Prunus armeniaca L., two chloroplast DNA fragments (trnL-trnF and ycf1) and the nuclear ribosomal DNA internal transcribed spacer (ITS) were employed to assess genetic variation across 12 P. armeniaca populations. The results of cpDNA and ITS sequence data analysis showed a high the level of genetic diversity  and a low level of genetic differentiation  in P. armeniaca. Analysis of molecular variance (AMOVA) revealed that most of the genetic variation in P. armeniaca occurred among individuals within populations. The value of interpopulation differentiation (NST) was significantly higher than the number of substitution types (GST), indicating genealogical structure in P. armeniaca. P. armeniaca shared genotypes with related species and may be associated with them through continuous and extensive gene flow. The haplotypes/genotypes of cultivated apricot populations in Xinjiang, North China, and foreign apricot populations were mixed with large numbers of haplotypes/genotypes of wild apricot populations from the Ili River Valley. The wild apricot populations in the Ili River Valley contained the ancestral haplotypes/genotypes with the highest genetic diversity and were located in an area considered a potential glacial refugium for P. armeniaca. Since population expansion occurred 16.53 kyr ago, the area has provided a suitable climate for the population and protected the genetic diversity of P. armeniaca.To clarify the phytogeography of  Understanding the effects of climate change on the spatial genetic patterns of species, particularly endangered species, can help reveal not only the evolutionary history of species but also conservation strategies3. The origins of mountain biodiversity are complex and may include the immigration of preadapted lineages5, in situ diversification6, or the continuation of ancestral lineages7. Compared with those of other mountains, such as the Hengduan Mountains, the organismal evolution and diversity of the Tianshan Mountains are still poorly understood. The Ili River Valley is located in the western part of the Tianshan Mountains in China and is surrounded by mountains on three sides. This valley was the main northern crossing of the ancient Silk Road. In the late Tertiary period, a large number of species, including wild apricot, wild apple and wild hawthorn, remained in the Ili River Valley and were important components of the deciduous broad-leaved forest at elevations below the coniferous forest and above the mountain grassland belt in the Xinjiang Uygur Autonomous Region, China8. However, there have been few studies on the phylogeography of plant species in the arid region of Northwest China10.The evolutionary history of organisms, including their genetic diversity, population structure and historical dynamics, is critical to species conservation12. Many systematic geographic studies have shown that the Last Glacial Maximum (LGM) of the Pleistocene strongly influenced the genetic variation and biodiversity of plants throughout the Northern Hemisphere14. Especially during the Quaternary glacial period, species in the ice-free zone were mainly affected by the cold and dry climate15. Climatic fluctuations cause the distribution of species to shrink and expand16, and cold and dry climates prompt plant and animal species to retreat to refugia, which provided shelter17. As temperatures rose after the glacial period, species underwent population expansion, changes that left a genetic signature in their current populations18. Although no unified glacial sheet was present in Asia, paleodata suggest that the distribution ranges of woody species in this region were similarly changed by Quaternary climatic oscillations19. Based on the geographical distribution of genetic variation, the glacial refuges and postglacial revegetation routes of most woody plants are roughly consistent with fossil evidence20. However, due to the lack of fossil evidence, genetic evidence has become an important means of providing information on the distribution range of some woody species and the history of glacial refugia22. Volkova23 interpreted the observed genetic structure [nuclear  and plastid DNA] of the Eurasian populations of Prunus padus as plausibly resulting from at least two cycles of glacial survival in refugia followed by postglacial colonization events17. The complex geographic history in the area may have provided a refuge for species in the last glacial period17. Xu et al.24 proposed two possible independent glacial refugia in Northwest China: the Ili River Valley and the Northern Junggar Basin. Su et al.9 speculated that intensification of the dry and cold climate during the early Quaternary, combined with plant physiological features, contributed to the lineage split, and climate oscillations most likely led to the Ili range expansion. Therefore, it is of great interest to study the systematic geography of endemic plants in Northwest China against the background of Quaternary climatic fluctuations.In recent decades, the method of combining molecular data with paleoclimatic and geographical evidence has been effective in the study of systematic geographyhttp://www.fao.org/home/zh). Apricot belongs to section Armeniaca (Lam.) Koch, subgenus Prunophora Focke and genus Prunus (Rosaceae)25. Almost all cultivated apricots originated from Prunus armeniaca26. In China, wild apricot is distributed in the Ili River Valley (Tianshan Mountain area). The species is also distributed along the Tianshan Mountains and westward to Kazakhstan, Kyrgyzstan and Uzbekistan. It is a relic of a broad-leaved forest from the late Tertiary period, which played a decisive role in the domestication of cultivated apricots worldwide27. As the world experienced extreme weather events, especially glacial periods, and most species went extinct, some of the more complex valleys may have become refugia for surviving forests and local species. As a result, traces of the species may be gradually shrinking in such areas. Therefore, is the Ili River Valley a glacial refugium for wild apricot?Apricot is a fruit of temperate and subtropical regions. Turkey, Uzbekistan, Iran, Algeria, Italy, Spain, China, Pakistan, France and Japan are the main producers of apricots  and nuclear ribosomal DNA (nrDNA) sequences to (1) reveal the haplotype/genotypic diversity and population genetic structure of the species and (2) examine the demographic history of P. armeniaca during Quaternary climate oscillations, and further explore the origin and evolution of this species.Generally, the dispersal distance of seeds is much shorter than that of pollen, and population divergence due to genetic drift will be more marked for chloroplast DNA (cpDNA) than for nuclear DNA. Indeed, cpDNA is considered to evolve very slowly, with low recombination and mutation ratesP. armeniaca and related species . Prunus davidiana (T) was used as the outgroup.The samples used for cpDNA analysis included 123 individuals from 20 populations. A total of 171 individuals from 19 populations were used for the ITS analysis, of which 38 samples were obtained from the NCBI database Table . The samP. armeniaca) populations covered most of the natural distribution in China, including Huocheng County , Yining County (DZGyn population), Gongliu County (DZGglb and DZGgld populations) and Xinyuan County . The distance between individuals sampled in each population was at least 100 m. Young leaves were collected and dried immediately with silica gel.Our collection of wild apricot , the North China apricot group  and the foreign apricot group . Detailed sample information is provided in Table 8.The cultivated populations of 34. The quality and concentration of the extracted DNA were determined by 1% agarose gel electrophoresis and ultraviolet spectrophotometry, respectively.Total genomic DNA was extracted from the silica gel-dried leaf materials using a Plant Genomic DNA Kit trnL-trnF and ycf1) and two nuclear ribosomal ITS regions (ITS1 and ITS2) were polymorphic. cpDNA and ITS fragments were amplified by polymerase chain reaction (PCR), and the details of their primers are provided in Table S337. PCR was performed in a total volume of 25 µL that contained 1 µL DNA, 5.5 µL PCR mix, 16.5 µL double-distilled water and 1 µL each forward or reverse primer. PCR amplifications were performed under the following conditions: 5 min of initial denaturation at 94 °C and 35 cycles of 0.5 min at 94 °C, 0.5 min of annealing at 58°, and 0.5 min of extension at 72 °C, with 10 min of final extension at 72 °C. A CASpure PCR Purification Kit 32 was used for purification. The purified PCR products were sequenced on an ABI PRISM 3730XL DNA Analyzer 38.cpDNA and nrDNA sequences from 15 samples were initially screened using universal primers. The sequencing results showed that the sequences of cpDNA  into a separate matrix for subsequent analysis. We used DnaSP ver. 5.10 to identify different haplotypes (cpDNA sequences) or genotypes (ITS sequences). A haplotype network was constructed using TCS ver. 1.2.143. ArcGIS ver. 10.2 (http://desktop.arcgis.com) software was used to create a haplotype geographical distribution map.We used BioEditHd) and