Partitioning drivers of spatial genetic variation for a continuously distributed population of boreal caribou: Implications for management unit delineation

Isolation by distance (IBD) is a natural pattern not readily incorporated into theoretical models nor traditional metrics for differentiating populations, although clinal genetic differentiation can be characteristic of many wildlife species. Landscape features can also drive population structure additive to baseline IBD resulting in differentiation through isolation‐by‐resistance (IBR). We assessed the population genetic structure of boreal caribou across western Canada using nonspatial (STRUCTURE) and spatial (MEMGENE) clustering methods and investigated the relative contribution of IBD and IBR on genetic variation of 1,221 boreal caribou multilocus genotypes across western Canada. We further introduced a novel approach to compare the partitioning of individuals into management units (MU) and assessed levels of genetic connectivity under different MU scenarios. STRUCTURE delineated five genetic clusters while MEMGENE identified finer‐scale differentiation across the study area. IBD was significant and did not differ for males and females both across and among detected genetic clusters. MEMGENE landscape analysis further quantified the proportion of genetic variation contributed by IBD and IBR patterns, allowing for the relative importance of spatial drivers, including roads, water bodies, and wildfires, to be assessed and incorporated into the characterization of population structure for the delineation of MUs. Local population units, as currently delineated in the boreal caribou recovery strategy, do not capture the genetic variation and connectivity of the ecotype across the study area. Here, we provide the tools to assess fine‐scale spatial patterns of genetic variation, partition drivers of genetic variation, and evaluate the best management options for maintaining genetic connectivity. Our approach is highly relevant to vagile wildlife species that are of management and conservation concern and demonstrate varying degrees of IBD and IBR with clinal spatial genetic structure that challenges the delineation of discrete population boundaries.     

Landscape genetic analyses reveal fine‐scale effects of forest fragmentation in an insular tropical bird

Within the framework of landscape genetics, resistance surface modelling is particularly relevant to explicitly test competing hypotheses about landscape effects on gene flow. To investigate how fragmentation of tropical forest affects population connectivity in a forest specialist bird species, we optimized resistance surfaces without a priori specification, using least‐cost (LCP) or resistance (IBR) distances. We implemented a two‐step procedure in order (i) to objectively define the landscape thematic resolution (level of detail in classification scheme to describe landscape variables) and spatial extent (area within the landscape boundaries) and then (ii) to test the relative role of several landscape features (elevation, roads, land cover) in genetic differentiation in the Plumbeous Warbler (Setophaga plumbea). We detected a small‐scale reduction of gene flow mainly driven by land cover, with a negative impact of the nonforest matrix on landscape functional connectivity. However, matrix components did not equally constrain gene flow, as their conductivity increased with increasing structural similarity with forest habitat: urban areas and meadows had the highest resistance values whereas agricultural areas had intermediate resistance values. Our results revealed a higher performance of IBR compared to LCP in explaining gene flow, reflecting suboptimal movements across this human‐modified landscape, challenging the common use of LCP to design habitat corridors and advocating for a broader use of circuit theory modelling. Finally, our results emphasize the need for an objective definition of landscape scales (landscape extent and thematic resolution) and highlight potential pitfalls associated with parameterization of resistance surfaces.     

Complex effects of scale on the relationships of landscape pattern versus avian species richness and community structure in a woodland savanna mosaic

