Timema stick insects, some estimated to be older than 1 Myr. Low genotype diversity, homozygosity of individuals and high linkage disequilibrium (LD) unaffected by marker distances support exclusively parthenogenetic reproduction in six populations. However, in two populations  we find strong evidence for cryptic sex, most likely mediated by rare males. These populations had comparatively high genotype diversities, lower LD, and a clear LD decay with genetic distance. Rare sex in species that are otherwise largely parthenogenetic could help explain the unusual success of parthenogenesis in the Timema genus and raises the question whether episodes of rare sex are in fact the simplest explanation for the persistence of many old parthenogens in nature.Obligately parthenogenetic species are expected to be short lived since the lack of sex and recombination should translate into a slower adaptation rate and increased accumulation of deleterious alleles. Some, however, are thought to have been reproducing without males for millions of years. It is not clear how these old parthenogens can escape the predicted long-term costs of parthenogenesis, but an obvious explanation is cryptic sex. In this study, we screen for signatures of cryptic sex in eight populations of four parthenogenetic species of  These species can reproduce without males, either exclusively (obligate parthenogens) or facultatively  . ParthenArtemia parthenogenetica [Adineta vaga [Macrotrachella quadricornifera [Despite the expected long-term costs of parthenogenesis, several parthenogenetic species are thought to have been reproducing solely parthenogenetically for millions of years ,10. Thisgenetica , and theeta vaga  and Macrornifera ,17, quesornifera ).Artemia brine shrimps it has been demonstrated that cryptic gene flow can be mediated by rare males [Artemia, rare males are documented in many obligately parthenogenetic species [In bdelloid rotifers, males have never been observed , howeverre males . Similar species –23, but  species ). In som species ,26, theyTimema (Phasmatodea). This genus comprises five described parthenogenetic species [Timema is thought to have been reproducing via parthenogenesis for over 1 Myr [Timema are very homozygous as they most likely reproduce via a form of automictic parthenogenesis where heterozygosity is completely lost each generation [Timema species [Timema [In this study, we assess whether cryptic gene flow occurs in parthenogenetic species of the stick insect genus  species –29 which species . These ler 1 Myr , making neration ,32. Rare species ,29,33, a [Timema , if an X [Timema ,36.Timema, we use RADseq genotyping to estimate linkage disequilibrium (LD) in eight populations of parthenogenetic species. A population specific approach is appropriate for studies with these species, as they live in patchy habitats and have very limited dispersal [Timema, LD within and between chromosomes is very low, and there is a consistent LD decay along the chromosomes. In one of these populations, we also find several individuals with unusually high heterozygosity, indicative of recent sexual events. The results presented here are the first evidence for cryptic sex, most likely mediated by rare males, in ‘obligately' parthenogenetic Timema and contribute to the growing evidence that rare or non-canonical sex might have to be considered when studying the long-term persistence of (mostly) parthenogenetically reproducing species.To test for cryptic sex in ispersal . LD can ispersal . Under sispersal ,40. In s. 2 (a)Timema , in a total of eight populations collected between 2010 and 2018. A population of the sexual species T. cristinae was also included to establish a sexual reference for LD and heterozygosity patterns based on RADseq genotypes. We used a population specific approach because Timema are wingless, and it has been estimated that they can travel a maximum of 128 m per generation [T. monikensis, T. shepardi, T. douglasi and T. genevievae, respectively).We looked for evidence of gene flow in two populations from each of four parthenogenetic species of neration , which iT. monikensis (see below) we included additional samples of T. monikensis from different collection years for both populations under study, which included four males from the FS population .We genotyped 24 females for each of the eight parthenogenetic populations  and 24 individuals  for the sexual population . Additionally, because we found evidence for recent gene flow in one population of DNA was extracted from the head or legs  using the Qiagen DNeasy Blood and Tissue Kit, following the manufacturer's instructions. ddRAD libraries were prepared following the protocol from , with th (b)https://github.com/SusanaNFreitas/cryptic_gene_flow). We used the STACKS software (version 2.5) [reeBayes (v. 1.3.3) [vcfallelicprimitives function of vcflib [All scripts used in this pipeline are available online (ion 2.5)  to de-muion 2.5)  and aligion 2.5)  with BWA. 1.3.3)  was used. 1.3.3) , keeping. 1.3.3) , only pof vcflib  was used (c)T. genevievae, T. monikensis, T. douglasi and T. shepardi, respectively. These were then used to infer the number of clones in our populations by estimating pairwise genetic distances between individuals, visualized as neighbour-joining networks /(square root of the sample size)), adding (for the upper limit) or subtracting (for the lower limit) to the mean twice the respective s.e. Relative heterozygosity was estimated for each individual by dividing the number of heterozygous positions by the total number of called polymorphic positions.The total number of SNPs per species was estimated with the R package ADEGENET 2.1.3 ,49. Generdinates ,51, whicbly v1.3 ). The bept the X ,51. r2 v ggplot2  in R. In. 3 (a)Timema populations, as exemplified by the T. cristinae population included here, present high relative heterozygosity and genotype diversity , even between SNPs separated by small distances (b).Sexual see also ). As expvery low a, even bistances b.FigureTimema, strict parthenogenesis would further be associated with very low heterozygosity for all individuals in a population, since their parthenogenesis mechanism leads to the complete or largely complete loss of heterozygosity every generation [Timema populations are undergoing rare sex we would expect to see an increase in the genotypic diversity, and perhaps some individuals with elevated heterozygosity, which would indicate that they were produced via sex.In contrast to the sexual population with high genotype diversity, populations reproducing solely by parthenogenesis are prone to undergo recurrent sweeps. This should result in the fixation of one or few clones (genotypes), with minor genetic differences between individuals within clonal lineages. In neration ,32. On ta; electronic supplementary material, figures S2–3, S4-A and S5-A), and did not decay over increasing genetic distances . We found clear evidence for gene flow in the remaining two populations, with high diversity of genotypes  among the populations of the parthenogenetic species. In six of the eight populations, we found no evidence for cryptic sex, as the observed patterns clearly matched the expectations for a purely parthenogenetic population: genotype diversities were very low T. genevievae—Antonio and T. shepardi—Elk Mountain, or two clonal lineages, T. genevievae—HW20, T. shepardi—Spring Road, T. douglasi—Black Bart Rd and T. monikensis—Sycamore , indicative of a tight linkage between polymorphic positions. There was also no evidence of LD decay , consistent with linkage between polymorphic positions and the absence of recombination. Finally, individuals from all six obligately parthenogenetic populations (including the two for which LD estimates are not informative due to low polymorphism) presented consistently low heterozygosity (The six populations that presented no evidence for cryptic sex (hereafter obligately parthenogenetic populations), had either one clonal lineage, namely Sycamore . Even thzygosity .Figure  (c)T. douglasi—Manchester, and T. monikensis—FS) are, however, much different and best explained by (rare) sexual reproduction between parthenogenetic individuals. Both populations present a high diversity of genotypes, with eight different clonal lineages in T. douglasi—Manchester, but no clonal lineages and only independent genotypes (21) in T. monikensis—FS , suggesting that segregation contributes more strongly to the high genotype diversity in this population than recombination.The patterns in the remaining two populations T. douglasi—Manchester and T. monikensis—FS populations, we investigated whether we could identify sexually produced individuals. Parthenogenetically produced Timema individuals are largely or completely homozygous [Given the evidence for cryptic gene flow in the mozygous ,32. By cT. monikensis females from the population FS showed elevated relative heterozygosity . If produced sexually, males would present elevated heterozygosity, similar to the four heterozygous females, but if resultant from X loss, males would present low heterozygosity levels. All four males were heterozygotic outliers, similar to the four highly heterozygous females T. douglasi—Manchester and the T. monikensis—FS populations we performed three additional complementary analyses. First, we found no evidence that highly heterozygous individuals were triploid, as would be expected from the rare fertilization of parthenogenetically produced diploid eggs . Second, we tested if our LD findings in the T. monikensis—FS population were driven by the heterozygous outliers. To do this, we repeated our LD analyses with heterozygous outlier individuals removed and found that LD and LD decay remained very similar . Finally, to test whether LD and heterozygosity in T. monikensis populations (both Sycamore and FS) were consistent between different years, we analysed additional individuals from previous collection years . We found similar results to our main analyses: no evidence for sex in Sycamore, and strong evidence for cryptic sex in the FS population from low LD , evident LD decay  and heterozygotic outlier individuals .To corroborate our conclusions of cryptic gene flow in the . 