nucleotide diversity (π) were calculated using DnaSP ver. 5.10 software44. The within- population gene diversity (HS), gene diversity in all populations (HT), interpopulation differentiation (GST) and number of substitution types (NST) were calculated using PERMUT45 ver. 2.0. The last two indexes (GST and NST) were analyzed via permutation tests with 1000 permutations. When NST is greater than GST, it indicates the existence of genealogical geographic structure45. Analysis of molecular variance (AMOVA) was performed using Arlequin ver. 3.5.2.246 to partition the genetic variation at different levels, with statistical significance determined by 1,000 permutations.Haplotype/genotype diversity , Harpending's raggedness index (HRI)45 and corresponding P values were calculated using Arlequin ver. 3.5.2.246. Neutrality tests based on Tajima’s D and Fu’s FS were conducted to detect departures from the population equilibrium by Arlequin ver. 3.5.2.246. According to the formula of Rogers and Harpending47, T = τ/2u, where “τ” is the parameter value from the mismatch distribution model. In the formula u = μkg, “μ” is the base substitution rate (chloroplast angiosperms48: 1.1 × 10–9), “k” is the fragment length (cpDNA length after combination: 2062 bp), and “g” is the generation time (20 years)49.To investigate the historical dynamics of P. davidiana, and the time point of peach-apricot differentiation was used as the calibration point50. The BEAUti interface was used to create an input file for BEAST51, for which the GTR + I + G nucleotide substitution model was used. The data were analyzed using a relaxed log-normal clock model and the Yule process speciation model for the tree priors. Prior settings for calibrating nodes were an offset of 55.1 Ma and a log mean of 1.0 (log stdev of 0.5). The Bayesian Markov chain Monte Carlo simulation was run for 100 million generations with a sample frequency of 1000, and the first 20% of generations were discarded as burn-in. Three independent analyses were conducted and their results combined by LogCombiner ver. 1.8.4. Finally, annotation and visualization of the maximum clade credibility tree were performed in TreeAnnotator 1.8.4 and FigTree ver. 1.4.3, respectively.The outgroup was trnL-trnF and ycf1), 33 haplotypes (H1-H33) were identified among 123 individuals from 20 populations of P. armeniaca and related species   was higher than that of the CAG  and NCG  accessions.Based on the concatenated cpDNA sequences   was higher than that of the CAG  and NCG (0.0047 and 0.818) accessions ; the second group was composed of related species (green branch), including P. sibirica, P. mandshurica, P. dasycarpa, P. mume, P. zhengheensis and P. limeixing. T3 was widely distributed in most populations. The genetic backgrounds of the related species had the same genotypes  as P. armeniaca, indicating that they are associated with P. armeniaca through continuous and extensive gene flow.A phylogenetic tree of all 57 genotypes was constructed to better understand their relationships Fig. . The phytrnL-trnF and ycf1), 19 haplotypes (G1-G19) were identified among 107 individuals from 12 populations of P. armeniaca . The Hd and π values detected at the ITS sequence level in P. armeniaca were 0.865 and 5.4 × 10–3, respectively.Based on the concatenated cpDNA sequences  was higher than that within populations  , indicating that P. armeniaca has significant geographical structure (Table In 4) Table . A permure Table .Table 3EP. armeniaca , with most of the genetic diversity occurring within the populations and relatively little occurring among them  and Fu’s FS  and the mismatch distribution analysis  nor the HRI  showed no significant positive values  and those of related species (green)  = 28.47–61.87). The onset of intraspecific divergence in P. armeniaca was estimated to have occurred 25.55 (95% HPD = 12.93–39.63) Ma.The cpDNA dataset was employed to estimate when the onset of divergence between red Fig. . Thirty-en) Fig. . The div32 used cpDNA trnL-trnF, rpl16 and nrDNA ITS sequences to infer the evolutionary history of S. sinomontana. Yang et al.52 used cpDNA psbA-trnH, trnL-trnF, ycf1, and matK sequences to access the demographical history and genetic diversity of a Deciduous Oak (Quercus liaotungensis) in Northern China. Zhang et al.53 used cpDNA markers to successfully determine the genetic diversity, genetic structure, and demographic history of 7 Michelia yunnanensis populations. Many scholars57 believed that the diversity of wild apricot is richest in the Ili River Valley, with low levels of genetic differentiation and genetic variation mainly occurring within populations. Hu et al.56 used simple sequence repeat markers to analyze the diversity of 212 apricot germplasms from 14 populations in the Ili River Valley. Among the populations, that from the Tuergen township in Xinyuan County had the highest genetic diversity, and the genetic distance between populations was significantly correlated with geographical distance. The self-incompatibility, wide distribution, and long-distance transmission of pollen through insects and strong winds of apricot are the main factors affecting its genetic structure56. Based on cpDNA and ITS data, we concluded that the haplotype/genotype diversity of wild apricot populations distributed in Ili River Valley was relatively high , but there were also significant differences among populations , which was consistent with previous results based on simple sequence repeat markers56. The relatively high genetic diversity also confirmed the Tianshan Mountains as the origin center of cultivated apricot56. The limited informative mutation sites among the ITS genotypes led to very little resolution for the construction of genotype relationships  as P. armeniaca had the same ancestral haplotype, G1. The haplotypes of the CAG, EG and NCG populations were mixed with the haplotypes/genotypes of the large wild populations (P. armeniaca). According to coalescent theory59, chloroplast haplotype G1, which was widely distributed and located in the center of the chloroplast network , southern Central Asia  and China, with both ancient gene flow and recent gene mixing occurring. Central Asia harbors the highest diversity of wild apricots, with genetically differentiated populations that may have resulted from population isolation in glacial refugia58. In this study, the nucleotide diversity and haplotype/genotype diversity of wild apricots (DZG accessions) were higher than those of CAG and NCG accessions. These findings are reasonable from a historical perspective, as there was extensive cultural contact along the Silk Road from 207 BCE to 220 CE60. Therefore, historical and commercial influences may have contributed to the development of this unique species of cultivated apricot.This study indicated that the cultivated and wild populations of ork Fig. , should 29. It is of great significance for us to understand the origin of species and the evolution of geographical patterns, and to better protect existing biodiversity. Phylogeographic studies have shown that ancient haplotypes and high genetic diversity can be used to identify refuges61. Populations in refugia usually display more genetic diversity and exclusive haplotypes than migratory populations14. Many scholars24 have suggested that the complex geographic history of Northwest China may have provided refuges for species during glacial periods. By combining two markers, we showed that all the wild populations of apricots distributed in the Ili River Valley contained ancestral haplotypes/genotypes and had high genetic diversity , the divergence time estimates in this study should be interpreted with caution because the limited coverage and low number of calibration points may lead to an overly high divergence time estimate for P. sibirica (mean age of 33.43 Ma).The selected taxon-sampling and fossil calibration strategies will influence the age estimationP. armeniaca exhibits genealogical structure. Affected by the Quaternary glaciation of the Pleistocene, the Ili River Valley in Northwest China served as a glacial refugium for P. armeniaca, providing the species with a suitable climate and preserving its genetic diversity. During the interglacial period, the species underwent a recent expansion in the face of favorable climatic and environmental conditions. Apricots originated during the middle Eocene, and the cultivated apricot in Xinjiang originated from apricots in the Ili River Valley in Northwest China.Based on cpDNA and ITS data, the haplotype/genotype diversity of wild apricot populations distributed in the Ili River Valley was relatively high, and the haplotype/genotype diversity of DZGhcmd and DZGyn populations was greater than that of other populations. The experimental research and field studies on plants in this work comply with the IUCN Policy Statement on Research Involving Species at Risk of Extinction and the Convention on the Trade in Endangered Species of Wild Fauna and Flora.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.Supplementary Information 6.Supplementary Information 7.Supplementary Information 8.Supplementary Information 9.


