In this study, we integrated targeted sequencing of genes of known function and genotyping of single-nucleotide polymorphisms to examine spatial, environmental, and species-specific patterns of potential local adaptation in two co-occuring turtle species: the Blanding’s turtle (Emydoidea blandingii) and the snapping turtle (Chelydra serpentina).Rapid anthropogenic climate change will require species to adapt to shifting environmental conditions, with successful adaptation dependent upon current patterns of genetic variation. While landscape genomic approaches allow for exploration of local adaptation in non-model systems, most landscape genomics studies of adaptive capacity are limited to exploratory identification of potentially important functional genes, often without We documented divergent patterns of spatial clustering between neutral and putatively adaptive genetic variation in both species. Environmental associations varied among gene regions and between species, with stronger environmental associations detected for genes involved in stress response and for the more specialized Blanding’s turtle. Land cover appeared to be more important than climate in shaping spatial variation in functional genes, indicating that human landscape alterations may affect adaptive capacity important for climate change responses.Our study provides evidence that responses to climate change will be contingent on species-specific adaptive capacity and past history of exposure to human land cover change.The online version contains supplementary material available at 10.1186/s12864-021-08151-7. Climate change will be among the greatest challenges facing biodiversity through the 21st century . The intGiven that adaptive genetic variation is a function of species-specific traits, adaptive potential is expected to vary among species , 11–14. Since local adaptation represents an interaction between genetic polymorphisms with functional roles, spatial variation in selection pressures, and phenotypic effects on fitness, focusing on genes of known functional significance allows for tests of ecological hypotheses related to adaptation , 10. IndIdentifying functional genes important for climate change adaptation in non-model organisms often relies on landscape genomic approaches for investigating adaptive genetic variation , 21. TesWhile substantial progress has been made in identifying and exploring local adaptation, several limitations challenge landscape genomic studies. First, most studies do not compare genes of known function to independent, unlinked neutral regions, instead relying on screening large numbers of loci to identify either markers that are more related to environmental gradients or more divergent between populations than others; this relies on the assumption that these outlier loci hold adaptive significance, yet very few outlier loci are often found to match with genes of known function , 23. SecIn this study, we addressed several of the aforementioned limitations by characterizing how putatively neutral and putatively adaptive genetic variation in two turtle species is affected by species-specific life history and habitat utilization traits and environmental characteristics. We addressed questions related to adaptive genetic variation using turtles for several reasons. Turtles, with their long lifespans and delayed sexual maturity, may have a limited capacity to respond rapidly to environmental change –28. HoweEmydoidea blandingii) and the snapping turtle (Chelydra serpentina). The Blanding’s turtle (E. blandingii) is a medium-bodied freshwater turtle that requires a juxtaposition of open terrestrial habitat for nesting with permanent wetlands for foraging and overwintering [E. blandingii is currently listed on the IUCN Red List as “Endangered”, and declines have been observed across much of the species’ range[C. serpentina) is a large-bodied freshwater generalist that is widely distributed throughout the United States and Canada [E. blandingii females are produced at incubation temperatures higher than a certain pivotal temperature, whereas for C. serpentina males are produced at intermediate temperatures and females are produced at extreme temperatures [We focused our analysis to two species: the Blanding’s turtle  of 0.210, a mean HO of 0.192, and a mean HE of 0.294. The E. blandingii SNP dataset global FST was 0.0300, and 20/21 pairwise comparisons between sites were significant despite low sample sizes . The C. serpentina SNP dataset consisted of 1,606 SNPs across 33 individuals, and had a mean MAF of 0.169, a mean HO of 0.169, and a mean HE of 0.244. Global FST was 0.068, and 9/10 pairwise comparisons between sites were significant despite low sample sizes .The E. blandingii, a total of two variant sites were identified for CIRBP, six for HSPA8, one for Sox9, and one for TRPV1; after excluding variant sites with the minor allele represented in fewer than two individuals for the unfiltered dataset, this left two for CIRBP, three for HSPA8, zero for Sox9, and one for TRPV1 (Table HO = 0.0795), but was highest for one of three variants discovered in HSPA8 (0.278). Across all target genes and sample sites, global FST was lower than global FST for the SNP dataset (0.0246), and 33/120 pairwise comparisons between sample sites were significant .For V1 Table . GeneticC. serpentina, with zero variant sites for CIRBP, six for HSPA8, zero for Sox9, and nine for TRPV1; however, the nine variant sites in TRPV1 characterized only two distinct haplotypes, and were thus analyzed as one distinct marker , and only 18/91 pairwise comparisons between sites were significant .We observed more variants in target genes overall for er Table . Mean HOE. blandingii target dataset, the number of inferred clusters varied between approaches, with K = 4 for DAPC and K = 6 for TESS3 based on observed inflection points in cross-validation scores; in contrast, the number of inferred clusters for the E. blandingii SNP dataset was lower, ranging from K = 1 for DAPC to K = 2 for TESS3, with no clear inflection points observed in cross-validation scores , distance to wetlands , precipitation , and maximum temperature , variance partitioning revealed that both climate and land cover covariates explain very little variance overall , and indicated that presence of the major allele was associated with higher log-transformed road densities , and indicated that presence of the major allele was associated with greater distance to open water , and indicated that presence of the minor allele was associated with reduced distances to agricultural cover  and distance to agricultural cover  were both significant, and indicated positive associations with both maximum temperature  and distance to agricultural cover  and allele presence. Finally, a model of presence/absence of the major allele of the second HSPA8 variant site against distance to agricultural cover was significant, and indicated a negative association between presence of this allele and distance to agricultural cover . For gene-specific multivariate partial redundancy analyses conditioned on latitude and longitude, we failed to detect any significant associations between climate or land cover variables and genetic differentiation.In contrast, for the C. serpentina) and a more range-restricted specialist with substantially delayed maturity (E. blandingii), we explored the genetic consequences of life history traits and habitat specialization [In this study, we leveraged both genome-wide SNPs and targeted sequencing of genes potentially important for local adaptation to explore how life history affects patterns of local adaptation. By exploring potentially adaptive genetic variation in a widespread generalist with an earlier age of maturity  may have produced differences in spatial genetic structure purely due to sampling effects, rather than effects of ecological and evolutionary processes. Second, any clustering in target gene datasets incorporated variant sites identified across all genes, as low numbers of variant sites identified overall made gene-specific clustering intractable. Given that we observed differences in environmental associations in both gene and allele-level analyses (see below), the spatial overlap in target gene clusters likely indicates that different processes are producing patterns in each gene region. Third, low sample sizes per site likely led to incomplete depictions of spatial patterns in allele frequencies. Although we attempted to use approaches throughout that do not have documented biases at low site-level sample sizes, clustering algorithms are affected by choices in sampling strategy . DespiteE. blandingii, requires co-occurrence of undisturbed wetlands habitat for residency and upland sandy bluffs for nesting [In addition to documenting divergent patterns of spatial clustering across datasets, we also documented significant environmental associations with adaptive genetic variation, suggesting that environmental heterogeneity may exert selective pressure on these genetic regions , 14. Fur nesting , 31, 47, nesting , 48, 49;C. serpentina and two in E. blandingii); furthermore, these nonsynonymous substitutions were observed in different genes across both species, with all three in C. serpentina observed in TRPV1 and both in E. blandingii in CIRBP. In contrast, climate and land cover covariates explained more variance in the E. blandingii target gene dataset than the SNP dataset  the number of nonsynonymous substitutions in each species-specific dataset, (b) the relative explanatory power of environmental variables for target vs. SNP datasets for each species, and (c) the relative explanatory power of environmental variables for target gene datasets across species. Below, we discuss each of these criteria in turn. First, we documented similar numbers of nonsynonymous substitutions for each species  explained more genetic variation in target genes than climate covariates for E. blandingii . As with the species-specific environmental association analyses, this may indicate that genetic variation in genes critical for adequately adapting to climate change is in some ways decoupled from contemporary climate conditions, and is instead shaped by the demographic and genetic impacts of anthropogenic habitat alterations [Since such plasticity would depend upon adequate sensing and response to increasing temperatures and TRPV1 has functional associations with temperature-sensing , we expeerations .E. blandingii, anthropogenic landscape alterations may have exerted selective pressures upon this gene [Unlike sex-determining genes, we documented more polymorphisms and stronger environmental associations in our stress response gene (HSPA8) than expected. While several previous studies have suggested the importance of heat shock proteins and other genes associated with stress response for screens of local adaptation, little is known about the environmental factors that might select for particular polymorphisms in heat shock proteins , 56–59. his gene , 60, 61.his gene , 60, 61.his gene , 63. Althis gene .In summary, we leveraged one of the many strengths of landscape genomic