4While obligately parthenogenetic lineages are expected to be short lived, many are thought to have been able to persist in the long term, sometimes even for millions of years , we need to rely on indirect evidence. Indirect evidence may include the presence of functional males, unexpectedly high genetic diversity in parthenogenetic populations or signatures of recombination . Here, we searched for signatures of recombination and sex in parthenogenetic species of the genus regation ,54, but  [Timema ,32, thesTimema, the population Manchester of T. douglasi and the population FS of T. monikensis. Cryptic sex, at least in T. monikensis, is almost certainly mediated by rare males, known to occur in Timema parthenogens. Three lines of evidence indicate that cryptic sex within parthenogenetic populations is a much more likely explanation for our findings than ‘contagious parthenogenesis', where males produced by parthenogenetic lineages mate with sexual females and thereby generate new parthenogenetic strains via introgression. First, sexual and parthenogenetic sister species do not overlap geographically in Timema, with the closest known populations being separated by hundreds of kilometres [While we found no evidence for cryptic sex in six populations, we detected signatures of recombination and cryptic sex in two populations of parthenogenetic lometres . Second,lometres , such thT. douglasi), LD within and between chromosomes was generally low (and similar), and a clear pattern of LD decay was evident across all linkage groups. Genotype diversity was relatively high , and clones were often represented by multiple individuals (one to five per clone). Finally, the heterozygosity of all females was as low as in the strictly parthenogenetic populations, indicating they were produced via parthenogenesis. These findings are best explained by rare sexual events that occurred in the past, leading to a high diversity of genotypes, but also allowing for the spread of these genotypes into clonal lineages.The uncovered patterns in the populations with signatures for cryptic sex, Manchester and FS populations, reflected different scenarios. Regarding the Manchester population  were characterized by elevated heterozygosity, and were most likely produced from a sexual cross between different genotypes . Timema parthenogenetic females were previously screened for their ability to fertilize eggs by using crosses with males from related sexual species , but none of the 322 genotyped hatchlings were sexually produced [T. cristinae females mated to T. monikensis males) result in large numbers of hybrid offspring [T. monikensis are capable of reproducing sexually, this suggests that sexually produced offspring were too rare to be detected in the analysed sample of hatchlings, yet we here report that as many as 19% of the adult females of the FS population were sexually produced. One possible explanation for such a frequency difference could be that sexually produced females have a survival advantage over parthenogenetically produced ones. If that is the case, then the maintenance of mostly parthenogenetic reproduction in the FS population in spite of this apparent selection for sex will be an interesting focus for future research.The second population with evidence for gene flow, the FS population of produced . Even thffspring . Given ode facto parthenogenetic, and do not present any evidence of gene flow at present or in the recent past. Two scenarios could potentially explain this patchy occurrence of sex. In the first scenario, a parthenogenetic all-female population can produce males spontaneously (via e.g. X aneuploidies), and parthenogenetic females are then able to mate and produce at least some sexual offspring. Such sexual offspring would comprise males, which would then allow for the propagation of males in the population. In the second scenario, males and sexual reproduction were originally present in all populations since the split with the closest sexual ancestor and were gradually lost or even extinct in the majority of recent parthenogenetic populations. In this scenario, populations that have males at present have somehow maintained rare sex and sexually produced offspring  perhaps through selection. In both scenarios, de facto parthenogenetic populations have been reproducing asexually for the last generations, but we cannot rule out these populations have not undergone episodes of (rare) sexual reproduction in the distant past.Even though we do find evidence for genetic exchange in two parthenogenetic populations, most of the populations analysed here are Timema, an ecologically important short-term advantage of parthenogenesis is the ability to rapidly recolonize areas following fire with a single  individual. As such, the recurrent selection for parthenogenesis following fire, combined with the inherent stochasticity of extinction–recolonization dynamics of fire-prone habitats, may drive a patchy distribution of sex in Timema parthenogenetic populations.Why sex occurs in some populations but not others is unclear and may also be a dynamic process. For example, if sex can emerge spontaneously in a population it may persist for multiple generations before going extinct due to the short-term advantages of parthenogenesis. In species that inhabit fire-prone habitats, such as Timema, including T. monikensis [T. monikensis now raise the question of whether males in other Timema species could also mediate cryptic sex in some populations, albeit much more rarely than in the FS population of T. monikensis. In combination with other recent reports of gene flow in parthenogenetic animal species [As mentioned above, males are known in four out of the five described parthenogenetic species of nikensis –29,33. O species –17, our  species –23), and species ,57, but  species . Further species . Males p species ,58. Acco species –61 and n species  and orib species . Since b species .In conclusion, independently of how many additional parthenogenetic species will be discovered to reproduce sexually (on rare occasions), the fact that at least some presumed obligate parthenogens can reproduce sexually questions our understanding about the plasticity of sexual and parthenogenetic reproduction modes in animals. When classifying a species as sexual, we expect that all offspring are produced by sex, yet many species are capable of spontaneous parthenogenesis . Accordi


An. funestus is a major vector of human malaria and is responsible for high transmissions in sub-Saharan Africa. In the malaria endemic region of western Kenya, it has adapted and colonized different ecological niches owing to its high resistance to pyrethroids and changing breeding environments. The genetic basis of its ecological adaptations to various settings, which could enrich our understanding of how the population is structured or segregated, is poorly understood. This study sought to evaluate the population structure and genetic diversity of Anopheles funestus in different landscapes in western Kenya. To achieve this, the cytochrome oxidase subunit II gene (COII) was PCR-amplified and sequenced. This study revealed an excess of low-frequency variations that are likely due to population expansion or possibly negative selection pressure. Our findings could serve as a guide for future genomic research to facilitate the design of control strategies.COII, was employed to assess the genetic structure and diversity of Anopheles funestus, a very important malaria vector in Africa that adapt and colonize different ecological niches in western Kenya. Mosquitoes were collected using mechanical aspirators in four areas  in western Kenya. Following morphological identification, PCR was used to confirm the species. The COII gene was amplified, sequenced, and analyzed to determine genetic diversity and population structure. A total of 126  sequences of COII were used for population genetic analysis. Anopheles funestus had a high haplotype diversity (Hd = 0.97 to 0.98) but low nucleotide diversity (Π = 0.004 to 0.005). The neutrality test revealed negative Tajima’s D and Fs values indicating an excess of low-frequency variation. This could be attributed to either population expansion or negative selection pressure across all the populations. No genetic or structural differentiation (Fst = −0.01) and a high level of gene flow  were observed among the populations. Population expansion suggests the high adaptability of this species to various ecological requirements, hence sustaining its vectorial capacity and malaria transmission.The mitochondrial marker,  Anopheles funestus group and the Anopheles gambiae species complex [An. funestus group comprises five subgroups, of which three of these subgroups contain at least thirteen (13) species that are identified in various ecological niches across Africa [Anopheles funestus sensu stricto (s.s.) (hereafter An. funestus) belongs to the Funestus subgroup, which has seven members, namely, An. funestus, An. aruni, An. vaneedeni, An. funestus-like, An. confuses, An. longipalpis type C and An. parensis [An. funestus is a major vector that is responsible for high malaria transmission in sub-Saharan Africa. Three of the members of the An. funestus group, An. funestus, An. Rivulorum, and An. Leesoni were found sympatrically in various ecological zones in Kenya [Malaria is a public health problem in Sub-Saharan Africa and is spread mainly through members of the  complex . The An.s Africa . Anophelparensis ,3. Of thin Kenya , Sudan [in Kenya , and Nigin Kenya ,7, suggeAnopheles funestus group can be found only in certain geographical areas in Africa, An. funestus has a wide range of geographical distributions across various climatic types. This vector remains one of the most devastatingly efficient human malaria vectors exhibiting consistently notorious anthropophilic (preferring human habitation), anthropophagic (biting humans), endophilic (indoor resting), and endophagic (indoor biting) behaviors [Anopheles gambiae and Anopheles arabiensis in some endemic areas in Africa [While most of the species of the ehaviors ,9. Its cn Africa ,10.An. funestus can adapt and colonize different ecological niches owing to its high resistance to insecticides and changing breeding environments. A previous study in Kenya revealed that An. funestus breeds in various habitats and co-breeds with An. gambiae sensu lato, Culex spp., and other vectors in the same habitats [An. funestus clusters namely: FUN1, FUN2, and FUN3 in Kenya [habitats . The vechabitats . These ehabitats . A studyin Kenya . The larThe mitochondrial DNA (mtDNA) is sensitive to genetic drift and has a high copy of numbers, and highly conserved primer binding and ease of amplification making it a good marker for interpreting molecular taxonomy, phylogenetic relationships, population structure, and genetic diversity . Indeed,COII has a number of properties such as maternal inheritance, which is devoid of recombination, intraspecific polymorphism, a higher level of differentiation between populations, and small effective population sizes which make it a good marker for studying genetic diversity, gene flow, and population structure [As a mitochondrial marker, tructure . Mitochotructure .An. funestus might influence its adaptation and efficiency of malaria transmission in western Kenya. Delineating the fine-scale population structure of vectors might be useful for investigating the genetic basis of speciation and local adaptation processes. Moreover, understanding the gene flow among An. funestus populations could help to assess their movement in natural populations and, therefore, how the populations are segregated. This study was designed to investigate the genetic structure, diversity, and gene flow of a major vector, An. funestus in a malaria-endemic region of western Kenya using the mitochondrial marker COII.The population structure and genetic diversity of Anopheles funestus mosquitoes were collected from four malaria transmission areas  in four counties in western Kenya from November 2020 to October 2021. The sample collection sites are shown in An. funestus sensu lato was stored in 1.5 mL Eppendorf tubes containing cotton wool and silica gel [lica gel . SamplesThis map was prepared with ESRI ArcGIS Pro 2.8 using field survey results and publicly available datasets. The Open Database License, which is used to make the data available, was used to compile the material on the map from OpenStreetMap and the OpenStreetMap 115 Foundation.