their definition remains challenging, not just because of various species concepts, but also because genome-wide assessments have demonstrated that the genome is often porous with gene flow across previously delineated species –5. GenetBecause some barriers to gene flow are temporary, their disappearance allows previously isolated populations to come into secondary contact. If gene flow is reinstated, previous genetic differentiation is eroded and the two lineages may fuse . SecondaPhyllopteryx taeniolatus , Syngnathidae) is distributed across Bass Strait and mitochondrial and nuclear analyses have left two interpretations of their divergence. While mitochondrial haplotypes found only shallow structure over Bass Strait (minimum uncorrected distance 0.12%) [ST, Structure, DAPC) were not explicitly designed to uncover potential gene flow, leaving possible demographic connectivity across Bass Strait insufficiently addressed. Further, sampling was limited to the southeastern part of the range and therefore could only assess a subset of the genetic makeup of common seadragons.The common seadragon (e 0.12%) , a studye 0.12%) . In the e 0.12%) . HoweverPhycodurus eques) and ruby seadragons (Phyllopteryx dewysea) that are all endemic to southern Australia, common seadragons have the largest range. They inhabit shallow  kelp and seagrass beds [Common seadragons have in fact a much broader distribution, spanning a total of 5500 km from the temperate west coast to the east coast extending north to Sydney and south to Tasmania Fig. a. Among ass beds  to whichass beds , 26 and ass beds . A rangeass beds , but thiass beds , 29, a gEnvironment Protection and Biodiversity Conservation Act 1999. Even though illegal capture does not seem to occur on large scales [Range-wide sampling of common seadragons allows us to address three currently unknown issues that have direct conservation relevance. First, obtaining population characteristics for common seadragons across their entire range is critical to identify the number of distinct genetic units and to underpin their potential monitoring. Second, estimates of genetic diversity for populations across the range are useful as they are often seen as a proxy for the evolutionary potential and resilience to changes in their environment . Third, e scales , 36, a bPhyllopteryx taeniolatus) sampled along the entire range to investigate genetic structuring and genetic diversity. Because UCEs can be identified across divergent species (an advantage over most RADseq approaches), we were able to integrate the data with published UCE sequences of ruby seadragons and leafy seadragons to provide a time frame for inter- and intraspecific divergences. In order to assess the species status of common seadragons, we assess divergence and potential connectivity over Bass Strait. Lastly, we use the range-wide genetic framework to infer the geographic origin of two confiscated common seadragons.Here, we sequence genome-wide markers  for 198 individuals of common seadragons  obtained a similar topology Fig. : Fig. S1The three main lineages of common seadragons  were also found in clustering analyses of individual samples. PCA showed a west–east orientation along the main axis , two in the central group , and three in the eastern group .Individual assignment to genetic clusters showed increasing substructure at larger values of ancestral populations , and highest between the extremes of the range . The western and the central groups were significantly differentiated, albeit moderately considering the large geographic distance between the closest sites (FST = 0.15 over ca. 1300 km separating Esperance and Pearson Island). Between the central and the eastern group, minimal differentiation was higher (FST ≥ 0.41), yet differentiation was generally also high between adjacent localities of the east coast, for example Eden versus Kurnell (FST = 0.41).Most sampling sites differed significantly in their allele frequencies  adjusted p < 0.05). The same population trios involved in introgression were identified by HyDe, but not all individuals in the potential hybrid population showed statistically significant deviation from expected site frequencies .In order to account for possible gene flow, we used TreeMix to infer population splits and superimpose potential migration events. TreeMix recovered the same topology as the other methods based on the nuclear data Fig. b, but inWe used a supervised machine learning algorithm (DIYABC Random Forest) to select among models of divergence and potential secondary contact across Bass Strait. The selected scenario was significantly favored over the alternatives , and declining both east- and westward . The three populations at the range edges all had the lowest values of genetic diversity .Genetic diversity  strong genetic structuring in three major groups with substructure in each group, but evidence for genetic cohesion among the groups, (2) a significant genetic disjunction across the Bass Strait with evidence for secondary contact, and (3) a cline in genetic diversity from the central regions