Canis lupus) genetic profiles from the Carpathian Mountains in Central Europe and the Dinaric Mountains in Southeastern Europe, with the latter as our reference population. We compared each population with Dinaric wolves, considering only shared markers (range 4–17). For each population, we calculated standard genetic diversity indices plus calibrated heterozygosity (Hec) and allelic richness (Ac). Hec and Ac in Dinaric (0.704 and 9.394) and Carpathian wolves (0.695 and 7.023) were comparable to those observed in other large and mid-sized European populations, but smaller than those of northeastern Europe. Major discrepancies in marker choices among some studies made comparisons more difficult. However, the yardstick method, including the new measures of Hec and Ac, provided a direct comparison of genetic diversity values among wolf populations and an intuitive interpretation of the results. The yardstick method thus permitted the integration of diverse sources of publicly available microsatellite data for spatiotemporal genetic monitoring of evolutionary potential.Integrating data across studies with traditional microsatellite genetic markers requires careful calibration and represents an obstacle for investigation of wide-ranging species where populations require transboundary management. We used the “yardstick” method to compare results published across Europe since 2002 and new wolf ( This has negative consequences for evolutionary research and conservation management of wide-ranging species where many populations range across multiple international boundaries4 and single studies rarely include entire populations. Rapid environmental changes and associated range shifts in many wild species further augment the need to understand their population genetic structure and evolutionary potential across wider scales . New genomic techniques are increasingly permitting broad-scale investigation also for non-model organisms, including marine and terrestrial taxa . However, many genetic monitoring programs still use microsatellite markers, which offer important temporal perspectives in cases where DNA sources no longer exist, but legacy data are available in the form of microsatellite profiles8. Given the comparative strength of microsatellite markers for detecting changes across limited temporal and spatial scales9, more efforts are needed to mitigate the problems that researchers and conservation managers encounter when seeking to integrate these valuable datasets. Efforts toward standardizing non-invasive genetic methods would also greatly facilitate the study of dispersal, and in turn help discern biological differences in dispersal parameters from variability owing to differences in methodology (reviewed in Ref.10).Comparison of results from traditional microsatellite genetic markers across studies requires calibration and thus complicates broad-scale studiesUrsus arctos) as a relevant example11. The method calibrates genetic diversity indices to a common denominator, the reference or “yardstick” population, allowing comparisons to be made. The authors suggested that this method may be even more useful for species with more diverse  sets of genetic markers used in different studies, as is the case with the grey wolf (Canis lupus)3. The yardstick method could facilitate comparison of existing data from populations with diverse demographic histories11, including habitat fragmentation, bottlenecks, and founder effects. Notably, allelic diversity has been proposed as a better predictor and candidate proxy than heterozygosity of evolutionary potential12. Recent works maintain that genetic diversity is vital for evolutionary fitness (reviewed in Ref.13) and that within-species genetic diversity merits wider recognition in international conservation planning14. The yardstick method’s inclusion of allelic richness could thus provide a flexible and cost-effective measure for genetic monitoring, research, and applied conservation management.To help alleviate these constraints, a “yardstick” method was proposed for comparison of genetic diversity indices among populations and applied to brown bears  overlapping loci.The wolf is a wide-ranging species where discrepancies in genetic markers among studies limit comparisons at relevant spatial scales in Europe2 are thought to represent vital genetic diversity for long-term preservation of evolutionary potential. Whereas wolves in many parts of Europe are well-studied at various spatio-temporal scales, those in Central and Southeastern Europe have received less attention  and have high priority for conservation16.In recent years, new genetic data on wolves has become available, allowing comparison of findings across regional scales, based on a larger number of common markers. These new data include populations in Central and Southeastern Europe where relatively abundant and persistent populations of large carnivores18, although there is evidence of recent gene flow into the Carpathians19. The Carpathian population spans the Czech Republic, Slovakia, Poland, Hungary, Ukraine, Serbia, and Romania and is among the largest in Europe20, estimated to number c. 3900–4700 individuals16.Analyses across genetic markers—including neutral and potentially adaptive genetic variation—show that wolves from the Carpathian Mountains in Central Europe are separated from wolves farther north19. Wolves were almost extirpated twice from Slovakia during the twentieth century but recovered and have since become relatively abundant in the central and eastern regions22. Moreover, further research is needed to evaluate connectivity across the region. A recent genome-wide analysis found signs of weak genetic structure between wolves in Romania and the Carpathian Mountain region of Ukraine23. Additional analyses with microsatellite markers could help clarify the extent of genetic exchange across short spatio-temporal scales9 and whether there are signs of recent resistance to gene flow.Recent population genetic analyses of microsatellite markers showed spatial structure within Slovakia that may have been influenced by environmental and anthropogenic factors25. The latter spans ten countries, from the Italian—Slovenian border in the west, across the Dinaric Mountains, to the Balkan Peninsula and Rhodope Mountains in Greece and Bulgaria in the southeast, with an estimated size of c. 5000–5500 individuals26. Wolves in the northwestern Dinaric Mountains (NW Dinaric) were subject to severe persecution in the past and were almost extirpated during the eighteenth and nineteenth centuries. The population started to recover in the second half of the twentieth century28 and is currently expanding its range into the Alps, where contact with the Alpine population has already been established30. Earlier findings from Croatia and Bosnia & Herzegovina have reported findings of genetic structure based on microsatellite markers31 and although such differentiation was not observed in Serbia32, there was evidence of structure based on mitochondrial DNA33, suggesting the need for broader-scale analyses.The Carpathian Mountain wolves were reported to be genetically distinct from the Dinaric-Balkan populationThe main objectives of this study were to (1) place new findings on wolf genetic diversity in the Carpathian and Dinaric regions into a broader context at the European level using previously published studies and (2) apply the yardstick method to a model species where conservation managers require a better understanding of genetic diversity at the continental scale. We also extended the yardstick method with calculation of calibrated heterozygosity and allelic richness estimates, enhancing the interpretability of the results. Finally, we discuss the relevance of these measures for conservation management of wolves and other wide-ranging species.We included 345 wolf profiles from tissue and noninvasive samples from the Dinaric Mountains  and the Carpathian Mountains , after removal of individuals showing any sign of possible hybridization with dogs. For details about sampling, laboratory processing, genotyping and error checking, assessment of hybridization, and identification of wolves, see Supplementary Note S1, Supplemental Tables Ho), expected heterozygosity (He) and allelic diversity (A)—and estimation of genotyping error rates, we used the package adegenet v.2.1.834 in R v.4.2.235. We used the R package pegas v. 1.0-136 to estimate departures from Hardy–Weinberg (HW) equilibrium and corrected for multiple testing with the Holm-Bonferroni multiple test correction with alpha = 0.0537.For data handling, calculation of genetic diversity indices—observed heterozygosity . We performed pairwise comparison whereby each population of interest was compared with a reference population, considering only the genetic markers shared between the two studies. Differences in sample size were corrected through resampling with replacement multiple times (~ 1000) from the larger sample size to the sample size of the smaller data set. For the resulting subsamples, genetic diversity indices with their standard errors were calculated . Heterozygosity ratio (Her) and allelic richness ratio (Art) indices were calculated with the corresponding reference population values in the denominator11. As an extension of the method described in the original paper, we multiplied the Her and Art ratio indices with heterozygosity or, respectively, allelic richness of the reference population, obtaining directly interpretable calibrated expected heterozygosity (Hec) and allelic richness (Ac) values. As the values of these parameters were on the same scale as the original heterozygosity and allelic richness, this allowed both direct comparison between otherwise incompatible datasets as well as a more intuitive interpretation of the obtained values. As the relationship between observed and expected heterozygosity is better estimated in the original data , we maintained the relationship between these two parameters in each population by calculating the calibrated observed heterozygosity (Hoc) from Hec and the ratio between observed (Ho) and expected (He) heterozygosity in the original data as Hoc = Hec * (Ho/He).We collected published genetic diversity data based on microsatellites for other European gray wolf populations through literature review  and implemented in the function divRatio of the diveRsity package41). For each comparison we ran 1000 random subsamples. When using the yardstick method to calculate diversity indices for populations with sample sizes larger than the reference population, we compared the published indices directly  using the loci in common. In these cases, the number of samples was large in both populations and the relative sample sizes were very similar, and any bias should therefore be minimal. Figures were created using base R functions and the R package ggplot242. Maps were created using QGIS 3.24.043.The NW Dinaric population with a relatively large sample size and locus set was used as the reference population  and was therefore excluded  and highest for wolves in European Russia (Art = 2.252).Heterozygosity and allelic richness were generally higher in populations in N and NE Europe  Fig. . In manyart Fig. , suggestCalibrated heterozygosity and allelic richness in Dinaric wolves were higher than those found in Scandinavian, Italian and Iberian wolves. In Carpathian wolves, both parameters were higher in Romania than in Slovakia, and comparable