Phyllodactylus tuberculosus) at multiple sites near the northernmost limit of this ecosystem at Alamos, Sonora, Mexico. Traditional analyses of population genetics are combined with multivariate GIS-based landscape analyses to test hypotheses on the potential drivers of spatial genetic variation. Moderate levels of within-population diversity and substantial levels of population differentiation are revealed by FST and Dest. Analyses using structure suggest the occurrence of from 2 to 9 genetic clusters depending on the model used. Landscape genetic analysis suggests that forest cover, stream connectivity, undisturbed habitat, slope, and minimum temperature of the coldest period explain more genetic variation than do simple Euclidean distances. Additional landscape genetic studies throughout TDF habitat are required to understand species-specific responses to landscape and climate change and to identify common drivers. We urge researchers interested in using multivariate distance methods to test for, and report, significant correlations among predictor matrices that can impact results, particularly when adopting least-cost path approaches. Further investigation into the use of information theoretic approaches for model selection is also warranted.Habitat fragmentation due to both natural and anthropogenic forces continues to threaten the evolution and maintenance of biological diversity. This is of particular concern in tropical regions that are experiencing elevated rates of habitat loss. Although less well-studied than tropical rain forests, tropical dry forests (TDF) contain an enormous diversity of species and continue to be threatened by anthropogenic activities including grazing and agriculture. However, little is known about the processes that shape genetic connectivity in species inhabiting TDF ecosystems. We adopt a landscape genetic approach to understanding functional connectivity for leaf-toed geckos ( Dispersal is a fundamental process that can greatly influence ecological and demographic trajectories within and between subpopulations Differences in dispersal rates can result from factors including species-specific philopatry, intra- and interspecific interactions, predation, physiological tolerances, and simple geographic (Euclidean) distance Tropical deciduous or dry forests (TDF), which also occur in the Neotropics, are a major biodiversity hotspot PhyllodactylusThe diversity of Mexico's herpetofauna is substantial, with approximately 1,000 described species and many more awaiting formal description Phyllodactylus tuberculosus, to understand the effects of landscape configuration and anthropogenic influence on functional connectivity for a small terrestrial vertebrate presumably dependent on TDF. This species is an ideal choice to examine the relationship between landscape and genetics for several reasons. First, the geographic distribution of the gecko mirrors the distribution of TDF in Mexico. Second, abundance is relatively high when populations are isolated, providing a statistically suitable model. Third, along with others in the genus, this species is at risk of local and area-wide extirpation due to habitat fragmentation and recent introductions of non-native, all female species, such as geckos of the genus Hemidactylus, which appears to be displacing leaf-toed geckos (pers. obs.). Further, local people actively kill leaf-toed geckos as they are presumed to be venomous and dangerous to humans. For these reasons, leaf-toed geckos may soon pose a conservation concern and identifying landscape components that maximize genetic connectivity may be necessary for managing the persistence of populations. Our study site lay in the northernmost limit of TDF near Alamos, Sonora. We specifically test the following hypotheses: 1) high levels of genetic diversity and differentiation occur over small spatial scales; 2) anthropogenic fragmentation influences functional connectivity; 3) riparian connectivity predicts dispersal patterns, yet 4) some rivers act as dispersal barriers. We also test for an influence of slope and temperature on genetic patterns because the species is generally restricted to warm lowland habitats. Testing these hypotheses and drawing robust conclusions requires principles and practices drawn from diverse research disciplines Herein we use the Mexican yellow-bellied gecko, Our study area  has a reP. tuberculosuseneMarker v.1.95 (SoftGenetics). We re-ran PCRs for approximately 10% of our samples to quantify any potential errors in genotyping.DNA was extracted using standard phenol-chloroform procedures. We used polymerase chain reaction (PCR) to amplify 12 polymorphic microsatellite loci developed specifically for microsatelliteanalyser v.4.05 HO) and expected (HE) heterozygosites were calculated in tfpga v.1.3 enePop v.4.0.10 We calculated diversity statistics for each site including number of alleles and allelic richness using FST and Jost's D between populations. Unlike FST, D measured the degree of allelic differentiation between populations and was particularly useful for highly polymorphic markers such as microsatellites icrosatelliteanalyser was used to calculate both global and pairwise multilocus FSTsmogd v.1.2.5 Dest based on harmonic means estimated over all loci. Unbiased estimates of all metrics were employed to account for artefacts of sample size We calculated both traditional structure v.2.3.3 K-values (1–12). For simplicity, we created a batch run specifying the range of K-values to be evaluated and implemented 10 independent runs per K. Each run was composed of a burn-in of 30,000 followed by 100,000 iterations, which was sufficient to reach convergence. We evaluated the most likely number of clusters using both the ln Pr (X/K) and the ΔK method tructureharvesterK based on both methods. To deal with the multimodality of utilizing multiple independent runs, we used clumpp v.1.1.2 K-value using the “Greedy” algorithm with 1,000 random input orders. Distruct v.1.1 clumpp.We tested for the presence of genetic clusters using structure results to those that incorporated information about sampling locations as prior information structure under the same conditions as the aspatial model.Geographic  information has often provided valuable insights in population genetic structure We first calculated effective distances between populations using least-cost path modeling Dest. Landscape variables were selected based on expert knowledge of which habitat characteristics were most likely important in shaping patterns of gene flow in the species ecodistr correlation statistic using 10,000 randomizations.We tested for the relative influence of several landscape variables on genetic differentiation based on pairwise P. tuberculosus occurred in lowland tropical environments only, our second set of analyses developed least-cost paths based on slope. These data were derived from a GTOPO30 digital elevation model (DEM) with a 1 km2 spatial resolution produced from Natural Resources Canada and the U.S. Geological Survey. This layer consisted of seven elevation classes that were reclassified into slope to test the prediction that gene flow occurred primarily throughout lowland habitats. Our study area encompassed an elevational range between 100 to 500 m above sea level. Because slope represented continuous data and because we assumed a linear relationship between slope and gene flow Because 2 and assigned different cost values to cells encompassing buffered streams versus those that did not. We tested the same relative cost values as our land cover analysis  and selected the best values based on Mantel correlations.Because we often captured geckos adjacent to streams and arroyos, we tested if dispersal occurred primarily via stream corridors. We first obtained a polyline file representing all of Mexico's streams and tributaries from the GISDataDepot, a site that compiled multiple data layers based on ESRI's Digital Chart of the World (DCW). To represent riparian corridors, we created 1 km buffers around stream networks in the polyline file. We then converted these data into a raster file with a cell size of 100 m2. As with slope, we assumed a linear relationship between temperature and gene flow and we reclassified the data into a continuous distribution with 32 classes. We assigned higher cost values to cells representing lower minimum temperatures.We tested for effects of minimum temperature of the coldest period of the year because these lizards are predominantly found in warm tropical lowland habitats. A significant correlation between gene flow and minimum temperatures was predicted. Temperature data were obtained from the WorldClim database at a resolution of 1 km2. Cell values ranged between 0 and 64, with 0 representing no anthropogenic influence and 64 representing maximum influence. To create least-cost paths, we reclassified the data into a continuous distribution with 32 classes and assigned cost values ranging from 0 to 31, with 31 representing the highest cost to gene flow for cells with the highest anthropogenic influence. Like slope and temperature, we assumed a linear relationship between the degree of disturbance and gene flow.Finally, we utilized a multivariate resistance surface representing the combined effects of anthropogenic land-use (anthropogenic model). Data were obtained from the Wildlife Conservation Society (WCS) and the Center for International Earth Science Information Network (CIESIN). These data represented the combined effects of population density, built-up areas, roads, railroads, navigable rivers, coastlines, land-use, and nighttime lights . The data were categorized based on the Human Influence Index at a spatial resolution of 817 mlandscapegeneticstoolbox 1.2.3 rcMap 10 to calculate effective distances between sampling localities. This calculated both the cumulative cost-distance and the length of the least-cost path between any two sampling points. Because both distances could have been sensitive to relative cost values For all least-cost path-analyses, we used the circuitscape 3.5.7 We also modelled patterns of gene flow using circuit theory ecodist to evaluate landscape-genetic relationships. Although a potentially powerful method for landscape-genetic inference Dest) and a number of indicator matrices and identify the contribution of each explanatory variable to the overall fit of the model Dest) was then regressed against each indicator vector (i.e. least-cost or resistance distances) and the significance of the model was assessed by permuting the objects of the response vector.We used multiple regression analyses on distance matrices MRM;  in ecodicircuitscape, we included all variables in the model selection procedure. We predicted a negative relationship between genetic differentiation and several of our landscape features including stream networks and the degree of undisturbed habitat. Because multiple regression models account for the effects of all included predictors, we anticipated that the regression coefficients may change depending upon the other variables included in the model (e.g. Euclidean distance). Univariate MRM models all resulted in positive coefficients due to spatial autocorrelation. Thus, particular attention was placed on regression coefficients in highly supported models containing Euclidean distance as a predictor.MRM models using all six explanatory variables were not created for the least-cost analyses due to a relatively high degree of collinearity among the predictors, which could have resulted in coefficients with large variances and lead to erroneous conclusions regarding the direction and magnitude of slope. Some authors have suggested calculating Variance Inflation Factors (VIF) for each predictor in a model to ascertain if collinearity might be a problem in parameter estimation Dest. We followed previous recommendations in assessing the relative importance of models uMInWe utilized information theoretic criteria to select among candidate