and the eastern and western parts of the range and (4) allowed us to infer that two illegally exported samples likely originated from around Albany in Western Australia.We used UCEs and mitochondrial genomes to study the range-wide genetic structure and genetic diversity of the common seadragon Our analyses of mitochondrial genomes and genome-wide SNPs from UCEs showed strong geographic structuring into three main groups, in the west, central, and east coast of Australia, which each showed additional population structure among sampling sites Fig. : Fig. S5Despite the strong geographic structure, our data clearly shows shared genetic variants between the three main groups and adjacent populations, indicating genetic connectivity between neighboring populations. When viewed in relation to the age of divergence of common seadragons and the other seadragon species, the intraspecific divergences among common seadragons lineages were much more recent Fig. a. The diThe genetic divergence in the southeast across Bass Strait separating the central clade from the eastern group was pronounced across all analyses. As in other marine taxa, this genetic break of common seadragons was likely caused by repeated isolation on either side of Bass Strait during sea level lows , 21. In Despite this main pattern of isolation across the genome, we also found substantial evidence for secondary contact between the previously isolated lineages east and west of Bass Strait, estimating that 16–21% of genetic variation is shared between Eden and Melbourne. Secondary contact may also explain the atypical position of the Eden population in species tree analyses, where it was the sister group to all other east coast populations, albeit being geographically located in the center of the eastern populations. The D statistics further indicated that the exchanged genetic material has spread along the east coast into all sampled populations, although these results were not statistically significant after correcting for multiple testing. We suggest that greater genetic marker density and sampling around the secondary contact zone could identify additional admixed populations. Overall, these analyses provide evidence for reinstated genetic exchange across Bass Strait.The sustained imprints of a historical landbridge that disappeared 14,000 years ago suggest that rates of admixture are low. It is possible that there is some pre- or postzygotic incompatibility that may slow down genetic exchange , but theST) and genetic diversity  estimated from the UCE loci used here and from the RADseq loci used in [ST, DAPC, and structure analysis [D statistics, Gamma statistics, model testing). Models that only consider lineage divergence are now known to oversplit population-level differentiation [Our results stand in contrast to a previous study that concluded that there is no evidence of gene flow and suggested raising a subspecies . The dis used in   are notanalysis , while wntiation , 14. OurA notable difference in the inferred patterns between mitochondrial genomes and genome-wide nuclear SNPs concerns the degree of divergence between the western and the central group. In agreement with a previous study based on two mitochondrial fragments , we founThe low divergence in nuclear DNA could be evidence of high gene flow but this is not expected given the considerable geographic distance and the low dispersal of common seadragons. Another explanation is a recent population divergence. It is possible that the ancestral populations were geographically closer during sea-level lows and then separated geographically when the coastlines shifted with rising sea levels. This is similar to leafy seadragons, for which genetic and geological modeling suggested that localities around South Australia and the central coast may have served as refugia from which recolonization occurred after sea-level rise . Such a Posidonia australis also has lower genetic diversity through New South Wales compared to Victorian and South Australian populations [Ecklonia radiata shows a strong decline in genetic diversity northward on Australia’s west coast [We found a pronounced cline in genetic diversity with high diversity in central locations and tapering diversity going both east- and westward. The pattern is remarkably similar to the leafy seadragon, which shows a cline from the central regions westward but is not distributed east of South Australia . The patulations , and genulations . The kelst coast . AdditioTwo explanations have been proposed to explain the often reduced genetic diversity at range edges. The center-periphery hypothesis states that genetic diversity is highest in the center of a species’ range where environmental conditions are ideal, while the range edges have suitable conditions . The reaThe genetic assignment of the two confiscated individuals of common seadragons to populations in southwestern Australia was unambiguously supported by all analyses, with the highest support for an origin west of Albany. The coast west of Albany encompasses several remote National Parks, with much of the