Astacus astacus, is an indigenous European freshwater species. Its populations show significant declines caused by anthropogenic pressure on its habitats, climate change and the spread of invasive species. Diminishing populations’ trends and loss of genetic diversity highlight the need for effective conservation that will ensure their long-term survival. We combined population genetics and species distribution modelling (SDM) to reveal the impact of climate change and invasive species on the noble crayfish, and to guide future conservation programs of current populations. Our study showed that Croatian populations of A. astacus harbour an important part of species genetic diversity and represent significant genetic reservoir at the European level. The SDM results predicted substantial reductions of suitable habitats for A. astacus by the 2070; only 13% of its current potential distribution is projected to remain stable under pessimistic Representative Concentration Pathway (RCP 8.5) emission scenario. Moreover, most of the populations with high genetic diversity are located in the areas predicted to become unsuitable, and consequently have a high probability of being lost in the future. Further, SDM results also indicated considerable decrease of future habitat suitability for invasive crayfish species in Croatia, suggesting that climate change poses a major threat to already endangered A. astacus. The obtained results help in the identification of populations and areas with the highest conservation value which should be given the highest priority for protection. In order to preserve present diversity in areas that are predicted as suitable, we propose assisted migration and repopulation approaches, for enhancing populations’ size and saving maximum genetic variability. The result of our research emphasizes once again the benefits of multidisciplinary approach in the modern biodiversity conservation.The noble crayfish,  Indigenous crayfish species are among the most threatened animal taxa in European freshwaters where they are experiencing substantial population declines across their entire distribution ranges3. Thus, the requirement for appropriate conservation actions and policies are urgently needed4.One of the greatest challenges faced by humanity is the mitigation of rapid biodiversity loss, associated with negative anthropogenic activitiesAstacus astacus, is a keystone species of high ecological, economic, and cultural importance in Europe5. It is an indigenous European freshwater species whose gene pool and wide current distribution have been shaped by geo-climatic events (i.e. Pleistocene glaciations) and anthropogenic impacts . In Croatia, A. astacus is recorded in all three biogeographical regions: Continental, Alpine and Mediterranean. It is naturally distributed in the waterbodies of the Black Sea drainage, with a few recorded populations in the Adriatic Sea drainage that are of anthropogenic origin6. Large-scale genetic analyses revealed that A. astacus encompasses several mitochondrial lineages that have separated and diversified during the Pleistocene glaciations in the western and southern Balkans9, as well as in the lower Danube basin7. Results of microsatellite analysis revealed a differentiation of northern European populations from central European populations, with the former exhibiting a lower genetic diversity10. Furthermore, Schrimpf et al.11 and Laggis et al.8 revealed that this species harbours the highest genetic diversity in south-eastern Europe, while, low genetic diversity was detected in central and northern Europe, resulting from founder effects due to postglacial re-colonization and frequent human translocations for economic reasons7. Climate change is also among the major pressures for a decline in A. astacus populations, and its recognised vulnerability12 prompted the protection afforded by international legislation . In Croatia, its conservation status in all three biogeographical regions is unfavourable-inadequate following EU Habitats Directive (details are provided at https://nature-art17.eionet.europa.eu/article17/); during the last decade, 55% of A. astacus populations extirpated6, demonstrating the inadequacy of the species conservation. Consequently, as decreasing population trends continued13, A. astacus is listed as vulnerable even though protected by national legislation (NN 80/13).The noble crayfish, 5. The NICS displace indigenous crayfish species through transmission of diseases such as crayfish plague caused by the oomycete Aphanomyces astaci, which is listed among the World’s 100 Worst Invasive Alien Species14. Furthermore, NICS’ success is attributed to competitive exclusion, tolerance to poor water quality and altered habitat5. In addition, apart from direct competition with indigenous crayfish populations, NICS possess the ability to change food webs and entire ecosystems5. During the last two decades, three NICS have been recorded in Croatian freshwaters: the marbled crayfish , the spiny-cheek crayfish (Faxonius limosus) and the signal crayfish (Pacifastacus leniusculus)15. Since the presence of P. virginalis remains limited to one local population16, and P. leniusculus and F. limosus have more extensive ranges, they represent the most problematic NICS for A. astacus populations in Croatian freshwaters. Faxonius limosus was first recorded in the Nature Park Kopački rit  in 2003 where it has spread naturally along the Danube River from Hungary6. Compared to previous data this species has significantly expanded its range and continues to successfully spread in Croatian freshwater ecosystems displacing native A. astacus and Pontastacus leptodactylus15. Pacifastacus leniusculus is one of the most successful crayfish invaders in Europe2. Indeed, Chucholl3 found P. leniusculus to be the greatest threat to indigenous crayfish species among six NICS, in south-western Germany. This species is the most widespread NICS in Croatia due to its high dispersal rate which is among the highest recorded rates in Europe15. It is distributed in the continental part of the country, particularly in the Mura and Drava Rivers6, as well as in the Korana River, where it was illegally introduced15. The Korana River and its tributaries are the hotspots of indigenous Croatian astacofauna diversity, namely, A. astacus, Austropotamobius torrentium and P. leptodactylus, which encompass various divergent lineages, are distributed in those freshwater ecosystems17. To guide best-practice conservation and management actions for A. astacus populations in Croatia, we aimed to identify areas of potential current and future habitat suitability overlap between the indigenous A. astacus and the two problematic NICS using Species Distribution Modelling (SDM) (details are provided below). Identifying such areas will enable perceiving locations where endangered A. astacus may overlap with its invasive competitors, currently or in the future, under different climate change scenarios. This information, combined with genetic data, is a crucial piece of information for selecting A. astacus populations with the highest priority for protection.Apart from being threatened by climate change, habitat loss and deterioration, the introduction of non-indigenous crayfish species (NICS), and their pathogens, are of additional major concern for indigenous crayfish species decline in freshwater ecosystems across Europe4. Contemporary, conservation planning includes genetic screening and selection of potentially suitable habitats for long-term preservation4. Genetic variability within the species is essential for species survival by securing its evolutionary and adaptive potential which result in populations’ capability of responding to new environmental conditions18. Reintroductions and/or restocking, as an approach for conservation of endangered species, are frequently disputed20, but they are still emphasized as management strategies for the conservation of indigenous crayfish species21. For successful conservation, greater insight into genetic diversity and structure of endangered crayfish populations is needed4. Microsatellites are among the most popular and versatile genetic markers with wide applications in population genetics, conservation biology, and evolutionary biology22. Population genetics analyses using microsatellites have been used successfully in several studies of A. astacus by providing insights into patterns of contemporary genetic diversity and structure, gene flow, effective population sizes, evolutionary history, and fates of introductions24. Conservation of indigenous crayfish species does not only include the assessment of a population’s genetic diversity in order to obtain restocking material for restoring their populations, but also includes the selection of potentially suitable habitats/sites in the future. The translocation of crayfish for reintroduction or restocking has been attempted many times across Europe, but the low rate of this measure’s success showed the need to improve site selection4. Species distribution modelling, also known as ecological niche modelling, is increasingly suggested as part of conservation decision making by forecasting environmental suitability for an endangered species26. Species distribution models require georeferenced biodiversity observations  and geographic layers of environmental information . This approach represents a useful tool for selecting suitable habitats for conservation actions, such as translocation, and selection of sites for protection, and at the same time taking into consideration impacts of invasive species and climate change on species and habitats30. Global climate changes impact the size and extent of areas that may potentially be inhabited by many species32. Moreover, climate change is causing distributional shifts of many species worldwide due to altering environmental conditions to which they are adapted32. Factors like increasing water temperatures and long-term droughts could, undoubtedly impact dispersal‐limited freshwater crayfish impairing their survival33, while favouring the future spread of warm-water adapted NICS34.Recent substantial declines and local extinctions of indigenous European crayfish populations have highlighted the need for developing appropriate conservation programmes and policiesA. astacus genetic diversity. To provide such a baseline for conservation programs, the aims of our study were:A. astacus from 17 localities in Croatia , and to identify areas of their potential current and future distribution overlap in Croatia using SDM;To assess potential suitable habitats for the current and future period under different climate change scenarios for endangered A. astacus with its potential future distribution areas, as