to those of Dinaric wolves. Our results from the Dinaric and Carpathian regions showed lower values for heterozygosity and allelic richness than those observed in NE Europe.11 allowed us to compare our results with genetic diversity parameters published in various studies from across Europe, which included samples originating from 1829 to 2018  among wolf populations are considerable and, in many cases, correspond better with known population histories than the heterozygosity estimates. Ref.15 noted that allelic richness was distributed relatively evenly across Europe, showing only a weak spatial signal. In our study, Ac nonetheless shows a general increase from south-west to north-east, which corresponds with the distribution of genetic diversity across European populations observed by Ref.15. As allelic richness seems to be a good indicator of severe, short bottlenecks53, and may more effectively reflect a population’s long-term evolutionary potential54, temporal data on allelic richness can provide an effective means of monitoring smaller and relatively isolated populations identified as vulnerable (Ref.15 and references therein).The observed distribution of genetic diversity in European wolves fits well with the general picture found in earlier studies55, but deviations occur at a relatively small spatial scale for a highly mobile mammal56. A simple explanation is that because many populations survived the persecution of previous centuries in very small numbers, high genetic drift caused by low effective population sizes leads to detectable genetic differentiation between populations, or even between population fragments within the same metapopulation, also at relatively small spatial scales. Founder effects during recolonization have resulted in persistent genetic drift57. Moreover, some areas were colonized from populations that were quite recently established by a limited number of founders, such as wolves in Denmark58, and a “double” founder effect could thus be affecting wolves in such areas.There is a general pattern of deviations from HW equilibrium in most reported wolf studies Fig. . In some59. Such deviations seem more frequent in populations that are geographically close to other genetically differentiated wolf populations, than in divergent populations separated by larger geographic distances, as observed in our study. This general pattern of deviations from HW equilibrium at the continental scale may suggest that, as wolves in Europe continue to recover, gene flow may be starting between previously isolated populations. However, the detectable effects of gene flow usually follow clear and predictable patterns. At first, direct immigrants  are expected to cause the Wahlund effect55 resulting in excess homozygotes and a positive Fis60, which may be misinterpreted as a sign of assortative breeding if no other analysis is done. This can be an explanation for populations that are above the diagonal dashed line in Fig. 55), shifting the deviation from HW equilibrium in the other direction and causing negative Fis, which can also be misinterpreted as a sign of inbreeding avoidance. This may be the case for the populations below the dashed diagonal line in Fig. However, in other cases there are clear deviations from HW equilibrium where a hidden substructure would be unexpected given the spatial and landscape characteristics of the areaOriginally, the yardstick method calculates the ratios between the genetic diversity indices in target versus reference populations, but they are not always intuitive to interpret. We extended the method to calculate calibrated heterozygosity and allelic richness, which enables a much more intuitive interpretation. Moreover, we compared the “original” genetic diversity values obtained in the reviewed studies with the calibrated values Fig. . The dif11 used bootstrap resampling to account for this issue when demonstrating the yardstick method. In our study the sample size for the reference (NW Dinaric) population was lower than in nine of the populations used for comparison. These nine studies all had relatively large sample sizes, and we therefore compared the published He and Ho for the shared loci directly .If sample size is small, estimates of some parameters, particularly allelic diversity, can become severely biased, and Ref.24 (study SK1). Our sampling area was focused on northern Slovakia, and we included only 20 individuals, whereas Ref.24 had a sample of 194.One challenge for our investigation was the availability of data from the published studies. In some cases, we were unable to calculate the calibrated allelic richness or observed heterozygosity ratio with the yardstick method for some of the reported population clusters, because the authors did not report locus-level allelic diversity for each separate unit. Neither were the temporal extents, sampling periods, or sampling areas clearly defined in all cases, and the results should therefore be interpreted with caution. For example, we noticed quite a difference between the genetic diversity observed in Slovakia in our study (study SK2) and that of Ref.45 among the compared studies reported analyzing more markers (42 loci). However, the numbers of overlapping markers available for comparison between the reference and other populations varied widely, ranging from 21 to only four loci. The marker panels differed considerably among studies, reflecting the high diversity of markers available for canids, and nearly every study included here had its own marker panel. Conversely, in brown bears, where the number of markers routinely investigated is smaller, more than half of the compared studies had eight loci available for comparison with the reference population11. Whereas the high diversity of canid markers presents some challenges and complicates the comparison of populations that have few overlapping markers with the reference population, it also highlights the yardstick method’s utility in our objective of comparing wolves across Europe.We genotyped 33 microsatellites, and only Ref.Lynx lynx) extend across multiple countries (Ref.61). The same holds for many ungulate species, such as red deer (Cervus elaphus) and wild boar (Sus scrofa) that are broadly distributed across Europe63. As with wolves in parts of our study area64, red deer, wild boar and other ungulate species in Europe have been affected by hybridization in parts of their range (reviewed in Ref.4). Such events confound analyses of genetic structure and variability, and make it difficult to distinguish natural patterns of gene flow and diversity from those influenced by human activities62. Broad-scale investigations that permit direct comparison of results across geographic regions therefore remain an important priority4.The yardstick method can inform conservation management for wide-ranging species where populations encompass multiple jurisdictions, such as large carnivores in the Carpathian and Dinaric-Balkan regions where populations of wolves, bears, and lynx  and Italian (study IT2) populations that are known to have gone through severe bottlenecks. Recent findings also suggest a dynamic situation with signs of recent gene flow between the Carpathian Mountains and wolves farther north and west67, which requires further monitoring and investigation.Within the Carpathian Mountains, wolves in Romania seem to have higher genetic diversity than wolves in Slovakia, which is expected as they represent a major part (70%) of the Carpathian population29, in the Alps and Dinaric Mountains30, in this study, and possibly in the Carpathians Mountains19, and Poland67. Hence, this may be a wider phenomenon connected with the ongoing continent-wide recovery of wolves20.Although we observed considerable differences among wolf populations in Europe, even at finer spatial scales, these differences may gradually dissolve as recovering wolf populations become increasingly reconnected. This process is already seen in the Apennine Mountains59. For certain populations showing structure across relatively short geographic distances, natural selection has also been suggested as a possible contributing factor68 although it is difficult to see how this could have occurred in recent times. Given that most of these populations have been considerably reduced and their effective population size was (and probably still is) low, selection would need to be extremely strong not to be overpowered by genetic drift69, making adaptive evolutionary changes in such populations unlikely. However, this selection could have occurred in the past, in which case some of the currently observed population structure in wolves might not be a result of human activity, but possibly a result of natural selection . These issues have consequences for applied conservation of wild species, for example, whether some populations should be conserved in situ and admixture with other populations encouraged only via natural dispersal. These questions require further genomic research that can evaluate both neutral and functional genetic variation.The newly presented measures for calibrated heterozygosity and allelic richness allow a direct comparison of genetic diversity values among populations and a more intuitive interpretation of the results. However, it is still crucial to interpret the results carefully, including deviations from HW equilibrium, as this may indicate the presence of cryptic population structure72, and it would therefore seem farsighted to preserve the genetic diversity currently found in habitats where organisms are more likely to have experienced such selective pressures. The role of adaptive potential at range edges is important also for highly mobile species73, and future research could help clarify the extent to which allelic richness may function as an indicator of adaptive potential, where the yardstick method could be informative.Gene flow from populations adapted to warmer and drier conditions could become increasingly important given rapid environmental changes74. On the other hand, practical conservation in Europe is frequently done at the level of administrative units—countries, or even provinces—causing further “administrative fragmentation” of conservation management. To promote conservation, restore genetic diversity, and facilitate increased gene flow in wolves and other large carnivores, conservation managers should move towards population-level actions75. This process should be supported by the scientific community by shifting the focus from national-level research towards broader studies coordinated at the level of biogeographic regions  or even the entire continent. For highly mobile species such as large carnivores, broad-scale approaches likely present the best option to reduce human-wildlife conflicts and ensure their sustainable future in the human-dominated landscapes of modern-day Europe. Tracking allelic richness across the spatial scales relevant for gene flow in wolves and other highly mobile mammals could thus provide a cost-effective measure for genetic monitoring, especially given its capacity to connect past and present data across multiple jurisdictions.Much conservation effort is focused at the species level, including the backbone of European biodiversity conservation policy, the Habitats Directive . Extinction rates for populations, however, are estimated to be three to eight times higher than those for speciesSupplementary Information 1.Supplementary Tables.


