Habitat-linked genetic structure for white-crowned sparrow (Zonotrichia leucophrys): Local factors shape population genetic structure

Ecological, environmental, and geographic factors all influence genetic structure. Species with broad distributions are ideal systems because they cover a range of ecological and environmental conditions allowing us to test which components predict genetic structure. This study presents a novel, broad geographic approach using molecular markers, morphology, and habitat modeling to investigate rangewide and local barriers causing contemporary genetic differentiation within the geographical range of three white-crowned sparrow (Zonotrichia leucophrys) subspecies: Z. l. gambelii, Z. l. oriantha, and Z. l. pugetensis. Three types of genetic markers showed geographic distance between sampling sites, elevation, and ecosystem type are key factors contributing to population genetic structure. Microsatellite markers revealed white-crowned sparrows do not group by subspecies, but instead indicated four groupings at a rangewide scale and two groupings based on coniferous and deciduous ecosystems at a local scale. Our analyses of morphological variation also revealed habitat differences; sparrows from deciduous ecosystems are larger than individuals from coniferous ecosystems based on principal component analyses. Habitat modeling showed isolation by distance was prevalent in describing genetic structure, but isolation by resistance also had a small but significant influence. Not only do these findings have implications concerning the accuracy of subspecies delineations, they also highlight the critical role of local factors such as habitat in shaping contemporary population genetic structure of species with high dispersal ability.

MrIML: Multi-response interpretable machine learning to model genomic landscapes

We introduce a new R package "MrIML" ("Mister iml"; Multi-response Interpretable Machine Learning). MrIML provides a powerful and interpretable framework that enables users to harness recent advances in machine learning to quantify multilocus genomic relationships, to identify loci of interest for future landscape genetics studies, and to gain new insights into adaptation across environmental gradients. Relationships between genetic variation and environment are often nonlinear and interactive; these characteristics have been challenging to address using traditional landscape genetic approaches. Our package helps capture this complexity and offers functions that fit and interpret a wide range of highly flexible models that are routinely used for single-locus landscape genetics studies but are rarely extended to estimate response functions for multiple loci. To demonstrate the package's broad functionality, we test its ability to recover landscape relationships from simulated genomic data. We also apply the package to two empirical case studies. In the first, we model genetic variation of North American balsam poplar (Populus balsamifera, Salicaceae) populations across environmental gradients. In the second case study, we recover the landscape and host drivers of feline immunodeficiency virus genetic variation in bobcats (Lynx rufus). The ability to model thousands of loci collectively and compare models from linear regression to extreme gradient boosting, within the same analytical framework, has the potential to be transformative. The MrIML framework is also extendable and not limited to modelling genetic variation; for example, it can quantify the environmental drivers of microbiomes and coinfection dynamics.

A machine-learning approach to map landscape connectivity in Aedes aegypti with genetic and environmental data

Aedes mosquitoes are projected to continue expanding their ranges, which could expose millions more humans to the diseases they carry. The implementation of vector control methods ranging from traditional (e.g., insecticides) to cutting edge (e.g., genetic modification) could be improved with landscape connectivity maps and increased understanding of the factors that affect mosquito dispersal. Here we present an iterative random forest method for integrating genetic and environmental data to map landscape connectivity. We achieve a correlation of 0.83 between the model’s predicted genetic distance and actual genetic distance. We produce a genetic connectivity map for the southern tier of the United States and discuss important factors to consider in mosquito control, e.g., the release of genetically modified mosquitoes.

Mapping landscape connectivity is important for controlling invasive species and disease vectors. Current landscape genetics methods are often constrained by the subjectivity of creating resistance surfaces and the difficulty of working with interacting and correlated environmental variables. To overcome these constraints, we combine the advantages of a machine-learning framework and an iterative optimization process to develop a method for integrating genetic and environmental (e.g., climate, land cover, human infrastructure) data. We validate and demonstrate this method for the Aedes aegypti mosquito, an invasive species and the primary vector of dengue, yellow fever, chikungunya, and Zika. We test two contrasting metrics to approximate genetic distance and find Cavalli-Sforza–Edwards distance (CSE) performs better than linearized F_ST. The correlation (R) between the model’s predicted genetic distance and actual distance is 0.83. We produce a map of genetic connectivity for Ae. aegypti’s range in North America and discuss which environmental and anthropogenic variables are most important for predicting gene flow, especially in the context of vector control.