coastline between Albany and westward to the Capes being inaccessible and exposed to inclement weather. These types of areas are challenging for ongoing surveillance.The forensic approach employed here could also be useful for other ornamental fish species. Currently, forensic approaches on aquarium fishes are used mostly for species identification employing mitochondrial barcodes . HoweverThe question of whether the east coast common seadragons are demographically independent from the central coast has practical implications if conservation actions become necessary. Separate management of the east coast and the central populations, as suggested before , may havOur genomic assessment of common seadragons provided a detailed view of their range-wide structuring, connectivity, and genetic diversity. These insights will inform conservation prioritization and may be used to address illegal wildlife trade. They also emphasize that species delimitation needs to consider gene flow. The results indicate a strong geological legacy in the genomes of common seadragons, with lineages shaped by vicariance and reunion, and their genetic diversity tapering towards the range edges. The historical geology of the temperate Australian coast likely impacted most shallow-water species, although the imprints on the genome may vary depending on the dispersal rate. In order to understand the history of inhabitants of the Great Southern Reef, we therefore need to consider life history and geological history driving divergence, secondary contact, and demographic fluctuations.P. taeniolatus) were sampled across the species’ range , one from the holotype and one beach-washed individual [Phycodurus eques) [A total of 198 individuals of common seadragons  , 61.N = 26). Of these, tissue clips were taken from wild fish (N = 14) and others came from preserved samples (N = 10), specifically from the Western Australian Museum Perth (N = 4), the South Australian Museum Adelaide (N = 1), the Australian National Fish Collection Hobart (N = 2), our own tissue collection , and from individuals held at Birch Aquarium at Scripps, which were raised from a brood collected in Melbourne (N = 2). Samples of common seadragons collected illegally (N = 2) were provisioned by the Western Australian Museum Perth, donated by the Department of Fisheries, Western Australia.Common seadragon samples came from a previous study , as welST > 0.08, see Results; Additional File N < 5) were analyzed with the population they clustered most closely in individual-level assignments (see Results): Dunsborough (N = 2) was analyzed with Perth, Hopetoun (N = 3) with Esperance, samples from localities in South Australia were analyzed together (Pearson Island (N = 1), Spencer Gulf (4 sites with N = 1–4), Fleurieu Peninsula (3 sites with N = 1–2), Bawley Point/Ulladulla (N = 2) with Jervis Bay, Triabunna (N = 1) with Hobart.For analyses that require population assignment, samples were grouped by sampling locality as these were genetically identifiable  using the DNeasy Blood & Tissue kit (Qiagen). We quantified DNA using a Qubit fluorometer (Life Technologies). DNA was sheared by sonication with a Bioruptor Standard (Diagenode) into fragments of an average size of 400–700 bp. Genomic DNA libraries were prepared using a commercial kit following the manufacturer’s instructions (KAPA Biosystems LTP Library Preparation Kit or KAPA Hyper Prep Kit) with an input of 60–1200 ng DNA. DNA was cleaned using a SPRI bead substitute . IndividSamples were sequenced using two runs of MiSeq  with 600 cycle  v3 chemistry and with five runs of 500 cycle (= 250 bp PE) v2 chemistry at the UCSD Stem Cell Genomics Core and one run of 500 cycle v2 chemistry at the UCLA Genomics Core. We also shared three lanes of HiSeq2500  in rapid run mode (100 bp PE), and one lane of the HiSeq4000  (100 bp PE) at the UCSD IGM Genomics Center.Raw reads were cleaned from adapter contamination and low-quality bases with trimmomatic v.0.39  using ilN = 268), and the other of common seadragons only (N = 198). Both sample sets were processed and filtered in the same manner. SNPs for each sample were called with HaplotypeCaller in GVCF mode, combined with CombineGVCFs, and genotyped using GenotypeGVCFs. Variants were filtered for quality using VariantFiltration and only biallelic SNPs were retained. vcftools v.0.1.17 [We created two sample sets, one including all samples of common, leafy, and ruby seadragons . We inferred suitable models incorporating a parameter for polymorphisms (+ P) using ModelFinder [For the nuclear data, we used a polymorphism-aware (PoMo) model, which reconstructs the evolution of lineages considering both changes due to mutations and changes in frequency of alleles for example due to genetic drift , 87. TheelFinder  and asseelFinder . We usedelFinder  in PAUP elFinder , which relFinder . A totalK), repeated each 20 times, and summarized repeated runs on the clumpak web server [In order to elucidate population structure within common seadragons (198 samples), a principal component analysis (PCA) was performed on the dataset