®-100 method [An. funestus-specific PCR was conducted to confirm species using the species-specific primers (ITS2A/FUN) that could amplify the internal transcribed spacer region (ITS2) on the ribosomal DNA as described by [COII gene was amplified using forward (5′-TCTAATATGGCAGATTAGTGCA-3′) and reverse (5′-ACTTGCTTTCAG TCATCTAATG-3′) primers [® ToughMix® (5×), and 7.5 µL of PCR water were used. The PCR conditions included initial denaturation at 95 °C for 3 min followed by 35 cycles, denaturation at 95 °C for 15 s, and annealing at 41 °C for 30 s, with extension at 72 °C for 1 min and 30 s and final extension at 72 °C for 7 min. The resulting amplicons were separated by agarose gel electrophoresis in 1.5% w/v agarose gel stained with 2 µL smart glow. The SmartDoc imaging system (Accuris Tm instruments) was used to visualize the DNA bands and were imaged. All the amplicons were bidirectionally sequenced using the primers used in PCR amplification. Sequencing was performed on an ABI PRISM® 3700 DNA Analyzer  platform using the 3730 BigDye® Terminator v3.1 chemistry.Genomic DNA was extracted from the whole mosquito using the Chelex0 method . An. funribed by . The COI primers . A finalThe de novo assembly of paired raw reads was performed using the Geneious prime software version 2022.0.1 . Low-quaAnopheles gambiae, Aedes albopictus, and Culex pipiens pallens were the “outgroup” . The previously reported COII sequences of An. funestus from western Kenya (GenBank accession: MT917174) and other locations in African countries, including southern Ghana (MT917179 and MT917180), northwestern Tanzania (MT917176 and MT917177), eastern Uganda , eastern Zambia (MT917178), and northern Malawi (MT917161), were also included in the phylogenetic tree. The 50% majority rule consensus tree was constructed with Bayesian posterior probabilities of the nodal supports. The output tree was visualized and edited with an online tool called the Interactive Tree of Life (iTOL) v5 [The best-fitting nucleotide substitution model was estimated with the Akaike Information Criterion (AIC) and the Bayesian information criterion (BIC) implemented in MEGA version 11.0.13 . The BayiTOL) v5 .2 = 181.744, df = 189, p = 0.635).A total of 126  amplicon sequences of COII were used for population genetic analysis. A total of 64 haplotypes were identified , suggestAMOVA results showed that there was no genetic differentiation across all four populations (Fst = −0.01). Specifically, our result revealed that there was no genetic differentiation between Port Victoria and Migori (Fst = −0.010), Bungoma and Kombewa (Fst= −0.016), Port Victoria and Bungoma (Fst = −0.020), Migori and Bungoma (Fst = −0.008), Port Victoria and Kombewa (Fst = −0.009), and Migori and Kombewa (Fst = −0.003) . The lacCOII haplotypes. The phylogenetic tree was inferred by Bayesian analyses with the standard deviation of split frequency values (p < 0.01). The phylogenetic estimates from the MCMC analyses strongly supported the monophyletic group (posterior probability = 1) (An. funestus-like (accession # MT917161) from Malawi, with the closest haplotype (Hap 40) coming from the Migori and Port Victoria populations, which border Tanzania and Uganda, respectively. Anopheles funestus samples from Port Victoria and Bungoma (Hap 2) shared a recent common ancestor with An. funestus (accession # MT917175) from Uganda.The best-fit model for nucleotide substitution was identified by MEGA 11 as a Tamura 3-parameter with gamma-distributed rate heterogeneity (T92 + G) according to the Bayesian information criterion for ity = 1) . There wCOII gene revealed the genealogy of each of the observed haplotypes, with haplotypes S1-S20 being shared across the study populations (An. funestus in western Kenya. Port Victoria and Bungoma populations had the most shared mutations (52.9%), followed by Migori and Port Victoria, as well as Kombewa, each at 50%. Port Victoria and Kombewa populations had the least shared number of mutated sites (42%). Nucleotide sequences of the 64 identified haplotypes were submitted to GenBank and assigned accession numbers ON931353-ON931416.Haplotype networks were constructed using the median-joining method in PopART software. Out of the 64 distinct haplotypes identified in western Kenya, 30 (46.9%) were found in the Port Victoria population . Twenty ulations . In the An. funestus population across four counties in western Kenya using a mitochondrial marker, COII. In the context of malaria control strategies, including the genetic alteration of vector species, information on the genetic diversity and population structure of vectors is critical [An. Funestus a highly adaptable dominant vector species [An. funestus population resulting in genetic diversity.This study revealed the population genetic structure of critical . In rececritical . High to species . Due to An. funestus from areas with varied p. falciparum transmission intensities in western Kenya. Generally, An. funestus exhibited low nucleotide diversity, with Port Victoria exhibiting a higher number of haplotypes than other regions. In western Kenya, the high genetic diversity of the An. funestus population compared to coastal regions based on microsatellite markers was reported a decade ago [Plasmodial transmission in these study areas compared to other malaria vectors [Anopheles populations. However, none of these factors served as a barrier to gene flow in the An. funestus population in western Kenya. The Rift Valley is known to serve as a barrier to gene flow [An. funestus populations [This study revealed the signature of population expansion, with weak population structure and high levels of gene flow among the population of cade ago . The obs vectors ,36. Port vectors . This mi vectors . The hig vectors . Mountaiene flow ; nonetheulations .COII gene might be not directly associated with geographical divisions. However, with the excess frequency of rare alleles, a genetic signature for population expansion could persist for a long period, masking any genuine ecological population or genetic structure that may exist [An. funestus populations in western Kenya. The presence of a negative signature of selection on this gene is an indicator of purifying selection, which acts on the gene to preserve the genetic structure by eliminating deleterious mutations [Given the low Fst values and high Gamma St, Nm values, there was a strong indication of a high gene flow between populations as well as high breeding. Not only has a high gene flow reduced the heterozygosity between populations, but it has also resulted in most haplotypes being shared among the western Kenya region, contributing to a weak or lack of population structure. The shared haplotypes were observed between western Kenya and other African countries, suggesting that the genetic diversity of the ay exist . The obsutations .An. funestus population in western Kenya could be the usage of long-lasting insecticidal nets, indoor residual spraying, and insecticide use for agricultural activities, especially those affecting larval breeding sites [An. funestus to various ecological requirements, hence sustaining its vectorial capacity and malaria transmission.With evidence of purifying selection and population expansion (or possibly negative selection pressure), it is possible that evolutionary forces shaping the ng sites ,43. The An. funestus population in western Kenya is under selection pressure leading to demographic expansion and the spread of variants through breeding among varied transmission sites in western Kenya. Population expansion (or possibly negative selection pressure) suggests there is high adaptability of this species to various ecological requirements hence allowing them to sustain their vectorial capacity and transmission of malaria.This study has shown that the


Cyclopterus lumpus L.) has become a large, lucrative industry owing to the escalating demand for “cleaner fish” to minimise sea lice infestations in Atlantic salmon mariculture farms. We used over 10K genome-wide single nucleotide polymorphisms (SNPs) to investigate the spatial patterns of genomic variation in the lumpfish along the coast of Norway and across the North Atlantic. Moreover, we applied three genome scans for outliers and two genotype–environment association tests to assess the signatures and patterns of local adaptation under extensive gene flow. With our ‘global’ sampling regime, we found two major genetic groups of lumpfish, i.e., the western and eastern Atlantic. Regionally in Norway, we found marginal evidence of population structure, where the population genomic analysis revealed a small portion of individuals with a different genetic ancestry. Nevertheless, we found strong support for local adaption under high gene flow in the Norwegian lumpfish and identified over 380 high-confidence environment-associated loci linked to gene sets with a key role in biological processes associated with environmental pressures and embryonic development. Our results bridge population genetic/genomics studies with seascape genomics studies and will facilitate genome-enabled monitoring of the genetic impacts of escapees and allow for genetic-informed broodstock selection and management in Norway.Aquaculture of the lumpfish ( Salmo salar L.) aquaculture industry in Norway [Land- and marine-based farming of aquatic animals (aquaculture) is a lucrative industry that produces high amounts of seafood estimated at a record high 87.5 million tonnes, valued at USD 264.8 billion in 2022, to meet the increasing consumer demand . Aquaculn Norway ,4. SevenLepeophtheirus salmonis K.; specialist; specific for salmonids) and the sea louse  [Sea lice cause direct damage to farmed stocks by feeding on their hosts’ skin, mucous, and blood , with sepecific) ,9,13, copecific) . Consequpecific) ,7,14. Nupecific) . Sea licpecific) ,17 and tpecific) ,18,19. Cpecific) .Oncorhynchus mykiss W.), lumpfish is currently the third most profitable marine aquaculture species in Norway [Aquaculture of the lumpfish has become a large, lucrative industry owing to the escalating demand for cleaner fish to minimise sea lice infestations in Atlantic salmon mariculture farms ,22,23,24n Norway ,26. Howen Norway ,28. Accun Norway ,31,32,33n Norway ,30,31, an Norway ,35,36,37n Norway ,39,40.Gene flow plays a complex and multifaceted evolutionary role in local adaptation as it has the capacity to either enhance or disrupt local adaptation, specifically by spreading advantageous (“adaptive”) alleles or by homogenizing the gene pool ,43,44,45In order to further investigate the genetic basis of local adaptation in lumpfish along the Norwegian coastline, this study builds upon our previous research in population genetics. Our earlier work focused on the lumpfish population genetics using non-functional short tandem repeats (STRs) as genetic markers , followeThe lumpfish is a benthopelagic, cold-adapted marine species found throughout the North Atlantic that is characterised by a globose body, skin that is typically covered with tubercles, the pelvic fins modified to form a sucking disc, and the usual presence of two short dorsal fins ,57,58. Ln = 15) and Canada (n = 15) in the northwestern Atlantic . We normalised each DNA sample to 10 ng/µL in nuclease and ion-free water and stored the working stocks at −21 °C prior to 3RAD library preparation.We primarily utilised the same samples from previous studies to avoid sampling and temporal variation issues between studies ,31, whicMspI (C|CGG) as the frequent cutter and BamHI-HF (G|GATCC) as the rare cutter, which fit the 3RAD Design 2 with ClaI (AT|CGAT) as the third enzyme for suppressing phosphorylated ends in MspI recognition sites. A detailed account of the laboratory procedure of the 3RAD library preparation can be found within the We prepared RADseq libraries using the Adapterama III library preparation protocol of Bayona-Vásquez et al.   for quality control and subsequent final size selection using a one-sided bead clean-up (0.7:1 ratio) to capture 550 bp ± 10% fragments, and the final paired-end (PE) 150 bp sequencing on one lane of the Illumina HiSeq 4000 platform.FastQC [MultiQC v.2.31 [process_radtags.pl included as part of Stacks v.2.51 [c) and discarded reads with low quality scores (-q) with a default sliding window of 15% of the length of the read and raw Phred score of 20 to retain high-quality reads. We specified Read 1 (MspI) and Read 2 (BamHI-HF) restriction enzymes, and we rescued sequence tags  and RAD tags (enzyme overhang) within 2 mismatches of their expected sequence (-r); otherwise, reads were discarded. We truncated (-t) PE150 reads to 140 nt to have equal length among all reads with different barcodes. Second, we downloaded the reference genome of the lumpfish  from NCBI Genome database  under BioProject PRJNA562003, the Vertebrate Genomes Project (G10K Consortium) [SAMtools v.1.9 suite [Qualimap v.2.2.20 [ref_map.pl also implemented in Stacks, which consisted of two components (gstacks and populations). We ran population analysis in ref_map.pl, with (i) a minimum of 75% of individuals in a population required to process a RAD locus for that population (-r 0.75), (ii) a minimum of 75% of populations a RAD locus must be present to process a locus (-p 9), (iii) by discarding unpaired reads (--rm-unpaired-reads), (iv) a minimum mapping quality of 20 to consider a read (--min-mapq 20), and (v) a minimum minor allele count (MAC) of 3 required to process a SNP at a RAD locus (--min-mac 3).After assessing the quality of the sequencing run post demultiplexing to plate-level using the plate-specific i7 indexes using FastQC  plus MulC v.2.31 , we procs v.2.51 . We ran sortium) . Third, sortium) , excludi.9 suite ; we obtav.2.2.20 . FinallyStacks into two datasets, one containing single-copy loci (hereafter singleton SNPs) and the other containing multiple-copy loci . We filtered genotype data and characterised singleton and duplicated SNP loci using filtering procedures and custom scripts available in STACKS Workflow v.2 . First, we filtered the ‘raw’ VCF file keeping only SNPs that (i) showed a minimum depth of four (-m 4) and (ii) were called in at least 80% of the samples in each site (-p 80), (iii) for which at least two samples had the rare allele, i.e., Minor Allele Sample , using the python script 05_filter_vcf_fast.py. Second, we excluded those samples with more than 20% missing genotypes from the dataset. Third, we calculated pairwise relatedness between samples with the Yang et al. [vcftools v.0.1.17 [SNPRelate v.1.28.0 [tiger [_filter_vcf_fast.py, keeping the above-mentioned data filtering cut-off parameters . Fifth, we calculated a suit of four summary statistics to discriminate high-confidence SNPs (singleton SNPs) from SNPs exhibiting a duplication pattern (duplicated SNPs): (i) median of allele ratio in heterozygotes (MedRatio), (ii) proportion of heterozygotes (PropHet), (iii) proportion of rare homozygotes (PropHomRare), and (iv) inbreeding coefficient (ISF). We calculated each parameter from the filtered VCF file using the python script 08_extract_snp_duplication_info.py. The four parameters calculated for each locus were plotted against each other to visualise their distribution across all loci using the R script 09_classify_snps.R. Based on the methodology of McKinney et al. [_split_vcf_in_categories.py for classify SNPs to generate two separate datasets: the “SNP dataset”, based on SNP singletons only, and the “CNV dataset”, based on duplicated SNPs only. Seventh, we postfiltered both datasets by keeping all unlinked SNPs within each 3RAD locus using the 11_extract_unlinked_snps.py script with a minimum difference of 0.5 (-diff_threshold 0.5) and a maximum distance 600 bp (-max_distance 500) to consider linked SNPs. Then, for the “SNP dataset”, we filtered out SNPs that were located in unplaced scaffolds, i.e., contigs that were not part of the 25 chromosomes of the lumpfish genome. We used PGDSpider v.2.1.1.5 [vcftools and performed normalisation using the trimmed mean of M-values method originally described for RNAseq count normalisation and implemented in the R package edgeR [Recent research on marine species indicates that copy number variants (CNVs) are a significant source of genetic variability in genomes ,77. Therg et al.  algorithv.0.1.17 . Additiov.1.28.0 . Then, w0 [tiger . Finallyy et al.  and Dora.2.1.1.5  to transge edgeR . The corBayeScan) and two individual-level methods (pcadapt and OutFLANK). For BayeScan v.1 [pr_odds) at 10,000 after explorative runs , which is appropriate for the number of markers in our datasets [coda v.0.19.4 [BayeScan output in R, we defined a false discovery rate (FDR) q-value threshold of 0.05 and defined the respective α and q combinations to classify SNPs: (i) positive α and q ≤ 0.05 as suggestive of diversifying selection, (ii) negative α and q ≥ 0.05 as suggestive of balancing selection, and (iii) positive α and q ≥ 0.05 as suggestive of neutrality. For the respective individual-level methods, we used pcadapt v.4.3.3 [OutFLANK v.0.2 [K) and used the first 10 components that captured the majority of the population structure in the data. To control the false positive, we set the STF threshold value to 0.05 in the OutFLANK approach and the FDR threshold value to 0.05 in the pcadapt approach. While BayeScan is suitable for our population-level sampling design, PCAdapt and OutFLANK are more reliable for species with complex, hierarchical population structure and are less sensitive to admixed individuals and outliers in the data [We investigated if SNPs were putatively under selection using a population-level method , and within population gene diversity (sH), Wright’s inbreeding coefficient (ISF), and a population-specific index of differentiation relative to all other sampling populations (WTβ) [hierfstat v.0.5-11 [ISF and WTβ values using the bootstrap method (1000 bootstraps) as implemented in hierfstat. We also calculated the number of private alleles at each locus and sampling population with the function ‘privateAlleles’ of the R package StrataG v.2.5.01 [STF estimates using the Weir and Cockerham [pairwise.WCfst’ in hierfstat and computed the 95% confidence intervals of STF values using the bootstrap function ‘boot.ppfst’ (nboot = 1000) as implemented in hierfstat. Indices were considered significant if the 95% credible set did not include zero. We estimated the population genetic differentiation of loci identified as CNVs by calculating the variant fixation index STV [STF estimator of population differentiation [STV as follows:TV is the total variance of normalised read depths among all individuals from populations i and j; iV and jV are the normalised read depth variance for populations i and j, respectively; iN and jN are the sample size for populations i and j, respectively; TN is the total sample size; and -SV is the average of the variance within each population, weighed for population size [i and j individuals and calculated a new STV for every CNV. This process was repeated 1000 times creating a distribution of STV values for each CNV using the R script available at https://github.com/DaRinker/PolarBearCNV (accessed on 10 February 2023). We calculated 95% confidence intervals, and indices were considered significant if the 95% credible set did not include zero. We also established genome-wide standard cut-offs of STV > 0.1 < 0.3 and STV > 0.3 based on the results from population comparisons of a marine species [To characterise genetic diversity, we calculated the average observed heterozygosity (ns (βWT)  using thv.0.5-11 ,95. We cv.2.5.01 . We calcockerham  method wndex VST , an analntiation , commonlntiation ,99,100. ion size ,98,100.  species  and avaiAD [stamppNeisD of the R package StAMPP v.1.6.3 [pcoa of the R package ape v.5.6. [adegenetv.2.1.5 [find.clusters testing up to 30 clusters and using Bayesian information criteria (BIC) to identify the best-fit number of clusters for our data. Using the number of clusters with the lowest BIC value, we performed a DAPC with the adegenet function dapc and plotted the results in R. The number of PCs was set based on the a-score. Third, we inferred the genomic admixture of lumpfish using a sparse non-negative matrix factorisation (snmf) as implemented in the snmf function in the R-package LEA v. 3.8.0 [K values (1–10). We determined the number of ancestral populations (K) using the entropy criterion [Q) were visualised by plotting the Q-values of each individual in a bar plot using the R package pophelper v.2.3.1 [fineRADstructure [RADpainter. Subsequently, clustering was performed with the Markov chain Monte Carlo method of fineRADstructure, running for 500,000 generations and sampling every 1000 generations; the first 200,000 generations were discarded as burn-in . We also inferred a tree for visualisation with fineRADstructure using the tree-building algorithm of Lawson et al. [m T -x 10,000). fineRADstructure results were plotted with R scripts included in the fineRADstructure package. We only used PCAs to visualise the pattern of genetic differentiation based on all CNVs in R.To investigate population clustering, we first computed a matrix of Nei’s genetic distance DA ,102 for  v.1.6.3 . The dise v.5.6. . Second,tv.2.1.5 ,106, botv. 3.8.0 ,108. We riterion ,109. Anc v.2.3.1 . Last, wtructure . We firsn et al.  with 10,2) concentration (hereafter abbreviated as DO), nitrate (NO3) concentration (hereafter abbreviated as NO3), phosphate (PO4) concentration (hereafter abbreviated as PO4), silicate (SiO44−) concentration (SI), dissolved iron (Fe) concentration (hereafter abbreviated as FE), surface partial pressure of carbon dioxide in sea water , and pH (hereafter abbreviated as PH). We obtained these variables from five respective georeferenced datasets describing atmospheric and seawater conditions from the Copernicus Marine Environment Monitoring Service  [KlaR v.1.7-2 [In order to avoid multicollinearity and select the most seemingly ecologically relevant variable, we used the im v.4.3  with a tor (VIF) ,123 amon v.1.7-2  for the STF-based genetic distance matrix calculated from the neutral SNP datasets consisting of the (i) global  and (ii) local (Norway) sampling sites. For SEA, we used distance-based Moran Eigenvector Maps (dbMEM) [geoXY function available in the R package SoDA v.1.0-6.1 [dist function and also transformed geographical distances as in-water distances using the lc.dist function of the R package marmap v.1.0.6 [dbmem function implemented in the R package adespatial v. 0.3-20 [cor.test function to determine the correlation between ‘Euclidean’ and ‘in-water’ dbMEMs. Last, we performed two site-level RDAs on sites’ allelic frequencies and geographical distances using (i) in-water distances and (ii) significant dbMEM with the rda function. To test for IBD, we used the mantel.rtest function of the R package ade4 v.1.7-20 [We implemented the spatial eigenfunction analysis (SEA) and isolation-by-distance (IBD) approaches to evaluate the relative contribution of the geographic distance on multiscale spatial patterns of genetic variation in our system. For both analyses, we used the  (dbMEM) ,126 to m (dbMEM) ,128. Imp.1.0-6.1 . Then, w v.1.0.6 . We gene. 0.3-20 . We usedv.1.7-20  with 10,We carried out a seascape genomics analysis to investigate the possible correlation between environmental variables and the frequency of particular genotypes of lumpfish globally and regionally. Associations of this kind might reveal an environmental constraint requiring adaptation in lumpfish at different scales, as well as the genetic features conferring the selective advantage. For genotype–environment association analyses, we combined univariate and multivariate methods, namely latent factor mixed models (LFMM) and redundancy analysis (RDA). While LFMM identifies associations between single loci and single predictors, RDA can detect multilocus signatures of selection as a function of a multivariate set of predictors ,134. Therda function in vegan v. 2.6-4 [screeplot function in vegan, which suggests retaining only those components whose eigenvalue is larger than the value given by the broken-stick distribution [scores function as implemented in vegan to determine correlations between the PC axis and predictors. Similarly, we applied the same approach to estimate population structure in the genotypic data and determined the optimal number of principal components (PCs) to retain. After determining how many PCs explained most of the genotypic variance  and predictor variance, we conducted the LFMM analysis with the newer lfmm2 implementation that uses a least-squares estimation method to compute LFMMs using the R package lffm v.1.1 [lfmm_ridge function for estimating the parameters of LFMMs, where the ridge estimates are based on minimising a regularised least-squares problem with an L2 penalty [lfmm_test to perform association testing using the fitted model and calibrated p-values using the gif option that calculated the genomic inflation factor . The lfmm_test function returns p-values describing the statistical significance of every genotype–environment association under different values of K. For each association model related to the same environmental variable, we converted GIF-adjusted p-values to q-values and applied the FDR control method to correct for multiple testing with the qvalue function of the R package qvalue v.2.28.0 [q < 0.05 under at least one level of K.To apply LFMM, we first used the R function v. 2.6-4  to perfov. 2.6-4  with theribution ,139. We fm v.1.1 . We impl penalty . We thenv.2.28.0 , and therda function for modelling genotypes as a function of predictor variables and produced as many constrained axes as predictors [ANOVA.cca’ function, and we then used the significant axes to identify candidate loci. Candidate loci were identified using a Mahalanobis distance-based approach [p-values using the genomic inflation factor (λ) and setting FDRs to q = 0.05, as described above.We then performed individual-level RDA using the edictors . We asseapproach , which mSnpEff v.5.1 [BLASTp [UniProtKB/Swiss-Prot database (Release 2023_01) [–7. Lastly, we performed pathway and process enrichment analysis for Gene Ontology terms, KEGG Pathway, Reactome Gene Sets, and WikiPathways among the predicted genes using the Metascape web server v.3.5.20230101 [Given that landscape/seascape genomics analyses can suffer from the issue of false positives, we annotated the genomic neighbourhood of each SNP associated with a particular environmental variable(s), i.e., environment-associated SNP, and determined whether the molecular functions of the genes surrounding an environment-associated locus were consistent with an assumed adaptive role. For every environment-associated SNP, we performed the annotation procedure as follows. First, we annotated environment-associated SNP with the NCBI General Feature Format (GFF) annotation file of the reference genome of the lumpfish using ff v.5.1 , and we ff v.5.1 ,144,145. [BLASTp  against 2023_01) . For eve20230101 .process_radtags recovered a high percentage of retained reads (92.7%) with a mean of 1.2 million reads per sample. After removing samples that did not sequence well, with <500,000 retained reads, the gstacks program generated a catalog of 457,751 putative RAD loci with a mean insert length of 442.8 bp (±155.7 bp) and a mean effective per-sample coverage of 18× (±14.3×). After the initial SNP filtering with the populations program, we retained 57,840 RAD loci composed of 40,972 SNPs for the global dataset and 60,540 RAD loci composed of 40,877 SNPs for the local dataset. After secondary filtering, thinning the step in STACKS Workflow, purging closely related individuals, and removing SNPs within non-chromosomal scaffolds, we retained a final panel of 21,202 and 14,613 polymorphic SNPs for the global  and regional  datasets, respectively  and regional  sampling populations. From these datasets each, we derived a SNP subset dataset of 150 SNPs with the highest locus-specific STF. We identified 803 CNV, and after normalizing the data and removing all CNV with a variance of zero, we retained a final panel of 27 CNV for each dataset.Although our HiSeq4000 run yielded approximately 330 million PE reads, the total number of raw reads reported in this study is 162,423,062, and the rest of the reads correspond to those from other studies on the species in our group. Processing of raw Illumina data by the program ectively . We deteOH and SH ranging from 0.135 to 0.163 and 0.135 to 0.165, and ISF ranging from −0.023 to 0.056  harboured the highest PA of 243 and 180 alleles. Within Norway, the northernmost sampling populations NAL and NHE harboured the highest PA of 25 and 8. The overall βWT index for the global dataset was moderate , where the highest observed WTβ within sampling populations was among lumpfish from NRG  and CAN . Whereas the overall βWT index for the regional dataset was shallow , where the highest observed WTβ was among lumpfish from NRG  and NSA .The overall patterns of the genome-wide diversity dataset for global and regional samples were similar across sampling populations, with to 0.056 . Most noSTF ranging from −0.002 to 0.182 , where outgroup sampling populations were the most divergent followed by the southern Norway sampling population NRG , where NRG and NAU were the highly differentiated sampling populations. We found a similar trend of a limited discernible geographic pattern with the CNV dataset  and the northwestern Atlantic sampling populations shared more co-ancestry with each other than between Norwegian sampling populations in the northeastern Atlantic , where samples from respective regions formed individual clusters with a hierarchical pattern of genetic structuring within Norway. Overall PCoA clustering patterns revealed at least three main genetic clusters based on the first three principal components (PCs), which together accounted for 26% of the genetic variation in the global datasets A. A simiectively A,B. Notaectively A,B. FinaAtlantic . GenotypAtlantic . MoreoveIn contrast to the SNP datasets, the global and regional CNV data revealed three and two clusters, respectively, which do not correspond to the geographic proximity among the sampling population . The gloSTF and Euclidean geographic distance  and in-water geographic distance  with the Mantel test, indicating a significant effect of IBD in the global dataset. However, within Norway, we detected no significant effect of IBD with the regional dataset . We retained the first four axes from the PCA of Hellinger-transformed allele frequencies, which cumulatively explained 56.8% of the total neutral genetic variation for the Norwegian sampling populations. Forward selection of the Euclidean dbMEM variables identified two significant predictors (dbMEM6 and dbMEM8) representing regional-scale spatial structure. RDAs with only the selected dbMEM variables were significant (p = 0.013) with a coefficient of determination (adjusted R2) of 22.3%. The first two RDA axes were marginally significant and explained 20.8% and 18.8% of the genetic variation summarised in the PC axes, respectively  and eastern Atlantic (Norway), congruent with previous findings ,31,32,33The intensity of divergent natural (or Darwinian) selection frequently fluctuates in heterogeneous environments across ecological clines , resulting in genotype–environment interactions for Darwinian fitness ,161. MarFunctional annotation analyses of the environment-associated RAD loci revealed that the gene sets linked to these loci play a key role in biological processes associated with environmental pressures. The retrieved top GO categories  have been reported in other studies that measure transcriptomic responses under experimentally induced heat stress ,166, oceIn the upheaval of environmental change and commercial exploitation of the lumpfish, applied genomics is paramount for efficient conservation and sustainable species management. This study conducted analyses of population structure on all SNPs but also independently on putatively neutral and outlier SNPs to aid in the inference of neutral versus adaptive processes underlying the observed genetic patterns. Our study provides important clues on the definition and delineation of stocks in Norway and their potential to respond to environmental variability. Additionally, our analyses of population structure support the subdivision of lumpfish into at least two stocks in Norway, where one of the southernmost sampling populations, NRG, was the most divergent sampling population. Our regional sampling regime allowed us to resolve the contradictory evidence relating to genetic homogeneity versus structuring among Norwegian lumpfish and identify the likely causes for the conflicting results of the spatial scale of sampling design given the complex bathymetry, topography, and oceanography of the coastline. Moreover, the patterns of local adaptation in lumpfish uncovered in our study provide further valuable information that can help identify the best potential source populations for broodstock development and provenance sourcing and to identify escapees from farms.Future sampling design should focus on embayment and/or fjord locations shielded from ocean currents to account for habitat patchiness with increased likelihood to capture genetic disjunction at smaller spatial scales. This is crucial in order to accurately investigate the number of genetic clusters and to avoid misattributing them to interbreeding between farmed escapees and wild individuals. Furthermore, it is imperative to conduct a comprehensive investigation into the mechanisms of local adaptation under high gene flow by employing a population transcriptomics framework. This approach will allow for a conclusive examination of the genetic and expression patterns, as well as an exploration of adaptive divergence at various levels, including sequences, genes, and biological metabolic pathways, among natural populations. This will further establish fundamental data regarding the genetic makeup of broodstock and assess the impact of artificial selection on adaptive polymorphisms, as well as their potential linkage with causal variants underpinning production-related traits in breeding programs.


