Analyses resolve from 4 to 8 spatially well-defined clusters across the range. Western, central, and southern populations within the Western Mojave recovery unit are consistent throughout, while analyses sometimes merge other recovery units depending on the level of clustering. Causal modeling consistently associates genetic connectivity with least-cost distance, based on multiple landscape features associated with tortoise habitat, better than geographic distance. Some features include elevation, soil depth, rock volume, precipitation, and vegetation coverage, suggesting that physical, climatic, and biotic landscape features have played a strong evolutionary role restricting gene flow between populations. Further, 12 highly differentiated outlier loci have associated functions that may be involved with neurogenesis, wound healing, lipid metabolism, and possibly vitellogenesis. Together, these findings have important implications for recovery programs, such as translocations, population augmentation, reproduction in captivity and the identification of ecologically important genes, opening new venues for conservation genomics in desert tortoises.Distance, environmental heterogeneity and local adaptation can strongly influence population structure and connectivity. Understanding how these factors shape the genomic landscape of threatened species is a major goal in conservation genomics and wildlife management. Herein, we use thousands  of single nucleotide polymorphism markers and spatial data from hundreds of individuals ( Heterogeneous environments, geographic distance and life-history traits, such as longevity, mating behavior, and potential for dispersal, can affect rates of gene flow across a species’ range. Agassiz’s desert tortoises (Gopherus agassizii) are long-lived, have low-motility, and inhabit one of the most arid environments in North America4. Populations of G. agassizii have been assessed genetically10 since the species was prioritized for conservation12. One focus has been the redefinition and delineation of recovery units, originally proposed in the Desert Tortoise Recovery Plan12, based on clustering methods from population genetic data. While some patterns of population genetic structure have been resolved consistently, marked differences in experimental design, numbers of samples, and sampling strategies fostered inconsistent results, in particular with respect to the resolution of the Western Mojave recovery unit12.A major goal in conservation genetics involves understanding how landscape features influence population connectivity and structure14. IBD is an evolutionary process by which genetic differences between individuals and/or populations increase with geographic distance16. The main biological assumption is that many organisms have limited dispersal leading to geographically restricted mating. While geographic distance is an important part the landscape, it is insensitive to the environment, which can be an important source of divergence18. Isolation-by-environment (IBE) and isolation-by-resistance (IBR) are two ways in which the effects of landscape heterogeneity can be measured with respect to genetic connectivity. Because IBR can conflate both IBE and IBD in empirical data17, it can be desirable to explicitly test for IBE, together with IBR and IBD, to untangle multiple competing patterns. A means to accomplish this is to treat IBD as the null hypothesis, against which multiple IBE and IBR models can be tested. While IBE models use overall environmental distances, IBR uses environmental friction or resistance as a proxy for probability of dispersal, where lower resistance leads to higher dispersal16. Environmental friction can be quantified by the least-cost distance (LCD), which is the path between two points that accumulates less friction and resistance-distance (RD), which uses circuit-theory to simultaneously weigh many possible routes across a landscape18.Previous studies have recognized the importance of isolation-by-distance (IBD) as a factor modulating genetic connectivity among desert tortoisesG. agassizii have reported stronger support for IBD than for IBR14. However, at a fine spatial scale (<100 km2), Latch et al.14 found weak population structure and weak influence of natural  and anthropogenic  factors on genetic connectivity in one population in the central Mojave Desert. Over a broader scale, Hagerty et al.13 used habitat suitability scores from a model of the distribution of desert tortoises19 to quantify landscape friction with LCD and RD. Their results suggested distance due to barriers, such as mountains and deep valleys, are major landscape features limiting gene flow. However, their barrier model was not better than the null IBD expectation13. Moreover, these landscape genetic studies have relied on Mantel tests to identify explanatory variables, a method which has been heavily critiqued22. After comparing a suite of popular methods for assessing spatial correlation, Shirk et al.23 suggested that linear mixed-effect models using maximum-likelihood population effects24 and reciprocal causal modeling25 were among the most consistent methods; these have not been applied yet for desert tortoises. Furthermore, previous studies on tortoises have relied on at most 20 selectively-neutral microsatellite markers, which inform only about random, stochastic changes in allele frequencies. Genome-wide single nucleotide polymorphisms (SNPs), alternatively, are generally more abundant and can potentially inform about adaptive processes acting upon specific alleles27.Landscape genetics models on 27. For instance, thousands of selectively neutral markers can accurately estimate effective population sizes  and migration rates , both of which are evolutionary measures critical for assessing conservation status. Further, genomic sites under selection can identify adaptations associated with geographic features, adding potential links to the environment28. Landscape genomics extends the amalgamation of population genetics and landscape ecology (landscape genetics) on two fronts: (1) access to thousands of putatively independent markers across the genome, which should increase analytical accuracy; and (2) access to genetic data that may be subject to evolutionary forces other than drift, such as natural selection and linkage29. The distribution of genetic variation across landscapes can reflect intricate interactions between the environment and evolutionary processes affecting population structure and adaptation to local conditions30.The genome era promises to resolve many conservation genetics issues associated with breadth of data, marker evolution  and scalabilityG. agassizii provides a unique resource to study the genomic basis of desert adaptations, and factors related to health, disease, and longevity31. Likewise, such data can facilitate population genomic analyses by serving as a reference for mapping variants that can be linked to functional regions. Reduced-representation-sequencing approaches, such as double-digest restriction-site associated sequencing (ddRAD-seq), are rapid, reliable, and cost-effective for generating thousands of SNPs across the genome for hundreds of individuals32. These approaches may significantly advance evolutionary and conservation analyses for many species33.The recently published genome of 34.Herein, we report an analysis of ddRAD-seq data comprising thousands of markers for hundreds of Agassiz’s desert tortoises. Our aim is to comprehensively re-assess population structure throughout the species’ range and provide an understanding of how landscape features influence genetic connectivity. We also seek to associate outlier loci with the functions of neighboring genes and model their distribution in an attempt to better understand local adaptation using reverse ecology. Lastly, we anticipate that our results will better inform wildlife management decisions for recovering declining desert tortoise populationsG. agassizii from the Marine Air Ground Task Force Training Command, Marine Corps Air Ground Combat Center, Twentynine Palms, California. These samples came mainly from 23 locations in the southern Mojave region. In addition, we used archival DNA samples employed in Murphy et al.9 (n = 494), which came from hand-captured tortoises whose blood was salvaged from other research projects37. For these, samples were collected at 31 locations across the Mojave and Colorado deserts , followed by size-selection, PCR amplification, quantification, and adaptor ligation. Barcode adapters, which recognized individual samples, were ligated to each fragment. Samples were pooled as equimolar concentrations, having 43–48 samples per pool (7 pools per sequencing lane). Libraries were later massively pair-ended sequenced using 4-5 flow-cell lanes with Illumina HiSeq2500 at a read length of 100 bp.Total genomic DNA was isolated from 50–100 µl of whole blood in 750 µl lysis buffer  by overnight lysis with proteinase K at 55 °C, followed by robotic extraction using a QIAGEN BioSprint 96 robotic magnetic-particle purification system  and Invitrogen Dynal bead extraction chemistry . The recovered DNA was quantified using a BioTEK Synergy HT  and diluted working stocks to 5 ng/μl in Low TE . Genotyping was performed at the University of Arizona Genetics Core following the protocol of Peterson Stacks v1.4438, a software package for restriction-site associated sequencing analysis. Raw reads were initially processed with the program process_radtags, filtering all reads with at least one uncalled base (-c option), reads with at least 10% of their length (about 10 bp) having contiguous low-quality bases , and recovering ambiguous barcode tags (-r option). De-multiplexing involved looking for a four-base in-read barcode tag, followed by the restriction site of sphI on forward reads (–renz_1 sphI), and the restriction site of mluCI (–renz_1 mluCI) on the reverse end, and the Illumina library index in the FASTQ header (–inline_index). Next, de-multiplexed sequences were mapped  to the reference genome of G. agassizii31 using Bowtie239 and the following settings: -D 15 -R 2 -N 0 -L 20 -i S,1,0.75. The resulting sequence alignment/map file was converted to binary data, sorted, and indexed with Samtools v1.3.140. Locus identification, cataloging, and re-matching were performed with pstacks, cstacks, and sstacks, respectively. Only stacks with a read depth of ≥5 (-m in pstacks) were kept. For cataloging, loci were determined by genomic position (-g), allowing a maximum of 3 mismatches per sample locus (-n in cstacks). A variant-call format file was generated by the program populations (Stacks) reporting all variable sites per locus ; loci found in at least 50% of the samples were retained. In a second filtering step, SNPs with a minor allele frequency below 0.001 were excluded, retaining the SNP with the highest minor allele frequency per linkage group (scaffold) using a Perl script (https://github.com/santiagosnchez/sing_snp_vcf).Sequences were retrieved from the University of Arizona Genetics Core server and transferred to SciNet, a high-performance computing server at the University of Toronto. All Illumina pair-end sequence data were filtered for quality control, de-multiplexed  and assembled using Admixture41, which can efficiently handle thousands of SNPs and uses a block relaxation algorithm that accurately estimates ancestry coefficients per individual. To select the best genetic group size (K), 10 bootstrap runs were executed using a 5-fold cross-validation  for K values ranging from 1 to 10. Only the K cluster with the smallest cross-validation error and significantly (Wilcoxon rank sum test) different bootstrapped cross-validation distributions was used for subsequent analyses; other K clusters that were only marginally worse were reported. Q-matrices were