without missing data across all common seadragons with the ade4 R package . We usedb server .ST in GenoDive v.3.06 [We calculated pairwise differentiation between populations as Fe v.3.06  with 100Because the above species tree analyses cannot account for the possibility of gene flow across lineages, we used TreeMix v.1.13  to inferN = 268, 13,748 SNPs), we calculated the conservative Dmin statistic with Dtrios of DSuite [D statistic for each trio of populations irrespective of the relationship between them [In order to test for gene flow between the populations while accounting for the possibility of incomplete lineage sorting, we calculated ABBA-BABA or D statistics, which express the frequency of sites that are discordant with the species tree , 101. Saf DSuite , i.e., teen them . Supporteen them ) method p values for multiple testing.To account for the possibility that not all individuals of a population experienced the same degree of gene flow, we employed HyDe , which eTo test support for different divergence scenarios across Bass Strait and to estimate population parameters, we used DIYABC Random Forest v.1.0 . This mehttps://github.com/stephens999/scat) to interpolate the origin of individuals on a geographic grid. SCAT estimates smoothed maps of allele frequency variation based on reference populations and infers a probability distribution of the geographic source of each confiscated sample [https://www.keene.edu/campus/maps/tool/). We ran 10 replicate analyses from different starting seeds with 1000 MCMC iterations each, of which half were discarded as burnin. All post-burnin inferred geographic coordinates from the replicate runs were combined and plotted using a 2D kernel density estimation.In order to infer the geographic origin of two confiscated individuals of unknown origin, we used a population assignment test implemented in GenoDive that calculates the likelihood that the individual’s genotype is found in a reference population based on allele frequencies . Analysed sample . We defiAdditional file 1: Fig. S1. Multi-individual species trees for 268 individuals of leafy, ruby and common seadragons based on 891,858 base pairs including 13,748 SNPs. Fig. S2. Phylogenetic relationships based on mitochondrial genomesof 155 individuals of leafy, ruby and common seadragons. Fig. S3. Cross entropy values for 20 replicates of Admixture analyses, each runs for 1–15 ancestral populations. Fig. S4. Results of individual clustering of 198 individuals with Admixture. Fig. S5. Pairwise FST comparisons between populations. Fig. S6. Estimates of D statistics between pairs of populations. Fig. S7. Overview of the scenarios tested in DIYABC Random Forest. Fig. S8. Comparison of metrics of genetic differentiation and genetic diversity between the present study and the previous study by Klanten et al. 2020. Table S1. Sampling information for the 198 individuals of common seadragon. Table S2. Priors for the DIYABC Random Forest analysis. Table S3. Assignment of two confiscated samples based on population assignment in GenoDive using likelihood ratios.Additional file 2: Sequencing statistics, mapping and deduplication statistics and SRA accessions for each sequencing run, each sample. Genbank accessions for the mitochondrial genomes.


Bigfin squid is one of the economically important seafood resources in Vietnam’s fisheries and the waters around Con Dao and Phu Quoc islands are two major fishing grounds where this species has been actively exploited. The start codon targeted polymorphism (SCoT) and CAAT box–derived polymorphism (CBDP) techniques were used to generate DNA fingerprinting data to analyze the genetic diversity, variation, and structure of the two populations in the waters surrounding Phu Quoc and Con Dao islands together with mitochondrial cytochrome C oxidase subunit I (COI) gene sequence data.He) = 0.2254, Shannon index (I) = 0.3459, percentage of polymorphic bands (PPB) = 80.14%, nucleotide diversity (π) = 0.0336, haplotype diversity (h) = 0.910 with 16 haplotypes] than Phu Quoc population . The genetic diversity at species level in the investigated region was at level of He = 0.2169, I = 0.3399, PPB = 86.41, π = 0.0289, and h = 0.892 with 24 haplotypes. Based on DNA fingerprinting data, the pairwise genetic similarity coefficients among individuals of the Con Dao population were lower (average of 0.7977) than the Phu Quoc population (average of 0.8316). Based on mitochondrial COI data, the pairwise genetic distances among individuals of the Con Dao population were higher (average of 0.0361) than the Phu Quoc population (average of 0.0263). Gene differentiation (GST) between two investigated populations was 0.0316 and 0.0310 leading to the genetic distance was 0.0573 and 0.0213 and the gene flow between them was Nm = 8.2209 and 11.4700 migrants per generation among populations based on DNA fingerprinting and based on COI gene sequence data, respectively. Genetic variation within individuals of both populations (WP) played the key role in total genetic variation at species level in surveyed region.Con Dao population possessed