Impact of Landscape on Host-Parasite Genetic Diversity and Distribution Using the Puumala orthohantavirus-Bank Vole System

In nature, host specificity has a strong impact on the parasite's distribution, prevalence, and genetic diversity. The host's population dynamics is expected to shape the distribution of host-specific parasites. In turn, the parasite's genetic structure is predicted to mirror that of the host. Here, we study the tandem Puumala orthohantavirus (PUUV)-bank vole system. The genetic diversity of 310 bank voles and 33 PUUV isolates from 10 characterized localities of Northeast France was assessed. Our findings show that the genetic diversity of both PUUV and voles, was positively correlated with forest coverage and contiguity of habitats. While the genetic diversity of voles was weakly structured in space, that of PUUV was found to be strongly structured, suggesting that the dispersion of voles was not sufficient to ensure a broad PUUV dissemination. Genetic diversity of PUUV was mainly shaped by purifying selection. Genetic drift and extinction events were better reflected than local adaptation of PUUV. These contrasting patterns of microevolution have important consequences for the understanding of PUUV distribution and epidemiology.

Association of putatively adaptive genetic variation with climatic variables differs between a parasite and its host

Parasitism is a pervasive phenomenon in nature with the relationship between species driving evolution in both parasite and host. Due to their host-dependent lifestyle, parasites may adapt to the abiotic environment in ways that differ from their hosts or from free-living relatives; yet rarely has this been assessed. Here, we test two competing hypotheses related to whether putatively adaptive genetic variation in a specialist mistletoe associates with the same, or different, climatic variables as its host species. We sampled 11 populations of the specialist mistletoe Amyema gibberula var. tatei (n = 154) and 10 populations of its associated host Hakea recurva subsp. recurva (n = 160). Reduced-representation sequencing was used to obtain genome-wide markers and putatively adaptive variation detected using genome scan methods. Climate associations were identified using generalized dissimilarity modelling, and these were mapped geographically to visualize the spatial patterns of genetic composition. Our results supported the hypothesis of parasites and host species responding differently to climatic variables. Temperature was relatively more important in predicting allelic turnover in the specialist mistletoe while precipitation was more important for the host. This suggests that parasitic plants and host species may respond differently to selective pressures, potentially as a result of differing nutrient acquisition strategies. Specifically, mistletoes acquire water from hosts (rather than the abiotic environment), which may provide a buffer to precipitation as a selective pressure. This work deepens and complements the physiological and other ecological studies of adaptation and provides a window into the evolutionary processes that underlie previously observed phenomena. Applying these methods to a comparative study in a host-parasite system has also highlighted factors that affect the study of selection pressure on nonmodel organisms, such as differing adaptation rates and lack of reference genomes.

EST-SSR-based landscape genetics of Pseudotaxus chienii, a tertiary relict conifer endemic to China

Pseudotaxus chienii, belonging to the monotypic genus Pseudotaxus (Taxaceae), is a relict conifer endemic to China. Its populations are usually small and patchily distributed, having a low capacity of natural regeneration. To gain a clearer understanding of how landscape variables affect the local adaptation of P. chienii, we applied EST-SSR markers in conjunction with landscape genetics methods: (a) to examine the population genetic pattern and spatial genetic structure; (b) to perform genome scan and selection scan to identify outlier loci and the associated landscape variables; and (c) to model the ecological niche under climate change. As a result, P. chienii was found to have a moderate level of genetic variation and a high level of genetic differentiation. Its populations displayed a significant positive relationship between the genetic and geographical distance (i.e., "isolation by distance" pattern) and a strong fine-scale spatial genetic structure within 2 km. A putatively adaptive locus EMS6 (functionally annotated to cellulose synthase A catalytic subunit 7) was identified, which was found significantly associated with soil Cu, K, and Pb content and the combined effects of temperature and precipitation. Moreover, P. chienii was predicted to experience significant range contractions in future climate change scenarios. Our results highlight the potential of specific soil metal content and climate variables as the driving force of adaptive genetic differentiation in P. chienii. The data would also be useful to develop a conservation action plan for P. chienii.

Do all roads lead to resistance? State road density is the main impediment to gene flow in a flagship species inhabiting a severely fragmented anthropogenic landscape

AIM

Connectivity conservation is ideally based on empirical information on how landscape heterogeneity influences species-specific movement and gene flow. Here, we present the first large-scale evaluation of landscape impacts on genetic connectivity in the European wildcat (Felis silvestris), a flagship and umbrella species for connectivity conservation across Europe.

LOCATION

The study was carried out in the core area of the distributional range of wildcats in Germany, covering about 186,000 km2 of a densely populated and highly fragmented landscape.

METHODS

We used data of 975 wildcats genotyped at 14 microsatellites and an individual-based landscape genetic framework to assess the importance of twelve landscape variables for explaining observed genetic connectivity. For this, we optimized landscape resistance surfaces for all variables and compared their relative impacts using multiple regression on distance matrices and commonality analysis.