models hypothesized to be important predictors of spatial genetic variation in our system lme4 ixMod2βpbkrtestBecause it remained unknown how information theoretic criteria performed when evaluating models based on pairwise distances, we compared our MRM results with a linear mixed modeling approach All microsatellite data were deposited in Dryad . The genotyping and scoring of microsatellite alleles had an error rate of less than 1%, and, thus, high reproducibility. After controlling for false discovery rates, some loci showed significant deviations from Hardy-Weinberg expectations within collecting sites. For example, locus G2_96 showed heterozygote deficits at six of the 12 sites, locus P7 at eight of 12, and locus G2_59 at four of 12 . HoweverAfter controlling for false discovery rates, a few loci showed signs of linkage disequilibrium. For example, locus G2_22 showed linkage to loci G2_96, P2, P7, P12, and P19. Locus G2_85 showed signs of linkage with locus P7, and locus P2 with P15. However, linkage occurred at only two of our 12 sites (Arroyo Tabelo B and Mocuzari) suggesting that our loci were, in fact, independent, unlinked markers.2 = 0.5836;P = 0.004; In general, within-site diversity was moderate as shown by both expected heterozygosity and allelic richness . ExpecteFST  and D (Dest = 0.248). Pairwise measures of differentiation revealed moderate to high levels of population divergence  to 0.201 between La Sierrita and El Quintero. Further, the majority of FST values were significant based on 10,000 random permutations of alleles after a Bonferroni correction (P<0.00075). Values of Dest ranged from moderate to high, showing similar relative values between populations. El Quintero and Choquincahui were the most divergent from the remaining populations based on both statistics. We found a high correlation between FST and Dest (Pearson r = 0.940). Because of this correlation, all subsequent landscape genetic analyses were performed with Dest only. We detected a significant positive correlation between Euclidean distance and Dest .Moderate levels of global genetic differentiation were resolved based on both vergence . PairwisK-value based on ΔK suggested a K = 3  method  method . IndividK method suggested a K of 2  reached a slight peak at 9  showed that likelihood values began to stabilize at about K = 3, which was a value similar to that chosen using the ΔK method in the aspatial analysis. At K = 3, cluster memberships and admixture coefficients were very similar to those of the aspatial analysis.A second set of analyses utilized a model that explicitly incorporated prior information for sampling localities to aid in clustering. These results differed from the clustering results that did not utilize sampling localities. For example, whereas the Δeak at 9 . For K =d Tabelo . The plor values were highest with a cost ratio of 1∶2 . The MRM analysis based on least-cost path distances suggested that the incorporation of landscape variables explained significantly more variance in genetic differentiation than a simple IBD model  was based on a combination of Euclidean distance, stream connectivity, and the degree of anthropogenic disturbance . Our least-cost paths based on temperature and slope explained slightly more variation in Dest than Euclidean distance , with stream being the only variable with a negative coefficient. The next most supported model, which included all six variables, received considerably less support , although well within the estimated confidence set. Visual examination of cumulative current based on a composite map of Euclidean distance, stream networks, and slope was highly congruent with the genetic clusters inferred from the structure analysis  versus a model with Euclidean distance, slope, and stream connectivity  is highly sensitive to within-population heterozygosity, making it difficult to compare values between studies and markers FST is dependent on the number of alleles and heterozygosity, two populations can have low FST values despite sharing no alleles. For example, studies have shown that in two hypothetical populations with a total of 16 alleles, FST is constrained to less than 0.1 even when the two populations share no alleles G'stDFSTD is a useful metric for landscape-genetic inference, spatially explicit computer simulations may provide a greater understanding of how this measure compares to other commonly used differentiation statistics.Considerable recent debate exists as to the best structure analyses reveal cryptic population structure. We infer values of K both with and without incorporating prior information on sampling localities. Incorporating locality data as a prior changes our inference of K. The ΔK method suggests K = 2, whereas the plot of K versus ln Pr(X|K) suggests K = 9. Populations at El Quintero and Choquincahui always group together into a single cluster and the populations at Mocuzari and Tabelo fall into separate clusters from the remaining populations in several Structure analyses. Structure plots generally corroborate the levels of differentiation based on Dest and FST.Our K = 9 reveals different ancestries on opposite banks. Further, Mantel tests suggest a significant barrier effect for these features. Although Bayesian clustering methods, and structure in particular, can be a powerful tool for inferring recent linear barriers to gene flow We never detect structure on opposite banks of the Río Cuchujaqui or Arroyo Tabelo based on aspatial Bayesian clustering . However, spatial clustering at tructure incorporates geographic information by assigning different codes to different populations structure in comparison to the fully spatial models implemented in other software packages.New, sophisticated algorithms achieve difficult genetic clustering. Although early algorithms are entirely aspatial in nature P. tuberculosus also suggest that many gecko species may have limited dispersal abilities and rely on landscape characteristics to facilitate dispersal.To date, few studies examine the population genetic structure of other lineages of geckos. For example, a recent study on two species of sympatric gecko species report significant differences in genetic diversity and structure To date, few studies investigate landscape-genetic relationships in Neotropical vertebrates Dest or higher gene flow. These results are concordant with other studies that show a negative relationship between anthropogenic disturbance and rates of gene flow Our least-cost path analysis suggests that anthropogenic disturbance is influencing functional connectivity in geckos. Because we parameterize the multivariate anthropogenic resistance surface by assigning higher costs to disturbed areas, the significantly negative regression coefficient (after controlling for Euclidean distance) suggests that undisturbed habitat is associated with a lower P. tuberculosus are only present in and around houses in absence of introduced geckos of the genus Hemidactylus (pers. obs.). Very few leaf-toed geckos can be found syntopically with Hemidactylus and on one occasion we witnessed the head of a P. tuberculosus in the jaws of H. frenatus. Thus, in heavily anthropogenically influenced areas, it appears that introduced Hemidactylus are directly competing with native Phyllodactylus. To exacerbate this issue, people often kill leaf-toed geckos on site as they believe the darker colour of these geckos indicates that they are venomous. Conservation efforts should focus on educating local people on differences between native and non-native flora and fauna to aid in the maintenance and protection of the native species. This is especially important for species commonly found close to human settlements.Although geckos are common on abandoned houses in TDF habitat, individuals of Dest than Euclidean distance (0.548 vs. 0.444). TDF is a seasonal forest with approximately eight months of warm, wet conditions and four months of dry, cooler conditions The minimum temperature of the coldest period influences gene flow, which explains slightly more of the variance in Ambystoma tigrinum melanosticum) Ascaphus montanus) Zapus trinotatus) Ascaphus truei) and does not follow riparian corridors Many geckos are from near bridges and rocky outcroppings adjacent to streams and rivers. Thus, we test the hypothesis that riparian networks are an important component shaping patterns of gene flow. Our results suggest that riparian connectivity is an important predictor for patterns of dispersal, as this variable always occurs in our top models. However, these features may also serve as a genetic barrier. Thus, it appears as if geckos may disperse along riparian networks, but seldom cross them. Previous studies conflict as to the role riparian networks play in shaping functional connectivity in small terrestrial vertebrates. For example, streams facilitate gene flow among populations of blotched tiger salamanders  based on resistance distances explains similar variation in Dest as our least-cost models. However, unlike the least-cost analysis, we can combine all resistance distances into our models due to the lack of collinearity among the predictors. Our results corroborate previous findings and suggest that a combination of circuit theoretic and least-cost models provides a powerful tool for investigating functional connectivity in dynamic landscapes.Circuit theoretic approaches complement least-cost path modeling and often explain more of the variance in genetic differentiation than more traditional methods Landscape genetics is still a relatively new discipline Although powerful, MRM approaches have limitations that need to be addressed Although multicollinearity occurs in our least-cost path data, coefficients change little with different explanatory models . Thus, wIt remains unclear how the pairwise nature of distance matrices can influence model-selection using information theoretric metrics such as AIC, which generally assume independent observations. Other recent studies acknowledge this potential issue and propose alternatives through the use of Delaunay triangulation Habitat fragmentation and extirpation continue to threaten tropical ecosystems throughout the globe Figure S1tructure results illustrating changes in ln Pr(a) SX|K) under the aspatial model. b) Structure results based on the second order rate of change (ΔK method) under the aspatial model. For each K, 10 independent simulations were performed.(TIF)Click here for additional data file.Figure S2tructure results illustrating changes in ln Pr(a) SX|K) under the spatial model. b) Structure results based on the second order rate of change (ΔK method) under the spatial model. For each K, 10 independent simulations were performed.(TIF)Click here for additional data file.Table S1Genetic diversity statistics per locus and population for Phyllodactylus tuberculosus sampled throughout the Alamos, Sonora region.(DOCX)Click here for additional data file.Table S2Multiple regression on distance matrices (MRM) results showing the relationship between pairwise genetic distance (linearized Dest) and least-cost path cost distances incorporating landscape heterogeneity. Candidate models tested were based on a priori hypotheses and to minimize collinearity among predictors. Optimal cost values used to parameterize resistance surfaces prior to calculating each least-cost path were selected based on Mantel r correlation coefficients. VIF = Variance Inflation Factor.(DOCX)Click here for additional data file.Table S3Multiple regression on distance matrices (MRM) results showing the relationship between pairwise genetic distance (linearized Dest) and resistance distances incorporating landscape heterogeneity. Candidate models tested were based on a priori hypotheses. Optimal cost values used to parameterize resistance surfaces prior to calculating resistance distances were selected based on Mantel r correlation coefficients. VIF = Variance Inflation Factor.(DOCX)Click here for additional data file.