N = 74) from three different populations of Montezuma quail (Cyrtonyx montezumae), a small ground‐dwelling bird that is sustainably harvested in some portions of its range but is of conservation concern elsewhere. Our historical demographic results indicate that Montezuma quail populations in the United States exhibit low levels of genomic diversity due in large part to long‐term declines in effective population sizes over nearly a million years. The smaller and more isolated Texas population is significantly more inbred than the large Arizona and the intermediate‐sized New Mexico populations we surveyed. The Texas gene pool has a significantly smaller proportion of strongly deleterious variants segregating in the population compared with the larger Arizona gene pool. Our results demonstrate that even in small populations, highly deleterious mutations are effectively purged and/or lost due to drift. However, we find that in small populations the realized genetic load is elevated because of inbreeding coupled with a higher frequency of slightly deleterious mutations that are manifested in homozygotes. Overall, our study illustrates how population genomics can be used to proactively assess both neutral and functional aspects of contemporary genetic diversity in a conservation framework while simultaneously considering deeper demographic histories.Populations with higher genetic diversity and larger effective sizes have greater evolutionary capacity  to respond to ecological stressors. We are interested in how the variation captured in protein‐coding genes fluctuates relative to overall genomic diversity and whether smaller populations suffer greater costs due to their genetic load of deleterious mutations compared with larger populations. We analyzed individual whole‐genome sequences ( It is oHerein, we report the data from whole‐genome sequencing (WGS) of 90 Montezuma quail from Arizona, New Mexico, and Texas. We used these WGS data to quantify the levels of overall genomic diversity, genic variation, differentiation, individual inbreeding, and the inferred genetic load in each population. We do so in a conservation context by comparing populations of different sizes. Our results indicate that Montezuma quail effective population sizes have decreased over much of the last million years, and their similar trajectories over time indicate that now‐disjunct populations in the United States were long connected demographically. Furthermore, we find that the small Texas population is isolated and genetically depauperate, and that its genetic load is mostly due to small‐impact deleterious mutations. Because inbreeding is also more pronounced in the small Texas population compared with the larger populations, these deleterious mutations are more likely to occur in homozygotes and thus contribute to a decline in fitness.22.1Montezuma quail samples were opportunistically collected  from three representative geographic populations in the United States: Arizona (AZ), New Mexico (NM), and Texas (TX) as described earlier  on one S4 flow cell using Illumina® NovaSeq™ 6000 sequencing system in Purdue University's Genomics Core Facility. The estimated genome size of Montezuma quail is 1.03 Gb  by creating individually barcoded dual‐index libraries using Illumina2.2We used FastQC v0.11.7 Andrews,  to qualimem algorithm. Our final dataset contained 74 individuals . We used the Genome Analysis ToolKit (GATK) “Best Practice Workflow” . We then used GATK v3.6.0  as reference. Both Galliformes, Montezuma quail, belong to the New World quail family Odontophoridae that diverged from junglefowl  approximately 30–40 million years ago , we used the high‐quality and contiguous chicken genome  and recalculated nucleotide diversity indices using 100 independent permutations.All mitogenomes were aligned as multiple sequence alignment using Clustal W v.2.1  of 0.05. We also used a maximum depth threshold of 500 to avoid calling SNPs from repetitive regions , we used a threshold of minimum 60 individuals to ensure genotypic information is captured in at least 80% of all samples and to avoid retaining segregating sites from only the Arizona population (N = 52) (“population dataset”). To avoid biases introduced due to uneven sample sizes, we re‐estimated GL and discovered SNPs from an equal subset of Arizona and Texas samples . For our subsamples, we chose samples with the highest depth and breadth of coverage to maximize the genomic spread of our variants (“genomic dataset”). For the subset, we used a minimum individual threshold of 15 and maximum depth threshold of 100.When estimating GL across all samples  retaining maximum individual information at the cost of markers per individual (“population dataset”) and (b) retaining maximum genomic information on each population at the cost of individuals analyzed per population (“genomic dataset”). The population dataset was used for the estimation of inbreeding and genetic structure, both of which can be inferred from a smaller set of widespread markers from more individuals  using PCAngsd v.0.982  and 5000 maximum iterations for estimation to assure a stricter stopping criterion and avoid convergence at a local minimum . We estimated heterozygosity for each individual as the total proportion of heterozygous sites from its SFS.For nucleotide diversity estimates, we only used the genomic dataset to avoid biases in estimating site frequency spectrum (SFS) due to uneven sample sizes and heavy data pruning, which was the case for our population dataset. We used ANGSD to generate a folded SFS by using the Montezuma quail reference genome and a minimum base quality of 20 and minimum mapping quality of 20 Figure . Next, wNe) from mean genomic θW, we first estimated the whole‐genomic mutation rate (µ) for Montezuma quail (θW = 4Neµ). Since no linkage map exists for Montezuma quail, we estimated µ following Zhan et al.  using LASTZ  between chicken and Montezuma quail was derived from www.timetree.org, and polymorphic loci were identified only if neither target nor query nucleotide was N/n and the locus was not in an alignment gap. The final µ per nt per year was calculated with the following formula: µ = (counts of mutated loci / sequence length) / 2t  and divergence in allele frequencies (FST)  for each population pair . We used ANGSD to calculate the 2D SFS for each population pair using the chicken genome (GRCg6a) as reference to polarize alleles as derived or ancestral. We quantified the levels of nucleotide divergence (DXY) using the calcDxy.R . In this case, we ran ANGSD for each population individually to get population‐level AF and GL information, but only for the SNPs previously identified in our genomic dataset. This ensured that sites with a fixed allele in one population were still included in our per population DXY calculations.Small populations in isolation can become genetically differentiated due to drift at neutral loci and positive selection at non‐neutral loci . Both processes lead to nucleotide divergence (FST around the mean for each sliding window and examined the outliers that had Z(FST) values outside 5 standard deviations from the mean . We ran our model using 5000 iterations and used different parameter values for thinning and regularization penalty to avoid degeneracy in the likelihood and overfitting  of a population as the proportion of deleterious variants of different impact classes across all annotated protein‐coding genes. We did so by predicting the effect of each nucleotide variant on the resulting amino acid sequence and then quantifying its putative deleterious impact using SnpEff 4.2  and k ∈ . LoadP is conceptually similar to the term “segregating load”  as the proportion of impactful variants that exist as homozygotes within individual diploid genomes. We computed the per‐individual proportion of deleterious variants of each impact class as the total number of deleterious alleles present within an individual divided by twice the number of segregating sites within each impact class  and, in the present study, k ∈ . To assess the zygosity of an impactful mutation in genic regions, we called individual genotypes at SNPs within the genes based on the posterior probability of the genotypes from GL using ANGSD. Genotypes were only called at sites with minimum individual depth of 5× to minimize technical biases  among different populations were identified using Welch's two‐sample t‐test.We note, however, that the proportion of potential load that is actually realized in individuals also depends on the mode of dominance and on zygosity. To illustrate how Load3N = 10) or were less than 50% of the total reads mapped to the Montezuma quail assembly (N = 6). However, we achieved a high level of read mapping for the remainder of the samples  as compared to Arizona. Haplotype diversity (Hd) did not significantly differ between Texas and Arizona mitogenomes (p = 0.70) but was significantly smaller in New Mexico as compared to Arizona  and Kimura 2‐P pairwise distances to be smaller in the Texas and New Mexico mitogenomes (N = 74). The genomic dataset contained GL information from 6,696,145 SNPs sampled across an equal subset of each representative population (N = 21). Using the population dataset, we first estimated the relatedness among our samples to determine whether we had close relatives in the study. Pairwise relatedness was measured for 2341 individual pairs. Almost all the pairs analyzed were either unrelated (99.5%) or 3rd‐degree relatives (0.21%). We found no full‐sibling or parent–offspring relationships (1st‐degree) in our samples; however, five pairs from Arizona, one pair from Texas, and one pair from New Mexico had 2nd degree or half‐sibling relationship . Mean genome‐wide θW was significantly lower for the Texas population  as compared to both Arizona  and New Mexico   scores for loci on chromosome 16 (NC_006103.5) for all population comparisons  values. Note there is a similar discontinuity at one end of chicken chromosome 26 , whereas both the Texas and New Mexico populations have been declining with Fu's F = 0.11 ± 0.02 and 0.22 ± 0.02, respectively  followed by a more stable period. A subsequent re‐expansion occurred around 10,000 years ago, and then, populations began to rebound until growth rates became negative around 3000–5000 YBP  genic nucleotide diversity as compared to both Arizona  and New Mexico  due to higher proportions of high‐, moderate‐, and low‐impact deleterious mutations, and lower proportions of noncoding variants, when compared to either the Texas or New Mexico populations  showed that Texas quail had no significant difference in the mean AF of highly deleterious mutations per individual  as compared to Arizona quail. Most exonic variants were classified as moderate‐ or low‐impact deleterious mutations, and we found them at higher frequencies and significantly more homozygous contributing to significantly higher LoadR in Texas quail as compared to Arizona quail    to summarize the population‐wide genetic load dependent on the proportion of deleterious mutations in a given gene pool. Our results indicate that Arizona quail carry significantly more high‐impact deleterious variants as compared to Texas quail, and this difference tends to diminish with variant impact  < 1). In addition to drift, individuals from smaller inbred populations tend to carry these small‐effect deleterious mutants as homozygotes, whereas they tend to be heterozygous in larger outbred populations  to better assess genomic vulnerabilities and estimate inbreeding depression in individuals of small populations. It seems clear that large effect deleterious mutations  than small populations. However, small populations are most susceptible to reduced fitness because small‐effect deleterious alleles are homogenized due to drift and inbreeding . Overall, we think these data will be useful to those interested in the conservation of Montezuma quail and that they illustrate the power of population genomics in evaluating adaptive potential in light of fragmented landscapes and rapid environmental change.The authors declare no conflict of interest.Supplementary MaterialClick here for additional data file.Table S1Click here for additional data file.