RESULTS

Genetic connectivity was best explained by a synergistic combination of six landscape variables and isolation by distance. Of these variables, road density had by far the strongest individual impact followed by synergistic effects of agricultural lands and settlements. Subsequent analyses involving different road types revealed that the strong effect of road density was largely due to state roads, while highways and federal roads had a much smaller, and county roads only a negligible impact.

MAIN CONCLUSIONS

Our results highlight that landscape-wide genetic connectivity in wildcats across Germany is strongly shaped by the density of roads and in particular state roads, with higher densities providing larger resistance to successful dispersal. These findings have important implications for conservation planning, as measures to mitigate fragmentation effects of roads (e.g., over- or underpasses) often focus on large, federally managed transportation infrastructures. While these major roads exert local barrier effects, other road types can be more influential on overall connectivity, as they are more abundant and more widespread across the landscape.

A machine learning approach to integrating genetic and ecological data in tsetse flies (Glossina pallidipes) for spatially explicit vector control planning

Vector control is an effective strategy for reducing vector-borne disease transmission, but requires knowledge of vector habitat use and dispersal patterns. Our goal was to improve this knowledge for the tsetse species Glossina pallidipes, a vector of human and animal African trypanosomiasis, which are diseases that pose serious health and socioeconomic burdens across sub-Saharan Africa. We used random forest regression to (i) build and integrate models of G. pallidipes habitat suitability and genetic connectivity across Kenya and northern Tanzania and (ii) provide novel vector control recommendations. Inputs for the models included field survey records from 349 trap locations, genetic data from 11 microsatellite loci from 659 flies and 29 sampling sites, and remotely sensed environmental data. The suitability and connectivity models explained approximately 80% and 67% of the variance in the occurrence and genetic data and exhibited high accuracy based on cross-validation. The bivariate map showed that suitability and connectivity vary independently across the landscape and was used to inform our vector control recommendations. Post hoc analyses show spatial variation in the correlations between the most important environmental predictors from our models and each response variable (e.g., suitability and connectivity) as well as heterogeneity in expected future climatic change of these predictors. The bivariate map suggests that vector control is most likely to be successful in the Lake Victoria Basin and supports the previous recommendation that G. pallidipes from most of eastern Kenya should be managed as a single unit. We further recommend that future monitoring efforts should focus on tracking potential changes in vector presence and dispersal around the Serengeti and the Lake Victoria Basin based on projected local climatic shifts. The strong performance of the spatial models suggests potential for our integrative methodology to be used to understand future impacts of climate change in this and other vector systems.

Genomic data support multiple introductions and explosive demographic expansions in a highly invasive aquatic insect

The study of the genetic makeup and demographic fate of alien species is essential to understand their capacity to recover from founder effects, adapt to new environmental conditions and, ultimately, become invasive and potentially damaging. Here, we employ genomic data to gain insights into key demographic processes that might help to explain the extraordinarily successful invasion of the Western Mediterranean region by the North American boatman Trichocorixa verticalis (Hemiptera: Corixidae). Our analyses revealed the genetic distinctiveness of populations from the main areas comprising the invasive range and coalescent-based simulations supported that they originated from independent introductions events probably involving different source populations. Testing of alternative demographic models indicated that all populations experienced a strong bottleneck followed by a recent and instantaneous demographic expansion that restored a large portion (>30%) of their ancestral effective population sizes shortly after introductions took place (<60 years ago). Considerable genetic admixture of some populations suggest that hypothetical barriers to dispersal (i.e., land and sea water) are permeable to gene flow and/or that they originated from introductions involving multiple lineages. This study demonstrates the repeated arrival of propagules with different origins and short time lags between arrival and establishment, emphasizing the extraordinary capacity of the species to recover from founder effects and genetically admix in invaded areas. This can explain the demonstrated capacity of this aquatic insect to spread and outcompete native species once it colonizes new suitable regions. Future genomic analyses of native range populations could help to infer the genetic makeup of introduced populations and track invasion routes.

Environmental association modelling with loci under divergent selection predicts the distribution range of a lizard

During the historical building of a species range, individual colonizers have to confront different ecological challenges, and the capacity of the species to broaden its range may depend on the total amount of adaptive genetic variation supplied by evolution. We set out to increase our understanding of what defines a range and the role of underlying genetics by trying to predict an entire species' range from the geographical distribution of its genetic diversity under selection. We sampled five populations of the western Mediterranean lizard Psammodromus algirus that inhabit a noticeable environmental gradient of temperature and precipitation. We correlated the genotypes of 95 individuals (18-20 individuals per population) for 21 SNPs putatively under selection with environmental scores on a bioclimatic gradient, using 1 × 1 km²  grid cells as sampling units. By extrapolating the resulting model to all possible combinations of alleles, we inferred all the geographic cells that were theoretically suitable for a given amount of genetic variance under selection. The inferred distribution range overlapped to a large extent with the realized range of the species (77.46% of overlap), including an accurate prediction of internal gaps and range borders. Our results suggest an adaptability threshold determined by the amount of genetic variation available that would be required to warrant adaptation beyond a certain limit of environmental variation. These results support the idea that the expansion of a species' range can be ultimately linked to the arising of new variants under selection (either newly selected variants from standing genetic variation or innovative mutations under selection).