approaches – the testing of evolutionarily-relevant hypotheses in field systems– to explore the impacts of habitat specialization and gene function on patterns of putatively adaptive genetic variation . While oGiven the scope of conservation challenges posed by climate change, landscape genomic studies will increasingly be leveraged to provide insights into effective conservation and wildlife management –68. In pE. blandingii), potentially adaptive genetic variation was more strongly associated with land cover than climate, with particularly strong effects of road density. Given previous findings that roads have demographic and genetic impacts on turtle populations, this may indicate an additional threat posed to turtles by roads, whereby roads reduce the potential for local adaptation [Studies of local adaptation provide additional advantages beyond spatial prioritization as well, particularly if significant environmental associations with adaptive genetic variation are detected, as these environmental associations may provide evidence of the selective forces underpinning the distribution of adaptive genetic variation , 13. Foraptation , 50, 51 aptation , 14. Nonaptation , 11–13. C. serpentina across 16 sites for SNP genotyping, 86 C. serpentina across 21 sites for target gene sequencing, 60 E. blandingii across 13 sites for SNP genotyping, and 95 E. blandingii across 22 sites for target gene sequencing.Blanding’s turtles and snapping turtles were sampled across more than 20 sites in Wisconsin between 2010 and 2015 , 48, 74 E. blandingii, 36 C. serpentina) were single-end 100 bp sequenced in their own lanes; given poor overlap in loci among individuals for these libraries, an additional library of 87 samples  was also pair-end 100 bp sequenced on a RAPID run in two lanes. Since genomes were not yet available for either species at time of analysis, we then used de novo assembly in iPyrad [We used genotype-by-sequencing (GBS) to discover and genotype SNPs for each species . Samples were submitted for library preparation and sequencing to the University of Wisconsin-Madison Biotechnology Center. Each sample was digested using apeKI, indexed and then sequenced on an Illumina HiSeq2500. Two libraries of the same 87 samples  for each locus using “gl.basic.stats”, and then used “gl.drop.loc” to drop any loci with HO > 0.5. The E. blandingii dataset comprised 557,829 SNPs across 64 individuals and 12 sites, represented at a mean coverage of 28.58x, each of which was present in at least four individuals. After filtering for MAF, missing data by individual and locus, Hardy-Weinberg Equilibrium, heterozygote excess, and for a single SNP per RAD locus, a total of 2,811 SNPs across 37 individuals and 11 sites were available for E. blandingii, with a total missing data percentage of 18.9 %. Excluding sites with fewer than three samples produced a final dataset of 2,810 SNPs across 31 individuals in seven sites. The C. serpentina dataset comprised 795,296 SNPs across 52 individuals and 15 sites, represented at mean coverage of 25.25X, each of which was present in at least four individuals. After filtering for MAF, missing data by individual and locus, Hardy-Weinberg Equilibrium, heterozygote excess, and for a single SNP per RAD locus, a total of 1,606 SNPs across 33 individuals and 13 sites were available for C. serpentina, with a total missing data percentage of 17.23 %. Excluding sites with fewer than three samples produced a final dataset of 1,600 SNPs across 23 individuals in five sites. Hereafter, we refer to these final genome-wide datasets as our SNP datasets. Given low sample sizes and limited spatial representation of sample sites, we primarily used these datasets as a point of comparison with the targeted sequencing efforts described below.Final SNP datasets for each species were exported as vcf files for further filtering using package “dartR” . First, a priori; as a result, some primers inadvertently targeted non-coding sequences  with Illumina TruSeq Universal adapters for downstream Illumina MiSeq sequencing. We then amplified each tagged primer set for each sample using Qiagen Taq PCR core kits and Phusion High-Fidelity DNA Polymerase kits. See supplementary materials We used QIIME2 , a progrE. blandingii and 79 individuals across 22 sites for C. serpentina. After further removing sites with fewer than 3 individuals, this left 77 individuals across 16 sites for E. blandingii and 67 individuals across 14 sites for C. serpentina.The first Illumina MiSeq nano run generated a total of 726,112 sequences of 250 bp each, whereas the second generated a total of 621,932 sequences of 250 bp each. For most genes, aligning forward and reverse reads resulted in relatively high data loss post-filtering; thus, only forward reads were retained for downstream analyses, most of which were either truncated to 200 bp or left unaltered  was used for multiple sequence alignment of all genetic variants across samples, and variant sites present in greater than one individual were identified using MEGA7 and exported to comma-separated value (csv) format. Genotypes were numerically coded based on minor allele frequencies of each polymorphic site for each individual, and converted to a “genind” object using package “adegenet” in R . For allHe) and observed heterozygosity (Ho) for the SNP and target gene datasets for each sample site and species. We also quantified pairwise hierarchical F-statistics between sample sites for each species and dataset using packages “hierfstat”, “pegas”, and “adegenet” based on the Weir and Cockerham (1984) estimator [FST calculations. In all cases, we assessed significance using permutation tests with 100 bootstrapped replicates. We tested for isolation by distance for each dataset and species using Mantel tests by relating a matrix of individual-level genetic distances to a matrix of Euclidian geographic distances between samples [In order to address our first hypothesis – that there are differences in spatial genetic structure between target gene and SNP datasets – we quantified population genetic summary statistics and inferred numbers of genetic clusters. First, we quantified expected heterozygosity  in package “adegenet” in R to capture spatial clustering in the SNP and amplicon datasets. In order to do so, we used the “find.clusters” function to detect the optimal number of clusters for each species and dataset based on Bayesian Information Criterion (BIC), which generally coincided with a sharp elbow in the curve of BIC values as a function of K [K, and inferred the optimal K based on when cross-validation scores plateau or start increasing [K= 1 to 12.In order to identify whether genetic variation was geographically structured among sample sites, we used several approaches to identify genetic clusters in each dataset. First, we used successive ion of K , 86. We ion of K . This apcreasing . For eachttps://dnr.wi.gov/maps/ WISCLAND.html) by first converting raster representations of land cover to polygons, and then calculating Euclidian distance from sample sites to each land cover type. Finally, since road density has documented impacts on genetic diversity for E. blandingii [2 pixel) from the TIGER dataset (https://www.census.gov/geo/maps-data/data/tiger.html). We grouped these covariates into three categories: climate/weather , land cover , and geographic (latitude and longitude).We sampled environmental covariates from a variety of sources, with a similar methodology to that described in . Rather andingii  we extraC. serpentina and E. blandingii), we used the variance partitioning function “varpart” in package “vegan” to assess the variance in allele counts explained by climate, land cover, and geographic variables for each genetic variant in each gene. Next, we ran similar pRDA models to those described above with forward and backward stepwise variable selection to select climate and land cover covariates associated with overall genetic variation in each dataset and species, and used permutation tests to assess variable significance. We used output from these tests to assess relative support for alternative interpretations of our hypothesis. If cumulative variance explained by land cover and climate covariates for the E. blandingii target dataset (a) has more nonsynonymous substitutions than the C. serpentina dataset, (b) exceeds the proportion for the putatively neutral E. blandingii SNP dataset and (c) exceeds the proportion explained for the C. serpentina target dataset, our prediction that habitat specialists will show stronger adaptive responses to environmental gradients would be considered supported. Alternatively, if the opposite of all three of these conditions is true, then we will interpret that as evidence that the sensitive conservation status and lower effective population sizes of the Blanding’s turtle may lead to a lower potential of adaptive responses to environmental gradients than the widespread C. serpentina. Between these two alternatives, mixed evidence in support of either or both predictions may be possible as well, and may illustrate the relative roles of drift and selection in driving adaptive divergence for these species.In order to address our hypothesis that species-level traits will lead to divergent environmental trends in adaptive differentiation, we used similar genotype-environment association tests as described above. First, for each overall dataset (SNP and target gene) and species (r > 0.6). We then used a forward and backward stepwise variable selection procedure in package “vegan” to select covariates associated with genetic variation in target genes while conditioning for geographic coordinates (latitude + longitude), and used permutation tests to assess variable significance [In order to test our hypothesis that environmental associations in targeted genes should reflect the putative functions fulfilled by each gene, we used a combination of variance partitioning approaches and multivariate environmental association tests. First, we classified each polymorphism as noncoding or coding based on alignment of amplicon sequences to previously-described GenBank records; if a polymorphism was determined to be in a coding sequence, we then classified polymorphisms as synonymous or non-synonymous using MEGA7. Next, we used a similar variance partitioning approach to the one described above to assess overall gene-specific differences in the proportion of the genetic variation explained by climate, land cover, and geography. Next, we used both univariate and multivariate tests of environmental associations to assess relationships between variants and environmental gradients potentially driving selection. For univariate tests, we used generalized linear models with allele presence/absence as the response and environmental variables as predictor variables. We limited inquiry to one-predictor models to facilitate downstream analysis, and evaluated significance using likelihood ratio tests. Finally, we used partial redundancy analysis (pRDAs) in the “vegan” library to conduct multivariate tests of genotype-environment associations , 95. BefAdditional file 1.


