Culex pipiens pallens is dominant species of Culex mosquito in northern China and a major vector for both West Nile virus and Bancroftian filariasis. Insecticide application were largely applied to control the mosquito-mediated spread of these diseases, contributing to increasing rates of resistance in the mosquito population. The voltage-gated sodium channel (Vgsc) gene is the target site of pyrethroids, and mutations in this gene cause knockdown resistance (kdr). While these kdr mutations are known to be critical to pyrethroid resistance, their evolutionary origins remain poorly understood. Clarifying the origins of these mutations is potential to guide further vector control and disease prevention efforts. Accordingly, the present study was designed to study the evolutionary genetics of kdr mutations and their association with the population structure of Cx. p. pallens in Shandong province, China.Mosquitoes are important vectors for a range of diseases, contributing to high rates of morbidity and mortality in the human population. Culex females were collected from Shandong province and subjected to morphological identification under a dissection microscope. Genomic DNA were extracted from the collected mosquitoes, the Vgsc gene were amplified via PCR and sequenced to assess kdr allele frequencies, intron polymorphisms, and kdr codon evolution. In addition, population genetic diversity and related population characteristics were assessed by amplifying and sequencing the mitochondrial cytochrome C oxidase I (COI) gene.Adult Cx. p. pallens specimens were used for DNA barcoding and sequencing analyses to assess kdr allele frequencies in nine Culex populations. The kdr codon L1014 in the Vgsc gene identified two non-synonymous mutations (L1014F and L1014S) in the analyzed population. These mutations were present in the eastern hilly area and west plain region of Shandong Province. However, only L1014F mutation was detected in the southern mountainous area and Dongying city of Shandong Province, where the mutation frequency was low. Compared to other cities, population in Qingdao revealed significant genetic differentiation. Spatial kdr mutation patterns are likely attributable to some combination of prolonged insecticide-mediated selection coupled with the genetic isolation of these mosquito populations.Totally, 263 kdr alleles associated with insecticide resistance are present within the Cx. p. pallens populations of Shandong Province, China. The geographical distributions of kdr mutations in this province are likely that the result of prolonged and extensive insecticide application in agricultural contexts together with frequent mosquito population migrations. In contrast, the low-frequency kdr mutation detected in central Shandong Province populations may originate from the limited selection pressure in this area and the relative genetic isolation. Overall, the study compares the genetic patterns revealed by a functional gene with a neutral marker and demonstrates the combined impact of demographic and selection factors on population structure.These data suggest that multiple  Culex species are the most common mosquitos in the world, contributing to pathogen emergence and spread through their ability to consume blood meals from humans and animals [Culex-mediated disease outbreaks are becoming increasingly frequent, as in the case of the 2019 outbreak of Eastern equine encephalitis virus (EEEV) in the United States, which caused 34 infections and 11 deaths [Culex mosquitos facilitate these spillover events in which EEEV and other deadly diseases are transmitted into humans, horses, and other so-called ‘dead end’ host species [Cx. p. pallens, which serves as the primary vector for bancroftian filariasis in Shandong Province [Cx. p. pallens population genetic structure and evolutionary genetics is potential to inform efforts to prevent vector-borne disease spread and to better evaluate the disease risk in different areas.Mosquitoes are important vectors for a wide range of pathogenic organisms . Culex s animals . Rates o species . In nortProvince , in addiProvince . StudiesVgsc mutation-mediated knockdown resistance is among the leading causes of pyrethroid resistance [kdr mutations among mosquito populations in Shandong province, revealing a positive correlation between kdr mutations and mosquito survival rate, suggesting that analyses of kdr mutations can offer value as a biomarker for studies of pyrethroid resistance in Cx. p. pallens populations [Insecticide application is the predominant mode of disease control at present , 9. Howesistance . Liu et ulations .Aedes albopictus to screen for resistance and found that the F1534S mutation was significantly associated with early resistance in this species, and that when resistance rates were high, a novel F1534L mutation was closely associated with resistance [Cx. p. pallens population using pyrethroids, Shi et al. found kdr to be a major contributor to the early stages of resistance development, with a positive correlation between L1014F mutation frequency and resistance levels together with reductions in kdr gene polymorphisms such that the final population only contained a single haplotype [Ae. albopictus [kdr mutation polymorphisms, frequencies, and haplotypes are used to trace the origins and development of resistance [kdr mutations are known to be key drivers of resistance to pyrethroid, the precise evolutionary origins of these mutations and their development in Cx. p. pallens populations from Shandong province remain to be characterized.It is worth nFoting that some seemingly contradictory results have been reported in prior studies. For one, there is some level of disagreement between studies screening mosquitoes for resistance and studying the role of knockdown resistance in the development of resistance. For example, Su et al. applied deltamethrin to a sensitive population of sistance . When scaplotype . At presbopictus , while Lbopictus . This masistance . Knowledsistance . While kkdr allele distribution among Cx. p. pallens in Shandong province, to determine whether these mutations were the result of one or more evolutionary origins, and to evaluate the impact of geographical isolation and demographic history on kdr mutation evolution in this mosquito population. Analyses of whether resistance-related mutations in a population are derived from a single emergence event and subsequent spreading or evolved independently on multiple occasions can highlight the relative importance that migration and mutation rates play in the dynamics of insecticide resistance. By analyzing neutral biomarker genes and assessing the genetic variation therein, it is possible to gain additional insight into demographic-related population structure. Mitochondrial DNA (mtDNA) markers have emerged as popular targets for analysis when studying genetic diversity and population structure given that they are maternally inherited, do not undergo recombination, are highly variable, and thus necessitate a relatively small target population to facilitate robust analyses [kdr introns in the Vgsc gene while utilizing COI gene as a marker in order to simultaneously study the evolutionary history of kdr mutations and the population structure of Cx. p. pallens communities in Shandong Province, China.The present study was conducted to assess the patterns of analyses , 23. TheCx. p. pallens breeding primarily takes place in moderately polluted standing bodies of water located near to human settlements. For this study, adult female Cx. p. pallens specimens were collected from Zibo and Linyi in the southern mountainous area of Shandong; Rizhao, Yantai, and Qingdao in the eastern hilly area of Shandong; and Heze, Dongying, Dezhou, and Liaocheng in the northwest plain region of Shandong from 2021 to 2022  and HCO2198 (5′-TAA ACT TCA GGG TGA CCA AAA AAT CA-3′) primers via PCR. Each PCR reaction contained 12.5 μl of GoTaq Green Master Mix, 1 μl of each primer (10 μmol/l), 2 μl of template DNA, and nuclease-free water to a final volume of 25 μl. Thermocycler settings were: 94 °C for 1 min; 5 cycles of 94 °C for 40 s, 45 °C for 40 s, and 72 °C for 1 min; 30 cycles of 94 °C for 40 s, 53 °C for 40 s, and 72 °C for 1 min; and 72 °C for 5 min. Amplicons were electrophoretically separated and directly sequenced with the Big-Dye kit .The 651 bp Vgsc gene including the kdr codon at position 1014 was conducted with the kdr-F (5′-C CT GCC ACG GTG GAA CTT C-3′)and kdr-R (5′-GGA CAA AAG CAA GGC TAA GAA-3′) and primers each PCR reaction contained 12.5 μl of GoTaq Green Master Mix, 1 μl of each primer (10 μmol/l), 2 μl of template DNA, and nuclease-free water to a final volume of 25 μl. Thermocycler settings were: 94 °C for 1 min; 30 cycles of 98 °C for 10 s (denaturation), 55 °C for 15 s, and 68 °C for 30 s; and 68 °C for 1 min. Amplicons were electrophoretically separated and directly sequenced with the Big-Dye kit .The amplification of 521 bp of the Cx. p. pallens gene sequences in the GenBank repository using NCBI’s  online BLAST  algorithmic program. The COI gene exhibited a > 99% match to the expected Cx. p. pallens gene sequence, and Bioedit v 7.0 was used to align COI and kdr sequences. DnaSPv6 [Fst) and gene flow (Nm) among groups. Departures from the expected neutral DNA sequence variability were detected through statistical neutrality testing [D [D and F tests developed by Fu & Li, with the latter being based on haplotype frequency distributions [kdr haplotypes. MEGA v.7.0 was used to construct a Neighbor-Joining phylogenetic tree model based upon genetic distance values [Compared to  DnaSPv6  was used testing . Tajima’sting [D  is basedibutions . All calibutions , as weree values .Vgsc gene in these Cx. p. pallens specimens generated a 521 bp PCR fragment. The sequence data for this gene were analyzed to assess kdr allele frequency distributions, kdr codon evolution, polymorphisms in the neighboring intron downstream of the kdr codon, and population genetic diversity. PCR amplification of the COI gene generated a 651 bp PCR fragment. As the mitochondrial genome is maternally inherited and haploid, drift towards different haplotype frequencies will occur more rapidly within isolated populations, resulting in differentiation roughly twice that observed for nuclear markers. Accordingly, these mtDNA sequences were analyzed to assess population variations, population expansion, spatial population structure, genetic differentiation, and patterns of gene flow.PCR amplification of the kdr mutant allele frequencies were found to vary with geographic location. L1014F and L1014S were observed in the northwest plain region  and eastern hilly area  of Shandong Province. In the northwest plain region, L1014F was the predominant mutation and L1014S was also common. However, only the L1014F was detected in the southern mountainous area and Dongying city at a low mutational frequency  and the mutant alleles  were detected at codon 1014. A total of eight genotypes, including the wild-type genotype TTA/TTA (L/L), the wild-type/mutant heterozygotes TTA/TTT (L/F), TTA/TCA (L/S), and the mutant genotypes TCA/TCA (S/S), TTT/TTT (F/F), TTC/TTC (F/F), TTC/TTT (F/F) and TTT/TCA (F/S), were detected at codon 1014 Table . The kdrFs and Tajima’s D) were performed using the neighboring intron downstream of the kdr allele in Cx. p. pallens, with 520 kdr intron sequences being included in these analyses. As these polymorphic sites exhibited heterozygous phenotypes, a phase analysis approach was used for haplotype predictions, leading to the detection of 31 haplotypes from 9 populations (Table Hd) and nucleotide diversity (Pi) values were 0.664 and 0.0521. The Heze populations exhibited low Hd (0.351) and Pi (0.01261), whereas these values were high in other populations. No significant departures from neutrality were detected in any of these populations using Tajima’s D test, whereas Fu’s F test detected significant departures in the Qingdao and Zibo populations, with positive F test results indicating