Glycine soja Sieb. & Zucc. is the wild ancestor from which the important crop plant soybean was bred. G. soja provides important germplasm resources for the breeding and improvement of cultivated soybean crops, however the species is threatened by habitat loss and fragmentation, and is experiencing population declines across its natural range. Understanding the patterns of genetic diversity in G. soja populations can help to inform conservation practices.G. soja at different sites and investigated the gene flow within the species. We obtained 147 G. soja accessions collected from 16 locations across the natural range of the species from China, Korea and Japan. Samples were analyzed using SLAF-seq (Specific-Locus Amplified Fragment Sequencing).In this study, we analyzed the genetic diversity and differentiation of G. soja harbors relatively high diversity and that populations of this species are highly differentiated. The populations harboring high genetic diversity, especially KR, should be considered first when devising conservation plans for the protection of G. soja, and in situ protection should be adopted in KR. G. soja populations from the Yangtze River, the Korean peninsula and northeastern China have a close relationship, although these areas are geographically disconnected. Other populations from north China clustered together. Analysis of gene flow suggested that historical migrations of G. soja may have occurred from the south northwards across the East-Asia land-bridge, but not across north China. All G. soja populations could be divided into one of two lineages, and these two lineages should be treated separately when formulating protection policies.We obtained a total of 56,489 highly consistent SNPs. Our results suggested that  The species grows as a weed in cultivated land, on banks and in wetlands, from sea level to altitudes of 2,650 m  technology for G. soja, which together cover almost all of the main distribution area of the species. All 16 populations sampled lie outside of reserves or conservation areas, and the sample collection met the requirements of the local government in each area. Within each population, plants were randomly sampled at a minimum distance of 15 m from each other, in order to avoid the collection of ramets of the same clone. All samples were collected directly from the wild. Field experiments were approved by the National Natural Science Foundation of China (Project number: 31500459). Young, healthy leaves were collected from individuals and were dried immediately in silica gel. Between nine and ten individuals were collected from each population, with the exception of population DQ3, from which only three individuals were collected  was added to the 3′ end of each of the obtained SLAF fragments, and the fragments were then connected to the Dual-Index sequencing joint. Fragments were amplified using PCR, were purified, and target fragment sizes were selected using gel tI if they passed the library quality inspection. High-throughput sequencing was performed on an Illumina HiSeqTM-2500 platform  at the Biomarker Technologies Corporation in Beijing.Specific-locus amplified fragment sequencing (SLAF-seq) is an efficient method of large-scale genotyping, and is based on a reduced representation library (RRL) and high-throughput sequencing. We used a modified SLAF-seq strategy in our experiment, with fragment sizes (including adaptors and indexes) ranging from 364 to 444 bp. The DNA was then cleaned and digested into fragments using the enzymes RsaI+HaeIII  which have been previously applied to G. max . ConsideStacks1. 0 pipeline  were discarded and the reads were filtered to remove adapter contamination. The program ustacks (stack depth parameter (−m) = 5; a mismatch parameter (−M) = 2, maximum stacks per locus = 3) was then used to group the sample data into loci. The locus data were then merged into a catalog in cstacks. The alleles in each sample were determined by comparing the loci from each sample to the catalog in sstacks.In order to reconstruct the loci, the raw data were analyzed using the pipeline , 2013b. G. soja was assessed using the program populations, with all 147 samples treated together as a single population. A locus was required to be present in at least 67% of all samples in order to be eligible for inclusion in this analysis. Analysis of population level genetic diversity was conducted with each collection area treated as a population and loci were required to be present in all individuals (r = 1) in at least six populations (p = 6).Species level genetic diversity in Stacks was used to calculate population genetic statistics for each SNP ; expected heterozygosity (HE); nucleotide diversity (π); Wright’s F statistics FIS and FST)  . The inbpulation . We calcMega 6.0 . The corMega 6.0  and a maMega 6.0  using thStructure format files containing the SNP data were output from the populations program in Stacks to allow analysis of population level genetic structure (Genepop and Structure files using the parameters r = 1 and p = 6. GenePop v 4.3 (p) were adjusted for multiple comparisons with sequential Bonferroni correction (α = 0.05)  .Structure files was then analyzed using program Structure 2.3 (K) was set to 1–20 with 10 replicates for each value. The Structure Harvester program was applied to calculate the optimal K for each analysis  analysis based on the Euclidian distances of individual genotypes.The ture 2.3 . The inianalysis . In order2 = 0.1 was used for the evaluation of LD decay for each population, with the r2 value for a marker distance of 0 kb assumed to be 1. Distances between the SNPs and r2 were plotted as the LD-Decay curve. r2 is usually larger where SNPs are closer, and smaller when SNPs are further far apart. The LD decay distance (LDD) is the distance during which r2 is reduced to half of its maximum value Low recombinant frequencies within a particular distance tend to result in longer LDDs while higher recombinant frequencies within the same distance result in shorter LDDs. Plink (A nonlinear regression of linkage disequilibrium (LD) between polymorphic sites against distance (bp between sites) was run to estimate LD decay with physical distance. A cut-off value of s. Plink  was usedMaximum likelihood trees describing the historical relationships between the study populations and to infer potential migration events between them were generated in TreeMix v1.13 . TreeMixG. max) was used for program prediction in this project. The clean reads derived from each sample ranged between 453 and 2,202 Mb for each individual, with most reads being about 800 Mb. The average number of reads assigned to each individual was 3,859,551, with minimum and maximum read numbers per individual of 2,266,715 and 11,010,066, respectively (p < 0.001). A total of 56,489 SNPs were retained for downstream genetic diversity analysis. The SNPs showed a largely even distribution throughout the genome  was 0.0157 for all loci polymorphic at the species level, with the expected heterozygosity (He) being 0.1459, a nucleotide diversity (π) of 0.1465, and an inbreeding coefficient (FIS) of 0.8533. When considering all nucleotide positions, including the non-polymorphic ones, the observed heterozygosity decreased to 0.0004, with the expected heterozygosity decreasing to 0.0035, the nucleotide diversity decreasing to 0.0035, and the inbreeding coefficient decreasing to 0.0205 under the same conditions.The observed heterozygosity (Ho) was found to range between 0.0199 (DQ) and 0.0460 (KR), the expected heterozygosity (He) to range between 0.0119 (DQ) and 0.3492 (KR), the nucleotide diversity (π) between 0.0130 (JK) and 0.3789 (KR), and inbreeding coefficient between −0.0003 (JK) and 0.0230 (KR).Statistical analyses for each population are given in If all nucleotides, including nonpolymorphic nucleotides were considered, the observed heterozygosity was found to lie between 0.0005 to 0.0016, with the expected heterozygosity ranging from 0.0003 to 0.0121. The observed nucleotide diversity ranged between 0.0003 and 0.0131, and the inbreeding coefficient from −0.0003 to 0.0230. The number of private alleles observed for each population ranged between 1,755 (HH) and 12,083 (QQHE). From all the measures, the highest genetic diversity was found in the KR population, followed by QQHE. The lowest nucleotide diversity and heterozygosity was seen in the JK population, with the lowest observed heterozygosity was found in the DQ population.r2 = 0.0268, p = 0.104)  . The aveega v6.0 . In geneG. soja may have occurred, from the south northwards across the East-Asia land-bridge. The phylogeographic history of G. soja provides us with new insights into the migration patterns of herbaceous plants across the Sino-Japanese Floristic Region.Analysis of gene flow suggested that historical migrations of G. soja populations, “admixture” and “correlated alleles frequencies” models were used to analyzed the 56,489 generated SNPs in Structure2. Changes in LnP(D) and delta K were assessed. K = 11 was best model for our data . The result is also consistent with the structure of the neighbor-joining tree , 0.226 (ISSR) and 0.157 (SSR). In the current study, we applied the high throughput sequencing technology SLAF-seq to investigate the genetic diversity of G. soja populations across the known distribution of the species. We obtained a value of HO = 0.0157 and HE = 0.1459, and different markers behaved differently in our study. Because SLAF-seq markers are genome-wide DNA tags , they should represent the sequence characteristics of the entire genome. SLAF-seq markers are therefore believed to accurately reflect the true level of genetic diversity. Although HO and HE were different among different markers, all of the different markers show the same pattern: that the HO was much lower than HE in G. soja populations, indicating that there is a certain amount of inbreeding within the population, and that the species lacks heterozygotes. We also found that certain populations, such as KR from the Korean Peninsula, have especially high genetic diversity. This is consistent with our previous studies using SSR markers , but that there would have been a land-bridge formed by the exposed ECS basin when the sea levels decreased by c. 85–130/140 m during the glacial periods . Temperangeshoma , Platycrr arguta  and Croo discovered a natural population of G. soja plants covering an area of about 3,000 m2 in Tahe County. This area was designated as a “G. soja original habitat nature reserve” by the environmental protection department. However, original habitat nature reserves are insufficient for the complete protection of G. soja, and the protection of the species needs to be strengthened.Certain areas have begun to realize the importance of 10.7717/peerj.16415/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.16415/supp-2Supplemental Information 2Click here for additional data file.