Multiscale assessment of functional connectivity: Landscape genetics of eastern indigo snakes in an anthropogenically fragmented landscape in central Florida

Landscape features can strongly influence gene flow and the strength and direction of these effects may vary across spatial scales. However, few studies have evaluated methodological approaches for selecting spatial scales in landscape genetics analyses, in part because of computational challenges associated with optimizing landscape resistance surfaces (LRS). We used the federally threatened eastern indigo snake (Drymarchon couperi) in central Florida as a case study with which to compare the importance of landscape features and their scales of effect in influencing gene flow. We used genetic algorithms (ResistanceGA) to empirically optimize LRS using categorical land cover surfaces, multiscale resource selection surfaces (RSS), and four combinations of landscape covariates measured at multiple spatial scales (multisurface multiscale LRS). We compared LRS where scale was selected using pseudo- and full optimization. Multisurface multiscale LRS received more empirical support than LRS optimized from categorical land cover surfaces or RSS. Multiscale LRS with scale selected using full optimization generally outperformed those with scale selected using pseudo-optimization. Multiscale LRS with large spatial scales (1200-1800 m) received the most empirical support. Our results highlight the importance of considering landscape features across multiple spatial scales in landscape genetic analyses, particularly broad scales relative to species movement potential. Different effects of scale on home range-level movements and dispersal could explain weak associations between habitat suitability and gene flow in other studies. Our results also demonstrate the importance of large tracts of undeveloped upland habitat with heterogenous vegetation communities and low urbanization for promoting indigo snake connectivity.

The effect of sampling density and study area size on landscape genetics inferences for the Mississippi slimy salamander (Plethodon mississippi)

In landscape genetics, it is largely unknown how choices regarding sampling density and study area size impact inferences upon which habitat features impede vs. facilitate gene flow. While it is recommended that sampling locations be spaced no further apart than the average individual's dispersal distance, for low-mobility species, this could lead to a challenging number of sampling locations, or an unrepresentative study area. We assessed the effects of sampling density and study area size on landscape genetic inferences for a dispersal-limited amphibian, Plethodon mississippi, via analysis of nested datasets. Microsatellite-based genetic distances among individuals were divided into three datasets representing sparse sampling across a large study area, dense sampling across a small study area, or sparse sampling across the same small study area. These datasets were a proxy for gene flow (i.e., the response variable) in maximum-likelihood population effects models that assessed the nature and strength of their relationship with each of five land-use classes (i.e., potential predictor variables). Comparisons of outcomes were based on the rank order of effect, sign of effect (i.e., gene flow resistance vs. facilitation), spatial scale of effect, and functional relationship with gene flow. The best-fit model for each dataset had the same sign of effect for hardwood forests, manmade structures, and pine forests, indicating the impacts of these land-use classes on dispersal and gene flow in P. mississippi are robust to sampling scheme. Contrasting sampling densities led to a different inferred functional relationship between agricultural areas and gene flow. Study area size appeared to influence the scale of effect of manmade structures and the sign of effect of pine forests. Our findings provided evidence for an influence of sampling density, study area size, and sampling effort upon inferences. Accordingly, we recommend iterative subsampling of empirical datasets and continued investigation into the sensitivities of landscape genetic analyses using simulations.

Conservation insights from wild bee genetic studies: Geographic differences, susceptibility to inbreeding, and signs of local adaptation

Conserving bees are critical both ecologically and economically. Genetic tools are valuable for monitoring these vital pollinators since tracking these small, fast-flying insects by traditional means is difficult. By surveying the current state of the literature, this review discusses how recent advances in landscape genetic and genomic research are elucidating how wild bees respond to anthropogenic threats. Current literature suggests that there may be geographic differences in the vulnerability of bee species to landscape changes. Populations of temperate bee species are becoming more isolated and more genetically depauperate as their landscape becomes more fragmented, but tropical bee species appear unaffected. These differences may be an artifact of historical differences in land-use, or it suggests that different management plans are needed for temperate and tropical bee species. Encouragingly, genetic studies on invasive bee species indicate that low levels of genetic diversity may not lead to rapid extinction in bees as once predicted. Additionally, next-generation sequencing has given researchers the power to identify potential genes under selection, which are likely critical to species' survival in their rapidly changing environment. While genetic studies provide insights into wild bee biology, more studies focusing on a greater phylogenetic and life-history breadth of species are needed. Therefore, caution should be taken when making broad conservation decisions based on the currently few species examined.