Landscape pattern metrics are widely used for predicting habitat and species diversity. However, the relationship between landscape pattern and species diversity is typically measured at a single spatial scale, even though both landscape pattern, and species occurrence and community composition are scale‐dependent. While the effects of scale on landscape pattern are well documented, the effects of scale on the relationships between spatial pattern and species richness and composition are not well known. Here, our main goal was to quantify the effects of cartographic scale (spatial resolution and extent) on the relationships between spatial pattern and avian richness and community structure in a mosaic of grassland, woodland, and savanna in central Wisconsin. Our secondary goal was to evaluate the effectiveness of a newly developed tool for spatial pattern analysis, multiscale contextual spatial pattern analysis (MCSPA), compared to existing landscape metrics. Landscape metrics and avian species richness had quadratic, exponential, or logarithmic relationships, and these patterns were generally consistent across two spatial resolutions and six spatial extents. However, the magnitude of the relationships was affected by both resolution and extent. At the finer resolution (10‐m), edge density was consistently the best predictor of species richness, followed by an MCSPA metric that measures the standard deviation of woody cover across extents. At the coarser resolution (30‐m), NDVI was the best predictor of species richness by far, regardless of spatial extent. Another MCSPA metric that denotes the average woody cover across extents, together with percent of woody cover, were always the best predictors of variation in avian community structure. Spatial resolution and extent had varying effects on the relationships between spatial pattern and avian community structure. We therefore conclude that cartographic scale not only affects measures of landscape pattern per se, but also the relationships among spatial pattern, species richness, and community structure, often in complex ways, which reduces the efficacy of landscape metrics for predicting the richness and diversity of organisms.     

Isolation by resistance

Despite growing interest in the effects of landscape heterogeneity on genetic structuring, few tools are available to incorporate data on landscape composition into population genetic studies. Analyses of isolation by distance have typically either assumed spatial homogeneity for convenience or applied theoretically unjustified distance metrics to compensate for heterogeneity. Here I propose the isolation-by-resistance (IBR) model as an alternative for predicting equilibrium genetic structuring in complex landscapes. The model predicts a positive relationship between genetic differentiation and the resistance distance, a distance metric that exploits precise relationships between random walk times and effective resistances in electronic networks. As a predictor of genetic differentiation, the resistance distance is both more theoretically justified and more robust to spatial heterogeneity than Euclidean or least cost path-based distance measures. Moreover, the metric can be applied with a wide range of data inputs, including coarse-scale range maps, simple maps of habitat and nonhabitat within a species' range, or complex spatial datasets with habitats and barriers of differing qualities. The IBR model thus provides a flexible and efficient tool to account for habitat heterogeneity in studies of isolation by distance, improve understanding of how landscape characteristics affect genetic structuring, and predict genetic and evolutionary consequences of landscape change.     

Linking genetic and ecological differentiation in an ungulate with a circumpolar distribution

Genetic differentiation among populations may arise from the disruption of gene flow due to local adaptation to distinct environments and/or neutral accumulation of mutations and genetic drift resulted from geographical isolation. Quantifying the role of these processes in determining the genetic structure of natural populations remains challenging. Here, we analyze the relative contribution of isolation‐by‐resistance (IBR), isolation‐by‐environment (IBE), genetic drift and historical isolation in allopatry during Pleistocene glacial cycles on shaping patterns of genetic differentiation in caribou/reindeer populations Rangifer tarandus across the entire distribution range of the species. Our study integrates analyses at range‐wide and regional scales to partial out the effects of historical and contemporary isolation mechanisms. At the circumpolar scale, our results indicate that genetic differentiation is predominantly explained by IBR and historical isolation. At a regional scale, we found that IBR, IBE and population size significantly explained the spatial distribution of genetic variation among populations belonging to the Euro‐Beringian lineage within North America. In contrast, genetic differentiation among populations within the North American lineage was predominantly explained by IBR and population size, but not IBE. We also found discrepancies between genetic and ecotype designation across the Holarctic species distribution range. Overall, these results indicate that multiple isolating mechanisms have played roles in shaping the spatial distribution of genetic variation across the distribution range of a large mammal with high potential for gene flow. Considering multiple spatial scales and simultaneously testing a comprehensive suite of potential isolating mechanisms, our study contributes to understand the ecological and evolutionary processes underlying organism–landscape interactions.     