Neophron percnopterus) in central Spain. The results show that the genetic diversity remained relatively stable over a period of twelve years despite the decline in census and effective population sizes in the last decades. A relatively high proportion of nestlings from different and distant territories showed high relatedness in each study year. We also found support for an increasing impact of severe recent (contemporary) rather than distant  past demographic bottlenecks, and the first evidence of inbred mating between full siblings coinciding with lethal malformations in offspring. The inbred nestling with feather malformations was positive to beak and feather disease virus recorded for the first time in this species. These results alert on recent and novel threats potentially affecting health and reducing the adaptive potential of individuals in this threatened species.The assessment of temporal variation in genetic features can be particularly informative on the factors behind demography and viability of wildlife populations and species. We used molecular methods to evaluate neutral genetic variation, relatedness, bottlenecks, and inbreeding in a declining population of Egyptian vulture ( The assessment of genetic diversity is crucial to understanding trends of wildlife populations and to implementing adequate strategies for conservation of endangered species . The evaRelatedness levels among individuals can be used as an indicator of genetic diversity in wildlife populations and species. The mating between close relatives can cause the loss of genetic variation due to random genetic drift, increasing the expression of recessive deleterious alleles, the loss of heterozygosity, and the extinction of functionally important alleles in the population . InbreedMultiple life-history traits and environmental factors can modulate the gain and loss of neutral genetic variation of populations and species . In longNeophron percnopterus) in central Spain. The main causes of the generalized decline of the Egyptian vulture includes illegal persecution through poisoning, habitat degradation, and reduction of food resources . In addition, we evaluated patterns and temporal variation in genetic signature of recent and historic demographic processes through bottleneck analysis. To further investigate inbreeding and its potential negative consequences, we examined the genetic relationships among nestlings fledged from a particular territory during a period of 15 years. Two of these nestlings subsequently mated, which allowed us to retrospectively investigate genetic relatedness and pedigree relationships between them and its influence in breeding performance and offspring health. This aimed to determine the extent of inbreeding as a relevant factor to be considered in the conservation management of this declining species.The Egyptian vulture is a small (∼2 kg) obligate scavenger living in a variety of open, arid, montane, and rugged landscapes in Southern Europe, Africa, and Asia . BreedinThe study was conducted in the province of Segovia . This area holds a small and declining population of Egyptian vultures mostly nesting in cliffs located in two major systems of large gorges in two protected areas  and their surroundings, while isolated pairs nest in small and scattered cliffs. The breeding population has suffered a sharp decline in the study area since the 1990s  were measured, weighed, and banded with metal and plastic rings with alphanumeric codes allowing individual identification at long distance with terrestrial telescopes. A sample of blood as source of DNA was taken from the brachial wing vein and preserved in absolute ethanol.An intensive monitoring program, including searching for territories, ringing nestlings, and identifying banded breeding individuals at territories was conducted at the Segovia province and its surroundings during the last four decades . The nesn = 19 from 15 territories) and 2018 (n = 14 from 10 territories). The sampled nestlings correspond to about 40–50% of the active territories each year in the population, as a variable proportion of breeding pairs failed in the reproduction each year  born in 2004 (n = 1), 2005 (n = 1), 2006 (n = 1), 2007 (n = 2), 2010 (n = 2), 2018 (n = 2) and 2020 (n = 1) in the two nesting sites (distant <200 m) included in this single territory (a single nesting site used each year). This allowed us to evaluate the validity of the relatedness estimates in reflecting full-sib relationships. To determine whether the same breeders used the same territory across the years, we collected feathers of the breeders attending this particular territory during the nest access to band the nestlings in 2018. We wanted to determine whether these breeders were the parents of the nestlings raised in the same territory the previous years, and then to establish long-term nest re-use and changes in the composition of the pair mate.In the breeding season of 2017, we located a novel breeding pair composed of two individuals born in the same nest but in different years. Because breeders can use the same nest and territory year to year , we asseCHD1 gene (The DNA was isolated by using the Quick-DNA Miniprep Plus Kit (Zymo Research) according to manufacturer’s protocols. Molecular sexing was performed using specific primers for partial amplification of the HD1 gene .Seventeen microsatellite markers previously characterized for Egyptian vulture were analysed: BV9, BV13, BV14, BV20, NP39, NP51, NP93, NP140, NP141, NP155, NP163, NP166, NP229, NP249, NP257, NP259, and NP296 . MicrosaNA), observed and expected heterozygosities (HO and HE) were calculated with GENALEX v.6.5 (AR) values were obtained using FSTAT v.2.9.3.2  was estimated for a maximum of 9 distance classes . Significance tests for each distance class were performed using 9,999 random permutations and 9,999 bootstrap replicates. In addition, we performed a Bayesian clustering analysis using the program STRUCTURE v.2.3.4  may outperform microsatellite markers in kingship assignment and relatedness estimations e.g., . Howeverr) among individuals was calculated using different estimators to evaluate the robustness and reliability of our inferences, since the performance of the different relatedness algorithms are dependent of the dataset used (r), which minimizes a downward bias for small sample sizes (r) and relationship categories between pairs of individuals from genotypic data. The likelihood values of the relationships available from the software  were calculated to determine the relationship with highest likelihood for each pair of individuals. When individuals born in the same year were categorized as PO, kinship was assumed to be FS. The inbreeding coefficients (FIS) were estimated using GENEPOP v.4.4.2 .The spatial pattern of relatedness was tested by two-tailed Mantel test with 9,999 permutations considering the geographic distances (in km) between nests  and the The detection of potential bottleneck events that lead to severe population size reductions in the recent (contemporary) past over the last decades was performed using the program BOTTLENECK version 1.2.02 . The steNe) was estimated using the program LDNE v.1.31 . Specific molecular markers previously reported were used in the PCR screening of BFDV  and by using the real-time PCR IQ-CHECK™ Salmonella II kit (Bio-Rad) and Easy I protocol, according to the manufacturer’s instructions  were analysed for the presence of three potential infectious agents that can cause plumage alterations , and the regional government of Castilla y Léon (Expte: EP/CyL/298/2016).The probability of null alleles was negligible for all loci except for NP140 . Therefore, the subsequent analyses were carried out with and without this locus. The results were similar when NP140 was included and excluded from the data set. Therefore, we present the results including this locus. Only the loci NP140 (offspring 2018) showed a significant deviation from Hardy-Weinberg equilibrium (HWE) after Bonferroni correction. When all individuals were analysed in the same dataset no significant deviation from HWE was observed . Exact tAR, HO, and HE did not differ between nestlings from 2006 and 2018  to eight (NP39), with an average of four alleles . In the and 2018 .FST = 0.02, p = 0.38). No significant differences were detected when Fisher’s exact tests of genetic differentiation were calculated from allele frequency data (Genetic differentiation was not significant between offspring from 2006 and 2018 (ncy data . We founncy data . The resncy data .r) in the following analysis.The evaluation of the five genetic relatedness estimators showed a good performance for most of the estimators . The estr . The strategy used for categorizing the genetic relationships allowed the accurate assignment of all direct relationships in individuals with known pedigree, and only two deviations in the kinship assignation were observed for inferred relationships. Overall, we correctly assigned 93% (26 of the 28 pair-wise relationships) obtained for the group of individuals with known pedigree. Therefore, it is expected that relationship categories estimated for individuals with unknown pedigree reflect the actual relatedness with accuracy. For nestlings from 2006, 33 half-sib (19.8%) and three full-sib (1.8%) relationships were estimated, excluding the four full-sibs from broods of two nestlings reared in the same nest and year (p = 0.19), being about 20% those estimated being closely related individuals  when pooled years; for this analysis, we considered a single nestling selected at random from broods of two nestlings to avoid pseudoreplication. Mean values of relatedness and inbreeding were similar between nestlings from 2006 and 2018 (Measures of QG-r  and ML-rr (r  were higand year . For nesand year . The geoand 2018 . The HL and 2018 .n = 15 territories), and most territories in 2018 . The number of territories with closely related nestlings ranged from one to seven in 2006, and from zero to four in 2018  and 2018 .All territories showed at least one closely related individual  among the nestlings sampled in 2006  for each sampled year were 100 (CI = 24-inf) for 2006 and 48 (CI = 13-inf) for 2018. The value of Ne considering all sampled individuals (2006 + 2018) was 53 (CI = 26–314).Estimates of effective population size  nestlings from 2004–2006 were full sibs, and therefore shared the same progenitors; (ii) nestlings from 2007, 2010, and 2018 were also full sibs  but unrer = 0.62 ± 0.11; ML-r = 0.53 ± 0.12), as is expected for dyads of closely related individuals. Two of these full siblings mated between them  in 2017: a female (ref. 291) born in 2007 and a male (ref. 32U) born in 2010  betr = 0.72 ± 0.18; ML-r = 0.64 ± 0.19). This nestling showed alterations in feather structure and appearance across all the plumage . This nestling was confirmed as full-sib of the nestling with feather malformations in 2018 , and as full-sibs with all other nestlings from this family lineage in 2007–2018  that was confirmed to be daughter of their putative parents by microsatellite allelic patterns  and rela plumage . These a plumage . As a co plumage  and rela007–2018 . Values 007–2018 .Salmonella. All results were negative in the nestling apparently healthy (Ref. 9MF).The screenings of infectious agents performed in these inbreed offspring showed a BFDV positive result in the nestling with malformations (Ref. 9MC) and negative results for APV and A growing body of evidence has related reduction in genetic diversity with declining population size of wild species . Our datFIS values tend to be lower (more outbred) when estimates are calculated using only samples from juveniles, and higher (more inbred) in samples from adults (We found similar levels of genetic variability and non-significant differences between two breeding seasons twelve years apart. This lack of inter-year differences is not surprising considering that the time period between both study years (12 years) is shorter that the generation time in the Egyptian vulture  of dyads comprising half- and full-sibs relationships among nestlings born in different territories in each study year. Indeed, a proportion of nestlings from distant territories in each year were genetic full-siblings. However, we found no evidence of a relationship between pairwise geographic inter-territory distances and pairwise genetic relatedness between nestlings from different territories in each cohort. This suggests a low contribution of extra-pair paternity to these intra- and inter-nest kinship relationships. Although extra-pair copulation has been reported at low frequency in the Egyptian vulture , the conp = 0.153, from The analysis of bottlenecks detected a clear signature of genetic contractions during the last decades, coupled with the recent decline according to census and effective population size in the study area and other regions included in the same genetic cluster . The sign = 12), but no other with malformations has been recorded in previous studies (The increase of close kin among mated breeders in small populations can lead to inbreeding depression. The consequences of a reduction of genetic diversity on offspring health and fitness can be severe for the affected populations . This is studies .Our study highlights on high relatedness, recent demographic bottlenecks, and inbreeding as concerning and increasing threats potentially reducing the adaptive potential of this declining population. This may arise directly through the effects of genetic erosion on health and fitness, and in combination with the detrimental effects of food-born pharmaceuticals and pathogens, including BFDV recorded for the first time in this species. Further research is needed to understand the relationships between demographic bottlenecks, inbreeding, susceptibility to pathogens, and health in the Egyptian vulture and other species of conservation concern.10.7717/peerj.11139/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.11139/supp-2Appendix S1Click here for additional data file.10.7717/peerj.11139/supp-3Appendix S2Click here for additional data file.10.7717/peerj.11139/supp-4Appendix S3Click here for additional data file.10.7717/peerj.11139/supp-5Appendix S4Click here for additional data file.10.7717/peerj.11139/supp-6Appendix S5Click here for additional data file.10.7717/peerj.11139/supp-7Appendix S6Click here for additional data file.