Nacella magellanica has a wide distribution in this province and represents a suitable model to infer the Quaternary glacial legacy on marine benthic organisms. This species inhabits ice-free rocky ecosystems, has a narrow bathymetric range and consequently should have been severely affected by recurrent glacial cycles during the Quaternary. We performed phylogeographic and demographic analyses of N. magellanica from 14 localities along its distribution in Pacific Patagonia, Atlantic Patagonia, and the Falkland/Malvinas Islands.Patagonia extends for more than 84,000 km of irregular coasts is an area especially apt to evaluate how historic and contemporary processes influence the distribution and connectivity of shallow marine benthic organisms. The true limpet N. magellanica revealed an absence of genetic differentiation in the species with a single genetic unit along Pacific Patagonia. However, we detected significant genetic differences among three main groups named Pacific Patagonia, Atlantic Patagonia and Falkland/Malvinas Islands. Migration rate estimations indicated asymmetrical gene flow, primarily from Pacific Patagonia to Atlantic Patagonia (Nem=2.21) and the Falkland/Malvinas Islands (Nem=16.6). Demographic reconstruction in Pacific Patagonia suggests a recent recolonization process (< 10 ka) supported by neutrality tests, mismatch distribution and the median-joining haplotype genealogy.Mitochondrial (COI) DNA analyses of 357 individuals of Absence of genetic structure, a single dominant haplotype, lack of correlation between geographic and genetic distance, high estimated migration rates and the signal of recent demographic growth represent a large body of evidence supporting the hypothesis of rapid postglacial expansion in this species in Pacific Patagonia. This expansion could have been sustained by larval dispersal following the main current system in this area. Lower levels of genetic diversity in inland sea areas suggest that fjords and channels represent the areas most recently colonized by the species. Hence recolonization seems to follow a west to east direction to areas that were progressively deglaciated. Significant genetic differences among Pacific, Atlantic and Falkland/Malvinas Islands populations may be also explained through disparities in their respective glaciological and geological histories. The Falkland/Malvinas Islands, more than representing a glacial refugium for the species, seems to constitute a sink area considering the strong asymmetric gene flow detected from Pacific to Atlantic sectors. These results suggest that historical and contemporary processes represent the main factors shaping the modern biogeography of most shallow marine benthic invertebrates inhabiting the Patagonian Province. Climatic changes are considered as one of the main factors regulating abundance, composition, and distribution of species at different temporal and spatial scales -3. DirecThe application of molecular-based approaches in population genetics has provided new insights into the history of many species and helped us to further understand the impact of the Quaternary glacial cycles on patterns of genetic variation and structure ,3,6,7. HNacella (Patellogastropoda: Nacellidae) includes 15 nominal species distributed in different biogeographical provinces of the Southern Ocean  and geographical distance between localities using a Mantel test implemented in Arlequin; associated probabilities were estimated with 25,000 permutations.Genetic differentiation was determined in two ways following ,130 usinentities . Using Sentities  we definentities . This apN. magellanica using median-joining haplotype networks in Network v.4.6 (http://www.fluxus-engineering.com) [N. magellanica to determine whether N. magellanica has undergone sudden population growth. We compared the distribution of pairwise differences in N. magellanica with expectations of a sudden expansion model. Three main parameters were estimated: i) the date of growth/decline (τ=2μt) measured in units of 1/2 μ generations where t=time in years and μ=mutation rate per sequence per generation, initial population size (Θi) before the population growth/decline and a final theta (Θf) after population growth/decline. These demographic expansion parameters were determined using a nonlinear least squares approach implemented in Arlequin [http://www.abc.se/~nylander/) and a piecewise constant Bayesian skyline model with 10 groups. Before choosing this model we performed Bayesian Skyline Plot analyses using N. magellanica’s COI data set with three different models: an uncorrelated lognormal relaxed clock, an uncorrelated exponential relaxed clock, and a strict molecular clock. Estimated bayes factors among these models strongly supported the molecular clock hypothesis. We ran the analyses for 350 × 106 generations, making sure that the effective sampling sizes for each statistic were at least 1500. Convergence was examined in Tracer v. 1.5 (http://beast.bio.ed.ac.uk/Tracer) [We reconstructed genealogical relationships for ing.com) . To estiing.com)  which reArlequin . The gooArlequin , which dArlequin ,138. We Arlequin , using t/Tracer) .The authors declare that they have no competing interests.CG-W and E.P generated the ideas and design of the study, and all the authors took part in sample collections. CG-W produced the molecular data, and CG-W and E.P were responsible for data analyses. CG-W drafted the original version of the manuscript, and CG-W and E.P produced subsequent versions. All authors participated in a critical review of the manuscript and approved the final version for submission.A) Haplotype network including 357 Nacella magellanica mtDNA COI sequences. Each haplotype is represented by a circle and its size is proportional to its frequency in the whole data set. mv=median vector . B) Haplotype Network Puerto Deseado; C) Haplotype Network Falkland/Malvinas Islands. Click here for fileNumber of individulas presenting each haplotype and their corresponding locality.Click here for file


Dendroctonus ponderosae) and one of its fungal symbionts, Grosmanniaclavigera, in western Canada using neutral genetic markers. We examined how spatial heterogeneity affects genetic variation within beetles and fungi and developed a novel integrated landscape genetics approach to assess reciprocal genetic influences between species using constrained ordination. We also compared landscape genetic models built using Euclidean distances based on allele frequencies to traditional pair-wise Fst. Both beetles and fungi exhibited moderate levels of genetic structure over the total study area, low levels of structure in the south, and more pronounced fungal structure in the north. Beetle genetic variation was associated with geographic location while that of the fungus was not. Pinevolume and climate explained beetle genetic variation in the northern region of recent outbreak expansion. Reciprocal genetic relationships were only detectedin the south where there has been alonger history of beetle infestations. The Euclidean distance and Fst-based analyses resulted in similar models in the north and over the entire study area, but differences between methods in the south suggest that genetic distances measures should be selected based on ecological and evolutionary contexts. The integrated landscape genetics framework we present is powerful, general, and can be applied to other systems to quantify the biotic and abiotic determinants of spatial genetic variation within and among taxa.Spatial patterns of genetic variation in interacting species can identify shared features that are important to gene flow and can elucidate co-evolutionary relationships. We assessed concordance in spatial genetic variation between the mountain pine beetle ( Dendroctonusponderasae) outbreak in western Canada is unprecedented in terms of extent and severity and has had significant ecological and economic consequences Ophiostoma spp.), host trees (Pinus spp.), landscape features, and climate The current mountain pine beetle  genomes may interact directly through the selective advantage of particular combinations of alleles , (2) genomes may appear correlated because species share similar life histories and/or movement patterns , or (3) genomes may appear correlateddue to indirect factors such as shared responses to environmental heterogeneity . Because microsatellite markers aregenerally not correlated with adaptive variation Pinuscontorta) and hybrid jack (Pinusbanksiana)-lodgepole pine trees. Permits were obtained when required to cover both the collection and transport of beetle and fungal materials. Parks Canada provided permits for the mountain parks Kootenay and Yoho. Alberta Tourism, Parks, and Recreation provided permits for collection in the Wilmore Wilderness, Cypress Hills and around Canmore (Kananaskis). We also received permission from Tembec to collect in their forest around Sparwood.Beetles and fungi were collected in Alberta and British Columbia, Canada in two sample periods  from vis2, and extends from 49.6° to 56.9°north latitude and 114.4° to 121.9° west longitude. Beetle larvae and adults were sampled from galleries within 5″ phloem disks extracted from individual trees. Full details onspecimen collection are found in Roe et al. There were three levels to the sampling design: (1) landscapes, (2) sites within landscapes, and (3) individual trees within sites . Not allFst values summarized to the level of the landscape to ensure adequate sample sizes for both species. Werestricted our analysis to landscapes for which both fungal and beetle samples were available. Fungi were sampled from galleries and adjacent wood tissue In total, 2943 individual beetles and 174single spore fungal isolates were collected from 44sites within 16 landscapes. Fungal data were corrected for clonality by removing individuals with identical microsatellite profiles at all loci which resulted in 156 unique isolates. Analysis was performed on allele frequenciesor matrices of pair-wise G. clavigeraWe used 13 microsatellite loci to identify genetic structure in the mountain pine beetle Fst and the landscape as the sample units Fst against geographic distance (log-transformed) for the entire study area and within the northern and southern