Lepus yarkandensis Günther, 1875) is endemic to oasis and desert areas around the Tarim Basin in the Xinjiang Uyghur Autonomous Region of northwest China; however, genome-wide information for this species remains limited. Moreover, the genetic variation, genetic structure, and phylogenetic relationships of Yarkand hare from the plateau mountain regions have not been reported. Thus, we used specific-length amplified fragment sequencing (SLAF-seq) technology to evaluate the genetic diversity of 76 Yarkand hares from seven geographic populations in the northern and southwestern parts of the Tarim Basin to investigate single-nucleotide polymorphism (SNP) marker-based population differentiation and evolutionary processes. Selective sweep analysis was conducted to identify genetic differences between populations.The Yarkand hare (FST estimates, the north and southwest groups showed a moderate level of genetic differentiation. Phylogenetic tree and population structure analyses demonstrated evident systematic phylogeographical structure patterns consistent with the geographical distribution of the hares. Hierarchical analysis of molecular variation further indicated that genetic variation was mainly observed within populations. Low to moderate genetic differentiation also occurred among populations despite a common genomic background, likely due to geographical barriers, genetic drift, and differential selection pressure of distinct environments. Nevertheless, the observed lineage-mixing pattern, as indicated by the evolutionary tree, principal component analysis, population structure, and TreeMix analyses, suggests a certain degree of gene flow between the north and southwest groups. This may be related to the migration of hares to high-altitude water sources southwest of the basin during glacial climatic oscillations, as well as river re-diffusion and oasis restoration in the basin following the glacial period. We also identified candidate genes, and their associated gene ontology terms and pathways, related to the adaptation of Yarkand hares to different environmental habitats.Using SLAF-seq, a total of 1,835,504 SNPs were initially obtained, of which 308,942 high-confidence SNPs were selected for further analysis. Yarkand hares exhibited a relatively high degree of genetic diversity at the SNP level. Based on pairwise The identified genome-wide SNPs, genetic diversity, and population structure of Yarkand hares expand our understanding of the genetic background of this endemic species and provide valuable insights into its environmental adaptation, allowing for further exploration of the underlying mechanisms.The online version contains supplementary material available at 10.1186/s12983-021-00432-x. FST) . The Yarkand hare population differentiation events predominantly occurred approximately 0.81–0.32 Mya, exhibiting a general south-to-north trend, with the southwest KS population showing the earliest differentiation (0.81 Mya).The MCMC algorithm with a general time-reversible model base pair substitution model was used to construct a Yarkand hare differentiation time-merging tree Fig. a. The YaWe inferred Yarkand hare migration events using TreeMix to further reveal the population structure with ADMIXTURE Fig. b. ConsisFST values and the θπ ratio cut-off values between the following group pairs: north group vs. southwest group, north group vs. TX, and southwest group vs. TX, which identified 30, 17, and 15 candidate genes, respectively , represented by an arid, hot, and plain environment, into a single gene pool. We combined the southwest group (AKT and WQ), represented by an arid and medium-altitude (elevation between 1500 and 2500 m) environment, into another gene pool. The TX population in the cold, arid, and high-altitude (elevation > 3000 m) environment was considered as a separate group. As the KS population includes samples collected from both plain and medium-altitude (> 1500 m) areas, we excluded this population from this part of the analysis. Selection sweep analyses were performed by calculating the 5% highest In Xinjiang, Yarkand hares inhabit both natural and artificial oases, and are also found along the major river edges in the Tarim Basin encircling the Taklamakan Desert, which is the second largest sand sea on Earth. In this first genome-wide study of the Yarkand hare using SLAF-seq, we found low to moderate, yet significant, genetic differentiation between populations despite relatively long geographic distances. Interestingly, some genetic exchange among populations was also detected in the phylogenetic tree, PCA, and ADMIXTURE analyses. Indeed, the TreeMix analysis estimated a certain degree of gene flow among geographically separated populations. These results indicate that both genetic differentiation and gene exchange co-occur among the populations of this species. In addition, some selection signatures corresponding to genomic regions potentially under selection were identified in association with environmental differences, indicating a certain degree of local adaptation.π (0.0655), PIC (0.2543), and Ho (0.2582) of the Yarkand hare were slightly lower than those of the domestic rabbit (O. cuniculus)  and southwest  groups were significantly enriched in the same catabolic and metabolic processes, as well as in the corticosteroid and glucocorticoid receptor signaling pathways; these genes were also commonly enriched in development-related GO terms  and sheep breeds (Ovis aries) indicating adaptation to extreme drought environments  were significantly associated with responses to various stimuli , was significantly enriched in the KEGG pathways fatty acid degradation (KEGG pathway accession code: ocu00830), retinol metabolism (KEGG pathway accession code: ocu05223), and arachidonic acid metabolism pathway (KEGG pathway accession code: ocu00590). CYP4A5 plays an important role in converting arachidonic acid into 19(S)-HETE and 20-HETE through ω–hydroxylation , and in the TX population, such as leucine rich repeat kinase 2 (LRRK2), were significantly enriched in 18 GO terms related to the regulation and response to oxidative stress and peroxidase activity  selected in TX is involved in the regulation of smooth muscle cell and vascular-associated smooth muscle cell migration . Oxidative damage is induced by oxidative stress when ROS formation exceeds the antioxidant defense capacity of target cells and is associated with various disease states . The canp-values, such as Bonferroni correction, false discovery rate, and fold enrichment tests. Nonetheless, we identified noteworthy, overrepresented biological processes and pathways based on raw p-values, which may provide insights into the mechanisms involved in the environmental adaptation of this species. It is also important to note that many evolutionary processes can influence genomic variation. Without functional experiments, it is difficult to distinguish natural selection from genetic drift at a particular locus, which includes the candidate genes identified herein. Therefore, comprehensive studies integrating whole-genome re-sequencing, demographic history, candidate gene sequencing, and transcriptomes with adaptive traits and physiological mechanisms of Yarkand hare are warranted to confirm or refine our functional genomic hypotheses and further explore the genomic mechanisms underlying adaptation.Overall, we detected only a small number of candidate genes (58) within selected regions among Yarkand hare populations, which may be attributed to the limited sampling sites or the small and unequal sample sizes for the southwest group and TX population. Moreover, the reduced-representation sequencing approach—which may not detect low-frequency alleles that could have a substantial role in adaptation— exhibits some inherent limitations with respect to obtaining information regarding neutral or adaptive genetic variation on a genome-wide scale . BecauseWe performed SNP analysis via SLAF-seq to elucidate the genetic diversity, differentiation, phylogenetic relationships, historical pattern of divergence, and gene flow among seven Yarkand hare populations in the southwestern and northern regions of the Tarim Basin at the genome-wide level. We also conducted selective sweep analysis to assess differences in the SNP distribution across chromosomes and determine their associations with environmental stress or adaptation.Our genomic data revealed a relatively high degree of genetic diversity along with a high degree of admixture between populations through multiple analyses, although there was a clear population structure and low to moderate differentiation between the southwest and north groups. The coexistence of genetic differentiation and admixture among the Yarkand hare populations sampled in this study likely reflects the strong adaptability and enhanced dispersal characteristics of this species, as well as the important influence of historical and current environmental changes. We further investigated the selection signatures that may be caused by different types of environmental selective pressure, and identified genes and pathways that may be related to drought environment adaptation of the Yarkand hare. We also screened genes and pathways likely associated with altitude adaptation of the TX and southwest populations. Overall, this study highlights the value of genomic data in assessing the population genetics of this endemic species, thereby prompting future gene linkage and association analyses of key adaptive traits linked to the specific habitats of these and other hare populations.Additional file 1: Table S1. Summary of Yarkand hare sequencing data obtained in this study.Additional file 2: Figure S1. Distribution of SLAF tags on reference genome chromosomes. The abscissa represents the SLAF tag position on the chromosome and the ordinate represents each chromosome. The reference genome was divided into 1-Mb portions. The higher the number of SLAF tags per 1 Mb, the darker the color; the lower the number, the lighter the color.Additional file 3: Figure S2. Phylogenetic trees constructed using ML (a) and BI (b) methods based on the SNP matrix of 76 Yarkand hares. Oryctolagus cuniculus was used as the outgroup. AKS, Akesu; ALR, Alar; KRL, Korla; TX Taxkorgan; AKT, Aketu; KS, Kashgar; WQ, Wuqia.Additional file 4: Figure S3. Cross-validation errors in the ADMIXTURE analysis. The number of ancestry (K) was assumed to range from 1 to 10; K = 2 was the optimal number.Additional file 5: Table S2. List of genes in the overlapping regions selected by the top 5% highest log2 (θπ ratio) and top 5% highest FST values for Yarkand hare populations from different environments.Additional file 6: Table S3. The top 20 KEGG pathways and GO functional enrichment of candidate genes between the north and southwest groups via FST-θπ ratio analysis.Additional file 7: Table S4. KEGG pathways and GO functional enrichment of candidate genes between the north group and TX population via FST-θπ ratio analysis.Additional file 8: Table S5. KEGG pathways and GO functional enrichment of candidate genes between the southwest group and TX population via FST-θπ ratio analysis.