well as with future potential distribution of both NICS in order to identify populations and areas of the highest conservation value and priority for protection.To combine genetic data from Maintaining genetic diversity in an indigenous species is a pivotal goal at the global (European) and local (Croatian) levels (EU-Biodiversity strategy for 2030). Therefore, the present study has combined population genetic analyses and SDM as a guide for the future conservation actions of A. astacus. Genetic characterisation will help identifying populations that should be given the highest priority in conservation, and which can also serve as suitable donor populations for possible repopulation and reintroduction programs not only in Croatia, but also in other European countries. Furthermore, we will be able to define areas and habitats that will be under the greatest pressure from NICS and climate change, as well as potential ark sites for this species long-time survival.We expect that a combination of SDM and genetic data will provide the required information needed to develop conservation programs for endangered A. astacus were described by the Median-joining (MJ) networks   Fig. . The mos9. No evidence of linkage disequilibrium between pairs of loci tested over all populations was detected after Bonferroni correction (p = 0.0004), hence all microsatellites were considered as independent markers. Likewise, no signs for genotyping error due to stuttering or large allele dropout were observed as assessed by MICRO-CHECKER software. Null alleles were detected within several loci and populations . Furthermore, no locus showed null alleles across all populations and a bias on our evaluation of the population structure due to null alleles was very unlikely. Therefore, all subsequent analyses were conducted using the original data set.The final data set for the microsatellite analyses comprised 413 samples and 15 microsatellite loci; 269 successfully genotyped noble crayfish samples from 12 populations in this study, and reference data from five populations obtained in Gross et al.E from 0.355 (MAK) to 0.684 (ILO)  to 0.630 (ILO), and the HE from 0.355 (MAK) to 0.684 (ILO). HE and HO averaged to 0.560 and 0.524, respectively. The inbreeding coefficient per population was low to moderate and statistically not significant in the majority of populations (FIS = -0.027 up to 0.198), so the intra-population variability was still evident. Only populations SLO and VEL exhibited statistically significant higher values of FIS  indicating homozygote excess/heterozygote deficit. Significant deviations from Hardy–Weinberg equilibrium (HWE) were observed in four populations . Deviations from HWE were accompanied by positive FIS values, indicating the heterozygote deficit and homozygote excess. Also in those populations, null alleles were detected  to 0.556 (between MAK and BUR), with the global FST = 0.319  MOT, BED, JAN; II) BRE, VUK; III) BUR, GLO, KIK, JAR; IV) ILO, OTU, BIJ, SLO, KUT, VEL; and V) MAK, TOT . Individuals were assigned to a certain cluster if their assignment probability was ≥ 0.8, where individuals with membership to a cluster below this threshold were considered to be admixed. Most individuals showed a high assignment to one genetic cluster. The cluster I included individuals from populations MAK, TOT, JAN and BED, whereas the cluster II comprised crayfish from populations MOT, BRE, BUR, ILO, BIJ, GLO, KIK, SLO, KUT, VEL, JAR, VUK and OTU  Fig. , where tIn order to reveal partitioning of genetic variance by AMOVA, populations were grouped according to their affiliation to the genetic clusters inferred by the Bayesian clustering analysis Fig. . The res36. All SDMs for all species had excellent performance following interpretations for AUC values given in the literature38, with AUC > 0.9, regardless of the method used  AUC values were 0.999 for both species.We evaluated model performance using area under the receiver operating characteristic curve (AUC)A. astacus habitat suitability values  largely corresponded to current known distribution of this species in Croatia  of COI haplotypes recorded in Croatia may be lost   Fig. . Majorit24) Fig. .A. astacus, as well as the impact of climate changes and invasive species on its populations. Analyses of genetic data coupled with species distribution models revealed the vulnerability of this keystone species to climate change.In our research, the fine scale phylogenetics, population genetics and species distribution modelling were used to explore genetic diversity and structure of A. astacus consistent with the findings of previous studies9. Our results confirmed the existence of several genetic lineages, with the indication of novel divergent lineage containing haplotypes from Croatia and Slovenia (Hap51/Lsh18 and Hap61/Lsh19). Phylogenetic analysis indicates that all Croatian haplotypes belong to two mtDNA lineages  that were also recorded in different countries across Europe. Astacus astacus exhibits lower mtDNA diversity and lower genetic structuring, without an obvious geographical pattern7, compared to other native European crayfish species42. Precisely, the MJ network showed weak phylogeographic structure and high haplotype-sharing even between geographically distant populations. This finding is consistent with the results of previous studies11 showing that the contemporary distribution and genetic structure of A. astacus were shaped through past geo-climatic events, strong anthropogenic influence on its habitat and frequent human mediated translocations that partly eroded their genetic structure. Similarities between distant A. astacus populations in several cases were explained by artificial stockings from different countries or populations11. Such a case was also observed in our study; crayfish from population JAR were used for aquaculture in the geographically distant hatchery Otočac, and consequently samples from both populations belonged to the same mtDNA lineage and shared the same haplotype (Lsh2). The genetic lineages of A. astacus diversified during the late Pliocene and throughout the Pleistocene, within the period between 1.7 and 0.5 mya9. Current A. astacus lineage distribution shows a divergence pattern congruent with the phenomena of insularity and isolation of multiple southern glacial refugia during repeated climatic pulses in the Pleistocene that produced a mosaic of lineages43.The phylogenetic network based on mtDNA displayed intraspecific relationships within A. astacus across the sampled localities revealed high within-population genetic diversity and moderate differentiation among populations, that differed from the results of previous studies using the same9 or different microsatellite loci24. Overall, we detected a high number of alleles, proportion of polymorphic loci (P), allelic richness (AR) and observed heterozygosity (HO) in the study area. Genetic diversity, expressed as the P, AR and HO was higher in populations ILO, BED, KUT, VEL, while the level of genetic diversity was lower in populations MAK, VUK, BUR. Reduced genetic diversity in the populations MAK and BUR could be explained by the fact that they represent introduced populations45. Overall genetic diversity across the sampled localities of A. astacus was high when compared to the results of Gross et al.10, Schrimpf et al.11, Laggis et al.8 and Panicz et al.24 that used different set of microsatellite loci. A considerable number of private alleles was found in the majority of populations suggesting the presence of the unique genetic variation. Besides, private alleles are considered important in the long-term response to selection and the survival of populations and species46. We found that two populations (SLO and VEL), with significant homozygote excess, are vulnerable to inbreeding which may reduce the populations’ genetic diversity, and consequently lead to the loss of adaptive evolutionary potential of the species47. Furthermore, we analysed whether the recent bottleneck events influenced the observed genetic structure of the studied populations, and found that two populations did experience a recent bottleneck event (JAR and TOT). Bottlenecks in small remnant populations with limited gene flow could lead to low effective population sizes and cause fitness reductions across at least part of the species distribution. In Croatia, as elsewhere in Europe, A. astacus populations are mostly isolated by natural  or artificial  barriers, and their distribution being frequently limited to small fragmented areas . Therefore, there is a reasonable concern that they may undergo significant declines in effective population size and that much of their genetic diversity might be lost.Population genetic analyses on A. astacus populations in Croatia by identifying two main genetic clusters. Moreover, they revealed the presence of admixed individuals/populations assigned to different genetic cluster reflecting contributions of different ancestral groups or artificial translocation. Furthermore, results indicated to some extant populations’ structuring according to different river basins what is similar to that found by Gross et al.9. The pairwise FST values and AMOVA indicated moderate to high levels of genetic differentiation among studied populations demonstrating isolated populations with limited gene flow. The most genetically differentiated populations were MAK, TOT, JAN and VUK when compared to other studied populations. Contrary to other populations, the Vuka River (population VUK) flows directly into the Danube River which may explain the high FST. Lower FST values obtained in this study reflected well geographical proximity, with the exception of populations OTU and KUT. The native range of A. astacus is restricted to the rivers of the Black Sea basin, whereas population OTU belongs to the Adriatic Sea basin. Thus, low FST value obtained for this population pair could indicate anthropogenic translocation between those two populations. Likewise, high values of FST for MAK populations could also be explained by artificial stockings from an unknown source. Moreover, it should be pointed out that MAK is recorded in an urban lake in Zagreb City, and that it may have been introduced from the Sava River where Karaman48 recorded A. astacus. Therefore, it is possible that this population represents a remnant astacofauna formerly present in the Sava River, with unique genetics that no longer exists elsewhere. This study discovered a higher value of global FST (= 0.319) compared to the study of A. astacus in central and northern Europe by Gross et al.  and Schrimpf et al. , but a lower value than that was found by Laggis et al.  and Gross et al. , in A. astacus populations in Greece and across the Balkan