Soybean cultivars bred in the Huang-Huai-Hai region (HR) are rich in pedigree information. To date, few reports have exposed the genetic variants, population structure and genetic diversity of cultivars in this region by making use of genome-wide resequencing data. To depict genetic variation, population structure and composition characteristics of genetic diversity, a sample of soybean population composed all by cultivars was constructed. We re-sequenced 181 soybean cultivar genomes with an average depth of 10.38×. In total, 11,185,589 single nucleotide polymorphisms (SNPs) and 2,520,208 insertion-deletions (InDels) were identified on all 20 chromosomes. A considerable number of putative variants existed in important genome regions that may have an incalculable influence on genes, which participated in momentous biological processes. All 181 varieties were divided into five subpopulations according to their breeding years, SA (1963–1980), SB (1983–1988), SC (1991–2000), SD (2001–2011), SE (2012–2017). PCA and population structure figured out that there was no obvious grouping trend. The LD semi-decay distances of sub-population D and E were 182 kb, and 227 kb, respectively. Sub-population A (SA) had the highest value of nucleotide polymorphism (π). With the passage of time, the nucleotide polymorphism of SB and SC decreased gradually, however that of SD and SE, opposite to SB and SC, gave a rapid up-climbing trend, which meant a sharp increase in genetic diversity during the latest 20 years, hinting that breeders may have different breeding goals in different breeding periods in HR. Analysis of the PIC statistics exhibited very similar results with π. The current study is to analyze the genetic variants and characterize the structure and genetic diversity of soybean cultivars bred in different decades in HR, and to provide a theoretical reference for other identical studies. Glycine max (L) Merrill], which originated from China, is an important economic and oil crop2, and it has been cultivated for about 5000 years4. Soybean cultivars play a crucial role in soybean production, act as the most precious germplasm resources5 and are widely known as domesticated from wild materials through artificial directional selection and variation accumulation7. Huang-Huai-Hai region (HR) is the second main soybean-producing area in China only after Northeast China9. In recent years, HR soybean planting area has been stabilized at 2 million hectares, accounting for about 30% of China's total soybean planting area. At the same time, HR is also one of the areas where soybean breeding started very early10. From the earlier cultivated "Jinda 332", a large number of cultivated varieties suitable for landrace growth have been bred8. According to available statistics, a total of 550 varieties were bred in the region from 1986 to 2016 alone. In addition, HR soybean varieties are rich in pedigree information and have a long time span11. Consequently, they are ideal materials for studying the transmission and genetic relationship of excellent allelic variations in pedigree13. All of the above information highlights the vital importance of the HR region in China. The study on the population genomic variation, genetic diversity and population genetic structure of soybean cultivars in HR can provide reference for the selection of soybean breeding parents and broadening the genetic basis of soybean breeding.Soybean [16, the assembly of high-quality reference genomes18 and the advent of advanced bioinformatics methods21, we can study the genetic variations at the whole genome level quickly and accurately. Then the possible genomic regions of variations and their effects on genes can be inferred by means of variation annotation. Zhou et al.22 sequenced a total of 302 soybean accessions including wild, landrace, cultivated soybean germplasm and some foreign germplasm, and a total of 9,790,744 SNPs and 976,799 InDels were detected. It was found that the occurrence of variations led to the differentiation of genes in different geographical regions, which determined the model of regional adaptability of agronomic traits. Maldonado et al.23 sequenced 28 soybean cultivars of Brazilian and analyzed in detail the possible effects of SNPs and InDels with non-synonymous mutations on the function of the corresponding genes, which suggested that once the effects of these mutations on gene functions were confirmed, then the adaptation mechanism of soybean to tropical conditions in Brazil could be explained. Kim et al.24 carried out high-depth (> 13×) whole-genome (WGS) analysis of 418 domesticated soybean cultivars, 345 wild soybean materials and 18 natural hybrid varieties. The whole genome variation map of soybean was obtained, and 10.6 million SNPs and 1.4 million InDels were identified. Further analysis found that the emergence of a large number of putative variants led to changes in gene function, and the author concluded that harmful mutations were the genetic basis of crop inbreeding decline and heterosis.With the rapid development of high-throughput sequencing technology27. However, a practical problem that we should pay attention to is that the innovation of germplasm resources is regional adaptability, and our understanding of genetic diversity of germplasm resources should be multi-angle and multi-level. In order to apply the research results of genetic diversity of germplasm resources to breeding guidance in a better way, we must analyze the genetic diversity of soybean germplasm resources from different perspectives. So far, few relative studies have been conducted to analyze the genetic diversity of cultivated soybean populations in different decades in a single geographical region, which was thought to be very necessary28. Previous studies have pointed out that 80% of the cultivars bred in the HR were cultivated by cross breeding of high-yield varieties, and some breeders worried that such a "high by high" cross will reduce genetic diversity29, which was the vital factor that stimulate our interest in the study of population genetic diversity of bred varieties in HR.The rapid development of high-throughput sequencing technology not only enables us to obtain the information of genome-wide variation quickly and accurately, but also greatly facilitates the analysis of downstream population genetics. Now, we can study the population structure and genetic diversity at the genome-wide level to reveal the impact of genetic improvement on germplasm populations. Numerous previous studies have conducted in-depth analysis of soybean population genetic diversity, population structure and genomic LD changing patterns from the perspective of wild, landrace and cultivated or even different types and different ecological region materials, and the information obtained has been well used to guide breeding practice, meanwhile greatly promoted breeder's understanding of the genome of germplasm resourcesA large number of excellent varieties have been bred in HR in the past few decades. In line with the idea of analyzing the genetic information of germplasm resources in HR, the main purpose of the current study is to make utilization of the sequencing information of 181 soybean cultivars in HR, which were bred in the period of 1963 to 2017, and to do the researches as following: (1) figure out a large amount of important genes that affected by putative genetic variants; (2) clarify population structure and LD variation patterns of sub-populations in each breeding period in HR; (3) portray the genetic diversity of sub-populations in different ages.41. These SNPs were evenly distributed on all chromosomes, among which the number of SNPs on chromosome 18 was the highest and so was the density, while chromosome 15 took the second place (Supplementary Table We re-sequenced 181 soybean cultivars in Huang-Huai-Hai region (HR) and produced approximately 15.2 billion 100 bp pair-ended reads. The sample sequencing depth ranged from 6.64 ~ 16.22 fold of the reference genome, and the average sequencing depth was 10.38× , these 1314 genes were widely involved in biological processes such as metabolic process, cellular process, reproductive process, obsolete GTP catabolic process, regulation and so on , Glyma.18G161900 (binding) and Glyma.20G135500, but unfortunately, the biological process annotation information of Glyma.03G049800, Glyma.20G135500 was not included in SoyBase (http://soybase.org)   analysis to reveal the population structure. PCA1 and PCA2 explained the variation of 19.97% and 13.35% of the population respectively Fig. A but basNext, the genetic relationship matrix among all the samples was computed by using the core SNP data set, and the NJ tree between samples was drawn Fig. B, which 2 was used to reflect the degree of LD of sub-populations. The half-decay distance  of the entire HR population was 160 kb, and 182 kb, and 227 kb for sub-population D and E, respectively. The number of individuals in the three sub-populations A, B and C is relatively small. Considering that the use of a small number of samples to estimate the genomic LD may produce large errors, we had not evaluated the genomic LD of these three sub-populations , which was significantly higher than that in the later breeding stage sub-populations, while SB (1.27 × 10–3) and SC (1.24 × 10–3) got the lowest. With the passage of breeding period, the nucleotide polymorphism of SD (1.41 × 10–3) and SE (1.38 × 10–3) gradually increased, and significantly higher than that of SB and SC. The results of PIC analysis were consistent with π. SA had the highest polymorphic information content (0.242), followed by SD (0.234), SB (0.197) and SC (0.190). The analysis of the above two aspects showed that SA had the highest genetic diversity, followed by SD, while SB and SC had the lowest genetic diversity.In this section, we calculate the nucleotide diversity (π) Table  and the Glyma.02G17160030, Glyma.04G05020031 (J(E6)), Glyma.06G20780032 (E1), Glyma.10G22150033 (E2), Glyma.16G15100034 (GmFT2b), Glyma.17G03630035 and Glyma.19G19430036 (Dt1), respectively. These variants were likely to lead to changes in the products encoded by these genes. Notably, a "stop_gained" type variation was detected in Glyma.10G221500 and the results of snpEff annotation showed that this variation had a "HIGH" effect on Glyma.10G221500, and this gene is a famous soybean photoperiod related gene, which was named as E2. What is well known to us all is that soybean is very sensitive to changes in photoperiod and it is a typical short-day plant. Photoperiod affects the latitude adaptability and yield of soybean largely. The above findings will help us to understand the photoperiod adaptation mechanism of soybean in HR and provide genetic resources for soybean molecular breeding. In the near future, more work needs to be done to verify these putative variants affected genes, once the authenticity of the genetic effect of the variations were determined, these variations will guide us to understand the adaptation mechanism of plants to the environmental conditions in HR.Compared with the reference genome (Gmax_275_Wm82.a2.v1), many genetic changes were found in the germplasm resources of HR due to the occurrence of variations. Perhaps it was precisely because of the existence of these mutations that the function of the affected genes were enhanced or weakened or even lost its function. Except for the genes we mentioned in the second part of the results that may be affected by the putative variations, in fact, we also selected 18 genes in soybean that have been functionally verified in previous studies to study the mutations in these genes  and