imported into R v3.442 and bar plots with stacked proportions of ancestry per individual were generated.We assessed population structure with Admixture, a discriminant analysis of principal components (DAPC)43 was performed using the R package adegenet44. The discriminant analysis of principal components used ordination to graphically depict total genetic variation, maximizing between-group genetic variation, while minimizing within-group variation. Given that the analyses did not allow inclusion of missing data, missing genotypes were substituted for the mean of the available data per locus, where allelic data represented allelic counts separated into two columns . DAPC required the a priori selection of both the number of retained principal components (PCs) and the number of discriminant functions. Thus, analyses ran a 50-fold cross-validation with data separated into training (90%) and testing (10%) sets (maximum number of retained PCs = 100) using the function xvalDapc in adegenet. Afterwards, the optimization with the best trade-off between retaining too few or too many principal components was selected. Ordinated genetic distances were then visualized in a scatter plot and colored by genetic cluster in R42.In addition to Q-matrix generated by Admixture was used to predict ancestry coefficients on a spatial grid. An R implementation of Kriging interpolation (the Krig function from the package fields45) was applied using a scaling theta of 1, assuming that the unknown covariance was a realization of a Gaussian random spatial processes. To improve model predictions, 200 random locations from outside the predicted distribution were added. For this, a binary grid mask based on the species distribution model (SDM) was generated, which included habitat suitability values above a minimal threshold of 0.2 . To exclude internal areas with low suitability scores, the raster was converted to polygons (rasterToPolygons) and we used only the polygon with the largest area (electronic Supplementary data interpolate function (bilinear method) from the package raster46 was used to extrapolate ancestry coefficients to the whole surface. Finally, all cells that had ancestry coefficients below the 80% quantile were ignored. This approach was similar  to the package tess3r47.The G. agassizii was reconstructed based on 11 environmental variables that were proposed previously to represent desert tortoise distribution19. These variables encompassed topography (elevation and slope), climate (precipitation and temperature), soil  and vegetation (vegetation coverage) (Table aggregate and resample functions from the package raster. All layers were adjusted to the same coordinate projection system (EPSG: 4326 or +proj = longlat + ellps = WGS84 + datum = WGS84 + no_defs) and cropped to an extent delimited by longitude: −120, −112, and latitude: 32, 38.The distribution of G. agassizii from the Global Biodiversity Information Facility (GBIF) server within the area of study. After adding our own georeferenced data, 2,565 presence coordinates were obtained. To avoid data redundancy and same-cell overrepresentation, our presence data were reduced to 645 coordinates representing cells with presence data only (600 × 800 matrix). To generate pseudo-absence data, 10,000 random coordinate points were simulated and cells (1000 × 1000 m) were selected based on those points. Cells with presence data were excluded from the absence data set.Presence data consisted of 1,848 downloaded georeferenced records of MaxEnt48 was employed to model habitat suitability based on the environmental data as predictor variables, and presence and pseudo-absence coordinate data. MaxEnt, which is a statistical machine-learning model based on the principle of maximum entropy, has been used often for predicting distributions of species49. The model was evaluated using a 10-fold cross-validation approach where all the data were separated into small training and testing sets. All statistical evaluation parameters, such as the area-under-the-receiver-operating-characteristic curve (AUC), were summarized across replicates. Variable importance was determined using a jackknife approach. Threshold values were estimated for each replicate and averaged. Habitat suitability scores were inferred using the predict function of the package raster. Because G. agassizii mostly occurs west and north of the Colorado River, we excluded suitability scores east of the Colorado River in the SDM  and for all rasters. Then, we calculated Euclidean environmental distances between individuals.An individual-based approach was used to test for IBD, IBE and IBR. First, genetic distances were estimated with the function 53. Therefore, we assigned resistance values analytically. First, we extracted the values of cells with presence data  and calculated the density distribution (density) for each variable. Then, we fitted cubic smoothing splines (smooth.spline) with the values of the density distribution as the response variable and the sampled environmental values from the distribution as predictor variables. We used this model to extrapolate density values to all data cells for each raster. Resistance was assumed to be 1–density to assign lower values to cells with higher density or less friction. The cell values in all rasters were again rescaled . We did this for all environmental variables except for the SDM, where direct habitat suitability (1–suitability score) was taken as a proxy for resistance; low suitability scores equaled higher resistance. Similarly, slope was evaluated by using both the degree of the slope as a measure of friction and by using the density approach described above. To improve computational efficiency, all 12 layers were trimmed to a polygon defined by the convex Hull of the largest polygon in the SDM  was used to obtain a conductance matrix. An R script (https://github.com/santiagosnchez/runCS) that ran CircuitScape55 in the background was used to calculate RD, directly loading matrices as dist objects in R. Least-cost distance and resistance distance matrices were linearized as vectors and stored as data frames.LCD was calculated using the function et al.23, two approaches for model evaluation were used. The first approach used linear mixed-effect modeling with MLPE24. Models were fitted using the function MLPE.lmm in the R package ResistanceGA56. Population assignments (K = 5) for every pair of individuals or every distance value in the matrix were used to build the correlation structure. This information was specified as a sparse matrix, which was built using a matrix with two columns and n(n−1)/2 rows , to the ZZ argument in the MLPE.lmm function. The model considered the relationships between genetic distances and the predictor distances as fixed effects, and the population structure relationships as random effects. Univariate models were compared using the Akaike Information Criterion (AIC)57 and Bayesian Information Criterion (BIC) with maximum likelihood, not restricted maximum likelihood (REML), as REML has caused issues in mixed-effect model comparisons58. MLPE.lmm used the lme459 package internally.Following Shirk 25. The mantel function in the ecodist package60 was used to perform partial Mantel tests for each pair of variables. In every case, the Rm (Mantel’s R) was calculated using 999 permutations having one of the variables as the main variable and suppressing the alternative variable (model A). The reciprocal test (model B) was performed next. If the difference in Rm between model A and model B was positive, then the test favored model A, and if it was negative or zero it favored model B61. As in Ruiz-Gonzalez et al.61, RmA−RmB, results were reported in a colored heat map where red colors indicated negative values and blue colors indicated positive values. RmA−RmB values were summed for each column to simplify variable ranking. The heat map was plotted using the R package plotly.The second approach applied reciprocal causal modeling (RCM) with partial Mantel testsBayeScan v2.162 was used to detect outlier loci . This used a Bayesian multinomial Dirichlet model to estimate allele frequencies and FST coefficients, which were then decomposed into population-specific (beta) and locus-specific  components. Loci for which the locus-specific component was necessary to explain the observed variation were considered non-neutral . Positive alpha values indicated that the locus was under positive diversifying selection, while negative values indicated negative purifying selection. A reversible-jump Monte Carlo Markov chain was then used to select one of two models, in which the alpha component was added or not. Before running the program, a VCF file was converted to the input format required by BayeScan for SNP data (https://github.com/santiagosnchez/vcf2bayescan). Next, BayeScan was run with default MCMC settings (-n 5000 -thin 10 -nbp 20 -pilot 5000 -burn 50000), except for the posterior odds prior (pr_odds), which was set to 100. Increasing the posterior odds prior increased the sensitivity and made the analysis more conservative, particularly because more than 1,000 markers were analyzed. Outlier loci were detected by plotting the FST coefficients against the log10 of the posterior odds, and the q-values  against alpha. Thresholds were marked by 2 (log10(100)) on the x-axis, and q-value of 0.05 on the y-axis, for each case. The closest function in bedtools v2.2663 was used to find the closest annotated gene for each outlier locus.read.vcf from the package pegas64. Then each locus was converted to a numeric multivariate format in which both alleles were separated into homozygotes (1 or 0) and heterozygotes (0.5). The spatial analysis was done as described earlier using Krig interpolation. This analysis was only performed on loci that were close to annotated genes to facilitate physiological interpretation in a spatial context.The distribution of the minor allele  of outlier loci was modeled in a similar way as in the spatial ancestry interpolation. A VCF file with outlier SNPs was imported into R using https://github.com/santiagosnchez). All other data were supplied as online supplementary files.Sequencing data were deposited in NCBI under the BioProject ID PRJNA450441. Scripts used for bioinformatics were made available through GitHub (n = 646 after exclusion). We catalogued 1,046,121 loci, most of which were excluded for not being present in at least 50% of all samples. Ultimately, after filtering out low quality data, 6,859 SNPs were retained for analyses.An average of 930,203 reads  were generated per individual after quality control filtering. From these, an average of 906,208 reads were successfully mapped to the reference genome. Read depth averaged 13.6 across all samples . The average number of scored loci was 54,152  per sample after excluding samples with less than 5,000 scored loci. Due to poor data-yield, 386 samples were excluded  had the lowest cross-validation error (CVE = 0.42897), followed closely by K = 4 clusters  represented the central Mojave group; Cluster 2 (blue) the western Mojave group; Cluster 3 (green) the southern Mojave group; Cluster 4 (yellow) included the Eastern Colorado, Eastern Mojave, and Northern Colorado recovery units; Cluster 5 (red) included Northeastern Mojave and Upper Virgin River recovery units  and 0.283 (between Cluster 3 and 5). Recovery units that were recognized with other K values included Northeastern Mojave and Upper Virgin River groups (K = 6 and K = 7) and the Eastern Colorado (K = 8)  Fig. . Eastern= 8 Fig. . In addi= 8 Fig. . The DAP= 8 Fig.  was mostr 5 Fig. . Clusterr 5 Fig. . The DAPAn average AUC of 0.875 SD ± 0.021) from 10 cross-validation runs indicated a good model fit and a significant deviation from random or homogeneous prediction . Based on Jackknife and permutation analyses, the