Global wind patterns shape genetic differentiation, asymmetric gene flow, and genetic diversity in trees

Wind disperses the pollen and seeds of many plants, but little is known about whether and how it shapes large-scale landscape genetic patterns. We address this question by a synthesis and reanalysis of genetic data from more than 1,900 populations of 97 tree and shrub species around the world, using a newly developed framework for modeling long-term landscape connectivity by wind currents. We show that wind shapes three independent aspects of landscape genetics in plants with wind pollination or seed dispersal: populations linked by stronger winds are more genetically similar, populations linked by directionally imbalanced winds exhibit asymmetric gene flow ratios, and downwind populations have higher genetic diversity. For each of these distinct hypotheses, partial correlations between the respective wind and genetic metrics (controlling for distance and climate) are positive for a significant majority of wind-dispersed or wind-pollinated genetic data sets and increase significantly across functional groups expected to be increasingly influenced by wind. Together, these results indicate that the geography of both wind strength and wind direction play important roles in shaping large-scale genetic patterns across the world's forests. These findings have implications for various aspects of basic plant ecology and evolution, as well as the response of biodiversity to future global change.

Genetic data improves niche model discrimination and alters the direction and magnitude of climate change forecasts

Ecological niche models (ENMs) have classically operated under the simplifying assumptions that there are no barriers to gene flow, species are genetically homogeneous (i.e., no population-specific local adaptation), and all individuals share the same niche. Yet, these assumptions are violated for most broadly distributed species. Here, we incorporate genetic data from the widespread riparian tree species narrowleaf cottonwood (Populus angustifolia) to examine whether including intraspecific genetic variation can alter model performance and predictions of climate change impacts. We found that (1) P. angustifolia is differentiated into six genetic groups across its range from México to Canada and (2) different populations occupy distinct climate niches representing unique ecotypes. Comparing model discriminatory power, (3) all genetically informed ecological niche models (gENMs) outperformed the standard species-level ENM (3-14% increase in AUC; 1-23% increase in pROC). Furthermore, (4) gENMs predicted large differences among ecotypes in both the direction and magnitude of responses to climate change and (5) revealed evidence of niche divergence, particularly for the Eastern Rocky Mountain ecotype. (6) Models also predicted progressively increasing fragmentation and decreasing overlap between ecotypes. Contact zones are often hotspots of diversity that are critical for supporting species' capacity to respond to present and future climate change, thus predicted reductions in connectivity among ecotypes is of conservation concern. We further examined the generality of our findings by comparing our model developed for a higher elevation Rocky Mountain species with a related desert riparian cottonwood, P. fremontii. Together our results suggest that incorporating intraspecific genetic information can improve model performance by addressing this important source of variance. gENMs bring an evolutionary perspective to niche modeling and provide a truly "adaptive management" approach to support conservation genetic management of species facing global change.

Comparative analysis of spatial genetic structures in sympatric populations of two riparian plants, Saxifraga acerifolia and Saxifraga fortunei

PREMISE

The genetic structure between plant populations is facilitated by the spatial population arrangement and limited dispersal of seed and pollen. Saxifraga acerifolia, a local endemic species in Japan, is a habitat specialist that is confined to waterfalls in riparian environments. Its sister species, Saxifraga fortunei, is a generalist that is widely distributed along riverbanks. Here, we examined sympatric populations of the two Saxifraga species to test whether the differences in habitat preference and colonization process influenced regional and local genetic structures.

METHODS

To reveal genetic structures, we examined chloroplast microsatellite variations and genome-wide nucleotide polymorphisms obtained by genotyping by sequencing. We also estimated the gene flow among and within populations and performed landscape genetic analyses to evaluate seed and pollen movement and the extent of genetic isolation related to geographic distance and/or habitat differences.

RESULTS

We found strong genetic structure in the specialist S. acerifolia, even on a small spatial scale (<1 km part); each population on a different waterfall in one river system had a completely different predominant haplotype. By contrast, the generalist S. fortunei showed no clear genetic differentiation.

CONCLUSIONS

Our findings suggest that the level of genetic isolation was increased in S. acerifolia by the spatially fragmented habitat and limited seed and pollen dispersal over waterfalls. Habitat differentiation between the sister taxa could have contributed to the different patterns of gene flow and then shaped the contrasting genetic structures.