Salmo trutta L.) that have suffered dramatic declines in numbers over the past 40 years. Knowledge of levels and patterns of genetic diversity is essential for effective management of these vulnerable populations. Using restriction site‐associated DNA sequencing (RADseq) data, we describe the development and characterisation of a panel of 95 single nucleotide polymorphism (SNP) loci for trout from this region and investigate their applicability and variability in both target  and non‐target trout populations from northern Britain and Ireland. In addition, we present three case studies which demonstrate the utility and resolution of these genetic markers at three levels of spatial separation:(a) between closely related populations in nearby rivers, (b) within a catchment and (c) when determining parentage and familial relationships between fish sampled from a single site, using both empirical and simulated data. The SNP loci will be useful for population genetic and assignment studies on brown trout within the UK and beyond.The rivers of southern England and northern France which drain into the English Channel contain several genetically unique groups of trout ( We then examine the effectiveness of this panel to explore patterns of genetic diversity and structure using populations from within the English Channel area as well as populations outside of this region. We then explore three case studies using this panel to examine (a) structure and genetic diversity within a single catchment relating to population fragmentation, (b) structure and genetic diversity between small proximate coastal catchments and (c) the utility of this SNP panel in identifying full‐sib family structure within a population and comparing the results to those obtained using 18 microsatellite markers.22.1The work represented here did not require ethics approval.2.2et al., et al., et al., Sbfl, purified using AmpureXP magnetic beads , and phased P1 adaptors were ligated onto each fragment. Digested DNA was fragmented to an average size of 400 bp, blunt ends repaired and adenylated prior to P2 adaptor ligation. The libraries were PCR amplified for 14 cycles prior to 250 bp PE sequencing on an Illumina HiSeq 2500 .A pooled RADseq approach  gene and a C/G polymorphism in an intron of the metallothionein B (metB) gene]. Full details for these additional loci are given in Supporting Information. Hereafter, all loci are referred to as SNPs for clarity, including the three base‐pair indel.To the RADseq‐derived loci the authors added sequence from three additional genomic regions – a three base‐pair indel polymorphism and two additional SNPs  were calculated using GenAlEx v6.502  using the Holm–Bonferroni correction  were calculated using the divBasic function of the R package diveRsity v1.9 .Population pair‐wise values of We explored the utility of this SNP panel, designed around trout from multiple Channel/Manche rivers  sub‐structuring . RAD loci were built using optimised parameters  using the denovo_map.pl pipeline. Preliminary data analyses showed that three population pools had either low levels of sequence coverage (GB01A) or high levels of missing data  and were therefore removed from all subsequent analysis.Post process_radtag filtering resulted in the retention of 116,757,729 sequences from the 20 pooled libraries  were submitted for primer design. Initial testing of 135 loci with trout samples from a range of British, Irish and French rivers identified several loci that did not give the expected three genotype clusters, lacked one of the homozygous genotypes or had evidence for a high frequency null allele  to 0.276   was monomorphic in all samples other than the Flèche population. A comparison of genotypes from repeated samples gave an error rate of 0.0014% . No SNPs were found to be in significant linkage after Holm–Bonferri FDR correction. Across all populations, no SNPs were found to be out of Hardy–Weinberg equilibrium. Basic measures of diversity were comparable across all 11 samples. Observed heterozygosity ranged from 0.287  to 0.359 (Tamar), whereas expected heterozygosity ranged from 0.287  to 0.362 (Tamar)  and ranged between 0.121 (Crowlas and Trevaylor) and 0.284 (Penberth and Cober)  and highest in Crowlas (89.5%). Pairwise % and hig3.2FIS values were significantly different from zero. Pair‐wise FST values between sites varied between −0.001 (Pencarrow and Stannon) and 0.057 (Brynn and De Lank). FST values between the De Lank and each other site were all significantly greater than zero  and greatest in the Trehannick (0.351). Percentage polymorphism varied from 94.7% (De Lank) to 98.9% (Stannon and Trehannick). No FST and geographic distance between sampled populations  and, though still non‐significant, the strength of this relationship increased when comparisons between the De Lank and other sites were excluded from the Mantel tests .DAPC analysis of trout in the tributaries of the Camel produced three main clusters: (a) the Trehannick and Brynn; (b) Stannon, Tressarett and Pencarrow; and (c) the De Lank Figure . To exam3.3Correspondence between data from 18 microsatellites and the new panel of 95 SNPs in determining full‐sib relationships for the sample of trout from the Great Stour was strong . In the region studied – Mount's Bay, southern Cornwall – potential factors limiting gene flow include isolation by physical barriers and chemical pollutants from the industrial legacy of the region . This, alongside the relatively high pair‐wise The 95‐locus SNP panel presented here appears well suited to exploring this variation, as seen in several previous microsatellite‐based studies, together with additional fine levels of differentiation.4.3et al., et al., et al., et al., The brown trout populations of southwest England are typified by relatively small coastal catchments, with contemporary gene flow facilitated by the straying of anadromous individuals, with levels of genetic diversity within these small catchments often comparable to larger rivers . In addition, using simulated data, we found no 'spurious' familial relationships, thereby increasing the confidence in the ability of their SNP panel to correctly identify full sibs within 'real”' data sets.In‐river sampling of juvenile salmonid fishes for genetic analysis is often focused on just one or a few sites within a river, where juveniles  may originate from a very limited number of spawning adults . We find these loci to be highly polymorphic and suitable for defining population structure within the original target region – English and French rivers flowing into the Channel – and in populations around the wider British Isles. We have demonstrated the utility of this panel in three case studies: (a) an examination of relatedness between potentially linked proximate small populations; (b) an assessment of the impact of potential barriers to gene flow within a catchment; and (c) the detection of family relationships between individuals sampled from a single population. The ability to genotype large numbers of individuals across populations at relatively low cost in a short time period offers a flexibility often preferable to reduced representation sequencing approaches, while maintaining reproducibility and statistical power. The case studies also reveal some otherwise‐overlooked conservation concerns within fragmented populations, and we anticipate that this panel will be useful in future studies seeking to understand the impacts of potential stressors on genetic structure and health of threatened brown trout populations.Here we present a low‐density SNP panel of 95 loci as a low‐cost tool for use in population genetic studies of trout (FIGURE S1 Example genotype scatter plots for four brown trout SNP assays genotyped on the Fluidigm EP1 platform. Str_68875 shows the three expected genotype clusters with homozygous (red and green) and heterozygous (blue) genotypes, Str_5593 lacks one of the homozygous genotype clusters, Str_59578 is an assay with a high frequency null allele and Str_10193 failed to resolve the three genotype clustersFIGURE S2 Number of single nucleotide polymorphisms per linkage groups for the final panel of 95 Fluidigm assaysFIGURE S3 Position and nature  on each brown trout linkage group for the final 95 single nucleotide polymorphismsFIGURE S4 Results of COLONY analysis on the ability of the brown trout microsatellite and SNP panels to correctly identify familial relationships. (a) Assignment to full‐sib families and (b) assignment of parentage for 32 simulated trout genotypes. Results were identical for both marker typesTABLE S1 Details of DNA pools used for restriction site‐associated DNA (RAD) sequencing with numbers of sequences obtained and the number retained post‐processing through the process_radtags module in STACKS v2.41 TABLE S2 Details of fullsib families simulated using HYBRIDLAB ].Click here for additional data file.