variable depth-to-bedrock (soil) had the largest contribution to the model (average = 42.3%), followed by elevation , and mean temperature during the wet season . All other variables contributed from 7 to 1%, with the lowest being coarse fragment volume , habitat suitability (SDM), and depth-to-bedrock d2b)  > 2, which resulted in loci emitting a very strong to decisive signature . This identified 32 outlier loci  in Table Convergence of the MCMC chain in oci Fig. . Eighty-oci Fig.  were detThe distributions of the minor alleles of outlier loci that were on or close to annotated genes were modeled Fig. . Overall66. The extent to which the landscape limits genetic connectivity may potentially inform about the species potential for dispersal and interactions with the environment67. Analyses of vast genotypic data , ample sampling throughout the Mojave and Colorado deserts68, and high-resolution GIS data enable powerful insights about the spatial features and genomic composition of desert tortoise populations. The type and volume of data offer unprecedented value for desert tortoise conservation, making possible a more accurate assignment of individuals to spatially defined genetic units, measurement of intra- and inter-population relationships, and allowing for identification of candidate genes on which natural selection may be ocurring28.A major goal in conservation genetics involves performing spatial and genetic assessments of evolutionary significant units to gain knowledge about factors influencing population structure69. Our SDM constitutes ‘proof-of-concept’ by enhancing multiple analyses. For instance, it improves our spatial ancestry prediction  essentially correspond to the central, western, and southern Mojave populations proposed as recovery units in Murphy et al.9. Some agreement also occurs with respect to the Eastern Colorado, Northeastern Mojave, and Upper Virgin River populations at K = 6 and K = 8. To a large extent, our second DAPC analysis . The high resemblance in results might be partially due to the re-analysis of many of the same samples, albeit using considerably more, new and different data. Analyses of these data show strong genetic relationships within the southeastern , western , and northeastern (Northeastern Mojave and Upper Virgin River) groups.Our analyses detect from four to seven genetically distinct groups Fig.  with welsis Fig. , based ond 3 K =  essentiaet al.9, support the hypothesis that the Western Mojave recovery unit, as proposed originally in the Desert Tortoise Recovery Plan12, is a conglomerate of at least three distinct genetic groups. Our results contrast with the study of Hagerty and Tracy10, which supports the Western Mojave recovery unit as a single group. In support of our hypothesis, the genetic distinctiveness of these three groups remains consistent at K values ranging from 4 to 8 , which match our Northeastern Mojave and Upper Virgin River groups, respectively. Differences in sampling may account for discordance between our results and Murphy et al.‘s compared to Hagerty and Tracy’s. For instance, using stronger sampling in Nevada and Utah, Hagerty and Tracy resolved a higher level of structure than we resolve. In contrast, our study has stronger sampling in the western portion of the Mojave Desert, where we find a higher level of genetic subdivision. Recent genetic simulations on IBD models69 demonstrate that population-level sampling (e.g.9 and this study) may better resolve membership identification than does random sampling (e.g.10) in a IBD scenario. However, we suggest that for conservation purposes all information available should be synthetized into a single framework.Our genome-level analyses, together with the results in Murphy o 8 Fig. , and in o 8 Fig. . Other t9. Tortoises in the eastern and southern recovery units  suggest admixture patterns between the different groups. The southern Mojave population Cluster 3) seems to be the most admixed group Fig. . Cluster seems to14. As the authors suggested, limited dispersal, previous and present barriers, and climatic features are thought to be important factors effecting genetic differentiation74.The extent and directionality of admixture, together with the spatially explicit genetic structure Figs  and 3, s15. In contrast, more sophisticated landscape genetic approaches1 apply IBR models to evaluate the effect of landscape heterogeneity on genetic connectivity18. Because IBD can conflate IBR, our assessment uses multiple IBE models in a comparative framework. Our results do not find a direct influence of raw environmental distances on population structure. In contrast, IBD and IBR models are consistently better supported than IBE, suggesting that spatial features may be more important than raw environmental differences. Moreover, analyses find that several landscape features are better predictors of genetic connectivity than geographic distances  inter-individual or inter-population geographic distances and genetic distanceses Table , Fig. 4A14. Hagerty et al.13 reported that mountains and deep valleys serve to limit gene flow, albeit with marginal support. This reinforces field observations and distribution models suggesting that tortoises generally avoid steep slopes, high elevation areas, and playas75. Typical desert tortoise habitat can range from sandy plains to rocky or rolling foothills, including alluvial fans, washes, and canyons12. Tortoises also spend most of the year underground76, which means that they are likely to be found in areas with soils suitable for burrowing or caves in well-developed calcic layers78. This makes the idea of testing soil variables appealing. In fact, our analyses find support for variables such as the absolute depth-to-bedrock and fraction of coarse (>2 mm) soil fragments  that provide tortoises shade and shelter, plus stability to soils that support tortoise burrows. Interestingly, our analyses find average winter precipitation and vegetation coverage (winter and summer) to be good predictors of genetic connectivity  and/or the high variability of the landscape due to low primary productivity of deserts, could have hindered the relationship between vegetation coverage and genetic connectivity. However, our results show that even at broader scales the amount of vegetated land, in particular perennial plants, can have a substantial impact on how desert tortoise populations are structured.Rainfall has strong effects on food plant productionty Table . Because18. In contrast, RD is quantified as the average random walks between locations, assuming that gene flow happens through multiple, alternate routes56. LCD models for the best variables  in their home ranges. Thus, because genetic variation accumulates over time, it is important to frame genetic connectivity as a measure that represents evolutionary tendencies of genetic exchange, and not as a measure that represents contemporary movement.Circuit-based IBR models were introduced as more realistic alternatives to LCP analyses, which assume that individuals have preferred dispersal routeses Table , Fig. 4 12. It is possible to improve management techniques, including population augmentation , by incorporating knowledge of genetic boundaries and distances that tortoises should be moved. Our analyses delineate genetic population boundaries by using robust sampling for most of the species’ geographic range. These genetic boundaries are similar but not identical to those proposed by Murphy et al.9 and Hagerty and Tracy10.Understanding the genetic units of tortoises is important for managing this threatened species. Genetic units, among other factors, form the basis of recovery units for the Mojave population of desert tortoises89, using genetic boundaries drawn by Hagerty and Tracy10, wrote that populations “within a 200–276 km straight-line radius of each other (249–308 km measured around topographic barriers) tend to be genetically correlated and may be considered single genetic units for management purposes.”. Our findings, drawing on more robust genetic analyses, indicate the prudence of considering the importance of population boundaries in addition to distance. Distances of 200 km extend across several genetically identifiable populations  in the Western Mojave recovery unit, and across genetically identifiable populations in the southern Mojave to the eastern Colorado Desert. Mixing genetic populations could lead to outbreeding depression, failure to integrate, thrive, and survive91, or outbreeding vigor90, although there are no ‘common garden’ studies93 or other empirical investigations that explore these phenomena for G. agassizii. Via conservative management, however, we may limit risks by avoiding population augmentations across genetic population boundaries or over long distances94. Consistent with this approach, headstart tortoises in the western, central, southern, and northeastern Mojave genetic populations are being placed within their genetic population of origin.Averill-Murray and Hagerty95. Second, the approach can identify genes or neighboring genes of loci that strongly conform to a non-neutral model26. In a recent conservation genomics study of the Burmese roofed turtle (Batagur trivittata), from about 1500 SNPs, not a single locus departed significantly from neutrality96. In contrast, our analyses discover at least 32 loci under potential diversifying selection. Among 12 loci that associate with annotations, five variants occur within introns of five genes  of loci are neutral, which is an assumption in models of population structure100; ADAM9 (IPR006586), a membrane-anchored protein involved with cell adhesion, fertilization, muscular development and neurogenesis101; Roundabout homolog 2 (IPR032985) and SCO-spondin (IPR030119), which are independently related to axonal migration, growth, neural development, and tissue development102; and cholesteryl ester transfer protein (IPR017130), with functions related to lipid and cholesterol control103.Some genes in the vicinity of these loci have functions that may be involved with neurogenesis, wound healing, lipid metabolism and vitellogenesis. More specifically, noteworthy functions include the following: Beta-thymosin (IPR001152), a multi-function protein involved with cellular processes such as wound healing, actin formation (muscle development), embryonic organ development, and disease pathogenesis85. Lipid metabolism helps females increase body lipid reserves they use subsequently during periods of low resource availability, such as drought and brumation, and stimulates egg production85. Cholesterol and triglyceride levels have been found to vary between females and males, and among seasons, where they are high in spring when females are preparing eggs37. Egg production among female desert tortoises varies with the amount of rainfall or concomitant primary production104 and distribution . However, our results show that the minor allele associated with the cholesteryl ester transfer protein occurs mostly in northern areas  might point to interesting future research given the association of lipid metabolism with energy storage and vitellogenesis among desert tortoiseseas Fig. : 2293_0 Landscape genomics aims at identifying complex interactions between the environment and the genome of individuals in a population. These interactions include, but are not limited, to how the landscape limits gene flow between and within populations, and how genome-wide allele frequencies change as a function of space and the environment. With better inferences about these interactions and underling biological processes, better conservation and wildlife management actions can take place to restore major, threatened species, such as the desert tortoise. For the first time, we generate thousands of genome-wide genotypic data for hundreds of individuals in desert tortoises, which have helped to robustly assess the population structure in the species. By coupling genetic and spatial interpolation techniques, analyses delimit genetic clusters spatially, which can help inform potential locations for translocation and headstart releases, and where interpopulation interactions may occur. We also apply novel statistical methods to evaluate the effect of the landscape on genetic connectivity, using geographical distance as a null model. Our results allow us to build on previous studies, showing how several environmental, climatic, and biotic features explain genetic differences between populations. Finally, we identify potentially non-neutral loci that are in the vicinity of genes that may be involved with neurogenesis, wound healing, lipid metabolism and vitellogenesis. While their direct correlation to the environment is still uncertain, this research opens new directions for conservation genomics in desert tortoises.Supplementary informationSupplementary dataset