Peninsula, respectively. A pattern of isolated populations of freshwater species that contain high genetic diversity is characteristic for the Balkan Peninsula, that is recognised as one of the freshwater biodiversity hotspots50. Currently this region is characterised by fragmented and complex habitats with frequently no suitable surface water connections. Therefore, restricted dispersal and gene flow among populations probably led to genetic isolation of numerous freshwater species in this area, including crayfish. However, limited gene flow may lead to reduced effective population sizes, lower genetic diversity and increase the risk of local extinction, resulting in cascading effects through freshwater ecosystems51. Moreover, geographically isolated populations with low dispersal capabilities such as crayfish could experience problems in accommodating to ongoing climate changes due to limited possibilities for migration and a shift in their distribution toward more climate-suitable habitats.The results of STRUCTURE and PCoA indicated the presence of genetic structuring among 52, and vulnerability of freshwater crayfish to climate change, as well as to NICS has been demonstrated in many studies33. To evaluate the impact of climate change and NICS on the endangered A. astacus, we performed SDM. The models were able to capture the known ranges of A. astacus and two NICS in Croatia. Our predictions are concordant with previous studies of A. astacus distribution in Croatia6; majority of areas currently suitable for A. astacus are located in the area of Continental Croatia, including parts of Alpine Croatia and small isolated areas in Mediterranean Croatia where species was introduced6. Likewise, current projections for NICS, F. limosus and P. leniusculus, revealed highly suitable habitats corresponding to their present distribution in Croatia, but also encompassing areas for their potential spread. Our current projections suggested overlap between suitable habitats for A. astacus and P. leniusculus, a competitor which negatively affects A. astacus populations in the rivers of the continental part of Croatia through competitive exclusion and A. astaci transmission15. On the contrary, modelling the current potential distribution of F. limosus in Croatia did not detect any overlap between current suitable areas with A. astacus, as expected, since waterbodies of eastern Croatia are inhabited by P. leptodactylus6.Sensitivity to climate change in freshwater taxa was proved to be higher than in terrestrial taxaA. astacus by reducing the surface of climate-suitable areas available for this native European species. This result is in line with findings for other endangered aquatic species in Europe54. Change in thermal and precipitation regimes caused by global warming will probably lead to drastic range contractions of A. astacus. Consequently, this could drive population declines across the species distribution range in Croatia. This conclusion supports the alarming studies of Capinha et al.34 and Hossain et al.52 that predicted extreme loss of habitat suitability for freshwater crayfish due to climate change. Thus, our results indicate that climate change‐driven habitat loss represents a greater threat to A. astacus than the potential future distribution of the two studied NICS. A similar scenario was found for Austropotamobius pallipes in relation to the invasive P. leniusculus .Overall, our future projections demonstrated that climate change may have major negative effects on the distribution of A. astacus will likely shift towards the north-west and practically disappear from the easternmost parts of Croatia due to the severe reduction (87% of currently suitable habitats) in habitat suitability by 2070. Furthermore, the most suitable areas for A. astacus in the future were forecasted to be in the western Croatian waterbodies, some of which are at high altitudes where Austropotamobius torrentium is currently recorded6. Even though the Alpine region and its freshwater ecosystems represent suitable habitats for most crayfish species53, these two indigenous species might compete for habitats and resources29. To overcome this, potential ark sites for A. astacus should be placed in the rivers and artificial lakes at lower altitudes in the Alpine region, as well as within gravel pits and oxbows alongside the Drava and Sava Rivers in the north-western part of Continental Croatia that were predicted as suitable in the future. These lower altitude waterbodies of the Alpine region would provide suitable ark sites for the crayfish from mtDNA Lineage 2 and/or Genetic cluster II, while crayfish from mtDNA Lineage 4 and/or Genetic cluster I could find refugia within gravel pits along the Drava and Sava Rivers in the north-western part of Continental Croatia. Keeping in mind that those suitable habitats are inaccessible to A. astacus due to natural dispersal barriers, human interventions would be needed. Assisted migration (AM) as an adaptation strategy for mitigating the projected effects of climate change on species is widely proposed21, especially for those with a life history features that prevents them from migrating to suitable habitats. However, it is a controversial topic among conservation biologists, with numerous identified risks. Arguments against AM include: risk of translocated species becoming invasive with associated negative biological, ecosystem and socioeconomic effects; spread of diseases and pathogens that can be transferred into new host species; removing individuals from existing populations increases the extinction risks facing those source populations55. In order to overcome those arguments, careful planning encompassing risk assessments, cost–benefit analyses, conducting AMs on a small scale, with robust monitoring that would enable prompt corrective actions to be taken if needed, along with political and public promotion, could insure successful AM implementations21.Future SDM projections suggested that the suitable habitat for 56; (c) the naturalised climatic niches of NICS that can differ from their natives’ climatic niches28; and (d) underestimated potential range expansion in the future due to the known issue of non-equilibrium of NICS with the environment within the invaded range57. Nevertheless, our continuous future habitat suitability projections showed that, even though the projected future suitable areas for NICS were more extensive than the current ones, drastic decrease in habitat suitability values for both NICS were displayed under future climate predictions. Explicitly, potential areas where A. astacus would overlap and compete with NICS virtually disappeared by 2070 under both climate change scenarios of high-warming (RCP 8.5) and low-warming conditions (RCP 4.5). This result is consistent with the results of Préau et al.30 showing no overlap between future suitable areas for A. pallipes and P. leniusculus in France based on SDMs, despite substantial ecological niche overlap between the two species. Likewise, Gallardo & Aldridge54 found that both endangered A. pallipes and invasive P. leniusculus were predicted to be negatively affected by climate changes in Europe. However, the range contraction was predicted to be more dramatic for the invasive P. leniusculus, leading to decreased overlap and consequently competition between the two species in the future, particularly in our study area. A more recent study by Zhang et al.58 confirmed that invasive P. leniusculus may lose a substantial portion of suitable habitat in Europe by 2070 in response to climate change. Moreover, Capinha et al.34 studied the potential distribution of indigenous crayfish species and NICS in Europe and found that climate-suitable areas were predicted to decrease by nearly 70% for A. astacus, 42% for P. leniusculus, and 49% for F. limosus by 2080. However, their models predicted that overlap of suitable ranges for native European crayfishes and invasive crayfishes would increase in the future which is contrary to our results. This may be because south-eastern Europe seems to be less suitable for P. leniusculus under changing climatic conditions58. It is therefore crucial to continue the monitoring of NICS invasions in the future.Regardless of the RCP scenarios, our binary projections did not forecast any suitable areas for NICS in the future which should be interpreted with caution due to (a) the small number of available occurrences for NICS in the Croatian waterbodies used for SDMs; (b) models that do not account for human-mediated dispersal of NICSA. astacus genetic variability, especially in the eastern part of Continental Croatia that may potentially lose populations with high and unique genetic diversity. Minimising such possible losses in the future requires viable A. astacus populations to be established and maintained in ark sites/climate change refugia. Our results exposed an alarming need to prioritise conservation planning and management that will support existing populations and potentially establish new ones in the areas of stable habitat suitability that are expected to sustain A. astacus into the future. Species responses to climate change will depend on their distribution shifts to accommodate climate changes, and/or rely on the adaptation based on the standing genetic variation. Keeping in mind low dispersal abilities and isolated populations, we argue that assisted migration and population mixing approaches will be probably needed in the future to enhance the size and genetic diversity of remnant populations in order to maintain the long-term survival of the species59. Based on our results, we propose several donor populations for future restocking and reintroduction strategies. Namely, populations ILO, KUT, VEL, BAČ and BIJ, contain high and unique genetic diversity both at the mitochondrial and nuclear level, but they are predicted to be lost due to unsuitable habitats in the future. Dispersal as a fundamental behavioural mechanism is of great importance for adaptation and species’ responses to rapidly changing climate26. Strong dispersal limitations, habitat discontinuities and limited gene flow have a major effect on the ability of crayfish populations to withstand climate changes. Thus, assisted migration in climate change refugia seems a logical solution for slowing down genetic diversity erosion, reducing genetic load and the detrimental consequences of inbreeding, but also allows variations in allele frequencies62.Estimated reduction in habitat suitability by the end of this century indicates potential loss of a significant portion of the A. astacus into restored waterbodies has become common practice, even though the genetic origin of stocking material has rarely been considered10. Thus, potential