Carya illinoinensis), caused by A. alternata, in South Africa is known. The pecan industry is growing rapidly, so it is essential to understand the impact of the fungal pathogen to pecan health. In this study, the genetic variation of 364 A. alternata isolates was investigated by two RAMS primers (CCA5 and CGA5). In total, 6,525 alleles were produced, with a minimum of 3,182 alleles on the CGA5 primer and maximum of 3,343 alleles for CCA5 primer. Further analysis of the primers showed relatively low genetic diversity of A. alternata isolate populations, with mean values; (H = 0.12) and Shannon’s information index (I = 0.20). The analysis of molecular variance (AMOVA) revealed significant differences between populations, with 88% of the genetic variation was found within populations , and were not significantly different (p > 0.001). While 12% variation was observed among populations  and the estimates were statistically significant (p < 0.001). STRUCTURE HARVESTER output showed that K value is K = 8, where ΔK cannot find the true number of populations because of less variation. The dendrogram cluster tree generated by Ward’s analysis unveiled two main distinct clades and 10 sub-clades, revealing similar findings as those of PCoA analysis clusters. Therefore, it was evident that these analyses depicted no distinct relationship between the A. alternata isolates and their geographic locations or the prevalence of distribution among the populations.Limited information regarding the occurrence of black spot disease of pecan ( Carya illinoinensis (pecan) is one of the premier tree nut crops experiencing a rapid upsurge in nut production and sales across the global tree nut market  in plants , random amplified polymorphic DNA (RAPD) and inter-simple sequence repeat (ISSR) markers were successfully used to determine the genetic variability of A. alternata causing black spot in potato in China (Alternaria species causing early blight of potato (Solanum tuberosum) and tomato (Solanum lycopersicum) in Algeria and India  in South Africa without the need for expensive sequencing  have also been successfully used to assess the intraspecific genetic variation among 112 isolates of an A. alternata population from Pinus tabulaeformis in India (Random amplified microsatellites (RAMs) are based on Polymerase Chain Reaction (PCR) and is effective to detect high polymorphism levels for genetic diversity studies . RAMs marma spp. , and Perfraxinea . RAMs main India  and Chinin India .A. alternata populations, sampled from pecan leaves, nuts-in-shucks, and shoots in the FS, EC, GP, KZN, LP, NC, and NW. This is to test the hypotheses that population subdivision exists among isolates of A. alternata based on geographic location. The information derived from this study could provide insight to the genetic diversity of this pathogen and help identify future genetic threats that could have a positive impact on the pathogenic ability of A. alternata on pecans.No data have been generated regarding the genetic diversity of the ABS pathogen across the major pecan production regions in the Free State (FS), Eastern Cape (EC), Gauteng (GP), Kwazulu-Natal (KZN), Limpopo (LP), Northern Cape (NC), and North West (NW) provinces of South Africa. This hinders effective management of the pathogen. In the current study, RAMs was used to evaluate the genetic variation within and between A. alternata isolates sampled from eight geographical locations were used in this study  (Genomic (gDNA) was extracted from the newly isolated cultures using the ZR Quick DNA Fungal/Bacterial Microprep™Kit . The DNA concentration and purity were determined with the NanoDrop Lite ND-2000 spectrophotometer . The acquired gDNA purity for all the A280 nm) , with thA280 nm) .5 and CGA5) , and ultra-pure dH2O to a final volume of 25 μL. The cycling conditions consisted of an initial denaturation step of 3 min at 95°C followed by 32 cycles of 20 s at 98°C, annealing 30 s at 56.3°C for CCA5 and 53.8°C for CGA5, extension at 72°C for 40 s, and a final extension step of 3 min at 72°C. The PCR reactions were performed in a T100TM Thermal Cycler . For confirmation, RAMs amplicons were stained with GelRed nucleic acid stain (Thermo Fisher Scientific), separated by 5% agarose gels electrophoresed at 100 V—constant power, estimated using 1 Kb DNA ladder molecular weight marker, and visualised under UV light “Gel Doc EZ Gel Documentation System” (Bio-Rad).DNA samples were amplified in two separate PCR, using two modified labelled degenerate RAMs primers (CCAnd CGA5)  as descrTM) to analyse amplicons amplified with RAMs primers labelled with fluorescent dyes, namely, 6-FAM and VIC (Thermo Fisher Scientific). The total reaction volume of 10 μL consisted of 8.6 μL Hi-Di™ formamide (Thermo Fisher Scientific) mixed with 0.4 μL GeneScan™2500 ROX™ and 1 μL of PCR product. Only the fragments shorter than 1,060 base pairs (bp) were considered reproducible because of the limited fragment size  of the Genetic Analyser capillary. Data containing the alleles , using standard protocols. The Genetic Analyser used matrix standard dye set  was determined with the following parameters: Alleles: Na , Ne , I (Shannon’s information index), H (observed diversity), Uh (unbiased expected diversity), and PPL (percentage of polymorphic loci) by using GenAlEx v6.5 software. Notably, identical samples of individuals were identified using the probability identity matrices.GenAlEx v6.5 software was used to evaluate the genetic diversity within the 8 ulations . Each frGD) for the combined data by covariance-standardised method as implemented using the same statistical package. Genetic structure for the eight populations was analysed using STRUCTURE v2.3.4  method described by K). The genetic relationships between isolates were further estimated by submitting the combined data set to a cluster analysis (Ward´s method) using Past 4.05 package  was evaluated using GenAlEx v6.5 . The anaE v2.3.4 . K-value package .5 primer and 15 and 1,060 bp for CGA5 primer (data not shown). The result of the band patterns of the combined primers across populations revealed that there were 4,319 (66%) polymorphic bands, 142 (2%) bands were unique to a single population and 2092 (32%) were monomorphic bands (Freq. >= 5%) found in 50% or fewer populations  to 1.61 (NW), and the number of effective alleles ranged between 1.16 (NW and MP) and 1.23 (NC). The Shannon’s information diversity index (I) ranged from 0.16 (GP) to (LP and NC), with average at 0.20. Nei’s observed genetic diversity (H) ranged between 0.11  and 0.15 (NC). Nei’s unbiased genetic diversity (Uh) ranged from 0.11 (MP) to 0.15 (LP and NC). The mean expected diversity for the total of isolates was 0.13, indicating about 13% were expected to be heterozygous at a given locus. There was a slight increase in the mean value of Nei’s unbiased estimate of expected genetic diversity (Uh), contrary to the observed diversity (H) across both individual primers and combined for each population.The results showed various levels of genetic diversity indices of ulations . The twop < 0.001) within and among A. alternata populations (p > 0.001) genetic differentiation . A 12% variation was observed among populations with significant (p < 0.001) genetic differentiation . The estimated gene flow value of 3.59 and 2.89 indicated high gene flow of A. alternata within and among populations, respectively. Nm is the product of population size and the proportion of the number of alleles exchanged between populations per generation  showed that there was a significant difference   depictedenotypes , where tK value as K = 8 , EC (25), LP (57), and NW (92). A small number of isolates from MP (3), NC (3), KZN (6), and FS (7) were found in the clade. Five subclades  were formed within clade 1. Subclade “1A” contained 16 isolates from NW, while subclade “1B” had 70 isolates distributed amongst LP (9), MP (3), NC (3), EC (3), KZN (1), and NW (46). Subclade “1C” consisted of 51 isolates from KZN (5), EC (22), LP (7), and NW (17). Twenty-six A. alternata isolates from FS (1), LP (14), GP (2), and NW (9) were grouped in subclade “1D”, and sub-cluster “1E” had 35 isolates from FS (1), GP (3), NW (4), and LP (27).The dendrogram cluster tree generated by Ward’s analysis unveiled two main distinct clades consisting of 10 subclades  that revA. alternata isolates between EC (3), FS (4), KZN (4), NW (5), LP (6), MP (7), NC (14), and subclade “2E” included 25 isolates from KZN (5), NC (6), MP (7), and LP (7).Clade 2 had a cluster pattern similar to clade 1, comprised of 166 isolates, the majority of which came from FS (30), KZN (31), MP (39), and NC (42) and few isolates from EC (5), NW (6), and LP (13). Five subclades were distinguished . Subclade “2A” had 11 isolates from EC (1), and NC (10), subclade “2B” consisted of 62 isolates from EC (1), FS (11), NC (12), KZN (13), and MP (25), and subclade “2C” had 25 isolates shared between NC (1), KZN (9), and FS (15). Subclade “2D” had 43 A. alternata populations sampled from South African pecans (C. illinoinensis) in the FS, EC, GP, KZN, LP, NC, and NW. The study is the first to document the use of RAMs primers to evaluate the genetic diversity of A. alternata isolates causing black spot disease on pecans in South Africa. The analyses showed low genetic diversity in all A. alternata populations. There were no major dominant genotypes present across all provincial geographical areas, but two major groups from the Northern and Southern parts of South Africa were formed. This fungus has low genetic variation, which has positive implications for the efficacy of fungicides and reduced genetic fitness due to the low pathogen variability.The present study was conducted to evaluate the genetic diversity and population structure in A. alternata. In this study, RAMs assisted to produce a high number of 6,525 alleles from the 364 A. alternata isolates across the eight populations. The percentage polymorphism of the number of alleles was above average and effective alleles. The number of alleles obtained exceeded the only previous study reported to describe the genetic diversity (H = 2.79) of 112 A. alternata isolates from Pinus tabuliformis in China , number of observed alleles (1.19), number of effective alleles (1.19), Shannon’s information index (0.20), Observed diversity (0.12), and Unbiased expected diversity (0.13) across the eight populations. Similar, but generally lower numbers. Similar levels (H = 0.11 and 0.16) of genetic diversity was found by A. solani and A. alternata, known to cause early blight in potato (S. tuberosum) and tomato (S. lycopersicum). In South Africa, A. solani isolates from potatoes (S. tuberosum) using similar RAMS primer combinations. In contrast, relatively high genetic diversity