Liriodendron chinense is a Tertiary relict tree largely endemic to subtropical China. In this study, we aimed to achieve a better understanding of the phylogeographical pattern of L. chinense and to explore the role of mountains in the conservation of L. chinense genetic resources.Subtropical China is a global center of biodiversity and one of the most important refugia worldwide. Mountains play an important role in conserving the genetic resources of species. psbJ-petA, rpl32-ndhF, and trnK5’-matK) were sequenced in 40 populations of L. chinense for phylogeographical analyses. Relationships among chloroplast DNA (cpDNA) haplotypes were determined using median-joining networks, and genetic structure was examined by spatial analysis of molecular variance (SAMOVA). The ancestral area of the species was reconstructed using the Bayesian binary Markov Chain Monte Carlo (BBM) method according to its geographic distribution and a maximum parsimony (MP) tree based on Bayesian methods.Three chloroplast regions (L. chinense. SAMOVA revealed seven groups matching the major landscape features of the L. chinense distribution area. The haplotype network showed three clades distributed in the eastern, southwestern, and northwestern regions. Separate northern and southern refugia were found in the Wu Mountains and Yungui Plateau, with genetic admixture in the Dalou Mountains and Wuling Mountains. BBM revealed a more ancient origin of L. chinense in the eastern region, with a west–east split most likely having occurred during the Mindel glacial stage.Obvious phylogeographic structure was found in L. chinense. The east–west lineage split was most likely a process of gradual genetic isolation and allopatric lineage divergence when the Nanling corridor was frequently occupied by evergreen or coniferous forest during Late Quaternary oscillations. Hotspots of haplotype diversity in the Dalou Mountains and Wuling Mountains likely benefited from gene flow from the Wu Mountains and Yungui Plateau. Collectively, these results indicate that mountain regions should be the main units for conserving and collecting genetic resources of L. chinense and other similar species in subtropical China.The clear geographical distributions of haplotypes suggested multiple mountainous refugia of  Climate oscillations during the Late Tertiary and Quaternary are believed to have had profound influences on species variation and extinction and to have shaped the population structures of relict species , 2004. DSubtropical China, located between the Qingling Mountains-Huai River line (at approx. 34 °N) and the tropical south (≤22 °N) and between 99 °E and 123 °E, is characterized by evergreen broad-leaved forests . This arSoutheastern China was never covered by large ice sheets , and eviLiriodendron (Magnoliaceae) was widely distributed in the northern region of the Northern Hemisphere during the early to mid-Miocene but became extinct in Europe during the Pleistocene (Liriodendron chinense (Hemsl.) Sarg. and Liriodendron tulipifera Linn. Here, we focus on L. chinense, which naturally occurs in subtropical China and North Vietnam (L. chinense by using simple sequence repeat (SSR) markers from both the chloroplast and nuclear genomes to explore the roles of historical and present population genetics in determining the range limits of this species. However, the homology of chloroplast SSRs (cpSSRs) limited our ability to reveal the phylogeographic pattern of L. chinense, and a more complex phylogeographic pattern was found in the western region of L. chinense. Thus, in the present study, we applied three chloroplast DNA fragments  and increased sampling to 40 populations throughout the distribution area of L. chinense, especially surrounding the Sichuan Basin in the western regions, aiming to draw a clearer phylogeographic pattern of L. chinense and to examine the role of mountains in gene conservation in subtropical China. Here, we address the following questions: (1) Is there a clear relationship between the pattern of genetic divergence and the pattern of geographic distribution? (2) Is there an obvious lineage split or obvious genetic admixture between the continuous western mountain region and scattered areas in the eastern mountain region? (3) Are there any hotspots of haplotype diversity? If such regions do exist, what is the likely underlying process creating them? The results of this study should be valuable in furthering our understanding of the present genetic distribution of L. chinense and in helping to formulate appropriate strategies for the conservation of L. chinense and other plant species with similar distribution patterns in subtropical China.The genus istocene , leaving Vietnam . In earl Vietnam , we inveL. chinense is a long-lived and monoecious tree species that inhabits subtropical forests and is the only Liriodendron species in Asia. Liriodendron trees are easily recognized by their distinctive leaves, with four lobes in most cases and a cross-cut notched or straight apex. Flowering occurs between April and May. L. chinense is pollinated by bees, flies, and beetles  reported by L. tulipifera. Only trnK5’-matK amplification produced polymorphic and stable results; in contrast, the psbA-trnH fragment contained too many SSRs, reducing the accuracy of the sequencing results. Additionally, trnL intron-trnF was too conservative. Hence, psbJ-petA, rpl32-ndhF, and trnK5’-matK were selected for a large-scale survey of haplotype variation in Liriodendron (MH717109–MH717129 (psbJ-petA), MH717130–MH717142 (rpl32-ndhF), and MH717143–MH717150 (trnK5’-matK) (We designed eight noncoding plastid DNA region primers as suggested by odendron . The cpD5’-matK) . The haph; Pi; hT and vT) and population genetic diversity (hS and vS) were calculated by PermutCpSSR 2.0 . Nonparametric correlations (Spearman) between genetic diversity (h and π) in the western regions and latitude were also tested.The haplotype diversity h;  and nuclpSSR 2.0 . GeneticK) with a simulated annealing approach to maximize the proportion of total genetic variance observed between groups (FCT). SAMOVA was conducted with SAMOVA 1.0  with 10,000 permutations in Arlequin version 3.1 . SpatialMOVA 1.0  by usingRST  was significantly higher than that of GST (depending on the frequencies of haplotypes), with 1,000 random permutations in PermutCpSSR version 2.0 clade. We calculated the expected frequency based on a population growth-decline model. The sum of squared deviations (SSDs) between the observed and expected mismatch distributions and the raggedness index . Two independent runs were carried out for a chain length of 1 × 108 with a burn-in of 10%. The results of each run were visualized using Tracer v 1.7  of the haplotype lineages was inferred from their net pairwise sequence divergence per base pair (dA) using the Kimura 2-parameter model. T was calculated as T = dA/2μ, where μ is the rate of nucleotide substitutions  (BBM) analysis was performed in RASP v3.0 (http://mnh.scu.edu.cn/soft/blog/RASP) using trees retained from Mega 6 (see above). L. tulipifera was used as an outgroup for our ancestral state reconstructions. Three geographic regions were defined according to the distribution pattern of L. chinense , three indels (8–9 bp in length), and four SSRs were also identified  and nucleotide diversity (πT = 1.756 × 10−3), though the average population haplotype diversity was relatively low . The unbiased haplotype diversity (h) for each population was zero to one (LA), and the nucleotide diversity ranged from 0 to 0.128 × 10−3 (ZY and K). No significant deficiency in genetic diversity was detected in the scattered eastern mountain regions , but hotspots of haplotype diversity occurred at mid-latitudes close to the current northern edge, around the Wu Mountains, Dalou Mountains, and Wuling Mountains. The number of haplotypes was clearly low in the southwestern range . Hierarchical AMOVA revealed that 17.74% of the variation between the two groups and 56.97% of the total variation in cpDNA of the species was distributed within the eastern and western regions (FCT (minimized) and FSC (maximized) for different values of K, the best population grouping scheme for a set of sample populations can be identified in SAMOVA , indicating significant phylogeographic structure across the range of L. chinense. Four main clades (A–D) were revealed by the three cpDNA regions in the median-joining network , indicating that the clock-like evolution model  was suggested.The MP tree showed a pattern similar to that in the haplotype network . Obviouson model  was applD were significant for any cpDNA (sub)clade of L. chinense. Although all Fu’s FS values were positive, only the Fu’s FS over the entire range (P < 0.01) was significant. The observed mismatch distributions of haplotypes for each (sub)clade failed to reject the spatial expansion model in most cases . The results of BSPs showing the estimated changes in effective population size over time for the whole and each (sub)clade were concordant with the results from the neutrality tests. They showed that L. chinense had a constant population size during the last 15,000 years . Although recently occurring and not as pronounced, a west–east lineage split has also been detected in many other species, such as Fagus engleriana  occurred before the LGM (~0.018 Ma), and the Nanling Mountains were climatically unsuitable during the LGM  contained the ancestral haplotype H17 from clade C (northwestern clade) and tip haplotypes  from clade B (southwestern clade). In contrast, populations in the Wuling Mountains  all exclusively contained ancestral haplotype H13 from clade B and one tip haplotype, H6, from clade C  and Wuling Mountains , where it encountered northwestern genetic resources .The next question, then, is what the underlying process is. In contrast to wave-like migration fronts, expansion from local refugia followed by subsequent population admixture is more likely to be characteristic of postglacial forest development . In west Plateau , gene fldenopoda , the YunL. chinense, the southward introgression of northern refugial resources greatly contributed to the genetic admixture in the Dalou Mountains and Wuling Mountains. The northern refugium was centered in the Wu Mountains, where all the haplotypes in clade C were located , Yungui Plateau (southwest), Luoxiao Mountains (center), Tianmu Mountains (northeast) and Wuyi Mountains (southeast), falling almost entirely within the main mountain ranges –central China split, which was mainly caused by the ancient uplift of the Himalayan–Tibetan Plateau and the formation of Asian monsoons  Whole range; (b) East lineage; (c) Southwest subclade; (d) Northwest subclade.The Click here for additional data file.10.7717/peerj.6355/supp-5Supplemental Information 5Click here for additional data file.