ark sites should represent areas that maintain the highest contemporary genetic diversity in the species and predicted climate-suitable habitats for the future. Astacus astacus relocation should be preceded with a careful assessment regarding potential negative consequences of assisted gene flow that can impact the success of relocated populations, particularly when populations exhibit local adaptation to factors other than climate64. Also, introgression between local and translocated populations could result in outbreeding depression61. Still, Bláha et al.23 found no significant decline in genetic diversity between the source and translocated A. astacus populations after introduction. Furthermore, their study showed that even though the source populations did not possess high genetic diversity, their distinctiveness still made them suitable for conservation purposes. In addition, it is critical that climatically suitable sites outside A. astacus historical range for conservation purposes should be free from diseases, such as crayfish plague caused by oomycete Aphanomyces astaci.The adoption of such approaches for conservation purposes has gained significant momentum over the last few decades; reintroduction of the A. astacus will require significant interventions. This paper provides a baseline to guide these actions. Specifically, SDM combined with population genetics provided essential guidance for conservation actions aimed at safeguarding endangered A. astacus in Croatia by revealing genetic structure and identifying sites most suitable for protection and sites where climate change constitutes a threat. In addition, our study corroborates SDM as a valuable tool for conservation planning of threatened crayfish species by identifying areas within a species' distribution that may be vulnerable and suitable areas for assisted migration as shown in studies on European crayfish52, A. pallipes complex65, and A. torrentium3.In conclusion, our results suggest that securing the future of A. astacus samples across its entire distribution range in Croatia , purification and sequencing were performed according to Gross et al.9. Sequences were edited and aligned in Bioedit v. 7.2.569. The final COI alignment did not contain any length variants or ambiguous sites and its final length was 623 bp, while 16S alignment contained 1 length variation and its final length was 475 bp. In order to perform phylogenetic analysis comparable with previous studies of A. astacus, COI sequences (trimmed to 350 bp length) and 16S sequences (475 bp length) from the same individual were concatenated . Sequences were collapsed to unique haplotypes using FaBox70, and all newly obtained haplotypes were submitted to GenBank  from Croatian populations obtained in this study and in the study by Gross et al.9. Median joining network II included concatenated 16S + COI sequences (825 bp long) obtained in this study and assembled with all available sequences at European level68. This approach enabled us to associate haplotypes obtained in the present study to the haplotypes obtained in previous research and indirectly to the lineages sensu Schrimpf et al.7 and groups sensu Laggis et al.8.Median-joining (MJ) network approach74, and following modified protocols and procedures as in Gross et al.9. Microsatellite loci were genotyped on Applied Biosystems 3500 XL Genetic Analyser  using internal GeneScan 600 LIZ Size Standard v2.0 . Genotypes were scored using GeneMapper v.5 software , and were double-checked manually by two experts. Since four loci had overlapping allele size ranges  they were omitted from further data analyses which were performed using 15 microsatellite loci. Also, several samples from different populations with more than two non-amplified loci were omitted from further analysis. Microsatellite loci were tested for potential presence of genotyping errors due to null alleles, stutter peaks and large allele dropout using MICRO-CHECKER v.2.2.375. Pairwise linkage disequilibrium between all pairs of loci was tested using Fisher's exact test in GENEPOP v. 4.7.276. Null allele frequencies based on the expectation–maximization (EM) algorithm77 and corrected FST values using the ENA method were estimated using FreeNA35 with a number of bootstrap replicates fixed to 10,000. The estimations of FST, with and without null allele correction, were compared for each population using t-test in STATISTICA 13 (StatSoft. Inc).For microsatellite analyses we amplified 19 species-specific tetranucleotide repeat microsatellite loci developed by Gross et al.A. astacus populations obtained in this study that were supplemented with the microsatellite genotype data of five Croatian A. astacus populations  from the study by Gross et al.9 in order to enlarge data set and make analyses more robust.Population genetics analyses were conducted with the microsatellite genotype data of 12 78. Statistics included the percentage of polymorphic loci (P), mean number of alleles (NA), observed heterozygosity (HO) and unbiased expected heterozygosity (HE). Further, FSTAT v.2.9.479 was used for estimation of allelic richness (AR) that was calculated as the number of alleles per locus independent of sample size, and the inbreeding coefficient (FIS).Population genetic diversity was assessed with standard descriptive statistics using GenAlEx v. 6.51PR) was estimated by rarefaction method using HP Rare v.Feb-2-200980 and multiplied by the number of used loci. Deviations from the Hardy–Weinberg equilibrium (HWE) for each population across all loci were tested using GENEPOP v. 4.7.276. All probability tests were based on the Markov chain algorithm using 10,000 dememorization steps, 100 batches and 5000 iterations per batch. Significance levels were adjusted applying the Bonferroni correction to correct for the effect of multiple tests. Recent reductions in the effective population size using allele frequency data and potential signatures of recent bottlenecks were tested using the heterozygosity excess method implemented in BOTTLENECK v.1.2.0281 under three different mutational models: infinite allele model (IAM), stepwise mutation model (SMM) and two-phase model (TPM). Significant deviations from mutational-drift equilibrium were tested using the Wilcoxon sign rank test with 10,000 simulations.The number of private alleles  was assessed using the Bayesian model-based clustering approach implemented in STRUCTURE v.2.3.4.82. The conditions performed were 10 runs for each genetic cluster (K) between 1 and 17 using a 100,000 burn-in period followed by 100,000 MCMC iterations, under the admixture model, with correlated allelic frequencies. The number of optimal K was inferred using the protocol defined by Evanno et al.83 as implemented in STRUCTURE HARVESTER v. 0.6.9384. STRUCTURE graphical results were plotted with CLUMPAK85. In addition, structure in the distribution of genetic variation was visualized by principal coordinates analysis (PCoA) using Nei's genetic distance in GenAlEx v. 6.51. Hierarchical analysis of molecular variance (AMOVA) was carried out using ARLEQUIN v. 3.5.1.286 in order to estimate partitioning of genetic variance among groups, among populations within groups and within population. Populations were grouped according to their affiliation to the genetic clusters inferred from STRUCTURE; The cluster I included individuals from populations MAK, TOT, JAN and BED, and the cluster II comprised crayfish from populations MOT, BRE, BUR, ILO, BIJ, GLO, KIK, SLO, KUT, VEL, JAR, VUK and OTU  from across Croatia from our own published and unpublished field sampling6. This resulted in a total of 174 occurrences for A. astacus, 22 for F. limosus and 17 for P. leniusculus  elevation data (https://www2.jpl.nasa.gov/srtm), while the variable percentage of forest cover in 1 km2 was calculated from the Corine Land Cover 2018 dataset (https://land.copernicus.eu/pan-european/corine-land-cover). All environmental variables were used at a spatial resolution of ~ 1 km2. Predictor variables for SDMs of A. astacus and the two NICS were then selected based on our expert knowledge about their ecological relevance for the target species , excluding highly correlated ones based on variance inflation factor, VIF < 10 . Thus, the final predictor set for A. astacus included ten, and for NICS nine environmental variables , we developed SDMs using an ensemble approach implemented in R package BIOMOD2 ver. 3.3-790. For each species we applied three different modelling methods  with ten replicates for each method . Occurrences were combined with 10,000 random pseudo-absences drawn across the study area for methods that require absences91. In each run, 70% of the occurrences were used for model calibration, and the remaining 30% were used for model evaluation using AUC36.To assess the potential current and future habitat suitability of 37 and obtained this ensemble as an AUC weighted average. The obtained current ensemble model was then projected under both current and future environmental conditions to obtain potential habitat suitability maps for each species. For future projections we used two RCP scenarios (mid-range emission scenario RCP 4.5 and pessimistic scenario RCP 8.5) and four global circulation models (GCMs) suitable for Europe92  for the 2070-time period (average for 2061–2080). Since future projections for variables slope, altitude and forest cover were not available, we kept them as constant in our future projections, assuming that they will not change for our study area during the considered time period. The available data on forest cover change in Croatia during the last decades and current forest management structure and practices provide confidence that at least for our study area, forest cover may remain stable in the future93 [https://forest.eea.europa.eu]. An ensemble future projection for each RCP scenario was obtained by taking an average of the four GCM projections. Multiple RCPs and GCMs were used to address the associated uncertainties arising from different climate change predictions94.To build the current ensemble model we used only highly reliable models with AUC > 0.9A. astacus, we applied a threshold maximizing the sum of sensitivity and specificity95 to ensemble current and future continuous habitat suitability maps.To obtain binary presence/absence maps helpful in model interpretation and for calculating changes in habitat suitability for A. astacus with its potential current and future suitable areas, as well as with future potential distribution of both NICS.Finally, to estimate the effects of climate change on genetic diversity and structure of the focal species, we overlapped genetic data of Supplementary Table S1.Supplementary Information.Supplementary Legends.


