Ne). Kit foxes (Vulpes macrotis) are a species of conservation concern across much of their range. In western Utah, kit fox abundance has declined precipitously from historical levels, causing concern about population persistence. We analyzed genetic samples from museum specimens and contemporary scats to evaluate temporal changes in (a) genetic diversity and (b) Ne for kit foxes in western Utah, and (c) discuss our findings with respect to population risk and conservation. The Ne of kit foxes in western Utah has decreased substantially. When compared to established conservation thresholds for Ne , observed levels suggest the population may be at risk of inbreeding depression and local extinction. In contrast, we found no significant decrease in genetic diversity associated with declining Ne. We detected evidence of low levels of immigration into the population and suspect genetic diversity may have been maintained by this previously undescribed gene flow from adjacent populations. Low or intermittent immigration may serve to temper the potential short‐term negative consequences of low Ne. We recommend that kit fox conservation efforts focus on evaluating and maintaining landscape connectivity. We demonstrate how historical specimens can provide a baseline of comparison for contemporary populations, highlighting the importance of natural history collections to conservation during a period of declining funding and support.Loss of genetic diversity has serious conservation consequences , but is difficult to evaluate without developing long‐term, multigenerational datasets. Alternatively, historical samples can provide insights into changes in genetic diversity and effective population size ( Considering the precipitous decline in kit fox densities, we hypothesized that genetic diversity would be decreased in the contemporary population relative to the historical population. Similarly, we expected that contemporary Ne would be significantly lower than historical Ne. Based on recent abundance estimates . The region is characterized as Great Basin Desert with low‐lying basins separated by mountains. Habitat varies from playa, vegetated and unvegetated dunes, grasslands, and shrublands at lower elevations, to shrubland and open woodland at higher elevations , tentorium and internal occipital protuberance , and toepads of each kit fox specimen  setup to dedicated laboratories to minimize contamination risk. For historical samples, DNA extraction and PCR set up were conducted in a laboratory that had not previously been used to house or process vertebrate DNA (including noninvasive samples) and was spatially separated  from areas in which DNA amplification was performed. Noninvasive contemporary samples were processed  in a laboratory dedicated to low quality samples that was also spatially separated  from areas in which DNA amplification was performed. Protocols restricted movement of supplies, equipment, and people from the historical to noninvasive laboratories, and from the noninvasive to postamplification laboratories . For contemporary samples, DNA storage, extraction, amplification, and scoring methods are detailed in Lonsinger, Gese, and Waits . We deteWe amplified kit fox samples with nine nuclear DNA (nDNA) microsatellite loci , 63°C for 90 s , and 72°C for 60 s (elongation), 20 cycles at 94°C for 30 s (denaturation), 55°C for 90 s , and 72°C for 60 s (elongation), and a final elongation at 60°C for 30 min. For historical samples, the PCR thermal profile had an initial denaturation of 95°C for 15 min, 15 touchdown cycles at 94°C for 30 s (denaturation), 63°C for 90 s  and 72°C for 60 s (elongation), 35 cycles at 94°C for 30 s (denaturation), 55°C for 90 s , and 72°C for 60 s (elongation), and a final elongation at 60°C for 30 min.genemapper 3.7 (Applied Biosystems) to visualize and score allele sizes.We conducted all PCR procedures for contemporary and historical samples on a Bio‐Rad Tetrad thermocycler  with negative and positive controls. We used a 3130×l DNA Analyzer  to obtain results and congenr . This restricts analyses to those individuals that survived until at least their first breeding season and therefore had the opportunity to contribute to the breeding population. For historical samples, we restricted our analyses to samples with date and location (county) of collection data, and where the location aligned with , or was adjacent to , our contemporary sampling area. Closely related individuals may bias the results of some genetic analyses  and linkage equilibrium across loci for historical and contemporary populations with the probability test in structure v2.3.  that best reflect HWE and linkage equilibrium. For each sample , we performed 10 independent runs of structure, identifying the most supported number of K from a range of K = 1–4 clusters. We used the admixture model with correlated alleles. Each run included 50,000 burn‐in and 50,000 Markov Chain Monte‐Carlo iterations; runtime evaluations of summary statistic stability suggested that these run lengths were sufficient , Nei's unbiased expected heterozygosity (HE), and the inbreeding coefficient (FIS), independently for contemporary and historical populations with genalex  with fstat v2.9.3.2  that have been shown to generally produce accurate and precise estimates  and filtered out rare alleles occurring at frequencies lower than these values. We generated 95% confidence intervals based on the jackknife‐across samples method  and single‐sample methods to estimate kit fox  Pollak,  and Fc 2.6geneclass2 v2.0  loci (range = 5–8). We collected 120 samples including 45 cranial bones, 47 nasal bones, and 28 toe pads from 56 historical specimens. Seven specimens failed to produce sufficient amplifications and were dropped. Of the 49 (M:F ratio = 1.9:1) historical individuals retained, 69% achieved complete multilocus consensus genotypes across loci; individuals without complete multilocus genotypes had consensus genotypes at an average of 7.8 (±0.14 SE) loci (range = 6–8). Genotyping error rates per multilocus genotype  were higher for contemporary samples  than historical samples , but both were relatively low. Consequently, the probability of observing a genotyping error in the consensus genotype  was low at the average number of replicates performed for contemporary (5.25 ± 0.08 SE) and historical (7.69 ± 0.27 SE) individuals. We did not detect evidence of contamination in any extraction or PCR negatives.We surveyed 570–870 km of transects during each season and collected 810 scats confirmed as kit fox with mitochondrial DNA. Of these, we identified 76 (M:F ratio = 1.7:1) contemporary individuals and achieved complete multilocus consensus genotypes for 91% of individuals; individuals without complete genotypes had consensus genotypes at an average of 7  exceeded the number of individuals required to accurately characterize population‐level genetic diversity  at K = 1  or HE  between historical and contemporary populations. For both historical and contemporary populations, mean HO was comparable to mean HE, and mean FIS was not different from zero, aligning with tests for HWE . As expected, both temporal methods produced similar estimates of Ne that were intermediate with respect to the linkage disequilibrium estimates . Linkage disequilibrium estimates of contemporary Ne were well below the Ne = 500 threshold and near the critical threshold of 50 individuals .Linkage disequilibrium estimates of historical e Figure . Upper cs Figure . Tempora0 Figure . Still wAmong 76 contemporary individuals, two individuals (M:F ratio = 1:1) were identified as having a probability of population membership <1%, whereas five individuals (M:F ratio = 4:1) had a probability of population membership <5%. Thus, naïve estimates of contemporary immigration ranged from 2.6% to 6.6%. Among 49 historical kit foxes, four individuals had a probability of population membership <5% .42; Egoscue, 2; Lonsinger et al., Ne of kit foxes decreased substantially, and that contemporary Ne was precariously close to levels identified as being at risk of inbreeding depression and local extinction. Interestingly, we found no significant decrease in genetic diversity associated with declining abundance and Ne, and hypothesized that genetic diversity was maintained by undescribed immigration from other populations.Comparing historical specimens from NHCs with contemporary populations can provide insights into how natural and anthropogenic changes influence populations and inform conservation  and to ensure long‐term adaptive potential and persistence . Genetic evidence from wild populations has suggested these thresholds are too low, and Frankham, Bradshaw, and Brook  estimates of variance Ne were intermediate and suggested the harmonic mean Ne between sampling periods was likely greater than the lower thresholds to avoid inbreeding depression, but less than thresholds to maintain adaptive potential. Collectively, these findings suggest Ne of the kit fox population has decreased from relatively secure levels prior to 1970 to levels that warrant increased conservation attention.Franklin's  50/500 rnd Brook  suggesteNe decreases, inbreeding and drift are both expected to lead to declines in genetic diversity  experienced significant decreases in genetic diversity during a population bottleneck  and attributed to immigration from neighboring metapopulations . We detected only low levels of immigration and therefore our estimates of Ne should represent the Ne of the population under study, rather than the entire metapopulation  apply these methods to species with overlapping generations  of inbreeding depression. In contrast to these findings, we found no evidence of declining genetic diversity when considering the historical baseline data for genetic diversity. We hypothesize that genetic diversity has been maintained through low or intermittent immigration and argue that conservation efforts should prioritize assessing connectivity further. Identifying, maintaining, and potentially promoting or restoring gene flow with adjacent populations would likely decrease risks to kit foxes from short‐term genetic stochasticity and help promote long‐term conservation. Advancements in the field of landscape genetics provide a framework for effectively evaluating patterns of gene flow and identifying key corridors for conservation (Balkenhol, Cushman, Storfer, & Waits, Much of our broad understanding of kit fox ecology comes from the early research at Dugway Egoscue, , 1962 . None declared.RCL and LPW conceived the research. RCL and JRA performed data collection and conducted laboratory procedures. All authors conducted analyses. RCL led the manuscript preparation. JRA and LPW assisted with manuscript preparation and review.https://doi:10.5061/dryad.kc5j299.Genetic data are available from the Dryad Digital Repository:


A common measure employed to evaluate the efficacy of livestock improvement schemes is the genetic trend, which is calculated as the means of predicted breeding values for animals born in successive time periods. This implies that different cohorts refer to the same base population. For genetic evaluation schemes integrating genomic information with records for all animals, genotyped or not, this is often not the case: expected means for pedigree founders are zero whereas values for genotyped animals are expected to sum to zero at the (mean) time corresponding to the frequencies that are used to center marker allele counts when calculating genomic relationships.The paper examines estimates of genetic trends from single-step genomic evaluations. After a review of methods which propose to align pedigree-based and genomic relationship matrices, simulation is used to illustrate the effects of alignments and choice of assumed gene frequencies on trajectories of genetic trends.The results show that methods available to alleviate differences between the founder populations implied by the two types of relationship matrices perform well; in particular, the meta-founder approach is advantageous. An application to data from routine genetic evaluation of Australian sheep is shown, confirming their effectiveness for practical data.Aligning pedigree and genomic relationship matrices for single step genetic evaluation for populations under selection is essential. Fitting meta-founders is an effective and simple method to avoid distortion of estimates of genetic trends. Genetic evaluation based on the use of genomic information has become a routine procedure in numerous livestock improvement schemes. Many employ the so-called single-step procedure for best linear unbiased prediction (SS-GBLUP) which allows for joint evaluation of genotyped and non-genotyped animals ; see LegA problem inherent to combining genomically-derived and pedigree-based relationships arises due to different conceptual founder populations with potentially different means. For the NRM, the (unknown) parents of the first generation of pedigreed animals are considered to be the unrelated and non-inbred founder animals. Thus, the base generation is determined by the point in time where pedigree recording began. In contrast, genomic relationships are based on ancestral founders many generations back. Combining the NRM and GRM without accounting for these differences can result in biased predictions of breeding values, in particular EBV for genotyped animals may be biased downwards. This is akin to problems that have been encountered earlier on in genetic evaluation of beef cattle when PEBV of imported, superior bulls without appropriate local pedigree ties were found to be severely underestimated because they referenced the wrong, lower base . SeveralA standard measure, which is routinely computed to demonstrate the efficacy of selection programmes in livestock is the genetic trend. This is usually obtained as the mean EBV of cohorts of animals in a generation or born within a given time period. To date and to our knowledge, there are no studies that have examined estimates of genetic trend in the context of SS-GBLUP. This paper considers the effects of different modifications, which are suggested for the relationship matrices involved in SS-GBLUP, on the estimates of genetic trend. After a review of the methods proposed in the literature, we demonstrate by using simulated data that different ways of centering marker counts or aligning GRM and NRM can substantially affect estimates of trends, especially for populations that are subject to intense selection. This is followed by an application to data from LAMBPLAN, the Australian sheep genetic improvement scheme , represem markers summarized in matrix M of size A and G denote the NRM and GRM, respectively, and H the joint relationship matrix. Assume animals are ordered so that A can be partitioned into blocks pertaining to genotyped ; see their papers for details. This was modified slightly to consider a trait recorded on both sexes and by reducing the number of markers. In brief, this yielded records and genotypes comprised of allele counts for 46,500 loci for 2800 unselected animals with unknown parents in generation 0 and 2600 animals in each of 10 overlapping generations, 28,800 in total. Records were sampled for a trait with a phenotypic variance of 10 and heritability of 0.3. Parents of the next generation were selected based on their breeding values, obtained by BLUP using the (pedigree) numerator relationship matrix. Selection involved replacement of 80 out of 200 sires and 520 out of 2600 dams in each generation. A total of 25 replicates were obtained and analysed.To examine the effects of different methods of aligning e QMSim   adoptinge QMSim   and 13]\documenti through to 10, for Data were analysed by considering successive subsets of genotypes in generations The inverse of the joint relationship matrix between genotyped and non-genotyped animals was built in three ways. First, a et al. . Prelimia et al. , and theentclass1pt{minimao et al.  and \doca et al. .M. The first, denoted as all, was calculated using all observed genotypes in the subset, as is common practice. Second, only the genotypes for the first generation available were used, yielding case 1st. For comparison, the third scheme, fou, considered the founder frequencies, i.e. for individuals in generation 0. Finally, all frequencies were assumed to be equal to 0.5 . No weighted averaging between In turn, The examined summary statistics included mean EBV for each generation and coefficients for the regression of true on predicted breeding values for individuals in generation 10. In addition, mean GEBV per generation for each genomic analysis were deviated from the corresponding PEBV values, after standardising the ‘location’ of curves by subtracting the mean GEBV or PEBV for generation 0 from the respective means for all generations. A measure of discrepancy between estimates of genetic trends for different analyses was then calculated as the Frobenius norm of the resulting vector. Standardisation ensured that this quantity reflected only differences in shape of the trajectories.Analyses were carried out using our mixed model software WOMBAT, building ulations , 34.Methods were also tested for Australian sheep data, using records from the LAMBPLAN  maternalData for ynlb were analysed using WOMBAT which involved fitting single trait animal models with a fixed effect for contemporary group (250 levels) and an additional random effect for service sire (291 levels). The genetic effect was again fitted either without genotypes as ‘pedigree BLUP’, or with genotypes as SS-GBLUP fitting (see Eq. ) were usan sheep .Estimates of genetic trend for different analyses and amounts of genotypes available are shown in Fig. Without modifications of As fewer generations of genotypes were considered, the discrepancy between curves increased. For this method of centering, the mean of the genotyped animals used to determine the allele frequencies is forced to be zero . As espeEstimates of entclass1pt{minimaFinally, modifying There was little variation in results over replicates. Table Simulations considered the scenario where all animals in a generation were either genotyped or not. In practice, this is unlikely. Simulations were thus repeated deleting genotype information for every third animal (not shown). For case M0 using observed frequencies, either all or 1st, reduced regression coefficients further, the more so the fewer genotyped generations were considered. For instance, for all and genotypes in generation 10 only, the regression of true on predicted breeding values dropped to 0.55. Whereas standard deviations across replicates increased slightly, regression coefficients for the other cases differed little from the values given in Table Estimates of genetic trend for different analyses of the sheep data are shown in Fig. As for the simulated data, estimates of genetic trend for SS-GBLUP without any attempts to align Results for analyses fitting a meta-founder shown in Fig. Estimates of heritability and phenotypic variances varied little between analyses, with the estimated variance ratio for the service sire effect equal to 0.04 throughout. Values for the discrepancy in genetic trends between SS-GBLUP and pedigree BLUP analyses reflected observations for Fig. Joint genetic evaluation of genotyped and non-genotyped animals in a single-step analysis has become a standard procedure. Results clearly illustrate that care has to be taken to model different means and conceptual founder populations appropriately, especially for populations under selection. In particular, estimates of genetic trends are easily distorted and can differ with the assumptions on gene frequencies or the way they are estimated. As reviewed briefly, various methods have been suggested to combine or align the pedigree-based and genomically-derived relationship matrices or scale selected components of Current livestock improvement schemes typically have genotype information for the most recent generation(s) of animals only, especially for the more extensive industries such as sheep and beef cattle. Results show that both scaling of p effect , but thiMoreover, for the meta-founder approach coefficients for the regression of true on predicted breeding values (simulation) were essentially equal to unity, while adding all loci .Alignment of pedigree-based and genomic relationship matrices for single-step genetic evaluation of populations under selection is essential. Making the pedigree based relationship to be compatible with genomic information by fitting meta-founders is simple and effective.