regions.Genetic structure for each species was assessed using a global estimate of rda function in the veganpackage in R protest function in the vegan package in R Variation in allele frequencies among landscapes for each species was summarized and visualized using principal components analysis (PCA) calculated using the FstFstFst values. PCoA summarizes and plots differences among sites based on an input distance matrix Fst) was assessed using a Procrustestest, as described above. Pair-wise Fst values (Nei 1973) were calculated using the pairwise. fst function in the adegenet package in R capscale function in the vegan package in R PCA calculates distances among sites in multi-dimensional space based on allele frequencies but does not make many of the evolutionary assumptions of more traditional distance measures such as G. clavigera.After characterizing genetic structure in both species, we investigated whether landscape features could explain further marginal variation in allele frequencies among landscapes using redundancy analysis (RDA), a form of constrained ordination. RDA is the canonical extension to PCA in whichthe principal components produced are constrained to be linear combinations of a set of predictor  variables ordistep function of the vegan package in R varpart function in vegan.Within RDA models we used log-transformed allele frequencies as the response matrixand several measures of spatial connectivity as predictors. Predictor variablesincluded connectivity among locations based on geographic (Euclidean) distance, elevation, climatic suitability Fst-based PCoAdescribed above, we compared models constructed using: (1) RDA in which Euclidean distances are the response variable, and (2) distance-based redundancy analysis  was calculated using a general ecological connectivity metric often used in meta-population studies A challenge to using constrained ordination in landscape genetics studies is to convert spatial environmental data into location-specific information rather than measures of dissimilarity as are often employed in Mantel testing frameworks iS is the connectivity of patch i to the rest of the points in the network (j), ijd is the effective distance calculated from the least-cost path between points i and j based on the resistance surface, and β is the estimated mean beetle dispersal distance that parameterizes a kernel that weights a connection between a given pair of locations.Connectivity of a site was represented as the average connectivity of that point to all othersites in the network for a given resistance surface. Connectivity for each site for each of the four resistance surfaces  was calcβ was set to 40 km to accommodate recently observed dispersal events of greater than 100 km that brought beetles over the Rocky Mountains into Alberta The mountain pine beetle is capable of long-distance dispersal Resistance surfaces  were useFst = 0.036 and 0.039, respectively) across the entire study area (Fst = 0.01) whilethe fungus resembled the entire study area (Fst = 0.034). Both species were very weakly structuredin the southern region (Fst<0.01). The 95% confidence intervals around these Fst estimates did not include zero for the total and northern regions (Fst estimates for both species did include zero and indicate non-significant genetic structure.Beetles and fungi exhibited similarly weak genetic structure (udy area . In the  regions . HoweverFst) distance. However, much of the structure identified was related to comparisons between regions. When examining each region separately, we found IBD for the beetles in the north, but not for the fungus  than fungi from wood tissue (Fst = 0.086). However, the proportion of variance explained by the fungal source (wood vs. fungus) was not significant . Therefore, fungal data from both sources were pooled for further analysis.Fungi sampled from larvae within galleries were less genetically structured  or in the beetle  in the four sites that were sampled in both periods. Here, the negative variance (σ2) simply indicates that the predictor performs more poorly than random values. Therefore, differences among sites are not due to differences between sample periods for both fungi and beetles  were highly correlated with the ordination solutions from the PCA. Fungal variation was more similarly described by the two methods than the beetles except in the southern region. Correlations between the PCA and PCoAfor the beetles were equal to 0.77 (p = 0.001), 0.898 (p = 0.001), and 0.818 (p = 0.027) in the total, northern, and southern regions, respectively. Correlations for the fungus were equal to 0.90 (p = 0.001), 0.93 (p = 0.001), and 0.79 (p = 0.03) also for the total, northern, and southern regions, respectively.The first beetle principal component indicated a very strong north-south gradient in beetle genetic structure  and acco2adjR = 0.389, F = 9.89, p = 0.005), but not for fungi . This same pattern was found in both sub-regions. In the north, beetle genetic variation covaried with latitude  but fungalvariation did not . We also found this pattern in the south for beetles  and fungi .Latitudeaccounted for a significant proportion of the beetle allele frequency variation acrossthe entire study area , whereas the best beetle RDA model included both pine volume and climate (2adjR = 0.32). The unique contributions (2adjR) of climate and pine volume were 12.8% and 9.6%, respectively, with 9.1% of the variation explained shared between the two predictors. For both approaches, nothing predicted fungal genetic variation. Models in the southern region differed between methods. Using dbRDA, nothing accounted for beetle genetic variation, whereas using RDA, fungal genetic variation (PC1) was a significant predictor  of 9.2% and 9.3%, respectively, with 14.6% of the variation explained shared.The different ordination methods also identified similar spatial predictors of genetic variation for both species . In the G. clavigera we expected the genetic structure of these two species to be similar. This was the case when we examined the entire study area (Fst∼0.04) but species differed within regions. The fungus retained its structure in the north, while the beetle did not. Both species demonstrated very weak genetic structure in the south (Fst<0.01). Similarly, the correlations in genetic structure between species were not consistent between regions or between methods . Previous studies of the concordance of genetic structure between symbionts have suggested that different scales of dispersal between species may account for the incongruence in spatial genetic variation G. clavigeradoes not disperse independently of the beetle Beetle and fungus allele frequencies respond differently to environmental heterogeneity . Most imWhen examining the study area as a whole, Euclidean connectivity was found to best predict genetic variation in both species, likely due to the strong differences between regions. Similarly, landscape genetic models differed between regions for both methods used and within regions, the influence of environmental heterogeneity on genetic variation differed between species. In the north, pine volume significantly predictedbeetle genetic connectivity using both methods. Using RDA, which does not make any evolutionary assumptions, climate was also identified as a significant predictor. In the south we found some evidence for significant reciprocal genetic influences, although these influences were not symmetrical between methods: the fungus helped explain the beetle using RDA, but beetles helped explain the fungus using dbRDA.Fst may be more appropriate as a distance measure as its assumptions are more likely to be met. Differences between the two methods in the south may reflect these differences in population genetic structure, history of association, or temporal stage of the outbreak. Interestingly, Fst and Euclidean distance perform similarly in the area of recent expansion.The differences between regions have most likely arisen because each region has had a different period of association with the mountain pine beetle and different processes determine beetle and fungal movement and hence spatial genetic variation. The northern region was recently colonized and is at the front of the current outbreak G. clavigera in northern Alberta. Recent work on the MBP-associated fungal community found that G. clavigera is typically found in the south, whereas L. longiclavatum, is more typically found in the north of Alberta G. clavigera in the north. It will be valuable to contrast these results with those from other fungal species.With specific regard to the fungus, the different structure between regions can also explained by spatial variation in MPB-associated fungal community and the relative frequency of In the north, pine volume affectedbeetle spatial genetic variation  accordinClimate also explained a significant proportion of variance in beetle genetic variation in the northern region, but only in the RDA model . In thisGenetic correlations were identified in the southern region using both ordination approaches. Using RDA, asignificant proportion of variation in beetle allele frequencies (∼11%) was explained by the first fungus PCA axis (PC1). There was not an equivalent effect of beetles on the fungus. The opposite effect was found using dbRDA and the beetles, in combination with Euclidean distance, significantly described the fungus. Thesefindings represent an important aspect of the symbiotic relationship that we identified using the integrated landscape genetics approach. Most likely, these effects represent either an unmeasured environmental variable to which both species respond or demographic interactions between species and highlights the utility of using additional biotic information in landscape genetic studies. Previous work has demonstrated the importance of host plant genotypes on the dynamics of associated insect herbivores 2adjR) explained by the environment suggests that landscape resistancemay not be the most suitable model for examining the landscape genetics of a highly mobile species such as the mountain pine beetle, at least at the spatial and temporal scale we examined. Indeed, much of the genetic variation resides within individuals and within sample sites  or genetic