a higher diversity [expected heterozygosity (For the bigfin reef squid species in the surveyed region, the Con Dao population had the higher genetic diversity than the Phu Quoc population, between them existed a low to moderate genetic differentiation and a genetic exchange via gene flow. The DNA fingerprinting data better revealed the genetic differentiation between the two surveyed populations while the mitochondrial COI gene sequence data could show the phylogenetic relationship among the surveyed individuals and the other from the sea regions in Southeast Asia. Based on the results obtained, fisheries management strategies are suggested toward the conservation and sustainable exploitation of this species.The online version contains supplementary material available at 10.1186/s43141-023-00573-y. Sepioteuthis lessoniana d’Orbigny, is one of the members of the Loliginidae family. This species is considered to be the most widely distributed loliginid squid in the Indo-West Pacific region because besides its largest distribution range from Japan to northern Australia, it also occurs in many other places in the Pacific and Indian Oceans such as New Zealand, the Hawaiian Islands, South India, West Africa, the Madagascar island, Red Sea, and Western Mediterranean  to the corresponding sequence recorded for bigfin reef squid in Bali, Indonesia (GenBank accession number of KF052372).The gene flow between the two surveyed populations could be supported by the dispersal of planktonic paralarvae by two weak currents connecting the Con Dao and Phu Quoc sea waters in the two monsoon seasons .DNA fingerprinting data, which showed close grouping of individuals by population, again better reflects the genetic differentiation and the genetic variation between the two populations compared to mitochondrial COI sequence data. This could be explained by the fact that the SCoT and CBDP data reflect the genetic variation of many genes across different regions of the genome while the mitochondrial COI sequence data only reflect the genetic variation of a single gene.Analysis of molecular variance (AMOVA) revealed high genetic variation within populations and low genetic differentiation among two populations in investigated regions. Based on the mitochondrial DNA non-coding region, Aoki et al. showed that pairwise gene differentiations among 7 bigfin reef squid populations in Japan, Taiwan, and Gulf of Tonkin of Vietnam were in range of 0.0041 to 0.8593 with the average of 0.3695 , 40, theD = 0.0361) was significantly higher than between Nagasaki and Rayon populations  [Based on DNA fingerprinting data, the genetic distance between Con Dao and Phu Quoc populations in current study (chnique) , althougUsing mitochondrial COI sequence to investigate 11 populations of bigfin reef squid in Southeast Asia, Cheng et al. showed that the significantly different genetic variation distribution among populations (77.51% or 23.24%) and within populations (22.49% or 76.76%) depended on the investigate lineages [As mentioned above, the information on the genetic diversity, differentiation, and gene flow among the populations is necessary to establish the strategies for conservation and sustainable exploitation of the species. The findings from the current study indicate that the Con Dao population had a higher genetic diversity than the Phu Quoc population. The main reason for that difference may be that Con Dao island is exposed to strong ocean currents from two different directions, while there has been only a weak ocean current bringing planktonic paralarvae to Phu Quoc island. Accordingly, to maintain the long-term genetic diversity of the populations in surveyed regions, several fishery management strategies including the promotion of programs of artificial propagation and local as well as translocational releasing to increase the size of local populations and to overcome barriers to gene flows among populations; establishing regulations to limit fishing in the breeding season; and enhancing the protection and restoration of coral reefs and seagrass beds to ensure the quality of such habitats, especially at spawning grounds for the species should be fully considered.For the bigfin reef squid species in the surveyed region, the Con Dao population had the higher genetic diversity than the Phu Quoc population; however, the levels of genetic diversity based on COI data of these populations were at relatively higher compared to other populations in Southeast Asia. Between the investigated populations existed a low to moderate genetic differentiation and a genetic exchange via gene flow.DNA fingerprinting data better showed the gene differentiation between the two investigated populations than the mitochondrial COI gene sequence data; the COI gene sequence data reflects the phylogenetic relationships of the individuals investigated in the current study with bigfin reef squid in some other sea areas in Southeast Asia.From this study, several fishery management strategies for conservation and sustainable exploitation of bigfin reef squid are proposed.Additional file 1.