then displayed an obviously increase in SD  and later in SE the genetic diversity showed a slightly changing. Why was there an inflection point between SC and SD? It was speculated that the varieties bred in HR region might have experienced the most stringent artificial selection during this period37, which leads to a violent decrease in genetic diversity. Yet, after the period of SC, some breeders gradually realized that only relying on local varieties for variety improvement was not cost-effective and in breeding, we should pay attention to the diversity of genetic resources, make full use of various types, broaden the genetic basis, select parents with distant genetic relationship, and make full use of germplasm resources at home and abroad9. As a result, a large number of exotic varieties were introduced, and new variants were entered39. At the meantime, some breeders have discovered a wealth of excellent allelic variants contained in wild materials and further used them as breeding parents in the subsequent breeding work, which resulting in a high genetic diversity in SD. Moreover, there existed another latent factor that should be considered, which was the small number of samples in SC. There were only seven cultivars in SC, and it is possible that this number of cultivars was not enough to represent the genomic information of this breeding period, and further resulted in false positive in calculations of some statistics. In the future, we will try our best to collect more representative varieties to improve the relevant research content involved in this study.SA had the longest breeding time, due to the frequent recombination in the genome, SA exhibited the highest genetic diversity (π and PIC). However, in this series of changes, the genetic diversity reached the lowest peak in SC . According to the breeding time, they were divided into five sub-populations: SA (1963–1980), SB (1983–1988), SC (1991–2000), SD (2001–2011) and SE (2011–2017), and the five sub-populations contain 12, 8, 7, 90 and 64 accessions respectively. Notably, among the 12 varieties in SA, there are 2 ancestral parents and 10 cultivars that were bred in the year from 1963 to 1980  of the extracted DNA sample was determined by spectrophotometer, and the DNA was examined by 1% agarose gel electrophoresis to ensure that the extracted DNA was not degraded or contaminated by impurities. Still further, common concentration and purity of DNA were detected by Nanodrop, and the concentration of DNA was accurately quantified by Qubit fluorescence. It was required that the total amount of genomic DNA of non-pollution and non-degradation DNA should be more than 10 μg for sequencing.Following to the hexadecyltrimethylammonium bromide (CTAB) method42 (version 0.7.12) was used to map the preprocessed paired-end reads onto the reference soybean genome Williams8243 (Gmax_275_Wm82.a2.v1) with a -M added to mark shorter split hits as secondary and other parameters used default values. The original “.fastq” files were converted into “.bam” files by samtools44, and the Markduplicates tool of Picard45 (version:1.87) was used to mark possible PCR duplicates, and the flagstat tool of samtools45 was used to compile statistics of mapping information.The WGS-Seq (Whole Genome Resequencing) was done for variant genotyping for all the 181 selected cultivars in the present study, which was conducted at SHBIO, Shanghai, China. With the help of the Illumina Hiseq2500 sequencing platform and combined with the paired-end sequencing method, the sample genome resequencing analysis was carried out. BWA-MEM46 (version 4.1.1.0). The minimum read mapping quality was set to 20, and the default parameter settings were selected for the rest. First, we used GATK to perform a mutation detection on the data after mapping, sorting, and marking duplications to obtain a VCF file containing the mutation set. Second, we treated the first step obtained variant data as a known mutation set, and adopted the BaseRecalibrator and ApplyBQSR module to re-calibrate the sequencing bases’ quality of all reads in the “.bam” file, and utilized the newly generated bam file together with the HaplotypeCaller and GenotypeGVCFs modules of GATK to generate a second mutation detection result. Next, we made use of GATK's VariantFiltration module for quality control for the second mutation detection result, and all the quality control parameters were the default settings of the software. Finally, a VCF file containing the original SNPs and InDels were ready for the next step analysis.Mutation detection was done by employing the HaplotypeCaller and GenotypeGVCFs module of the GATK47.After all the procedures above, A total of 11,624,289 SNPs and 2,520,208 InDels  were identified from the analyses of the genomes of 181 cultivars in HR. Two subsets of all SNPs detected were defined using the following filtering criteria: (1) a simply filtered data set containing 11,185,589 SNPs by removing SNPs, which was monomorphic or having more than two alleles; (2) a core SNP set containing 4,666,538 . The genome region classification of SNPs and InDels in the present study was consistent with that in Zhou22, with just one point in difference, which was that the present study defined a 5 kb interval from the start or stop codon sites as an upstream or downstream variation. The simply filtered SNP set and all the InDels were used for the annotation analysis, and for the following analysis below, they were all basing on the core SNP data set. There existed one more thing needing to be made clear, which was that in the present study we refer to a variant site from the simply filtered SNP set, of which the missing rate was less than 10%, and all the rest accessions showing a mutation as the site that mutations had occurred in all samples.SNP and InDel annotation were both performed according to the soybean reference genome (Gmax_275_Wm82.a2.v1) using snpEff50. The neighbor-joining tree was constructed using MEGA51 (version: 7.0) with neighbor-joining algorithm52, and the bootstrap value was set to 1000. Terastructure53 basing on the machine learning algorithm was used to estimate the structure of population in the present study. The range of k value was set to 2–20, and the calculation of each K value was repeated for three times. Then, we extract the final validation likelihood for each run and averaged overall reps and drawn the averaged values into a line chart, and finally, we chose the value of K where the validation likelihood plateaus. We used Plink50 to compute the degree of linkage disequilibrium (LD) of each sub-population. We set 1 Mb as the window length to calculate the LD value between SNP pairs, the LD of SNP pairs between different chromosomes were ignored in this study, and finally, we used the in-house R scripts to visualize the LD decay trend.We conducted the principal component analysis (PCA) using Plink54 (π) and polymorphic information content (PIC)55 were both used to describe population genetic diversity. Nucleotide diversity refers to the average value of nucleotide difference at each site between any two nucleotide sequences in a population, which can be computed as xi is the frequency of sequence i, xj is the frequency of sequence j, and πij is the number of nucleotide differences between sequence i and j. Polymorphic information content (PIC) is a measure of the amount of information that can be provided by the polymorphism of a genetic marker in linkage analysis. Now it was often used to measure the degree of locus polymorphism, which can be computed as Plu is the frequency of the uth allele of the l marker and Plv is the frequency of the vth allele of the l marker, and v is bigger than u in number. π was calculated by -window-pi command of vcftools-0.1.1556 software with 100 kb (no overlap between windows) as the calculation window and a step length of 10 kb, π of sub-populations were the average of results from all calculation windows on the genome. According to its definition, PIC was calculated by a self-written Python (version: 3.7.1) script, and the average PIC of all loci was the population PIC value.Nucleotide diversityThe use of plants in the present study complies with international, national and/or institutional guidelines.Experimental research on the plants and the writing process of this manuscript comply with the current laws of China.Supplementary Information.


Quercus humboldtii, which is a typical species in northern Andean montane forests. Then, these patterns were compared with the palynological and evolutionary hypotheses postulated for montane forests of the Colombian Andes under climatic fluctuation scenarios during the Quaternary. Our results indicated that populations of Q. humboldtii have high genetic diversity and a lack of genetic structure and that they have experienced a historical increase in connectivity from the last glacial maximum (LGM) to the present. Furthermore, our results showed a dramatic reduction in the effective population size followed by an expansion before the LGM, which is consistent with the results found by palynological studies, suggesting a change in dominance in Andean forests that may be related to ecological factors rather than climate change.The climatic and geological changes that occurred during the Quaternary, particularly the fluctuations during the glacial and interglacial periods of the Pleistocene, shaped the population demography and geographic distribution of many species. These processes have been studied in several groups of organisms in the Northern Hemisphere, but their influence on the evolution of Neotropical montane species and ecosystems remains unclear. This study contributes to the understanding of the effect of climatic fluctuations during the late Pleistocene on the evolution of Andean mountain forests. First, we describe the nuclear and plastidic DNA patterns of genetic diversity, structure, historical demography, and landscape connectivity of  Quercus humboldtii. We found a high population genetic diversity, lack of genetic structure, and evidence of a demographic expansion predating the LGMThis study describes the nuclear and plastidic DNA patterns of genetic diversity, structure, historical demography, and landscape connectivity of  Several groups, such as birds, mammals, and plants, have been used as models to document the role of climatic fluctuations, vulcanism, and tectonics on species evolution, mostly in the Mexican transition zone of the Neotropics Guevara, . Such stQuercus have been intensively studied from macro‐ and microevolutionary perspectives, allowing us to understand not only how Nearctic lineages have diversified and radiated into the Neotropics  is a characteristic element of montane ecosystems and the single representative of the genus. Interestingly, numerous paleoecological studies of montane forest evolution during the Quaternary, including the role of Quercus in forest dynamics, have been conducted in this region  is a large tree that produces mid‐sized acorns (2–3 cm long) annually and has evergreen lanceolate leaves  for the molecular procedures. Total DNA was extracted using a commercial DNA isolation kit under standard protocols (QIAGEN DNeasy Plant Mini Kit). A set of nine mononucleotide repeat chloroplast DNA microsatellite (cpSSR) loci  and 10 nuclear DNA microsatellite (nSSR) loci  designed for Fagaceae species by Sebastiani et al.  two primer groups for cpSSRs  and (ii) four primer groups for nSSRs (a) quru‐GA‐IF02 and ‐IF07; (b) ‐OE09, ‐OM07, and ‐2F05; (c) ‐OC11, ‐IC08, and ‐OC19; and (d) ‐OI01 and ‐OA01). PCR and fragment sizing were performed following the procedures described by Rodríguez‐Correa