low levels of high-frequency populations consistent with a population size reduction and/or balancing selection  and H02 (23.8%)  and nucleotide diversity (Pi) values were 0.306 and 0.00052, respectively. The low haplotype diversity and low nucleotide diversity indicated that different geographic populations may have experienced a “bottleneck effect”. Dezhou population exhibited the highest levels of diversity, whereas the Dongying and Zibo populations were the least diverse. The haplotype network graph constructed based on Median-joining method shows a radiative evolutionary center with a star-shaped distribution  tree incorporating these 9 COI sequence-based haplotypes was constructed , while moderate differentiation (0.05 < Fst < 0.15), high differentiation (0.15 < Fst < 0.25), and very high differentiation (Fst > 0.25) were respectively observed among 7 (19.4%), 8 (22.2%), and 7 (19.4%) pairs of populations. The highest Fst value was observed between the Qingdao-Heze, Qingdao-Zibo, Qingdao-Dongying populations , owing to the presence of the H4 haplotype in Qingdao and its absence in Dongying/Zibo.An analysis of molecular variance (AMOVA) revealed that genetic differences of ns Table . A rangeFst < 0.0; 38.9%, Fst < 0.0; 38.9%, Cx. p. pallens populations in Shandong Province, Tajima’s D and Fu’s Fs tests were next conducted, with the results and simulated P-values being shown in Table D and Fu’s Fs values were obtained for the Liaocheng, Dezhou, Linyi and Heze populations, suggesting high numbers of low-frequency mutations in these populations and that they are actively undergoing expansion. The frequency distributions for pairwise differences between sequences were also assessed to evaluate historical demographic expansions. Mismatch distributions can be used to visually assess historical population dynamics. Two models correspond to expected DNAsp values, with one assuming that the population size remains constant and the other assuming that the population grows or declines. When observations conform to one of these models, it indicates that the population is in line with the expected model-derived values and the population is constant or shrinking, whereas if the values deviate from expected values this indicates the population is expanding with a single-peak curve. The present results indicated that actual observations were consistent with hypothetical model values, thus indicating that the Cx. p. pallens populations in Shandong were in a state of genetic equilibrium.In an effort to infer the demographic history of kdr mutations associated with insecticide resistance in Cx. p. pallens populations were studied by the Vgsc gene from mosquito populations in Shandong Province. The origins of these kdr mutations were explored through haplotype networks and comparisons of regional variability in mutation frequencies among these populations. The COI gene was further used to analyze the population structure in these study sites to clarify the differences among regions, supporting a model in which both geographical isolation and varying levels of selection pressure have shaped these communities.In the study, patterns of Fst value was 0.28 consistent with substantial genetic differentiation. Notably, the Fst values when comparing the Qingdao population and other populations ranged from 0.059–0.469. Nm values were consistent with these findings, reaffirming the higher level of genetic differentiation for the Qingdao population as compared to the other studied populations. This may be a result of the fact that the sampling site was located in the Huangdao district of Qingdao. Historically, Huangdao was an isolated island surrounded by the sea, making travel to inland areas challenging. However, the Qingdao Jiao Zhou Bay Bridge and the Jiao Zhou Bay Subsea Tunnel were completed in 2010 and 2011 respectively. The development of transportation methods has facilitated transportation between Huangdao and other cities, and economic advances continue to contribute to rising transregional, transnational, and transcontinental vessel exchanges. Beginning in 2010, Liu et al. began investigating the association between kdr mutations and insecticide resistance in Cx. p. pallens of Shandong province, they found lower frequencies of mutations in Qingdao populations as compared to other study sites. The World Health Organization (WHO) standard resistance bioassay was applied to test insecticides resistance in Cx. p. pallens, Huangdao district populations exhibited a mortality rate of 83.8% consistent with preliminary resistance, whereas mortality rates in Jining populations were just 37.3% consistent with high levels of resistance [kdr mutations frequency in Qingdao populations significantly increased in this study, that may be because the Qingdao Jiao Zhou Bay Bridge and the Jiao Zhou Bay Subsea Tunnel were completed, promoting communication among different populations, but the main reason is related to the using of pesticides. The Huangdao port thus provides a potential site not only for the interactions among people and the dissemination of goods and services, but also for the spread of infectious diseases. The Huangdao port is currently an important node in over 150 international routes, engaging in trade with over 450 ports in over 130 countries. Over 60 international long-distance sea routes connect this port to far-flung areas including South Africa, the Black Sea, Europe, the Americas, the Mediterranean Sea, and Australia. Invasive mosquito vector species are being passively transported to new areas by humans for decades. However, current levels of mobility with a worldwide increased flow of people and commodities have raised the risk of the introduction of invasive mosquito species considerably. Mosquito surveillance in maritime ports of entry is crucial to allow the early detection of invasive mosquito species [Based on mtDNA sequencing, significant genetic differentiation was detected between populations from eastern hilly area of Shandong and southern mountainous area of Shandong. When comparing these two regions the sistance . Compare species . A totalCx. p. pallens population from Heze exhibited lower levels of genetic variation in the intronic regions associated with and a high frequency of kdr mutation (L1014F), indicating that the low levels of intronic variation may be attributable to a selective sweep, referring to a rapid increase in the frequency of a given allele as a consequence of directional selection, tied to high levels of intensive insecticide application and/or from the fixation of this resistance genotype. During a selective sweep, linked alleles in regions flanking the allele subject to selective pressure can also rise in frequency, contributing to a drop in overall genetic variation [kdr L1014 codon indicated a high degree of genetic differentiation between the Heze population and other analyzed populations consistent with strong local selection for this resistance allele in Heze. In contrast, these mutational frequencies were low in Dongying, Linyi, and Zibo, with only one mutant allele (L1014F) being present. Zibo is south of the Tai-Yi Mountain range, Linyi is adjacent to Zibo. From north to south, the three main mountains in Linyi are the Yi, Meng, and Ni mountains, which probably due to interrupted gene flow from eastern hilly or northwest plain region to southern mountainous area, caused by geographical isolations. While Dongying is in the northwest plain regions of Shandong province, it is near the end of the Yellow River and the coast of the Bohai sea where the flow of the Yellow River into the sea slows, forming a large delta that has arisen through the rapid deposition of high levels of silt sediment, resulting in shallow belowground water levels and extremely salinized soil that contributes to high levels of ecological fragility. On the one hand, currently, the saline-alkali land area in Dongying city is 350 km2, most crops cannot grow in saline-alkali soils, agricultural cultivation is relatively poor [kdr codon L1014 sequences, significant departures from neutrality were observed in Linyi and Zibo, resulting from population bottleneck events. Rates of agricultural insecticide application in Dongying may be relatively low, potentially explaining the lack of insecticide resistance alleles in this population. Of the different neutrality tests, Fu’s Fs statistic exhibits the greatest power when detecting departures from neutrality based on a genetic hitchhiking model [Fs measures for most of the study sites in Shandong province may be attributable to the high levels of insecticide use and strong selection placed on both the kdr locus and other regions of the genome. Over the past 50 years, insecticides have been widely used in agricultural settings and for vector control throughout central China [kdr mutation distributions observed among these Cx. p. pallens populations underscore the need for the establishment of resistance surveillance plans and efforts to better manage insecticide efficacy.Heze is situated in southwestern Shandong Province in the lower reaches of the Yellow River, and contains extensive areas of flat terrain with deep soil that is well-suited to agricultural use. It is a primary site of grain production in this province and bordering Anhui, Henan, and Jiangsu provinces to which good transportation is available. Vector-borne disease outbreaks have been historically common in Heze, and it was the last city in Shandong Province to eliminate new malaria cases . Relativariation . Compariely poor , 34. Comely poor . Mosquitely poor . Saline-ng model . High Fual China . The rapkdr codon together with the observed patterns of geographic distribution strongly indicate that these kdr mutations are derived from multiple mutational events rather than a single origin. Two non-synonymous codon L1014 mutations were detected among analyzed mosquito populations. While just one of these mutations was evident in central Shandong Province, both were found in the western and eastern coastal regions of the province. This is consistent with prior kdr genotyping results published for Cx. p. pallens, which revealed that the L1014F mutation was widely distributed throughout the Shandong region [kdr mutations. Specifically, these mutations may have arisen once and then spread over time, or they may have independently evolved in multiple geographically distinct populations. At least two independent mutational events were identified as having given rise to the L1014F or L1014S haplotypes through a single mutational step . These analyses would provide both valuable insight into the molecular evolution of pyrethroid resistance and a foundation for subsequent vector control efforts aimed at controlling the spread of mosquito-borne illness.The geographic distribution of