Plasmodium falciparum, coupled with a low‐threshold, Cas9‐based gene drive. When released into a wild vector population, GEMs preferentially transmit these parasite‐blocking genes to their offspring, ultimately modifying a vector population into a nonvector one. Deploying this technology awaits ecologically contained field trial evaluations. Here, we consider a process for site selection, the first critical step in designing a trial. Our goal is to identify a site that maximizes prospects for success, minimizes risk, and serves as a fair, valid, and convincing test of efficacy and impacts of a GEM product intended for large‐scale deployment in Africa. We base site selection on geographic, geological, and biological, rather than social or legal, criteria. We recognize the latter as critically important but not as a first step in selecting a site. We propose physical islands as being the best candidates for a GEM field trial and present an evaluation of 22 African islands. We consider geographic and genetic isolation, biological complexity, island size, and topography and identify two island groups that satisfy key criteria for ideal GEM field trial sites.Novel malaria control strategies using genetically engineered mosquitoes (GEMs) are on the horizon. Population modification is one approach wherein mosquitoes are engineered with genes rendering them refractory to the malaria parasite,  These GEMs are designed to offer safe, cost‐effective, and sustainable malaria control in sub‐Saharan Africa. This will be achieved using a population modification strategy . Henceforth, when we refer to a GEM, we mean a mosquito engineered with anti‐Anopheles coluzzii and A. gambiae, and is ultimately intended for malaria elimination throughout sub‐Saharan Africa. Early field trials will provide critically important data to inform subsequent trials and influence decisions regarding large‐scale deployment. These data include factors that cannot easily be assessed in caged populations because cages do not adequately replicate natural conditions and cannot assess phenomenon on a meaningful spatial scale. We consider a trial successful when the quality and utility of the data it generates justifies the time, effort, and cost that goes into conducting it. Phase 2 field trials are intended to measure entomological endpoints including GEM survival, mating competitiveness, gene drive spread, and construct stability  Isolation Index, which is calculated as "the sum of the square roots of the distances to the nearest equivalent or larger island, the nearest group or archipelago, and the nearest continent Dahl, .” The hiThe third isolation index is surrounding landmass proportion (SLMP) where the isolation of the focal island is proportional to the area of the surrounding landmass  generated 167 genomes  following our established protocol  <1%. In addition, population structure analysis was based on chromosome 3 SNPs only. This was done to avoid confounding signals from polymorphic inversions on chromosomes 2 and X  was calculated in nonoverlapping windows of 10 kb on euchromatic regions of chromosome 3 using VCFtools  after pruning for LD using scikit‐allel v1.2.0  number of international departures from airports within a specific country were obtained from CIRIUM, an aviation data analytics provider  is an important parameter influencing the biology of populations residing on an island. Large island areas typically include more habitat types and can support larger populations. This characteristic can increase the rate of speciation and lower extinction rates over time . The position of individuals in the space defined by the principal components can be interpreted as revealing levels of genetic similarity/dissimilarity among the populations from which those individuals were sampled. Populations occupying the same space are presumed to be very similar genetically and those widely separated, very different.A. gambiae populations are illustrated in Figure A. coluzzii  can be thought of as including the genotypes of the individuals doing the moving and, in this context, migration results in gene flow. Genetic divergence can be described using the statistic FST, which is the genetic variance in a subpopulation (S) relative to the total variance (T). FST values range between 0 and 1 and are higher when populations are considerably diverged. The relationship between FST and m is complex, but excluding the effects of drift and selection, the more gene flow between two populations, the lower the FST value. All pairwise FST values for the populations of A. gambiae and A. coluzzii analyzed in this study  between two populations can be approximated by measuring the level of genetic divergence between those populations. Migration . Divergence between Bioko island and the nearest mainland in Cameroon is higher (FST = 0.036). The pattern is quite different in east Africa, wherein mainland populations are far more diverged (FST = 0.033–0.090). This pattern is consistent with the theory that A. gambiae originated in west Africa and dispersed eastward through a series of population bottlenecks . Considerably higher divergence is observed between the Comoros islands and the nearest mainland populations in Tanzania (FST = 0.130–0.169) and between the Comoros and Madagascar (FST = 0.126–0.196).Pairwise FST values for populations of A. coluzzii are likewise consistent with the PCA . Populations of A. coluzzii on the oceanic islands of São Tomé and Príncipe were, by far, the most genetically isolated from mainland populations . In addition, the two islands were highly diverged from each other (FST = 0.130). The islands within Lake Victoria were excluded from consideration because the A. gambiae populations residing on them lacked the high level of divergence that would indicate genetic isolation.The A Figure . DivergeTaken together, the data summarized in Figure 3.5A. coluzzii and A. gambiae is generally high suggesting that dispersal off the islands is low. Nonetheless, dispersal that may occur is most likely to rely on anthropogenic conveyance  species present on island sites and select locations on the mainland are illustrated in Figure Anopheles species are present, positively impacting the time and resources required for successful assessment.Assessment of entomological endpoints following a GEM release requires repeated mosquito collections to quantify changes in the ratio of GEM to wild‐type mosquitoes. This necessitates sorting large numbers of individual field‐collected mosquito samples to separate target from nontarget species. For members of sibling species complexes, which are morphologically indistinguishable, this requires the application of PCR‐based diagnostics to each individual specimen. If collections include larvae, time‐consuming microscopic examination or individual PCR assays to identify species is required even for those species distinguishable morphologically in the adult stage. Logistically these procedures are greatly simplified where fewer nontarget Although entomological endpoints are the main consideration in evaluating the outcome of a PHASE 2 trial, epidemiological impacts should be considered where feasible. If epidemiological endpoints are to be assessed, the presence of multiple primary and secondary vectors is problematic as they can lengthen the season of malaria transmission  had between 14 and 21 species. As expected, oceanic islands contained far fewer, ranging from 1 to 7 species. These results favor the selection of the oceanic islands, São Tomé and Príncipe, Annobón, and the Comoros, for field trials. The oceanic islands including the Canary Islands, Cape Verde, Mauritius, and Reunion likewise had low numbers of anopheline species, but these were excluded because our target species 3.7A. gambiae‐ 0.80%, A. coluzzii‐ 0.88%) were significantly lower (p < 0.0001) than mainland population means  for both species , likely due their small size and high degree of isolation.Genetic complexity was measured using the nucleotide diversity statistic (π), defined as the average number of pairwise nucleotide differences per nucleotide site. Mean nucleotide diversities (π) on oceanic islands  can be elevated from “potential” to “candidate” GEM field trial sites. The Mascarene (Mauritius and Réunion) and Cape Verde Islands fit many criteria, but Therefore, we propose the following as the lead candidate sites for a PHASE 2 GEM field trial: the Comoros Islands, São Tomé and Príncipe, and Bioko.4Our early decision to consider physical islands as the ideal sites for a GEM field trial was guided by contemporary IBT. This theory provides the basis for certain expectations concerning species richness, in our case, anopheline species richness and also features such as genetic isolation and diversity. Consistent with IBT, anopheline mosquito species richness was lowest on small, isolated oceanic islands, higher on continental islands, and highest at mainland continental sites Figure . Our resThe framework described here has been applied by the University of California Irvine Malaria Initiative (UCIMI) as they enter PHASE 2 of GEM research. It is our belief that this comprehensive framework provides identification of site(s) that will maximize the prospect for success, minimize risk, and will serve as a fair, valid, and convincing test of the efficacy and impacts of the UCIMI GEM product, meeting the goal of a PHASE 2 field trial. Furthermore, this process provides a well‐reasoned, science‐based justification for selecting these sites for GEM field trials and a solid foundation on which to approach ethical, social, and legal considerations with field site stakeholders.The authors declare no conflict of interests.Fig S1Click here for additional data file.Fig S2Click here for additional data file.Fig S3Click here for additional data file.Supplementary MaterialClick here for additional data file.Table S1Click here for additional data file.Table S2Click here for additional data file.Table S3Click here for additional data file.Table S4Click here for additional data file.Supplementary MaterialClick here for additional data file.