测量的区域土地覆盖格局研基于多尺度遥感究

 利用1km、4km和8km 3种空间分辨率的NOAA/AVHRR数字影像,对中国NECT样带西部地区进行了土地覆盖分类及其景观特征的比较研究。重点比较了几种空间分辨率遥感数据分类结果边界的一致性和空间差异,以及影像所记录的景观格局的差异。为进一步在不同尺度上研究景观变化过程以及尺度转换研究奠定了基础。研究表明：3种空间分辨率的遥感影像所反映的区域土地覆盖的宏观空间格局是一致的,但类型的边界、每一类型斑块的形状和数量均产生较大的差异;经过对反映景观空间结构的4种指标（分维数、破碎度、多样性、优势度）的比较显示出随着遥感影像空间分辨率的变化,影像所反映的景观结构发生了较大的变化。其中,各覆盖类型的分维数表现出最大差异,表征着空间分辨率的变化对斑块复杂程度的影响最大。     

测量的区域土地覆盖格局研基于多尺度遥感究

利用1km、4km和8km 3种空间分辨率的NOAA/AVHRR数字影像,对中国NECT样带西部地区进行了土地覆盖分类及其景观特征的比较研究。重点比较了几种空间分辨率遥感数据分类结果边界的一致性和空间差异,以及影像所记录的景观格局的差异。为进一步在不同尺度上研究景观变化过程以及尺度转换研究奠定了基础。研究表明：3种空间分辨率的遥感影像所反映的区域土地覆盖的宏观空间格局是一致的,但类型的边界、每一类型斑块的形状和数量均产生较大的差异;经过对反映景观空间结构的4种指标（分维数、破碎度、多样性、优势度）的比较显示出随着遥感影像空间分辨率的变化,影像所反映的景观结构发生了较大的变化。其中,各覆盖类型的分维数表现出最大差异,表征着空间分辨率的变化对斑块复杂程度的影响最大。     

Performance of partial statistics in individual‐based landscape genetics

Individual‐based landscape genetic methods have become increasingly popular for quantifying fine‐scale landscape influences on gene flow. One complication for individual‐based methods is that gene flow and landscape variables are often correlated with geography. Partial statistics, particularly Mantel tests, are often employed to control for these inherent correlations by removing the effects of geography while simultaneously correlating measures of genetic differentiation and landscape variables of interest. Concerns about the reliability of Mantel tests prompted this study, in which we use simulated landscapes to evaluate the performance of partial Mantel tests and two ordination methods, distance‐based redundancy analysis (dbRDA) and redundancy analysis (RDA), for detecting isolation by distance (IBD) and isolation by landscape resistance (IBR). Specifically, we described the effects of suitable habitat amount, fragmentation and resistance strength on metrics of accuracy (frequency of correct results, type I/II errors and strength of IBR according to underlying landscape and resistance strength) for each test using realistic individual‐based gene flow simulations. Mantel tests were very effective for detecting IBD, but exhibited higher error rates when detecting IBR. Ordination methods were overall more accurate in detecting IBR, but had high type I errors compared to partial Mantel tests. Thus, no one test outperformed another completely. A combination of statistical tests, for example partial Mantel tests to detect IBD paired with appropriate ordination techniques for IBR detection, provides the best characterization of fine‐scale landscape genetic structure. Realistic simulations of empirical data sets will further increase power to distinguish among putative mechanisms of differentiation.     

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.     

Landscape Features Fail to Explain Spatial Genetic Structure in White-Tailed Deer Across Ohio,USA