Salix humboldtiana) in three basins of Central Mexico.To infer the geological and climatic factors that have shaped the genetic diversity and structure of a willow species  and one population from another basin (Ameca) within the Mexican Central Plateau (MCP). Individuals were analyzed using sequences of two chloroplast DNA (cpDNA) regions and eight nuclear simple sequence repeats (nSSR). Population genetic diversity and structure were determined from these data. To evaluate whether genetic structure was associated with ecological niche differentiation, we determined whether there is niche equivalence, overlap, or divergence between the Balsas and Lerma basins. Also, we evaluated the relative contributions of geographic distribution and climatic variation on population genetic structuring through redundancy analysis (RDA) and partial RDA.Both cpDNA and nSSRs data indicated the presence of three highly differentiated genetic groups, mostly geographically congruent with the three main hydrological basins. According to nSSRs, the three genetic groups can be further subdivided into eight subgroups corresponding to different rivers within the main basins. The niche equivalency test showed that the niches of the species in the Balsas and Lerma basins are significantly nonequivalent. The RDA indicated a significant association of genetic variation among populations with climate variables (particularly those related to the precipitation regime), while controlling for geographic distribution.S. humboldtiana is strongly associated with the historical and current geological configuration of the basins and the rivers within basins. The observed hierarchical genetic differentiation can be due to gene flow limitation resulting from physical barriers to the dispersal of S. humboldtiana, but also to some degree of isolation by environment, as suggested by the significant association between genetic variation among populations and precipitation regime.The genetic structure of  As the majority of willows, S. humboldtiana is an insect‐pollinated dioecious species that reproduces sexually by seed, but also shows frequent vegetative propagation through the establishment of branch fragments. It is the single Salix species distributed from the south of the United States to the Patagonia in Argentina in tropical and subtropical climates. In Mexico, it is commonly found from sea level up to 2,300 m, forming patches of riparian forest along river courses. Salix humboldtiana is commonly found in the Lerma and Balsas river systems within the MCP. Therefore, this species represents a good model to infer how topography and climate have affected the patterns of structure and gene flow of riparian forests in this region. The first goal of the present study was to determine the genetic structure of S. humboldtiana in two basins within the MCP using both nuclear and chloroplast DNA markers. Then, by combining genetic data, ecological niche modeling, and spatial analyses, we aimed at elucidating the consequences of historical changes in the distribution of the rivers. The specific questions addressed were as follows: (a) How is the genetic structure of S. humboldtiana related to the past or the current geological configuration of these two basins within the MCP? (b) Are climatic conditions in the two basins different and how is this related to the genetic structure within this species?2Fully detailed methodology, sampling, molecular protocols, and species records are available in Appendix 2.1Leaf tissue was collected from a total of 133 individuals representing 12 sampling sites located in the basins of the rivers Balsas and Lerma and one additional sampling site from the Ameca river basin Table . At eachSalix burjatica  size standard. Resulting electropherograms were analyzed in peak scanner 1.0 (Perkin Elmer Applied Biosystems) for allele sizing. Only eight nSSRs and one cpSSRs were polymorphic for this species and were used for the whole sample  markers previously designed for 2.2S. humboldtiana populations in the three basins, we sequenced 444 bp from the psbA‐trnH and the 5′‐trnS intergenic spacer regions of the cpDNA from four individuals from each basin  chosen to represent the main river systems within them  and 5′‐trnS (GenBank accession numbers MH924999–MH925010) sequences of S. humboldtiana individuals. A Salix babylonica sequence (GenBank accession number EU750539.1) was used as outgroup. Alignment of the sequences was performed manually with BioEdit 7.0.5 , and haplotype diversity (Hd), were estimated in dnasp5.10.01  values among populations with the method of Weir (HE) and observed (HO) heterozygosity and the inbreeding coefficient (FIS) in each locality were estimated using the software ARLEQUIN v 3.5.1.2  iterations. To determine the most probable K value from the analysis, the highest value of ΔK was calculated implementing the Evanno method  using both the infinite alleles and the stepwise mutation models  were performed with ARLEQUIN v 3.5.1.2  approach was used. We collected environmental data from 65 S. humboldtiana spatially unique presence records from our own sampling and from botanical collection records (www.conabio.gob.mx/remib/doctos/remib_esp.html). To avoid biases resulting from unequal sampling across the area, we only considered records that were separated at least 10 km from each other. Final analyses included 24 records from the Balsas Basin and 33 from the Lerma Basin. To reduce the overprediction of area suitability as well as to perform a better model validation . To avoid over‐parameterization of the models, we performed Pearson's correlation tests among all pairs of variables. From each pair of highly correlated variables (r ≥ .8), we chose the more temporally inclusive one . These analyses were performed with XLSTAT Pro 7.5 (www.xlstat.com/en/products-solutions.html). As a result, 10 climatic variables , we converted each model into binary (presence–absence or 1–0) format using as a threshold the 10‐percentile training presence, that is, the probability assigned to the 10th percentile of training occurrence records  and niche similarity (I) with ranges from 0 (no overlap) to 1 . The second test, the niche equivalency (or identity), determines whether the niches of, in this case, the populations in the two basins are more different than expected by chance even if they are drawn from the same underlying distribution. The latter was done based on D and I values randomly generated by 100 pseudo replicates and compared against empirical values with a one‐tailed t test  using the “vegan” library in R Oksanen, . The env33.1Hd = 0.691). Each of the three haplotypes corresponded to one of the basins, Ameca, Balsas, and Lerma  and observed (HO) heterozygosity, inbreeding coefficient (F), number of alleles per locus (Na), number of effective alleles per locus (Ne), allelic richness (Ar), and number of private alleles (Pa) per population are listed in Table For the eight nSSRs, we found 101 alleles, 28 of which are private alleles to one locality Table , but thePopulations with significant heterozygote deficit were evaluated with the INest v2.2 software  indicated that 13.65% of the variation is found among the Balsas and Lerma basins, while 18.41% is among populations within basins, and 67.94% is within populations. In turn, the grouping of individuals according to the three genetic groups identified by STRUCTURE indicated that 18.7% of the variation is among groups, 12.93% among populations, and 68.38% within populations. Finally, the grouping according to the eight genetic groups identified by GENELAND indicated that 21.38% of the variation is among the genetic groups, 7.37% among populations, and 71.25% within populations. The analysis with the SMM (RST) showed similar trends, but the proportion of the genetic variation explained at the among‐groups level was considerably higher, indicating that mutations are contributing to the differentiation among population groups. Pairwise genetic differentiation values using RST and FST were statistically significant (p < .05) for almost all pairs of populations and ranged between 0.036 and 0.524 for FST and between −0.01 and 0.846 for RST . Regarding estimates of relative contributions of the environmental variables to the MAXENT models, results indicated that the topographic variable (slope) and the precipitation of the warmest quarter (Bio 18) were highly informative for both Balsas and Lerma models . Precipitation of the wettest quarter (Bio 16 = 28.9%) resulted highly informative for the Balsas model, while temperature seasonality was informative for the Lerma model (Bio 4 = 25%).The performance of the two models  produced by MAXENT was significantly better than expected by chance alone according to the partial‐ROC bootstrap tests Figure a, which Our results indicated that the niche models for the two basins exhibited an overlap index of 54.37 for the Balsas to Lerma comparison and 45.62% for the Lerma to Balsas comparison Appendix . InterprResults of the principal components analysis showed that ~85% of the environmental variation is explained by the first two principal components. All 10 climatic variables (previously chosen) and the slope were negatively correlated to the first PCA axis. Precipitation seasonality (Bio 15) was correlated positively to the second PCA axis, and precipitation of the warmest quarter (Bio 18) was negatively correlated to the second axis .The niche equivalency test showed that the niches of the Balsas and Lerma basins are not identical, showing a low similarity value of 3.4p = .001) and accounted for a considerable proportion of the variance . In this analysis, the most important variables were annual precipitation and precipitation of the wettest month, followed by precipitation of the warmest quarter, temperature mean diurnal range, and slope . Finally, climate and geography together accounted for 25% of the variation (p = .001).The full RDA model considering the effect of both geography and climate on genetic structuring among populations was significant  distributed in an east–west gradient in the MCP occurred ca. 3.3 Mya based on the molecular clock calibration and was associated with the disconnection of water bodies during a dry period and the formation of geological isolation barriers. But dispersal of these organisms is restrained to river courses, and these connections have never been examined in an organism that is dispersed by both wind and water. Our results indicate very restricted gene flow among S. humboldtiana populations situated in different basins. Phylogenetic analysis of the psbA‐trnH and 5′trnS sequences revealed only three haplotypes, each restricted to one of the three basins, with a closer relationship between the haplotypes in the Ameca and Balsas basins  and the scarce genetic variation found. A similar pattern was obtained, though, from nuclear and chloroplast SSRs from all individuals, supporting in general a clear genetic differentiation among basins, with only a few populations showing evidence of admixture among basins. Particularly, in the case of the cpSSR locus, populations Tocumbo (6) and Sn. Juanico (7) belonging to the Balsas Basin, showed the haplotype present in all populations of the Lerma Basin, and population Ameca  was fixed for the same haplotype present in almost all populations in the Balsas Basin  and only Yautepec showed a high frequency of null alleles  associated with different rivers within basins. In other Salicaceae species, lack of genetic structure is commonly attributed to the absence of geographic barriers and long‐distance dispersal of seeds and pollen through wind  were highly informative for both Balsas and Lerma, while precipitation of the wettest quarter (Bio 16) was highly informative for the Balsas model and temperature seasonality (Bio 4) was informative for the Lerma model. Considering the differentiation of niches, the combination of a high proportion of overlap (54.37%–45.62%) and interpredictability (90.62%–100%) of potential distributions, and the results of the equivalency and similarity tests, in which Lerma and Balsas ecological spaces are significantly nonidentical and also significantly not similar, a complex biogeographical scenario can be proposed in which probably vicariance primarily drove the spatial isolation of The RDA analysis also showed an association between climate and genetic variation, with precipitation variables as the most important ones  followed by temperature mean diurnal range and slope. It has been shown that morphological and genetic variation is associated with climatic variables in other tree species like oaks (Riordan et al., S. humboldtiana and many other organisms. However, this is the first study in the MCP to examine patterns of genetic differentiation in a riparian tree that is dispersed by wind and water. We found that even for an organism like Salix, characterized in general by a high dispersal capacity, gene flow in this region is restricted between basins and that genetic structure is strongly mediated by river configuration, limiting genetic exchange even between close populations. These results contrast with other studies of Salix species in the world, which show efficient gene flow though wind‐dispersed seeds and scarce isolating barriers between populations (Perdereau et al., In conclusion, the complex geological and climatic history of the MCP, including the volcanic activity of the TMVB, has had a profound impact on gene flow patterns in None declared.Mariana S. Hernández‐Leal and Daniel Piñero designed the study and collected the data. Marco Suárez‐Atilano and Mariana S. Hernández‐Leal analyzed the data and wrote the manuscript with input from all authors. All authors discussed the results and commented on the manuscript. Antonio González‐Rodríguez and Daniel Piñero supervised the project. Click here for additional data file.