et al. (Polymerase chain reactions (PCRs) were performed using the QIAGEN multiplex PCR kit with a final volume of 5 μl containing 1X multiplex PCR master mix, 0.25 mM of each primer, 20 ng of DNA, and dHa et al. , Rodrígua et al. .2.3STF with and without the ENA correction. Finally, nuclear loci were tested to detect significant departures from Hardy–Weinberg equilibrium and pairwise linkage disequilibrium among loci using the program Arlequin 3.5.2.2 , effective number of alleles (Ae), number of private alleles (Ap), observed (Ho) and unbiased expected (u He) heterozygosity, and the rarefied allele richness (AR) were calculated for the 22 sampling locations  using nSSR loci and the programs Arlequin 3.5.2.2  and RST  using 10,000 permutations in Arlequin 3.5.2.2  were performed to describe the partitioning of the genetic variation between groups, between sampling locations within groups, and within sampling locations for both cpSSR and nSSR datasets. The group categories were defined as the regional levels described above, which correspond to the three main cordilleras of the Colombian Andes. The AMOVA was calculated considering both tess3r  was further evaluated considering the geographic location. For this purpose, the package NST  were compared. STN is an FST analog that takes into account the genetic distances between haplotypes. The pNST value should approximate the value of GST, which considers only the probability of identity by descent among individuals among populations. Therefore, significant differences between NST and pNST are indicative of phylogeographic structure, meaning that related haplotypes are located in the same populations  for nSSRs. RST is a SMM‐based measure of differentiation, which is calculated from differences in allele sizes. Permuted RST is expected to approximate the value of FST; therefore, these values can be used to infer the relative importance of stepwise mutation versus drift in population differentiation  cons Figure  using th–3 to 1 × 10–4 . Similarly, the third method calculates the proportion of times in which scenario X has the highest posterior probability given that a different scenario is true. This proportion is calculated for all the other scenarios (type II error).Confidence in DIYABC scenario choice was evaluated in three ways. All the methods are based on simulating test datasets (pseudo‐observed datasets) and estimating their respective posterior probabilities. The first method checks the posterior error rate. It selects the 500 closest datasets to the observed data and uses them as test datasets. Then, it calculates the proportion of times the wrong scenario had the highest posterior probability. The second method uses 1,000 test datasets randomly chosen from the whole prior space of the hypothesized best scenario (X) and calculates the proportion of times in which other scenarios have the highest posterior probability given that scenario X is true  First, we used ecological niche modeling to build a climatic suitability‐based distribution model for the species through the Colombian Andes in different time periods. (ii) Then, we selected suitable habitat patches for each period. (iii) Subsequently, we constructed a graph using the least‐cost path among patches. (iv) Finally, we calculated and compared the equivalent connectivity area (ECA) among time periods.For the purpose of evaluating the geographic connectivity dynamics of Q. humboldtii used for the ecological niche modeling (ENM) were gathered from public databases , field observations, and bibliographic references. Occurrence records were mapped and filtered by altitude, latitude, and longitude based on the natural history of the species. Finally, to diminish the possible spatial autocorrelation effects of the aggregation of occurrence records on the ENM, all the records except one within a 10 km radius were deleted from the database.The geographic records of The climatic layers used for the present day (PD) were developed by Fick and Hijmans  mid‐Hol; (ii) thQ. humboldtii in the Colombian Andes during the MHol and LGM were obtained by projecting the present distribution model into six MHol and LGM scenarios . The models were evaluated using the average AUC (area under the curve) as an independent threshold after a 100‐replication bootstrap procedure. A subset formed by 30% of the total records was used for training the AUC, and the remaining 70% of the data were used to test the AUC.Ecological niche modeling was performed using the maximum entropy algorithm executed in MaxEnt version 3.3.3a  was evaluated using the graph‐based equivalent connectivity area (ECA) index  a collection of suitable habitat patches and (ii) the least‐cost distances among them. To obtain the first input, a resistance layer was constructed using the average MaxEnt output model for each period and the present‐day topographic ruggedness index (TRI), which is the sum of the changes in elevation among neighboring cells  showed significant deviations from equilibrium in at least one population. However, deviations were not consistently observed in the same loci across populations Table . The anaFST with and without adjustment for null alleles showed very similar values . Therefore, we concluded that the influence of null alleles on the estimation of genetic structure was low and decided to continue the analysis using all the analyzed loci. Finally, the linkage disequilibrium test detected significant linkage among every pair of loci in at least one sampling location. However, there was no consistent pattern across the sampling locations.The results for the simultaneous calculation of inbreeding coefficients and null allele frequencies by INEST showed that the model with the lowest DIC included the three possible parameters . The average inbreeding coefficient was 0.0875 , and the null allele frequency was lower than 0.2 for all loci Table . The est3.1h) varied between 0.154  and v (SE) values of 0.69 (0.209) and 1.74 (1.25), respectively.For the 22 sampling locations evaluated, 52 different cpSSR haplotypes with 38 unique haplotypes (present in a single population) and three widely distributed haplotypes were identified Figure . The numA mean allelic richness per population of 6.095 (0.047) and a mean effective allelic richness of 3.93 (0.0343) were found for the nuclear microsatellites Table . The num3.2FST = 0.18, p < .001; RST = 0.21, p < .0001), which indicated low‐to‐moderate levels of differentiation among the sampling locations. Moreover, genetic differentiation among regions  was not supported by the AMOVA results under either mutation model .The AMOVA for the cpSSR haplotypes suggested that considering both the infinite allele mutation model (IAM) and the stepwise mutation model (SMM), most of the genetic variation was distributed within the sampling locations (81.11% for the IAM and 78.7% for the SMM). The fixation indices had low values . However, the results obtained using the SMM indicated a higher percentage of variation among the sampling locations within regions . With regard to the comparisons among regions, the nuclear microsatellites showed no variation for either mutation model.Similar to the chloroplast results, the AMOVA calculation for nSSR using the IAM showed that most of the genetic variation was distributed within the sampling locations (92.82%), and a very low In the analysis of the genetic structure accounting for geographic distance, the lowest median nSSR value for the cross‐validation criterion had an RMSE = 0.198, indicating the presence of eleven clusters. The interpolated results showed only eight groups due to the low ancestry proportion represented in the remaining genetic groups Figure , which a3.3NST) was 0.168 and the pNST was 0.0569 (0.0132). The observed value was higher than expected under random permutations (p = .000), suggesting the presence of phylogeographic structure based on the cpSSR loci. The allele size permutation test (RST vs. pRST) resulted in a significantly larger value of RST (0.53) than of pRST (0.11) , also indicating the presence of phylogeographic structure in the nSSR loci. The calculated haplotype network showed 52 haplotypes, which were separated in most of the cases by a single mutational step  and logistic regression tests   Table .3.52), followed by the CCSM  and MIROC  models. During MHol, the CCSM and the MIROC models resulted in a slight decrease in connectivity , while the MPI model predicted a substantial increase , and the ECA for the present time was 8,348 km2.Our estimations of habitat connectivity among the three time periods  showed an increase in the ECA from the past to the present Figure . There w4Q. humboldtii, a lonely oak species that has reached and successfully colonized the Colombian Andes.In recent years, complex phylogeographic patterns have been described for Neotropical species  indicated a low genetic structure. Strong physiographic features, such as the Cauca and Magdalena valleys, which descend by up to 100 and 900 m, respectively  have had higher immigration rates and maintained higher demographic stability than the peripheral populations. Interestingly, they proposed that the persistence of these populations is related to the relatively stable temperature and humidity conditions in Colombia during the last 350 ka.It is difficult to compare our results with those of previous studies in the region due to the few phylogeographic analyses of mid‐elevation species similar to Ceroxylon populations in the northern Andes. A similar scenario of persistent connectivity throughout its geographic range may also explain the lack of genetic structure observed in Q. humboldtii. However, it is worth mentioning that important differences in the ecology of these palm species, such as climatic specificity, endemism, and relatively short pollination and dispersal distances, have resulted in different patterns of genetic structure than those found in Q. humboldtii.Sanín et al.  mentione4.3Q. humboldtii went through a bottleneck in the recent past: the higher expected than observed heterozygosity of the nuclear microsatellites, the star‐shaped haplotype network observed in the chloroplast data, and the ABC demographic scenario of a contraction of the population (47.5 kyr BP) followed by a demographic population expansion (28.4 kyr BP) just before the LGM (~21 kyr BP) . This epoch has been described as climatically cool and wet, although characterized by several stadial and interstadial fluctuations that caused variation in the extension of the glaciers and the altitudinal position of vegetation belts  Figure . InteresQuercus became a common element of the Andean forest , while Quercus gradually decreased (down to 20%)  and an altitudinal displacement of the highly suitable areas for A similar conclusion has been reached in the Hengduan Mountains in China, where range expansions have been reported in subalpine and mid‐elevation species from the LGM to the present ; formal analysis ; investigation ; methodology ; writing‐original draft ; writing‐review & editing . Antonio González‐Rodríguez: Conceptualization ; funding acquisition ; investigation ; methodology ; writing‐review & editing . Ken Oyama: Conceptualization ; funding acquisition ; investigation ; methodology ; writing‐review & editing . Mailyn A. González: Data curation ; funding acquisition ; investigation ; writing‐review & editing . Hernando Rodríguez‐Correa: Conceptualization ; formal analysis ; funding acquisition ; investigation ; methodology ; writing‐original draft ; writing‐review & editing .Fig S1Click here for additional data file.Supplementary MaterialClick here for additional data file.