Cyrtonyx montezumae), a small game bird that rarely flies long distances. In the northern limit of its distribution, it inhabits oak-juniper-pine savannas of Arizona, New Mexico, and Texas. Understanding genetic structure can provide information about the demographic history of populations that is also important for conservation and management. The objective of this study was to determine patterns of genetic variation in Montezuma quail populations using nine DNA microsatellite loci. We genotyped 119 individuals from four study populations: Arizona, Western New Mexico, Central New Mexico, and West Texas. Compared to other quail, heterozygosity was low (Ā = 2.41 ± 0.27). The global population genetic differentiation index STR = 0.045 suggests little genetic structure, even though a Bayesian allocation analysis suggested three genetic clusters (K = 3). This analysis also suggested admixture between clusters. Nevertheless, an isolation-by-distance analysis indicates a strong correlation (r = 0.937) and moderate evidence (P = 0.032) of non-independence between geographical and genetic distances. Climate change projections indicate an increase in aridity for this region, especially in temperate ecosystems where the species occurs. In this scenario, corridors between the populations may disappear, thus causing their complete isolation.Restricted movement among populations decreases genetic variation, which may be the case for the Montezuma quail ( Cyrtonyx montezumae Vigors 1830) is a small game bird with limited dispersal by flight, that has a naturally disjointed geographic distribution. Montezuma quail inhabit temperate woodlands and savannas associated with mountains ranges in southeastern Arizona, southern New Mexico, and west Texas in the United States , and sedge (Cyperus spp.), but also feed on acorns (Quercus spp.), and a large variety of seeds and insects  in southeastern Arizona. Differences in management by state agencies may have also affected quail genetic structure. Montezuma quail are hunted in Arizona and New Mexico . Since unbalanced sampling affects the inference of population structure , Central New Mexico , Western New Mexico  and West Texas (WTX). These geographic designations produced an unbalanced sample size, with a relatively low sample size allocated to CNM , 12.5 µL of MasterMix , 1 µL of each oligo and 8.5 µL of PCR water without endonucleases. Thermocycler conditions for the amplification were modified according to the MasterMix manufacturer specifications and the alignment temperatures of the oligos, which varied between 45 °C and 57 °C. The program used was as follows: a cycle of 95 °C for 2 min; five cycles of 94 °C for 30 s, 45 °C for 30 s and 72 °C for 30 s; then 35 cycles of 94 °C for 45 s, 45 °C for 45 s and 72 °C for 1 min; followed by a cycle of 72 °C for 2 min. Ten microsatellite loci with the highest polymorphism and concordance in the fragment size as reported by the author were chosen. Selected loci were marked with fluorophores in the sequence 5′–3′. A post-PCR multiplex array for fragment reading was performed. This was performed on an ABI 3730xl sequencer  of Macrogen. We used GeneScan™ 350 ROX™ dye Size Standard for sizing DNA fragments. Allele scoring was conducted using program GeneMarker v. 2.6.4 (A) expected heterozygosity (He) and observed heterozygosity (Ho) were obtained for each locus and population using GenAlEx v 6.5  between 1–4, an ancestral mixing model, and a correlated allelic frequency model. Outputs from program STRUCTURE were analyzed using the method of K (ΔK), which is the difference between the values of the logarithmic likelihood in each analysis iteration for the four clusters.Population genetic differentiation index in v 3.5  to deterin v 3.5 , which eRST was used as a measure of genetic distance between populations. We performed 10,000 permutations to estimate the statistical significance (P < 0.05) of the null hypothesis of independence between genetic and geographic distance.A Mantel test was conducted using program Genepop 4.3  to deter2 = 9.58, d.f. = 4, P = 0.048) and Quail 27–Quail 31  and no evidence of linkage disequilibrium for the other 23 pairs (P > 0.05). Hence, no locus was discarded due to linkage disequilibrium. In the end, all samples (n = 119) were analyzed using 9 DNA microsatellite loci.A reduced number of DNA microsatellite loci out of the 20 loci tested were used for genetic analyses. We use locus (Quail) names as in Overall estimators of genetic variability had the following values: mean number of alleles 2.41 ± 0.27 per locus (range = 1–11), mean observed heterozygosity of 4 ± 0.04 . GeneticRST = 0.045). However, pairwise differentiation between distant populations AZ–WTX (RST = 0.094. P = 0.001), AZ–CNM (RST = 0.061. P = 0.009), and WTX–WNM had statistical significance  (K = 3)  . We foun (K = 3) , whose m (K = 3) ; study p (K = 3) . The wit (K = 3) . The pro (K = 3)  had no g (K = 3) . WNM (47RST) were strongly correlated across populations (r = 0.937)  of an association between geographical distance and genetic distance. According to RST statistics, AZ and WTX populations had the greatest genetic and geographic distances, while CNM and WNM populations had a shorter genetic and geographical distance.Geographical distance and genetic distance (= 0.937) . NeverthA = 2.41 ± 0.27) and a high proportion of fixed loci (3 out of 9 loci) demonstrate low genetic variability of the Montezuma quail in the southwestern United States. For instance, Ho = 0.58 in seven DNA microsatellite loci for bobwhite (Colinus virginianus) populations in southern Texas. Ho from 0.250–0.928 and an average A = 7 in 23 DNA microsatellite loci for scaled quail  populations in Arizona, Colorado, Oklahoma, and Texas. Similarly, Ho = 0.32 ± 0.17. Low genetic variability of Montezuma quail populations may be caused by drift in small and isolated populations inhabiting oak-pine-juniper savanna islands separated by vast desert grasslands. In this regard, low migration rates between the peripheral populations and those closer to the center of the geographic range could promote gene drift . Low heterozygosity  and WTX . In addition, AZ is not differentiated from WNM, suggesting an extant or recent corridor for Montezuma quail between these two study populations. Montezuma quail occur through the forested Mogollon Rim, which may connect WNM to central Arizona and to mountain ranges to southern Arizona (AZ) as both eBird sightings . Although some CNM individuals appear to be geographically isolated from the rest of the population , Montezuma quail can occur in areas with riparian vegetation near oak forest  and WTX–WNM populations  were the highest among all pairwise comparisons. In contrast, the previous mtDNA survey by RST = 0.033) was suggestive (P = 0.096) that gene flow occurs between these populations >200 km apart. This area is approximately 20 km away from the Guadalupe Mountains, which is near the Texas–New Mexico state line, thus representing a possible route of exchange between the two study populations. However, the current existence of a large extension of unsuitable arid land between the two mountainous areas and the RST may suggest recent divergence. Furthermore, our Bayesian analysis suggested three clusters, whose assignment probabilities among Montezuma quail followed a longitudinal gradient  in Arizona .Input file for program Genepop (Click here for additional data file.


Aedes aegypti Linn, the vector of dengue, chikungunya, and Zika viruses, across different geo-climatic zones of India and also elucidated the impact of ecological and topographic factors. After data quality checks and removal of samples with excess null alleles, the final analysis was performed on 589 individual samples using 10 microsatellite markers. Overall findings of this study suggested that, Ae. aegypti populations are highly diverse with moderate genetic differentiation between them. Around half of the populations (13 out of 22) formed two genetic clusters roughly associated with geographical regions. The remaining nine populations shared genetic ancestries with either one or both of the clusters. A significant relationship between genetic and geographic distance was observed, indicating isolation by distance. However, spatial autocorrelation analysis predicted the signs of long-distance admixture. Post-hoc environmental association analysis showed that 52.7% of genetic variations were explained by a combination of climatic and topographic factors, with latitude and temperature being the best predictors. This study indicated that though overall genetic differentiation among Ae. aegypti populations across India is moderate (Fst = 0.099), the differences between the populations are developing due to the factors associated with geographic locations. This study improves the understanding of the Ae. aegypti population structure in India that may assist in predicting mosquito movements across the geo-climatic zones, enabling effective control strategies and assessing the risk of disease transmission.The present study explicitly evaluated the genetic structure of  Aedes aegypti is a mosquito species that transmits several arboviruses including dengue, chikungunya, Zika, etc. This species was originally found in Africa but is now extensive in all the continents except Antarctica. The spread of this species is of concern as the movement of infected mosquitoes can spread the viruses from one place to another. Anthropogenic changes such as climate warming and increasing urbanization are boosting the spread. This study focuses on analyzing the genetic structure of Ae. aegypti in India and how it is affected by the local topological and climatic factors. We analyzed Ae. aegypti populations from different geographical regions in India covering the parts from North to South and East to West and analyzed them using a panel of microsatellite markers. Our analysis suggested that Ae. aegypti populations in India are genetically closer to each other irrespective of geographical distance between them which indicates high gene flow among the populations. However, signs of genetic structure were also observed owing to the factors associated with geographical locations. This research provides a broader picture of the nature and dynamics of the mosquito populations among eco-geographical regions across a larger area of the country, which would provide a valuable reference base for genetic studies and for novel mosquito control strategies. One of the fundamental challenges in population genetics is to understand the pattern of genetic structure and delineate the factors shaping it. Genetic differentiation is determined by the amount of gene flow (migration) across populations. The migration of mosquitoes across space is generally influenced by their biological and ecological behavior, population history, eco-geographic barriers, and other local environmental factors –5. A cleAedes aegypti is one of the most important mosquito species worldwide as it is the major vector of dengue, chikungunya, Zika, and other arboviruses and has contributed to several disease outbreaks worldwide. It is a human commensal that not only feeds on humans [Ae. aegypti populations from urban areas with good connectivity are less differentiated than the populations from areas with restricted human movements [n humans ,12 but cn humans –17. Howen humans –21. For ovements –27. ThusAe. aegypti has been studied worldwide [Ae. aegypti. Genetic distinctness and limited gene flow among populations are crucial factors in mosquito control and thus understanding the local vector population dynamics is important for maximum benefits.Genetic structure of orldwide . The mosorldwide . This anorldwide . The popAe. aegypti is most prevalent in urban areas [Aedes control mainly relies on source reduction, environmental manipulation, and sanitation [Ae. aegypti and Aedes albopictus from desert and coastal regions of India [Ae. aegypti was found better suited to desert conditions while Ae. albopictus was more adapted to coastal environment of Kolkata. Understanding the population dynamics of the mosquito species from different eco-geographical regions is important. The genetic studies on Ae. aegypti populations in India available so far are geographically localized to a particular city or state [Ae. aegypti at the national level. With this in mind, the present study was carried out to explore the genetic structure of the species across the country and the possible factors that determine the spatial structuring among them.In India, an areas –31, but an areas . Aedes cnitation ,35. Notanitation –38. Howeof India ,40. Ae. or state ,33,41–43Aedes aegypti mosquitoes were collected from 22 different locations across India, from 2018 to 2021. The sampling locations and their characteristics are given in Ae. aegypti mosquitoes were stored in 95% ethanol for further processing. A minimum of 30 samples per site were randomly selected for genetic analysis, but all samples were used for the sites where less than 30 mosquitoes were collected.et al., (2011) such as A9, AC4, AC1, AC5, A1, AC2 were tagged with FAM and B2, B3, AG1, AG5, CT2, AG2 with HEX [Genomic DNA was isolated from individual mosquitoes