Eucalyptus behriana, to build hypotheses regarding evolution of major disjunctions in the species' distribution and to expand understanding of the biogeographical history of southeastern Australia.To infer relationships between populations of the semi‐arid, mallee eucalypt, Southeastern Australia.Eucalyptus behriana .E. behriana using double digest restriction site‐associated DNA sequencing (ddRAD‐seq), to determine genetic relationships between the populations. These relationships, along with species distribution models, were used to construct hypotheses regarding environmental processes that have driven fragmentation of the species’ distribution.We developed a large dataset of anonymous genomic loci for 97 samples from 11 populations of Greatest genetic divergence was between populations on either side of the Lower Murray Basin. Populations west of the Basin showed greater genetic divergence between one another than the eastern populations. The most genetically distinct population in the east (Long Forest) was separated from others by the Great Dividing Range. A close relationship was found between the outlying northernmost population  and those in the Victorian Goldfields despite a large disjunction between them.Patterns of genetic variation are consistent with a history of vicariant differentiation of disjunct populations. We infer that an early disjunction to develop in the species distribution was that across the Lower Murray Basin, an important biogeographical barrier separating many dry sclerophyll plant taxa in southeastern Australia. Additionally, our results suggest that the western populations fragmented earlier than the eastern ones. Fragmentation, both west and east of the Murray Basin, is likely tied to climatic changes associated with glacial‐interglacial cycles although it remains possible that major geological events including uplift of the Mount Lofty Ranges and basalt flows in the Newer Volcanics Province also played a role. Eucalyptus behriana, which has a distribution divided by several major biogeographical barriers. By reconstructing the relationships between isolated populations and building species distribution models to investigate potential environmental factors influencing the species distribution, we build hypotheses regarding how the species scattered distribution comes about. Both climatic and edaphic changes are shown to likely have influenced the species distribution, with current isolated populations representing refugia where the species has survived as intervening populations have become extinct.In this study, we investigate the phylogeography and biogeographic history of the southeast Australian mallee eucalypt,  Single ) Figure ; howeverNicolle, , and it Nicolle, . FurtherNicolle, . Other iNicolle, . At LongNicolle, .E. behriana to build hypotheses regarding evolution of major disjunctions in the species' distribution and to expand understanding of the biogeographic history of southeastern Australia. Specifically, we sought to assess the significance of the Lower Murray Basin as a barrier to gene flow, relative to the other geographic disjunctions in E. behriana, to contribute to understanding of the importance of this feature in the history of the flora. We also sought to assess whether genetic patterns showed any support for recent dispersal, over a null hypothesis of vicariance, in the history of the species. To achieve these aims, we analyzed anonymous nuclear genome loci generated through double digest restriction site‐associated DNA sequencing (ddRAD‐seq) and built species distribution models (SDMs).Our aim in the current study was to assess patterns of genetic variation in 22.1E. behriana . Data were treated as paired‐end ddRAD data and assembled using the reference option, mapping to the eleven chromosomes of the Eucalyptus grandis genome  and population expected heterozygosity (Table He = 0.0685–0.0815). Interpopulation FST values were low ; however, no confidence intervals overlapped with zero. Highest average FST values were observed between the two larger western populations and all other populations, include between these two populations .Observed heterozygosity of individuals ranged from 0.0107 to 0.0141  was the highest so far reported for any species in Eucalyptus section Adnataria   and that intervening populations have been lost, presumably because of subsequent environmental changes.The low level of genetic distance between the Goldfields and West Wyalong populations suggests that these populations diverged comparatively recently relative to other eastern populations Figure , despites Figure  returns s Figure . It is ts Figure  identifi4.6E. behriana through the Kilmore Gap  on which the species occurs at Long Forest and the Victorian Goldfields Figure . The claThe authors have no conflicts of interest to declare.Patrick S. Fahey: Conceptualization ; Data curation (lead); Formal analysis (lead); Investigation (lead); Methodology ; Project administration ; Visualization (lead); Writing‐original draft (lead); Writing‐review & editing (lead). Rachael M. Fowler: Formal analysis (supporting); Investigation (supporting); Methodology ; Writing‐review & editing (supporting). Todd G. B. McLay: Formal analysis (supporting); Investigation (supporting); Methodology ; Software (supporting); Writing‐review & editing (supporting). Frank Udovicic: Conceptualization ; Funding acquisition (lead); Project administration (supporting); Resources ; Supervision (supporting); Writing‐original draft (supporting); Writing‐review & editing (supporting). David J. Cantrill: Conceptualization ; Funding acquisition ; Project administration (supporting); Resources ; Supervision (supporting); Writing‐original draft (supporting); Writing‐review & editing (supporting). Michael J. Bayly: Conceptualization ; Funding acquisition (lead); Investigation (supporting); Methodology ; Project administration ; Resources (lead); Supervision (lead); Visualization (supporting); Writing‐original draft (supporting); Writing‐review & editing .


AbstractTeraponjarbua is a coastal Indo-Pacific species, sourced for human consumption. This study examined 134 samples from the central west and east coasts of Peninsular (West) Malaysia and East Malaysia. A 1446-bp concatenated dataset of mtDNA COI and Cyt b sequences was used in this study and 83 haplotypes were identified, of which 79 are unique haplotypes and four are shared haplotypes. Populations of T.jarbua in Malaysia are genetically heterogenous as shown by the high level of haplotype diversity ranging from 0.9167–0.9952, low nucleotide diversity ranging from 0.0288–0.3434, and high FST values (within population genetic variation). Population genetic structuring is not distinct as shown by the shared haplotypes between geographic populations and mixtures of haplotypes from different populations within the same genetic cluster. The gene flow patterns and population structuring observed among these regions are likely attributed to geographical distance, past historical events, allopatric speciation, dispersal ability and water currents. For instance, the mixture of haplotypes revealed an extraordinary migration ability of T.jarbua (>1200 km) via ancient river connectivity. The negative overall value of the neutrality test and a non-significant mismatch distribution are consistent with demographic expansion(s) in the past. The median-joining network concurred with the maximum likelihood haplotype tree with three major clades resolved. The scarcity of information on this species is an obstacle for future management and conservation purposes. Hence, this study aims to contribute information on the population structure, genetic diversity, and historical demography of T.jarbua in Malaysia.A background study is important for the conservation and stock management of a species.  A population’s genetic structure describes the total genetic diversity in the population, which is shaped by several factors, including the life history, geographical barriers, gene flow, selection and bottlenecks . The patTeraponjarbua  is a medium-sized fish commonly known as crescent perch, and it is locally known as “ikan mengkerong” in Malaysia  under the IUCN Red List due to its widespread distribution with no known threats  and reproductive biology , cytochrome b (Cyt b) and microsatellites molecular markers. Also, a phylogeographic survey of T.jarbua along with other reef fauna of the western Indian Ocean was reported by COI gene sequences. Mitochondrial DNA (mtDNA) has been widely utilized as the marker of choice to examine the genetic diversity and population structure of marine fishes due to its strict maternal inheritance, rapid mutation rates and the absence of recombination in most species  Malaysia and East Malaysia (Sabah and Sarawak) which are located in the tropical Indo-west Pacific region Fig. . These tT.jarbua. Populations were provisionally divided into five groups according to region: 1) Kuala Selangor  of west Peninsular which is surrounded by the Straits of Malacca; 2) Kuantan, Pahang  of east Peninsular which is adjacent to the South China Sea; 3) Mukah, Sarawak  of East Malaysia which is surrounded by the South China Sea; 4) Sandakan  and 5) Tawau  of East Malaysia which are surrounded by the Sulu Sea and the Celebes Sea, respectively  amplification of approximately 1000 bp from the 5’-end of the Cyt b gene was performed using the primer pairs Glu31 and Thr33  and 25μl reaction mixtures consisting of 12.5μl exTEN 2X PCR master mix , 9.5 μl of sterile distilled water, 1μl each of forward and reverse primers, and 1μl of DNA template. PCR cycling conditions were as follow: initial denaturation for 1 min at 96°C, 36 cycles of denaturation at 95°C for 30 s, annealing for 30 s at 44°C (CO1) or 48°C (Cyt b), elongation for 1 min at 72°C, and final elongation for 10 min at 72°C. The amplicons were checked for correct length via electrophoresis on a 1% agarose gel (90V for 25 min). PCR products were sent to Apical Scientific Sdn. Bhd.  for purification and DNA sequencing.COI and Cyt b sequences. Analyses performed in this study were based on the final truncated length of 1446-bp concatenated sequences. All haplotype sequences were deposited in Genbank under the accession numbers MN529663–MN52993.Multiple sequence alignment was first performed separately for each gene region using the CLUSTAL X  program CT), among populations within regions (FSC), and within populations (FST). The significance of the F-statistics for population comparisons was assessed using 1000 permutations. The Tamura Nei plus gamma rate model (TN93+G) was selected by MEGA v. 7.0 . The demographic changes were also examined using the mismatch distribution analysis (SSD) between observed and expected mismatch for each of the populations under the model of constant population size were analyzed according to SSD) was tested using a parametric bootstrap approach with 1000 replicates.In addition, a neutrality test of the pairwise differences among all populations was performed to infer historical demographic and deviation of sequence variation from evolutionary neutrality. Deviations from neutrality were evaluated using Fu’s Fs  and TajiD  via DNASanalysis  in ARLEQanalysis  and the COI (631 bp) and Cyt b (815 bp) sequences were analyzed for 134 individuals obtained in five different locations  of T.jarbua populations in this study ranged from 0.0288 ±0.0158 (mean ±SD) to 0.3434 ±0.1722 while haplotype diversity (h) ranged from 0.9167 ±0.0482 to 0.9952 ±0.0165  and four were shared haplotypes (4.82%). The dominant haplotype of Malaysian populations is Hap5  while other shared haplotypes are Hap3 , Hap8 (KS and KN) and Hap45 (TW and SN). The population from KS recorded the highest total number of haplotypes (22) of which 19 were unique haplotypes, while Tawau recorded the lowest number of haplotypes (15) with 12 unique haplotypes. The nucleotide diversity   