ROH = 0.034), while the maximum was found in the Alai breed (FROH = 0.071). Short ROH segments (ROH ≤ 4 Mb) were predominant in all breeds. Long ROH segments (ROH > 16 Mb) were absent in the Gissar breed. The Gissar and Aykol breeds had the highest values of the effective population sizes estimated for five generations ago (5Ne = 660 and 563), whereas the Alai and Kyrgyz coarse wool displayed lower values . The synthetic origin of the Aykol breed was clearly evidenced by all analyses applied. Based on the network and admixture analyses of the Kyrgyz and global sheep breeds, the Tien-Shan and the Russian semi-fine wool breeds demonstrated a common ancestry that most likely is due to a contribution of the Lincoln breed. The Gissar, Aykol, and Kyrgyz coarse wool breeds showed a genetic background predominating in sheep populations from Iran and China whereas the Alai demonstrated the different ancestry type. The revealed admixture patterns probably resulted from the exchange and trade during the era of the Great Silk Road, which partly overlapped with historical and archeological findings.Sheep are a main livestock species of Kyrgyzstan, a Central Asian country with predominating mountain terrain. The current gene pool of local sheep resources has been forming under diverse climate conditions from the era of the trading caravans of the Great Silk Road, through the Soviet period of large-scale livestock improvements, which was followed by the deep crisis at the end of the 20th century, up to now. However, not much is known about the genetic background and variability of the local sheep populations. Therefore, our aims were to provide a characterization of the population structure and genetic relations within the Kyrgyz sheep breeds and to study their genetic connections with the global sheep breeds using SNP analysis. Samples of the Alai (n = 31), Gissar (n = 30), Kyrgyz coarse wool (n = 13), Aykol (n = 31), and Tien-Shan (n = 24) breeds were genotyped with the OvineSNP50 BeadChip or the Ovine Infinium HD BeadChip . The measure of inbreeding based on runs of homozygosity showed a minimum value in the Aykol breed (F The Kyrgyz Republic (Kyrgyzstan) is a Central Asian country comprising the Western part of the Tien-Shan and the Northern regions of the high Pamir Mountains and of the Alay Range . AccordiLivestock farming  has been the foundation supporting the livelihoods of local people from nomadic times until the present day. The contribution of the agricultural production to the economy is crucial (about 45% of gross income) . HoweverThe traditional agricultural management and extensive mountainous land use setting in the Kyrgyzstan, which is referred to as «Central Asian mountain agro-pastoralism» , was basFurthermore, during the Perestroika and the following economic crisis, farmers intensively crossed the populations of fine wool and local coarse wool sheep with Gissar rams to obtain flocks with good meat and fat quality to overcome the food shortage in the country . The newThe sheep breed resources are among the most valuable bases of economic prosperity and food security as well as cultural heritage of the Kyrgyzstan. In this regard, an awareness of the current status of their gene pool including estimation of levels of inbreeding and an evaluation of possible admixture patterns are crucial for both the rational management of the sheep populations and the conservation of their unique traits.The breeding of the native and locally developed livestock resources is usually based on using a restricted number of sires and mating between close relatives over generations. This process causes consecutive changes in the population means starting from an increase in inbreeding through the following reduction of genetic diversity and resulting in deterioration of fitness and survival-related traits. Furthermore, frequency of homozygous recessive defects is raised .Assessment of genetic diversity based on DNA marker data is especially beneficial when pedigree records are not available or unreliable . The devTo estimate inbreeding, diverse methods are currently available. Besides well-established classical approaches such as the calculations of observed and expected heterozygosity and Wright’s F statistic indices (Fis), there are the evaluation of effective population size (Ne) based on linkage disequilibrium (LD)  and the Runs of homozygosity are contiguous stretches of homozygous loci that inbred offspring inherit from both parents originated from a common ancestor . The lenFurthermore, it is worth mentioning that the Kyrgyzstan, which is geographically located between Kazakhstan, Uzbekistan, Tajikistan, and China, played a crucial role as a gateway of the ancient Great Silk Road . In factTherefore, our aims are to provide a detailed characterization of the population structure, to infer inbreeding levels and genetic relations within the Kyrgyz sheep breeds, and to study their genetic connections with the worldwide sheep breeds using the OvineSNP50K BeadChip and the Ovine Infinium HD BeadChip .In total, 129 animals representing five local and locally derived breeds of the Kyrgyzstan were selected for our study including Alai (n = 32), Aykol (n = 31), Gissar (n = 30), Kyrgyz coarse wool (n = 13), and Tien-Shan (n = 24). Three of these breeds  are fat-rumped coarse wool sheep; the Alai is a fat-rumped semi-coarse wool sheep; the Tien-Shan is a long-thin-tailed sheep with crossbred semi-fine wool.To infer the genetic relationships of the Kyrgyz sheep breeds and to study their historical admixture patterns in the context of the worldwide gene pool, we combined our data set with SNP genotypes of 61 global sheep populations, including 22 Russian, four Central European, 23 South-West European, three Asian, three Southwest Asian, and one American breeds. The sheep data made available by the International Sheep Genomics Consortium  and wereDNA was extracted from ear tissue samples using Nexttec columns  according to the manufacturer’s instructions. The concentrations of the dsDNA solutions were measured with a Qubit 3.0 fluorimeter . The OD260/OD280 ratio of DNA solutions was determined with a NanoDrop-2000 . Additionally, DNA quality was checked by 1% agarose gel electrophoresis.SNP genotyping was performed using the OvineSNP50 BeadChip  or the Ovine Infinium HD BeadChip  . GenotypAfter merging the genotypic data from the 600K and 50K arrays, a total of 42,230 autosomal SNPs that overlapped between the two arrays were left in the analysis. SNPs with a call rate below 0.90, a minor allele frequency (MAF) lower than 0.05, or that were located on sex chromosomes were discarded. A Hardy-Weinberg equilibrium test was not performed for comparisons with worldwide breeds because too many SNPs would have been excluded due to the Wahlund effect . After pTo account for the effects of family structures within the subpopulations, closely related animals (relationship >0.35) were discarded based on estimation of a unified additive relationship (UAR) matrix (relationship > 0.35) according to E(u)) (R) and the inbreeding coefficient (ISF) based on the unbiased expected heterozygosity using the R package “diveRsity” , unbiased expected heterozygosity (HE(u)) , rarefieveRsity” .For estimation of runs of homozygosity (ROH), a window-free method for consecutive SNP-based detection [consecutive runs method ] implemeTo minimize false positive results, the minimum number of SNPs (l) was calculated as was initially proposed by s = the number of genotyped SNPs per individual; ni = the number of genotyped individuals; α = the percentage of false positive ROH (set to 0.05 in our study); and het = the mean heterozygosity across all SNPs. Calculated l was equal to 18.where nROH were estimated for each individual and then categorized in the following ROH length classes: . We computed the total number of identified ROH for each length category in each of the individuals of each breed. The mean sum of ROH was calculated by adding up the length of all ROH for each individual in the sheep populations and then the results were averaged per breed population.ROH) was computed as the sum of the length of all ROH per animal as a proportion of the total autosomal SNP coverage (2.44 Gb). To measure the overall inbreeding, including recent and ancient common ancestors, we calculated the FROH based on the ROH with the minimum length of 1 Mb (FROH(1Mb)). More recent inbreeding was estimated based on the ROH with minimum length of 5 Mb (FROH(5MB)) as well as with minimum length of 10 Mb (FROH (10Mb)).The genomic inbreeding coefficient based on ROH (FNe) were estimated from linkage disequilibrium (LD) as implemented in SNeP (Ne was taken five generations back (5Ne) to make our data comparable with previous sheep studies , where c is the distance between the SNPs in Morgans, were calculated.Trends of effective population size    between FST) . Using tsoftware .A multidimensional scaling analysis (MDS) based on pairwise identical-by-state (IBS) distances and Principal Component Analysis (PCA) were performed with PLINK v1.90 and visualized with the R package “ggplot2” .Genetic admixture was inferred using Admixture v1.3 software  and plotInference of population splits and mixtures was performed using the TreeMix program . To set R version 3.3.2 was used to create the input files .The main characteristics of the breeds are summarized in The levels of genetic diversity were similar within the Kyrgyz sheep breeds, as presented in 5Ne values (660 and 563) among all breeds, whereas the Alai and Kyrgyz coarse wool displayed lower values  and Tien-Shan took an intermediate position (5Ne = 259). A similar trend was recorded for the 50Ne values. Thus, both the Gissar and Aykol breeds demonstrated high 50Ne values around 3000, while the Alai had the minimum 50Ne value (695) within the studied populations.The Gissar and Aykol breeds had the highest NeC analysis identified several changes in effective population size of the studied Kyrgyz populations , the troughs of decline in ROH were found in all breeds, with short ROH segments being predominant , while 938 ROH segments were found in the Kyrgyz coarse wool  = 0.034), while the Alai breed had the highest FROH (1MB) with a value of 0.071 (ROH (5MB) ranged from 0.004 in the Gissar breed to 0.03 in the Alai breed, whereas FROH (10MB) varied from 0.005 in the Gissar breed to 0.026 in the Kyrgyz coarse wool.The lowest FThe first component of the MDS analysis . The pairwise FST values between the Aykol breed and the other breeds varied from 0.017 with the Kyrgyz coarse wool breed to 0.029 with the Alai and 0.035 with the Tien-Shan breed. The FST values between the Alai and Gissar breeds and the Alai and Kyrgyz coarse wool breeds were similar .The relationships between the studied sheep breeds were assessed by calculating a pairwise The ADMIXTURE analysis   and by 5Ne = 65). Effective population sizes estimated for 50 generations ago for the studied populations were higher  or in aul breed .Ne values  in the Gissar and Aykol breeds probably corresponded to the wide spreading and high popularity of this type of the fat-rumped breed in Central Asian countries  of the Tien-Shan breed, we could assume that some rotation of rams takes place between the bloodlines within the breed to prevent inbreeding depression.The low values of effective population size in the Alai as well as the moderately small values in the Tien-Shan breed may reflect the small founding populations. Both breeds were created