Latitudinal clines in sexual selection, sexual size dimorphism and sex-specific genetic dispersal during a poleward range expansion

Range expansions can be shaped by sex differences in behaviours and other phenotypic traits affecting dispersal and reproduction. Here, we investigate sex differences in morphology, behaviour and genomic population differentiation along a climate-mediated range expansion in the common bluetail damselfly (Ischnura elegans) in northern Europe. We sampled 65 sites along a 583-km gradient spanning the I. elegans range in Sweden and quantified latitudinal gradients in site relative abundance, sex ratio and sex-specific shifts in body size and mating status (a measure of sexual selection). Using single nucleotide polymorphism (SNP) data for 426 individuals from 25 sites, we further investigated sex-specific landscape and climatic effects on neutral genetic connectivity and migration patterns. We found evidence for sex differences associated with the I. elegans range expansion, namely (a) increased male body size with latitude, but no latitudinal effect on female body size, resulting in reduced sexual dimorphism towards the range limit, (b) a steeper decline in male genetic similarity with increasing geographic distance than in females, (c) male-biased genetic migration propensity and (d) a latitudinal cline in migration distance (increasing migratory distances towards the range margin), which was stronger in males. Cooler mean annual temperatures towards the range limit were associated with increased resistance to gene flow in both sexes. Sex ratios became increasingly male biased towards the range limit, and there was evidence for a changed sexual selection regime shifting from favouring larger males in the south to favouring smaller males in the north. Our findings suggest sex-specific spatial phenotype sorting at the range limit, where larger males disperse more under higher landscape resistance associated with cooler climates. The combination of latitudinal gradients in sex-biased dispersal, increasing male body size and (reduced) sexual size dimorphism should have emergent consequences for sexual selection dynamics and the mating system at the expanding range front. Our study illustrates the importance of considering sex differences in the study of range expansions driven by ongoing climate change.

Landscape Genetic Connectivity and Evidence for Recombination in the North American Population of the White-Nose Syndrome Pathogen, Pseudogymnoascus destructans

White-Nose Syndrome is an ongoing fungal epizootic caused by epidermal infections of the fungus, Pseudogymnoascus destructans (P. destructans), affecting hibernating bat species in North America. Emerging early in 2006 in New York State, infections of P. destructans have spread to 38 US States and seven Canadian Provinces. Since then, clonal isolates of P. destructans have accumulated genotypic and phenotypic variations in North America. Using microsatellite and single nucleotide polymorphism markers, we investigated the population structure and genetic relationships among P. destructans isolates from diverse regions in North America to understand its pattern of spread, and to test hypotheses about factors that contribute to transmission. We found limited support for genetic isolation of P. destructans populations by geographic distance, and instead identified evidence for gene flow among geographic regions. Interestingly, allelic association tests revealed evidence for recombination in the North American P. destructans population. Our landscape genetic analyses revealed that the population structure of P. destructans in North America was significantly influenced by anthropogenic impacts on the landscape. Our results have important implications for understanding the mechanism(s) of P. destructans spread.

Agricultural Landscape Heterogeneity Matter: Responses of Neutral Genetic Diversity and Adaptive Traits in a Neotropical Savanna Tree

Plants are one of the most vulnerable groups to fragmentation and habitat loss, that may affect community richness, abundance, functional traits, and genetic diversity. Here, we address the effects of landscape features on adaptive quantitative traits and evolutionary potential, and on neutral genetic diversity in populations of the Neotropical savanna tree Caryocar brasiliense. We sampled adults and juveniles in 10 savanna remnants within five landscapes. To obtain neutral genetic variation, we genotyped all individuals from each site using nine microsatellite loci. For adaptive traits we measured seed size and mass and grown seeds in nursery in completely randomized experimental design. We obtained mean, additive genetic variance (V_a) and coefficient of variation (CV_a%), which measures evolvability, for 17 traits in seedlings. We found that landscapes with higher compositional heterogeneity (SHDI) had lower evolutionary potential (CV_a%) in leaf length (LL) and lower aboveground dry mass (ADM) genetic differentiation (Q_ST). We also found that landscapes with higher SHDI had higher genetic diversity (He) and allelic richness (AR) in adults, and lower genetic differentiation (F_ST). In juveniles, SHDI was also positively related to AR. These results are most likely due to longer dispersal distance of pollen in landscapes with lower density of flowering individuals. Agricultural landscapes with low quality mosaic may be more stressful for plant species, due to the lower habitat cover (%), higher cover of monocropping (%) and other land covers, and edge effects. However, in landscapes with higher SHDI with high quality mosaic, forest nearby savanna habitat and the other environments may facilitate the movement or provide additional habitat and resources for seed disperses and pollinators, increasing gene flow and genetic diversity. Finally, despite the very recent agriculture expansion in Central Brazil, we found no time lag in response to habitat loss, because both adults and juveniles were affected by landscape changes.