while SN and TW showed the least differentiation . The pairwise nucleotide divergence among populations  estimated among populations was low at 0.82. The sequence divergence was calculated using the Kimura two parameter (K2P) distance model for both genes (Table COI: 0.019 and Cyt b: 0.029) were observed between MH-KN, MH-SN and MH-TW. The T.jarbua populations displayed a low level of conspecific divergence within 2% (COI).Pairwise FSN Table . PopulatT.jarbua populations analysed by AMOVA showed little (39.52%) genetic differentiation among regions but high (62.13%) variation within populations , indicating the presence of non-equilibrium and a population expansion event in T.jarbua. Besides that, the studied populations showed non-significant raggedness index (p > 0.10) indicating the data has relatively good fit to a model of population expansion  reported similarly high genetic diversities ranging from 0.216 to 0.698. The high number of haplotypes (83 haplotypes) in the present study is likely due to the high mutation rate of the mtDNA genes. Most of the haplotypes are unique to its region which may indicate the presence of different founding populations in the studied localities  which suggests limited genetic connectivity among the five populations.FT.jarbua. The significantly higher genetic differentiation between populations of KS-SN (pairwise FST = 0.2681) compared to KN-SN (pairwise FST = 0.0702) may be attributed to distance and physical barrier. Some genetic exchange can be expected since the Straits of Malacca connects the Andaman Sea and South China Sea via the narrow Tebrau strait. It is likely that mixing between the two bodies of water is very limited which supports the FST value obtained. However, the higher genetic variation between populations of MH-SN (pairwise FST = 0.5038) as compared to MH-KS (pairwise FST = 0.3310) implies that geographical distance is not the only driving factor of genetic variation among populations of T.jarbua in the Malaysian waters, similar to the results observed in the wider Gulf of Aden  where populations of adjacent locations showed low genetic connectivity despite the absence of a geographic barrier. Populations bordering a common origin such as the South China Sea  may have evolved independently of each other over time, but there might have been insufficient time for genetic divergence to accumulate in these populations.Populations from the same region, i.e., TW and SN of Sabah, were the least genetically variable Tables  and 3, wCOI haplotypes between both regions. The haplotype sharing and their consequent gene flow may also be attributed to breeding migration, mutation, pelagic larvae, and sharing of common ancestors . Southeast Asia is believed to have experienced simultaneous glaciation and consequent deglaciation along with its associated decrease and increase of seawater levels during the Pleistocene period, which greatly influenced continental and oceanic configuration . The shancestors .The MH population is the most genetically distinct with the highest between-group mean distances, haplotype and nucleotide diversity among the five populations. Geographical isolation of allopatric populations restricts gene flow between two populations, which in turn allows the evolution of a genome adapted to local condition . ClusterST = 0.0702). This could probably be explained by the high migration ability of T.jarbua , thus allowing gene flow among these drainages . Gene flD and Fu’s Fs statistics estimate the deviation from neutrality, which is based on the expectation of a constant population size at mutation-drift equilibrium. Here, a negative Tajima’s D signifies an excess of low frequency polymorphisms relative to expectation, indicating population size expansion or positive selection  present across the range while the rest of the haplotypes are unique. The range expansion was a recent phenomenon and may not have achieved the migration-drift equilibrium, as shown by the lack of phylogeographical structure. Neutrality test statistics were in overall negatively significant and not consistent with a population at drift-mutation equilibrium.Historical demographic expansions were determined by analysing the frequency distributions of pairwise differences between sequences . Neutralelection . The negelection . The T.SSD and the raggedness index. Here, non-significant values for SSD signifies that the observed data do not deviate from that expected under the model of expansion. Non-significant raggedness index also indicates population expansion. Our observations of non-significant values in goodness-of-fit distribution for all populations suggest that population expansion occurred recently  significant results suggesting population expansion of T.jarbua in this region; 3) despite the three genetic clusters observed in the haplotype tree and median-joining network, no obvious population structuring was detected among geographically distinct populations. Common haplotypes among populations and haplotypes from several populations in each genetic cluster indicate high genetic connectivity among the populations. This study assesses the genetic diversity and population structure of T.jarbua in Malaysia for appropriate conservation and management strategies. Conservation of crescent grunter at its natural variation level is required as it forms a diverse group of taxa with 83 haplotypes distributed across Malaysia. The haplotype composition surveyed in the present study may provide a baseline for future comparisons to monitor the temporal variability of haplotype frequency and population structure. This study also has indirectly revealed the dispersal power of T.jarbua through its high mobility and rapid adaptability to a newly colonized area. Further studies can be conducted using larger sample size and temporal replicates, samples collected from other areas of geographical distributions, and sequence data from other mtDNA genes or information based on nuclear DNA. This research contributed useful data for future large scale biogeographical and taxonomic studies of this species.To summarize, we found 1) high haplotype diversity but low nucleotide diversity among The fish species that was employed in this study is not categorized as endangered species under the IUCN list and all the samples were collected from fish markets and landing sites.


Isocladus armatus. Because our sampling range is intersected by two well‐known biogeographic barriers (the East Cape and the Cook Strait), our study provides an opportunity to understand how such barriers influence dispersal in direct developers. On a small spatial scale (20 km), gene flow between locations is extremely high, suggestive of an island model of migration. However, over larger spatial scales (600 km), populations exhibit a clear pattern of isolation‐by‐distance. Our results indicate that I. armatus exhibits significant migration across the hypothesized barriers and suggest that large‐scale ocean currents associated with these locations do not present a barrier to dispersal. Interestingly, we find evidence of a north‐south population genetic break occurring between Māhia and Wellington. While no known geophysical barrier is apparent in this area, it coincides with the location of a proposed border between bioregions. Analysis of loci under selection revealed that both isolation‐by‐distance and adaption may be contributing to the degree of population structure we have observed here. We conclude that developmental life history largely predicts dispersal in the intertidal isopod I. armatus. However, localized biogeographic processes can disrupt this expectation, and this may explain the potential meta‐population detected in the Auckland region.Population genetic structure in the marine environment can be influenced by life‐history traits such as developmental mode  or habitat specificity, as well as geography and selection. Developmental mode is thought to significantly influence dispersal, with direct developers expected to have much lower dispersal potential. However, this prediction can be complicated by the presence of geophysical barriers to dispersal. In this study, we use a panel of 8,020 SNPs to investigate population structure and biogeography over multiple spatial scales for a direct‐developing species, the New Zealand endemic marine isopod  Isocladus armatus exhibits both isolation‐by‐distance and geographic concordance with a PCA. Unusually, we find evidence of high and ongoing gene flow across a well studied biogeographic break, which in other species hinders connectivity.We show, for a New Zealand isopod, a strong relationship between geographic and genetic relationships.  For example, variation in life‐history traits, such as habitat specificity or dispersal ability can result in different opportunities for gene flow. Population genetic structure can also arise as a consequence of the presence of biogeographic barriers such as ocean currents, land masses, or continental shelves limiting gene flow across the barrier. Alternatively, genetic structure can result from selection acting in different populations, for example, differential adaptation in heterogeneous environments (divergent selection).Austrolittorina unifasciata, and the carpet sea star, Meridiastra calcar  on the Southern Californian coastline  or selection (such as isolation‐by‐adaptation). In particular, geographic distance has the potential to influence population structure, especially for coastally restricted direct‐developing species with purported limited dispersal , biogeography, and selection in determining population genetic structure. To shed light on the relative importance of these factors on population genetic structuring, we examined population structure across various spatial scales in a direct‐developing marine isopod endemic to New Zealand, I. armatus. This species exhibits several characteristics that make it an ideal candidate for study. First, populations are found in abundance on easily sampled semisheltered rocky shorelines. Second, they are found across a wide geographic range throughout the country  how population structure and dispersal is influenced by geographic distance, (b) whether known biogeographic barriers such as the East Cape and the Cook Strait influence gene flow and dispersal, and (c) whether there are divergent selective pressures among populations that are responsible for population genetic divergence.In order to shed light on these factors, we used genotyping‐by‐sequencing (GBS) to resolve population genetic structure in 22.1I. armatus between May and July 2018, from around the North Island, New Zealand, from locations where I. armatus had previously been recorded  Ltd using DArTseq, a genotyping‐by‐sequencing (GBS) approach. The restriction enzymes PstI and SphI were chosen for complexity reduction, the complete methodology for this approach is outlined in Wells and Dale , but seeWe analyzed the dataset provided by Diversity Arrays Technology together with the data from Wells and Dale . To ensuq‐value of ≤ 0.05 were excluded from any further analyses.We required SNPs to have a call rate ≥ 0.9 , a minor allele count of at least 3   – this was because we wanted to retain any SNPs under putative selection, and both heterozygosity and minor allele count can signal non‐neutrality  using the R package adegenet  as a meRousset,  and fossRousset,  R packagade4  using the R packages Paradis, . We partI. armatus, we repeated the AMOVA and Fst analyses for SNPs that were deemed to be under selection in our BayeScan analysis  could be occurring among diverse lineages of 3I. armatus populations, we isolated 261 individuals distributed across eight populations across New Zealand  was very low  for population comparisons that included populations south of Māhia Peninsula. Kaikoura and Māhia Peninsula (separated by 730 km) exhibited the greatest differentiation, with