distance (dbRDA) and environmental connectivity, and dimensionally reduced symbiont genetic variation. Recently, Balkenhol et al. G. clavigera but did not address whether the two species are adaptively responding to each other or their environment. This study represents one of the first to use a biological predictor, specifically genetic information, to describe genetic variation of an interacting species in a landscape genetics framework. Future work using this integrated framework will assess adaptive genomic interactions among taxa in the mountain pine beetle system as well as concordance in evolutionary responses to environmental heterogeneity. Opportunities also exist to further investigate the relative strengths of different measures of genetic distance  for describing landscape genetic relationship in different spatial contexts. Ongoing research to identify SNP markers and to characterize functional genes in the beetle, the fungus, and host pine trees will be used to investigate these relevant evolutionary questions in a spatially explicit and integrated context.We assessed concordance in genetic patterns and gene flow between the mountain pine beetle and G. clavigera. We also identified important differences in the genetic congruence between species within the historical southern range of the MPB that depend on the assumptions underlying different measures of genetic distance. Finally, we identified genetically-based support for the role of host connectivity (pine volume) and climate in the spread of the outbreak in the north. The different patterns of genetic structure between regions likely reflect the different processes that determine endemic vs. epidemic population structure and the recent northward population expansion. The next challenge will be to examine the three-way genetic correlationsamong the beetle, its fungal symbionts, and its host pine trees.We presented a novel integrated landscape genetics framework to investigate reciprocal genetic correlationsbetween species while also testing for the influence of spatial heterogeneity. Contrary to expectation, we found differences in genetic structure between the mountain pine beetle and Table S1Summary of all sample sites arranged from north to south. Site names include the location followed by a letter indicating the unique site within the sample landscape. Single letters after the underscore indicate year sampled: “_A” represents a sample from Feb.–May 2007; “_B” represents a sample from Sept.–April 2008. Elevation is indicated in metres above sea level.(DOCX)Click here for additional data file.


Aspidosperma polyneuron and Cariniana estrellensis, known as peroba-rosa and jequitibá-branco respectively, are characteristic of late secondary semideciduous Atlantic forest in submontane formation. Their wood is widely used for carpentry and construction. Increasing logging, intensive agriculture and urban expansion placed A. polyneuron in the Red List of Threatened Species [http://www.iucnredlist.org/apps/redlist/details/32023/0] while C. estrellensis is considered an endangered species. So, in situ conservation of these forest species is critical while ex situ collections as gene banks is an important complementary approach. Our study assessed and compared the genetic diversity of A. polyneuron and C. estrellensis in two stands aiming to provide subsidies for the ex situ conservation of these important genetic resources.The neotropical tree species A. polyneuron and C. estrellensis were sampled and stored at -20°C: SI was composed by isolated individuals of an extensive area located among Pardo river and Mogi-Guaçu river basins used as seed donor of a Forest Genetic Bank, at the University of São Paulo in Ribeirão Preto campus (BG-USP/RP), SP-Brazil. SII was one of the last natural populations around Ribeirão Preto region of an isolated fragment of 7.5 ha located on Águas Claras farm in Cravinhos Municipality .Leaf tissues of mature trees of two disturbed stands (SI and SII) of C. estrellensis[A. polyneuron ] were used for this study . Microsatellite loci were amplified individually according [3]. FSTAT software [http://www2.unil.ch/popgen/softwares/fstat.htm] was used to calculate the genetic parameters per locus and sample: mean number of alleles (A), the observed (oH) and expected heterozygosities (eH). Wright’s fixation index was calculated as 1 - o/HeH. Deviation from Hardy-Weinberg equilibrium (HWE) was measured using GDA software[http://hydrodictyon.eeb.uconn.edu/people/plewis/software.php].DNA extraction was performed according to . SSR marrellensis and for e SI/SIIH= 0.65/0.47, e SI/SIIH= 0.72/0.61 for A. polyneuron and C. estrellensis, respectively). For A. polyneuron, the expected heterozygosity (eH) was higher than observed heterozygosity (oH) in SI and SII. For eC. estrellensis H was higher than oH in SI and the opposite was found in SII. Fixation index was higher in SI than SII for both species , suggesting inbreeding. Significant departures from HWE were observed for most loci in both species mainly in SI, which might be due to population substructure (Wahlund effect).For both species, the genetic diversity was higher in SI than SII (A. polyneuron and C. estrellensis (SI), suggesting that this diversity was incorporated into BG-USP/RP. Indeed future progeny studies will be able to confirm these results. The low diversity found in SII for A. polyneuron highlights its threatened situation in this stand, on of the last natural populations in Ribeirão Preto (SP-Brazil). For C. estrellensis, a higher genetic diversity was observed which may be due to greater number of trees in the population, preferentially outcrossed mating system and probably gene flow from outside the fragment studied.In summary, our study revealed a high diversity for the seed donor trees of Financial Support: FAPESP, CNPq, CAPES-PROEX, FAEPA


Gopherus agassizii), using 859 tortoises genotyped at 16 microsatellite loci with associated data on geographic location, sex, elevation, slope, and soil type, and spatial relationship to putative barriers . We used spatially explicit and non-explicit Bayesian clustering algorithms to partition the sample into discrete clusters, and characterize the relationships between genetic distance and ecological variables to identify factors with the greatest influence on gene flow at a local scale. Desert tortoises exhibit weak genetic structure at a local scale, and we identified two subpopulations across the study area. Although genetic differentiation between the subpopulations was low, our landscape genetic analysis identified both natural (slope) and anthropogenic (roads) landscape variables that have significantly influenced gene flow within this local population. We show that desert tortoise movements at a local scale are influenced by features of the landscape, and that these features are different than those that influence gene flow at larger scales. Our findings are important for desert tortoise conservation and management, particularly in light of recent translocation efforts in the region. More generally, our results indicate that recent landscape changes can affect gene flow at a local scale and that their effects can be detected almost immediately.Characterizing the effects of landscape features on genetic variation is essential for understanding how landscapes shape patterns of gene flow and spatial genetic structure of populations. Most landscape genetics studies have focused on patterns of gene flow at a regional scale. However, the genetic structure of populations at a local scale may be influenced by a unique suite of landscape variables that have little bearing on connectivity patterns observed at broader spatial scales. We investigated fine-scale spatial patterns of genetic variation and gene flow in relation to features of the landscape in desert tortoise ( Features of the landscape can either restrict or promote movements of individuals in natural populations and consequently influence the degree of genetic connectivity among populations. Characterizing the effects of landscape features on genetic variation is essential for understanding how landscapes shape patterns of gene flow and spatial genetic structure of populations, and this objective forms the basis of landscape genetics Detecting cryptic population genetic structure and identifying features of the landscape that influence gene flow are central to understanding local- and regional-scale movements and barriers. Decomposing a genetic mixture of samples into their component, panmictic subpopulations  helps to identify genetic discontinuities that could restrict gene flow among populations e.g. . Both naMost landscape genetics studies focus on patterns of movements of individuals at a regional scale. For example, some of the best-known studies in landscape genetics of vertebrates  to investigate fine-scale spatial patterns of genetic variation and gene flow in relation to features of the landscape. The desert tortoise occupies portions of three major North American deserts  = 1.9×10−10; structure calculates q, the proportion of an individual's sampled genome that is characteristic of each subpopulation, and thus allows for individuals to be of mixed ancestry, whereas geneland calculates the probability that an individual belongs to each subpopulation; Clustering analysis revealed that desert tortoises were structured into two genetically distinct subpopulations. The most likely number of subpopulations in the dataset was two for both tructure , 2, 3. TST = 0.0046, p = 0.002), even when adjusted for the high heterozygosity of the microsatellites used (G′ST = 0.00931). This low level of genetic differentiation between subpopulations indicates either a recent separation between these two subpopulations or a high level of ongoing gene flow between them. As expected for weakly differentiated subpopulations, standard genetic diversity measures for each subpopulation were similar  was correlated with elevation , roads  and slope  is restricting local movements of the desert tortoise. Barriers to gene flow that are relevant over ecological time can be difficult to detect because genetic divergence may be low Our landscape genetic analysis identified both natural (slope) and anthropogenic (roads) landscape