Analysing landscape effects on dispersal networks and gene flow with genetic graphs

Graph-theoretic approaches have relevant applications in landscape genetic analyses. When species form populations in discrete habitat patches, genetic graphs can be used (a) to identify direct dispersal paths followed by propagules or (b) to quantify landscape effects on multi-generational gene flow. However, the influence of their construction parameters remains to be explored. Using a simulation approach, we constructed genetic graphs using several pruning methods (geographical distance thresholds, topological constraints, statistical inference) and genetic distances to weight graph links (F_ST , D_PS , Euclidean genetic distances). We then compared the capacity of these different graphs to (a) identify the precise topology of the dispersal network and (b) to infer landscape resistance to gene flow from the relationship between cost-distances and genetic distances. Although not always clear-cut, our results showed that methods based on geographical distance thresholds seem to better identify dispersal networks in most cases. More interestingly, our study demonstrates that a sub-selection of pairwise distances through graph pruning (thereby reducing the number of data points) can counter-intuitively lead to improved inferences of landscape effects on dispersal. Finally, we showed that genetic distances such as the D_PS or Euclidean genetic distances should be preferred over the F_ST for landscape effect inference as they respond faster to landscape changes.

Cropland connectivity affects genetic divergence of Colorado potato beetle along an invasion front

The population genetic structure of invasive species can be strongly affected by environmental and landscape barriers to dispersal. Disentangling the relative contributions of these factors to genetic divergence among invading populations is a fundamental goal of landscape genetics with important implications for invasion management. Here, we relate patterns of genetic divergence in a global invasive agricultural pest, Colorado potato beetle (CPB; Leptinotarsa decemlineata), to environmental and landscape factors along an invasion front in Northwestern China. We first used microsatellite markers and spatial-temporal samples to assess broad patterns of genetic diversity as well as fine-scale changes in patterns of genetic divergence. We then distinguished the relative contributions of five factors to genetic divergence among front populations: geographic distance (isolation by distance), climate dissimilarity (isolation by environment), and least-cost distances (isolation by resistance) modeled with three factors: climate suitability, cropland cover, and road networks. Genetic diversity broadly decreased from West to East, with the exception being Eastern China. Low levels of genetic diversity and varying degrees of divergence were observed in Northwestern China, reflecting the potential effect of landscape heterogeneity. Least-cost distance across cropland cover was most positively correlated with genetic divergence, suggesting a role of croplands in facilitating gene flow. The contribution of climate to genetic divergence was secondary, whether modeled in terms of local adaptability or connectivity of the climatic landscape, suggesting that constraints to CPB gene flow imposed by a harsh climate may be ameliorated in agricultural landscapes. No evidence was found for an obvious effect of road networks on genetic divergence and population structuring. Our study provides an example of how agricultural landscape connectivity can facilitate the spread of invasive pests, even across a broad climatic gradient. More broadly, our findings can guide decisions about future land management for mitigating further spread.

Conservation Genomics of the Threatened Western Spadefoot, Spea hammondii, in Urbanized Southern California

Populations of the western spadefoot (Spea hammondii) in southern California occur in one of the most urbanized and fragmented landscapes on the planet and have lost up to 80% of their native habitat. Orange County is one of the last strongholds for this pond-breeding amphibian in the region, and ongoing restoration efforts targeting S. hammondii have involved habitat protection and the construction of artificial breeding ponds. These efforts have successfully increased breeding activity, but genetic characterization of the populations, including estimates of effective population size and admixture between the gene pools of constructed artificial and natural ponds, has never been undertaken. Using thousands of genome-wide single-nucleotide polymorphisms, we characterized the population structure, genetic diversity, and genetic connectivity of spadefoots in Orange County to guide ongoing and future management efforts. We identified at least 2, and possibly 3 major genetic clusters, with additional substructure within clusters indicating that individual ponds are often genetically distinct. Estimates of landscape resistance suggest that ponds on either side of the Los Angeles Basin were likely interconnected historically, but intense urban development has rendered them essentially isolated, and the resulting risk of interruption to natural metapopulation dynamics appears to be high. Resistance surfaces show that the existing artificial ponds were well-placed and connected to natural populations by low-resistance corridors. Toad samples from all ponds (natural and artificial) returned extremely low estimates of effective population size, possibly due to a bottleneck caused by a recent multi-year drought. Management efforts should focus on maintaining gene flow among natural and artificial ponds by both assisted migration and construction of new ponds to bolster the existing pond network in the region.