using DNeasy Blood & Tissue Kit  as per the manufacturer’s protocol. Genotyping was performed using 12 microsatellite markers, including four tri-nucleotide repeats  and eight di-nucleotide repeats  ,44. Amplwith HEX . AmplifiNa), the number of effective alleles (Ne), expected and observed heterozygosity , and inbreeding coefficient (FIS) were estimated with GenAlEx 6.5 [Fst as well as locus-wise Fst was estimated with and without null alleles using FreeNA [Fst estimates was tested using t-test with p-value < 0.05. The linkage disequilibrium among the locus-pairs and Hardy Weinberg Equilibrium (HWE) for each location was tested by applying Fisher’s exact test in Genepop 3.4 [Standard genetic parameters such as the average number of alleles  in FSTAT [Fst values were constructed using complex heatmap package [HCPC) analysis with FactoMineR v.1.41 [et al., [Several methods were applied to determine the clustering pattern among mosquito populations across the wide geographical range. First, genetic differentiation among in FSTAT ,51, and  package  in R v 4 package . GeneticR v.1.41 . ClusterR v.1.41 . The relR v.1.41 . To dete[et al.,  ΔK metho[et al., . The fin[et al., . FinallyFst/(1-Fst)) and geographical distance using GenAlEx 6.5. To determine the impact at a finer scale; spatial autocorrelation analysis was done following Smouse & Peakall [The impact of geographic distance on the genetic structure between the populations was determined by Isolation-by Distance (IBD). As per the IBD hypothesis, the genetic distance between populations increases with the geographic distance between them. To test this, the Mantel test was performed between pair-wise genetic distance  was performed using the vegan package [2>0.7), the more biologically meaningful variables were selected for further analysis which resulted in the selection of four climatic variables  and three geographic variables . To disentangle the impact of each type of variable three models were tested in RDA; (i) a full RDA model testing all variables , (ii) a partial model in which geography explains genetic data keeping climatic variables as constant; and (iii) the third model determined the impact of geographical variables conditioned on climatic variables. This analysis determined the contribution of individual climatic and geographic variables, as well as the impact of the combination.Finally, to determine the impact of climatic and geographic factors on the genetic structuring of  package ,61 in R.Fst analysis using FreeNA was determined. Overall Fst with and without correction was 0.088 and 0.094, respectively, . Moreover, the relationship between the null allele frequencies for each locus with the expected heterozygosity of each marker was not significant (p> 0.05). This signifies that the null alleles have a negligible impact on genetic estimations. Therefore, 10 loci out of 12 were retained for further analysis.A total of 625 mosquitoes from 22 populations (20–32 mosquitoes per population) were analyzed using 12 microsatellite loci. None of the locus pairs were found in linkage disequilibrium after the Bonferroni correction . Howeverctively,  and the In addition to this, thirty-six individual samples having >30% missing alleles were removed from the data. The remaining 589 individuals showed 585 multilocus genotypes (MLGs); 581 of which were unique, while four MLGs were shared each by two mosquitoes. However, each mosquito belonged to a different population. Thus, the final dataset analyzed in this study included 589 individual mosquitoes genotyped using 10 microsatellite loci .Na) varied from 2.3 in WB1 to 6.0 in KR1 and an effective number of alleles (Ne) ranged from 1.8 in WB1 to 2.8 in TN2  were private (found in a single population only). A maximum number (20%) of the private alleles were found in marker CT2 followed by 13% each in AG2 and AC1.A total of 139 alleles were identified among 589 mosquitoes from 22 locations. The number of alleles varied from 9 in AC4 to 30 in AG2 . All the8 in TN2 . In eachHo) = 0.553 was similar to the expected heterozygosity He = 0.527 among all the 22 Ae. aegypti populations. However, the diversity level was variable among the populations. The Andaman & Nicobar Island  populations showed the highest genetic diversity and the WB1 population showed the lowest (Ho) among populations varied from 0.205 in WB1 to 0.856 in AN1 and He varied from 0.354 in WB1 to 0.598 in AN4 (He) was higher than Ho among ten populations with significantly positive FIS indicating an excess of heterozygotes. Among the remaining 12 populations, four showed significantly negative FIS  of 3.58. The Fst values varied from 0.00 (between TN populations) to 0.37 (between AN3 and WB1). Out of 231 pair-wise Fst comparisons, 80% were statistically significant , we grouped the populations into clusters. The populations from TN and AN grouped together forming cluster 1. The second cluster consisted of populations WB1, OD1, AS1, MP1, and KN1. The remaining populations had shared ancestries from both or either of the two clusters showing admixture. Notably, populations collected from within states  were found genetically closer to each other  between the pair-wise matrixes of genetic differentiation (Fst/1-Fst) and geographical distances was observed. Spatial autocorrelation analysis indicated a significantly positive autocorrelation coefficient (r) only up to ~100 km. The genetic similarity diminished after ~100km, however, random peaks were observed at larger geographical distances  and how the genetic structure among populations is influenced by the local ecological and geographical factors. The genotyping of 589 mosquitoes with 10 microsatellite markers revealed high genetic diversity with moderate genetic differentiation among Ae. aegypti populations.This study represents the first large-scale genetic analysis of 22 Ae. aegypti populations from India. A total of 139 alleles were observed with an average of >4 alleles from each population. Among 589 mosquitoes, 585 unique multilocus genotypes were observed. Moreover, overall He was 0.527 and Ho = 0.553 which depicts a high level of genetic diversity as mentioned in Takezaki 1996 [Ho) in this study was similar to that observed among invasive populations from other countries , however, less than the ancestral populations from Africa (Ho = 0.613) as determined by analyzing 79 Ae. aegypti populations worldwide [Aedes aegypti is an invasive species that has existed in India even before the 19th century [FIS value (-0.025). The high genetic diversity could also result from the continuous admixture of mosquito populations from other countries or geographical regions. Aedes aegypti is known to be transported as eggs or immature stages across countries [High genetic diversity was observed among 22 aki 1996 . Observeorldwide . Aedes a century ,30,63. Tountries  and due FIS and the significance of FIS indicates the complexity of genetic structure. Although overall He and Ho were almost similar, the level of diversity varied across the populations. Around half (10/22) of the populations had He>Ho with significant positive FIS indicating a deficit of heterozygotes which happens due to inbreeding among relatives [FIS was observed in WB1 followed by AS1, KN1, DL1, and others. Most of these populations belong to big cities/municipalities or the state capitals where mosquito control is more active compared to smaller cities/towns. Regular vector control could cause a reduction in mosquito effective population size through bottleneck and genetic drift leading to a loss of diversity as seen in the vector populations from Trinidad and Tobago, and Venezuela [Aedes mosquitoes having low dispersal capabilities (<500m) [Expected and observed heterozygosity are important parameters in genetic diversity analysis. The difference between the two is determined by elatives . The higenezuela –68. Inde (<500m) –17 can l (<500m) ,70. Howe (<500m) .i.e., 41% of the total populations spread across India showed an admixture of genetic ancestries. Based on the membership coefficient of the identified genetic ancestries through STRUCTURE, three populations  shared ancestry with cluster 1, two population (HR1 and KR2) shared with cluster 2 and the remaining four populations  had all the three identified genetic ancestries. This type of genetic structure is referred to as “chaotic genetic patchiness” [e.g., KN and AS, RJ and TN) was observed. This indicates that apart from the geographic distance, other factors determine the genetic relatedness between the mosquito populations. One of the widely known factors is the human-mediated dispersal of mosquito eggs, larvae and adults across broader geographical ranges [Clustering analysis based on PCoA, HCPC, POPTREE, and Bayesian approach consistently identified two major genetic clusters. One cluster consists of populations from Tamil Nadu (TN) and Andaman & Nicobar (AN), and the other from eastern India , Madhya Pradesh and Bangalore. The remaining nine populations chiness” . The popl ranges . Severall ranges ,19,74–762 = 0.023, p = 0.02), between geographic and genetic distance, the r2 was very low indicating a weak relation possibly due to the existence of long-distance gene flow. This is further supported by the spatial autocorrelation analysis which demonstrated the absence of genetic connectedness beyond ~100km, however, several random peaks were observed for higher geographical distances. Notably, the populations collected within states/UT  were found genetically closer. This might be due to connectivity which is more frequent within states compared to between states and thus could be responsible for population admixtures.In this study, there are two major observations from clustering analysis. One is the sharing of genetic ancestries across a broad geographic range indicating the admixture. The possibility of long-distance dispersal was also reflected in the IBD analysis. Although mantel test showed a significant positive relation  between them, however, our results indicate that either there is a continuous exchange of mosquito populations between these areas or the populations once introduced have not gone through enough generations to develop significant differences between them.The second important observation of the clustering analysis is the identification of two genetic clusters with Ae. aegypti populations can be explained by two hypotheses. One hypothesis is that the mosquito populations in each cluster might have entered separately from other countries or geographical regions [Ae. aegypti populations in different countries differ from each other [Ae. aegypti populations in India is that the mosquito species is strongly associated with humans and their surroundings and do not fly to far-off places if enough breeding places are available nearby [Ae. aegypti populations and the factors responsible for that. Moreover, an in-depth investigation of these two clusters would be interesting to understand if there is any difference in the level of vector competence, insecticide resistance, or other features related to the transmission of vector-borne diseases.Furthermore, the existence of genetic clusters among  regions ,78. Eachch other . Nonethee nearby –17. Thuse nearby –83. HoweAedes aegypti is closely associated with human dwellings, and thus its genetic structure is known to be influenced by anthropological and environmental factors like availability of breeding places, the intensity of vector control activities, connectivity, human population density, land use pattern, rainfall, temperature, etc. [Ae. aegypti in India. However, weak relations and the observations from autocorrelation analysis indicated the chances of long-distance gene flow between the populations indicating the role of other factors in shaping the genetic structure.re, etc. –3,84–86.RDA analysis identified that 52.7% of the genetic variation in the mosquito species is explained by the selected climatic and geographical variables. Among all the variables, latitude, and