Scophthalmus maximus), gilthead seabream (Sparus aurata), European seabass (Dicentrarchus labrax) and common carp (Cyprinus carpio) to obtain accurate estimates of the effective size for commercial populations.The high fecundity of fish species allows intense selection to be practised and therefore leads to fast genetic gains. Based on this, numerous selective breeding programmes have been started in Europe in the last decades, but in general, little is known about how the base populations of breeders have been built. Such knowledge is important because base populations can be created from very few individuals, which can lead to small effective population sizes and associated reductions in genetic variability. In this study, we used genomic information that was recently made available for turbot , and thus accounts for potential fluctuations in population size over time.Our results show that the current effective population size for these populations is small , potentially putting the sustainability of the breeding programmes at risk. We have also detected important drops in effective population size about five to nine generations ago, most likely as a result of domestication and the start of selective breeding programmes for these species in Europe.Our findings highlight the need to broaden the genetic composition of the base populations from which selection programmes start, and suggest that measures designed to increase effective population size within all farmed populations analysed here should be implemented in order to manage genetic variability and ensure the sustainability of the breeding programmes.The online version contains supplementary material available at 10.1186/s12711-021-00680-9. Ne) and therefore, to high rates of loss of genetic variability, high rates of inbreeding and restricted long-term selection responses.The success of any breeding programme depends critically on how the base population of breeders is built, since the genetic variability that is initially available in the founders will affect the genetic progress achieved in the subsequent selection programme –3. This Ne can be estimated for generations before pedigree recording began. This is of great importance in aquaculture species to determine the impact of domestication on the genetic variability present in the base populations and the potential long-term response to selection. Genomic estimates of Ne are obtained based on the linkage disequilibrium (LD) approach [Ne of a particular generation in the past can be estimated from LD between pairs of single nucleotide polymorphisms (SNPs) separated by a specific distance [Ne in consecutive generations, and this allows the detection of drastic changes in population size.With the rapid development of genomic tools, temporal series of approach , and difdistance . Howeverdistance . To circdistance  have recNe, estimates of this parameter are scarce for most aquaculture species. In this study, we used genomic information that was recently produced for important fish species in European aquaculture  to obtain recent and historical estimates of Ne for commercial populations, using the novel method developed by Santiago et al. [Ne potentially associated with domestication or with the establishment of selective breeding programmes.In spite of the importance of estimating o et al. . These ewww.fishboost.eu) (Table Data were derived from broodstock (and their offspring) sampled in 2014 from different European breeding programmes for turbot, gilthead seabream, European seabass and common carp within the framework of the FISHBOOST project  with a mirror phenotype for scaliness and males from a wild population  with a scaly phenotype. The Amur Mirror strain was founded from F2 crosses by selecting offspring that had the mirror phenotype. The population used in this study was obtained by artificial fertilization that involved four blocks of full factorial crosses each comprising five dams and ten sires [Turbot samples were obtained from an experimental population of Atlantic origin maintained at CETGA  through hierarchical matings. For gilthead seabream, data came from one of the four genetically linked yearly cohorts of the breeding nuclei of the Andromeda Group SL (Greece) and Ferme Marine de Douhet , where the main breeding objectives in the selection programmes are growth and body shape. The Andromeda programme applies mass spawning, while the FMD programme applies partial factorial mating designs . Europeaen sires .Ne were obtained using the software GONE and its auxiliary programs developed by Santiago et al. [https://github.com/esrud/GONE). Squared correlations between allele frequencies of pairs of SNPs .Estimates of LD between pairs of loci and temporal estimates of o et al.   were obquences; ) were obNe were also obtained using the previous method of Hayes et al. [Ne [Ne or linear changes in Ne.For the sake of comparison, temporal estimates of ancestral s et al.  as imples et al. . Althougs et al.  are base al. [Ne , the mair2) between SNPs separated by short distances (< 0.01 kb) was moderately low (0.15 for turbot and 0.24 for seabass) and decreased rapidly with physical distance. The average r2 was reduced by half in both cases for distances shorter than 5 kb.The pattern of LD decay with physical distance that was computed with offspring data for turbot and seabass is represented in Fig. Ne were equal to or less than 50 fish in all cases. When using offspring data, Ne of 31 for turbot, 46 for seabream_A, 32 for seabream_F, 40 for seabass and 33 for carp were found, and when using parents data, the corresponding values were 26, 50, 30, 32 and 15, respectively.Estimates of recent Ne were larger than 1000 fish for about 20 generations ago in all species. However, important drops were observed about five generations ago for turbot and seabream and about eight to nine generations ago for seabass, using data from parents or from offspring  were of the same order of magnitude as those obtained with the method of Santiago et al. [The LD method of Hayes et al.  led to lo et al.  and are Ne estimates were obtained for farmed populations of important European aquaculture species , using genome-wide SNP data from RAD-seq, and a novel accurate method based on LD measures [Ne for all the analysed populations were small and that important drops in ancestral Ne occurred in these populations about five to nine generations ago.In this study, recent and historical measures . Our resNe estimates for all the analysed populations were equal to or less than 50 fish. A value around 50 is considered to fit the minimum value recommended to avoid severe inbreeding depression and retain fitness in the short-term [Ne estimates for seabream and seabass could be slightly underestimated given that the data used came from breeding schemes with overlapping generations and the method asumes discrete generations [Recent ort-term –18. Howeerations , 19.Ne was within the range of those found in other farmed fish populations of different species [Ne in the GIFT  selection programme in which the creation of the base population was carefully planned, was equal to 88 after seven generations of selection for growth rate [Ne obtained for the farmed populations analysed here contrast with the large estimates (> 1000) found for wild populations of turbot, seabass and seabream [Ne for the wild common carp are not available, genetic variability analyses have shown that they are smaller in farmed than in wild strains [In general, the magnitude of our recent estimates of  species –30, alth species . For inswth rate . The smaseabream –34). Alt strains , 36.Ne for all the analysed populations revealed important drops occurring about five to nine generations ago. We obtained similar results using data from the reduced number of parental samples or from the more extensive number of offspring samples  but also in subsequent generations of selection if there is no optimal inbreeding control. Some caution must be taken in the interpretation of the drops observed as they could also be a consequence of population admixture or of the use of inaccurate genetic maps [Ne suggest that the combination of both domestication and the start of selection programmes is the most likely explanation for the important recent drops inferred in the populations analysed. Both events may have occurred too close in time to be disentangled by the method.A drop in tic maps . Neverthtic maps , with apr2 decreased by half at distances shorter than 0.02 Mb and it was maintained when the distance increased by one order of magnitude. At distances longer than 10 Mb, r2 reached values lower than 0.05. Similar LD values have been reported for coho salmon [Our results reflect a moderately low LD between SNPs that are separated by very short distances in turbot and seabass populations. In addition, a very fast LD decay with physical distance was observed in both populations. In fact, o salmon  and Nileo salmon  at shorto salmon , 39, 40 o salmon , with alNe is that it only holds for linear changes in population size. Indeed, previous studies applying this method have observed linear trends in Ne over time [Ne, such as those observed in Fig. Ne  across the last 100 generations for each population analysed. Straight lines represent estimates obtained using data from parents and dashed lines represent estimates obtained using data from offspring.Additional file 2. Estimates of Ne  obtained with the LD method of Hayes et al. [s et al.  across tAdditional file 3. Estimates of Ne  obtained by computer simulations with the LD methods of Hayes et al. [s et al.  and Sants et al. , using ts et al. .