Sylvilagus transitionalis) is the only native cottontail in the Northeast US, an early successional habitat specialist, and a species of conservation concern. Eastern cottontail (S. floridanus) is an invasive species in the Northeast US and a habitat generalist. We characterized each species’ genomic variation by developing double-digest Restriction-site Associated DNA sequence single nucleotide polymorphism markers, quantified their habitat with Geographic Information System environmental variables, and conducted our analyses at multiple scales. Surprisingly, both species had similar levels of genetic diversity and eastern cottontail’s Ne was only higher than New England cottontail in one of three subregions. At a regional level, the population clusters of New England cottontail were more distinct than eastern cottontail, but the subregional levels showed more geographic areas of restricted gene flow for eastern cottontail than New England cottontail. In general, the environmental variables had the predicted effect on each species’ gene flow. However, the most important environmental variable varied by subregion and species, which shows that location and species matter. Our results provide partial support for the SGVH and the identification of environmental variables that facilitate or impede gene flow can be used to help inform management decisions to conserve New England cottontail.The environment plays an important role in the movement of individuals and their associated genes among populations, which facilitates gene flow. Gene flow can help maintain the genetic diversity both within and between populations and counter the negative impact of genetic drift, which can decrease the fitness of individuals. Sympatric species can have different habitat preferences, and thus can exhibit different patterns of genetic variability and population structure. The specialist-generalist variation hypothesis (SGVH) predicts that specialists will have lower genetic diversity, lower effective population sizes (Ne), and less gene flow among populations. In this study, we used spatially explicit, individual-based comparative approaches to test SGVH predictions in two sympatric cottontail species and identify environmental variables that influence their gene flow. New England cottontail ( The environment has an important effect on the movement of individuals and their associated genes among populations. Gene flow can help maintain the genetic diversity both within and between populations and counter the negative impact of genetic drift. When populations become isolated, random alleles can become fixed. This results in the loss of genetic diversity and an increase in inbreeding, which both have negative impacts on the fitness of individuals . GeneticSylvilagus aquaticus) and habitat generalist eastern cottontail (S. floridanus) in Southern Illinois  is the only native cottontail in the Northeast US and is listed as vulnerable by the IUCN Redlist , West , and East . The subregions were selected to maximize the number of samples from each species, while still remaining in the general area of a single population for both species (We received 191 and 274 tissue samples from New England (NEC) and eastern cottontail (EAC), respectively, collected by zoo personnel, state biologists, and federal biologists. The samples were collected from 2010 to 2016 from the New England area and eastern New York. The full range wide samples for each species were subdivided into three subregions: Cape Cod (NEC  species . The tisTM dsDNA Assay Kit (Invitrogen) on a BioTek SynergyTM H1  microplate reader. The initial quantity of DNA was standardized to 400 ng for each sample using an epMotion 5075 TMX  and digested using the restriction enzymes SphI-HF  and MluCI . Digested DNA was purified using NucleoMag 96 PCR cleanup kit , quantified, and standardized to 150–250 ng. Sets of 12 samples were barcoded with unique P1/P2 adapters, purified, and combined at equal concentrations. The adapter labelled pools were size selected for 333 bp to 414 bp using ethidium free gels on the Pippin Blue  system and cleaned using Dynabeads  to remove fragments of DNA that did not have a forward and reverse adapter attached. Four sets of pooled samples containing 48 samples were polymerase chain reaction (PCR) amplified for 12–15 cycles using the Kapa Library Amplification Kit  according to manufacturer’s instructions to add Illumina  flowcell annealing sequences, multiplexing indices, and to increase the amount of labelled fragments. Index labelled pools were purified and quantified using the Agilent High Sensitivity DNA kit  and the Kapa Library Quantification Kit  on a LightCycler® 480  according to manufacturers’ instructions. The index labelled pools were combined at equal concentrations, purified, and quantified again using the Agilent High Sensitivity DNA kit. The final pooled library was submitted to the Northwest Laboratory Bauer Core Facility at Harvard University for single end 50 bp read sequencing using an Illumina HiSeq™ 2000 . A total of six libraries were sequenced.We followed the process_radtag program was used to conduct the initial quality control filtering and assign reads to individuals with the settings -c, -q, and -r. We qualitatively assessed the sequence read quality using the software FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). The cleaned reads were aligned to whole genome sequences generated from New England cottontail DNA using the program BWA v.0.7.17 , which retained 89% of the contigs. The ref_map.pl pipeline in STACKS was used to process the cleaned reads that were aligned to the New England cottontail contigs. The populations program in STACKS was used to call SNPs using the parameter settings -R 0.8, --min-maf 0.01, --min-mac 3, and --max-obs-het 0.7. The retained SNPs were further filtered using the programs vcftools v.1.16 (mawk (https://invisible-island.net/mawk/) was used to filter samples that had more than 50% missing data. Potential adaptive SNPs were filtered using the program PCAdapt  with a minimum p-value cutoff of 0.01 and all samples considered one population. Replicate samples were processed for New England cottontail (n = 47) and eastern cottontail (n = 23) within each library run. The program SNPRelate (n = 39) and eastern cottontail (n = 23).The Bauer Core Facility staff demultiplexed the raw next-generation sequencing data by each index. The University of Rhode Island’s Bluewaves high performance computing and R version 3.6.2  through v.0.7.17 . The whos v.1.16  and mawk PCAdapt  with an NPRelate  was usedIS) was estimated at the regional and subregional scales using the program populations in STACKS. The R package sGD (Average observed heterozygosity (Ho), expected heterozygosity (He), and inbreeding  using a deme size of 100. We conducted three independent runs with different deme sizes , averaged the runs, and displayed the results using R scripts described in the EEMS instruction manual (https://github.com/dipetkov/eems/blob/master/Documentation/EEMS-doc.pdf). The MEMGENE analyses were conducted on the proportion of shared alleles (Dps). We used the R package propShared in adegenet to estimate Dps, which is a measure of gene flow. The MEMGENE analyses identifies independent spatial axes of genetic variation and estimates the percent of genetic variance explained by each axis. This analysis determines if environmental variables are potentially influencing gene flow and quantifies the amount of genetic variation explained by a given spatial pattern. The MEMGENE analyses were conducted at the regional and subregional scale.The presence of isolation by distance can confound population structure analyses . We tested for isolation by distance at the individual level by comparing the genetic difference matrices to geographic distance using GenAlEx 6.502  with 9,9adegenet  to conduadegenet . We usedadegenet ; EEMS anadegenet , to idenadegenet  and idenrandomForest , aspect (landfire.gov), forest height which is a LiDAR derived product (landfire.gov), shrub vegetation cover (landfire.gov), forest and herbaceous wetlands , and roads (United States Major Roads from ESRI). The expected impact of each environmental variable on the genetic differentiation among individuals of each species is described in importance function in the randomForest package, which estimates the percent increase in mean square error (MSE) and increase in node purity. The percent increase in MSE is estimated by first permuting the out of bag (OOB) data, which are the data removed for testing the prediction accuracy, and estimating the MSE. Each predictor variable is then permuted and the difference between the permuted variable and OOB data is averaged across all trees and normalized by their standard deviation. The increase in node purity is estimated using residual sum of squares and is the impact of splitting on a given predictor variable, which is averaged over all trees (https://cran.r-project.org/web/packages/randomForest/randomForest.pdf). The influence of the top importance variables was visualized by creating partial dependence plots in R using the partialPlot function.The individual Dps values were analyzed using the R package omForest . The CapomForest , which domForest  is a reg product , distancprocess_radtags. The average number of reads per individual New England cottontail was 1,695,987 +/− 907,637 (standard deviation) and per individual eastern cottontail was 1,425,021 +/− 1,549,282. The PCAdapt and HWE outlier filtering removed 113 (57 PCAdapt + 56 HWE) and 429 (41 PCAdapt + 388 HWE) SNPs from the New England cottontail and eastern cottontail data sets, respectively. The number of retained SNPs ranged from 1,725 to 3,318 for New England cottontail and 4,085 to 14,029 for eastern cottontail. The cleaned fastq files are deposited in NCBI’s SRA database under BioProject accession number PRJNA736852.The six library runs produced 807,714,829 million reads after filtering with IS value was over three times higher than New England cottontail. Estimates of Ne for each species at the subregional scale ranged from 14.9 (NEC East) to 264.1 (EAC East) with New England cottontail having lower overall values across subregions. However, eastern cottontail only had significantly higher Ne  than New England cottontail in the East subregion. For New England cottontail the highest Ne estimate was in the Cape Cod subregion. The sGD estimate of individual Ho ranged from 0.125 to 0.300 for New England cottontail and 0.100 to 0.270 for eastern cottontail, but the values changed depending upon the scale of analysis  and East  regions showed a significant isolation by distance pattern, while the Cape Cod analysis was not significant . All three subregional analyses for eastern cottontail were significant: West , East  and Cape Cod .The regional analyses of isolation by distance was significant for both species with an Rttontail . At the The regional DAPC analyses showed the expected clustering of New England cottontail samples into distinct West, East, and Cape Cod clusters . The easOverall, the EEMs results showed a more complex pattern of gene flow for eastern cottontail compared to New England cottontail due to more areas of facilitated and restricted gene flow . At the The adjusted R-squared values, amount of genetic variation explained by a given spatial pattern, for the New England cottontail MEMGENE analyses ranged from a negative value to 0.13. The highest amount of genetic variation was explained at the regional scale, whereas Cape Cod and the West subregions essentially had zero. The first eigenvalue axis for the regional and East subregion explained the majority of genetic variation at 55 and 69%, respectively. The adjusted R-squared values for eastern cottontail MEMGENE analyses ranged from 0.01 to 0.11, with the regional scale explaining the highest amount of genetic variation and the West subregional scale the lowest. The first eigenvalue axis for the regional, Cape Cod, and West subregions explained the majority of genetic variation at 44, 54, and 35%, respectively. The percent of variation explained by the East subregion’s eigenvalues was more evenly distributed among numerous axes and ranged from 10 to 17%.The regional map of New England cottontail’s first MEMGENE axis showed four groups that generally corresponded with samples from New Hampshire/Maine, Cape Cod, eastern Connecticut, and western Connecticut/New York . The strrandomForest analyses were 25, 19, and 65% for the Cape Cod, West, and East subregions, respectively. The percent of variation explained by the eastern cottontail randomForest analyses were 38, 62, and 41% for the Cape Cod, West, and East subregions, respectively. The two measures for the most important variables in the randomForest analyses agreed in eight out of 12 instances . Elevation was the only other variable that placed in the top two more than one time (2 out of 6).A map of each environmental variable is shown in nstances . The mosa priori expectation for an impact of elevation or slope aspect on Dps, but in