Acanthisitta chloris granti). While this species has a historically widespread distribution, anthropogenic forest clearance has resulted in a highly fragmented current distribution. We conducted analyses of mitochondrial DNA (COI and Control Region) and 12 nuclear DNA microsatellites to test for population divergence and estimate times of divergence. diyabc and biogeobears were then used to assess likely past dispersal scenarios based on both mtDNA and nDNA. The results reveal several significantly divergent lineages across the North Island of New Zealand and indicate that some populations have been isolated for extensive periods of time (0.7–4.9 mya). Modeling indicated a dynamic history of population connectivity, with a drastic restriction in gene flow between three geographic regions, followed by a more recent re‐establishment of connectivity. Our analyses indicate the dynamic influence of key geological and climatological events on the distribution of genetic diversity in this species, including support for the genetic impact of old biogeographic boundaries such as the Taupo Line and Cockayne's Line, rather than recent anthropogenic habitat fragmentation. These findings present a rare example of an avian species with a genetic history more like that of flightless taxa and so provide new general insights into vicariant processes affecting populations of passerines with limited dispersal.This research investigates the extent and causal mechanisms of genetic population divergence in a poorly flighted passerine, the North Island Rifleman or Titipounamu ( This research uncovers significant genetic divergence between populations of a basal passerine with restricted dispersal potential, New Zealand's North Island Rifleman. These results demonstrate the impact of ancient biogeographic events on the distribution of this species and present a rare example of an avian species with a genetic history more like that of flightless taxa. This work provides new insights into vicariant processes affecting populations of passerines with limited dispersal more broadly. The Rifleman is a member of the endemic New Zealand wren family (Acanthisittidae), now considered as the sister group to all other passerines  and the Rock Wren (Xenicus gilviventris). Both species have reduced dispersal ability due to their reduced tail and wing morphology and are therefore likely to have limited long‐range dispersal potential  and the South Island Rifleman (Acanthisitta chloris chloris). The North Island sub‐species was once found throughout the North Island of New Zealand  Is there evidence for significant genetic divergence among currently fragmented and isolated North Island populations? and (b) Is genetic isolation of populations likely to be related to past geological and climatological factors, or due to more recent anthropogenic forces of change? Given their low dispersal capability, and their previously widespread North Island distribution, we predicted that North Island Rifleman populations would show evidence of genetic population divergence following an isolation‐by‐distance pattern. We used a combination of mtDNA and nDNA to analyze population genetic divergence and to test several different models of past dispersal to determine which model best explains the current genetic distribution for Rifleman.22.1Six locations spread across the North Island of New Zealand were selected for sampling, including Hauturu‐o‐Toi/Little Barrier Island , Taranaki National Park , Pureora Forest Park , the Maungaharuru Ranges (Boundary Stream Mainland Island) , Mohi Bush , and Tararua Forest Park  160 µl of SET buffer replaced the recommended 200 µl of PBS buffer, (b) 40 µl of Proteinase K replaced the recommended 20 µl, (c) 30 µl of blood in Seutin buffer replaced 10 µl of whole blood, (d) incubation was carried out overnight as opposed to 10 min, and (e) final elution stages were done with two rounds of 100 µl of buffer AE as opposed to 200 µl.2.32, 2.5 µl of BSA, 0.2 mM of dNTP’s, 0.17 µl of Taq Ti polymerase, 1.25 µl of each primer (1.25 µM) and 3 µl of template DNA (10–40 ng/µl). Thermal cycling conditions for amplification involved an initial partial denaturation phase at 94°C for 2 min and 35 cycles of denaturation at 94°C for 30 s, annealing at 57.5°C for 30 s, and extension at 72°C for 30 s, followed by a final extension step at 72°C for 4 min.Mitochondrial DNA (mtDNA) polymerase chain reaction (PCR) targeted two regions of the mitochondrial genome for amplification. A 751 bp region of the cytochrome oxidase 1 gene (COI) was targeted using the forward primer AWCF1  were designed specifically for Rifleman using a published mitochondrial genome from the South Island sub‐species of Rifleman  methods for visual representation of haplotype relationships.Sequences were viewed and aligned manually using Geneious Pro . All variable sites were confirmed by visual inspection of chromatograms. Resulting alignments were edited, and low‐quality ends were trimmed to create a 377 bp alignment for the Control Region and a 652 bp alignment for COI. Separate mitochondrial gene alignments were used for subsequent analyses, plus a concatenated dataset was created by combining Control Region and COI sequences. Variable sites were identified using MEGA (version 5.05) to enable manual haplotype designation. Haplotype (h) and nucleotide (π) diversities Nei, , and meaFST (using haplotype diversities only) and ФST (including nucleotide divergences). Statistical adjustment for multiple simultaneous tests was carried out using false discovery rate correction . Population divergence based on the concatenated COI and Control Region dataset was analyzed in Arlequin using AMOVA, along with pairwise calculations of beast 2.6.2 was used to estimate divergence dates by a coalescent method, based on the concatenated alignment, using reference sequences , 1.5 μl of DNA template (10–40 ng/μl), 0.1 μl of the forward primer (10 μM), 0.4 μl of the reverse primer (10 μM), 0.4 μl of fluorescent dye at a concentration of 10 μM , 0.1 μl of dNTP mix (20 mM), and 0.045 μl (0.125 Units) of Platinum® Taq DNA Polymerase . PCR cycling was performed using the following amplification temperature profile: initial denaturation at 95°C for 1 min, followed by 40 cycles of 95°C for 15 s, 56°C for 30 s and 72°C for 60 s, and a final extension at 72°C for 5 min. PCR products were electrophoresed on a 1.6% agarose gel, stained with RedSafe, to determine whether amplification was successful. Successfully amplified PCR products were diluted 1:10 in ddH2O. 1 μl of diluted PCR product was mixed with 10 μl of HiDi™ formamide (Applied Biosystems) and 0.4 μl GeneScan™ 600 LIZ® Size Standard (Applied Biosystems), in preparation for genotyping. This mix was heat‐shocked at 95°C for 5 min and then cooled at 4°C. Genotyping was performed using an ABI3130xL Genetic Analyzer (Applied Biosystems). Individuals were genotyped based on band length using the microsatellite plugin in Geneious Pro Version 9 (Biomatters Inc.). Genotypes were binned to ensure a constant difference in allele length names of either four (for tetramer repeats) or two (for dimer repeats).Polymerase chain reaction (PCR) was set up to a total volume of 10 μl, containing 6 μl of water, 1 μl of 10× PCR buffer, 0.5 μl of 25 mM magnesium chloride (MgClk) best explains the data , lositan , and genalex . Evidence for the effects of a bottleneck on allelic variation within species was tested using the program bottleneck Version 1.2.02  was used to estimate the number of divergent population groups in the microsatellite data, without location being used as a prior  was used to estimate the number of divergent population groups in the microsatellite data  variation observed within the species revealed significant historical divergences among some populations, but that these genetic divergences may have begun to erode in more recent times. As such, we undertook a test of different biogeographic models of past dispersal, in order to determine whether a model that incorporated a recent change in the pattern of dispersal was the best at explaining the patterns of genetic diversity within the species. Three potential dispersal histories were modeled, based on the geographic pattern of genetic variation and the region's known paleogeographic history. The first null model (H0) simulated