an Fst of 0.409  consistently clustering together, while Kaikōura and Wellington also always clustered together across all ranges of K Figure . No addiK Figure .K, additional population genetic structure became apparent. At K = 3, Māhia individuals formed a distinct cluster exhibiting high admixture with populations to the north (Mt. Maunganui and Opito Bay). Increasing K further suggested genetic admixture between all adjacent populations with the exception of Wellington and Māhia. With K = 4, there was clear evidence of two Māhia individuals with admixture profiles more similar to Mt. Maunganui individuals, and one Mt. Maunganui individual with a profile similar to Māhia individuals  and the southernmost (Wellington and Kaikōura). However, at K = 7, Wellington and Kaikōura again clustered separately, with no admixture indicated with the northern populations. These results may be an artifact of overfitting which is also seen for greater values of K . Finally, a partial Mantel test of Fst of neutral SNPs against distance, using a matrix of Fst of non‐neutral SNPs as a covariate, showed a significant correlation , suggesting that this signature of IBD cannot be attributed solely to divergent selection operating across an environmental gradient and that the physical distance between populations is responsible for IBD, especially among the northern populations.We next tested for a correlation between geographic and genetic distance. The Procrustes transformation of PC1 and PC2 revealed high correspondence between genetic distance and geographic distance, suggestive of an isolation‐by‐distance (IBD) effect Figure . This waFinally, we also performed an analysis of molecular variance to allow for the partitioning of genetic variation among various hierarchical levels. This analysis confirmed the existence of strong genetic structure between the southern group (Wellington and Kaikōura) and the northern group. The greatest source of variation was within individuals 56.23%), and the second largest source of variation (28.91%) occurred between regions , although these values were very low relative to our other pairwise population comparisons of Fst. Further finer‐scale sampling across the Auckland region would be required to determine whether weak population structuring truly begins to accumulate at this spatial scale.Despite our large dataset of 8,020 SNPs, there was minimal evidence of population structure of I. armatus exhibited strong patterns of isolation‐by‐distance (IBD). The Procrustes‐transformed PCA indicated that the first two principal components were highly concordant with the geographic arrangement of populations. A Mantel test of the IBD hypothesis also indicated a strong and almost linear correlation between geographic distance and genetic distance within the northern populations, indicative of a stepping stone model of distribution.Over larger spatial scales, I. armatus can be found on the same rocky coastlines as New Zealand kelps such as Ecklonia radiata and Durvillaea antarctica, and are known to cling to seaweeds in disturbed water . Thus, the lack of population structure seen among the Auckland populations could be due to a migration‐drift disequilibrium that has arisen from a recent range expansion, rather than extensive gene flow between populations. This possibility could be further investigated by conducting population expansion tests on each population.While these results suggest that isolation‐by‐distance is responsible for small‐ to medium‐scale population structure in Actinia tenebrosa, and two species of amphipods , or the effects of a geophysical barrier that prevent dispersal in this region.The authors have no conflicts of interest to declare.William S. Pearman: Conceptualization ; data curation ; formal analysis ; investigation ; methodology ; visualization (lead); writing – original draft (lead); writing – review and editing . Sarah J. Wells: Conceptualization ; data curation ; formal analysis ; funding acquisition (lead); investigation ; methodology ; writing – review and editing . Olin K. Silander: Formal analysis (supporting); investigation (supporting); methodology (supporting); supervision (supporting); validation (supporting); writing – review and editing (supporting). Nikki E. Freed: Conceptualization (supporting); methodology (supporting); project administration (lead); resources (supporting); supervision (lead); writing – review and editing (supporting). James Dale: Conceptualization ; funding acquisition ; investigation ; methodology ; project administration (lead); resources (supporting); supervision (lead); writing – review and editing .No ethics approval or permits were required to conduct this research.


Ticks , after mosquitoes, are the second most prevalent vector of infectious diseases. They are responsible for spreading a multitude of pathogens and threatening the health and welfare of animals and human beings. However, given the history of tick-borne pathogen infections in the Inner Mongolia Autonomous Region of China, surprisingly, neither the genetic diversity nor the spatial distribution of haplotypes within ticks has been studied.Dermacentor nuttalli in four main pastoral areas of the Inner Mongolia Autonomous Region, by sampling 109 individuals (recovered from sheep) in April–August 2019. The 16S rRNA gene, cytochrome c oxidase subunit I (COI), and the internal transcribed spacer 2 region (ITS2) were amplified and sequenced from extracted DNA.We characterized the haplotype distribution of ST) indicated that the degree of genetic differentiation among some sampled populations was small, while for others it was moderate. Analysis of molecular variance (AMOVA) revealed that the variation within populations was greater than that among populations. The mismatch analysis of D. nuttalli exhibited double peaks.Twenty-six haplotypes were identified using 16S rRNA sequences, 57 haplotypes were identified with COI sequences, and 75 haplotypes were identified with ITS2 sequences. Among the three genes, total haplotype diversity was greater than 0.7, while total nucleotide diversity was greater than 0.06. Neutrality tests revealed a significantly negative Tajima’s D result, while Fu's Fs was not significantly positive. Fixation index values (FD. nuttalli populations in our region can likely adapt to different geographical environments, thereby leading to genetic diversity, and creating genetic differentiation among different populations. However, genetic differentiation is cryptic and does not form a pedigree geographical structure.The genetic diversity of  Dermacentor nuttalli (Acari: Ixodidae) is widely distributed across northern China, Russia and Mongolia ), and annealing temperatures are presented in Table To analyze the genetic structure of our the four populations, we amplified the 16S rRNA, COI and ITS2 gene regions. The primer templates for all three genes were downloaded from the NCBI database. The upstream and downstream primers of 16S rRNA gene were located in the v4 and v7 variable regions, respectively. The COI and ITS2 amplification products were approximately full length (Oligo7). The primer sets of the 16S rRNA, COI and ITS2 gene sequences were synthesized by Shanghai Sangon Biotechnology . The 40 μL PCR reaction mixture contained 20 μL Taq PCR Master Mix (Sangon), 1 μL of each primer, 16 μL ddHD. nuttalli populations, we estimated a number of parameters under a range of neutrality tests  in DnaSP 5.10, including the number of segregating sites, the average number of nucleotide differences, the number of haplotypes, haplotype diversity and the distribution pattern of DNA variation [ST). In order to determine the relationship between haplotypes [D. nuttalli in MEGA7.0 using the adjacency method. We downloaded the 16S rRNA, COI and ITS2 gene sequences of the three genes from different geographic populations in China (NCBI), and the higher homology of D. nuttalli with Haemaphysalis longicornis was chosen as the outgroup. The model was the Kimura two-parameter model, and the stability of the tree was evaluated by 1000 bootstrap repeats [Nucleotide sequences from the three amplified regions  were spliced by SeqMan 7.1. Our sequences were compared to those previously uploaded to GenBank using the Basic Local Alignment Sequence Tool (BLAST) search engine. In order to ascertain the degree of genetic diversity among these four ariation , 31. Theplotypes , PopART  repeats –35.h = 0.946), while EEDS had the lowest diversity (h = 0.849). Regarding nucleotide diversity, the lowest diversity was found in SZWQ (pi = 0.00721), and the highest (pi = 0.10363) in CF  that contained seven shared haplotypes , with shared haplotypes varying in certain genetic differentiation among populations. The most frequent haplotype detected was S1, with 55 sequences . S1 was located in the center of the haplotype network diagram and was a shared haplotype of the four geographic populations, indicating that these populations were relatively stable and could adapt to different environments. The shared haplotype was the dominant haplotype among groups and likely could adapt to the environment more successfully than the remaining 19 unique haplotypes .Negative neutrality results obtained for most of the locations analyzed indicated an excess of recently derived haplotypes. While Tajima's D values were significant, Fu’s Fs values were not significant, also indicating that the population had not experienced expansion recently. The Fow Table . The resll Table a.Table 4h = 0.997), while SZWQ had the lowest diversity (h = 0.957). Regarding nucleotide diversity, the lowest diversity was found in EEDS (pi = 0.00698) and the highest (pi = 0.16498) in CF  contained 6 shared haplotypes . The most frequent haplotype detected was C12, with a total of 24 sequences , although C35 was located in the center of the network. Given that C35 possessed the greatest number of connections and was most closely related to other haplotypes, it is likely an ancient haplotype , while the FST values for CF, SZWQ and HLBE were all greater than 0.05, indicating that there was moderate genetic differentiation among populations with a small degree of gene flow. The FST values among the three regions of SZWQ, EEDS and HLBE were all less than 0.05, indicating that the genetic differentiation among these populations was very small, likely as a result of high gene flow , while CF had the lowest diversity (h = 0.931). Regarding nucleotide diversity, the lowest diversity was found in CF (pi = 0.21148) and the highest (pi = 0.28319) in SZWQ  were obtained through ITS2 gene analysis, including three shared haplotypes. The most frequent haplotype detected was I1, represented by 17 sequences . I19 was in the center of the network map, while other shared haplotypes formed small cluster centers. The various clusters were surrounded, which may be related to the close geographic distance. There were no shared haplotypes in CF or HLBE, indicating that there was genetic differentiation between these two geographical populations (Fig. ST values for CF and SZWQ, and CF and HLBE were all greater than 0.05, and the FST values of other regions were all less than 0.05, indicating that the genetic differentiation among these populations was very small, likely as a result of high gene flow (Table Neutrality results were the same as that of 16S rRNA and COI. The Fow Table . The resow Table , Fig. 3cD. nuttalli were clustered into one group, and the four geographic populations were mixed, with no obvious geographical differentiation structure. This implies that the genetic evolution among populations was not closely related to the geographical structure. Genetic analysis found that D. nuttalli and D. silvarum were closely related, and others were distributed in the periphery (Fig. We found that the inner populations of ery Fig. .Fig. 4PhD. nuttalli was related to D. silvarum and D. marginatus (Fig. As in the case of 16S rRNA analysis, the haplotypes were gathered as a branch and mixed with each other. However, an additional phylogenetic tree revealed that tus Fig. .Fig. 5PhThe phylogenetic tree based on ITS2 gene sequences was identical to that created using the 16S rRNA sequences (Fig. Dermacentor nuttalli, which is the dominant tick species in the Inner Mongolia Autonomous Region [D. nuttalli populations was characterized among four different geographical populations in the Inner Mongolia Autonomous Region with three genes, revealing high haplotype diversity and high nucleotide diversity.This is the first report on the genetic structure of s Region . Haplotys Region . In thisD. nuttalli may have the ability to adapt to different environments within its large geographical range in Inner Mongolia. Our haplotype network diagram revealed highly connected shared haplotypes, indicating that not only was there frequent gene communication, but that there was also no effect of geographical isolation.Our results indicate that only a few shared haplotypes were present across our four sampled populations, but that these shared haplotypes were also highly abundant. Shared haplotypes can persist for long periods in a population and can adapt to different environments . While mST values represent the genetic differentiation index of populations, which can measure the genetic differentiation among different populations [D. nuttalli populations within Inner Mongolia did not conform to geographical isolation, and the reason for the formation of this structure was the combination of multiple factors.The neutral test results of Tajima's D and Fu's Fs can be used to analyze the historical dynamics of the population. If both are significantly negative, it means that the population has experienced rapid population expansion in history. Otherwise, there is no population expansion, and the population may differentiate. Therefore, all three genes exhibited genetic differentiation with no population expansion. The mismatch analysis maps are both bimodal, with no peak, which is consistent with the results of the neutrality test. Fulations . In our D. nuttalli across four sites characterized with 16S rRNA and ITS2 obtained only 11 haplotypes, which is significantly fewer than in our study [D. nuttalli was D. silvarum and the next closest related species was D. marginatus. In this study, only four areas with D. nuttalli from Inner Mongolia were selected, which did not cover the distribution area of D. nuttalli in China. Further studies should increase the geographic sampling range to lay a foundation for controlling the further spread of tick-borne diseases and provide more information with which to study the origin and evolution of ticks.In a study in Eastern Siberia, the genetic specificity and phylogeny of D. nuttalli sampled from four geographic localities in Inner Mongolia. The genetic diversity of D. nuttalli populations in the Inner Mongolia Autonomous Region reflects adaptation to different geographical environments, and the adaptability of different populations resulted in genetic diversity that led to genetic differentiation among them. However, the genetic differentiation among populations was cryptic and did not form a pedigree geographical structure. The genetic diversity was related not only to gene mutation, but to natural selection and other environmental factors [The 16S rRNA gene, COI and ITS2 were used to study the genetic diversity of  factors . It was


Arabidopsis thaliana across the Iberian Peninsula, the region with the largest genomic diversity. To that end, we sampled, genotyped with genome-wide SNPs, and analyzed 1772 individuals from 278 populations distributed across the Iberian Peninsula.Disentangling the drivers of genetic differentiation is one of the cornerstones in evolution. This is because genetic diversity, and the way in which it is partitioned within and among populations across space, is an important asset for the ability of populations to adapt and persist in changing environments. We tested three major hypotheses accounting for genetic differentiation—isolation-by-distance (IBD), isolation-by-environment (IBE) and isolation-by-resistance (IBR)—in the annual plant A. thaliana. In other words, dispersal limitation, genetic drift, and to a lesser extent local adaptation to environmental gradients, accounted for the within- and among-population distribution of genetic diversity. Analyses applied to the four Iberian genetic clusters, which represent the joint outcome of the long demographic and adaptive history of the species in the region, showed similar results except for one cluster, in which IBR (a function of landscape heterogeneity) was the most important driver of genetic differentiation. Using spatial hierarchical Bayesian models, we found that precipitation seasonality and topsoil pH chiefly accounted for the geographic distribution of genetic diversity in Iberian A. thaliana.IBD, and to a lesser extent IBE, were the most important drivers of genetic differentiation in A. thaliana.Overall, the interplay between the influence of precipitation seasonality on genetic diversity and the effect of restricted dispersal and genetic drift on genetic differentiation emerges as the major forces underlying the evolutionary trajectory of Iberian  Genetic diversity is an important asset for the ability of populations to adapt and persist in changing environments –8. The sThe inherent spatial nature of genetic differentiation defines the theoretical and methodological framework of three models, which are not mutually exclusive, testing the major drivers of genetic differentiation: isolation-by-distance (IBD hereafter), isolation-by-environment (IBE hereafter) and isolation-by-resistance (IBR hereafter) models. In the classical IBD, genetic differentiation among populations exhibits a positive relationship with geographic distance –18. In tIn contrast, the IBE model deals with the effects of environmental differences on genetic differentiation. IBE posits that gene exchange is strongest among populations located in similar environments, which would be mediated by environmental heterogeneity, the extent of local adaptation and spatial variation in gene flow across space , 21, 22.Finally, the IBR model takes environmental heterogeneity across landscapes into account as a modulator of gene flow and its effects on genetic differentiation . The IBRArabidopsis thaliana across the Iberian Peninsula. This is the region of the species’ distribution harboring the largest genomic diversity [A. thaliana occurs in a wide array of natural environments practically across the whole region, spanning from seaside to sub-alpine locations [A. thaliana’s history in the Iberian Peninsula [A. thaliana. In fact, habitat suitability of relict populations has been associated to more stable vegetation dynamics since the Last Glacial Maximum and during the Holocene in the Iberian Peninsula [A. thaliana an appropriate study system to disentangle the drivers of genetic differentiation at a regional scale.Here, we tested these three hypotheses to identify the major drivers of genetic differentiation in the annual plant iversity –30. In aocations –33. Theseninsula , 31, 34,eninsula , 34 alsoeninsula . OverallA. thaliana populations collected over a decade. About six individuals per population, totaling 1772 individuals, were genotyped with genome-wide, putatively neutral SNPs to estimate genetic diversity, differentiation and structure. We hypothesized that IBD and IBE largely accounted for genetic differentiation for two reasons. First, IBD is a common result in A. thaliana genetic studies—including the Iberian Peninsula [A. thaliana shows significant adaptive variation in fitness-related traits across environmental gradients [A. thaliana populations. For the sake of completeness, we also examined the geographical distribution of genetic diversity in Iberian A. thaliana. To this end, we employed a spatial hierarchical Bayesian model to identify regional hot and cold spots of genetic diversity and their potential environmental predictors. Overall, we stress the importance of identifying the drivers of genetic differentiation among populations, but also of pinpointing the forces that determine the amount of genetic diversity within populations, to understand the evolutionary dynamics of any organism.Given the fact that a small sample size seriously reduces power and accuracy of spatial analyses , IBD, IBeninsula –33—due tradients , 37, 38.A. thaliana in the Iberian Peninsula. In this set of populations, the percentage of polymorphic loci (PL) ranged between 0 and 56.7% (mean ± SD = 24.4 ± 17.7%), the mean number of observed alleles per locus (na) between 0.95 and 1.56 (mean ± SD = 1.23 ± 0.18), and mean gene diversity (HS) between 0 and 0.224 (mean ± SD = 0.09 ± 0.07). HS exhibited a bimodal distribution clearly differentiated in two groups of populations. On the one hand, 66 populations had very low HS values .We genotyped 240 genome-wide SNPs in 1172 individuals collected from 278 populations  in the 1772 individuals, which showed an average proportion of allelic differences between haplotype pairs of 0.30 ± 0.05. We only found five non-redundant multilocus genotypes in different populations. In particular, two (separated by 0.6 km) and three populations (separated by 7.6–12.8 km) included the same non-redundant multilocus genotypes.Overall, we found 1613 non-redundant multilocus genotypes .To assess whether populations experienced major or sudden disturbances that could affect their genetic diversity and differentiation during the last decades, we retrieved temporal series of publicly available orthophotographs from all populations. The analyses of the habitats interpreted from digitalized orthophotographs indicated that land-used changes and major disturbances  over the time considered did not affect landscapes. In particular, the average change per habitat type between year intervals was concentrated around zero Fig. d, indicaHS in A. thaliana populations, we used a spatial hierarchical Bayesian modeling to explain HS as a function of environmental variables. The model considered simultaneously the populations with zero (N = 66 populations) and non-zero (N = 212 populations) HS values. The best fitting model indicated that the occurrence probability of populations with non-zero HS values (populations with genetic diversity) was concentrated towards the center and eastern areas of the Iberian Peninsula , precipitation of the warmest quarter (BIO18) and topsoil pH. The contributions of precipitation variables were negative for the occurrence of populations with no genetic diversity, as well as for the genetic diversity of populations with non-zero HS values  the species is very rare .The environmental predictors with more influence on the spatial distribution of ality BIO5, precipality BIO5, precipA. thaliana populations in different genetic clusters with the 1613 non-redundant multilocus genotypes using Bayesian , occurred scattered across the Iberian Peninsula  were homogeneous, with 118, 44, 35 and 33 belonging to genetic clusters NW-C1, NE-C2, relict-C3 and SW-C4, respectively  to evaluate simultaneously the effects of the geographic (IBD), environmental (IBE) and resistance (IBR) variation on the genetic differentiation among Iberian le Table a. In addle Table b. These We found that the single model including IBD and IBE was ranked as the top model for three genetic clusters  with no models differing less than 2 in AIC Table a. AlthouA. thaliana.Genetic differentiation is a dynamic process because any population is constantly under the effect of ecological, genetic and evolutionary forces modifying the amount of genetic diversity within and among populations. Disentangling such forces accounting for genetic differentiation, which underlies major evolutionary processes from local adaptation to speciation, has long promoted the development of a strong theoretical and methodological framework since practically the birth of the Modern Synthesis. Here, we addressed this question by testing three hypotheses  accounting for regional-scale genetic differentiation in Iberian A. thaliana was mostly accounted for by IBD, and to a lesser extent by IBE , indicated that genetic differentiation in IBE Fig. a. In otheninsula –33 and eeninsula , 40–48.  