Landscape features influence wildlife movements across spatial scales and have the potential to influence the spread of disease. Chronic wasting disease (CWD) is a fatal prion disease affecting members of the family Cervidae, particularly white-tailed deer (Odocoileus virginianus), and the first positive CWD case in a wild deer in Ohio, USA, was recorded in 2020. Landscape genetics approaches are increasingly used to better understand potential pathways for CWD spread in white-tailed deer, but little is known about genetic structure of white-tailed deer in Ohio. The objectives of our study were to evaluate spatial genetic structure in white-tailed deer across Ohio and compare the support for isolation by distance (IBD) and isolation by landscape resistance (IBR) models in explaining this structure. We collected genetic data from 619 individual deer from 24 counties across Ohio during 2007–2009. We used microsatellite genotypes from 619 individuals genotyped at 11 loci and haplotypes from a 547-base pair fragment of the mitochondrial DNA control region. We used spatial and non-spatial genetic clustering tests to evaluate genetic structure in both types of genetic data and empirically optimized landscape resistance surfaces to compare IBD and IBR using microsatellite data. Non-spatial genetic clustering tests failed to detect spatial genetic structure, whereas spatial genetic clustering tests indicated subtle spatial genetic structure. The IBD model consistently outperformed IBR models that included land cover, traffic volume, and streams. Our results indicated widespread genetic connectivity of white-tailed deer across Ohio and negligible effects of landscape features. These patterns likely reflect some combination of minimal resistive effects of landscape features on white-tail deer movement in Ohio and the effects of regional recolonization or translocation. We encourage continued CWD surveillance in Ohio, particularly in the proximity of confirmed cases. © 2021 The Wildlife Society. This article has been contributed to by US Government employees and their work is in the public domain in the USA.     

Drivers of genetic differentiation in a generalist insect‐pollinated herb across spatial scales

The isolation‐by‐distance model (IBD) predicts that genetic differentiation among populations increases with geographic distance. Yet, empirical studies show that a variety of ecological, topographic and historical factors may override the effect of geographic distance on genetic variation. This may particularly apply to species with narrow but highly heterogeneous distribution ranges, such as those occurring along elevational gradients. Using nine SSR markers, we study the genetic differentiation of the montane pollination‐generalist herb, Erysimum mediohispanicum. Because the effects of any given factor may depend on the geographic scale considered, we investigate the contribution of different environmental and historical factors at three different spatial scales. We evaluate five competing models that put forward the role of geographic distance, local environmental factors [biotic interactions (IBEb) and climatic variables (IBEa)], landscape resistance (IBR) and phylogeographic patterns (IBP), respectively. We find significant IBD regardless of the spatial scale and the genetic distance estimator considered. However, IBEa and IBP also play a prominent role in shaping genetic differentiation patterns at the larger spatial scales, and IBR is significant at the fine spatial scale. Overall, our results highlight the importance of combining different estimators, statistical approaches and spatial scales to disentangle the relative importance of the various ecological factors contributing to the shaping of genetic divergence patterns in natural populations.     

High resolution land cover data improve understanding of mechanistic linkages with stream integrity

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Urban landscape genetics: canopy cover predicts gene flow between white-footed mouse (Peromyscus leucopus) populations in New York City

In this study, I examine the influence of urban canopy cover on gene flow between 15 white-footed mouse (Peromyscus leucopus) populations in New York City parklands. Parks in the urban core are often highly fragmented, leading to rapid genetic differentiation of relatively nonvagile species. However, a diverse array of 'green' spaces may provide dispersal corridors through 'grey' urban infrastructure. I identify urban landscape features that promote genetic connectivity in an urban environment and compare the success of two different landscape connectivity approaches at explaining gene flow. Gene flow was associated with 'effective distances' between populations that were calculated based on per cent tree canopy cover using two different approaches: (i) isolation by effective distance (IED) that calculates the single best pathway to minimize passage through high-resistance (i.e. low canopy cover) areas, and (ii) isolation by resistance (IBR), an implementation of circuit theory that identifies all low-resistance paths through the landscape. IBR, but not IED, models were significantly associated with three measures of gene flow (Nm from F(ST) , BayesAss+ and Migrate-n) after factoring out the influence of isolation by distance using partial Mantel tests. Predicted corridors for gene flow between city parks were largely narrow, linear parklands or vegetated spaces that are not managed for wildlife, such as cemeteries and roadway medians. These results have implications for understanding the impacts of urbanization trends on native wildlife, as well as for urban reforestation efforts that aim to improve urban ecosystem processes.     