the scenario where there were no differences in dispersal restrictions among any locations, and there had been no changes over time. The first alternative hypothesis (H1) simulated the scenario suggested by the evidence of three distinct phylogeographic mtDNA lineages—that is, after initial colonization, there were strong dispersal restrictions between the three main regions , and these were maintained until recent times. The second alternative hypothesis (H2) simulated the scenario suggested by the additional evidence of recent sharing of mtDNA and nDNA between Mainland and southeastern populations, but no recent sharing between the two southeastern locations—that is, after initial colonization, there were strong dispersal restrictions between the three main regions, and this was followed by recent breakdown of the barrier between Mainland and southeastern regions, and a new barrier to dispersal between the two southeastern locations .diyabc 2 distance for microsatellites. The posterior probabilities of each scenario were calculated and compared using the 500 closest simulated datasets in the direct approach, and confidence in the scenario choice was evaluated using 500 datasets from the posterior distribution  were observed that defined 17 different haplotypes among the six geographic regions (4% mean sequence divergence). The mtCR region contained 46 variable sites that were used to characterize 33 haplotypes in total (12% mean sequence divergence).Haplotype and nucleotide diversity varied significantly among populations Table . The InsFew haplotypes were shared among locations for both the COI and Control Region. The two mitochondrial regions displayed slightly different geographic patterns and were thus analyzed both separately and together in a concatenated dataset. Of the 17 haplotypes represented by COI sequences, only a single haplotype was shared between more than one population Figure . The mosThe mtCR exhibited high diversity with only one haplotype shared between any two populations . The phylogeographic patterns seen in the mtCR dataset were somewhat different from those seen in COI, but were highly similar to those displayed in the concatenated dataset, and so only the concatenated data are shown here and are best visualized in a phylogenetic tree Figure  due to tFST and ФST values both showed highly significant isolation among all populations . Pairwise population comparisons were all significant at the p < .05 level for both FST and ФST  and population divergence times varied depending on the assumptions incorporated in the divergence dating analyses Table . Using t3.2No microsatellite loci were found to depart from neutral expectations for tests of selection or departure from Hardy–Weinberg equilibrium. The population with the lowest observed heterozygosity (0.586) and allelic richness (1.55) was the Insular population, which also has low mtDNA diversity. As with the mitochondrial diversity indices, the Southern, Western, and Central mainland populations are the most diverse populations, for both heterozygosity (0.787–0.811) and allelic richness (1.81–1.84). Moreover, despite having very low mtDNA haplotype diversity, the Eastern Coastal population has average nDNA allelic diversity (1.71) and relatively high heterozygosity (0.808). Based on the Bottleneck results, four populations  showed significant evidence for the presence of a bottleneck under at least one model of evolution . Hedrick's F′ST, which is the FST corrected for within‐population variation, was 0.657 across all loci  .3.3The combined genetic results above revealed significant historical divergences in both mtDNA and nDNA among the Insular, Mainland, and southeastern regions. However, the mtDNA data also suggested recent sharing of mtDNA between the Mainland and Eastern Coastal populations, and between the Mainland and Southern populations  using the summary statistic approach of c Figure  showed tc Figure . This wabiogeobears, also showed that model H1 did not confer greater likelihood on the data over the null model H0, and that the model that conferred much higher likelihood across all biogeographic models was H2 , the southeastern (Tararua Ranges and Mohi Bush populations), and the mainland (remainder of the mainland populations) clades. Although the nDNA data largely concur with this, it does suggest a more recent and closer relationship between the Southern (Tararua Ranges) and central mainland populations (see below). The significant levels of inter‐population divergence from our analyses of both mitochondrial and nuclear DNA indicate that North Island Rifleman populations have been separated for extensive periods of time, consistent with geological impacts stretching back into the Pliocene.Our estimates of the divergence times among populations are likely to have some error associated with them. Firstly, they are derived from the mtDNA, which is effectively a single locus, and thus subject to considerable stochastic error  and is consistent with the long geological isolation of this volcanic island (Hauturu‐o‐Toi/Little Barrier), which is estimated to have emerged approximately 3 mya with subsequent eruptions at approximately 1.6 mya . Research on a number of species of both plant and animal land taxa has also demonstrated a pattern of population divergence or genetic diversity variation across this divide. Two predominant biogeographic boundaries have been proposed to explain this pattern across the central North Island  during the Pliocene. This strait effectively restricted dry land to a series of islands in the upper region of the North Island and an emergent range in the southernmost part of the current North Island from approximately 23 mya up until as recent as 1.5–2 mya  and Eastern Coastal (Mohi Bush) populations, accompanied by reduced dispersal between the Southern and Eastern Coastal populations. The nDNA data also suggest that nuclear gene flow has now eroded much of the divergence of the Southern (Tararua Ranges) population, but little of the Eastern Coastal (Mohi Bush) divergence. One reason for this difference between mtDNA and nDNA divergence is likely to be the greater dispersal of males, which will more rapidly break down nDNA divergence among populations. This evidence for changing patterns of dispersal over time concurs with known paleogeographic patterns and may also demonstrate the dynamic nature of forest distribution throughout the late Pleistocene in response to glacial cycles and geological impacts  and to a lesser extent in the Eastern Coastal (Mohi Bush) populations and by the microsatellite data that suggest extensive recent dispersal between the Mainland region and the Southern population. Dispersal modeling using both Populations in the central and north‐west of the mainland (the Mainland clade) were much more closely related to each other than to the southeastern populations. The shallow phylogenetic differentiation of sequences and the star‐shaped COI haplotype network evident within the Mainland clade are both evidence of more recent population expansion Dixon, , possiblClitarchus sp.; Buckley et al., Kikihia sp.; Ellis et al., Dactylanthus ; Data curation (lead); Formal analysis (lead); Funding acquisition ; Investigation (lead); Methodology (lead); Validation (lead); Visualization (lead); Writing‐original draft (lead); Writing‐review & editing . Stuart Parsons: Conceptualization ; Data curation (supporting); Formal analysis (supporting); Funding acquisition ; Investigation (supporting); Methodology (supporting); Project administration ; Resources ; Supervision (lead); Writing‐review & editing . Mark Hauber: Conceptualization ; Data curation (supporting); Formal analysis (supporting); Funding acquisition ; Investigation (supporting); Methodology (supporting); Project administration ; Resources ; Supervision (lead); Writing‐review & editing . Alistair Kendrick: Data curation ; Formal analysis (lead); Investigation ; Methodology (lead); Visualization (lead); Writing‐original draft (supporting); Writing‐review & editing (supporting). Shane Lavery: Conceptualization ; Data curation ; Formal analysis (lead); Funding acquisition ; Investigation ; Methodology ; Project administration ; Resources ; Supervision (lead); Validation ; Visualization ; Writing‐review & editing (lead).Appendix S1Click here for additional data file.