temperature contributed significantly. Geographical factors were found to be more important than climatic factors indicating that mosquito genetic structure is influenced by the factors associated with geographical locations. Notably, around half of the variation remained unexplained. There are many other important factors like number of breeding places, land use pattern, population size, infrastructure, vegetation, etc. that have not been analyzed in this study due to unavailability of the data estimates.Ae. aegypti was observed, probably due to its long-term establishment and continuous expansion across the country. Three genetic clusters were identified. Two clusters were moderately differentiated from each other. The third cluster contained populations with shared genetic ancestries reflecting genetic connectivity among populations irrespective of geographical locations or distance between them. Although signs of genetic connectivity at a larger geographical distance were observed the species seems to be structured largely due to the impact of local factors associated with geographic locations. The populations within state/union territories were genetically similar indicating the role of human movements and connectivity on mosquito dispersal which is more frequent within the states than between the states. Vector management has to be focused at the state level considering the means of connectivity and the environmental factors which can dilute the impact due to gene flow from other areas. Further, fine-scale analysis of Ae. aegypti populations at the state level could be insightful to determine the role of possible factors on the genetic structure and dispersal of this mosquito species in understanding dispersal patterns of mosquitoes and thus guiding the effective vector management.Overall high genetic diversity in Ae. aegypti population dynamics across India and also facilitated the development of a large-scale reference base for future genetic studies in the country. Moreover, the data would provide a basis to predict the mosquito dispersal patterns across geographical regions, which is important for formulating effective control strategies as well as assessing the risk of disease transmission. Understanding genetic connectivity across climatic zones is particularly important in India which encompasses huge climatic variations from extreme cold in the northern parts to the tropical climates in the southern regions. The rainy season in India is generally July to September. However, the southern coast that includes the parts of Tamil Nadu receives around half of its annual precipitation from the retreating monsoons extending the rainy season from October to December [The results of this study improved the overall understanding of December ,88. ThisDecember –91 may gS1 Fig(XLSX)Click here for additional data file.S2 FigA) Principal Coordinate Analysis (PCoA) based on genetic distance between each mosquito sample, B) Hierarchical Clustering on Principal Components (HCPC) plot for individual mosquito samples.(XLSX)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 TableSample location codes are given in (XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 TableFst values after Bonferroni correction (p<0.00024) are indicated in bold.Codes for the population names are given in (XLSX)Click here for additional data file.S1 Data(XLSX)Click here for additional data file.


Puccinia triticina, is one of the most important fungal diseases of wheat in China. However, little is known about the dynamic changes of population structure and genetic diversity of P. triticina during a period of time. In this study, 247 isolates of P. triticina collected from Hebei Province from 2001 to 2010 were tested on 36 Thatcher near-isogenic lines for virulence diversity and detected by 21 pairs of Expressed Sequence Tag derived Simple Sequence Repeat (EST-SSR) primers for genetic diversity. A total of 204 isolates were successfully identified as 164 races, and THTT, THST, PHRT, THTS, and PHTT were the most common races in Hebei Province from 2001 to 2010. The cluster analysis based on virulence showed that P. triticina has a rich virulence polymorphism, which had a certain correlation with the years, while the cluster analysis based on EST-SSR showed that the genetic diversity of the P. triticina population was significantly different between years in Hebei Province from 2001 to 2010. In addition, the population structure of P. triticina may have changed greatly in 2007 and 2009, which was significantly different from that of 2001–2006 on either virulence or genetic characteristics. The variation frequency of the population structure had an increasing trend during this period. From 2001 to 2010, there was a certain degree of gene flow among the P. triticina populations. No significant correlation was found between virulence and molecular polymorphism. The genetic differentiation analysis of the 10 tested populations (each year as a population) showed that the coefficient of genetic differentiation (Gst) was 0.27, indicating that there was a certain genetic differentiation among or within populations of P. triticina in Hebei Province. The genetic variation within populations (73.08%) was higher than that among populations (26.92%), which indicated that the genetic variations were mainly found within populations. Our study provides the foundation for a better understanding of the population structure change and genetic diversity of P. triticina over a period in Hebei Province of China.Wheat leaf rust, caused by  Puccinia triticina Erikss., is the most common fungal disease of common wheat (Triticum aestivum L.) in major wheat-growing regions worldwide  among 61 single-uredinial isolates, and little evidence was found for robust distinct clusters among the isolates. In 2001, P. triticina from Canada. In 2007, P. triticina collected from common wheat in Central Asia and the Caucasus regions based on near-isogenic lines of Thatcher and SSR molecular markers and found that all populations of P. triticina from Central Asia and the Caucasus regions were significantly differentiated from that of North America. In addition, P. triticina populations collected in Hebei Province of China based on universally primed PCR (UP-PCR) primers. The Expressed Sequence Tag derived Simple Sequence Repeat (EST-SSR) markers of P. triticina have been developed and used in genetic diversity analysis for P. triticina in Canada and China  based on the results of P. triticina and the molecular mechanism of race virulence evolution.In addition to the methods of the race and virulence identification of nd China . The ESTnd China . P. trit EST-SSR . HoweverP. triticina sampled from a single plant or cultivar were treated as a single sample. The sample treatment and single-uredinial isolate multiplication were performed as described by Leaf rust samples were collected from 10 regions of Hebei Province from 2001 to 2010 during the wheat filling period by researchers from the College of Plant Protection, Hebei Agricultural University  and 20 extra differentials . The susceptible Thatcher was used as a control. When uredinia were fully developed on the leaves of Thatcher approximately 12 days post-inoculation (dpi), infection types (ITs) on the near-isogenic lines were recorded based on a 0–4 scale  method described by P. triticina isolates, two binary matrices based on virulence ITs and EST-SSR data were constructed respectively as described by P. triticina. To evaluate population genetic differentiations, the analysis of molecular variance (AMOVA) was performed using the AMOVA model of Arlequin3.ll software (http://cmpg.unibe.ch/software/arlequin3).To analyze the genetic relationships among selected In total, 247 single-uredinial isolates were selected from Hebei Province from 2001 to 2010, of which 204 isolates were successfully identified as 164 races  when the similarity coefficient was 0.764. While the other 17 isolates, clustered into V2–V13, were quite different compared with other isolates and scattered in different years. These clusters contained rare isolates, such as SHRT07-24 , KHNS08-25, DBGN05-18, DHGK02-17, PHDT08-13, or LBGL04-16. The subcluster V1-2 was the largest subcluster consisting of 110 isolates and included three sub-subclusters (V1-2-1–V1-2-3) at a similarity coefficient of 0.793. The isolates collected from 2001 to 2006 were mainly distributed in the subclusters V1-1, V1-2-1, V1-3, and V1-7, and the isolates collected from 2007 to 2010 were mainly distributed in V1-2-2 and V1-2-3. These results indicated that the populations of P. triticina in Hebei Province from 2001 to 2010 had high polymorphism in virulence phenotypes. In addition, the virulence polymorphism had a certain correlation with the year, and the populations from 2007 to 2010 differed from those from 2001 to 2006.The similarity coefficient of virulence cluster analysis between 204 isolates was 0.53–0.98 by constructing dendrograms of virulence based on Unweighted Pair-Group Method with Arithmetic (UPGMA) method (H) and Shannon’s information index (I) of P. triticina populations from Hebei in 2001–2010 were 0.16–0.27 and 0.27–0.42, respectively. In addition, H and I had similar trends from 2001 to 2010  at a similarity coefficient of 0.74. Cluster S1 contained 214 isolates (86.64%), including two distinct subclusters (S1-1 and S1-2) at a similarity coefficient of 0.758. The isolates of subcluster S1-1 and sub-subcluster S1-2-1 mainly came from 2001 to 2006, those of S1-2-2 were from 2009, those of S1-2-3 were from 2007, those of S1-2-4 and S1-2-6 were from 2008, and those of S1-2-5 were from 2010. While the isolates of clusters S2–S4 were from 2007 to 2009. These results indicated that the molecular genetic structure of P. triticina in Hebei Province from 2001 to 2006 was relatively consistent, and the genetic structure of P. triticina after 2007 had significantly changed compared with 2001–2006. Most of the isolates originating from the same or neighboring years were clustered into the same group or several neighboring subclusters with a high-level similarity, indicating that the genetic structure in these isolates might be related to their occurrence year.The genetic similarity coefficient based on EST-SSR analysis of the 247 isolates was 0.67–0.99   by 2D-PCA plot analysis of virulence Figure 3P. triticina pathotypes with virulence polymorphism or EST-SSR polymorphism, the cluster analysis was carried out on 49 isolates of five predominant races . In the polymorphism analysis based on virulence phenotypes, most of the same races tended to group in the same or adjacent clade , and the correlation coefficient between pathotypes and EST-SSR polymorphisms was 0.025 (<0.3), which was analyzed by the MXCOMP module of NTSYS-pc. These results indicated a correlation between pathotypes and virulence polymorphism, but there was no correlation between pathotypes and EST-SSR polymorphism.In order to further determine the relations of nt clade Figure 5P. triticina isolates tested from 10 years (2001–2010) in Hebei Province have been designated as 10 corresponding populations by the year. The H of these P. triticina populations ranged from 0.13 to 0.26, and I ranged from 0.20 to 0.38, while the highest and lowest levels of genetic diversity were found in 2009 and 2010, respectively  was detected by POPGENE software among the populations in 2001–2010. These results indicated that there was a certain degree of exchange of inoculum sources among populations of different years, which may play a role in the changes of population genetic structures.In addition, the analysis results of POPGENE software showed that the coefficient of genetic differentiation (Gst) was 0.27, of which genetic diversity within the population accounts for 73.08% of the total genetic diversity, and genetic diversity among the populations accounts for 26.92% of the total population genetic diversity. There were also certain genetic differentiations among or within r 27.46% Table 5.P. triticina