ability , 47–49.A. thaliana. We believe that the lack of IBR found across the Iberian Peninsula is likely due to the species’ cosmopolitan habit and a distribution without major discontinuities . Thus, most population pairs are connected by relatively high habitat suitability, diminishing resistance distances. The demographic history, characterized by the presence of the relict African lineage and other non-relict Eurasian lineages [A. thaliana.In contrast, IBR did not account for genetic differentiation at a regional scale Fig. a, suggeslineages , 35, 50,lineages , 51–55, We also explored the effect of IBD, IBE and IBR for each of the four genetic clusters, which represent the joint outcome of the long demographic and adaptive history of the species in the region , 39, 56.A. thaliana. For example, previous studies showed that geographic variation in fitness-related traits in Iberian A. thaliana (flowering time and seed dormancy) strongly co-varied as a function of temperature and to a lesser extent of precipitation [A. thaliana adjusts its life cycle by advancing flowering time and increasing seed dormancy as the environment becomes warmer, drier and more seasonal [Finally, we also conducted NMLPE using principal component (PC) axes to pinpoint the major environmental predictors underlying IBE. We found that the three PCs depicting gradients of temperature and/or precipitation across the region made substantial contributions to IBE in NW-C1, NE-C2 and relict-C3 Iberian clusters Fig. b, probabpitation , 62, 63.seasonal , 55, 63.A. thaliana that are worth considering. For example, analyses of the genetic diversity in this large number of populations showed that 24% of them had no or very low genetic diversity  Heyhn. (Brassicaceae) across the entire Iberian Peninsula , The CORINE Land Cover 2000 , and The Soil Geographical Database from Eurasia v.4 .We sampled 278 natural populations of the annual plant Geographic distance among populations varied between 1 and 1059 km  and Sierra de Grazalema Natural Park . Based on the Royal Decree of the Spanish legislation , the genetic resources included in this study fall within the definition of “exclusively for taxonomic purposes” as they were used for scientific, educational or non-commercial purposes.We collected all seeds from wild populations. Recent historical changes in land use or large-scale perturbations may dramatically affect the amount of genetic diversity in plant populations. Such changes can be a misleading factor when attempting to evaluate the joint effects of IBD, IBE and IBR on genetic differentiation. This is because IBD, IBE and IBR do not contemplate perturbations producing sudden or erratic changes in genetic diversity that eventually may mask the patterns of interest. To make sure that historical factors were not an issue in this study, we estimated the temporal landscape dynamics of all study populations. We examined all aerial orthophotographs available for each population (spanning between 1945 and 2016), which were retrieved from different public regional administrations in Spain  with a tN = 2252 orthophotographs) with QGIS v.3.4 [To quantify temporal landscape dynamics, a circular area (500 m radius) around the GPS coordinate was divided into a regular grid of 80 squares (100 m side) for each aerial orthophotograph available per population. Based on vegetation and land use, each square was categorized as forests, scrublands, dehesa , bare soil, crops, urban, infrastructures, and water, when one of these categories occupied at least more than half of the square. Categories were assigned to each square by digitalizing manually all squares from all aerial orthophotographs available  through the CEGEN Genotyping Service (www.usc.es/cegen/). These genome-wide SNPs are frequent polymorphisms in Central Europe, the Iberian Peninsula, and worldwide collections, which altogether minimized ascertainment bias [A total of 1772 ent bias , 32, 34.A. thaliana population, we calculated the percentage of polymorphic loci (PL), the mean number of observed alleles per locus (na) and mean gene diversity (HS) using FSTAT v.2.9.3 [FST values according to Weir and Cockerham [For each  v.2.9.3 . In addi v.2.9.3  to estimockerham , which rA. thaliana populations across the Iberian Peninsula was examined using two different methods: the Bayesian clustering method implemented in STRUCTURE v.2.3.3 [P < 0.005; Wilcoxon tests for related samples) gave the final K number. This was supported by a high similarity among the ancestry membership matrices from different runs of the same K value (H′ = 0.99). We used CLUMPP v.1 [http://www.cesga.es/).The genetic structure of  v.2.3.3 , 84 and  v.2.3.3  availabl v.2.3.3 . In the UMPP v.1  to calcuFor the DAPC analysis, we assessed the number of clusters using the ‘find.cluster’ function in adegenet, which runs successive K-means clustering with an increasing number of clusters to determine the best supported number of genetic clusters using the Bayesian Information Criterion (BIC). The K value with the lowest BIC value represents the optimal number of clusters, although BIC values may keep decreasing after the true K value in case of genetic clines and hierarchical structure . TherefoA. thaliana’s distribution [We modeled the effects of environmental heterogeneity on the spatial distribution of genetic diversity by using a modified version of a spatial hierarchical Bayesian model recently used to model the effects of warming on the Iberian ribution . The aimA. thaliana, 66 of 278 populations had very low mean gene diversity (HS) values (HS < 0.009), whereas the rest of populations exhibited non-zero HS values  was modeled with a Bernoulli distribution, whilst the continuous component (presence of genetic diversity) was modeled with a Beta distribution (see the full development of the model in the Supporting Information section). The predictors of the model were the 19 bioclimatic variables available from WorldClim and topsoil pH available from The Geographical Database from Eurasia. The Watanabe–Akaike information criterion (WAIC) [We used a Bayesian approach to handle the particularities of genetic diversity data, such as the semi-continuous nature of the variable. In the case of Iberian ues Fig. c. To hanan model  to separn (WAIC) ; was comn (WAIC) .A. thaliana as follows. IBD was based on geographic distance. However, instead of using the Euclidian distance among population pairs, we created a raster by assigning a 0.5 value to all 1-km2 pixels [A. thaliana populations employing the new raster. We did not use the Euclidean distance to compute IBD because straight lines between population pairs may introduce some bias when such straight lines traverses natural barriers for the study organism (i.e. the coastline or open sea) [We estimated the ecological, genetic and evolutionary drivers of genetic differentiation of Iberian 2 pixels , 91 of tpen sea) .N = 278; r = − 0.95; P < 0.001; Pearson’s correlation) and nearly summed to 100%. Finally, a topsoil pH layer was also used. Combining these 21 environmental variables, we conducted a principal component analysis (PCA) with varimax rotation using the software SPSS v.25 . Thus, we obtained the PC scores of the first three principal components (PC) for each population. We then used the PC scores to calculate environmental dissimilarity between populations as Euclidean distances derived with the ‘dist’ function in R. Furthermore, we also calculated dissimilarity matrices among populations for each PC axis separately, as they were interpretable in terms of environmental gradients.IBE was based on the 19 bioclimatic variables. In addition, we used The CORINE Land Cover to obtain the percentage of human-modified habitat  within a 500 m radius from the population GPS coordinate . The perA. thaliana across the Iberian Peninsula with the maximum-entropy modeling technique implemented in the software Maxent 3.3.3 k [A. thaliana populations available to date , and the same environmental variables used in a previous habitat suitability model [A. thaliana with lower sample sizes [To compute IBR, we first estimated habitat suitability for  3.3.3 k , 94. To ty model . In partle sizes , 56.A. thaliana (higher resistance) and vice-versa. Using circuit theory [A. thaliana populations was accounted for by IBR. We used Circuitscape v.4.0 [Next, we transformed the resulting habitat suitability into resistance values as 1 minus the value of each pixel. Thus, greater pixel values represented lower occurrence probability of t theory , 25, we pe v.4.0  to calcuFST values) among A. thaliana populations, we fitted maximum-likelihood population effect models (MLPE) [A. thaliana populations, all MLPE considered spatial locations. To this end, we used a modification of the MLPE model incorporating the correlation between pairwise measurements due to comparison of populations and spatial locations (nested MLPE or NMLPE) [FST values. We conducted NMLPE on the complete dataset (N = 278 populations) as well as on the subset of those populations exhibiting HS different from zero (N = 212 populations with HS > 0.009). The results were consistent between the two datasets, showing that the 66 populations with no genetic diversity were not biasing the results.To quantify the relative contributions of IBD, IBE and IBR on the genetic differentiation (pairwise s (MLPE) . Code imr NMLPE) . Since tA. thaliana in the region. We considered each population to belong to a specific genetic cluster when the average value of the membership proportions of its individuals was ≥0.3 for that specific cluster. Given the marked genetic structure of the study system, threshold values of 0.25 and 0.40 were consistent, but 0.3 provided a clearer classification of individuals into genetic clusters. The majority of populations were assigned to a single cluster (N = 230). We assigned populations reaching the minimum average membership proportion for more than one cluster to multiple clusters. Overall, 47 populations were assigned to two clusters, whereas only one population had average membership proportions higher than 0.3 for three clusters.In addition, we ran NMLPE for each genetic cluster to evaluate whether the effects of IBD, IBE and IBR on genetic differentiation differed among genetic clusters. This is because the number of genetic clusters represents the outcome of the demographic and evolutionary history of r < 0.6), created with the ‘dredge’ function from the R package MuMIn v1.4 [β) with standard errors for the explanatory variables included in all top-ranked models also using MuMIn. We considered effect sizes as significant when 95% confidence intervals did not overlap zero.We also ran NMLPE on the complete dataset and each cluster separately, but replacing IBE by the PC axes. Thus, we used IBD, PC1, PC2, PC3 and IBR as independent variables. We used the Akaike information criterion (AIC) to compare models containing all possible combinations of non-collinear predictions (MIn v1.4 . In thos