Myriophyllum alterniflorum DC., we explored the spatial genetic variation of eleven populations from the Iberian Plateau by means of microsatellite loci, and examined if the results obtained through genetic methods match modern perspectives of metacommunity theory. To do this, we applied a combination of robust statistical routines including network analysis, causal modelling and multiple matrix regression with randomization. Our findings revealed that macrophyte populations clustered into genetic groups that mirrored their geographic distributions. Importantly, we found a significant correlation between genetic variation and geographic distance at the regional scale. By using effective (genetic) dispersal estimates, our results are broadly in line with recent findings from metacommunity theory and re-emphasize the need to go beyond the historically predominant paradigm of understanding environmental heterogeneity as the main force driving macrophyte diversity patterns.The degree to which dispersal limitation interacts with environmental filtering has intrigued metacommunity ecologists and molecular biogeographers since the beginning of both research disciplines. Since genetic methods are superior to coarse proxies of dispersal, understanding how environmental and geographic factors influence population genetic structure is becoming a fundamental issue for population genetics and also one of the most challenging avenues for metacommunity ecology. In this study of the aquatic macrophyte  Today, metacommunity ecology has rapidly become a dominant framework through which ecologists understand the natural world2. Both, population genetics and metacommunity ecology, posit that it is not only the local environment that dictates patterns of species distributions, but these patterns also depend on processes such as the movement of organisms at the regional scale4. Each has generated an impressive body of theoretical and empirical research over the past two decades, yet dispersal processes operating in aquatic organisms remain little explored5. This deficit is a major hindrance to our understanding of dispersal as a force structuring regional patterns of biodiversity6, and is also the main reason why ecologists usually need to rely on proxies for dispersal. Recent simulation studies have shown that this typical coarse interpretation of spatial-based processes, which is primarily derived from the use of orthogonal spatial eigenvectors (MEM7) in variation partitioning analysis, is to some degree flawed, resulting in greatly inflated estimates of the role of environmental filtering8. Consequently, much of modern freshwater ecology is founded on the principle of environmental determinism and its findings are still subject to revision. In this vein, the recent macroecological study of Alahuhta et al.9  showed that environmental filtering overrode the effects of potential connectivity in explaining local communities across the world. However, such a statistical approach is highly correlative and was recently shown to overlook the role of dispersal-related processes on species distributions at a landscape level8.The degree to which dispersal limitation interacts with environmental filtering has intrigued metacommunity ecologists and population geneticists since the beginning of both research disciplines10. In this context, it is important that ecologists take advantage of modern techniques that have the potential to inform robust mechanistic models11. These techniques include molecular tools, which have been historically employed to study gene flow and potential dispersal limitation in biological populations13. Since gene flow estimations are certainly superior to coarse proxies of dispersal, understanding how environmental and geographic factors influence the genetic structure of biodiversity is becoming one of the most fundamental issues for population genetics14 and also one of the most challenging avenues for metacommunity ecology10. Consequently, progress in the synergistic potential of population genetics and metacommunity ecology may help in elucidating the degree to which dispersal limitation interferes with the local environment in determining geographic patterns of biological diversity.Recent recognition of the limitations affecting the traditional community-based approach to assess the relationships between dispersal limitation and environmental filtering called for the development of more sophisticated empirical methods15. Thus, genetic differentiation is the result of drift acting within populations more quickly than it is mitigated by gene flow among populations15.Landscape genetic scenarios, including ‘isolation by distance’ (IBD) and ‘isolation by the environment’ (IBE), rely on inferring the role of dispersal limitation and environmental variation from observed patterns of genetic structure. The theory of isolation by distance describes the local accumulation of genetic differences when dispersal among populations is limited by geographic factors, and therefore gene flow is inversely proportional to the distance between populations4. This model suggests that environmental variables can influence the colonisation success of individuals and groups of individuals via environmental filtering, with higher effective gene movements among similar environments14. Hence, regional variation in the environment may influence species-specific colonisation rates and establishment success when geographic distance allows dispersing immigrants to reach nearby habitat patches14. A classic example of this scenario in the freshwater realm comes from plants growing on and near the reaches of lakes and rivers, where local adaptations to different sediment and soil types have occurred4. Both patterns of IBD and IBE are usually present simultaneously in nature4 and represent one of the most important approaches with which to assess the relative importance of geographic distance and environmental heterogeneity in shaping patterns of dispersal and genetic variation17.Conversely, when genetic differentiation positively aligns with environmental dissimilarity among sites, a pattern of isolation by environment emerges18. The few existing studies19 seem to reveal simultaneous IBD and IBE patterns in shaping the genetic structure of different aquatic macrophyte species. Importantly, the interaction between spatial and environmental dynamics in structuring macrophyte genetic differentiation is broadly in line with recent findings from metacommunity theory21, and emphasizes the need to go beyond the historically predominant paradigm of understanding environmental specificity as the main force driving macrophyte gene flow patterns23.Relatively few empirical studies have examined the contribution of dispersal limitation and environmental filtering on macrophyte genetic divergenceMyriophyllum alterniflorum DC. (2n = 14) from 11 ponds located in the Iberian Plateau. More specifically, our main aims were to: (i) explore geographic patterns of population genetic structure and gene flow in the alternateflower watermilfoil; (ii) assess the influence of geographic distance and environmental dissimilarity on genetic differentiation of M. alterniflorum in Mediterranean pond environments; and (iii) disentangle if results obtained through population genetic methods match modern perspectives of metacommunity theory in these landscapes. Based on evidence from previous genetic studies on aquatic macrophytes19, we expected both geographic distance and environmental filtering to influence genetic variation of M. alterniflorum at the regional scale (H1). We also assumed that habitat fragmentation in Mediterranean landscapes would require that gene flow occurs primarily between neighbouring populations (H2), supporting recent metacommunity empirical research21 (H3) that suggests that spatial structuring accounts for much of the variation in aquatic macrophyte diversity patterns.Here, we present an analysis of geographic genetic variation using microsatellite markers on a total of 11 populations of 4. However, it is now becoming feasible to compare community assessments with genetic variation of single or a few species10. Whatever the case, we strongly believe that the alternateflower watermilfoil offers an ideal example for us to assess the spatial genetic patterns of aquatic macrophytes because it occurs irrespectively in still or slow-moving water of lakes, ponds and rivers and it is also widely distributed in different biogeographic realms of the Earth19.Since no academic work has yet examined the actual patterns of gene flow in aquatic macrophytes from a Mediterranean perspective, we hope that the baseline genetic information of our work may provide ground-breaking insights into the role of geographic isolation and environmental filtering on macrophyte metapopulation structuring. Similarly, our findings may have important and widespread implications for integrating population genetics into the full inference space of metacommunity ecology, helping us obtain a deeper understanding of whether or not spatial processes may hinder aquatic macrophytes from tracking environmental variation at the regional scale. It is important to emphasise that our study is limited to a single macrophyte species, and since most metacommunities comprise dozens to hundreds of species, findings should therefore be handled with caution. This is because, although the fundamental units are analogous , population genetics and metacommunity ecology use different routines and approaches to disentangle geographic patterns of biological diversityM. alterniflorum. Total genetic diversity (HT) and mean within-population diversity (HS) were 0.65 and 0.56, respectively. Considering all study populations, the number of genets, the mean number of alleles (Na) and the mean effective number of alleles (Ne) ranged from 6.0 to 20, 2.9 to 5.4, and 2.0 to 3.0, with a mean of 13, 4.4 and 2.6, respectively. The mean observed heterozygosity (Ho) and the mean unbiased expected heterozygosity (uHE) were 0.60 and 0.56, varying between 0.47–0.71 and 0.45–0.66, respectively  plotted by EDENetworks Fig.  revealedAfter the PCA-based model selection procedure, we kept the first two axes as synthetic environmental variables since these two principal components explained ~98% of the variance in the environmental attributes. The first axis was closely associated with turbidity, nutrient content and hydroperiod length, while pH and conductivity had the largest independent contribution for variation in the second axis , whereas non-significant associations were detected between genetic differentiation and environmental dissimilarity . When the influence of the environmental factor was controlled, the genetic-spatial association remained highly significant , while the effects of environmental heterogeneity were again statistically non-significant .Our analyses by Mantel test suggested a significantly positive correlation between genetic and geographic distances  for M. alterniflorum populations were somewhat high compared to the average values reported for other aquatic macrophytes with similar life history traits at both the species and the population level27. The possible reason for the relatively high genetic variation uncovered here may be explained in terms of high landscape heterogeneity and the wide range of local ecological conditions of the study ponds28. Likewise, all the obtained genotypes were population-specific, suggesting a high rate of recombination due to sexual reproduction and uncommon interpopulation dispersal of vegetative propagules29. In this regard, García-Girón et al.30 recently showed that seeds of M. alterniflorum were far more frequently encountered in surface sediment than its vegetative plant fragments, supporting our assumption that the spread of vegetative propagules is a relatively rare event. In any case, these patterns of genetic variability suggest that Mediterranean ponds are important reservoirs of genetic