Aedes (Stegomyia) aegypti represents a major challenge for public health due to its proliferation in many regions of the world and its capacity to disperse arboviruses. The verification of the genetic characteristics of this mosquito and the differentiation of these characteristics both within and between its populations in the Mid-North region of Brazil will be fundamentally important for the development of effective measures for the control of this disease vector. Previous studies of mitochondrial markers in the Mid-North region revealed two genetic lineages of Ae. aegypti, and high levels of genetic differentiation, which represent a potential risk for the occurrence of epidemics and a major problem for local public health authorities. Using microsatellite markers, the present study identified distinct genetic lineages of Ae. aegypti in areas with high rates of infestation. This analysis revealed the coexistence of two different genetic lineages, which were highly differentiated in genetic terms. These findings on the genetic status of the populations will be extremely important for the development of innovative strategies for the control of the populations of this disease vector, and effective measures for the protection of local human populations.The mosquito Aedes aegypti is the principal vector of the arboviruses—yellow fever, dengue virus, chikungunya, and zika virus. Given the epidemiological importance of this mosquito, its capacity to adapt to different habitats, and its resistance to many types of control measures, systematic research into the genetic variability of the populations of this mosquito is one of the most important steps toward a better understanding of its population structure and vector competence. In this context, the present study verified the presence of distinct genetic lineages of Ae. aegypti in areas with high infestation rates, based on the analysis of microsatellite markers. The samples were collected in nine municipalities with high building infestation rates in the Mid-North region of Brazil. Six microsatellite loci were genotyped in the 138 samples, producing a total of 32 alleles, varying from one to nine alleles per locus in each of the different populations. The AMOVA revealed greater within-population genetic differentiation with high fixation rates. The general analysis of population structure, based on a Bayesian approach, revealed K = 2, with two Ae. aegypti lineages that were highly differentiated genetically. These data on the connectivity of the populations and the genetic isolation of the lineages provide important insights for the development of innovative strategies for the control of the populations of this important disease vector. Aedes (Stegomyia) aegypti  is an invasive mosquito of the family Culicidae, which is native to Africa but is now widely distributed in the tropical and subtropical regions of the world [he world ,4,5.The control of this vector is considered to be a major challenge for public health authorities around the world, given the capacity of the mosquito to adapt to different types of habitat, the hardiness of its eggs, passive dispersal, and its resistance to control measures ,7,8. TheAe. aegypti was eradicated in the 1950s, according to the Pan American Health Organization (PAHO) but was subsequently reintroduced in the 1970s [Ae. aegypti has been considered to be one of the vectors that have had the greatest impact on public health in the country. Up until June 2022, for example, 1.172.882 probable cases of dengue fever were recorded in Brazil, in addition to 122.075 cases of chikungunya, and 5.699 of zika [In Brazil,  of zika . This sc of zika .Ae. aegypti populations. The epidemiological importance of the species demands reliable data on its population structure. The SSRs are good molecular markers for the study of population genetics, given their high rates of mutation and polymorphism [These findings reinforce the need for the genetic monitoring of morphism ,13. In pmorphism ,15,16,17Ae. aegypti populations are extremely important for the Mid-North region of Brazil, where a large proportion of the municipalities are at risk of infestation by this vector and are thus faced with the potential spread of the dengue, chikungunya, and zika arboviruses [Ae. aegypti, with evidence of the coexistence of two distinct genetic lineages in the state. It is important to note that populations of vectors with high levels of genetic differentiation within a given (micro or macro) region may present a greater competence for the transmission of diseases.Studies of the genetic variability of oviruses ,20,21,22oviruses  and Sousoviruses  found hiAe. aegypti in order to support the development of effective measures for the control of these populations within a given area. In this context, the present study builds on the results of three previous population studies of Ae. aegypti, two of which [Ae. aegypti infestation rates in the Mid-North Region of Brazil.Given this, it will be essential to compile reliable data on the patterns of genetic structure and gene flow and the within- and between-population differentiation of of which ,24 focusof which  analyzedAe. aegypti Survey (LIRAa) provided by the Brazilian Ministry of Health [The Mid-North (Meio-Norte) is a region in the western extreme of Northeastern Brazil, which encompasses the ecological transition between the Amazon and Cerrado biomes, including the state of Maranhão and the western half of Piauí . The samf Health ,19,26. TWe obtained the samples using 20 ovitraps , which wAll the biological specimens were identified as species using the taxonomic key of Consoli and Lourenço-de-Oliveira . Once idThe total DNA was extracted from the specimens using the Wizard Genomic DNA Purification kit , following the manufacturer’s instructions. The six polymorphic loci were amplified by Polymerase Chain Reaction (PCR) using the specific primers  describeThe PCR products were diluted by 10× in ultrapure water, with 0.3 μL of 600 LIZ Size Standard and 8.7 μL of formamide being added to 1 μL of this product. The plates containing 10 μL of each sample were placed in a thermocycler and heated to 95 °C for 5 min, after which the samples were sequenced in an ABI PRISM 3500  automatic DNA sequencer, following the manufacturer’s recommendations.The results of the genotyping were analyzed in the GENEMARKER program  to verifThe genetic differentiation of the populations was evaluated based on an Analysis of Molecular Variance (AMOVA), the FST index (genetic differentiation), and Nm (gene flow), all of which were calculated in ARLEQUIN 3.5  with 10,The effective size Ne(pop) of each population was estimated in NEESTIMATOR 1.3 , based oAe. aegypti specimens collected from the nine populations surveyed in the Mid-North region of Brazil, producing a total of 828 genotypes and 32 alleles. Between one and nine alleles were recorded per locus per population, with the largest number (9) being recorded for locus B07 from Piauí, which was also the study area with the highest allelic richness (5.368). The smallest number of alleles (1) was identified for locus 3472 from Governador Eugênio Barros and locus H08 from Senador Alexandre Costa, with both these populations also having the lowest allelic richness , the highest frequency was recorded in the population of Barra do Corda (B07), while the lowest frequencies were registered in Governador Eugênio Barros (3472) and Senador Alexandre Costa, H08 . A totalp < 0.05), most (13) indicating heterozygote deficiency. The populations of Barra do Corda (3), and Piauí (4) had the highest numbers of loci in disequilibrium, whereas Senador Alexandre Costa presented the highest number of loci in equilibrium (The Hardy–Weinberg Equilibrium (HWE) analysis was based on 36 tests, of which 15 (41.66%) indicated a significant deviation after applying the Bonferroni correction (ilibrium .p = 0.00000 ± 0.00000). A second scenario was generated with the samples arranged in two groups, as indicated by the STRUCTURE analysis  revealed highly significant genetic variability when all the Mid-North populations were arranged in a single group, with 14.34% of the variation being found among populations and 85.66% within populations, with FST = 0.14340 , although significant genetic differentiation was found between samples in the same group, with FSC = 0.12144 (p = 0.00000 ± 0.00000). In both scenarios analyzed, the percentage of within-population genetic variation  was much higher than that found between populations (14.34%) or among populations within groups (13.47%)  .p = 0.00000 ± 0.0000) in all the populations, with the highest fixation index (FST = 0.378) being found between the samples from Governador Eugênio Barros (GEB) and Vargem Grande which, in turn, had the lowest gene flow (Nm = 0.819). By contrast, the lowest genetic differentiation (FST = 0.013) was recorded between Timon and Teresina and, as expected, the highest gene flow (Nm = 35.328) (The estimates of genetic differentiation based on the pairwise FST and gene flow (Nm) values indicated highly significant genetic differentiation ( 35.328) .The general analysis of population structure based on the Bayesian approach revealed that the most probable number of genetic groups in the populations sampled was K = 2, indicating a mixed ancestry from two lineages . The firEffective population size Ne(pop) was estimated based on the Linkage Disequilibrium (LD) model, with the lowest value being recorded for the populations from Maranhão (9.6) and the highest value (45.5) for the populations from Piauí. The heterozygosity tests were based on the three mutation models , and the populations from both Maranhão and Piauí presented a significant excess of heterozygosity for all three of the models tested, which indicates that these populations passed through a recent bottleneck .Ae. aegypti from other locations around the world. A total of 10 alleles were recorded here for the B07 locus, for example, in comparison with 13 alleles in the populations from São Paulo [The loci analyzed in the present study presented levels of polymorphism similar to those reported for ão Paulo  and onlyão Paulo . Similarão Paulo  in northão Paulo .Ae. aegypti populations of the Brazilian Mid-North revealed the existence of two lineages that are present in higher or lower proportions at the different localities, with mixed ancestry and significant genetic differentiation  in the region. The genetic composition of the Ae. aegypti, which may range from only 50 m to 800 m [The Bayesian analysis revealed clear evidence of genetic sorting in the samples, with the geographically closest populations grouping in the same cluster, given their genotypic similarities. However, some geographically distant populations were also genetically similar and were grouped in the same lineage, as in the case of the Governador Eugênio Barros (GEB) and Piauí populations. Given the reduced dispersal capacity of to 800 m ,50, the to 800 m ,52,53. Ae. aegypti populations in Maranhão [The clustering pattern of the first lineage indicated a low genetic connectivity between the populations (Nm = 0.819–5.505). This is evident in the high indices of genetic differentiation (FST = 0.083–0.378), which indicate limited levels of gene flow between populations. Other similar studies involving mitochondrial markers have revealed high levels of genetic differentiation in Maranhão , with FSMaranhão  and SousMaranhão  using miAe. aegypti.Maranhão state straddles the transition zone between the Amazon and Cerrado biomes, an ecotone with a high diversity of ecosystems, species, and climatic conditions . The staAe. aegypti throughout the region through the interconnection of long-distance transportation routes [The second lineage also presented a high level of genetic connectivity, reflecting a high level of gene flow among its populations (Nm = 6.050–35.328), which is also reflected in its low FST values (0.013–0.076). This high level of gene flow may reflect the passive dispersal of n routes . The genAe. aegypti populations, based on 12 microsatellite loci Mendonça et al. [The effective population Ne(pop) sizes recorded in the present study were relatively small but are consistent