and reared within the scientifically designed breeding programs in the Soviet period . There iROH (7.1%) and the longest mean ROH segments (2.28 Mb) in the Alai breed seemed to be plausible. Summing up, the Alai breed is probably in critical state.The current situation with the Alai breed is even more ambiguous. In the 1980s, there were five different intra-breed bloodlines developed through selection and moderate inbreeding . CurrentROH value (4.3%). This pattern is in accordance with the fact that crossbreeds of the Kyrgyz coarse wool sheep and various breeds frequently form shared herds with purebred animals , we found no strong traces of recent inbreeding in the studied populations of the Kyrgyz sheep. We have a few assumptions on why this pattern arose. According to Considering the different methods of estimating genetic diversity  breed was clearly evidenced by all analyses. The Aykol’s position in the global network , the AykFST value (FST = 0.017)  . Being t= 0.017) , the Gisenturies . As Ivanenturies . The Gisenturies . The Gisenturies , which iThe creation of the Alai (Alaiskaya) breed was a multistep process and lasted from 1934 to 1981 . The eweAnalyzing the combined dataset of the SNP genotypes of the Kyrgyz, Russian, and worldwide breeds, the genetic background present in sheep from Iran  and China  as well as in Indian Deccani was found in the Alai, Gissar, Aykol, and Kyrgyz coarse wool breeds . We assuIn the current study, the assumption of an influence of the Great Silk Road on the gene pool of local sheep seems to be plausible. Due to a specific geographical location and mountain landscape, the territory of modern Kyrgyzstan was the crossroad of the ancient civilizations and great empires involved in the world’s famous exchange networks connecting China and South Asia with Central Asia and Europe . Based oThe exchange and trade of livestock animals was one of the important elements of functioning of the West-East networks . For exath to 13th centuries CE  in the implementation of the veterinary control (supervision) approved by Council Decision Eurasian Economic Commission № 80 .NZ and GB designed the study and participated in project coordination. EL and AZ collected the samples. TD and HR conducted the molecular genetic work. AD, ASh, HR, AS, EK, and IM processed the molecular genetic data. TD, AD, JS, HR, KW, NK, EL, GB, and NZ analyzed and discussed the data. TD, NZ, EL, and EK helped to draft the manuscript. TD wrote the final manuscript. All authors read and approved the final manuscript.The 50k SNP genotypes of the studied breeds were obtained within the framework of the Ministry of Science and Higher Education of Russia theme No. 0445-2019-0026 (АААА-А18-118021590138-1). The 600k SNP genotypes of the studied sheep breeds were obtained with financial support by the Russian Scientific Foundation (RSF) within Project No. 19-16-00070. The authors declare that the RSF and the Ministry of Science and Higher Education of Russia financed the study and did not have any influence on the results and their interpretation.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Passerculus sandwichensis) breeding in a large agricultural landscape. We used microsatellite analysis to determine a genetic pedigree, revealing a high level of extra-pair paternity (63%) within a relatively philopatric population. For the entire population, heritabilities varied from low to high (bill width: 0.160±0.182 to tarsus length: 0.651±0.155), while evolvabilities were low across all traits (wing length: 0.035±0.013 to body mass: 0.066±0.106). Our results indicate that any directional selection from agricultural management practices will produce negligible changes in basic morphometrics of Savannah sparrow populations occupying the Champlain Valley of Vermont, USA.Heritability and evolvability estimates of adult traits from free-living bird populations can be used to gauge the ability of populations to respond to selection, but are rare due to difficulties in gathering detailed pedigree information. The capacity to respond to selection is particularly important for species occupying managed habitats such as agricultural grasslands because of the potential for humans to accidentally influence traits. We calculated heritability and evolvability of six morphological traits in a population of Savannah Sparrows ( Human habitat alteration and other management activities are known to affect several evolutionary processes in birds, such as restricting or enhancing gene flow between populations , and alth2) is the proportion of a trait’s total phenotypic variance, VP, that is attributable to additive genetic variance, VA . Na. Na6]. Nance, VA . While hspective . Both esspective . Howeverspective  for a brThe ‘animal model’ can estimate heritability and evolvability parameters for unbalanced and/or incomplete pedigree datasets collected from wild populations . This moPasserculus sandwichensis), breeding in an agriculturally managed grassland landscape. These estimates will provide an important early indication of the potential for quantitative traits to evolve under continued selection from agricultural management, and will lay the groundwork for future studies of traits with more intimate links to individual fitness.Grassland birds, in particular, are under selective pressure from human activity because most of their native habitat has been replaced with row crops or intensely managed hayland and pasture . In thesAppropriate animal care was approved under the University of New England IACUC protocol number UNE010-2009, and banding under U.S. federal permit 17610#23540. Our research took place during 2002–2014 within the Champlain Valley of Vermont, USA, a relatively isolated region containing approximately 146,000 ha of managed grassland . In thisWe collected morphometric and genetic data on the Savannah Sparrows at five and four focal study fields at Shelburne and Hinesburg, respectively. The study fields were under diverse management schemes, which varied from low to high intensity depending on how often hay was harvested, where high intensity was characterized by both earlier and more frequent harvests or by being grazed by cows . Two of Each year during early- to mid-May, we passively netted adult birds with an array of 20–24 mist nets 1–3 times at each field. We banded all adults with a unique combination of three color bands and one metal US Geological Survey (USGS) band, and measured six morphological traits: overall body mass, wing length, tarsus length, and bill length, depth, and width. One observer (N. Perlut) took >99% of all measurements. We also collected a small blood sample (20–60 μL) from the brachial vein. Blood was dried onto Whatman disc filter paper and stored in a bag with silica desiccation beads.To help inform our pedigree analysis, we attempted to locate and record GPS coordinates of all nests on all study fields beginning in mid-May and ending in early-August. We determined female association by flushing them from the nest, and male association by observation of nestling provisioning and territory defense by males. If an adult associated with a nest remained unbanded, we attempted to capture it at the nest using mist nets. We checked nests every 1–2 day(s). When the nestlings reached 6–7 days of age we banded them with a metal USGS band and collected blood samples.To increase our sample of birds with known parents, we searched for natal dispersers across years using binoculars, identifiable by their single metal band. We searched weekly on focal Shelburne fields in all years and focal Hinesburg fields during 2002–2009. We also searched all fields within a 1.5-km radius of Shelburne focal fields during 2005 to 2014, and all fields within a 1.5-km radius of Hinesburg focal fields in 2006; see  for detaWe collected adult morphometrics on 95 locally hatched individuals. Egg dumping is not known in this species, so we were generally able to determine the identity of genetic mothers through field observations of incubation and feeding . HoweverPsa12 and Psa29 developed from P. sandwichensis . W. W32]. WSparrows , it is ug by 100 . Followig by 100 , we usedWe were able to successfully determine the genetic father of43 of the 95 recruits considered in our animal model, although one of these recruits was dropped from further analysis because maternity could not be confirmed. Twenty seven (63%) of these 43 individuals were extra-pair young based on exclusion of the territorial male as the genetic father. In 4 additional recruits the presence of extra-pair young was suggested by exclusion of the territorial male as the genetic father, but no genetic father was identifiable on the study field. All mothers identified through field observations were confirmed by maternity analysis, although in the case of 1 recruit no mother was identifiable by either observational or genetic methods.h2 ± standard error: 0.160±0.182) and tarsus length the highest heritability , as expected from similarly high rates previously observed in this population . ). 25]). Geospiza fortis, ; . S. SGeospiesticus, ; 38]) o) oGeospiesticus, ) have alChroicocephalus scopulinus), Barn Swallows (Hirundo rustica), Collared Flycatchers , Great Reed Warblers, House Sparrows, and Blue Tits (Cyanistes caeruleus). Similar to our estimates, these morphological traits tended to have low evolvabilities (less than 0.1), although mass in some species was slightly more evolvable (0.1–0.2). The evolvability of several of the morphological traits measured in our study were also estimated by [Teplitsky et al.  reviewedmated by  for an imated by  separatemated by  were bodIA = 0.04; [IA = 0.178). Human land management, however, may disrupt , t  the evoIn general, agricultural management has the potential to directly exert selection pressures on morphological traits. However, the most pronounced effects on fitness are likely to come from hay harvest or grazing events, which may be mostly nondiscriminatory with regard to morphological phenotypes, and which may actually dilute the effects of natural selective pressures. Nevertheless, one can envision specific scenarios under which birds with certain features, such as greater overall body mass, might gain advantages under agricultural management, such as the ability to initiate post-harvest nesting or to produce offspring that reach full size and develop flight more quickly. Unfortunately, our findings of low evolvability of morphological traits including body mass suggest a minimal potential to respond to management pressures in the near term, even if those pressures are consistent over evolutionarily meaningful time-spans. On the other hand, life-history traits such as the minimum time between reproductive events may have a much greater potential to respond to selection, considering that a short post-harvest period would necessarily select for birds capable of reproducing quickly following harvest events because only offspring that are produced during these constrained time-periods are able to recruit into this breeding population. Thus, future research should focus on the heritability and evolvability of other types of traits such as life-history and/or life-cycle traits to determine if and how they may respond to human activities, as well as the strength and direction of selection on both morphological and other traits, which were outside the scope of this study.S1 Table(DOCX)Click here for additional data file.S1 Data(XLSX)Click here for additional data file.