diversity25.Compared with previous works on patterns of genetic differentiation in aquatic macrophytesH2), populations clustered into genetic groups that mirrored their geographic patterns. Indeed, the Bayesian clustering approach and the discriminant analysis of principal components found only two genetic clusters in the data, one comprised of the eight populations north of SE and the other comprised of SE and the two populations south of this pond. This grouping was supported by the EDENetwork analysis, which also found modest levels of population connectivity. Sites spanning the northern cluster were relatively well connected, with the centrally located CAN being an important node linking several sites . Accordingly, the Monmonier’s algorithm indicated that there was a single barrier to gene flow, isolating the southernmost populations front the rest of the species’ range. This means that geographic limits will decrease the probability of effective dispersal among distant populations and thus enhance differentiation through genetic drift31.In spite of the relatively short distances between ponds (mean pairwise geographic distance ~ 15 km), and following our expectations . Under this scenario, increasing geographic distances among populations is expected to lead to enhanced genetic differentiation15, which essentially requires that gene flow occurs primarily between neighbouring populations31. Several previous studies revealed strong interpopulation genetic divergence and patterns of IBD in aquatic macrophytes at the continental scale18. However, to our knowledge, our study is the first empirical evidence that links macrophyte genetic variation and geographic isolation at the regional scale in Mediterranean landscapes. Globally, our study suggests that the dispersal of M. alterniflorum individuals among Mediterranean ponds was limited by geographic isolation and argues in favour of the classical stepping-stone model in which gene flow is mostly restricted to neighbour populations31.In our study, a strong association between genetic and geographic distances revealed a pattern of IBD across the distributional range of et al.5 found that hydrochory is a common means of long-distance dispersal in wetland species, facilitating gene exchange among geographically isolated populations and reducing the effect of founder events and genetic drift37. In fact, water dispersal has long been recognized as the main reason for the wide distribution of freshwater macrophytes38. Given the few hydrological connections among the study ponds39, wind and animals were likely to play the primary role for inter-population gene flow, transporting pollen, seeds and vegetative propagules to other ponds over the landscape20. Since wind-mediated gene flow seems to be effective only for distances of less than 1 km40, anemochorous dispersal may have failed to homogenize allele frequencies across distant populations. This reasoning is also in agreement with other experimental studies that have investigated spatial genetic structure of submerged macrophyte species in wetlands34. On the other hand, some waterbirds are known to play an important role in the seed dispersal of watermilfoils41. However, given the small populations of indigenous and migratory waterfowl in the study area39, we speculate that bird-mediated dispersal was likely to be of minor importance for gene flow patterns. Therefore, the single means of dispersal and lack of mediators (i.e. water and birds) may have enhanced genetic differentiation of M. alterniflorum populations in Mediterranean ponds, which could explain the greater prediction of IBD on genetic divergence as a result of genetic drift and dispersal limitation16.Wind, water and animals are the three main agents of dispersion for aquatic macrophytes, but their relative roles are very different. For example, Sommers 42, the results we report here may seem surprising. However, recent empirical research20 suggests that spatial structuring and environmental control together accounts for much of the variation in aquatic macrophyte communities at different spatial scales and geographic areas. For example, using a novel combination of metacommunity assembly modelling and multivariate multiscale codependence analysis, García-Girón et al.20 showed that dispersal limitation acted in concert with species sorting to influence macrophyte community assembly processes in Mediterranean landscapes. In this regard, a growing number of studies43 recognize that metacommunity theory must go beyond the historically predominant thinking of considering environmental determinism as the main scenario of macrophyte community assembly. For example, Brown et al.2 emphasized the degree to which dispersal limitation interferes with environmental filtering by hindering species’ tracking of local environmental conditions. By using effective (genetic) dispersal estimates, our results are broadly in line with recent findings from metacommunity theory21  and re-emphasize the need to go beyond the historically predominant paradigm of understanding environmental heterogeneity as the main force driving macrophyte gene flow patterns.Since the traditional view of macrophyte community studies is founded on the principle of environmental determinism14. However, since most metacommunities comprise dozens to hundreds of species, adding additional species to the analysis would provide a big step further for examining how ecological and landscape variation shapes the distribution of genetic diversity in nature14. For the moment, our study is an important step towards integrating population genetics into the full inference space of metacommunity ecology. Different types of approaches for molecular-based studies, including large multi-species population genetic data and DNA barcoding of entire assemblages10, will play a major part in the next big steps, providing an exciting frontier for metacommunity ecology that may open up many advances of scientific inquiry.Studies of single species are undoubtedly valuable for population genetics, often providing greater power and resolution for examining patterns of biological variation than coarse proxies of dispersal from spatial eigenfunction analysesM. alterniflorum under a relatively wide environmental gradient in Mediterranean ponds. Despite the relatively short distances between the study ponds (mean pairwise geographic distance ~ 15 km), plant populations clustered into genetic groups that mirrored their geographic distributions. Perhaps more importantly, we found a significant correlation between genetic variation and geographic distance at the regional scale, which essentially requires that gene flow occurs primarily between nearby populations. Accordingly, these findings emphasise that dispersal limitation at the landscape level may be an additional point of major conservation concern for aquatic macrophytes. Further studies examining the processes structuring genetic variation of multiple aquatic macrophyte species are needed to demonstrate whether the pattern provided by M. alterniflorum is typical or anomalous for macrophytes in this kind of highly fragmented landscapes. Hence, comparative studies, either of population genetics, metacommunity ecology or both in combination, will help us obtain a deeper understanding of whether or not spatial processes may hinder aquatic macrophyte species from tracking environmental variation.In conclusion, we highlighted the influence of spatial processes on patterns of genetic differentiation in 2 in northern Spain  of approximately 200 kmM. alterniflorum) is an anchored submerged aquatic macrophyte that is native to Europe, North America and Asia44. This widespread aquatic species occurs in still or slow-moving, neutral to basic water (pH = 6.0–8.8) of lakes, ponds and rivers with typically nutrient-poor and fine mineral sands sometimes mixed with muck45. M. alterniflorum presents diverse reproduction modes dispersing through both sexual and vegetative propagules (rhizomes and plant fragments). Similarly, this macrophyte species is typically dispersed by wind, water and waterfowl45. Together, gamete vectors and reproductive traits of this aquatic macrophyte species are expected to result in high gene flow and dispersal rates among nearby populations22.Alternateflower watermilfoil  was measured on high resolution aerial images with ArcMap version 10.6 . Maximum depth (m) and Secchi depth (m) were recorded in the deepest area of each waterbody using calibrated sticks and a Secchi discs (diameter = 20 cm), respectively. The ratio Secchi depth:maximum pond depth was used as a variable instead of Secchi depth since most of the ponds were shallow enough to keep the disc visible up to the bottom. Hydroperiod length (i.e. water residence time) was coded as a set of dummy variables, one for each category of the variable . Several water samples were randomly collected at different depths along a shore-centre transect using a cylindrical corer . All samples from each pond were subsequently mixed to form a single composite water sample (volume = 5 l). Conductivity and pH were measured in the field from the composite water sample using WTW probes . The integrated water samples were preserved in Pyrex glass bottles at 4 °C and then analysed in laboratory following standard methodsM. alterniflorum were collected from 11 ponds in July 2018. Six to 20 young leaf fragments from each population were randomly sampled at 2–3 m intervals to avoid collecting ramets from a single genet. Fresh leaf samples were immediately dried in allochroic silica gel in the field and then stored frozen at −80 °C before being processed further.A total of 142 individuals of et al.47, we selected a total of nine loci with clear polymorphic and reproducible bands , SSR primers , dNTPs , buffer and Cl2Mg 25 mM (2.5 µl each). PCR reactions consisted of an initial denaturation period of 5 min at 94 °C, followed by 35 cycles of 94 °C for 30 s, 52–59 °C for 30 s and 72 °C for 1 min, and a final 10 min extension at 72 °C, after which the samples were preserved at 4 °C. Genotyping was performed on an ABI 3130XL  automated DNA sequencer using an internal size standard  for accurate sizing. Then, GeneMapper version 4.0 (Applied Biosystems) was used for allele calling.Total genomic DNA was extracted from fresh leaf samples using the DNeasy® Plant Mini Kit  and following the manufacturer’ protocol. From the 20 microsatellite primers designed by Wu a and Ne), total genetic diversity (HT), mean genetic diversity (HS), and observed and expected heterozygosities (Ho and uHE), were computed with GenAlEx version 6.548. Nei’s unbiased genetic distance49 (DA) and Wright’s F statistics50, including inbreeding coefficient (FIS), total inbreeding (FIT) and fixation index (FST), were also determined with the same statistical software. Clone assignment was conducted with the criterion of treating individuals with the same multilocus genotype as a clone, and only the genotypes of the genets were kept for subsequent analyses.Genetic diversity estimates, such as allele richness  in ten independent runs from 1 to 11 , without using sampling site as a prior to assess convergence of the estimated In probability of the data, In P (D). Runs were carried out under the admixture model with independent allele frequencies. The best-fit number of clusters was calculated based on the second order rate of change of the likelihood function, ∆K52. Discriminant analysis of principal components53 (DAPC) was also carried out as an alternative method for determining broad-scale population structure using the dapc function from the ‘adegenet’ package version 2.1.154 in R. DAPC is a multivariate approach that combines principal component analysis together with discriminant analysis to summarize genetic differentiation between groups. DAPC is free of assumptions about Hardy–Weinberg equilibrium or linkage disequilibrium and provides graphical representation of the divergence among