Sirex noctilio F. (Hymenoptera: Siricidae: Siricinae), a new invasive species in China, is a significant international forestry pest which, transported via logs and related wood packing materials, has led to environmental damage and substantial economic loss in many countries around the world. It was first detected in China in 2013, and since then infestations have been found in 18 additional sites. Using a 322 bp fragment of the mitochondrial barcode gene COI, we studied the genetic diversity and structure of S. noctilio populations in both native and invaded ranges, with a specific focus in China. Twelve haplotypes were found across the native and invaded distribution of the pest, of which three were dominant; among these there were only one or two mutational steps between each pair of haplotypes. No obvious genetic structure was found other than in Chinese populations. China has a unique and dominant haplotype not found elsewhere, and compared with the rest of the world, the genetic structure of Chinese populations suggested a multiple invasion scenario. The current primary route for alien forest species invading China is through human activity. This is mainly unintentional introduction; transported seedlings and their propagation materials carry 46% of human activity-generated invaders, with packaging materials or wood accounting for 21%  [Dendroctonus valens, fall webworm Hyphantria cunea and loblolly pine mealybug Oracella acuta, have already caused the destruction of forestry resources in China, negatively influencing species diversity and ecological security, and resulting in major economic loss [Biological invasions are increasing exponentially as globalization progresses, without any sign of saturation, especially in insects ,2. At prndtjgb/) . Some mamic loss ,10,11. IPlutella xylostella, moves within China from south to north, with rare effective travel in the reverse direction [Grapholita molesta, which originates in southern China and has dispersed to the north [Frankliniella occidentalis, Cai (2017) recently determined the 13-year history of its invasion of China, revealing multiple introductions and further human-assisted spread, using mitochondrial genes and 18 microsatellite loci. In order to infer the invasion routes of the Asian long-horned beetle (Anoplophora glabripennis) in Europe, Javal (2019) used a 485 bp fragment of the mitochondrial barcode gene COI, which revealed multiple introductions from China and a bridgehead effect [COI gene (mtDNA cytochrome oxidase I) has been of widespread use in tracing invasions [Molecular markers, such as mitochondrial, ribosomal, chloroplast, and microsatellite DNA, and others, have become increasingly popular for investigating phylogenetic relationships between insect populations. By using mitochondrial genes and microsatellite loci, Wei 2013) showed that the invasive diamondback moth, irection . Similar showed td effect . The COInvasions ,16,17,18Sirex noctilio, a woodwasp native to Eurasia and North Africa, is a significant forestry pest, subject to quarantine regulations around the world [S. noctilio outside its native Eurasian range was in the southern hemisphere during the 1900s [Pinus radiata planted there, killing trees across approximately 120,000 ha of the north–central region and thereby causing huge economic and ecological loss. During 1987–1989, an outbreak in Australia and Tasmania killed more than 5 million P. radiata trees [he world . Its larhe 1900s . Followita trees ; it was ta trees . SeveralS. noctilio was first detected in 2013 in the northeast, at Daqing, Heilongjiang Province [S. noctilio is found in China at similar latitudes to its native sites; moreover, its native regions in Russia are near the Chinese border. Based on climatic conditions and host distribution, domestic and foreign experts analyzed the potential woodwasp distribution area in China. Their results indicated that a band stretching from Yunnan to Heilongjiang represents a highly suitable area for its establishment [In China, Province ,24. Six Province . MoreoveProvince . S. noctlishment . Its potS. noctilio in nine populations from New York and Pennsylvania (US), Ontario (Canada), and Queensland , showing that multiple introductions occurred in northeastern North America. However, a more comprehensive understanding requires the collection of more samples from its native Europe and Asia.In order to investigate the origins and pathways of invasion, Bernard (2012) used microsatellite and sequence data collected from five continents. Two major sources were identified, although the invasion history was quite complex, with most of the populations containing admixtures of independently introduced European insects that subsequently spread through the invaded regions. Similarly, Bittner (2017) investigated the genetic diversity of 924 S. noctilio populations were surveyed using the mtDNA cytochrome oxidase 1 (COI) gene. Our objective was to investigate the genetic structure and genetic diversity of S. noctilio populations around the world, especially in relation to Chinese infestations. We compared patterns in invaded continents and native regions, analyzing for the first time 778 individuals from 6 continents. In particular, samples from original European populations and recent Chinese infestations were included. Overall, this study aims to elucidate the invasive routes of this pest as well as provide data enabling the prevention of the harmful consequences of S. noctilio spread.In this study, the genetic structure and invasion routes of S. noctilio samples were collected from native European, non-Chinese invaders, and Chinese populations, comprising 44, 127, and 607 specimens, respectively. A summary of sampling sites and common parameters of the genetic diversity of S. noctilio in this study is provided in https://datadryad.org/stash/dataset/doi:10.5061/dryad.37mm8; from Genebank: https://www.ncbi.nlm.nih.gov/nuccore/?term=sirex+noctilio. Finally, we obtained a total of 607 specimens from 19 Chinese populations , we used only 20 of the sequenced individuals, which included all the haplotypes found in each population. We selected individuals from each haplotype based on the selection ratio and the number of samples in each haplotype. The final total of 364 samples for analysis consisted of 44, 127, and 193 individuals from Europe, non-Chinese invaded areas, and China, respectively.COI barcoding fragment was amplified via PCR using the forward primer LCO1490 (5ʹ-GGT CAA CAA ATC ATA AAG ATA TTG G-3’) and the reverse primer HC02198, (5’-TAA ACT TCA GGG TGA CCA AAA AAT CA-3’) . PCR reagents were used and reactions performed following Sun [COI fragment was amplified at the Canadian Centre for DNA Barcoding  using a slightly different sequencing set (C-LepFolF/C-LepFolR), following the standard high-throughput protocol [For the native European samples not supplied as sequences directly from GenBank, DNA extraction, PCR amplification and sequencing of the barcode fragment were performed for 15 specimens at the Laboratory of Forest Zoology URZF, INRAE . DNA extracts were prepared from one adult hind leg or thorax muscle using NucleoSpin tissue XS kits  according to the manufacturer’s protocol. The wing Sun . PCR proprotocol .COI amplification primers were synthesized by a commercial company . PCR primers LCO1490 and HC02198 were used as above. Reactions were performed in a total volume of 25 μL, containing 12.5 μL of 2 × GoTaq® Green Master Mix , 1 μL of each primer, 1.5 μL of DNA template, and 9 μL of ultrapure water. The reaction procedure was as follows: initial denaturation for 2 min at 94 °C, followed by 35 cycles of denaturation at 94 °C for 30 s, annealing at 45 °C for 30 s, and extension at 68 °C for 2 min, with a final extension at 68 °C for 10 min. PCR products (3 μL) were analyzed by electrophoresis on a 1.5% (w/v) agarose gel (1 × TAE), alongside a DNA marker . After electrophoresis at 130 V for 20 min, the PCR products were visualized using ethidium bromide and ultraviolet light. Finally, products clearly visible after electrophoresis were sent to a commercial company  for sequencing in both directions.DNA was extracted from the leg or thorax muscle of each Chinese specimen using the Multisource Genomic DNA Miniprep Kit , following the manufacturer’s protocol to prevent cross-contamination, all tools were sterilized by flame or 75% pure anhydrous ethanol. DNA was eluted using 70 μL of elution buffer and stored at −20 °C until use. Species-specific COI sequences were trimmed to the same length (322 bp) for final alignment.All electropherograms were checked manually in CodonCode Aligner v3.7.1.  to assess their quality. The alignment was done independently using Clustal W implemented in MEGA v6.06 with default parameters, and no pseudogenes or stop codons were detected in the sequences ,29. All www.boldsystems.org).Details on the collecting data for each specimen, as well as photographs, sequence records, trace files, and primer sequences used for PCR amplification, together with GenBank accession numbers, are available through the dataset in BOLD  were performed on native European populations and Chinese populations to measure the partitioning of genetic variation between populations and groups of populations in Arlequin 3.5.2.2 ,35. The There were 11 variable sites among all insects sampled. Three dominant haplotypes, H1, H2, and H9, were present among the total of 12 haplotypes; the haplotype diversity was 0.4207. Only one or two mutation sites existed across the different haplotypes . There wp < 0.01) was between geographic clusters, with a relatively small part of the genetic variance  between populations within geographic clusters. When sites were grouped by latitude (p < 0.01) between clusters was slightly higher than that  between populations within clusters. Within this study, no clear genetic structure emerged within native European populations.AMOVA analysis of the genetic structure of Chinese populations  showed tlatitude , the varP. sylvestris var. mongolica seedlings are taken for distribution and planting elsewhere. H3 is present in 5% of DM individuals, and so does H4, H5, H6, H7, these haplotypes occurred in 10%, 5%, 7%, and 25% of sampled insects of HG, FJ, YS and HHEJ, respectively. The latitude 46.204°N was used to group samples into the categories north and south. Haplotype diversity in the northern area is obviously higher than that in the south. The northern cluster has seven haplotypes, H1, H2, H3, H4, H5, H7, and H8, from nine populations, compared to two haplotypes (H1 and H6) from ten populations in southern China.The Chinese sampling sites are divided into four groups based on geographical proximity: west for one group, middle for two groups and east for one group. The middle and eastern groups, where there are cities, contained 4 haplotypes, whereas the western group, which is mainly mountainous, had 3 haplotypes. The first site discovered in China, DM, contained 40% H1 and a similar proportion, 55%, of H2. There is no H1 in the western group, although the proportion of H1 in the two middle area groups is quite high as is also found in the eastern group. There is a decreasing trend from west to east in the proportion of H2. The representative populations on this axis were HDQ, DM, and HG with 100%, 55%, and 20% H2, respectively. Among the other six unique haplotypes in China, H8 is located in MZL, on the border between China and Russia. Four of the remaining five haplotypes are distributed in the populations from which large samples were taken and are located in city areas. The sixth, HHEJ, is located in the area from which S. noctilio is rather large, although it is known that the pest is harmful to various kinds of conifers around the world, especially Pinus, Picea, Abies, Larix, and Pseudotsuga [P. sylvestris var. mongolica is in the area of Honghuaerji in Inner Mongolia and north of the Huma River in Heilongjiang [S. noctilio in the wild distribution areas of P. sylvestris var. mongolica in either mountain or sandy ecological zones  and Jinbaotun (Inner Mongolia). The former is a mixed forest of Pinus koraiensis, L. olgensis, Picea koraiensis and P. sylvestris var. mongolica. The latter contains Pinus tabuliformis and P. sylvestris var. mongolica. However, fieldwork has indicated that the only