variables that have significantly influenced gene flow within this local population. The higher correlation of genetic distance with slope compared to roads indicates that gene flow is influenced predominantly by the slope of the landscape. Slope has been found as a barrier to gene flow in a limited number of other species, mostly pond-breeding amphibians Roads also influence desert tortoise movements. Desert tortoise pairs from the same side of a road exhibited significantly less genetic differentiation than tortoise pairs from opposite sides of a road. This pattern was found for both Manix Trail and Fort Irwin Road, despite differences in traffic volumes and construction materials. Both roads were constructed relatively recently (expanded to current size ∼1970 s), and it may seem that road construction is too recent to have impacted desert tortoise population structure. However, our results confirm simulation studies that suggest that new landscape features can have rapid effects on genetic structure that are detectable almost immediately There are several possible ways in which roads could impact desert tortoises. First, roads may influence gene flow in desert tortoises directly via increased mortality during road crossings Our study indicates that both natural and anthropogenic features of the landscape influence patterns of genetic structure and gene flow in desert tortoises at a local scale. This is in contrast to patterns observed at a regional scale, where geographic distance plays a major role in shaping patterns of genetic structure Although genetic structure in this population is currently affected by slope more than by roads, the extremely short time lag between the emergence of roads as a barrier and detection of a genetic effect indicates that roads may become increasingly important in shaping the evolutionary trajectory of desert tortoise populations. In addition, our results empirically support recent research suggesting that landscape effects on population structure may be limited in scale Permits to collect blood samples and transfer them to the laboratory included US Fish and Wildlife Service permit TE-102235-3 and California Scientific Collecting Permit SCP 801179-015.Blood samples were collected from desert tortoises in the Southern Expansion Area (SEA) of the National Training Center in Fort Irwin, CA, and within the Southern Translocation Area (STA) to the south of Fort Irwin. The SEA is an area of approximately 9,700 ha that was systematically searched for desert tortoises during a clearance operation to completely remove desert tortoises in this area for subsequent translocation to the STA. All desert tortoises found (the entire population) were sampled. In the STA, samples were collected opportunistically. Desert tortoises in this region are part of the western Mojave Recovery Unit G. polyphemus) tortoises genemapper version 3.7. Repeated genotyping for quality control was performed for 9% of the samples in the dataset ; 7 repeated genotypes (0.5%) exhibited dropout of the larger allele and were reconciled in the final dataset. The final dataset consisted of 859 desert tortoises with a complete suite of data: multilocus microsatellite genotype, geographic location, sex, elevation, slope, and soil type.DNA was extracted from blood samples using the BioSprint 96 robotic workstation (Qiagen) and the manufacturer's protocol. Desert tortoises were genotyped at 16 microsatellite loci developed for desert and gopher . Allele frequencies were drawn from independent Dirichlet distributions To infer the number of subpopulations in the dataset and to assign individual samples to these subpopulations, we employed two Bayesian clustering techniques, structure, we performed 5 runs at each value of the fixed parameter K . Each run consisted of 500,000 replicates of the MCMC after a burn-in of 100,000 replicates. We used the admixture model and allowed allele frequencies to be correlated. All other parameters were set to default values Ks plateau and the variance among runs increase K measure that has been proposed to alleviate this problem and provide a more robust estimate of Kq, the proportion of an individual's sampled genome characteristic of each subpopulation, were used to assign individuals to subpopulations at both a 50% and 90% threshold for assignment.In geneland assignment) and the divergence among subpopulations with several standard measures; the number of alleles per locus (A), expected heterozygosity (He), and genetic variation among individuals within subpopulations (FIS) for each locus and the average multilocus value for each population. To compute FST, we used the method of spagedi (version 1.2; STWe assessed genetic diversity within each subpopulation (as determined by the 50% threshold genalex version 6.2 To assess the spatial pattern of genetic variation in desert tortoises, we performed a spatial autocorrelation analysis with ra; zt software We used Mantel tests to determine correlational significance between matrices of inter-individual genetic distance (Rousset's


Pristimantis (Anura: Craugastoridae). In order to better understand the initial stages of species formation in these frogs, this study quantified local-scale spatial genetic structuring in three species of Pristimantis. DNA sequences of two mitochondrial gene fragments (16S and COI) were obtained from P. brevifrons, P. palmeri and P. jubatus at different locations in the Cordillera Occidental. We found high levels of genetic diversity in the three species, with highly structured populations (as measured by FST) in P. brevifrons and P. palmeri while P. jubatus showed panmixia. Large effective population sizes, inferred from the high levels of genetic diversity, were found in the three species and two highly divergent lineages were detected within P. jubatus and P. palmeri. Estimated divergence times among populations within P. brevifrons and P. palmeri coincide with the Pleistocene, perhaps due to similar responses to climatic cycling or recent geological history. Such insights have important implications for linking alpha and beta diversity, suggesting regional scale patterns may be associated with local scale processes in promoting differentiation among populations in the Andes.The Andes of South America hosts perhaps the highest amphibian species diversity in the world, and a sizable component of that diversity is comprised of direct-developing frogs of the genus  The Andes of South America contain the highest levels of total species richness in the world A given geographic barrier may not have the same effect on all species, however. Organismal biology and natural history interact with landscape features to determine to what extent a potential barrier will affect dispersal Using molecular data it is possible to quantify the magnitude of genetic variation within populations and patterns of gene flow among conspecific populations. Moreover, if shared past events led to the formation of current genetic patterns, then comparing phylogeographic patterns of sympatric species may enhance our understanding of the relative roles performed by the forces of climatic, geological and ecological conditions in structuring within-species genetic variation Pristimantis (Anura: Terrarana: Craugastoridae) is the dominant component of the total amphibian fauna of Colombia, with 200 species, i.e., 27% of the country’s 750 species Pristimantis with distributions in the Cordillera Occidental of Colombia. Pristimantis jubatus2 in the department of Cauca. This species is considered to be locally abundant, inhabits forests with high (>90%) average humidity and dense vegetation cover (>80%) and appears to be intolerant of habitat modifications Pristimantis palmeriP. brevifronsThe genus These species were chosen because the accessibility of the area, availability of samples, their co-distribution in the Cordillera Occidental, and their contrasting ecological requirements. Thus, they should provide a useful opportunity to investigate the local historical demographic processes behind the larger scale biogeographic patterns in an ecoregion with a dynamic geologic history Pristimantis and focuses on the following questions: 1. Is genetic diversity randomly distributed in each species with respect to geography? 2. Do species with broad versus narrow distributions show similar patterns in the distribution of genetic variation? 3. Do sympatric species have common phylogeographic patterns because they share similar environmental histories, or do species with contrasting life histories show distinct phylogeographic patterns?Determining the processes promoting diversification and the geographic and temporal scales at which they operate is key to understanding how this group of direct-developing frogs has attained such remarkable levels of beta diversity across the Andes mountains P. brevifrons , and 537 bp for P. palmeri (N = 32) and P. jubatus (N = 30). This locus showed the number of segregating sites, S = 18 for P. brevifrons, S = 12 for P. palmeri, and S = 26 for P. jubatus. The COI alignment contained 658 bp for P. brevifrons (N = 16), and 618 bp for P. palmeri (N = 35) and P. jubatus (N = 31). Pristimantis brevifrons showed S = 30 polymorphic sites, while P. palmeri and P. jubatus contained S = 110 and S = 50 sites, respectively. The translation of amino acids using the genetic code for vertebrates showed that COI variants were synonymous (silent).The 16S alignment contained 536 base pairs (bp) for P. brevifrons (Los Paraguas and Peñas Blancas), located 151.8 km apart and about at roughly same altitude, did not share haplotypes  either in 16S or COI, which is consistent with a long period of isolation. As expected from the geography   . ChicoraP. palmeri that were 17% (p-distance) diverged from what were presumably conspecific samples found at the same site. We were unable to amplify 16S from these same two specimens, unfortunately. Furthermore, there was one haplotype from the Chicoral population closely related to samples found at Peñas Blancas, indicating recent gene flow between these populations  from the site Observatorio was highly diverged  from its conspecifics, with 22 nucleotide differences at 16S and 46 differences at COI from others at the same locality  within Peñas Blancas