Dichroplus vittatus, distributed in two Argentinean Biomes (Grassland and Savanna), by sequencing a 543 bp of the mitochondrial COI gene. Overall, we detected considerable haplotype diversity and low nucleotide diversity. AMOVA analyses showed a significant degree of differentiation among Biomes and between populations. Two major mitochondrial lineages can be distinguished. The haplogroup containing the most common haplotype split 17,000 years BP while the haplogroup including the second most common haplotype has a divergence date of about 11,700 years. Approximate Bayesian Computation (ABC) analyses showed that the palaeodemographic scenario that best fitted our data is consistent with a hypothesis of divergence from an ancestral population and subsequent admixture with Grassland-Savanna (South–North) direction. Our results suggest that populations located in both Biomes would derive from a single ancestral population that colonized the region after the Last Glacial Maximum and Grassland would have a more ancestral origin than Savanna. Further, our results emphasize the importance of human-mediated dispersal in the reconfiguration of genetic diversity of species with potential pest capacity.Quaternary climate oscillations and modification of the environment by humans have played an important role in shaping species distribution and genetic structure of modern species. Here, population genetic parameters were inferred from the analysis of 168 individuals belonging to 11 populations of the South American grasshopper,  Climate change, such as Quaternary oscillations, as well as dispersal are two major factors determining the contemporary distribution and genetic diversity of organisms2. Demographic events, such as stepping-stone expansion3, human-related dispersal4 and active migration6 of species, blurred their distribution ranges7.The environment of South America changed dramatically between glacial and interglacial intervals (ca. 100 ka. cycles for most of the Quaternary) in response to natural fluctuations in the Earth’s physical system9. The places where species persist during glaciations have generally been described as refugia10.The way in which species are isolated within such refugia, and the timing and mode of expansion from them after the improvement of environmental conditions, have become highly important topics to get deeper insight into the evolutionary processes that shaped the current genetic diversity.Genetic methods are widely applied to test phylogeographic hypotheses on Quaternary population dynamics, glacial/interglacial fluctuations, glacial refugia and postglacial recolonization12.Pleistocene events, mainly the Last Glacial Maximum  are thought to have affected the species range and the distribution of genetic variation among current populations of South America even in non-glaciated regions13. Particularly, there are few studies on the biogeography and species diversification in the tropical and temperate plains of southern South American grasslands15. Some papers proposed survival areas during glaciation periods in species distributed in non-glaciated Argentine regions far away from Central Argentina19. Particularly, there are not many examples analyzing the effect of past climatic events in Central Argentina. The most relevant report proposed that this region is climatic and demographically unstable for an endemic shrub17.Biogeographical approaches consider the Andes as a driver of species diversification; however, historical linkages among South American biomes are still under examination20). In South America TeGSS, also known as “Grassland”, include Pampean Prairies, Gramineous Steppe and Patagonian Mallines whereas the TrSGSS, belongs to the southern edge of the region well known as “Savanna”, include the Savannah itself, the Park, and the Shrub Steppe21.In Central Argentina converged the Temperate Grasslands, Savannas and Shrublands (TeGSS) and the Tropical and Subtropical Grasslands, Savannas and Shrublands (TrSGSS) biomes  to the Andean piedmont (west), and between the Gran Chaco (north) and Patagonia (south). The Pampean Ranges are located in the easternmost part of the Central Andes, formed by elongated mountain chains that rise sharply from the surroundings and alternate with narrow valleys and mud-flats, predominantly trending north–south.Between the natural quaternary regions comprising the central area of Argentina, and described by Iriondo23 and Clapperton24 indicated for the LGM a generalized advance of glaciers in the Andes Cordillera and South Patagonia and a widespread aridity in the lowlands. After that, many authors29 based on multi-proxy climate reconstructions inferred extensive dry conditions for the Pampas plain during the LGM.Iriondo & García30. Iriondo & García23 estimated for the LGM a shifting of the present Patagonian climatic province of ca. 750 km to the NE of their present position, covering the entire Pampa plain. An important influence on conditions over Patagonia and the adjacent Pampas during the LGM was the growth of a large conterminous ice cap over de Southern Andes, increasing the strength and consistency of anticyclonic circulation and generating strong drying (cold) winds from the SW24. Geological proxy data suggest a northward shift (up to 33°S) of the Westerlies31.In the South Pampa plain, the cold desert environment of the LGM is represented by the development of dune fields (Pampean Sand Sea), formed by winds of SW–NE and S–N directions. In the North Pampa, the climate during the LGM was peridesertic, promoting sedimentation of windblown dust in a steppe environment to form a loess belt10. Some species of Orthoptera are distributed in Central Argentina and constitute good models to analyze this issue.The analysis of new examples can improve our understanding of how species have responded to past climate changes, and where they endured periods of adverse climates in Central Argentina, forecasting how current climate change may affect species34.Orthoptera species of the Acrididae family are an essential component of both, healthy, and disturbed ecosystems. As primary consumers, they are important from a nutrient cycling perspective in ecosystem35. The genus Dichroplus included 18 species which are frequently grassland residents and are considered of economic importance because of the damages they cause to crops and pastures38.Some species of Acridids may be also considered of economic interest due to their ability of colonize modified environments and later experience a demographic expansion in their distribution range causing damage to crop and even to other components of biological diversityDichroplus vittatus is an especially harmful species of this genus. A South American nonselective polyphagous grasshopper with agronomic importance in Argentina, it is distinctive since in natural populations it may display wing size polymorphism41.D.vittatus a poorly developed wing morph (brachypterous) can coexist with a flight-capable morph with fully developed wings (macropterous) in populations from Central-West Argentina41.In fact, in D. vittatus have differences in their dispersal capacity. Populations with high frequency of long-winged individuals (macropterous) would show greater colonization potential revealing information for possible implementation of pest management plans41. To date, there are neither studies about genetic differentiation, nor about demographic and phylogenetic history of this species.Recent analyses indicate that dimorphic populations of D.vittatus in populations from Central Argentina that belong to two Biomes  by using sequences of mitochondrial DNA. We analyzed populations from the northwestern border of the Grassland Biome (TeGSS) (corresponding mainly to South Pampa quaternary region) and in the southwestern edge of the Savanna Biome (TrSGSS)   to infer genetic diversity (2) to estimate population structure (3) to analyze relationships and divergence among lineages (4) to explore historical demography and its association with Quaternary glacial or interglacial periods (5) to infer which hypothesis best explains the evolutionary history of the species in the studied region.Our analysis will help to determine if the populations located in the studied area derived from multiple or a single glacial refuge and identify the historical colonization routes and the demographic or historical events that shaped the genetic diversity observed today in this species.D.vittatus from 11 populations distributed in Central-West Argentina , differences among populations within Biomes accounted for 8.28%  of the total variation, while 75.91% of the variance could be attributed to within-population variability . AMOVA based on FST statistics revealed that 15.7% of genetic variation is distributed among Biomes , while 7.4% of variance is explained by differences among populations within Biomes  and 76.8% corresponded to differences between individuals within populations   Fig. B, althouThe median joining network illustrating the relationship of haplotypes indicated two haplotype lineages, with the overall network characterized by irregular clustering of haplotypes by population or Biome Fig. C. The moD.vittatus haplotypes, we implement a Bayesian approach through BEAST software considering D.elongatus haplotypes as outgroup. The BI tree showed strongly significant support for the divergence between haplotypes grouped by species   during the Upper Pleistocene. The haplogroup containing the most common haplotype (DV08) split 17,000 years BP  while the haplogroup including the second most common haplotype (DV03) has a divergence date of about 11,700 years   change through time we constructed Bayesian skyline plots (BSPs). The demographic scenario for Savanna presented by the BSP analyses showed a dual pattern, a short time of constant population size, followed by a long period of constant demographic expansion estimated to have started between 23,000 and 20,000 years BP Fig. . This anST approaches demonstrated significant number migrants per generation (greater than 1) between regions belonging to different Biomes (Table emf) of 7.42 whereas gene flow in the opposite direction was 2.77 (Nemf).Coalescent estimates of effective migration rates revealed a complex pattern of asymmetrical gene flow among Biomes Table . The estes Table . The anaD. vittatus  as the most highly supported event to explain the current phylogeographical structure of us Table . Both thus Table .Table 6AThe type I error indicates that the probability of excluding the best