populations. The method requires a priori clustering algorithms determined by k-means. We evaluated up to k = 11 groups, and Bayesian information criterion53 (BIC) was used to assess the number of clusters best fitting the data. However, the value of BIC kept decreasing with the increase of k. We therefore set an identical k values as the K of STRUCTURE for comparison16.The number of genetic clusters of the 11 alternateflower watermilfoil populations was assessed by using a Bayesian clustering method implemented in the software STRUCTURE version 2.355. The method is a divisive-hierarchical clustering-like process in which the network is scanned from a fully connected state to a critical threshold distance (i.e. percolation threshold) without a priori assumptions of the clustering of populations. The software plots all populations as nodes in a network graph with connections (links or edges) between nodes weighted by their pairwise Nei’s unbiased genetic distance (DA). The layout of the MSN was recomputed 10 times to test for possible alternative network shapes.We created a minimum spanning network (MSN) to illustrate patterns of gene flow between populations using EDENetworks version 2.1856 as implemented in the package ‘adegenet’ to identify the geographic areas associated with genetic discontinuities in the study populations. The initial connection network was built using UTM coordinates for the sites. Detection of genetic discontinuities was based on the Delauney triangulation and the resulting Voronoi tessellation. Each edge of the Voronoi polygons was associated with the value of the corresponding DA between pairs of populations. The algorithm then built genetic boundaries based on maximum pairwise distances56. Statistical confidence of the genetic barriers detected, corresponding to an abrupt change in the patterns of genetic variation among populations, was evaluated using 1,000 bootstrap replicates that were simulated with the function writeBoot from the package ‘diveRsity’ version 1.957. Analyses were also conducted separately for each amplifying microsatellite locus.We applied the Monmonier’s maximum difference algorithms < 0.7) with the princomp function from the ‘vegan’ package version 2.458. After dealing with multicollinearity, local environmental attributes included: hydroperiod length, aerial extent, depth, relative Secchi depth, pH, conductivity, total suspended solids, ammonium, total phosphorous and chlorophyll “a”. The environmental (Euclidean) distances between populations were calculated from the values obtained from the PCA-based model selection procedure. Both geographic (Euclidean distances between pond UTM coordinates) and environmental distance matrices were constructed using the vegdist function from the ‘vegan’ package. The correlations between geographic/environmental factors and Nei’s unbiased genetic distances were assessed by a combination of partial Mantel tests59 and multiple matrix regression with randomization60 (MMRR). Partial Mantel tests with 10,000 permutations were performed between genetic distances and one factor under the influence of the other (as covariate) using the mantel.partial function implemented in the ‘vegan’ package. Similarly, MMRR was implemented with 10,000 iterations to estimate the independent effect of geographic/environmental factors using the MMRR function script60. The main advantages of this method are that it produces appropriate levels of Type-I error60, and it uses multiple regression, assessing the independent contribution of each variable in the model.We reduced the available environmental variables to a more parsimonious set by performing principal component analysis (PCA) on independent environmental attributes (Spearman’s rank correlation rSupplementary Information


Melanitta sp.) is challenging as these species have large spatial distributions that span remote landscapes, have varying nesting distributions (disjunct vs. continuous), exhibit unknown levels of dispersal, and vary in the timing of the formation of pair bonds  that may influence the distribution of genetic diversity. Here, we used double‐digest restriction‐associated DNA sequence (ddRAD) and microsatellite genotype data to assess population structure within the three North American species of scoter , and between their European congeners . We uncovered no or weak genomic structure  within North America but high levels of structure among European congeners . The pattern of limited genomic structure within North America is shared with other sea duck species and is often attributed to male‐biased dispersal. Further, migratory tendencies (east vs. west) of female surf and white‐winged scoters in central Canada are known to vary across years, providing additional opportunities for intracontinental dispersal and a mechanism for the maintenance of genomic connectivity across North America. In contrast, the black scoter had relatively elevated levels of divergence between Alaska and Atlantic sites and a second genetic cluster found in Alaska at ddRAD loci was concordant with its disjunct breeding distribution suggestive of a dispersal barrier . Although scoter populations appear to be connected through a dispersal network, a small percentage (<4%) of ddRAD loci had elevated divergence which may be useful in linking areas  throughout the annual cycle.Dispersal shapes demographic processes and therefore is fundamental to understanding biological, ecological, and evolutionary processes acting within populations. However, assessing population connectivity in scoters ( Dispersal also shapes demographic processes by forming linkages among populations that can promote stability via immigration and ultimately affect population vital rates  are a group of sea ducks that inhabit Holarctic waters using freshwater or brackish lakes and ponds for nesting . In North America, scoter nesting distribution extends throughout most of the boreal and arctic zones of Alaska and Canada with surf and white‐winged scoters having the most extensive breeding range  and near‐threatened for black scoter . The influence of environmental change, harvest pressure, and other mechanisms regulating population dynamics, however, is poorly known for all scoter species. Scoter population recovery following declines, regardless of cause, may be longer when compared to other waterfowl  due to life‐history characteristics of this group, and therefore, estimating levels of genetic structure among scoter populations is needed  and ultimately linkages among populations. North American scoters vary in their nesting distributions Figure  as well For this study, first, we used genome‐wide scans  and microsatellite genotype data to assess population genomic structure of the three North American scoter species across four regions . Second, we assessed levels of intercontinental dispersal between North American and European forms as vagrancy from migratory routes has been observed in all scoters , three occur in North America  and two in Eurasia . Traditionally, black scoter and common scoter were treated as conspecifics and were only recently regarded as different species, as they show distinct phenotypic characters  and velvet/white‐winged scoter . Samples were grouped into four broad North American regions: Alaska, Pacific, Central, and Atlantic. Regions do not necessarily correspond to migratory flyways, as some sampled locales are comprised of birds that use different flyways (see Discussion). See Sonsthagen, Pierson, Wilson, and Talbot . Extractions for the ddRAD protocol were quantified using a NanoDrop 2000 Spectrophotometer (Thermo Fisher Scientific Inc.) to ensure a minimum concentration of 0.02 µg/µl and visualized on an 1% agarose gel for high molecular weight bands. Library preparation for multiplexing followed steps outlined in  and following steps outlined in Lavretsky et al.  were >10, and if >3 SNPs showed strong linkage. This information is produced in the “clustersummary.out” output file of the genotyping step of the DaCosta and Sorenson  were generated with custom python scripts that set a higher minimum sequencing depth to score an allele ; and Smo7  across ddRAD autosomal markers in the R package PopGenome  was estimated in the R package PopGenome  within and among species were calculated in Arlequin 3.1 .We calculated allelic richness, observed, and expected heterozygosities, Hardy–Weinberg equilibrium (HWE), and linkage disequilibrium (LD) for microsatellite loci in FSTAT 2.9.3 Goudet, . PairwisK (from K of 1–10). Each analysis used a block relaxation algorithm for point estimation and terminated once the change  in the log‐likelihood of the point estimations increased by <0.0001. The optimum K was based on the average of cross‐validation (CV) errors across the 100 analyses per K; however, additional Ks were analyzed for further population structure resolution  were based on CLUMPP analyses of all 100 replicates per K.For ddRAD data, maximum‐likelihood estimates of population assignments for each individual were obtained with ADMIXTURE v.1.3  ranging from 1 to 8; the analyses were repeated 10 times to ensure consistency across runs. We followed the method of Evanno, Regnaut, and Goudet , we recovered 3,487–3,999 ddRAD autosomal markers, comprising ~500K bp and ~15,000 SNPs Table . Final d3.2ΦST < 0.019; Figure ΦST (>0.2) were observed for all North American scoters, with the highest proportion observed for comparisons involving black scoters breeding in Alaska and surf scoters breeding in the Central region , however, is likely driving those high proportions. High genomic differentiation was estimated among North American locales for black scoter and common scoter (ΦST = 0.196–0.120) and among white‐winged and velvet (ΦST = 0.155–0.160) scoters  and white‐winged scoters (K = 2), respectively  as compared to black scoters from the Central and Atlantic regions (ΦST = 0.001). Finally, regardless of K value, surf scoters showed no structure across North American sampling sites  and white‐winged and velvet (FST = 0.172–0.193) scoters  and white‐winged (π = 0.0031–0.0036) scoters was slightly higher than those of surf scoters (π = 0.0028–0.0029). Diversity metrics were similar between the North American species and their European counterparts. Diversity metrics estimated from the microsatellite loci were similar across regions and species  as well as between European species (COSC vs. VESC ΦST = 0.730) based on the ddRAD data. Similar levels of structure were observed at microsatellite loci  and between European species . No identifiable hybrids between species were found in either the ddRAD or microsatellite datasets  and nucleotide diversity  were significantly correlated whether analyzing species separately (more loci) or together (fewer loci)  genetic structure across their ranges, suggestive of high gene flow and connectivity among regions. The pattern of high connectivity among regions within North America is similar to patterns found in other sea duck species, such that spatial patterns in genetic variation are often not detected or have a weak signal at autosomal loci  switched between Atlantic and Pacific wintering areas among years  winter areas between years  are comprised of birds that winter on the Atlantic and Pacific coasts  within black scoter and not detected at microsatellite loci , with no evidence of intercontinental gene flow based on ADMIXTURE  were below 0.090 with incomplete lineage sorting posited for lack of partitioning observed , Kraus et al.  maintaining species boundaries. Due to the high rate of genomic connectivity found within sympatric dabbling ducks  among regions were uncovered  may limit their capacity to recover from population declines and events (either stochastic or deterministic) that reduce adult survival (Koneff et al., d Figure . Analysid Figure  and provNone declared.All authors conceived of the project, collected the data, performed the analyses, and contributed to the writing of the manuscript.