values  were reported for A. alternata populations across the mainland in the United States  for the combined dataset also explains the level of genetic variability in the populations. High level of gene flow can only indicate a more homogeneous population structure and a lower degree of genetic differentiation among populations of A. tenuissima from wheat kernels in Russia (Nm = 3.59) within populations and among populations (Nm = 2.86) in the present study can be associated to the low genetic differentiation. Similar to those reported in other studies by , where all individual isolates tested share genetic information  inherited from the sub-groups. Thus, this corroborates the presence of high potential of genetic admixture and closed relationships among isolates of the different sampling pecan sites as a result of high gene flow. In accordance with STRUCTURE analyses in this study, no population substructure in A. alternata populations were found from wheat (Triticum aestivum) in Kazakhstan (Citrus spp.) in Morocco  in China revealed two subpopulations that showed the ability to recombine through many parasexual cycles of asexual propagation with fewer cycles of cryptic sexual reproduction (MAT1-1 and MAT1-2).These overall findings have implications for the management of ABS on South African pecans. Firstly, asexually reproducing populations of ent loci . A propooduction , based oA. alternata can be ascribed to the nature of microsatellites. For example, A. alternata , which makes it difficult for A. alternata spores to be disseminated through wind for such a distance. Long-distance movement of spores can most likely be attributed to human-mediated gene flow, which has been reported in A. alternata populations in potato growing areas in China (A. alternata could already be present in the plant tissue in nurseries. Another possibility could be that the diversity of genotypes was already present in the environment from different plant hosts, soils, and decaying organic material. This then acts as a reservoir of inoculum to infect pecans, especially newly planted seedlings, or planting of trees in virgin land.Large quantities of fungal spores can be spread mostly through wind, rain, and human-mediated dispersal . South Ain China . How pecin China  and A. aA. alternata across pecan orchards in South Africa. Low genetic diversity was established in A. alternata populations, brought about by the high gene flow within and among the populations. This may explain the non-correlation with the geographic distribution of the A. alternata pathogen, which is predominantly isolated in the eight major pecan producing provinces. Cautious intervention could be implemented by pecan farmers when applying different fungicides that can specifically inhibit the growth of this fungus. Finally, the production of pathogen free pecan trees in nurseries, whether through resistance breeding or careful selection of plant material, could improve the control of ABS, since pathogen populations with high evolutionary potential are more likely to overcome host genetic resistance (This study laid the groundwork for future studies on the population of sistance .


Climate fluctuations during the Pleistocene and mountain uplift are vital driving forces affecting geographic distribution. Here, we ask how an annual plant responded to the Pleistocene glacial cycles.Swertia tetraptera Maxim (Gentianaceae) endemic to Qinghai-Tibetan Plateau (QTP). A total of 301 individuals from 35 populations of S. tetraptera were analyzed based on two maternally inherited chloroplast fragments (trnL-trnF and trnS-trnG). Phylogeographic analysis was combined with species distribution modeling to detect the genetic variations in S. tetraptera.In this study, we analyzed the population demographic history of the annual herb S. tetraptera was high, likely due to its wide natural range, high proportion of endemic haplotypes and evolutionary history. Fifty-four haplotypes were identified in S. tetraptera. Only a few haplotypes were widespread , which were dispersed throughout the present geographical range of S. tetraptera, while many haplotypes were confined to single populations. The cpDNA dataset showed that phylogeographic structuring was lacking across the distribution range of S. tetraptera. Analyses of molecular variance showed that most genetic variation was found within populations (70.51%). In addition, the relationships of the haplotypes were almost completely unresolved by phylogenetic reconstruction. Both mismatch distribution analysis and neutrality tests showed a recent expansion across the distribution range of S. tetraptera. The MaxEnt analysis showed that S. tetraptera had a narrow distribution range during the Last Glacial Maximum (LGM) and a wide distribution range during the current time, with predictions into the future showing the distribution range of S. tetraptera expanding.The genetic diversity of S. tetraptera is likely to have been shaped by Quaternary periods. Multiple microrefugia of S. tetraptera existed during Quaternary glaciations. Rapid intraspecific diversification and hybridization and/or introgression may have played a vital role in shaping the current distribution patterns of S. tetraptera. The distribution range of S. tetraptera appeared to have experienced contraction during the LGM; in the future, when the global climate becomes warmer with rising carbon dioxide levels, the distribution of S. tetraptera will expand.Our study implies that the current geographic and genetic distribution of The online version contains supplementary material available at 10.1186/s12870-023-04471-w. Howeea level . Among aea level , whereasUsing the phylogeographic method , the phyThe history of a species in space as well as time can be revealed by phylogeography , howeverSwertia tetraptera, belonging to the genus Swertia in the Gentianaceae, is an annual herb endemic to the QTP. The main distribution of S. tetraptera is in Qinghai, Gansu, and Sichuan Provinces, occurring primarily in moist hillsides and shrub locations with an elevation of 2,000–5,000 m. The main characteristic that distinguishes S. tetraptera from other Swertia plants is its heteromorphic flowers, that is, every plant has two kinds of flowers: normal ‘open-pollinated’ flowers and ‘closed’ or cleistogamous flowers. As an endemic species of the QTP, S. tetraptera formed within its strong uplift (approximately 3.97 Ma) [S. tetraptera based on molecular biology have been reported [S. tetraptera based on only one chloroplast DNA (cpDNA) fragment. However, this previous study did not concentrate on phylogeographic structure or deep evolutionary history.3.97 Ma) . Thereforeported , 34. Yanreported  clarifieS. tetraptera were collected from the entire geographic distribution in the high-altitude QTP and adjacent areas. To characterize the population histories of this species, two chloroplast DNA (cpDNA) markers were used to detect genetic variations. The aims were (1) to explore the genetic structure and diversity level of S. tetraptera in the Qinghai-Tibetan Plateau; (2) to identify the evolutionary history of S. tetraptera; and (3) to infer on the reasons for the existing geographic distribution patterns of S. tetraptera.In the current study, 301 individuals from 35 populations of trnL-trnF and trnS-trnG) were applied to analyze 301 individuals from 35 populations of S. tetraptera. The total length of the fragments was 1215 bp, and the lengths of the trnL-trnF and trnS-trnG regions were 761 and 454 bp, respectively, which included 22 mutation sites  within the 35 populations ranged from 0.182 to 0.982, and nucleotide diversity (π) ranged from 0.00015 to 0.00362 (Table HT) was estimated to be 0.912, and the average gene diversity within populations (HS) was 0.768. The values of NST and GST were 0.158 and 0.315, respectively. No phylogeographic signal in the haplotype distribution was detected by means of a standard phylogeographic analysis because NST was non-significantly larger than GST. AMOVA suggested that most of the genetic variation was found within populations (70.51%) as opposed to between populations (29.49%)    Fig. , while p) Figure . However) Figure  and 3. TS. tetraptera. The results showed that Tajima’s D was − 0.12975 (P < 0.001) and Fu’s Fs was − 1.28549 (P < 0.001); that is, all were significantly negative values, and the observed mismatch distribution analysis results were single-peak curves . The results indicated that the distribution area or quantity of S. tetraptera in its current distribution area had experienced a recent expansion.DnaSP software was used to test the neutrality of all populations based on the chloroplast data of S. tetraptera during three time periods, LGM , current (1960–1990), and future , based on selected predictor variables , BIO03 (100)), BIO04), BIO10(Mean Temperature of Warmest Quarter), BIO11(Mean Temperature of Coldest Quarter), BIO14 (Precipitation of Driest Month), BIO15, BIO18 (Precipitation of Warmest Quarter)  Table . The modurrent 190–1990, aurrent 190–1990, aurrent 190–1990, aent Fig. .S. tetraptera in each period, the potential distribution changes of S. tetraptera from LGM to current and from current to future were calculated , and the ones with good models were finally selected as predictions . Taking the fitness index value 0.0612 as the threshold value for the existence and distribution of ted Fig. . The greS. tetraptera in eastern and central of Qinghai-Tibetan Plateau has contracted, while the distribution area in few regions of southern Qinghai-Tibetan Plateau has expanded  and π (nucleotide diversity) of S. tetraptera were 0.908 and 0.0257, respectively. According to statistics, these values were higher than those of other herbaceous plants on the QTP, such as Rhodiola chrysanthemifolia  [Notopterygium incisum  [Meconopsis integrifolia  [S. tetraptera. The high level of genetic diversity in S. tetraptera may have been caused by the following. First, there is a strong correlation between plant genetic diversity and the geographical distribution of species [S. tetraptera is an annual herb, it has a wide distribution range and is widely distributed in Qinghai, Gansu, Sichuan and the Tibetan Plateau, China. It can thus be predicted that S. tetraptera should have a high level of genetic diversity. This is consistent with the genetic diversity parameters obtained from our analysis of 35 populations. Second, the proportion of endemic haplotypes was high in the populations of S. tetraptera, which may have correspondingly increased the level of diversity within populations. Third, the high genetic diversity of S. tetraptera may be related to its evolutionary history. According to a previous study, Swertia originated in the early Miocene of the Tertiary (29.60 