population of ulations . Pristimlocality . These dP. palmeri and P. brevifrons , which are geographically closer. The greatest population structuring was observed between Paraguas and Peñas Blancas (the two most distant sampling sites) in both P. brevifrons  and P. palmeri . However, substantial polymorphism within populations resulted in low but significant proportions of the total variation partitioned among localities in the AMOVA . In the more range-restricted and habitat-specialized species P. jubatus, in contrast, we observed low mtDNA sequences divergence among all three localities (FST = 0.0076–0.2749) and a low percentage of the total variation partitioned among localities . These results suggest that the three sampling localities 3 km apart, Observatorio, Santa Ana and Charguayaco , showed a significantly site-frequency skew for the mitochondrial dataset  since initial divergence was very low between the two sampling localities of P. brevifrons, and within P. palmeri between the northerly Los Paraguas and the two southern sites. In contrast, significant and asymmetric gene flow was detected between the two adjacent populations of P. palmeri, with migration from Chicoral into Peñas Blancas being lower than in the reverse direction  for P. brevifrons of 38,000  and 68,000  reproductive individuals in Paraguas and Peñas Blancas, respectively. For P. palmeri we estimated eN at 10,000  individuals in Paraguas, 50,000  for Peñas Blancas and 100,000  at Chicoral  from Peñas Blancas. Including these samples would inflate the number of segregating sites and bias estimates of Ne upwards. Because the three collecting localities for P. jubatus apparently represented a single genetic population, their effective population size was obtained using MIGRATE P. jubatus caused significant departures from standard neutral expectations (see above), we removed this sample prior to calculating Ne, yielding a more conservative estimated effective population size for this species of 94,000  individuals.Based on estimates of rogs see , we calcChicoral , Fig. S2P. palmeri populations was about 0.58 million years ago (Mya) (90% HPD roughly 0.40–1.8 Mya) between Paraguas and Peñas Blancas, and divergence time between adjacent populations Chicoral and Peñas Blancas was 0.14 Mya (0.06–0.90 Mya)  than in P. palmeri (0.58).The upper tail of the posterior distribution for divergence times did not reach zero probability so we were unable to estimate a strict 90% HPD value for this parameter for any species. The partial posterior distributions, however, suggested the following tentative approximations of divergence times. The mode of the posterior distribution of divergence time among .90 Mya) , Fig. S3Studies on the intraspecific genetic differentiation and population structure of anurans in montane regions are sorely needed, considering that work in montane areas lags behind work done in low elevation environments of the Neotropics  P. brevifrons, P. palmeri and P. jubatus using the mitochondrial markers COI and 16S. Estimates of gene flow, population sizes, and divergence time revealed important aspects of the historical demography of these three Neotropical montane species. Our data highlighted the undifferentiated structure of P. jubatus at a local geographical scale as well as the similar phylogeographic structure in P. brevifrons and P. palmeri, two species with similar life histories.This study characterizes the genetic diversity, population structure and phylogeography of P. brevifrons and P. palmeri analyses of the two mitochondrial loci revealed high genetic differentiation in space cross the same geographic scale landscape (approx. 150 km.) and a similar inferred age of population differentiation in both species (around 0.80 Mya) (For .80 Mya) , Table 4The northern population of Los Paraguas showed low migration rates with the two southern sites, and a corresponding high pairwise population differentiation. This population appears to have split from the other locations during the Mid-Pleistocene (0.58–0.80 Mya), i.e., following the last episode of uplift of the northern portion of the Andes in the Pliocene (5.0–2.0 Mya) Pristimantis, these frogs are thought to have small home ranges along with low vagility P. jubatus, showed no significant spatial differentiation. We expected that the habitat specialist P. jubatus would show high structure but we likely need more geographically extensive sampling. On the other hand, we might have expected direct-developing frogs to show minimal population structuring as these amphibians do not require a water source for ovipositing or mating, so they may breed anywhere within a continuous forest of high relative humidity Pristimantis palmeri perhaps fits this prediction an intermediate spatial scale, i.e., the 18 km between Chicoral and Peñas Blancas. We estimated the asymmetric migration rates at 1.2 (90% HPD 0.003–13.9) and 0.004 (0.001–10.0), thus we could not reject the hypothesis of substantial levels of gene flow at this distance.Despite the dearth of ecological data for species of Dendropsophus labialis. These results indicate that Andean frogs may disperse long distances at least in some cases or in some geographic context To date, there is a limited yet growing number of studies on genetic structure at local or regional scales in Neotropical frogs e.g., P. brevifrons, P. palmeri or P. jubatus to corroborate the genetic-based estimates, however, the estimated population sizes for P. brevifrons, P. palmeri and P. jubatus are quite similar compared to those obtained by Crawford Craugastor bransfordii (3.1×105) and C. stejnegerianus (1.0×105). Thus, the effective population sizes are potentially very large in our three species of Pristimantis. Large population sizes could be positively correlated to higher speciation rates Pristimantis in montane regions has been promoted by barriers, such as the Peñas Blancas ridge. Our results here inform these biogeographic models by confirming that even localized and isolated populations of these relatively small frogs may hold substantial amounts of genetic diversity, thus contributing to the maintenance of viable populations through subsequent vicariant events during montane diversification Pristimantis frogs showed signs of demographic changes associated with recent Pleistocene and geological events. It will be interesting to examine similarly widespread species to evaluate the generality of these patterns and conduct more fine-scaled phylogeographical analyses to explore the possible mechanisms leading to speciation within this group of direct-developing frogs.Our IMa analyses produced robust historical demographic parameter estimates, with the exception of divergences times. Features of the evolutionary history of our three study species revealed by coalescent analyses are particularly informative because few data on historical demography are currently available for tropical amphibians. Demographic estimates of population size are not available for Convocatoria Interna number CI-746. Permit for sampling of specimens was obtained from Ministerio de Medio Ambiente, Vivienda y Desarrollo Territorial de Colombia (Resolución 573 del 10 de abril/08). Euthanasia of frogs was performed with Chloretone, and all efforts were made to minimize suffering. Specimens were deposited at Herpetological Collection – Universidad del Valle (Voucher numbers: UVC15812– UVC15954)   and DNA Sampling was carried out at four localities : La SerrPristimantis jubatus was sampled at three different locations in the Munchique National Park , while widespread, generalist species P. brevifrons and P. palmeri were sampled at La Serranía de Los Paraguas and Peñas Blancas, and Serranía de Los Paraguas, Peñas Blancas and Chicoral, respectively. Tissues were collected in the field and kept in a solution of 1 M Tris HCl, 0.5 M EDTA, 5 M NaCl and SDS to 20%. Specimen identification was confirmed by Dr. John D. Lynch, Curator of Amphibians at the Instituto de Ciencias Naturales, Universidad Nacional de Colombia, Bogotá.At each locality we established approximately straight-line transects across which samples were collected. Samples sizes ranged from 7–15 individuals per location . Pristim2, 0.3 mM dNTPs, 0.3 µM of each Primer, 0.625 U of Taq polymerase and 1–2 µl total DNA. PCR conditions followed Goebel et al. Total DNA was extracted from liver or thigh tissue using the Qiagen Tissue DNeasy kit (Qiagen) following the manufacturer’s instructions. Mitochondrial DNA (mtDNA) fragments were amplified using PCR primers 16 Sbr plus 16 Sar for the ARN region of the 16S ribosomal RNA gene π ) S), Watterson’s estimator of the per-site population mutation rate (DT). Sites with indels or missing data were eliminated in pairwise sequence comparisons. Coalescence simulations were run in DnaSP to test for significant departures from the expected value DT = 0 under the standard neutral model using 5,000 replicates conditional on sample size and S.Sequences were edited, assembled and aligned using the program Sequencher 4.6 (Gene Codes Corporation) and verified by eye. COI sequences were translated into amino acids with the program DnaSP 5.0 FSTFST was chosen as the metric parameter of the genetic differentiation among populations because it provides a more effective summary of the effects of population structure We used the programs Arlequin 3.1 Ne), asymmetric pairwise migration rates (Nm) and time since population divergence (t) with the programs IMa Ne from θ  we assumed a mutation rate, μ, of 1.9×10−8 per site per year, based on estimated rates of silent site divergence in a genus of frogs closely related to Pristimantisθ, m, t). We compared the convergence of the marginal distributions of each parameter among multiple runs and obtained the credibility intervals based on 90% highest posterior density (HPD).We estimated the effective population sizes Click here for additional data file.Figure S2Posterior probability distributions for effective population sizes from IMa analysis.(TIF)Click here for additional data file.Figure S3Posterior probability distributions for divergence times from IMa analysis.(TIF)Click here for additional data file.Table S1Sample information for taxa analyzed in this study.(DOC)Click here for additional data file.