infested conifer species in China to date is P. sylvestris var. mongolica, even when other potential hosts are present in the same forest [S. noctilio appears to be harmless in the natural distribution area of its favorite host tree species.Currently, the main infestation zones in China are those where e forest . As is cP. sylvestris var. mongolica, trees from this area will be planted elsewhere. We speculate that H1 is an invasion haplotype carried by humans that have entered the central and eastern urban areas of China. As it existed on all continents, its source is unclear. Based on our data, H2, H7 and H8 were found only in China, and given that they are unlikely to have originated there, we are unable to identify their origin. H2 is the main haplotype of the western component, with a distribution ratio that gradually decreases from west to east; this is in line with the characteristics of spread from a point of origin to other regions. This, as noted above, may represent an invasion originating with Russian wood imported for construction at Honghuaerji and spread eastward by planting. Severely endangered woodlands in Dumeng, Hegang, Fujin, and other eastern areas may have been invaded in this way. H7 and H8 are also unique to the western region; H8 occurs on the border with Russia.There are currently three more frequent haplotypes, H1, H2, and H9, which have a worldwide distribution. It is thus unsurprising that they are in the center of the haplotype network and closely connected with other well-established haplotypes . InteresS. noctilio populations than in those to the south. Damage to woodlands in the northeastern region occurs discretely, in multiple forest farms, parks, and other wooded land. This damage was concentrated, as is characteristic of the middle to late stages of an invasion. In contrast, in newly-invaded southern areas, such as Jinbaotun, damaged forests were localized and individual affected trees were distributed discretely in the affected area, indicating the initial stage of introduction. Although the 1130 km between Honghuaerji and Jinbaotun is shorter than the 1400 km between Honghuaerji and the affected area in the northeast, as the haplotype H1 found in the southern region is not present in Honghuaerji, the south regional populations may originate from northern China.Grouped by latitude, significantly more haplotypes exist in northern Chinese S. noctilio has a relatively strong natural flight ability. In the southern hemisphere, the maximum spread rate of an invading population of S. noctilio is 82 km per year [per year . In Chinper year . The strCOI) could prove effective, showing its utility as a method for determining invasion pathways and spread.When analyzing biological invasions, knowledge of the genetic structure of populations from the area of origin is vital in determining the source. Our target species is native to Eurasia and North Africa, but it does not cause serious harm there and has consequently received less attention, leading to difficulties in collecting samples. In particular, the lack of samples from the Russian region caused sampling breakpoints between the European and northeast Chinese populations. The observation that H2 occurred only in China may be due to the large sample size from some previously unsampled sites from this area. More reliable results may be obtained from sampling efforts focused on this region and on western China. Finally, microsatellite marker use involving cytochrome C oxidase subunit I (COI was used to analysis the genetic diversity and structure of S. noctilio populations in both native and invaded ranges, with a specific focus in China. Twelve haplotypes were found across the native and invaded distribution of the pest, of which three were dominant; among these there were only one or two mutational steps between each pair of haplotypes. No obvious genetic structure was found other than in Chinese populations. China has a unique and dominant haplotype not found elsewhere, and compared with the rest of the world, the genetic structure of Chinese populations suggested a multiple invasion scenario.In this study, a 322 bp fragment of the mitochondrial barcode gene


GD depend on both the age of the obstacle and the effective size of the populations it separates, making comparisons of the actual barrier effect of different obstacles difficult. Here, we developed a standardized genetic index of fragmentation (FINDEX), allowing an absolute and independent assessment of the individual effects of obstacles on connectivity. The FINDEX is the standardized ratio between the observed GD between pairs of populations located on either side of an obstacle and the GD expected if this obstacle completely prevented gene flow. The expected GD is calculated from simulations taking into account two parameters: the number of generations since barrier creation and the expected heterozygosity of the populations, a proxy for effective population size. Using both simulated and empirical datasets, we explored the validity and the limits of the FINDEX. We demonstrated that it allows quantifying effects of fragmentation only from a few generations after barrier creation and provides valid comparisons among obstacles of different ages and populations (or species) of different effective sizes. The FINDEX requires a minimum amount of fieldwork and genotypic data and solves some of the difficulties inherent to the study of artificial fragmentation in rivers and potentially in other ecosystems. This makes the FINDEX promising to support the management of freshwater species affected by barriers, notably for planning and evaluating restoration programs.Fragmentation by artificial barriers is an important threat to freshwater biodiversity. Mitigating the negative aftermaths of fragmentation is of crucial importance, and it is now essential for environmental managers to benefit from a precise estimate of the individual impact of weirs and dams on river connectivity. Although the indirect monitoring of fragmentation using molecular data constitutes a promising approach, it is plagued with several constraints preventing a standardized quantification of barrier effects. Indeed, observed levels of genetic differentiation  This is because genetic differentiation primarily stems from genetic drift, that is, from the random fluctuation of allelic frequencies naturally occurring in all populations  and the effective size eN of populations  is expressed as a percentage and directly quantifies the relative loss of gene flow resulting from the presence of an obstacle. It is based on the comparison of measures of genetic differentiation observed between populations located in the immediate upstream and downstream vicinity of a putative obstacle with the theoretical measures of genetic differentiation that would be expected if the obstacle was a total barrier to gene flow. These theoretical measures of genetic differentiation are inferred from numerous genetic simulations, here used to reflect the expected changes in allelic frequencies resulting from the interplay between the age of the obstacle and the expected heterozygosity of populations, a proxy for eN: the closer the observed measure of genetic differentiation from the one that would be expected in the worst‐case scenario , the higher the index of fragmentation. We first present the logic and principles underlying our index. We then use both simulated and published empirical genetic datasets to explore and discuss the validity and the limits of the proposed index. We finally propose several perspectives to use the index and, because setting bio‐indicators takes time, we present potential improvements that should be considered to make this index even more useful to managers.Here, we bridge that gap by developing a user‐friendly and standardized genetic index of fragmentation . GDmin should theoretically equal 0 but the background noise resulting from the concomitant influences of genetic drift, mutations, and incomplete genetic sampling may actually lead to non‐null—though very low—measures of genetic differentiation. On the other hand, GDmax stands for the theoretical measure of genetic differentiation that would be expected under the worst‐case scenario, that is, under the hypothesis that the considered obstacle is a total barrier to gene flow (m = 0). GDmax is expected to increase with time since barrier creation and to decrease with the increase in effective population sizes  to 100% . The FINDEX thus directly quantifies the loss of gene flow resulting from the presence of an obstacle.The obs is directly calculated from observed genotypic data collected in populations located at the immediate upstream and downstream vicinity of the obstacle , whereas GDmin and GDmax are predicted from theoretical datasets simulated according to three main parameters (see the next section for details): the mutation rate µ of considered genetic markers, and, for GDmax only, the age T of the total barrier to gene flow  and kept constant over time. We used forward simulations of gene flow between these two demes over 1,000 nonoverlapping generations. Genetic polymorphism was based on 15 microsatellite loci and 20 alleles per locus, which corresponds to the number of markers typically used in empirical studies focusing on functional connectivity  and each mutation rate µ (2 levels), we ran ten simulation replicates, and 30 genotypes were sampled every ten generations from generation 300 to generation 1,000 (71 levels) to monitor the setting up of genetic differentiation over time. This procedure resulted in a total of 93 × 2×71 × 10=132,060 simulated genetic datasets in the Fstat format , and the carrying capacity K of simulated populations.For each simulation, we computed the two following pairwise metrics of genetic differentiation: the Hedrick's G″st Hedrick,  and the  Winter, . Nine ot Winter, , in acco Winter, . All negT > 0  were used as a training set in the regression implementation of a random forest machine‐learning algorithm  computed after the creation of the barrier , we used the R‐package randomForest . Created randomForest R‐objects were saved in the form of.rda files  and were further used to predict the four possible expected measures of genetic differentiation GDmax (two possible metrics of genetic differentiation and two possible mutation rates) between pairs of populations according to both the mean expected heterozygosity eH (the proxy for effective population sizes) and the number of generations T elapsed since barrier creation, using the predict.randomForest function.The 111,600 simulations associated with Breiman, . This apBreiman, . The objT ≤ 0  were used to predict the four possible measures of genetic differentiation GDmin  that may be expected under the influence of mutations, drift, and incomplete genetic sampling between two adjacent populations not separated by any barrier to gene flow. For each of both mutation rates µ and each of both metrics of genetic differentiation GD (either G″st or φ′st) computed before the creation of the barrier , GDmin was computed as the fifth percentile of non‐null simulated GD values. These four predicted GDmin values were stored in the form of a.rda file.The 20,460 simulations associated with 2.3µ (5 × 10–5 or 5 × 10–4) and a metric of genetic differentiation GD (G″st or φ′st). The four indices are then averaged to get the final index of fragmentation FINDEX with a 95% confidence interval computed as 1.96 × SE, with SE the estimated standard error .Equation 1 allows computing a unique index of fragmentation for each combination of both a mutation rate FINDEX can also be estimated using an intercept‐only mixed‐effect linear model with the various indices as the response variable and the genotypic dataset as a random effect , and the corresponding 95% confidence interval is computed as 1.96 × SE, with SE the unbiased standard error