scenario (SC1) when it is the correct one is low , and the type II error indicates that the probability of selecting the best scenario when it is not the correct one is also relatively low . Posterior distribution of parameters for SC1 and RMAE values are shown in Table RMAEs are low, indicating that parameter estimates are reliable. As usual with demographic analyses, confidence intervals are wide however, broad intervals are mostly attributable to the limited information contained in the dataset rather than inaccurate choice of the priors or model misfit, as confirmed by model checking and RMAE values.7. The atmospheric circulation during the Upper Quaternary displayed patterns that differed significantly between glacial and interglacial periods. These differences may have been similar to those that existed between the LGM and a warm period represented by the mid-Holocene Climatic Optimum42. Particularly, the LGM is an exciting event for investigating ecosystem responses to climate changes ever since. Paleoenvironmental records suggest that full glacial climates throughout South America  were overall colder than today by about 5 °C44.Historical events, particularly Pleistocene glacial/interglacial cycles, have had a critical impact on generating phylogeographic structure in species47. However, some biomes have been more influenced by glacial impact than others. Here we report how a study of the wing dimorphic grasshopper D. vittatus, performed on part of the species distribution area across two Biomes of Southern South America (Grassland and Savanna) responded to some extreme climatic events of the Pleistocene, and what are the implications of these historical patterns in the present.Phylogeographic studies typically examined how species responded to paleoecological events within a single biome or biogeographic region17.Our results showed genetic differences between both Biomes. The significant population structure can be attributed to differences in topography and precipitations between the temperate (Savanna) and tropical and subtropical (Grassland) regions as previously proposedD. vittatus exhibited considerable haplotype diversity (h ˃ 0.5) and low nucleotide diversity (π < 0.5%) in Grassland and Savanna Biomes that could be attributed to population growth from a small number of individuals of an ancestral source48. The widespread distribution of the most frequent haplotypes (DV08 and DV03) throughout the studied area, as well as the weak phylogeographical signal, also supported the hypothesis of an ancestral expansion by long distance dispersal related to the colonization of isolated populations with very little genetic variation50. Furthermore, the leading-edge model of post-glacial expansion predicts there will be lower genetic diversity in recently colonized regions51. This would support the hypothesis that populations located in both Biomes in the studied area derive from a single ancestral population, since if there had been more than one; we should expect a higher genetic diversity and exclusive haplotypes in high frequency both in Savanna and in Grassland.After colonization, the estimates of migration rates suggested significant levels of genetic connectivity between both biomes with predominance of migration from South to North namely from South Pampa (corresponding to Grassland Biome) to Inter-mountain valleys of the Pampean Ranges (corresponding to Savanna Biome). The inferred historical gene flow would indicate some homogeneous environment in both natural quaternary regions in some past periods in the area analyzed here.41 suggested that the incidence of the macropterous morph (a proxy of contemporary migration events) is more frequent in populations from Grassland than in the Savanna ones. The frequency of macropterous individuals decreased with longitude and increase with precipitations, indicating that the macropterous morph is more frequent in the humid areas (Pampa plain) of the studied region (mostly populations of the Grassland Biome).Our previous work54. Over time the macropterous frequency can decrease if environment conditions are more suitable  and populations reach incidence of wing morphs like populations in stable habitat in a few generations53.It is traditionally accepted that the frequency of winged and wingless morphs can change through space and over time reflecting a trade-off between dispersal and reproduction in different environmentsA possible explanation for the current highest incidence of winged morph in Grassland may be that populations from Pampa plain correspond to an area influenced by agriculture and the pattern of wing variation would reflect events of contemporary migration and (re)colonization. Another tentative explanation can be related with different levels of trade-offs between dispersal and reproduction in different environments.The current gene flow (inferred by the incidence of macropterous morphs) from Grassland to Savanna is in line with the detected historic gene flow. However, as mentioned before the incidence of winged forms in addition to environmental factors may also be explained by anthropic activities.The demographic scenario showed by the Bayesian skyline plots indicated that both biomes experimented a long period of demographic expansion after LGM (started about ca 20 ka BP). The signals of population expansion were also detected through the analysis of mismatch distribution. Savanna’s mismatch analysis showed a distribution expected under a recent population expansion event, being CLA and RIO the populations that experience a clear pattern of expansion. The multimodal mismatch distribution revealed that Grassland represents a relatively stable area, with SRO being the only population showing signs of recent expansion. In general, our demographic studies pointed out Savanna Biome as an unstable area during Upper Pleistocene and Upper Holocene whereas Grassland Biome demonstrated more stability as expected in an oldest area.52. This would have allowed the expansion of D.vittatus in the Pampa plain currently represented by the Grassland Biome. Populations were settled later in currently Savanna, developed on the Pampean Ranges Region, perhaps because of a later improvement of environmental conditions.Our results evidenced that after ca. 20 ka BP, the two groups, established in Grassland and Savanna, are differentiated from an ancestral population . These results may be in concordance with paleoclimatic evidence. The last deglacial hemicycle, which marked the transition between the last glacial and present interglacial period, was characterized by a general increase in temperature and precipitation in Argentine plainsThe inference of population history with DIYABC confirmed that the population that established in Grassland was relatively larger than the Savanna one. In agreement with DIYABC, the two lineages identified by the Bayesian tree are roughly equally represented in the Savanna Biome whereas most of the haplotypes of the Grassland Biome (about 84%) correspond to the oldest lineage. These results support the hypothesis that Grassland would have a more ancient origin than Savanna, either due to higher proximity to the source ancestral population or due to that Grassland area (Southern Pampa) constituted an environment with better postglacial conditions.55.The signal of demographic expansion in Savanna is maintained at a slow but constant rate; however, Grassland reached a demographical stability at ca. 450 years BP. It could be related to the cold and arid period known as the Little Ice Age (LIA) occurred in the central region of Argentina between ca. 700–150 years BP59. Recent studies revealed insights about the survival areas during glaciation periods in species distributed in non-glaciated Argentine plains60. Lately was proposed a suitable refuge area for populations of an endemic shrub towards the north of the 35° south latitude of Central Argentina during Pleistocene glaciations and a southeast direction of the range expansion during interglaciations17.Several works suggested that terrestrial Patagonian taxa and some aquatic species probably survived glacial periods in southern refugia or alternatively recolonized the area from northern latitudes63. It can be hypothesized that the founders of the populations found today in the studied area of Grassland, could come from peripheral refuges or micro-refuges of the Atlantic Rainforest Ecoregion, since, the results show that populations were established in the studied Grassland area in a short term after the LGM and on the other hand, low general genetic diversity, widespread distribution of frequent haplotypes and weak phylogeographic signal indicates colonization by long distance dispersal from an ancestral population of low genetic diversity and effective size, compatible with the characteristics of a peripheral refuge.Another appealing hypothesis supporting the location of the glacial refuge closer to Grassland than Savanna would assume a micro-refuge located East of Grassland close to the Atlantic Rainforest Ecoregion (in Southern Brazil) where the existence of glacial refuges in general and in particular for insects is knownHowever, we studied only part of the geographic range of this species and further phylogeographic studies analyzing other geographic areas of this wide distributed species in Argentina may improve our knowledge about it evolutive history.64, as a short late occurrence of the LIA climate, which was characterized by a marked dryness in the Pampas. The Great Drought comprises part of the first half of the nineteenth century (AD 1800/1810 and AD 1827/183239). During most of the LIA, the Pampa plain registered an increasing demographic change after the Spanish conquer and the development of agricultural practices.Additionally, we detected an admixture event with Grassland-Savanna direction about 185 years ago. The admixture occurs due to gene flow of individuals that leave from Grassland and enter in the Savanna Biome . However, it turned out not to be a colonization process where individuals from Grassland totally or partially replaced those of Savanna; rather, a mixture of individuals occurred, what can also be seen in the BAPS analysis and through the haplotype network. It could be related to the climatic phase very well described by Darwin in his historical voyage around the world as “the Great Drought”, that was interpreted by Iriondo & Kröhling64. Human-mediated dispersal can reshape the genetic structure of animal populations and superimpose new signatures on existing natural phylogeographical patterns. Humans fundamentally affect dispersal, directly by transporting individuals and indirectly by altering landscapes. This human-mediated dispersal (HMD) modifies long-distance dispersal, changes dispersal paths, and above all benefits certain species or genotypes while disadvantaging others65.Patterns of genetic structure can change due to species dispersal ability frequently associated with environmental conditionsD. vittatus. One was the establishment of populations in Grassland and Savanna from a single glacial refuge located probably in the vicinity of the area that currently constitutes the Grassland Biome. This hypothesis is in line with our results which showed that Grassland is composed chiefly by haplotypes from the most ancestral haplogroup, constitutes an area that diverged earlier (approx. 