populations from 2001 to 2010, the population genetic distance was estimated using the POPGENE software (P. triticina could be clustered into three main branches (I–III), 2001–2006, 2007–2008, and 2009–2010. The genetic relationship of the six populations from 2001 to 2006 was relatively closer, while the two populations from 2009 and 2010 had the farthest relationship with other populations, followed by the two populations in 2007–2008. This indicated that the longer the year span, the greater the genetic difference of P. triticina populations.To clarify the genetic relationship between different software Table 6.P. triticina is to monitor the virulence changes based on the infection types of isolates to a set of Thatcher near-isogenic lines or currently planted cultivars, which can directly reflect the changes of pathotypes and virulence of P. triticina. While molecular marker technology can reveal the genetic structure, genetic differentiation, and genetic diversity of pathogens at the molecular level, which can reflect the difference between individuals of pathogens based on the variation of gene or genotype  >1, there was gene flow between populations, which could prevent genetic differentiations between populations. Gene flow has the role of homogenization, and the stronger the gene flow, the higher the similarity and the smaller the differentiation. On the contrary, the lower the gene flow, the lower the similarity and the larger the differentiation. The gene flow generated by the spread and diffusion of spores in different regions may change the gene frequency of the population. In addition, the interannual gene flow was mainly caused by asexual spores overwintering and oversummering, which was generally relatively stable, but low temperature or drastic environmental changes may also cause variations. The north and northwest of Hebei Province are located in mountainous areas, which have fewer wheat-planting areas, so no samples were collected for identification. The main wheat-growing region of Hebei Province is in the northern part of the North China Plain (only including Hebei Province), which is narrow compared with the southern part of the North China Plain . So, the plain is very conducive to the migration of P. triticina by the monsoon, and the gene flow caused by the migration of P. triticina among regions may reduce the diversity of P. triticina in the same year. Therefore, the difference of P. triticina population in the same year may come from the difference of wheat cultivars planted in different regions.Kosman et al.  and KolmP. triticina isolates in Canada. However, similar to the study of P. triticina. So, only 14 pairs of EST-SSR primers in this study were adapted to the genetic diversity analysis of P. triticina in China. Due to the significant difference in virulence of P. triticina between North America and China, we speculated that the genetic difference of P. triticina between the two regions may be the main reason for this phenomenon.Wang et al.  developeP. triticina populations showed that the genetic variations were mainly found within populations, which was higher than among populations  in 2009 and 2010 in China had a significant correlation with the virulence phenotypes, which may be related to the sample size or the regional source differences of the collections of P. triticina. The greater regional disparity, the greater possibility of virulence and genetic variation of P. triticina populations.Moreover, there is a contradiction in the correlation between virulence phenotypes and molecular genotypes of  regions . Howeverin China , which wrn China . WhereasLr2a and/or Lr17, were clustered into the same cluster. Similar phenomena have been found in other studies. Lr14a, were clustered into the same cluster. While some of the same races did not completely cluster into the same group, which indicated that these isolates have genetic differences, although they were of the same pathotype. Zhao and Wang (P. triticina, and the pathogenicity of different P. triticina isolates of the same pathotype was not completely consistent. The more differential hosts were used, the more pathotypes of P. triticina were identified. In this study, in addition to 16 standardized differentials that were used for race naming, 20 extra differentials were used to test the virulence of P. triticina isolates. Therefore, although the same races had the same infection types to those of 16 standard hosts, there may be differences in the infection types to additional extra differentials. For example, isolate THTT10-2 was virulent to Lr15 and avirulent to Lr28 and Lr36, but THTT09-25 was avirulent to Lr15 and virulent to Lr28 and Lr36. THTT09-2 was avirulent to Lr28 and Lr33+34 and virulent to Lr15 and Lr36, but THTT08-27 was virulent to Lr28 and Lr33+34 and avirulent to Lr15 and Lr36. Therefore, although these isolates are of the same pathotype according to the 4-letter nomenclature, some of them were different in the virulence to other Lr genes. With the increase of the differential hosts for pathotype identification, the diversity of isolates may be more abundant.In this study, the results of polymorphism analysis of the predominant pathotypes showed a correlation between pathotype and virulence polymorphism. However, the different pathotypes may also be grouped into the same cluster. For example, THTT and PHRT or PHTT and PHRT, which differed in virulence on and Wang  found th. triticina isolates collected in Hebei Province from 2001 to 2010 on 36 Thatcher near-isogenic lines and detected the EST-SSR genetic diversity. The cluster analysis based on virulence phenotype and EST-SSR genotype showed that P. triticina populations in Hebei Province have rich virulence and genetic diversity. Due to the alternation of wheat cultivars and climatic condition changes, the population structure of P. triticina has changed significantly since 2007. Moreover, there was a certain genetic differentiation among or within populations of P. triticina in Hebei Province, and the genetic variation of within populations was the main origin of virulence phenotype or genetic genotype variations in P. triticina populations in Hebei Province. Our study will provide a theoretical basis for a better understanding of the population structure and genetic variations of P. triticina over a period in Hebei Province of China.In the present study, we identified the virulence diversity of 247 PThe original contributions presented in the study are included in the article/HY and DL conceived and designed the research. LZ and LYZ performed the experiments and analyzed the data. QM participated and assisted in the virulence identification of isolates. LZ, LYZ, and HY wrote the manuscript. HY and DL reviewed and edited the manuscript. All authors contributed to the article and approved the submitted version.


Clethrionomys glareolus). We found that peripheral populations of bank vole in Britain are already at the extreme bounds of potential genetic adaptation and may require an influx of adaptive variation in order to respond. Analyses of adaptive loci suggest regional differences in climate variables select for variants that influence patterns of population adaptive resilience, including genes associated with antioxidant defense, and support a pattern of thermal/hypoxic cross-adaptation. Our findings indicate that understanding potential shifts in genomic composition in response to climate change may be key to predicting species’ fate under future climates.As climate change continues, species pushed outside their physiological tolerance limits must adapt or face extinction. When change is rapid, adaptation will largely harness ancestral variation, making the availability and characteristics of that variation of critical importance. Here, we used whole-genome sequencing and genetic-environment association analyses to identify adaptive variation and its significance in the context of future climates in a small Palearctic mammal, the bank vole ( A species’ response to anthropogenic climate change may depend on its adaptations to past climate changes. Here, the authors use whole-genome resequencing and genetic-environment association to identify genes important for local adaptation and project adaptation under future climate scenarios across bank vole populations in Britain. Because the emergence and spread of new mutations can be slow, adaptation often occurs through the redistribution of existing ancestral variation3, which can be more rapidly acted on by environmental change5. Therefore, the probability of avoiding demographic collapse or extinction largely depends on diversity within a species and whether populations that are already adapted to different climate extremes can provide those alleles to populations facing similar environmental stressors in the future6. Recent genomic studies have confirmed the role of existing  intraspecific variation in adaptation for some species9. However, for most species, the availability of adaptive variation and the potential for it to be accessible for future adaptation is largely unknown. The lack of relevant variation may be a limiting factor for species that need to cope with the pace of human-induced rapid climate change.As global climate continues to diverge from historical norms, species increasingly face conditions outside their physiological tolerance limits to which they must adapt or face extinction5. These scenarios are common for temperate species whose populations have experienced recurrent waves of extirpation and range expansion driven by glacial-interglacial cycles and can facilitate adaptation to new local environments12. For example, the allelic composition of populations colonizing newly available habitats at the end of the last glaciation was often highly heterogeneous, containing genetic variants arising in multiple glacial refugia17. This variation likely provided a useful pool of adaptive alleles to facilitate local adaptation to the diverse environmental conditions that populations encountered during range expansion, but may also be relevant to future climate change. Species whose populations evolved from lineages of different glacial origins and now inhabit geographic  environmental gradients may therefore offer particularly promising conditions for studying the role of standing variation in local adaptation and resiliency to future climate change.One potentially important factor shaping the distribution of variation is historical cycles of divergence and subsequent gene flow between previously isolated populations, which typically increase standing variation18. Small mammals, including voles and shrews, colonized Britain while it was still connected to continental Europe at the end of the last glacial period, in a two-stage process from two distinct glacial refugia for each species18. Based on a range of genetic markers in modern19 and ancient samples20, this process involved a partial displacement of the first wave of colonists by a second after the decline of the first population during the Younger Dryas, with the original group confined to peripheral regions of Britain  while being displaced in areas of Britain closer to continental Europe . However, whether the resulting distribution of genetic variation observed in these species shapes their local adaptation remains an unresolved question. Mixed ancestry combined with a gradient of climatically-driven selection pressure in Britain21, manifested as a transition from a relatively wet and cool periphery to a drier and warmer continent-adjacent area22, makes the species with Celtic fringes excellent model systems to study the effects of standing genetic variation segregating in natural populations on rapid adaptation to new climates.Here we examine adaptive variation and its significance in the context of future climate change in the Celtic fringe, a remarkably human-like geographic pattern of genetic variation that emerged from the end-glacial colonization of Britain by small mammalsClethrionomys