Humans have an exceptional capacity to mentally simulate sensory information of stimuli that are not physically present. This high-level cognitive ability—known as mental imagery—is a multisensory process that exploits internal representations of perception and/or action in working memory . ImageryAlthough considerable progress has been made in understanding imagery processes in healthy populations, less is known about these in the clinical domain. Specifically, the mechanisms driving the development, maintenance, and/or treatment of dysfunctional imagery in psychopathology remain largely unresolved . So the MI is a dynamic mental process that involves the performance of actions in the mind while inhibiting motor output . It is cMotor simulation theory MST; , 21 postProgress has also been made in understanding the specific cognitive systems supporting and constraining MI . Attentivia imagery, an alternative  mental representation of an event  across clinical disorders. The simulation mechanism driving MI—and the higher-order cognitive systems that support it—represent potential key targets for future intervention. Very often, imaginal exposure therapy and re-experiencing are associated with distress and high dropout rates, and preferred imagery techniques tend to involve imagery rescripting . TherefoBoth authors contributed equally to this article.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


STAT3. Within the first years of life she developed multiple, Staphylococcus aureus associated abscesses in the neck and face requiring frequent incision and drainage. Respiratory tract infections were not a feature of the clinical phenotype and a high resolution thoracic CT scan was unremarkable. Retained dentition was noted but fungal nail disease and recurrent thrush were absent. The total IgE was 970 IU/L, Lymphocyte counts and immunoglobulin levels were normal (IgG borderline 18.5 gr/L). There was suboptimal response to test immunisation with Pneumovax II vaccine. Th17 cell phenotyping revealed low levels of IL-17 expressing cells . Genetic analysis identified a missense mutation, N567D, in a conserved region of the linker domain of STAT3. Functional studies in HEK293 cells reveal that this mutation potently inhibits STAT3 activity when compared to the wildtype protein. This is consistent with other reported mutations in STAT3 associated with HIES. However, surprisingly, the magnitude of inhibition was similar to another STAT3 mutation (V637M) which causes a much more severe form of the disease.We present the case of a 19-year-old female with a mild form of Autosomal Dominant Hyper IgE syndrome (HIES) associated with a loss-of-function mutation in  Hyper IgE syndrome (HIES) is a rare primary immune deficiency and is characterised by elevated circulating levels of IgE. Patients typically experience eczema, lung, and skin infections, but other co-morbidities have also been described including brain and cardiac abnormalities. The autosomal dominant form of HIES is most commonly associated with inactivating mutations in STAT3 although HIES-associated mutations in DOCK8 and Tyk2 are reported .The transcription factor STAT3 is a multifunctional protein, whose activity is controlled by a plethora of cytokines and growth factors acting at their cognate cell surface receptors. Activated STAT3 translocates to the nucleus where it binds to consensus sequences in the DNA to regulate target gene expression. A variety of mutations in STAT3 have been implicated in disease and, in addition to loss-of-function mutations associated with HIES , variousStaphylococcus aureus associated abscesses in the neck and face requiring frequent incision and drainage. Respiratory tract infections were not a feature of the clinical phenotype and a thoracic CT scan was unremarkable. Retained dentition and mild eczema were noted but fungal nail disease and recurrent thrush were absent. The circulating total IgE was markedly elevated ; T and B cell counts were normal but IgG was raised (18.5 gr/L). Complement C3 and C4 levels, and complement function tests were normal. There was a suboptimal response to test immunisation with Pneumovax II vaccine. The patient is currently managed with flucloxacillin 500 mg BD for Staphylococcus aureus prophylaxis. The disease activity calculated via the score previously described by Grimbacher et al., was 36 [We present the case of a 19-year-old female with Autosomal Dominant HIES. She was born at 36 weeks gestation and early in life she developed multiple, Genomic DNA was isolated from whole blood. Coding genomic sequences and cDNA of STAT3 were purified with the QIAquick PCR purification kit . Subsequently, PCR products were sequenced using the ABI PRISM BigDye Terminator cycle ready reaction kit V3.1 (Applied Biosystems). The sequencing was performed on a 3130xl Applied Biosystems Genetic Analyzer. Data analysis was performed with DNA Sequencing Analysis software, v5.2 (Applied Biosystems) and Sequencher v4.8 .μg/ml streptomycin, and 100 U/ml penicillin was used to culture HEK293 cells. Cells were sub-cultured upon reaching approximately 80% confluency.A DMEM base medium supplemented with 10% foetal calf serum, 2 mM L-glutamine, 100 STAT3 gene  were introduced using the QuikChange site-directed mutagenesis kit . The custom primers used to generate STAT3 variants were N567D; Fd ACAAGGTCAATGATATCGTCCAGCCAGACCCAG Rv: TCTGGGTCTGGCTGGACGATATCATTGACCTTGTG Y640F; Fd: AGTCCGTGGAACCATTCACAAAGCAGCAGCTG Rv: AGCTGCTGCTTTGTGAATGGTTCCACGGACTG V637M; Fd; AGACCCAGATCCAGTCCATGGAACCATACACAAAG Rv; TGCTTTGTGTATGGTTCCATGGACTGGATCTGGGTC. The success of mutagenesis was confirmed by full sequencing of inserts (Source Bioscience). The STAT3 inserts were digested out of a pENTR221 vector using PsiI to produce a blunt-ended product. This was treated with a calf intestinal phosphatase enzyme prior to insertion into a pcDNA5/FRT/TO expression vector at the EcoRV restriction site within the polylinker.Mutations within the human HEK293 cells were seeded into 24-well plates and transfected with 200 ng of the STAT3-reponsive dual luciferase Cignal reporter construct (Qiagen) and 400 ng of the various STAT3 constructs. Attractene transfection reagent (Qiagen) was used to facilitate DNA uptake, and STAT3 activity was assessed 24 h after transfection using a dual luciferase reporter assay system  and with luminescence detected using a Pherastar FS . In some experiments, cells were treated with human IL-6  4 h after transfection.Cells were lysed and whole cell protein was extracted . Proteinµg/mL Staphylococcus enterotoxin B  for 2 h. GolgiStop (BD Biosciences) was then added and cells were incubated for a further 16 h. Subsequently, cells were surface stained with CD3 (V500), CD4 (V450), and CD45RO (PerCP-Cy5.5) and intracellular stained with IL-4 (PE-Cy7), IFN-γ (Alexa Fluor®488), and IL-17A (Alexa Fluor®647). Intracellular staining was performed by using the BD Cytofix/Cytoperm™ Fixation/Permeabilization Kit (BD Biosciences). All antibodies were from BD Biosciences. Cells were analysed using an 8 colour BD FACSCanto.PBMCs were isolated from a 10 ml heparin blood sample using LymphoprepTM (Axis-Shield). The PBMCs were re-suspended in 4 ml of RPMI with 10% foetal bovine serum. Cells were either untreated or stimulated with 1 HIES patients typically display reduced numbers of Th17 T-helper cells . Th17 ceSanger sequencing of the patient's DNA revealed a missense mutation in the STAT3 gene. The variant was heterozygous with a nucleotide exchange (A to G) at position 1699 in exon 19, leading to an aspartate for asparagine substitution at position 567 within the linker domain (N567D) . The amiThe N567D variant of STAT3 was generated by site-directed mutagenesis, and upon transfection into HEK293 cells, Western blotting analysis revealed an increase in STAT3 expression compared to cells transfected with empty vector . Importap < 0.05)  . IL-6 is < 0.05) . It is iTo validate the assay system, previously reported STAT3 mutations which enhance (Y640F) or reduce (V637M) STAT3 activity were tested in parallel with N567D. STAT3 activity was modified in the expected direction under both basal and IL-6 stimulated conditions upon the transfection of cells with these variants Figures .de novo mutation in the linker domain of STAT3 from a patient with a mild/moderate form of HIES that robustly inhibited the activity of STAT3 under both basal and IL-6 stimulated conditions. This mutation has been described in a single previous case with a more severe phenotype, although the authors did not examine its transcriptional activity in vitro [Autosomal dominant HIES is caused by loss-of-function mutations in STAT3 . Consistin vitro . Interesin vitro . This suStaphylococcus aureus. However, some differences in phenotype were noted. For example, unlike our patient, the earlier case exhibited an allergic history, bone fractures as well as oral candidiasis.In considering the effects of the present mutation, it is known that STAT3 is crucial for the differentiation of T-cells to a Th17 phenotype , and in α-helical linker between their SH2 and transactivation domains. Since this linker acts as a ridged spacer between functionally distinct groups, its disruption may result in altered protein function. In this context, targeted mutation of several conserved residues within the linker domain of STAT3 strongly reduced IL-6 stimulated transcriptional activity [in silico investigations, examining the intramolecular interactions that occur with the N567 residue, mutated in the present variant [G = −1.05) [The majority of STAT3 mutations were associated with HIES cluster in the SH2 domain, the DNA-binding domain and the transactivation domain of the protein . To our activity . Similaractivity . Merli a variant . This re= −1.05) . Taken tin vitro studies and these molecules were only effective against the mutations which destabilised STAT3, presumably due to their upregulation of chaperone proteins. Thus, alternative methods to improve the activity of STAT3 are needed. Perhaps, small molecules targeted to the linker region of STAT3 may be one mechanism to achieve this.The loss of STAT3 activity is an important driver of HIES, and therapeutic strategies to prevent or reverse this would be of interest to the field. In this context, a recent study revealed that certain small molecule compounds can improve the stability and activity of specific HIES-associated STAT3 mutants . Whilst