Landscape drivers of regional variation in the relationship between total phosphorus and chlorophyll in lakes

1. For north temperate lakes, the well‐studied empirical relationship between phosphorus (as measured by total phosphorus, TP), the most commonly limiting nutrient and algal biomass (as measured by chlorophyll a, CHL) has been found to vary across a wide range of landscape settings. Variation in the parameters of these TP–CHL regressions has been attributed to such lake variables as nitrogen/phosphorus ratios, organic carbon and alkalinity, all of which are strongly related to catchment characteristics (e.g. natural land cover and human land use). Although this suggests that landscape setting can help to explain much of the variation in ecoregional TP–CHL regression parameters, few studies have attempted to quantify relationships at an ecoregional spatial scale. 2. We tested the hypothesis that lake algal biomass and its predicted response to changes in phosphorus are related to both local‐scale features (e.g. lake and catchment) and ecoregional‐scale features, all of which affect the availability and transport of covarying solutes such as nitrogen, organic carbon and alkalinity. Specifically, we expected that land use and cover, acting at both local and ecoregional scales, would partially explain the spatial pattern in parameters of the TP–CHL regression. 3. We used a multilevel modelling framework and data from 2105 inland lakes spanning 35 ecoregions in six US states to test our hypothesis and identify specific local and ecoregional features that explain spatial heterogeneity in TP–CHL relationships. We include variables such as lake depth, natural land cover (for instance, wetland cover in the catchment of lakes and in the ecoregions) and human land use (for instance, agricultural land use in the catchment of lakes and in the ecoregions). 4. There was substantial heterogeneity in TP–CHL relationships across the 35 ecoregions. At the local scale, CHL was negatively and positively related to lake mean depth and percentage of wooded wetlands in the catchment, respectively. At the ecoregional scale, the slope parameter was positively related to the percentage of pasture in an ecoregion, indicating that CHL tends to respond more rapidly to changes in TP where there are high levels of agricultural pasture than where there is little. The intercept (i.e. the ecoregion‐average CHL) was negatively related to the percentage of wooded wetlands in the ecoregion. 5. By explicitly accounting for the hierarchical nature of lake–landscape interactions, we quantified the effects of landscape characteristics on the response of CHL to TP at two spatial scales. We provide new insight into ecoregional drivers of the rate at which algal biomass responds to changes in nutrient concentrations. Our results also indicate that the direction and magnitude of the effects of certain land use and cover characteristics on lake nutrient dynamics may be scale dependent and thus likely to represent different underlying mechanisms regulating lake productivity.     

Comparative landscape genetics reveals differential effects of environment on host and pathogen genetic structure in Tasmanian devils (Sarcophilus harrisii) and their transmissible tumour

Genetic structure in host species is often used to predict disease spread. However, host and pathogen genetic variation may be incongruent. Understanding landscape factors that have either concordant or divergent influence on host and pathogen genetic structure is crucial for wildlife disease management. Devil facial tumour disease (DFTD) was first observed in 1996 and has spread throughout almost the entire Tasmanian devil geographic range, causing dramatic population declines. Whereas DFTD is predominantly spread via biting among adults, devils typically disperse as juveniles, which experience low DFTD prevalence. Thus, we predicted little association between devil and tumour population structure and that environmental factors influencing gene flow differ between devils and tumours. We employed a comparative landscape genetics framework to test the influence of environmental factors on patterns of isolation by resistance (IBR) and isolation by environment (IBE) in devils and DFTD. Although we found evidence for broad‐scale costructuring between devils and tumours, we found no relationship between host and tumour individual genetic distances. Further, the factors driving the spatial distribution of genetic variation differed for each. Devils exhibited a strong IBR pattern driven by major roads, with no evidence of IBE. By contrast, tumours showed little evidence for IBR and a weak IBE pattern with respect to elevation in one of two tumour clusters we identify herein. Our results warrant caution when inferring pathogen spread using host population genetic structure and suggest that reliance on environmental barriers to host connectivity may be ineffective for managing the spread of wildlife diseases. Our findings demonstrate the utility of comparative landscape genetics for identifying differential factors driving host dispersal and pathogen transmission.     