with the values recorded by Maitra et al.  in 15 Bra et al.  recordeda et al.  also foua et al. .This pattern is likely the result of successive genetic bottlenecks and founder events occurring either during the colonization process or as a consequence of measures taken to control the local mosquito populations. These processes cause a reduction in effective population size Ne(pop), which may reinforce the differentiation of populations by genetic drift . This waThe analysis of genetic bottlenecks indicated that the study populations are not undergoing a process of expansion. However, there is clear evidence of the effects of genetic bottlenecks in both the Maranhão and Piauí populations, with all three mutation models tested here indicating that these populations have undergone a drastic reduction in size in the past, which is consistent with the low Ne values recorded for the populations. Invasive species often go through population bottlenecks, which tend to reduce their genetic diversity drastically, and in particular their allelic richness .Ae. aegypti populations may be influenced by environmental, climatic, and social factors (including human population density), and the intensive application of insecticides, which may generate distinct patterns of genetic structure in the Ae. aegypti populations [The populations from Maranhão and Piauí presented distinct demographic patterns. While evidence of genetic bottlenecks was found in both cases, effective population sizes were very small in Maranhão in comparison with the much higher Ne values recorded in Piauí. The demographic dynamics of ulations ,59,60. TThis intra-population variability may be derived from differences in the vectorial capacity of the mosquito, its predisposition to the virus, or even its resistance to insecticides or other ecological adaptations, all of which may limit the effectiveness of control measures .Ae. aegypti populations and their relatedness is fundamentally important for the development of effective programs of vector control, given that populations with a reduced effective size may be more subject to the effects of genetic drift, which may lead to an intense loss of genetic diversity over only a few generations. For programs that target the genetic diversity of the mosquito populations, the effective size of the target population must be taken into consideration to ensure the success of control measures [The understanding of the genetic structure of measures . The comAe. aegypti populations of the Mid-North region of Brazil, based on the analysis of microsatellite markers. The results of the analysis indicate high levels of genetic differentiation in the Maranhão populations and marked genetic connectivity among the Piauí populations, which present low levels of genetic differentiation that indicate frequent gene flow, presumably mediated by passive dispersal. The results of this study also indicate the possible introduction of a new set of genes from Piauí into Maranhão. These data on the connectivity and genetic isolation of the different Ae. aegypti populations represent valuable input for the establishment of guidelines for the development of innovative strategies for the effective control of the populations of this disease vector.The findings of the present study reveal the coexistence of two distinct sets of genes in the


Lissotriton vulgaris meridionalis and the role of drinking troughs in supporting viable breeding populations within a rural landscape interested by traditional husbandry and agriculture. Our genetic analysis highlighted the conservation value and the potential stepping-stone function of artificial aquatic sites in the dispersal of the species and for the gene flow maintenance. Indeed, populations of drinking troughs show allelic richness and heterozygosity levels comparable to those from natural ponds and there is no great evidence of genetic bottlenecks. A complex system of artificial aquatic sites and few natural wetlands was identified sustaining a well-structured network of demes highly interconnected with themselves and natural aquatic sites. The conservation of the identified genetic clusters may be useful to prevent further population declines and future loss of genetic diversity within the study area characterized by scarce natural wetlands that frequently dried because of agricultural practices and strong seasonality. Site-specific protection measures are needed to contrast the progressive disappearance of drinking troughs observed in the last years in Italy because of the abandonment of traditional farming practices in favour of modern agriculture and intensive farming.Amphibians are particularly sensitive to habitat loss and fragmentation caused by the intensification and modernization of farming occurring in the second half of the twentieth century in the Mediterranean basin. However, artificial water bodies, associated with traditional husbandry, proved to be important surrogate for amphibian feeding and reproduction. Here, multilocus genotypes were used to investigate the spatial population structure of  This is particularly true for the Mediterranean basin, a biodiversity hotspot, in which the current mosaic of agricultural lands, traditional pastures, evergreen woodlands and maquis habitats, is the result of anthropogenic activities over millennia3. Particularly, traditional agricultural systems, derived from well-integrated and sustainable management practices, being spatially heterogeneous, have supported a high level of biodiversity4. However, in the second half of the twentieth century, the intensification of modern agricultural practices has increased habitat loss and fragmentation, spatial homogenization, and a reduction in biodiversity5. In general, amphibians are particularly sensitive to habitat destruction and fragmentation that have a negative impact at a demographic and genetic level6 with habitat connectivity playing a key role in their viability at the regional scale7. Indeed, agriculture and urban development activities may create barriers to amphibian dispersal9 resulting in isolated subpopulations more subject to increased risk of inbreeding and local extinction11, and/or less likely to be recolonized by dispersers from neighbouring subpopulations12. In an isolated population, a decrease in the immigration rate of individuals from other populations (i.e. gene flow) will inevitably produce a genetic diversity loss6. This happens when population size is reduced13 and the isolation among populations is so strong that inbreeding and genetic drift are no longer counterbalanced by gene flow14. These negative effects are further exacerbated by the fact that amphibians, due to their habitat specificity, strict ecophysiological requirements15, low dispersal abilities, and philopatry16, do not behave as single, infinitely large panmictic population17 but as patchy demes representing unique genetic entities despite that geographic proximity. In addition, due to their biphasic life cycle in aquatic and terrestrial habitats, amphibians are vulnerable to the alteration of both environments and to any factors reducing population connectivity18. Despite human activities negatively impact amphibian populations, many studies in the Mediterranean highlighted the importance of artificial water bodies, associated with traditional agriculture and cattle watering, for amphibian feeding and reproduction22. This suggests that such artificial facilities, often connected to natural water bodies, may represent important surrogate breeding habitats for amphibians23. Indeed, the ability of amphibians to colonise artificial aquatic sites  has been well documented worldwide25. Moreover, they may act as stopovers during dispersal events, thereby facilitating amphibians’ movements by reducing effective inter-aquatic systems distances27. However, there are no studies on their role and importance as for the population genetic conservation and for stepping-stone function in the dispersal process and gene flow maintenance.The European landscape is the result of the dynamic interaction between people and their natural environments28. In this context, microsatellite markers have proved to be sensitive indicators of change in dispersal rates and suitable tools for addressing questions related to population subdivision and interconnection even at fine scale29. Studies on the individual movements and the spatial scale of dispersal among subpopulations are needed in addition to basic demographic research for improving our knowledge of amphibian population dynamics and sustainability, and to assist landscape management and conservation planning30. However, species genetic management is often overlooked in conservation programmes due to lack of resources or expertise31. However, the determination of genetic relationships among populations may be particularly useful to detect areas of high conservation priority, dispersal corridors, population declines and individuals and/or populations fundamental for augmentation/translocation or reintroduction programs32. Here, we described the spatial population structure of the Italian smooth newt Lissotriton vulgaris meridionalis (Linnaeus 1758) in a rural landscape of Central Italy (Latium) within a patchy system interested by infrastructural and agricultural development  were used to (i) evaluate genetic diversity within and among artificial and natural breeding sites; (ii) infer population structure in the study area; (iii) detect migration and gene flow among demes; (iv) establish whether drinking troughs may provide alternative and/or complementary aquatic habitats for amphibian conservation in rural landscapes.With increasing anthropogenic habitat modification, it is particularly important to understand the genetic consequences of changes in landscape composition and configurationGenotypic data were obtained from 464 individuals, excluding six samples that had more than 20% missing genotypes. One locus (Lm_013) was also excluded from data analyses because of a large amount of missing data, due to an amplification failure.Estimated null allele frequencies were not significant, indicating the absence of null alleles, large allele dropout and false homozygotes at any locus in all populations.He) ranged from 0.048 to 0.955, and observed heterozygosity (Ho) from 0.048 to 1.00  conformed to Hardy–Weinberg equilibrium after Bonferroni correction, with ten populations showing a slight deviation at one locus  to 10.6 (FER) in artificial sites and from 5.4 (RP) to 9.7 (RER) in natural water bodies. Allelic richness varied from 4.4 (FP) to 8.4 (FER) in drinking troughs and from 5.0 (RP) to 8.1 (RIC) in natural sites (Table He) heterozygosity varied from 0.507 (FT) to 0.659 (FER) in artificial sites, and from 0.547 (RP) to 0.681 (RR) in natural sites. Observed heterozygosity (Ho) ranged from 0.515 (FT) to 0.668 (FER) in artificial sites and from 0.550 (RP) to 0.670 (LE) in natural sites . Expected , except for three populations  showing FIS estimates significantly greater than zero after Bonferroni correction . No significant differences were observed in He values within each site pair represented by a natural and an artificial site .Heterozygosity did not vary between artificial and natural sites  to 0.239 (FT-FB) with an average FST = 0.066. FST values were significantly higher than zero in 202 of 210 cases after sequential Bonferroni correction for multiple comparisons. FST was not significant in three out five natural/artificial site pairs . Overall, low to moderate levels of population differentiation were observed except for FT showing the highest FST values (Table Pairwise Nm) between populations, calculated directly from the FST values  among pairs of all populations were significantly correlated with the geographical distance between localities . However, geographical distance explained less than 14% of the genetic variation (r2 = 0.137) in the study area  explaining about 43% of the genetic variation (r2 = 0.428) . No close relatives were present within the five site pairs  identified 5 clusters. The resulting scatterplot showed evidence of fair separation among clusters Fig. a, with sDAPC revealed a pronounced genetic differentiation