Ne values ranged from ~5900 in the Estonian population to ~2400 in the South Russian population. The divergence times between the Lithuanian and study populations ranged from 240 to 12,871 YBP. We also found evidence of selection in 24 regions, 21 of which have not been discovered in previous analyses of selection. Undoubtedly, the genetic diversity analysis of geographically specific regions may provide new insights into microevolutionary processes affecting local human populations.There are still several unanswered questions regarding about ancient events in the Lithuanian population. The Lithuanians, as the subject of this study, are of great interest as they represent a partially isolated population maintaining an ancient genetic composition and show genetic uniqueness in European comparisons. To elucidate the genetic relationships between the Lithuania and North-Eastern European and West Siberian populations, we analyzed the population structure, effective population size, and recent positive selection from genome-wide single nucleotide polymorphism (SNP) data. We identified the close genetic proximity of Lithuanians to neighboring populations  and in part with West and South Slavs , however, with particular genetic distinctiveness. The estimated long-term  LW*B gene and R408W phenylketonuria mutation together with VNTR and STR haplotypes, Balts are admixed with neighboring Slavic and Finno-Ugric populations. Additionally, Y chromosome STR haplotypes showed significant differences between the Lithuanian and Estonian populations, suggesting different origins or split time before Indo-Europeanization took place in the Eastern Baltic region [Recent genetic research has revealed that Lithuanians represent a partially isolated population maintaining an ancient genetic composition and genetic uniqueness within the European context . Lithuanc region . Otherwic region . Until tc region ,10, whicOur aim was to assess the genetic relationships between the Lithuanian and North-Eastern European and West Siberian populations by analyzing population structure, effective population size, and recent positive selection from whole-genome SNP data. To the best of our knowledge, little attention has been paid to the Lithuanian population elucidating genetic relationship between the Baltic people, who appeared as reference populations consisting of few individuals ,12. A dataset of 425 Lithuanian individuals who indicated at least three generations of Lithuanian nationality was used for analysis. The average age of the participants was ±53 years. In accordance with the Declaration of Helsinki, informed consent was obtained from all the study participants. Genomic DNA was obtained from whole blood using either a standard phenol-chloroform method of extraction or the automated DNA extraction platform TECAN Freedom EVO . A NanoDropR ND-1000 spectrophotometer  was used to assess DNA concentration and quality. This work is part of the ANELGEMIA project, which was approved by the Vilnius Regional Research Ethics Committee No. 2020/6-1243-724, date: 22 June 2020. P < 10−4) were also filtered out. We obtained 424 Lithuanian samples and 532,836 autosomal SNPs. To study genetic variation in broader geographical and historical context, we merged our genotyping data with the genome wide SNP data obtained from Kushniarevich et al. (2015) [N = 425), Latvia (N = 7), Estonia (N = 42), Belarus (N = 8), Central Russia (N = 2), South Russia (N = 4), Finland (N = 20), Khanty (N = 10), Mansi (N = 23), Poland (N = 5), Saami (N = 13), Saami Kola (N = 8), Slovakia (N = 15), and Slovenia (N = 15). The Lithuanian samples were genotyped on an Illumina HumanOmniExpress-12v1.1 (296 samples) and Infinium OmniExpress-24 (129 samples) arrays , which include an overlap of 707,138 SNPs. Genotyping cells and quality control were performed according to the standard manufacturer’s recommendations. The data were filtered with PLINK (v1.07) to remove individuals and SNPs with >10% missing data and SNPs with minor allele frequency (MAF) <0.01. SNPs with deviations from Hardy–Weinberg equilibrium  was performed with SmartPCA from EIGENSOFT (v7.2.1)  on the iNe for the populations under study was estimated using R Package NeON [Ne over time. It utilizes a relationship between the Ne and the average squared correlation coefficient of LD  . STF) [STF values were calculated with vcftools v.0.1.13 [STFP-value < 0.01. The regions under selection were annotated with ANNOVAR [Recent positive selection in the Lithuanian population was estimated using the cross-population extended haplotype-based homozygosity (XP-EHH) test  and the FST) , which wFST) . XP-EHH FST) . FST valv.0.1.13 . We kept ANNOVAR  using GR ANNOVAR , RefSeqG ANNOVAR . The enr ANNOVAR .To investigate the genetic diversity of the contemporary Lithuanian population among North-Eastern European  and West Siberian  populations, we first performed the principal component analysis (PCA) of 105,853 SNPs in 579 individuals . The firThe structure analysis was also supported by the ADMIXTURE plot  . The LitNe from LD patterns in genome-wide SNP using NeON [STF values  and Latvians (LVL), Estonians (EST), Mansi, Belarusians (BEL), Slovakians (SVK), Poles (POL), and Saami from the Kola Peninsula populations. Genome-wide distribution of detected signals between pairs of populations is shown in STFP value < 0.01. Detected signals comprise a total of 24 candidate regions, with signatures of recent selection in the Lithuanian population . The derived G allele at rs6591561 had low frequencies and was found at intermediate frequencies in Latvians and Poles. Interestingly, the rs6591561 variant was associated with reduced CSF sTREM2 (soluble triggering receptor expressed on myeloid cells 2) [NOS2 gene, produces nitric oxide (NO), which is a messenger molecule with diverse functions throughout the body. This gene carries a non-synonymous variant (rs2297518:1823C > T), which is associated with physical performance and longevity [ZKSCAN8; ZNF192P1; TOB2P1; ZSCAN9; ZKSCAN4; NKAPL; PGBD1; ZSCAN31; ZSCAN12; ZSCAN23; GPX6; GPX5; ZBED9) located on chromosome 6 was selected. One non-synonymous SNP (rs12000:287A > G) in the NKAPL gene was identified. The same region encompasses olfactory receptor family genes. Regarding the LT–Saami from Kola Peninsula comparison, one of the strongest signals identified in the Lithuanian population was found at a 70 kb region in chromosome 14, which includes immune regulator genes TCL1B and TCL1A. A non-synonymous variant in TCL1B (rs1064017:277G > A) was identified among the top XP-EHH and STF outliers along the region. This variant could contribute to the extreme IgE phenotype [Most recent positive selection signals were detected when comparing Lithuanians to the Latvians (five regions), Mansi (five regions), and Slovakians (five regions). One of the strongest signals identified in the LT–Mansi comparison was found in a 419 kb region in chromosome 1, which comprises the cells 2) , elevatecells 2) , and acccells 2) . In addiongevity . Among thenotype .SLC34A2, SLC37A1 . In the MF ontology, only one enriched category also satisfied the cut-off criteria, associated with sequence-specific DNA binding with six genes , p-value 0.02. The enriched KEGG pathway was involved in thyroid hormone synthesis accounting with three genes , and P-value 0.01.David 6.8 was used for enrichment analysis. We identified only one significantly enriched category in the BP ontology: GO:0035435, phosphate ion transmembrane transport, counting for two genes STF ≥ 0.05). There are still several unanswered questions about ancient events in the Lithuanian population. In the present research, we analyzed autosomal genome-wide SNPs to elucidate the genetic relationships between the Lithuanina and North-Eastern European and West Siberian populations. Through structure analysis, we both confirmed previously described findings and showed new findings between the Lithuanian and study populations. We identified the close genetic proximity of Lithuanians to neighboring populations  and in part with West Slavs , however, with particular genetic distinctiveness. The Lithuanians overlap with these populations only in one extreme. South Russians were differentiated from the rest of the samples, positioning in the vicinity of Finns and Saami from the Kola Peninsula according to their demographic history. The PCA plot revealed genetic differentiation between Siberian and Saami groups, as those populations harbor specific genetic variation. The observed MANSI distribution in structure analysis and close proximity with the Baltic populations including Slovaks and Slovenians could be explained by detected gene flows between population ancestral to Mansi and North-Eastern European hunter–gatherers that occurred before 6.6–8000 years ago . GeneticThe admixture analysis also showed genetic distinctiveness of Lithuanians as a predominant genetic component (in blue) was found . This coNe in the North-Eastern European, Slavic, and West Siberian populations. The detected Ne of the North-Eastern European populations ranged from 4126 in Latvians to 5967 in Estonians, as expected because the Ne of many Northern populations is about 5000, indicating the bottlenecks associated with the last glacial period in Europe. The smallest Ne was identified in Saami (2323), South Russia (2445), Saami from the Kola Peninsula (3140), Khanty (3298), and Poland (3660). In Slavic populations, Ne was similar to North-Eastern European estimates. The demographic history of each population is shown in We also estimated the long-term The evolutionary relationships among populations visualized in the UPGMA tree from the divergence time matrix showed that separations occurred more recently for populations from the same geographical region . Longer divergence times separates West Siberian populations from non-Siberian populations. Moreover, phylogenetic tree shows more recent separations between East, West, and South Slavs except for the Poles. To the best of our knowledge, the separation patterns described were not reported in previous works using similar methods. TYRP1 gene involved in light skin pigmentation in Europeans [MS4A4A gene and non-synonymous variant rs659156, which are associated with reduced CSF sTREM2 (soluble triggering receptor expressed on myeloid cells 2) [NOS2 gene. Some studies found that this variant is associated with physical performance, longevity, increases risk of primary headaches [MS4A1) and olfactory receptors , affecting odor perception in humans. The enrichment analysis with the selection signatures detected genes involved in phosphate ion transmembrane transport. One of those genes (SLC37A1) is a Pi-linked glucose-6-phosphate antiporter and appears to be involved in breast [We found evidence of selection in 24 regions, 21 of which had not been discovered in previous analyses of selection [uropeans  detectedcells 2) , increascells 2) , and acccells 2) . The sameadaches ,40, and eadaches . Howevern breast  and colon breast ,43 canceNe in the North-eastern European, Slavic and West Siberian populations ranging from ~5900 in Estonian population to ~2400 in the South Russia population. The estimated divergence times between the Lithuanian and study populations ranged from 240 to 12,871 YBP. Furthermore we found evidence of selection in 24 regions, 21 of which have not been discovered in previous analysis of selection. By analysing the genetic relationships between the Lithuania and North-eastern European and West Siberian populations we have identified the close genetic proximity of Lithuanians to neighbouring populations  and in part with West Slavs , however with particular genetic distinctiveness. Genome wide SNP data results support moderate impact of Finno-Ugrian populations on Lithuanians and Latvians. We have also estimated the long-term Our study contributes to the progress of scientific knowledge in understanding the genetic diversity of geographically specific region and provides new insights into microevolutionary processes affecting local human populations.