Effect of landscape features on genetic structure of the goitered gazelle (<Emphasis Type="Italic">Gazella subgutturosa</Emphasis>) in Central Iran

The populations of goitered gazelle suffered significant decline due to natural and anthropogenic factors over the last century. Investigating the effects of barriers on gene flow among the remaining populations is vital for conservation planning. Here we adopted a landscape genetics approach to evaluate the genetic structure of the goitered gazelle in Central Iran and the effects of landscape features on gene flow using 15 polymorphic microsatellite loci. Spatial autocorrelation, isolation by distance (IBD) and isolation by resistance (IBR) models were used to elucidate the effects of landscape features on the genetic structure. Ecological modeling was used to construct landscape permeability and resistance map using 12 ecogeographical variables. Bayesian algorithms revealed three genetically homogeneous groups and restricted dispersal pattern in the six populations. The IBD and spatial autocorrelation revealed a pattern of decreasing relatedness with increasing distance. The distribution of potential habitats was strongly correlated with bioclimatic factors, vegetation type, and elevation. Resistance distances and graph theory were significantly related with variation in genetic structure, suggesting that gazelles are affected by landscape composition. The IBD showed greater impact on genetic structure than IBR. The Mantel and partial Mantel tests indicated low but non-significant effects of anthropogenic barriers on observed genetic structure. We concluded that a combination of geographic distance, landscape resistance, and anthropogenic factors are affecting the genetic structure and gene flow of populations. Future road construction might impede connectivity and gene exchange of populations. Conservation measures on this vulnerable species should consider some isolated population as separate management units.     

Isolation by distance,resistance and/or clusters? Lessons learned from a forest‐dwelling carnivore inhabiting a heterogeneous landscape

Landscape genetics provides a valuable framework to understand how landscape features influence gene flow and to disentangle the factors that lead to discrete and/or clinal population structure. Here, we attempt to differentiate between these processes in a forest‐dwelling small carnivore [European pine marten (Martes martes)]. Specifically, we used complementary analytical approaches to quantify the spatially explicit genetic structure and diversity and analyse patterns of gene flow for 140 individuals genotyped at 15 microsatellite loci. We first used spatially explicit and nonspatial Bayesian clustering algorithms to partition the sample into discrete clusters and evaluate hypotheses of ‘isolation by barriers’ (IBB). We further characterized the relationships between genetic distance and geographical (‘isolation by distance’, IBD) and ecological distances (‘isolation by resistance’, IBR) obtained from optimized landscape models. Using a reciprocal causal modelling approach, we competed the IBD, IBR and IBB hypotheses with each other to unravel factors driving population genetic structure. Additionally, we further assessed spatially explicit indices of genetic diversity using sGD across potentially overlapping genetic neighbourhoods that matched the inferred population structure. Our results revealed a complex spatial genetic cline that appears to be driven jointly by IBD and partial barriers to gene flow (IBB) associated with poor habitat and interspecific competition. Habitat loss and fragmentation, in synergy with past overharvesting and possible interspecific competition with sympatric stone marten (Martes foina), are likely the main factors responsible for the spatial genetic structure we observed. These results emphasize the need for a more thorough evaluation of discrete and clinal hypotheses governing gene flow in landscape genetic studies, and the potential influence of different limiting factors affecting genetic structure at different spatial scales.     

Relating mesocarnivore relative abundance to anthropogenic land‐use with a hierarchical spatial count model