17 ka BP), and it showed to be more stable with larger populations sizes during the last part of the Upper Pleistocene. Savanna Biome was later recolonized from individuals of the same refuge, and it subsequently received migrants from Grassland more recently due to a predominant south-north migration.Our results would indicate that there were two mayor demographic processes in the last 20 ka for the studied distribution of On the other hand the mixing event detected by the DYABC analysis in the last 185 years with Grassland-Savanna direction that could be explained through the increase in agricultural exploitation in the Pampas Region, becoming the largest center of crop production in the country during the last decades, which led to the construction of roads and trade routes destined to distribute these products throughout Argentina; this man-mediated dispersal could have influenced the phylogeographic pattern that we see today.Further studies in other animal models may improve our understanding about the relationships between paleoclimatic changes and phylogeographic patterns in Central Argentina.Dichroplus vittatus were collected from 11 natural populations during the breeding season  across two Argentinian Biomes; Temperate Grasslands, Savannas and Shrubland (referred as “Grasslands”) and Tropical and Subtropical Grasslands, Savannas and Shrublands (referred as “Savanna”) biomes  , 10 X buffer and 100 ng DNA template. After an initial denaturalization at 94 °C for 3 min, we conducted polymerase chain reaction (PCR) via 35 cycles of 94 °C for 30 s, 47 °C for 45 s, and 68 °C for 55 s. The final extension was conducted at 68 °C for 10 min. After visualization on a 1% agarose gel with a 100 bp ladder, PCR products were purified and sequenced on an ABI PrismTM Sequencer 31309 l Genetic Analyzer  by the Macrogen INC., Seoul, Korea Sequencing Service.We extracted total genomic DNA using the QIAgen DNeasy Blood and Tissue Kit and amplified a 543 pb fragment of the mitochondrial gene cytochrome oxidase I (COI), using specific primers from Lizenberger & Chapco67 and edited using BIOEDIT 7.0.968. The haplotypes were identified and characterized using GENEALEX 6.0 software69.Sequences were aligned using CLUSTALX 1.8170 and nucleotide diversity π71 with the software ARLEQUIN 3.572.Genetic variation was estimated using haplotype diversity h73 was applied to the dataset to evaluate the hierarchical partitioning of genetic variation among Biomes, populations and individuals using ARLEQUIN 3.572. AMOVASs based on FST (using haplotypes frequencies) and ФST  were conducted to investigate differentiation among biomes and populations within biomes. The significance of the results was tested through 1000 non-parametric permutations. We used BAPS  5.074 to provide deeper insight into the species genetic structure by clustering genetically similar individuals into panmictic groups. BAPS adopts a Bayesian approach with a stochastic optimization algorithm for analyzing models of population structure. The analysis was run with the “mixture of groups of individuals” option. Ten replicate runs for each successive K from 2 to 11 were completed, and the K with the highest posterior probabilities was taken to be the best partition.Analysis of molecular variance (AMOVA)75 was used for haplotype network construction. The median-joining method, which combines the features of a maximum-parsimony heuristic algorithm and a minimum spanning tree search algorithm, was used to resolve the relationships of the haplotypes.NETWORK 4.676. Negative values of D and Fs may be evidence of recent population expansion77 although Fu’s Fs is more specifically indicative of population demographic processes78. The significance was assessed by 1000 permutations. We also generated mismatch distributions, which may provide additional evidence of recent expansion based on the modality of the frequency of pairwise differences between samples. A unimodal shape suggests population expansion, whereas demographically stable populations should display a multimodal distribution. For every distribution, we calculated the Harpending’s raggedness index (r), under the population stability model (r becomes larger the longer populations have been stable)80.To test for evidence of recent demographic expansion, we performed tests of Tajima’s D, Fu’s Fs and mismatch distribution analyses as implemented in DnaSP 5.181 to estimate an appropriate model of sequence evolution. The best fit sequence model was determined by use of the Akiake Information Criterion (AIC).Aligned sequences were run through the program Mr. MODELTEST 2.382 by comparing the likelihood of three different models for the coalescent tree prior: constant population size, exponential growth and Bayesian skyline, assuming strict molecular clock and a relaxed uncorrelated lognormal clock for each tree prior, a GTR + I sequence evolution model as given by Mr. MODELTEST and a fixed substitution rate of 1.15 × 10−2subs/site/lineage/million years, a rate estimated relative to insects used for COI mitochondrial gene83. To test for deviance from a strict clock, we assessed the distribution of the relaxed clock coefficient of variation in TRACER 1.484; if the distribution of variation in branch rates includes zero, the rates are similar enough that a strict clock cannot be rejected82.Phylogenetic dating and divergence time estimates were evaluated in the program BEAST 1.8.4D elongatus as outgroup85. For each analysis we conducted two independent runs of 60.000.000 generations, with trees sampled every 6000 generations. We used TRACER 1.484 to determine a burn-in of 10%  and to ensure all parameters of interest in the combined trace files had effective sample sizes (ESS) > 200. Results from replicate runs were pooled with LogCombiner v2.1.2. The analysis was run for 60 million generations and the posterior output was examined in TRACER 1.484 to assess mixing and convergence of MCMC chains. Tree files were summarized with TreeAnnotator 1.7.486 to estimate maximum clade credibility (MCC) tree after removing the burn-in. The final tree was displayed in TreeFig 1.4 (http://tree.bio.ed.ac.uk).After verifying that a strict clock can be discarded, a time-calibrated Bayesian tree was reconstructed, through a Bayesian skyline model , employing an uncorrelated relaxed clock model , previously identified sequences of D. vittatus . The analysis consisted of 40 million iterations sampled every 4000 iterations. Bayes factors were calculated by manually summing the tree likelihood and coalescent/skyline columns in the BEAST log file.To complement mismatch analyses and estimate timing of demographic events and most recent common ancestor (TMRCA), Bayesian skyline plots (BSP) were implemented in BEAST software, which shows a visual representation of the shape of population change over time. We used the same parameter settings and MCMC diagnostics as our BEAST divergence time analysis using a log-normal relaxed clock with a normally distributed tree height prior corresponding to MRCA for D. vittatus, we used the coalescent-based program MIGRATE-N version 3.7.287. The mutation-scaled population size parameter θ , and the pairwise population mutation-scaled migration rates M  were estimated using FST estimates and a UPGMA tree as starting parameters. The numbers of female migrants per generation 2Nfmf  were estimated from M and θ.To compare different biogeographic hypotheses on historical migration rates of ST = 1/(4 Nm + 1)88.Analyses were run with five Markov chains and gamma-distributed priors on M (range 0.01–4000) and θ (range 0.0001–0.1). MIGRATE was run 5 times, with the UST-based starting parameters and different random seed numbers. The static heating scheme was set to temperatures 1.0, 1.5, 3.0 and 10,000.0. Studies were conducted using the UST-based starting parameters, with five replicates, with 100,000 steps recorded every 100 generations and a number of discard trees per chain (burn-in) of 10,000 totaling 50 million parameter values retained. For the other settings, we used the default values. All individuals were included and Migrate was run three times with the conditions mentioned above and the run which exhibited higher likelihood score was used to infer historical gene flow. For comparison, the migration rate was also estimated according to F89 to explore putative scenarios of colonization and divergence in the studied area, posterior to LGM glacial refugia expansion. This approach simulates the data sets for several predefined scenarios and compares the summary statistics of these with the summary statistics of the observed data, making it feasible to test complex population genetic models. Then, statistical tests were used to define which scenario best fits the observed dataset. For this study, we tested 4 different paleodemographic hypotheses that could explain the current genetic structure of D. vittatus populations using a fragment of COI gene  and type II error  in the selection of scenarios. Posterior model checking was performed on the selected scenario of every analysis using a local linear regression on 1% of the simulated data sets closest to our real data92. All summary statistics were used for model checking.The DIYABC program allows for estimating the posterior probability of the different scenarios compared. Due to the absence of available data for the effective size related parameters , we used a uniform and broad priors with the limits exceeding the probable smallest and largest population sizes for this species . The prior distributions for time related parameters were set based in historical references of the Last Glacial Maximum (LGM) in South AmericaSupplementary Information.