glareolus . In the bank vole, admixture of the two colonist waves is associated with a gradient of genomic proportions of two distinct ancestors, with populations in the north (mainly Scotland) largely descended from the earliest colonists, while populations in the south  descended from the second colonization19. The bank vole is an excellent model for studying local adaptation in the Celtic fringe and in admixed populations in general. Not only is it an emerging model species representing forest ecosystems and potentially providing information on a wide range of species colonizing similar habitats, but it also exhibits polymorphism in hemoglobin (Hb), with differences between the two colonists25 shaping evolutionary potential for populations under future climate change26. Bank vole Hb appears subject to climate-driven selection, with two distinct alleles with functional and physiological differences restricted to the colder northern and warmer southern parts of Britain, respectively26. This variation is likely the result of selection on a charge-changing amino acid substitution (S52C) in the β-subunit that significantly increases erythrocyte resistance to oxidative stress25, an important source of selection pressure induced by environmental stressors such as extreme temperatures that affect survival and longevity of organisms under climate change28.We focus on one of the species, the bank vole, 29, genetic-environment association (GEA) approaches offer complementary insights into allele frequencies correlated with climatic variability. These approaches are powerful for detecting loci involved in climate adaptation, particularly when selective forces at individual loci are weak—which is likely for complex traits—and for postulating sources of selection pressure31. Importantly, GEA methods allow the separation of the respective effects of drift and selection in generating and maintaining variability by controlling for patterns of neutral population structure33. Once locally adaptive variation is identified, we can quantify the genomic offset of a population—i.e., the magnitude of change in the genetic composition of a population required to track future environmental conditions36. This provides insights into the risk of disrupting local adaptation  and the availability of adaptive variation needed to reverse disruptions33. This both advances our knowledge of local adaptation and provides insights into whether and how populations can survive in the context of future climates, which is critical for developing sound conservation and management strategies to address global biodiversity loss.Predicting organismal responses to climate necessitates moving beyond individual genes to detect signatures of local adaptation on a genome-wide scale. This will allow us to investigate the genetic basis of complex adaptive traits, such as resistance to climate-induced stress, and model their distribution in the future. While methods that identify targets of divergent selection relative to background differentiation are one important approach33. Britain, although only a subset of the bank vole range, represents an ideal microcosm for the study of genetic-environment associations in this species, combining a broad range of environments with a relatively simple biogeographic history for the species19. Results suggest that regional differences in climate select for traits that influence patterns of population resilience, including genes associated with antioxidant defense. Populations in southern Britain are predicted to have the greatest relative chance of surviving future climate change with standing variation, whereas populations in the north (Scotland) are likely to require an influx of adaptive variation from southern populations, paralleling previous results for Hb26. The concordance between genes associated with high temperatures and genes associated with high altitudes in other mammals may indicate a reciprocal adaptation to thermal and hypoxic conditions.By whole-genome resequencing of 111 bank voles, we identify genes and biological processes important for local adaptation, use future climate models to predict maladaptation across Britain and assess the availability of standing variation required by populations for future adaptationK = 2  separated from the northern cluster while still maintaining a high degree of shared ancestry with the site in southern Scotland (MLN), as expected under a two-phase colonization with ongoing isolation-by-distance  and pcadapt  detected 12,055 and 13,387 candidate SNPs, respectively , isothermality (BIO 03), maximum temperature of the warmest month (BIO 05), and annual precipitation . A total of 1075 SNPs (hereafter candidate loci) overlapped between all three approaches  and the Eftud2 (role in the pre-mRNA splicing process) genes and another 9 adaptive SNPs caused only one amino acid change (Supplementary Data 1). Adaptive candidates included genes involved in response to oxidative stress . Another GO category of interest included actin binding . Additionally, two genes, HBB-T1 and Epas1, frequently implicated in the response to hypoxia together with another 16 hypoxia-linked genes accounted for approximately 16% of all candidate loci , but there may not be a great risk of maladaptation for these populations because they are relatively close to sources of suitable adaptive alleles . Although the predicted genomic offset is lower for the two northernmost populations  and those in southwest England, they are already at the extreme bounds of potential genetic adaptation  and exhibit low standing genetic variation (SGV). When PAI is very high and most adaptive alleles are already or nearly fixed, populations have little potential for future adaptation with the standing variation they possess32. Because climate adaptation is often a polygenic process31, maintaining variability at adaptive loci may increase population viability under future environmental changes. The importance of SGV in local adaptation is supported by the higher SGV at adaptive SNPs compared to putatively neutral SNPs in all populations. Populations lacking adaptive variability may not be able to compensate for climate change over the next few decades without the influx of adaptive variation from other populations. Our results suggest that migration from central England and Wales may provide adaptive genetic material to these Celtic fringe populations; however, the distance to populations with suitable adaptive variants is great . In addition to the distance that alleles must travel, adaptation through gene flow also depends on population connectivity, and habitat fragmentation may prevent the migration of adaptive alleles even if they occur nearby. The bank vole is common in brushy edge habitats in Britain, and its population structure is not particularly high for a small mammal, suggesting that there are no strong geographic barriers and that the spread of adaptive alleles between populations may therefore be a viable adaptation mechanism in this system.Given the rapid pace of climate change, adaptation must necessarily rely on pre-existing genetic variation  within and among populations44. This aligns with expectations that many climate-associated traits are genetically complex, with variation among populations arising from small contributions at many segregating sites31. Polygenic traits may respond quickly to environmental change because the time to a new adaptive optimum is inversely proportional to the number of loci controlling the trait45. Here, we expand the set of candidate genes for bank voles to include 126 SNPs throughout the genome involved in a range of processes  implicated in responses to oxidative stress and reduction of oxidative damage. For example, Prkca is essential in the regulation of oxidative stress, shaping cellular processes by activating the mTOR signaling pathway with impacts on actin stress fibers53. An additional set of candidate genes  encode actin-binding proteins, regulate dynamics  of actin filaments, and reorganize F-actin proteins  during oxidative stress57. In particular, Arhgap32 was one of two genes with two nonsynonymous mutations and had the same allele fixed, or almost fixed, on both sides of the temperature gradient  are among the top candidates for hypoxia adaptation at high elevation in deer mice9. As little altitudinal variation was represented in our study , selective pressures on these genes are unlikely to be linked to altitude. Instead, these genes were associated with the annual mean temperature (BIO 01), suggesting selection of HBB-T1 and Epas1 may also be important in adaptation to thermal and heat-induced oxidative stress. Heat stress causes excessive oxygen demand in cells50 that can lead to inadequate oxygen delivery to the tissues. Consistent with this phenomenon, we detected several genes related to hypoxia  that may be important in the regulation of oxygen homeostasis and blood flow via contraction of the vascular smooth muscle and vasoconstriction under hypoxia60. Similar modifications of adaptive pulmonary function have been described in high-altitude deer mice9. Biological and cellular adaptations to heat may also improve tolerance to hypoxic or ischemic stress, as well as cross-tolerance to different stress stimuli61. These and other genes identified herein support a pattern of cross-adaptation along thermal and hypoxic gradients (similar to refs. 63), with the same genes responding to low oxygen at high altitudes and heat-induced hypoxia at low altitudes. Variation in these genes may be important in adaptation to climate change, particularly in populations and species where elevational range shifts are likely.Unexpectedly, ~16% of adaptive genes identified in British bank voles have previously been linked to responses to hypoxia in mammals in high-altitude environments64, understanding the geographic distribution of this variation and the underlying mechanisms  and drivers  becomes increasingly important. Our findings support a variety of adaptive mechanisms against heat and oxidative stress67 resulting from the cumulative effects of multiple adaptive loci of small effect which vary along environmental gradients. The characteristics and current distribution of standing adaptive variation suggest that bank voles throughout Britain can maintain climate adaptation under warming predicted for the next few decades, but an influx of adaptive variation from southern populations to those along the Celtic fringe may be important in preventing local maladaptation. The degree to which this pattern of relatively high peripheral vulnerability and central resilience is widespread, and the potential for genomic composition to shift in response to climate change, is key to the fate of biodiversity in a future, warmer world.As global warming increases temperature and drives oxidative stressPRJNA1017835.DNA was isolated using the DNeasy kit  from tissue samples  originating from 12 sites across Britain . Data were demultiplexed and converted to fastq formats with bcl2fastq conversion software . Quality control checks were performed by FastQC (v. 0.11.9)69. Reads were then processed with trimmomatic (v. 0.36)70 to remove adapters (ILLUMINACLIP 2:30:10:8) and low-quality bases . After filtering, we verified efficient adapter trimming . Reads for each individual were then mapped to the reference genome72 using bwa-mem algorithm of bwa (v. 0.7.10-r789)73 and resulting files were sorted and indexed with samtools (v. 1.6.0)74. Duplicate reads were identified with picardtools (v. 2.18.5-6) MarkDuplicates (http://broadinstitute.github.io/picard) and removed. Reads mapped well to the reference genome (only <1% pairs were not mapped). The mean coverage across all individuals was 6× (range 3×–46×). Prior to SNP calling, we down-sampled reads from two individuals sequenced with the highest coverage (44× and 46×) to approximately 15% of reads. SNP calling was then performed using samtools mpileup . Afterward, bcftools (v. 1.13)74 call was executed