among the five artificial-natural site pairs Fig. b, highliThe model-based clustering method suggested that the model with K = 5 (where K is the number of population genetic clusters) was substantially better than alternative models.Five clusters were identified with FT was split from the other populations. FB-RR and FP-RP formed two clusters on their own. All the other populations clustered in two distinct groups corresponding to north-western and south-eastern geographical areas Fig. .Figure 4Mean membership coefficients for all individuals into the north-western and south-eastern demes was generally high, with a mean of 0.796 (SD = 0.160) and 0.751 (SD = 0.235), respectively. A few populations , located on the borders of these two clusters, showed membership coefficients less than 70% and thus may be considered as admixed demes Figs. , 5.FigurThe hierarchical analysis of molecular variance (AMOVA) confirmed the genetic structure detected by clustering methods, with 86.73% of genetic variation resided within populations, 7.89% distributed among populations within clusters, and 5.38% of the variance possibly explained by differentiation among the five clusters Table . AlthougThe hypothesis of a recent bottleneck was tested with infinite allele model (IAM), stepwise mutation model (SMM) and two-phase model (TPM). We observed the occurrence of a bottleneck event in 7 out of 21 populations under IAM model  detected recent migrations. In 20 instances, the migration rate was greater than 0.04 indicating the occurrence of substantial contemporary gene flow. In 12 cases, migration rates were > 0.1 suggesting that more than 10% of the population is composed by recent immigrants. The highest level of gene flow was mostly detected in populations that were geographically closed and belonging to the same genetic cluster  within a rural area of Central Italy (Latium).To our knowledge, this is the first study to examine the distribution of genetic variation and the spatial genetic structure of Italian smooth newt  comparable to those from the closest brook. As the level of genetic diversity reflects population’s adaptive and evolutionary potential36, this finding demonstrates the importance of artificial habitats for the persistence of amphibians especially where original habitats have been degraded or lost20.Populations showed a similar genetic diversity with no differences between natural and artificial wetlands, comparable to other urodeles and temperate amphibians37, resulting in large genetic differentiation among populations that may facilitate local adaptation or cause genetic drift38. To date, for L. vulgaris meridionalis, genetic data are only available from17, who found moderate to high differentiation levels among close populations. In our study, genetic differentiation among populations was moderate (average FST = 0.066), thus indicating the existence of a network of populations connected by dispersing individuals39. This suggests that drinking troughs contributed to sustain gene flow, decreasing local genetic divergence, and mitigating genetic drift and population inbreeding40. Our findings are in agreement with previous fine-scale studies of urodeles employing microsatellite markers43.Smooth newts are highly philopatric, with short dispersal abilityFST-values not consistent with the geographic distribution . This may be due to the relatively longer timespan since isolation and the higher habitat fragmentation characterising FT and FP-RP demes. It is likely that, in the past, the study area consisted of a unique unfragmented woody landscape with many natural aquatic systems used by newts for reproduction. In the 1950s, wide-scale human impact caused habitat loss and fragmentation isolating some demes (i.e. FT and FP-RP) in less favourable areas. The transition forests-arable lands or grazed-cultivated lands, and the construction of roads may have removed suitable terrestrial shelters and artificial breeding sites used for reproduction44, affecting the connectivity of local populations, dispersal, and genetic diversity45. Our suppositions are in agreement with previous studies documenting the negative effects of habitat fragmentation on local genetic variability and connectedness in amphibians48.Some populations showed higher pairwise 50. Indeed, we found a significant IBD pattern between populations separated by a maximum distance of 16.7 km (FFA-FER) and 17.8 km (FB-FE). We thus confirm the limited dispersal ability of L. vulgaris meridionalis even at small spatial scale43, whose average dispersal distance has been estimated to be 400–500 m51. However, this moderate correlation coefficient detected suggests that further processes other than distance may have structured populations’ genetic composition48. Indeed, dispersal capability is known to be not only determined by species’ intrinsic biology but also by landscape features and habitat quality53. In this context, it is likely that natural and artificial water bodies have increased survival probability and facilitated juvenile dispersal and adult breeding migration43. Drinking troughs, in particular, seem to act as stepping-stone system connecting distant natural breeding sites and building a network able to face potential isolation.We also found a correlation between genetic and geographical distance measures. Isolation by distance is expected to shape genetic variation, even on small spatial scales, leading to populations more differentiated from each other with increasing distance54.Five statistically significant population clusters with a moderate genetic differentiation have been identified. Gene flow seems to be extensive among populations within each cluster determining quite genetically homogeneous assemblages, with a genetic variation attributable to within-populations differences. Moreover, the five artificial-natural site pairs were genetically interconnected representing each a unique nearly panmictic deme. Such a positive association confirms the potential of drinking troughs to contrast drift and local loss of genetic diversity providing a network of permanent aquatic sources used for breeding and migration55. This probably reflects the counterbalance of different effects currently ongoing or acted in the past. On one hand, natural limits to dispersal and population-level processes may have affected site fidelity and low vagility increasing genetic distance56; on the other, gene flow among pristine habitats, once widespread in the study area, may have reduced genetic differentiation. Two clusters drifted apart probably because of landscape features alone or in combination with topographical differences. Indeed, both natural barriers and human-induced environmental changes affect occasional migration events among distant populations in L. vulgaris meridionalis hindering gene flow and resulting in a strong genetic structure43.Three clusters had a very similar cohesiveness concordant with the geographic distribution evidencing a significant positive relationship between genetic differentiation and geographical distance41. Tolfa Mountains conform to this landscape scenario being characterized by large patches of natural habitats interspersed with areas subject to traditional agricultural and husbandry activities without absolute geographic barriers limiting individual exchange among populations57.Five populations showed evidence of mixed genetic ancestry. Such admixed populations are expected in systems where geographical structure exists but there are no barriers to dispersal58.Signals of current migration were found within clusters but not among populations from different sites. Interestingly, migration was unidirectional and influenced by the hydrology of the site (permanent vs. temporary) rather than its typology . Surprisingly, not only natural but also artificial permanent aquatic sites acted as sources of individuals. The observed configuration of water network and the connectivity among drinking troughs, and between drinking troughs and natural aquatic systems, suggests the existence of a metapopulation with a source-sink configuration, in which recolonization following local extinctions, and natural restocking after demographic contractions are likely to occur42 resulting in a genetic bottleneck effect at local scale. This also suggests that populations did not recently experience severe effects of natural and anthropogenic pressures, or such pressures were mitigated by the positive impact of the observed configuration of water network. RT, a brook population subject to periodic drought, seems to have experienced a recent bottleneck probably counterbalanced by individuals migrating from neighbouring natural and artificial sites. FFA and FBG drinking troughs populations probably suffered inadequate management practices, associated with mechanical and chemical cleaning activities and water extraction19. Such operations may cause early desiccation and metamorphosis failure of larvae, with massive mortalities every year and consequent repercussions on the occurrence and abundance of species at local scale20 and a decline in genetic variation13.We found evidence of a slight occurrence of bottleneck events . Amphibian populations are known to fluctuate from year to year with a demographic and connectivity instability57 and the current landscape. Drinking trough populations probably derived from individuals in dispersal from neighbouring natural sites subject to a relatively fast drift known to affect small and isolated populations59. Since 15–20 generations are needed for signals of genetic effects to be observable60, the genetic structure inferred is more likely to represent a snapshot of past landscape configuration and composition rather than contemporary ones61.Our study revealed that a complex system of artificial permanent aquatic sites and few natural wetlands can sustain a well-structured network of amphibian demes that seem to be quite connected. The current landscape matrix suggests that the observed genetic structure may not reflect present-day gene flow but an intermediate situation between the historic scenario after the 1950sL. vulgaris meridionalis conservation. The Maremmana cow seems to act as an ecosystem engineer (i) directly by structuring landscape features (i.e. increasing habitat heterogeneity) and (ii) indirectly by providing suitable artificial sites crucial for newt genetic diversity maintenance. We emphasize how drinking troughs may represent an interesting tool for promoting individual dispersal and gene flow, reinforcing metapopulation patterns observed in the study area. Intervention measures are timely to prevent the progressive abandonment of drinking troughs observed in the last 50 years in Italy due to expansion of modern and intensive farming63. At the local scale, conserving and managing the existing natural sites as well as incentivization of traditional farming accompanied by drinking trough restoration, preservation and proper management will increase the availability of water supplies to both the Maremmana cow and newts, preventing further population declines within the study area. Particularly, the correction of trap effects, invasive species eradication, cleaning practices respectful of newt phenology, intervention to avoid disrepair and permeability loss23 will help to preserve habitat suitability maximizing the effectiveness of drinking troughs as stepping-stones. At the regional scale, habitat suitability of both natural and artificial water bodies could be improved by increasing site connectivity64. Indeed, the co-occurrence of aquatic sites differing in sizes and other features  in the landscape is known to favour species’ dispersal reinforcing their metapopulations64.Our study highlights the significant role played by the Maremmana cow in 19.Our conclusions suggest that amphibian protection and social rural and cultural values preservation can be simultaneously achieved combining conservation biologists’ efforts and local livelihood needs65.The study area falls within the Maremma district, extended throughout Latium and Tuscany regions. Fieldwork was carried out in the Comprensorio Tolfetano-Cerite-Manziate  a Special Protection Area , spanning 70,000 ha, included