Glycine soja Sieb. et Zucc) is the most important germplasm resource for soybean breeding, and is currently subject to habitat loss, fragmentation and population decline. In order to develop successful conservation strategies, a total of 604 wild soybean accessions from 43 locations sampled across its range in China, Japan and Korea were analyzed using 20 nuclear (nSSRs) and five chloroplast microsatellite markers (cpSSRs) to reveal its genetic diversity and population structure. Relatively high nSSR diversity was found in wild soybean compared with other self-pollinated species, and the region of middle and lower reaches of Yangtze River (MDRY) was revealed to have the highest genetic diversity. However, cpSSRs suggested that Korea is a center of diversity. High genetic differentiation and low gene flow among populations were detected, which is consistent with the predominant self-pollination of wild soybean. Two main clusters were revealed by MCMC structure reconstruction and phylogenetic dendrogram, one formed by a group of populations from northwestern China (NWC) and north China (NC), and the other including northeastern China (NEC), Japan, Korea, MDRY, south China (SC) and southwestern China (SWC). Contrib analyses showed that southwestern China makes the greatest contribution to the total diversity and allelic richness, and is worthy of being given conservation priority.Wild soybean ( Glycine max (L.) Merrill, Fabaceae], is the world’s most important grain legume crop for its protein and oil  are commonly used for microsatellite. Considered Goldstein’s distance (δμ)2 showed bias at small sample sizes and the bias was directly related to the number of alleles and range in allele size [D) between groups was constructed using the UPGMA method implemented in the PHYLIP v. 3.68 [Nei’s genetic distance , the global RST across all samples was calculated in Arlequin v. 3.11 [RST into indirect estimates of the average number of migrants exchanged per generation among populations (Nm) [RST) was calculated using AMOVA analyses based on population levels, gene flow was estimated from RST (nSSRs: Nm = 0.25(1 − RST)/RST; cpSSRs: Nm = 0.5(1 − RST)/RST).Two commonly estimators of population differentiation are  SPAGeDI  to test  v. 3.11 . Gene flons (Nm) . Gene diNe to 4Ne generations) population bottlenecks that severely reduce effective population size (Ne) and produce an excess in heterozygosity. Heterozygosity excess test was performed under two mutation models: stepwise mutation model (SMM) and two-phase mutation model (TPM). The model of TPM include both 95% single-step mutations and 5% multiple-step mutations, as recommended by Piry [HE as much as common alleles, and thus distort the allele frequency distribution from its expected L-shaped distribution [We assessed demographic history based on microsatellite data using different and complementary methods. Heterozygosity excess test  and mode by Piry . Heteroz by Piry . Signifiribution .In summary, our results show a relatively high level of genetic diversity and genetic differentiation in wild soybean. Two major genetic clusters were revealed by both structure and phylogenetic reconstruction. The MDRY and Korea regions contain the highest genetic diversity, and SWC contributes the most to total diversity and allelic richness. Significant genetic bottlenecks have affected five populations with obvious human disturbance. Based on these results, conversation of wild soybean should reduce habitat loss by human interference, and the SWC region should be conserved with priority.


Currently, there is the potential to generate over 200,000 mutant mouse strains between existing mouse strains  and genetically modified mouse embryonic stem cells  that have been entered into the International Mouse Strain Resource Center (IMSR) from laboratories and repositories all over the world. The number of rat strains is also increasing exponentially. These mouse and rat mutants are a tremendous genetic resource; however, the awareness of their genetic integrity such as genetic background and genotyping of these models is not always carefully monitored. In this review, we make a case for the International Council for Laboratory Animal Science (ICLAS), which is interested in promoting and helping academic institutions develop a genetic monitoring program to bring a level of genetic quality assurance into the scientific interchange and use of mouse and rat genetically mutant models. The explosive growth of biomedical research over the past several decades has provided immense insight into the role of genes in human health and disease ]. In addition, several funded projects have generated thousands of mutant mice, including the NIH Knockout Mouse project  and the more ambitious mouse phenome project, which includes the goal of establishing baseline phenotypic data for the most common inbred mouse strains (http://www.ncbi.nlm.nih.gov/pubmed/15619963). To be sure, this effort is based on the genetic integrity of the mice that are being characterized. Therefore, to be able to compare and interpret experimental information, it becomes critical to have genetically well-characterized mouse and rat mutants. This trend of increasing the availability of genetically defined rodent strains, however, also begs the question of whether the dramatic increase in rodent models has been accompanied by a simultaneous increase in effort to monitor the genetic quality of these animals. In other words, do scientists, veterinarians, animal care personnel, and animal facility managers know that the specific strains of research animals they develop, use, care for, and house are genetically sound and truly representative of their assumed genotype or genetic background? With greater numbers of research mice and rats of profound genetic diversity being utilized and an increase in the exchange of genetically modified rodent models between scientists and animal facilities, what are the chances that a genetic contamination is present in these animals from an error in genetic modification, or a breeding error? What are the chances that genetic drift, an accumulation of mutations over time, has occurred even though the research animals look no different from their progenitors?Genetically defined mouse and rat models, including inbred, recombinant inbred, consomic, congenic, outbred, and genetically modified strains, have now become readily available to research institutions through the world [see The International Mouse Strain Resource (IMSR), Another important consideration that needs to be addressed when designing an experiment is the choice of the genetic background in which the mutation will be maintained. Phenotype variation due to genetic background is well documented  is interested in helping research institutions answer the question of genetic identity of their animals. For many years the ICLAS has sponsored programs dedicated to the promotion of high-quality animals for use in research. In particular, the ICLAS has focused on the ethical treatment of animals, animal health, and genetic quality Hedrich . In thisSeveral substantive reviews have been written that discuss the basic requirements for breeding programs designed to provide high-quality inbred strains of mice and rats for research  or microsatellite genotyping  that provides participating laboratories with a mechanism to self-assess their health monitoring and accuracy of testing for rodent pathogens. Similarly, the self assessment genetic monitoring program will enable institutions to determine whether their testing methods accurately identify the genetic background of the rodent strains being tested. The program will start with trials involving those research facilities currently performing rodent genetic monitoring and will be opened later to other institutions as they become confident in their capabilities. ICLAS will periodically publicize the means to access the Animal Quality Network program for genetic monitoring through this journal and other laboratory animal science journals, seminars, and special events as the program becomes firmly established.Availability of a variety of molecular biology technologies gives relatively easy access to different types of DNA testing that can be used in the establishment of a genetic monitoring program “in house.” A genetic monitoring program is necessary to safeguard the genetic quality of the mice/rats that are housed in a barrier. This genetic monitoring program is dependent on the type of mouse or rat model being bred and the genetic composition of other mouse/rat models in the particular barrier. Therefore, a genetic monitoring program needs to be inclusive of the genetic risks that other strains or models that are cohoused pose to the particular barrier. In order to achieve this, ICLAS, through its Animal Quality Network Program, is interested in (1) promoting training and education to increase awareness of the importance of genetic quality and genetic monitoring, (2) providing advice and guidance on genetic quality testing, and (3) establishing a self-assessment genetic monitoring program for interested institutions.