Lens culinaris Medik.) is one of the oldest domesticated crops and one of the most important grain legumes worldwide. The Mediterranean Basin holds large part of lentil biodiversity; however, no genetic structure was defined within the Mediterranean gene pool. In this study, we used high-throughput genotyping by sequencing to resolve the genetic structure of the Mediterranean ex situ lentil collection held at the Italian National Research Council. Sequencing of a 188-plex genotyping-by-sequencing library and bioinformatics treatment of data yielded 6,693 single nucleotide polymorphisms. Analysis of nonredundant genotypes with nonparametric and parametric methods highlighted the occurrence of five highly differentiated genetic clusters. Clustering could be related to geographic patterns and phenotypic traits, indicating that post-domestication routes introducing cultivation in Mediterranean countries and selection were major forces shaping lentil population structure. The estimation of the fixation index FST at individual single nucleotide polymorphism loci allowed the identification of distinctive alleles across clusters, suggesting the possibility to set up molecular keys for the assignment of lentil germplasm to specific genetic groups. Finally, significant associations between markers and phenotypic data were identified. Overall, the results of this study are of major importance for lentil conservation genetics and breeding and provide insights on the lentil evolutionary history.Cultivated lentil ( Lens culinaris Medik., 2n = 2x = 14) is one of the crops signing the birth of agriculture in the Neolithic Near East. Starting from the Fertile Crescent, lentil cultivation spreads westward to the Mediterranean Basin, the Nile Area, and Central Europe and eastward to Asia. More recently, lentil was introduced to North America and Australia  and pea (Pisum sativum L.)  . Lentil ivum L.) . Similarivum L.) . In addiivum L.) .Ex situ germplasm collections provide broad genetic variation to cope with future agricultural challenges, including climate changes, soil degradation, and water and land shortage  markers have been used in lentil to describe genetic variation of germplasm collections and link-specific markers to phenotypic traits, including seed quality, disease resistance, and micronutrient concentration . A comprGenotyping by sequencing (GBS) , based ohttp://ibbr.cnr.it/mgd/). At present, the collection includes 15,724 accessions, in most cases landraces collected from 1970s to 1990s in the Mediterranean area. In this study, we used GBS to explore the genetic diversity of 185 accessions selected within the IBBR-CNR Lens collection. The main aims of this study were to dissect lentil population structure and to provide a link between structure and geographic, phenotypic, and molecular variables. Genotypic and phenotypic data were finally merged aiming to detect significant associations.The Institute of Biosciences and Bioresources of the Italian National Research Council (IBBR-CNR) holds one of the most important legume collections worldwide . A set of 184 Lens culinaris accessions was selected to maximize variation with respect to geographic origin. The set mostly included Mediterranean germplasm  and Asian germplasm. In addition, four individuals obtained by self-pollination of the cultivar “Laird” were selected to provide a reference for the identification of redundant accessions  using the Illumina HiSeq 2500 system (Illumina). The TASSEL-Universal Network Enabled Analysis Kit pipeline (Genomic DNA was extracted from young leaf samples using the DNeasy Plant Mini Kit (Qiagen). A reduced representation library was prepared as described by pipeline , suitablpipeline . FinallyIn order to identify genetically redundant accessions, a matrix of pairwise identity by state (IBS) distance was calculated using SVS v.8.8.3 (Golden Helix). The mean and standard deviation of pairwise IBS distances calculated among four biological replicates of the cultivar “Laird” were considered to set up a threshold (mean – 3 × standard deviation) above, which two genotypes were declared redundant.2 < 0.5) using SVS 8.8.3 (Golden Helix).In order to minimize correlation among loci used to study population structure, the SNP input dataset referring to nonredundant accessions was pruned for linkage disequilibrium (ri) was higher than 0.6, otherwise they were classified as admixed.Analysis with the STRUCTURE (v.2.3.4) parametric model  was carrNonparametric study of population structure was carried out using the “find.clusters” function implemented in the stats R package, returning k-means clusters ranging from 1 to 40. The optimal number of clusters was inferred by the Bayesian Information Criterion statistics. Discriminant functions were calculated by the adegenet R package , using tST and Nei’s indexes  was computed at individual SNP loci, using the formula of ST values for each comparison.The pairwise fixation index (Fpost hoc detection of significant pairwise differences among clusters (p < 0.05). Boxplots were obtained to highlight the distribution of phenotypic traits in individual clusters.Nonredundant accessions of the lentil collection were grown at the experimental farm of the University of Bari Aldo Moro (41°01′22.1′′ N 16°54′21.0′′ E) during the growing season 2017–2018, according to a randomized block design with two replicates. Each experimental unit consisted of 10 plants arranged in a row of 1 m. The average flowering time was assessed at six different time points as the number of days after sowing in which about 30% of the plants exhibited flowers. Following harvest, plant height and the height of the flowering node were recorded. Seeds from both replicates were bulked, and a random sample of 50 seeds was taken for morphometric measurements. High-resolution scanner-based images were processed using the Image-Pro Plus 7.0 software , in order to estimate seed area, perimeter, and diameter. Significance of genotypic effects on phenotypic traits was assessed by analysis of variance. The Tukey’s honestly significant difference test was performed for the A GWA study was carried out to link phenotypic traits to markers identified by GBS analysis. The multi-locus mixed model implemented in SVS 8.8.3 (Golden Helix) was used for regression, using the IBS matrix as covariance matrix of random effects. The false discovery rate correction was used to declare significant associations (p < 0.05).http://www.ncbi.nlm.nih.gov/sra/) database under the accession number SRR8863759.Sequencing of a multiplexed GBS library prepared from 188 lentil genotypes  lower than 0.6  was observed between cluster SWM and clusters SEM–BM, whereas the lowest distance (0.21) was observed between clusters NM and A  that maximize variation among groups while minimizing within-group variation . The firGenetic relationships among individual accessions were finally assessed by the construction of a hierarchical tree based on the allele-sharing distance and the Ward’s clustering algorithm. In accordance with previous reports, genetic variation displayed by Mediterranean germplasm was higher than the one displayed by Asian accessions  recorded for the nonredundant lentil accessions genotyped in this study are reported in the ST) was computed at individual SNP loci in order to search for alleles that mostly differentiate each of the k-means clusters from the rest of the germplasm collection. The FST parameter reaches the upper limit of 1 when the two groups under comparison fix different alleles. As shown in ST > 0.8 could be assigned to all the clusters. In addition, alleles that are nearly private (FST > 0.95) for clusters SWM, SEM, and A were characterized. Overall, these results suggest the possibility to set up molecular keys enabling the assignment of genotypes to clusters and indicate polymorphisms that possibly arose from cluster-specific events of drift or selection.The pairwise fixation index  was significantly associated with all the three traits  obtained by Lens species. However, these authors used a different GBS protocol for library preparation, based on the simultaneous use of two restriction enzymes, the rare cutter PstI, and the frequent cutter MspI. Following the SNP filtering procedure, more than 6.5 K polymorphisms were detected, suggesting that GBS could be conveniently used in lentil for further characterization of ex situ collections and genomic studies.Here, we report the successful application of GBS for the study of genetic diversity in cultivated lentil. Sequencing of a 188-plex GBS library obtained with the post hoc statistics, estimating the plausibility of STRUCTURE clustering, not only for k = 5 but also for k = 2 and k = 3  and nonparametric (k-means) methods indicated that five genetic clusters (K = 5) could efficiently summarize patterns of variation in the data  and chickpea (Cicer arietinum L.) (microsperma (<6 mm) and macrosperma (>6 mm), would not efficiently distinguish the genetic clusters identified in this study, which were associated with average seed diameters ranging from 3.8 mm (cluster SWM) to 5.6 mm (cluster BM)  , thus suST distances at individual SNP loci highlighted several alleles that are nearly cluster specific , Fondazione Cariplo, and Daniel & Nina Carasso Foundation.This research has been performed within the project “LEgume GEnetic REsources as a tool for the development of innovative and sustainable food TEchnological system” supported under the “Thought for Food” Initiative by Agropolis Fondation (through the “The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