Ma), and the differentiation of S. tetraptera was completed in the late Pliocene of the Tertiary (3.97 Ma)  , Notopte0.00086) , and Mec.000144) . Althoug species –39. SpecGST=0.315, FST=0.2949) indicated a low degree of differentiation among populations of S. tetraptera. This may be caused by sufficiently strong gene flow between populations [S. tetraptera and which were endemic to most populations, suggesting that gene flow between populations was limited. Although the mechanisms of pollen and seed dispersal in this species are not well understood, a previous study showed that outcrossing and self-crossing existed simultaneously in S. tetraptera [S. tetraptera. Thus, rapid intraspecific diversification was one of the reasons for the low degree of differentiation among populations of S. tetraptera. Of course, it is possible that other factors, such as hybridization and/or introgression, contributed to the low level of differentiation between populations in S. tetraptera, but we do not currently know whether this phenomenon exists in this species. According to our long-term observations in the field, it was found that the distribution of S. tetraptera and Halenia elliptica was sympatric. When plant spacing between sympatric species is less than 10 cm, geographical opportunities for hybridization and/or gene introgression between them are provided. Therefore, considering the genetic structure of the population and the close relationship between S. tetraptera and H. elliptica, as indicated in previous studies [S. tetraptera and its sympatric species H. elliptica.In general, the chloroplast data  and unique haplotypes appeared in the same population  across its distribution range. After the repeated advance and retreat of the glaciers during the Quaternary, the distribution range of S. tetraptera may have fragmented into isolated patches, ultimately facilitating in situ allopatric divergence. Since then, affected by bottleneck effects caused by Quaternary glaciations, the number of individuals in S. tetraptera population decreased dramatically, and only a few individuals remained, the new population developed from these individuals retained only part of the ancestral population genotype (Hap_4 and Hap_3). Finally, S. tetraptera has lost some of the ancient genetic structure to some degree, producing a large number of unique haplotypes, which eventually formed the existing genetic structure.Ice refugium is the concentration of plants to escape from the harsh climate of ice age, and also the starting point of population redistribution after ice age . Compareon Table ; Fig. 1.QTP Fig. B. In facemifolia , Potentiruticosa  and P. g. glabra . As a reS. tetraptera were analyzed, while the natural geographical barriers such as mountains and rivers and human activities could also affect the evolution and dispersal of S. tetraptera, which were not involved in this study. Therefore, we should continue to study the differentiation pathway and evolutionary mechanism of S. tetraptera in the future, so as to provide a more comprehensive and richer theoretical basis for the utilization and protection of this species.In this study, only the effects of climate change on the distribution range, population structure and population dynamics of S. tetraptera is likely to have been shaped by Quaternary periods. Multiple microrefugia of S. tetraptera existed during Quaternary glaciations. Rapid intraspecific diversification and hybridization and/or introgression may have played a vital role in shaping the current distribution patterns of S. tetraptera. The distribution range of S. tetraptera appeared to have experienced contraction during the LGM; in the future, when the global climate becomes warmer with rising carbon dioxide, the distribution of S. tetraptera will expand.Our study indicates that the current geographic and genetic distribution of S. tetraptera. Fresh leaves were collected from 35 populations, and with few exceptions, 4–12 individuals that were at least 50 m apart from each other were sampled from each population  [trnS-trnG showed different sequences within the 10 individuals tested and were used thereafter for the large-scale survey of haplotype variation within S. tetraptera. PCR was performed in a 25 µL volume containing 10–40 ng plant DNA, 50 mM Tris-HCl, 1.5 mM MgCl2, 250 µg/mL BSA, 0.5 mM dNTPs, 2 µM of each primer, and 1 unit of Taq polymerase. Initial template denaturation was programmed at 95 °C for 5 min, followed by 35 cycles of 94 °C for 1 min, 52 °C for 1 min, and 72 °C for 1 min plus a final extension of 72 °C for 7 min. The BioDev Gel Extraction System B Kit (BioDev-Tech) was used to purify all successfully amplified DNA fragments. The PCR primers trnS and trnG were adopted to perform sequencing reactions using the ABI Prism Bigdye™ Terminator Cycle Sequencing Ready Reaction Kit. All trnL-trnF sequences of S. tetraptera used in this study were obtained from Yang et al. (2011) [Total genomic DNA was extracted from silica gel-dried leaves using the modified CTAB method  and usedn (1999) . The pri. (2011) .trnL-trnF and trnS-trnG gene segments, and the combined cpDNA gene segment was used to carry out the subsequent analysis.We used BioEdit v 7.0.9.0  softwareHd) and nucleotide diversity (π) indices for each population. Differentiations among populations and within populations were calculated by analysis of molecular variance (AMOVA) using ARLEQUIN version 3.5.2.We used the program ARLEQUIN version 3.5.2  to calcuHT), average gene diversity within populations (HS), coefficient of genetic variation over all populations (GST) and equivalent coefficient taking into account sequence similarities between haplotypes (NST) [GST and NST using a permutation test with 1000 permutations. The phylogeographic structure appears when the NST value is significantly larger than the GST value.The program PERMUT was used to calculate the total gene diversity (es (NST) . A compahttp://popart.otago.ac.nz/index.shtml) was used to construct the haplotype network based on Median joining network [We used DnaSP v5.10 to identify haplotypes of the cpDNA genes. Haplotype distribution was visualized by GenGIS . The pro network .Gentiana straminea (HM598090 for trnS-trnG intergenic spacer and HM598120 for trnL-trnF intergenic spacer) and Gentiana crassicaulis (DQ398745 for trnS-trnG intergenic spacer and KT907776 for trnL-trnF intergenic spacer) as the outgroup. MAFFT 7.205 software was used to compare the cpDNA sequences and remove the irregular sequences at both ends [In this study, a phylogenetic tree was constructed based on Bayesian inference (BI)  and maxioth ends . Before oth ends .S. tetraptera were conducted with DnaSP ver. 5.10 [Mismatch distribution and neutral tests for the existing populations of er. 5.10  softwareer. 5.10 . For theer. 5.10 –74, wereer. 5.10 , 74. Arler. 5.10  was usedS.tetraptera. The geographic distribution data of S.tetraptera were obtained mainly by the following methods: (1) field investigation , China plants subject database (http://www.plant.csdb.cn/) and Global Biodiversity Information Facility (https://www.gbif.org/). (3) Literature review, including Chinese and English journals, flora of China, flora of local areas, investigation reports of nature reserves, etc. We selected the reasonable sampling sites according to the following principles: First, only one record of the duplicate site data was retained, and the distance between the two sampling sites was more than 10 km. Second, the sampling points must have accurate latitude and longitude information (distribution points that had place name no geographic coordinate information were removed) to ensure the accuracy of geography. Third, to reduce the overfitting of model predictions due to species distribution clusters, spatial auto-correlation analysis was performed using the Perl script-based GUI ENMTools v1.4.4 (http://www.ENMTools.com) and followed the selection and elimination criteria provided by previous researchers [http://www.ENMTools.com). Finally, 83 records of species occurrence data were obtained for analysis ; 2070s , were used for the modelling. For future climate scenarios, we used one future climate projection, CCSM4 from CMIP5 [2) emission hypothesis over the period 2070 were selected for further projections. The RCPs reflect potential radiative forcing by 2100 compared with the pre-industrial values of + 8.5 W/m2 which is more pessimistic and reflects high emission levels of greenhouse gases [2) by using the spatial analysis tools in ArcGIS 10.6  [In this study, nineteen bioclimatic variables in the global circulation model of the Community Climate System Model (CCSM4) for the period of 1970‒2000 were obtained from the WorldClim dataset, with a resolution of 30 arcseconds or approximately 1 kmer pixel . To estise gases . To faciia, USA) , 82. TheS. tetraptera in the Qinghai-Tibetan Plateau. The main factors affecting the MaxEnt modelling results include highly multicollinear bioclimatic variables, regularization parameters and feature classes. Herein, we address this issue in two main ways. First, for the 19 climate variables, the “cor” function in R was used to remove the highly correlated variables and retain with Pearson statistic values < 0.85 [https://github.com/marlonecobos/kuenm) to optimize the regularization multiplier and feature class parameters in the R version 3.6.3 software , and regularization multiplier settings  [S. tetraptera and eight environmental variables are imported into MaxEnt v3.4 [S. tetraptera. And then, habitat suitability was divided into four classes based on nature breaks through ArcGIS with 0.0612 as a threshold: unsuitability (0-0.0612), low suitability (0.0612–0.3741), moderate suitability (0.3741–0.6871), and high suitability (0.6871-1).At present, maximum entropy (MaxEnt) modelling is one of the most popular distribution models , 84, whis < 0.85 , which csoftware . AICc va of 0.1) , 87. Fin of 0.1) . Based oEnt v3.4  for calcEnt v3.4 , 90–92. Ent v3.4 –95. The Ent v3.4 , 96, 97.Ent v3.4 –100. JacEnt v3.4 . The outEnt v3.4 . We seleEnt v3.4 . The thrS. tetraptera. To measure the predicted distribution changes for S. tetraptera, the binary ENMs were projected to Qinghai-Tibetan Plateau projection in ArcGIS 10.5 using SDMtoolbox v2.2 [S. tetraptera was inferred [For the three ENMs, ensemble models were averaged prior to conversion to a binary model. The final ensemble ENMs were converted to a binary model by classifying values greater than  0.5 to a value of 1 and those lower to a value of 0 . This mobox v2.2 . The Lasinferred , 78.Below is the link to the electronic supplementary material.Supplementary Material 1Supplementary Material 2Supplementary Material 3Supplementary Material 4Supplementary Material 5Supplementary Material 6