to call SNPs at each genomic position that passed through the quality control of the samtools mpileup step. We restricted our analysis to the 82 longest scaffolds in the reference genome, which account for over 99% of the genome length and include 28 chromosome-level scaffolds, excluding the scaffold representing the X chromosome. We then performed initial filtering with bcftools, removing SNPs that did not meet basic quality requirements , and vcftools (v. 0.1.17) was used to remove indels and SNPs with more than 50% missing genotypes. After this initial filtering step, the dataset contained 50,142,414 SNPs for 111 bank voles from Britain.Sequencing yielded an average of 1.3 × 1077) and inside the areas with mappability scores less than 1 (as defined by GenMap v. 1.3.0 using -k 100 and -e 2 options78). To produce the final dataset, SNPs were quality-filtered, retaining those that satisfied the following criteria: bialellic SNPs , p-value below 10−6, mean depth above 5 (MEAN (FMT/DP) < 5), minor allele frequency (MAF) above 5%, fraction of missing genotypes less than 10% (F_MISSING > 0.1). To remove SNPs exhibiting departure from HWE, we used the best practice recommendation for filtering genomic data (see ref. 79), where Out Within (SNPs removed from sampling location where they are out of HWE) and HWE < 0.001 criteria were applied. Finally, we used plink v. 1.9 (the –indep-pairwise 50 5 0.5 flag80) to generate a pruned subset of SNPs that are in approximate linkage equilibrium. After all filtering, the complete dataset contained 241,099 SNPs for 111 individuals from 12 localities across the bank vole distribution in Britain72.Next, we removed SNPs inside transposable elements , tandem repeats  as a two-dimensional summary of the observed genetic variation. We then used Admixture (v. 1.3.0)82 to estimate the number of genomic clusters (K) and the proportions of the genome derived from these clusters for each vole. The value of parameter K was estimated using the Admixture cross-validation procedure, which was run 10 times with random seeds, each time for values from 1 through 10, with 10 replications for each K.We calculated pairwise 37. We classified SNPs identified as outliers by all three methods as three-way candidates for adaptive loci. Because these approaches use different methods to estimate genome-wide signatures of local adaptation and to correct for higher false positive rates associated with some approaches under certain scenarios , SNPs identified by all three approaches represent high-confidence adaptive targets given the demographic background.We used a combination of three population differentiation and GEA methods to detect signatures of divergent selection in bank vole populations in BritainFST outlier approach and pcadapt—identify SNPs that exhibit above-average differentiation among populations based on the assumption that alleles involved in local adaptation should occur at higher frequencies where they increase fitness and at lower frequencies where they decrease fitness37. We calculated FST84 for each SNP within the set of populations (--within flag) in plink and considered the SNPs with the highest FST values above the 95th percentile threshold as candidate SNPs involved in local adaptation. We then used the R package pcadapt (v. 4.3.3)85 to identify SNPs whose allele frequency is excessively associated with the population structure characterized by PCA. The method performs correlative modeling between individual allele counts and the first K PCs, quantifies the strength of such association by the robust Mahalanobis distance, and identifies outliers by converting Mahalanobis distances to P-values based on a chi-squared distribution with K degrees of freedom. Catell’s rule was applied to the scree plot of variance explained by the first 20 PCs to determine K that best represents the observed population structure. Candidate loci were determined using a significance level equivalent to a genome-wide false discovery rate (FDR) of 0.1 using the qvalue R package (v. 2.15.0)86.The first two methods—the 37. pRDA uses constrained multivariate ordination to model linear relationships between genetic variation and environmental variables that are summarized by PCA33. There are two main potentially confounding factors in GEA analysis: population structure driven by genetic drift with reduced gene flow, which can result in genetic patterns similar to those caused by local adaptation, and recent demographic expansion, which can generate gradients in allele frequencies that are correlated with environmental variables that happen to change along the direction of expansion , thus mimicking a pattern of selective sweep or local adaptation32. To account for such potential confounding effects, pRDA adjusts the relationship between genomic response and environmental predictor variables through a set of conditioning variables, allowing partitioning of the percentage of genetic variance explained by each factor39. pRDA is capable of identifying signatures of weak and/or multilocus selection and adaptation87, and has been shown to have a low rate of false positives and a high rate of true positives across a range of demographic histories87 (but see ref. 83).The third method was partial redundancy analysis (pRDA), a GEA approach that identifies SNPs whose allele frequencies show an unusually strong correlation with environmental variables, suggesting that the alleles provide a selective advantage in a particular environment88 at 30-arc-second resolution and used the pairs.panels function of the vegan (v. 2.6.4) package89 in R to select a set of uncorrelated variables based on thresholds r < 0.7 and variance inflation factor < 1090. We used population ancestry coefficients from Admixture analyses for K = 3 and geographic coordinates of populations as two sets of conditioning variables to estimate the contribution of population structure and geography, respectively, to the observed pattern of genetic variation. We performed pRDA-based partitioning of the percentage of genetic variance explained by each specific set of predictor or conditioning variables using the rda function of the vegan package.We obtained bioclimatic variables from WorldClim v.233 with the first three RDA axes explaining most of the genetic variance associated with the predictors and conditioned the analysis using ancestry coefficients as proxies for neutral genetic differentiation due to isolation-by-distance and demographic history, such as end-glacial colonization32. Similar to pcadapt, we controlled for an FDR by converting P-values to q-values using the qvalue R package. Two different FDR values were used by pRDA to balance the known potential for a high proportion of false positives that may be associated with these approaches83 with the desire to minimize false negatives . Therefore, for most analyses, we focused on SNPs identified as outliers by all three methods but balanced this stringency by allowing a higher FDR . We then applied the more stringent cutoff (0.05) for the FDR to identify the adaptively enriched genetic space for our predictions of the genomic offset38.To identify candidate loci associated with environmental predictors, we performed a pRDA in vegan38. We first used the scores of the environmental predictors on the RDA axes to calculate the adaptive index of individuals in the current environment33. The adaptive index was estimated for each of the three RDA axes and each environmental pixel. We then extrapolated the genetic-environment relationship determined by pRDA to future environments to predict a potential shift in the adaptive optimum due to future climate change. We applied the same procedure used for the current climate to predict the optimal adaptive index using future environmental conditions represented by an average of projections for each of the four bioclimatic variables from six general circulation models for 2081–2100: ACCESS-ESM1.591, CNRM-CM6-1-HR92, FIO-ESM-2-093, GISS-E2-1-G, GISS-E2-1-H94, and MIROC695. In each model, we used the scenario with the highest greenhouse gas emissions (SSP5-8.5), which assumes continued growth of the fossil fuel-based global economy and projects the largest temperature increase96. The Euclidean distance between the adaptive index under current and future environmental conditions for each pixel provided an estimate of the genetic offset, i.e., the shift in adaptive genetic composition in a population that would be required to track predicted climate change. We summed the genetic offset across the three RDA axes and plotted it on a map to show the magnitude of genomic mismatch  for a population under future climate change. We used ArcMap (v. 10.8) and the Esri World Countries dataset (www.esri.com) to create the maps.To understand the impact of adaptive variation across the landscape and under future climates, we subjected the set of SNPs representing the adaptively enriched genetic space to a second pRDA32.We then measured the geographic distance for each pixel in the future adaptive landscape to the nearest population that has an equivalent adaptive index under current climatic conditions. This geographic offset estimates the potential for alleles favorable under future climatic conditions to arrive by migration from another population97. For comparison, we also calculated SGV for putative neutral SNPs, i.e., SNPs that were not identified as outliers by any method. We then estimated the population adaptive index (PAI), a measure of the difference between the adaptive allele frequencies of a given population and the mean adaptive allele frequencies of all populations98. This provides insights into how well-provisioned a population is to adapt in the future , with higher values indicating populations where most adaptive loci are fixed, or nearly fixed, and further response to selection is less likely.Finally, we quantified the amount of adaptive standing genetic variation (SGV) in each population by calculating the mean of the variances of allele frequencies across the adaptive candidate loci99. Similar to pRDA, GF predicts genomic offset as the differentiation for climatically adaptive SNPs between present and future climates at the same location and a geographic offset as the distance from a location in the future climate to a location with minimal offset in the current climate99. We used the R package gradientforest (v. 0.1.34)100 and fitted GF with 2000 regression trees per SNP, a variable correlation threshold of 0.5, and default values for the other parameters34.For comparison, we also estimated genomic and geographic offsets with GF using the approach of Fitzpatrick and KellerFST, pcadapt, and pRDA) on functional changes in bank voles, we used the CLC Genomics Workbench  and the positions of each SNP along the bank vole reference genome to identify SNPs located within coding regions and determined whether changes were synonymous or nonsynonymous. For SNPs located within or less than 1000 bp upstream or downstream of known genes in the bank vole genome, we identified the orthologous Mus musculus gene using the UniProt database101 to find the functional annotation of the gene and search for its involvement in adaptation in other species. To determine whether the list of annotated candidate genes was overrepresented for specific molecular functions, biological processes and cell components102, we performed Gene Ontology (GO)-term enrichment analysis using Panther v. 17.0103 and an FDR adjusted P-value  of 0.05. In addition, we used QuickGO v. 2022-11-18104 to identify the involvement of the genes in particular sub-processes, e.g., response to oxidative stress (GO:0006979), response to hypoxia (GO:0001666) and actin binding (GO:0003779). The Manhattan plots and distribution of allele frequencies were generated using the ggplot2 R package (v. 3.4.2)105.To understand the potential impacts of the three-way candidate loci (i.e., SNPs those identified as outliers by Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Reporting Summary