Ursus arctos) have so far focused on populations from Europe and North America, although the largest distribution area of brown bears is in Asia. In this study, we reveal population genetic parameters for the brown bear population inhabiting the Grand Kaçkar Mountains (GKM) in the north east of Turkey, western Lesser Caucasus. Using both hair (N = 147) and tissue samples (N = 7) collected between 2008 and 2014, we found substantial levels of genetic variation (10 microsatellite loci). Bear samples (hair) taken from rubbing trees worked better for genotyping than those from power poles, regardless of the year collected. Genotyping also revealed that bears moved between habitat patches, despite ongoing massive habitat alterations and the creation of large water reservoirs. This population has the potential to serve as a genetic reserve for future reintroductions in the Middle East. Due to the importance of the GKM population for on-going and future conservation actions, the impacts of habitat alterations in the region ought to be minimized; e.g., by establishing green bridges or corridors over reservoirs and major roads to maintain habitat connectivity and gene flow among populations in the Lesser Caucasus.Genetic studies of the Eurasian brown bear ( Ursus arctos Linnaeus, 1758) is a widely distributed Holarctic old world species. Although the main distribution of the species is in Asia, genetic studies have largely focused on Europe and North America  requires substantial logging to build new roads and high-voltage transmission lines at these high altitudes . For example, 28 big dams on Çoruh River (eastern part of GKM) and more than 1,000 small HEPP are to be constructed in the montane area of the Black Sea and Eastern Anatolia Regions at elevations between 750–2,500 m above sea level . The conltitudes . This loltitudes .Furthermore, the flooding of large areas by new water reservoirs and the construction of new big dams in the eastern GKM will likely hamper the movement of bears between GKM and the Lesser Caucasus Mountains in Georgia . A habitAn additional concern is human-induced fatalities, because bears are frequently involved in human-wildlife conflicts in the region and are thus the target of retaliatory poaching and trophy hunting . After aHabitat fragmentation and population isolation has been shown to result in low within-population genetic diversity in American grizzly bears . If largIn this study, we focus on the bear population of the Grand Kaçkar Mountains GKM;  in northWe collected 154 samples from the Grand Kaçkar Mountains (GKM) region in two neighboring Turkish provinces, Artvin and Bayburt ; Table 1N = 105), corral hair-traps (N = 7), and from barbed wires placed on rubbing trees (N = 12), at altitudes between 700–2,500 m above sea level (N = 3). Samples from the Bayburt province were collected from rubbing trees and power poles (N = 20). Hair samples were placed in paper envelopes without contacting human skin and were then stored at room temperature in zip lock bags with silica gel (N = 7) were obtained from bears captured in the Artvin province between 2010 and 2011    and seven tissue samples (Artvin N = 7) (Noninvasive genetic sampling was carried out from 2008 to 2014 (from May to October). In the Artvin province, we collected hair samples from rubbing trees and power poles impregnated with creosotes (ea level . Additiolica gel . In addiAffairs) , and wern N = 7) .For both hair and tissue samples, DNA was extracted using a commercially available kit  following the manufacturer’s instructions without any modifications.We genotyped all samples at 10 microsatellite loci: G10C, G10X, UarD1585, UarT739, UarD3139, UarD3684, G1D, Mu05, Mu23, Mu50, and at the sexing locus SRY (primer details in N = 2 replicates per sample) and if a mismatch occurred between replicates, results were discarded and genotyping was repeated on a new DNA extraction of that sample, resulting in 2–4 replicates per sample. The low number of hairs collected per sample limited us to two DNA extractions per sample. Thus, if a mismatch occurred in the second round of parallel genotyping, the sample was discarded.Following allelematch , or (ii) two allele differences between genotypes were observed but could be attributed to large allele drop-out, allele-size shift or missing data (N = 6). Genotypes from tissue samples were included in this analysis.As bears are highly mobile, we had to consider multiple sampling from a single individual. To remove such bias, we looked for matching genotypes using the R package  allelemach v2.5;  applyingMicro-Checker  and of the probability of identity given siblings (PIDsib) using the software package Gimlet  and expected heterozygosity (HE), and fstat .The following analyses infer population genetic structure using allele frequencies that do make such assumptions, and can be impacted by deviations from these assumptions . Arlequin  was evaluated in a range from K = 1 to K = 6 and was assessed in ten independent runs per K. We also used Bayesian inference implemented in the software tess  over the 20% runs with the lowest Deviance Information Criterion (DIC). Results were visualized using clumpp  and years (2008–2014) using Chi-square and contingency tables. Understanding how age and source of samples impact genotyping success can inform improving sampling strategy for further studies.To examine the impact of sampling date and sample source on genotyping success, we compared results for hair samples collected from different noninvasive sources (2).The microsatellite loci used were all highly polymorphic, with a range of seven to ten alleles . In totaIn order to reduce the impact of missing data on downstream analyses, we discarded six unique genotypes because there were missing data at more than one locus . Out of MicroChecker revealed that five loci had a significant probability for the presence of null alleles , and three of them  showed significant deviation from Hardy–Weinberg equilibrium  to PIDunb = 8.7 × 10−7 and PIDsib = 7.7 × 10−3 (5 loci).While inclusion of these loci did not alter the results of analyses on population structure, we restricted analyses to the five remaining loci  as these were still capable of distinguishing 41 out of the 42 unique genotypes identified using the full set of markers. Reducing the number of loci resulted in the cumulative estimates of ‘probability of identity’ dropping from N = 42), expected and observed heterozygosity was high , and slightly lower values for genotypes from the Bayburt province .Over all genotypes . When examining the proportion of shared alleles between individuals (following N = 42), no clustering of genotypes based on sample origin was apparent (N = 42) did not show a clustering of genotypes based on sample origin (Structure (N = 42) suggested the presence of a single population , we conducted the analysis for K = 2 to K = 6. Neither the Deviance Information Criterion (DIC) nor the plots of individual cluster membership provided unambiguous evidence for a population subdivision. Only up to five genotypes had a cluster membership probability above 0.8 (for K = 2) in any analysis  regardless of the collection year  (Hair samples collected from rubbing trees (with or without barbed wire) yielded DNA extracts that performed significantly better for genotyping than did samples collected from power poles treated with creosotes  .Measuring current levels of genetic variation is an essential aspect of conservation genetics , as it p2) as reported previously by Using both tissue and hair samples collected between 2008 and 2014 , we wereHE = 0.78, HO = 0.74; N = 36, HE = 0.79, HO = 0.74; Bayburt province: N = 5, HE = 0.68, HO = 0.72). While our estimates are not directly comparable with those obtained from other studies employing different microsatellite loci (We found high levels of allelic variation among bears in our study area (ite loci , they doite loci , are farUnfortunately, we were not able to satisfactorily resolve population structure in our study area because we were only able to obtain a moderate number of genotypes from the Bayburt province. None of the analyses could reject the hypothesis of a single population in our study area e.g., , and we Genetic monitoring of this population is also important to gauge the impact of habitat loss and habitat fragmentation in the region . These rIt was recently shown that hair samples obtained from rubbing trees perform better for genotyping than hair samples collected by other noninvasive methods, such as hair from corral traps . We simiAs reported for North American bears , rubbingAny conservation action plan for brown bears in the Lesser Caucasus needs to consider the influences of big dam and HEPP constructions on this genetically highly diverse bear population (high mtDNA diversity  and nucl10.7717/peerj.5660/supp-1Figure S1Click here for additional data file.10.7717/peerj.5660/supp-2Figure S2Click here for additional data file.10.7717/peerj.5660/supp-3Table S1Click here for additional data file.10.7717/peerj.5660/supp-4Supplemental Information 1Click here for additional data file.