Genetic structure across urban and agricultural landscapes reveals evidence of resource specialization and philopatry in the Eastern carpenter bee, Xylocopa virginica L

Human activity continues to impact global ecosystems, often by altering the habitat suitability, persistence, and movement of native species. It is thus critical to examine the population genetic structure of key ecosystemservice providers across human-altered landscapes to provide insight into the forces that limit wildlife persistence and movement across multiple spatial scales. While some studies have documented declines of bee pollinators as a result of human-mediated habitat alteration, others suggest that some bee species may benefit from altered land use due to increased food or nesting resource availability; however, detailed population and dispersal studies have been lacking. We investigated the population genetic structure of the Eastern carpenter bee, Xylocopa virginica, across 14 sites spanning more than 450 km, including dense urban areas and intensive agricultural habitat. X. virginica is a large bee which constructs nests in natural and human-associated wooden substrates, and is hypothesized to disperse broadly across urbanizing areas. Using 10 microsatellite loci, we detected significant genetic isolation by geographic distance and significant isolation by land use, where urban and cultivated landscapes were most conducive to gene flow. This is one of the first population genetic analyses to provide evidence of enhanced insect dispersal in human-altered areas as compared to semi-natural landscapes. We found moderate levels of regional-scale population structure across the study system (G'ST = 0.146) and substantial co-ancestry between the sampling regions, where co-ancestry patterns align with major human transportation corridors, suggesting that human-mediated movement may be influencing regional dispersal processes. Additionally, we found a signature of strong site-level philopatry where our analyses revealed significant levels of high genetic relatedness at very fine scales (<1 km), surprising given X. virginica's large body size. These results provide unique genetic evidence that insects can simultaneously exhibit substantial regional dispersal as well as high local nesting fidelity in landscapes dominated by human activity.

Dispersal ability predicts spatial genetic structure in native mammals persisting across an urbanization gradient

As the rate of urbanization continues to increase globally, a growing body of research is emerging that investigates how urbanization shapes the movement-and consequent gene flow-of species in cities. Of particular interest are native species that persist in cities, either as small relict populations or as larger populations of synanthropic species that thrive alongside humans in new urban environments. In this study, we used genomic sequence data (SNPs) and spatially explicit individual-based analyses to directly compare the genetic structure and patterns of gene flow in two small mammals with different dispersal abilities that occupy the same urbanized landscape to evaluate how mobility impacts genetic connectivity. We collected 215 white-footed mice (Peromyscus leucopus) and 380 big brown bats (Eptesicus fuscus) across an urban-to-rural gradient within the Providence, Rhode Island (U.S.A.) metropolitan area (population =1,600,000 people). We found that mice and bats exhibit clear differences in their spatial genetic structure that are consistent with their dispersal abilities, with urbanization having a stronger effect on Peromyscus mice. There were sharp breaks in the genetic structure of mice within the Providence urban core, as well as reduced rates of migration and an increase in inbreeding with more urbanization. In contrast, bats showed very weak genetic structuring across the entire study area, suggesting a near-panmictic gene pool likely due to the ability to disperse by flight. Genetic diversity remained stable for both species across the study region. Mice also exhibited a stronger reduction in gene flow between island and mainland populations than bats. This study represents one of the first to directly compare multiple species within the same urban-to-rural landscape gradient, an important gap to fill for urban ecology and evolution. Moreover, here we document the impacts of dispersal capacity on connectivity for native species that have persisted as the urban landscape matrix expands.

Spatial genetic structure and landscape connectivity in black bears: Investigating the significance of using different land cover datasets and classifications in landscape genetics analyses

Landscape genetic analyses allow detection of fine-scale spatial genetic structure (SGS) and quantification of effects of landscape features on gene flow and connectivity. Typically, analyses require generation of resistance surfaces. These surfaces characteristically take the form of a grid with cells that are coded to represent the degree to which landscape or environmental features promote or inhibit animal movement. How accurately resistance surfaces predict association between the landscape and movement is determined in large part by (a) the landscape features used, (b) the resistance values assigned to features, and (c) how accurately resistance surfaces represent landscape permeability. Our objective was to evaluate the performance of resistance surfaces generated using two publicly available land cover datasets that varied in how accurately they represent the actual landscape. We genotyped 365 individuals from a large black bear population (Ursus americanus) in the Northern Lower Peninsula (NLP) of Michigan, USA at 12 microsatellite loci, and evaluated the relationship between gene flow and landscape features using two different land cover datasets. We investigated the relative importance of land cover classification and accuracy on landscape resistance model performance. We detected local spatial genetic structure in Michigan's NLP black bears and found roads and land cover were significantly correlated with genetic distance. We observed similarities in model performance when different land cover datasets were used despite 21% dissimilarity in classification between the two land cover datasets. However, we did find the performance of land cover models to predict genetic distance was dependent on the way the land cover was defined. Models in which land cover was finely defined (i.e., eight land cover classes) outperformed models where land cover was defined more coarsely (i.e., habitat/non-habitat or forest/non-forest). Our results show that landscape genetic researchers should carefully consider how land cover classification changes inference in landscape genetic studies.