There is growing need to develop models of spatial patterns in animal abundance, yet comparatively few examples of such models exist. This is especially true in situations where the abundance of one species may inhibit that of another, such as the intensively‐farmed landscape of the Prairie Pothole Region (PPR) of the central United States, where waterfowl production is largely constrained by mesocarnivore nest predation. We used a hierarchical Bayesian approach to relate the distribution of various land‐cover types to the relative abundances of four mesocarnivores in the PPR: coyote Canis latrans, raccoon Procyon lotor, red fox Vulpes vulpes, and striped skunk Mephitis mephitis. We developed models for each species at multiple spatial resolutions (41.4 km², 10.4 km², and 2.6 km²) to address different ecological and management‐related questions. Model results for each species were similar irrespective of resolution. We found that the amount of row‐crop agriculture was nearly ubiquitous in our best models, exhibiting a positive relationship with relative abundance for each species. The amount of native grassland land‐cover was positively associated with coyote and raccoon relative abundance, but generally absent from models for red fox and skunk. Red fox and skunk were positively associated with each other, suggesting potential niche overlap. We found no evidence that coyote abundance limited that of other mesocarnivore species, as might be expected under a hypothesis of mesopredator release. The relationships between relative abundance and land‐cover types were similar across spatial resolutions. Our results indicated that mesocarnivores in the PPR are most likely to occur in portions of the landscape with large amounts of agricultural land‐cover. Further, our results indicated that track‐survey data can be used in a hierarchical framework to gain inferences regarding spatial patterns in animal relative abundance.     

Comparative landscape genetics of pond‐breeding amphibians in Mediterranean temporal wetlands: The positive role of structural heterogeneity in promoting gene flow

Comparative landscape genetics studies can provide key information to implement cost‐effective conservation measures favouring a broad set of taxa. These studies are scarce, particularly in Mediterranean areas, which include diverse but threatened biological communities. Here, we focus on Mediterranean wetlands in central Iberia and perform a multi‐level, comparative study of two endemic pond‐breeding amphibians, a salamander (Pleurodeles waltl) and a toad (Pelobates cultripes). We genotyped 411 salamanders from 20 populations and 306 toads from 16 populations at 18 and 16 microsatellite loci, respectively, and identified major factors associated with population connectivity through the analysis of three sets of variables potentially affecting gene flow at increasingly finer levels of spatial resolution. Topographic, land use/cover, and remotely sensed vegetation/moisture indices were used to derive optimized resistance surfaces for the two species. We found contrasting patterns of genetic structure, with stronger, finer scale genetic differentiation in Pleurodeles waltl, and notable differences in the role of fine‐scale patterns of heterogeneity in vegetation cover and water content in shaping patterns of regional genetic structure in the two species. Overall, our results suggest a positive role of structural heterogeneity in population connectivity in pond‐breeding amphibians, with habitat patches of Mediterranean scrubland and open oak woodlands (“dehesas”) facilitating gene flow. Our study highlights the usefulness of remotely sensed continuous variables of land cover, vegetation and water content (e.g., NDVI, NDMI) in conservation‐oriented studies aimed at identifying major drivers of population connectivity.     

Using simulations to evaluate Mantel‐based methods for assessing landscape resistance to gene flow

Mantel‐based tests have been the primary analytical methods for understanding how landscape features influence observed spatial genetic structure. Simulation studies examining Mantel‐based approaches have highlighted major challenges associated with the use of such tests and fueled debate on when the Mantel test is appropriate for landscape genetics studies. We aim to provide some clarity in this debate using spatially explicit, individual‐based, genetic simulations to examine the effects of the following on the performance of Mantel‐based methods: (1) landscape configuration, (2) spatial genetic nonequilibrium, (3) nonlinear relationships between genetic and cost distances, and (4) correlation among cost distances derived from competing resistance models. Under most conditions, Mantel‐based methods performed poorly. Causal modeling identified the true model only 22% of the time. Using relative support and simple Mantel r values boosted performance to approximately 50%. Across all methods, performance increased when landscapes were more fragmented, spatial genetic equilibrium was reached, and the relationship between cost distance and genetic distance was linearized. Performance depended on cost distance correlations among resistance models rather than cell‐wise resistance correlations. Given these results, we suggest that the use of Mantel tests with linearized relationships is appropriate for discriminating among resistance models that have cost distance correlations <0.85 with each other for causal modeling, or <0.95 for relative support or simple Mantel r. Because most alternative parameterizations of resistance for the same landscape variable will result in highly correlated cost distances, the use of Mantel test‐based methods to fine‐tune resistance values will often not be effective.