in the European Ecological Natura 2000 network. It is characterized by a high biodiversity value representing one of the highest wilderness areas in Italy despite the proximity to several main cities and its rural profile interested by traditional agriculture and husbandry. A mosaic of pastures, cultivated lands, and woodlands characterizes the vegetation landscape, mainly consisting of riverine woods, wide areas covered by bushy pastures and mesophilous forests66. Such artificial aquatic sites are particularly used by the Maremmana cow (Bos primigenius taurus), a robust and frugal native breed well adapted to difficult environments living freely in the marshes and maquis of the south coastal area of Italy for more than 1500 years68.In this area, natural wetlands are scarce and are frequently dried due to agricultural practices and their strong seasonality. Indeed, they are characterized by a short hydroperiod, especially in summer, because of high temperatures and irregular rainfalls. However, artificial drinking troughs built by farmers after World War II to facilitate livestock watering , are particularly widespread. Drinking troughs are small, shallow (20–60 cm depth) habitats, with rectangular shape and vertical walls, often in connection with natural permanent or semi-permanent aquatic systemsL. vulgaris meridionalis and a distribution across the study area aimed at minimizing spatial autocorrelation  were identified through orthophotos in Google Earth Pro software (version 7.3.1.4507). Overall, 7 natural aquatic sites  and 51 functioning drinking troughs were detected within an area covering approximately 18,000 ha. We focused on all 7 natural sites and on 14 drinking troughs  of a natural brook/pond  in 2015–2016 years. Since cohorts can vary in genetic composition, we avoided sampling the same site at different times during the yearTissue samples were obtained with no anaesthetic and by careful handling of animals. After this procedure, individuals were temporarily housed in tanks filled with water for approximately 30 min to verify their return to normal activity and released at the same point of capture. No individuals died or were otherwise harmed during samplings. This study was conducted according to the ethical scientific guidelines described by the American Society of Ichthyologists and Herpetologists (ASIH), The Herpetological League (HL) and the Society for the Study of Amphibians and Reptiles (SSAR).Experimental protocols were approved by Italian Ministry of the Environment that gave LV the permit [0001255/PNM] to manipulate amphibians and collect tissue samples.All methods are reported in accordance with ARRIVE guidelines.71. Three multiplex PCRs  were performed to save time and reagents. PCRs were carried out using Qiagen Multiplex PCR Kit in an 8 μL mix composed of 3.50 μL HotStarTaq Master Mix, 0.70 μL Qsol, 1.50 μL DNA, 0.20 μL each primer (10 μM), brought to volume with H2O. Amplification conditions for all loci included an initial denaturing at 95 °C for 15 min; 40 cycles of denaturing at 94 °C for 30 s, annealing temperature of 55 °C for 1.30 min, and 72 °C extension for 1 min; and a final 10 min extension at 72 °C. Two replicates for each sample at each locus were executed to confirm the genotypes.Total genomic DNA was extracted using DNeasy Blood and Tissue Kit (QIAGEN) and QIAcube robotic workstation, then amplified at 12 microsatellite loci (STRs) optimized for this speciesForward primers for each locus were 5’-labelled with a fluorescent dye for detection (Applied Biosystems). PCR products were electrophoresed with internal size standard  in an ABI Prism 3130XL sequencer and allele sizes were scored using the software GeneMapper v.5.0 (Applied Biosystems).72.The presence of null alleles was checked by Micro‐Checker v.2.2.374. Allelic richness was computed in Fstat 2.9.375. Arlequin v.3.5.276 was used to estimate observed (HO) and expected (HE) heterozygosity and to test genotypic frequencies at each population and locus for statistically significant deviations from Hardy–Weinberg equilibrium (HWE). We used a Monte Carlo approximation of the Fisher’s exact test77 and a standard Bonferroni correction for multiple comparisons . The Markov chain algorithm was run for 100,000 steps following 10,000 dememorization steps. The pairwise probability of linkage disequilibrium was estimated using a Fisher’s exact test implemented in Genepop v.4.278 with 10,000 steps following 1000 dememorization steps.Genetic diversity indices  were calculated for each population by GenAlex v.6.5Analysis of variance (ANOVA) was used to reveal differences in expected heterozygosity as a function of site type . Moreover, the Wilcoxon matched-pairs test was performed to compare heterozygosity values within each site pair represented by a natural and an artificial site. Statistical tests were performed using STATISTICA software (version 8.0 for Windows) with alpha set at 5%.75. Pairwise significance tests for FST79 were performed by permutation and resampling of multilocus genotypes among pairs of samples with 10,100 randomizations allowed for a table-wide significance at the 5% nominal level after standard Bonferroni corrections .Fstat 2.9.3 was used to perform a global test of overall population differentiation (not assuming HWE within populations)Nm estimates, derived from the fixation coefficient FST80.The degree of gene flow among demes, in terms of success of immigrants, was determined using FST values81 were correlated with geographical distances to test for patterns of spatial subdivision and isolation by distance (IBD) using a Mantel test with 1000 randomizations (implemented in GenAlex v.6.5). We performed two tests for IBD separately, on (i) all populations, and on (ii) populations with the exclusion of potential outliers (i.e. populations showing higher genetic distance despite the similar geographic distance in respect to the others).Pairwise population linearized 82 LR estimator by 2 to give a maximum value of 1 and minimum of − 1.Pairwise relatedness analysis was carried out to assess the presence of close relatives in our populations. We performed the analysis (implemented in GenAlex v.6.5) for each population and for the five artificial-natural site pairs using Lynch & Ritland’s84. We used “find.clusters” function to identify the optimal number of clusters. k-means algorithm was run comparing different clustering solutions using Bayesian Information Criterion (BIC) values with increasing values of k. The optimal number of clusters was selected corresponding to the lowest BIC. Then, we determined the number of PCs to retain considering the cumulated variance explained by the eigenvalues of the PCA. After the cross-validation step, DAPC was performed with the retention of 60 PCs (> 80% of variance explained) and four discriminant functions. A further DAPC was conducted to verify genetic differentiation within the five artificial-natural site pairs previously identified, with the retention of 60 PCs and four discriminant functions.A discriminant analysis of principal components (DAPC) was performed to assess the genetic divergence among populations and provide a description of the genetic clustering based on the largest between‐group and smallest within‐group variances in allele coefficients (R package “adegenet”)85. We ran 15 independent simulations of 1,000,000 iterations  for each value of K85 between 1 and 21. The admixture model was applied assuming gene flow among populations and allowing for correlated allele frequencies across populations. We performed two different models: (i) the default mode for STRUCTURE that uses only genetic information to infer population structure, and (ii) the LOCPRIOR model that uses sampling locations as prior information to assist the clustering.We performed Bayesian clustering analysis to characterize the population genetic structure of aquatic systems across the study area and assess the geographical scale of population differentiation using STRUCTURE v2.3.4K was selected based on the natural log of Pr(X|K) values and the ∆K criterion86 provided by STRUCTURE HARVESTER v.0.6.9487. Replicate runs were averaged using CLUMPP v.1.188 and plotted in DISTRUCT v.1.189 to generate graphical displays representing the mean membership of each individual over the replicate runs.Appropriate 90, implemented in Arlequin v.3.5.276, was performed on FST values to examine the distribution of genetic variation at three hierarchical levels: within populations, among populations within demes, and among demes. We assigned each population to a cluster based on its largest proportion of membership and then examined hierarchical genetic distribution considering only populations with a mean membership ≥ 0.7 and excluding admixed populations. The significance of the variance components was tested using 10,000 permutations.Considering the genetic clusters inferred from the Bayesian analyses as panmictic demes, an Analysis of Molecular Variance (AMOVA)91 was used to test for evidence of recent bottlenecks in each population. The main assumption of this test is that allele frequency distribution results from an equilibrium between mutation and genetic drift92. In non-bottlenecked populations, the expected heterozygosity at mutation-drift equilibrium (Heq) equals the measured Hardy–Weinberg equilibrium heterozygosity (He). Instead, in case of recent bottlenecks, a correlative reduction of allelic diversity and heterozygosity at polymorphic loci occurs and the mutation-drift equilibrium is temporarily disrupted: He will be significantly greater than Heq, calculated from the number of alleles sampled93. The calculation of Heq depends on the mutation model considered analysing the loci92. In the present study, Heq values were calculated under three mutation models: the infinite allele model (IAM)94, the Stepwise Mutation Model (SMM)95 and a combination of those two extreme hypotheses, the two-phase model 96. Significant deviations in observed heterozygosity across loci for each population were detected using a two-tailed Wilcoxon sign-rank test97 that allows the detection of the heterozygosity excess due to the faster loss of low frequency alleles and thus determines whether a population has been recently bottlenecked. Moreover, a qualitative descriptor of allele frequency distribution was used to infer bottlenecks. This method compares allele frequency distribution to the distribution expected at mutation-drift equilibrium (l-shaped distribution), considering that population bottlenecks cause a characteristic mode-shift distortion in the distribution of allele frequencies at selectively neutral loci97.The software BOTTLENECK v1.2.0291. Since the IAM is mainly recommended for allozyme data and microsatellites are often described as being closer to the single step mutation model91 or to the Two-Phase Mutation model96, we may consider the results obtained under SMM and TPM more reliable.For fewer than 20 loci (i.e. 11 loci in our study), the Wilcoxon’s test is the most appropriate and powerful98, which employs a Bayesian MCMC approach to identify migrants or recent descendants of migrants based on transient linkage disequilibrium among multilocus genotypes from different source populations. This program was run following the recommendation of99 concerning convergence issues. We performed 10 separate runs, with different seed numbers, using 107 iterations, with a burn-in of 106 and a sampling frequency of 1000. This run length was sufficient for the posterior probability to achieve convergence. The MCMC mixing parameters were set to 0.50, 0.80 and 0.25, for allele frequency, level of inbreeding and migration rate, respectively. These delta values resulted in acceptance ratios between 40 and 60% to maximize log-likelihood values98. Migration rates were averaged over the 10 independent runs and compared to average migration rates of randomly permuted data set, generated to assess significance. Estimated migration rates were considered significant when the 95% confidence interval (CI) did not overlap with the 95% CI of the randomly permuted data.We estimated recent migration rates between localities using the program BAYESASS 3.0Supplementary Information.