E. sibiricus L., the type species of the genus Elymus, is a perennial, self-pollinating and allotetraploid grass indigenous to Northern Asia, which in some countries can be cultivated as an important forage grass. In the present study, eighty-six Elymus sibiricus accessions, mostly from different parts of Asia, were assayed by gliadin markers based on Acid Polyacrylamide Gel Electrophoresis to differentiate and explore their genetic relationships. The genetic similarity matrix was calculated by 47 polymorphic bands, which ranged from 0.108 to 0.952 with an average of 0.373. The total Shannon diversity index (Ho) and the Simpson index (He) was 0.460 and 0.302, respectively. Cluster analysis showed a clear demarcation between accessions from Qinghai-Tibetan Plateau, China and the others as separate groups. The clustering pattern was probably dependent on geographic origin and ecological adaptability of the accessions. The population structure analysis based on Shannon indices showed that the proportion of variance within and among the five geographic regions of the Northern Hemisphere was 55.9 and 44.1%, respectively, or 63.4 and 36.6% within and among six Chinese provinces. This distinct geographical divergence was perhaps depended on ecogeographical conditions such as climate difference and mountain distribution. The results of gladin analysis in this study are useful for the collection and preservation of E. sibiricus germplasm resources. Elymus L. is the largest genus in the tribe Triticeae and by various estimates has 150 species distributed in most temperate regions of the World [Elymus, E. sibiricus L. (Siberian wildrye) is a perennial, caespitose, self-pollinating and allotetraploid grass (2n = 4x = 28) indigenous to Northern Asia, possessing the St and H genome [E. sibiricus usually serves as an important forage grass, and has been widely employed in establishing artificial grasslands to develop stock raising [H genome . It usuaass 2n = x = 28 inKnowledge of the genetic diversity in wild germplasm collections is central to the development of its effective conservation in genebanks, the insight of its evolutionary process and the efficient utilization in breeding strategies. Interest in the genetic structure of natural populations of grass species has been increased in the last few years owing to the necessity of broadening the knowledge of genetic variations in cultivated species. Triticeae, are monomeric proteins of 30–78 kD with poor solubility in dilute salt solutions and good solubility in aqueous ethanol [Triticeae are encoded by highly conserved multigenic families based on chromosomal locations, and the difference of copy number leads to gliadin polymorphism [E. sibiricus were more homologous to those from other Triticum species [Elymus species like genus Triticum and Hordeum. As a matter of fact, gliadin analysis based on Acid Polyacrylamide Gel Electrophoresis (A-PAGE) has been successfully applied in Elymus genus, mainly E.sibiricus and E. nutans, in which considerable interspecific or intraspecific variation were showed [E. sibiricus. Despite fewer detectable loci as compared to DNA molecular markers, gliadin markers still have great application potential in identification genotypes and characterization of genetic relationships of plant germplasm, because of its simplicity, rapidity and high repeatability.Gliadins, one of the main grain components of seed storage proteins in the tribe of  ethanol ,7. The s ethanol ,10,11,12 ethanol . All promorphism . Phyloge species . This mae showed ,17,18. Be showed , inter-se showed , and seqe showed  has beenE. sibiricus, however, wider collections need to be studied, analyzing the degree of diversity of the gliadins between different wild accessions and cultivated materials, and examining the relationship of this diversity with the geographical, ecological and/or climatic conditions of the places where samples are collected. A group of worldwide accessions of E. sibiricus, selected by their differences in terms of ecogeographic origin, formed the material examined in the present work. The aim of this study is to understand genetic variation and structure of eighty-six E. sibiricus accesssion that were collected worldwide from the main distribution areas including Western China, Mongolia, Central Asia, Siberia, etc., using a standard wheat variety ‘Marquis’ for A-PAGE analysis as control. Through an understanding of genetic diversity in E. sibicus from different regions, it will be helpful to produce a more efficient collection and preservation strategy for different regions and populations, and to maximize the use of germplasm resources.For an insight of the electrophoretic patterns variation of gliadins of E. sibiricus were used in this study: 10 from Mongolia, four from Kazakhstan, three from North America, 16 from Russia, and 53 from China  of which, were 90.4%. To measure the genetic content of accessions for four electrophoretic zones, we used the Shannon index (Ho), which is essentially a sum of frequency of occurrence of events (the gliadin bands in this case) (o) was highest in γ-zone (0.557), followed by β-zone (0.472), α-zone (0.431), and ω-zone (0.401). The high values of the total Shannon diversity index (Ho = 0.460) confirmed that gliadin markers are a very suitable tool for large-scale screening of E. sibiricus germplasm. The Simpson index (He) and the Shannon index were changed with a similar tendency. In addition, the high level of variation found in this study is consistent with the variability observed in morphological traits. Using the A-PAGE procedure with the wheat variety ‘Marquis’ as a control, a total of 52 electrophoretic bands were detected in 86 accessions of ectively . The aveis case) . The ShaE. sibiricus, such as gliadin (PPB = 92.86%) [E. sibiricus used 52 accessions from Qinghai-Tibet Plateau [The high polymorphism detected in the present investigation could be comparable to previous reports about  92.86%) , ISSR (P 92.86%) , SRAP (P 92.86%) , and SSR 92.86%) , of whic Plateau , and ISS Plateau . The higThe genetic similarities (GS) among 86 accessions were estimated by Jaccard coefficient based on gliadin markers. The GS for the 86 accessions ranged from 0.108 to 0.952 with a mean of 0.382. The lowest value of GS was 0.108, which was observed between accession PI598800 (Russia) and 205124 , while the highest GS (0.952) was found between accession PI372696 and PI348916, both collected from Alaska, USA. The average of GS among North America accessions was 0.859, while it was 0.636, 0.494, 0.418 and 0.391 for Mongolia, Kazakhstan, Russia and China. In the China group, the highest average of GS (0.555) was found among accessions from Qinghai, followed by Inner Mongolia (0.554), Xinjiang (0.514), Tibet (0.483), Gansu (0.454) and Sichuan (0.452). The UPGMA dendrogram resolving the genetic relationship among 86 accessions based on gliadin markers is presented in E.sibiricus as revealed by ISSR markers suggests that geographic isolation strongly inﬂuenced the evolution of the populations [E. sibiricus accessions, since Qinghai-Tibet Plateau is geographically isolated from the rest of the world by several of the world’s largest mountain ranges. However, in Cluster Ι the sub-cluster groupings based on gliadin data were generally not strongly associated with particular geographic origins , resulting from lack of exact definition of ecological zones for most accessions in this study, and the various selection forces that tended to produce the genetic heterogeneity under the different small niches, or gene mutation, natural factors and human activities. Moreover, genetic exchanges  among different accessions in the progress of reproduction also compromise origin data.According to the dendrogram displaying the genetic relationships among all the accessions, the distribution of the accessions within the clusters has apparent relation with the ecological or geographic origin. Geographic origin of accessions might be expected to represent different macro-environments and thus explain much of genetic variation among accessions from different regions . Nevo  with geographic group types. The North American group exhibited the lowest variabilities within group, but consisted of the smallest group size (two accessions). Both the Chinese and Russian groups exhibited high variabilities within group /Hsp, indicated that intra-group diversity component accounted for 55.9% of the total diversity, while 44.1% was due to diversity between groups. This result showed that intra-group diversity was higher than inter-group diversity. Similar result was observed among four Chinese sub-groups as well. In addition, Shannon indices also revealed that 63.4% and 36.6% variation accounted for within and among six Chinese geographical subgroups, respectively  and Russia (Ho = 0.340) contain the highest level of gliadin variation, whereas the lowest Ho value was observed in the accessions from North America. Significant correlation was found between genetic diversity and sampling size in the previous study [o, especially when most of the accessions in a group  were collected from a narrow area. Accessions from China , the Triticeae Research Institute , and Sichuan Academy of Grassland Science , were analysed in this study  ethanol and separated by Acid-PAGE at 10% (C: 3.84%) according to ISTA’s standard method . CanadiaoH = −Σπ lnπ, where π is the frequency of a band across all the samples [oH can be calculated and compared for the four zones of gliadins  separately. Shannon index within a subset of data was calculated as Hsp = −Σπi lnπi, where πi is the frequency of a band within a subset. The average diversity between different groups was calculated as Hpop = 1/n Σ Ho, where Hpop is the average group diversity over n groups. The proportion of diversity present within groups, wintinH = Hpop/Hsp, can thus be compared with variation between groups, beteeenH = (Hsp − Hpop)/Hsp. The Simpson index  was calculated as He = 1 − Σpi2, where pi is the band frequency of the ith allele [In the gliadin gel profiles the presence of a gliadin band was coded as ‘1’, whereas the absence of a band was coded as ‘0’.Total genetic diversity was estimated using the Shannon index as  samples . Ho can h allele . The binh allele . Based oh allele ,34. The h allele . E. sibiricus accessions. The dengrograms constructed by UPGMA method indicated that the genetic distance among accessions was related to the origin of these accesssions. And a clear demarcation was found between accessions from Qinghai-Tibetan Plateau of China and the other geographic areas as separate groups. This was confirmed by population structure analysis, which showed that the inter-group component accounted for 55.9% of the total diversity, while 44.1% was due to diversity within geographic groups. Thus, great importance should be attached to the ecogeographic diversification of germplasm collections as well as the enlargement of sampling size in order to maximize the use and conservation of genetic resources. All of the results of our study also showed that gliadin marker technique is suitable for genetic diversity analyses of E. sibiricus resources. In summary, the genetic diversity analyses indicated that a high level of genetic variance existed among the studied