both cases the influence pattern was non-linear for New England cottontail. However, for eastern cottontail the relationship between elevation and Dps was linear and the proportion of shared alleles increased as elevation increased. We expected distance to road and development to have a negative influence on gene flow with lower distance values correlating to lower Dps values. The relationship was non-linear for distance to road for New England cottontail, but linear in the expected direction for distance to development for eastern cottontail. The distance to wetlands variable was unexpectedly non-linear, but this variable was not identified as a top variable by both measures for New England cottontail on Cape Cod  on the y-axis. The slope of the partial dependence plots for the top two environmental variables for each region was as predicted for the most part . The preCape Cod . The disCape Cod .https://www.nae.usace.army.mil/Portals/74/docs/Recreation/CCC/Brochures/Canal_Overview_Trifold.pdf) before the documentation of eastern cottontail on Cape Cod . In general, these results support the second part of our first hypothesis. However, we expected a more drastic difference between the two species, given the known decline of New England cottontail and the geographic restriction of the species . Our Ne Genetic diversity and population structure of species inhabiting the same range depend on fine-scale responses to environmental factors . Our secWe found a significant isolation by distance pattern for eastern cottontail at the regional level. a priori expectation.We predicted the MEMGENE adjusted R-squared values would be higher for New England cottontail compared to eastern cottontail because we expected the environment to have a stronger impact in limiting New England cottontail gene flow. Eastern cottontail are thought to be better dispersers than New England cottontail and their movements less restricted by the environment . The adjOur two hypotheses were not fully supported by our results. Thus, our results only provide partial support for the SGVH because our expected outcomes were inconsistent. Prior to our study, the SGVH had yet to be explicitly applied to vertebrates. However, there are examples of vertebrates where the SGVH is supported, which include cottontails  and feliIn general, our predicted influence of a given environmental variable on genetic differentiation matched our expectations. Natural environmental variables that were important factors influencing cottontail gene flow included shrub, wetlands, and forest height. Distance to shrub was identified as a top important environmental variable the highest number of times, two times for each species in a subregion. The influence of distance to shrub was mostly as expected with a decreasing proportion of shared allele values as the distance to shrub habitat increased. The relationship was non-linear for New England cottontail on Cape Cod, but the two importance measures were inconsistent for that environmental variable. Distance to shrub habitat was expected to have a stronger influence on New England cottontail than eastern cottontail because New England cottontail are early successional habitat specialists. One of the main habitat characteristics of early successional habitat is dense shrub vegetation. Our results support the importance of shrub habitat in facilitating cottontail gene flow and support previous research that has found both species have high detection rates in this type of habitat . DistancAnthropogenic factors that influenced cottontail gene flow included development and roads. We expected distance to development to have a stronger impact on New England cottontail gene flow because eastern cottontail can inhabit urban areas. However, the variable was only identified as important for eastern cottontail in one subregion and the influence of the variable was as expected with a higher proportion of shared allele values as the distance to development increased. Roads have been identified as both facilitators and inhibitors of New England cottontail gene flow . The infExtensive efforts to create New England cottontail habitat have been conducted by state and federal biologists . The creation of early successional habitat is a major focus, which unfortunately will benefit the gene flow for both species. The main environmental variable we identified that could limit eastern cottontail gene flow is forest height. The variable results of which environmental variable was the most influential in affecting a given species’ gene flow demonstrates that location matters and makes it difficult to generalize results from one area of a species’ range. n = 57) compared to eastern cottontail (n = 41), but additional analyses would be needed to confirm that these SNPs are adaptive. Adaptive SNPs also are meaningless unless they can be associated with a particular environment, geographic location, or phenotype.The evolutionary potential is not always different between specialists and generalists. https://www.aphis.usda.gov/animal_health/downloads/rhdv2.pdf). Tularemia, a bacterial disease, also has recently been detected in an island population of New England cottontail. Retaining New England cottontail adaptive potential will be critical to allow them the best chance possible to survive these disease challenges and other threats, such as climate change. The identification of environmental variables that facilitate and inhibit gene flow also will help predict how diseases will be able to spread among and within both species. Analyzing populations throughout the species’ range will be critical to ensure informed management decisions.The analysis of adaptive markers could help identify evolutionary significant units for New England cottontail, as well as, the best source populations for reintroductions that are not too divergent from the target recipient population to avoid outbreeding depression . Convers


Leucadendron salignum or “common sunshine conebush” as a model to address the evolutionary and environmental factors shaping the vast CFR diversity. We found that high population structure, along with relatively deeper and older genealogies, is characteristic of the southwestern CFR, whereas low population structure and more recent lineage coalescence depict the eastern CFR. Population network analyses show genetic connectivity is facilitated in areas of lower elevation and higher seasonal precipitation. These population genetic signatures corroborate CFR species‐level patterns consistent with high Pleistocene biome stability and landscape heterogeneity in the southwest, but with coincident instability in the east. Finally, we also find evidence of human land‐usage as a significant gene flow barrier, especially in severely threatened lowlands where genetic connectivity has been historically the highest. These results help identify areas where conservation plans can prioritize protecting high genetic diversity threatened by contemporary human activities within this unique cultural UNESCO site.As human‐induced change eliminates natural habitats, it impacts genetic diversity and population connectivity for local biodiversity. The South African Cape Floristic Region (CFR) is the most diverse extratropical area for plant biodiversity, and much of its habitat is protected as a UNESCO World Heritage site. There has long been great interest in explaining the underlying factors driving this unique diversity, especially as much of the CFR is endangered by urbanization and other anthropogenic activity. Here, we use a population and landscape genetic analysis of SNP data from the CFR endemic plant  Genomic DNA was extracted using the DNeasy Plant Mini Kit (QIAGEN) according to the manufacturer's protocol. We designed a relatively simple and affordable approach that targeted hundreds of putatively neutrally evolving cpDNA and nuDNA fragments using degenerate PCR primers to generate SNP data Data . We used2.3PopGenome v. 2.7.5 R package  were generated for each of the cpDNA and nuDNA datasets and for each locale using the average number of pairwise nucleotide differences (θπ) and the number of polymorphic sites (θs). We estimated Tajima’s  for each of the cpDNA and nuDNA datasets. Samples were coded as individuals and run with 20 iterations for each K cluster. The program CLUMPAK  on each of the cpDNA and nuDNA datasets to infer genetic clustering of individuals using the ge Dyer, . We alsoge Dyer,  with staFST that make inferences about gene flow from pairwise dissimilarities only , we employed a permutation analysis using the 2.4L. salignum locales. Isolation‐by‐distance models predict that as geographic distance between populations increases, so does genetic differentiation between populations . As we have previously hypothesized, land‐usage change may have the potential to fragment population connectivity. Thus, we added a fifth landscape feature as an independent variable in the GLMM to test hypotheses about the potential impact of land‐usage (LAN). We used the GeoTerraImage , which accounts for nonindependence of distance matrices . The mean and variance calculated between all locales reflect different spatial properties. For example, the mean of an increase, followed by a decrease, and again an increase in elevation between two locales could be identical to a mean score that reflects “no change” in elevation between two locales. In this way, we can capture this variation in change between two points, which is what potentially influences gene flow between two points on the landscape. In addition, calculating landscape resistance distance as the difference between two locale points misses critical information. That is, elevation at locale 1 and elevation at locale 2 is identical and thus the difference between these two locale points is “0,” but dramatic changes in elevation occur between those two points, as we observe over even short geographic distances in the CFR. These mean and variance statistics were used as landscape resistance measures in separate GLMM analyses of each of the cpDNA and nuDNA datasets to test for relationships between landscape and genetic variables.We acquired raster image files for each of the five independent landscape variables, as noted above, and cropped them to fit the geographic area of the 51 sampled locales Figures . Becausegstudio v. 1.5 R package (as above) to identify significant relationships between genetic connectivity (estimated as cGD), and our landscape variables, as in previous analyses  to align with our L. salignum data generated in this study. With this outgroup and the approach of Valente et al.  are not significant and show no deviation from demographic equilibrium. Nucleotide sequence diversity for the overall dataset is higher for nuDNA than it is for cpDNA; however, estimates within locales are considerably lower, especially for cpDNA (Table FST = 0.81) compared to nuDNA (FST = 0.10).After multiple levels of postdata filtering, the resulting dataset from our sampling of 51 NA Table , suggestNA Table , and mucSeveral key observations emerge from the BEAST analysis of cpDNA Figure , which sK = 2–6  reflects multiple clusters moving geographically west to east , which includes individuals that are geographically distantly separated, but have in common their southwestern coastal locations. Notably, the central group additionally separates into multiple geographic clusters (K = 5), and coincidentally, the cpDNA PCA identified this more subtle genetic separation within the central region (PC5–6 = 8.2%). On the other hand, the nuDNA fastSTRUCTURE analysis found no genetic clustering  and are more genetically similar  in contrast to connections anywhere else in the popgraph  in both cpDNA and nuDNA datasets , but that landscape variables explain very little variance .The landscape genetic analysis performed in the GLMM shows that geographic distance explains a significant proportion of genetic differentiation , and evidence of invasion from older southwestern lineages. This latter explanation comes from the observation in our phylogenetic analysis that eastern lineages have a single common ancestor (derived from southwestern lineages) with reduced phylogenetic diversity. The southwestern CFR may act as an evolutionary genetic “source,” typified by genetic structure that is higher and older, that feeds into the eastern and northern CFR, which may appear as evolutionary genetic “sinks” typified by re‐occurring lineage extinction and colonization from the southwest.While the few previous CFR plant phylogeographic studies were limited in sample size and scope, they revealed consistently high population structure , other CFR species may reveal other priorities. For example, our study supports the need of fine‐scaled population network analyses that target contemporary factors of high threat such as human impact. We need a multi‐tiered approach to understanding CFR biome evolution (Potts et al., The authors declare no competing interests with research described in this paper.Supplementary MaterialClick here for additional data file.