as estimated from the mixed‐effect model.Note that when several genotypic datasets are available for the same obstacle, for instance when several sympatric species are sampled on either side of the obstacle or when several replicates are considered , an overall McNeish, . The ovegenepop format) and a parameter file indicating for each considered obstacle the name of the two adjacent populations (as given in the genotypic datasets) and the number of generations elapsed since barrier creation. This number of generations is to be estimated from the life‐history traits of the considered species. Figure The whole procedure was automated within a user‐friendly R‐function . All other simulation parameters were similar to previous simulations. For each carrying capacity K and each crossing rate m, we ran 20 simulation replicates, and 30 genotypes were sampled at generations t = 405 (age of the barrier T = 5), 410, 415, 420, 425, 450, 500, and 700 (T = 300), resulting in a total of 21,600 simulated genetic datasets in the Fstat format, further converted into the genepop format.To assess the validity of the proposed eH and the two pairwise measures of genetic differentiation G″st and φ′st. We then used parameters T and eH to predict the corresponding measures of genetic differentiation GDmin and GDmax (for both G″st and φ′st) expected under the two mutation rates 5 × 10–5 and 5 × 10–4 using the predict.randomForest function and the previously created.rda files  corresponding to each combination of K, T, and m.For each simulated dataset, we computed the averaged expected heterozygosity Appendix . For eacFINDEX to uncertainty in the estimates of eN and T and to reduced numbers of markers. Details are provided in Appendices We finally explored the sensitivity of the 2.5FINDEX in real situations, we used two published empirical datasets. The first one is from Gouskov, Reyes, Wirthner‐Bitterlin, and Vorburger  sampled across 47 sites and genotyped at nine microsatellite loci. We selected 6 pairs of populations according to the following criteria: upstream and downstream populations belonged to the same river, were separated by a single dam, were distant from a maximum of 5km  and 1359 Languedoc gudgeon (Gobio occitaniae) sampled across 47 sites (22 in the Célé River and 25 in the Viaur River) and genotyped at 11 and 13 microsatellite loci, respectively. We selected 8 pairs of populations according to the following criteria: upstream and downstream populations belonged to the same river, were separated by a single weir, were distant from a maximum of 1km, were not separated by any confluence with tributaries, and were sampled for both species. This selection corresponded to 8 independent weirs (~1 to 3 m high) created between the 16th and the 20th century  were used to predict GDmin values whereas data simulated after the creation of the barrier  were used to predict GDmax values. As expected with a migration rate of 0.5, GDmin values were always very close from 0 . With predicted GDmax values ranging from 0.031 to 0.898 for G″st and from 0.042 to 0.968 for φ′st, both metrics displayed similar distribution patterns across mutation rates, although φ′st systematically showed higher values at low eH.GD3.2FINDEX correctly reflected the actual level of gene flow between two populations separated by an artificial barrier, beyond the temporal inertia in the setting up of genetic differentiation. The mean FINDEX values computed over simulated replicates for each combination of K (carrying capacity), T (number of generations since barrier creation), and m (obstacle crossing rate) showed—as expected—an overall decrease with the increase in crossing rate, whatever the size of populations or the age of the barrier , the 95% confidence intervals about the FINDEX always included values lower than 20%. On the contrary, in absence of gene flow (m = 0), the 95% confidence intervals always included values higher than 90%, except within the first 10 generations after barrier creation  and 20% (full gene flow) providing robust benchmarks for future interpretation of the index, whatever the age of the obstacle (from generation 10 at least) or the effective size of populations.The second set of simulations was designed to assess whether the r Figure a–d. As en Figure a, b. In Appendix . Neverthm ≤ 0.1), the FINDEXshowed higher variability, with two noticeable trends. First, whatever the simulated carrying capacity, the FINDEXshowed a slight 10% to 20% decrease with the increase in time since barrier creation , the FINDEXshowed a slight 10 to 30% decrease with the increase in effective population sizes  to ~ 43% in largest populations .For low—though non‐null—crossing rates  and averaged expected heterozygosity .In the first empirical dataset , whereas observed measures of genetic differentiation were pretty low, ranging from 0 to 0.034 for φ′st and from 0 to 0.026 for G″st. The impact of weirs was variable across space and species  were found as responsible for a decrease in genetic connectivity since barrier creation (FINDEX > 20%), with FINDEX values ranging from 35% in the case of barrier SCA to 65% in the case of barrier SCC in the Célé River. In P. phoxinus, all weirs but CLA in the Célé River (FINDEX = 36%) were found as highly permeable (FINDEX < 20%), with five out of eight weirs showing a FINDEXof 0%. When computed across species, only the barrier CLA in the Célé River (multispecies FINDEX = 39%) was identified as an obstacle to overall genetic connectivity  and averaged expected heterozygosity .In the second empirical dataset . However, rivers are subject to many and sometimes contradictory uses  with respect to a theoretical range of variation spanning from the background noise expected in the absence of any barrier to gene flow  to the maximal level of differentiation expected if the obstacle was a total barrier to gene flow . The latter takes into account both the time since barrier creation and, using eH as a proxy, the effective size of populations, which makes the FINDEX a truly innovative tool as it makes it possible to compare the actual barrier effect of obstacles differing by their age and/or by the size of the populations they separate. Using numerous simulations, we were able to obtain GDmax values for a large range of biologically realistic parameters , whatever the number of generations since barrier creation and the effective population size , we found, however, that it could underestimate barrier effects in the first 5 to 10 generations after the creation of the obstacle. As a conservative strategy, we suggest that the FINDEX should not be used to assess the permeability of obstacles separating populations for fewer than 10 generations. However, it is noteworthy that the FINDEX can be applied to any type of organisms and thus that species with short generation time (such as some invertebrate species) may be considered as good candidates to investigate the impact of recently built barriers . For the lowest crossing rates (m ≤ 0.1), we also found that FINDEX values slightly decreased with both time since barrier creation (from generations 15 to 300) and effective population sizes  indicates no gene flow between populations , whatever the age of the obstacle or the effective size of populations.Interpretation forFINDEX < 20%: A FINDEX value lower than 20%  indicates full genetic connectivity (no barrier effect), whatever the age of the obstacle or the effective size of populations.Interpretation for intermediateFINDEXvalues: Intermediate FINDEX values can be used to rank obstacles according to their barrier effect. However, for FINDEX values ranging from ~40 to ~80%, the FINDEX tends to slightly decrease with both the increase in the number of generations since barrier creation and the increase in effective population sizes  should be considered as possibly having comparable barrier effects, except of course if the ranking of obstacles based on FINDEX values goes against these trends. Consider for instance an obstacle A of age 20 (in generations) and an obstacle B of age 300. If FINDEX(A) = 40% and FINDEX(B) = 20%, both obstacles should be considered as possibly having the same impact on gene flow. On the contrary, if FINDEX(A) = 20% and FINDEX(B) = 40%, obstacle B can be confidently considered as more impactful than obstacle A. Similarly, consider an obstacle C separating populations with low expected heterozygosity  and an obstacle D separating populations with high expected heterozygosity. If FINDEX(C) = 40% and FINDEX(D) = 20%, both obstacles should be considered as possibly having the same impact on gene flow. On the contrary, if FINDEX(C) = 20% and FINDEX(D) = 40%, obstacle D can be confidently considered as more impactful than obstacle C.s Figure e, f. ObsThe FINDEX provides a promising individual quantification of both the short‐ and long‐term genetic effects of dam‐induced fragmentation, allowing robust comparisons among species or populations with different population sizes and obstacles of different ages (from generation 15 at least) and types. When applied to empirical data, the FINDEX allowed identifying several obstacles partially limiting gene flow in the three considered freshwater fish species . More generally, minnows were much less affected by obstacles than gudgeons, in accordance with personal field observations and previous findings on the same two rivers . When the two sampled populations are distant from each other, GDobs values may nevertheless result from the interplay between the actual barrier effect (the quantity of interest) and other processes such as Isolation‐by‐Distance. In such situations, the FINDEX should be computed using ad hoc GDmin and GDmax. values, both taking into account the additional processes responsible for GDobs values. To that aim, a solution could be to consider a space‐for‐time substitution sampling design . Such a procedure might help answer very specific needs, but its complexity might restrict the direct use of the FINDEX to informed and trained managers. Furthermore, additional work would be required to determine to what extent FINDEX values computed in that way could still be comparable across obstacles.Handling other genetic markers: The proposed FINDEX relies on the use of microsatellite markers. Microsatellite markers are still widely used in the study of nonhuman organisms, especially by environmental managers. However, the reduction in sequencing costs and the development of an ever‐increasing supply of biotechnological services , the observed measures of genetic differentiation GDobs only stem from dam‐induced fragmentation. In other words, it is crucial to consider situations in which the focal populations would be fully connected if the obstacle did not exist. This assumption is true only when sampled populations are adjacent, that is, located in the immediate upstream and downstream vicinity of the obstacle  created by barriers, as fish might struggle or even fail to ascent an obstacle  and help them target their efforts and resources toward the most impactful ones. Similarly, it may allow tracking the expected temporal decrease in genetic differentiation after obstacle removal or fishpass setting and thus help evaluate the success of local mitigations and restoration measures in response to regulatory obligations. Finally, it might as well provide a standardized quantification of road‐induced fragmentation, a critical issue in terrestrial ecology.We here laid the groundwork for an operational tool dedicated to the individual and standardized quantification of the impact of artificial barriers on riverscape functional connectivity from measures of genetic differentiation. The proposed genetic index of fragmentation Appendix S1Appendix S2Appendix S3Appendix S4Appendix S5Appendix S6Appendix S7Appendix S8Appendix S9Appendix S10Appendix S11Appendix S12Appendix S13Appendix S14Click here for additional data file.