Landscape Resistance Models Research Articles

Landscape structure does not hinder the dispersal of an invasive herbivorous mammal in the New Caledonian biodiversity hotspot

Biological invasions are a major threat to biodiversity and have particularly devastating impacts on island ecosystems. The New Caledonia archipelago is considered a biodiversity hotspot due to its diverse native flora. Javan rusa deer (Rusa timorensis) were introduced to New Caledonia in 1870 and the population consists of several hundred thousand individuals today. They directly threaten rare endemic species and affect the composition and structure of the vegetation. While a rusa deer management plan has identified ten priority areas for deer control operations, removing deer could be offset by the dispersal of animals back into the control areas. Here, we genotyped 628 rusa deer using 16 microsatellite markers to analyse the genetic structure of the animals in New Caledonia. We aimed to assess fine-scale genetic structure, to identify natural barriers to deer movement and to assess functional connectivity by optimising individual-based landscape resistance models. Our results suggested that rusa deer formed a single genetic population on the main New Caledonian island. The isolation-by-distance pattern suggested that female dispersal was limited, whereas males had larger dispersal distances. We assessed functional connectivity using different genetic distance metrics and all models performed poorly (mR2 ≤ 0.0043). Landscape features thus hardly affected deer movement. The characteristics of our results suggested that they were not an artefact of the colonisation history of the species. Achieving an effective reduction of deer population sizes in specific management areas will be difficult because of the deer’s high dispersal capabilities and impossible without very substantial financial investment.

Demographic collapse threatens the long-term persistence of Andean condors in the northern Andes

Determining the conservation status of long-lived and highly mobile species is particularly challenging due to their long generational time and the spatio-temporal scale at which they interact with their landscape. Vultures are not only extremely vagile and long-lived but are also some of the most threatened species globally. However, the genetic status and connectivity patterns of most vulture species of the southern hemisphere remain poorly understood. Herein, we studied the patterns of neutral genetic variability in both the captive and remnant wild populations of Andean condors (Vultur gryphus) from the equatorial Andes and assessed their movement dynamic to infer current connectivity, as well as identify conservation corridors in one of the most critical areas for the conservation of the species. We found that the demographic collapse of condors in the region has severely affected their effective population size, which might lead to an extinction vortex in the near future. However, our results indicated that the captive population still harbors substantial genetic variation that could be harnessed to strengthen wild populations. Although our landscape resistance models revealed that the northern Andean corridor provides a continuum of suitable habitat for condors, our connectivity assessment identified important spatial disruptions, likely driven by anthropogenic processes. We discuss the implications of our findings to the conservation plan of Andean condors, while highlighting the importance of integrating multiple data sources to identify extinction risks in other species exhibiting high dispersal capabilities and long generational times.

Landscape genetics outperforms habitat suitability in predicting landscape resistance for congeneric cat species

AbstractAimThe use of landscape resistance maps to model connectivity has become an indispensable tool for species conservation. However, different methods can be used to estimate landscape resistance, but there is no consensus on which is the most reliable one. Therefore, comparing the performance of those methods in predicting resistance can be quite useful to understand their limitations and conservation implications. Our goal was to evaluate the accuracy of two commonly used approaches, habitat suitability modelling and landscape genetics, in estimating landscape resistance to genetic connectivity of two species of Neotropical cats (Leopardus guttulus and L. geoffroyi) across their ranges.LocationSouth America.TaxonFelidae—L. guttulus and L. geoffroyi.MethodsFor both species, we optimized a landscape genetics resistance surface using a restricted multivariate optimization approach and transformed a habitat suitability map into a resistance layer. We compared landscape resistance models created by these two approaches based on the models' Akaike information criterion scores and evaluated the similarities and differences in their predictions by calculating the correlation between the resistance layers and generating difference maps.ResultsThe genetic approach greatly outperformed the habitat suitability approach in explaining movement driving gene flow for both species. For the studied species, habitat preference and genetic connectivity are influenced by different landscape features. Habitat alteration imposes great resistance for genetic connectivity, and the presence of natural vegetation remnants within altered environments is essential for their conservation.Main conclusionsFor the studied species, the transformation of habitat suitability models into resistance surfaces is a poor proxy for permeability to dispersal, and the use of genetic data is more reliable in modelling connectivity for species conservation. Habitat suitability and landscape resistance are not equivalent or even proportional for these species.

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

ABSTRACTLandscape 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.

Landscape Genetics of American Beaver in Coastal Oregon

ABSTRACTAmerican beaver (Castor canadensis) have been translocated for population restoration, reduction of human‐wildlife conflict, and enhancement of ecosystem function. Yet few studies have assessed dispersal of beaver, making it difficult to determine at what scale translocations are appropriate. Genetic studies can provide inferences about gene flow, and thus dispersal. We used a landscape genetic approach to evaluate whether landscape features influenced gene flow among beaver in the Coast Range of western Oregon, USA, using samples collected April–September 2014. We collected genetic samples from live‐captured (n = 232), road‐killed (n = 2) and trapper‐provided (n = 58) tissue samples and genotyped them at 10 microsatellite loci. We mapped records of beaver translocations into or within the study area during the twentieth century to consider the effect of those movements on genetic structure. We used population assignment tests to delimit genetic clusters, evaluated correspondence of those clusters with watershed boundaries and translocation history, and then estimated differentiation between clusters and between watersheds using model‐based and model‐free approaches. We evaluated how individual genetic differences varied with geographic distance, and investigated related pairs within clusters. We developed landscape resistance models incorporating slope, distance to water, and watershed boundaries at 2 scales, and estimated effective distances between sample locations with least cost path and circuit theoretic analyses. We evaluated the correlation of individual genetic distances with effective distances using a pseudo‐bootstrapping approach. Landscape genetic models did not explain spatial variation in genetic structure better than geographic distance, but hierarchical genetic structure corresponded with watershed boundaries and suggested influences from historical translocations. Pairwise individual genetic distances were positively correlated with geographic distances to 61 km; highly‐related pairs mostly were detected <1 km apart (median = 1.0 km, = 14.6 ± 2.3 [SE] km, n = 77). We concluded that slope and distance to water did not strongly limit dispersal and gene flow by beaver in this system, but concordance of genetic structure with watershed boundaries suggests that dispersal is more common within than between watersheds. Genetic differentiation of beaver within this topographically complex system was much greater than reported in a study at similar spatial scales in relatively flat topography. We recommend that translocation efforts of American beaver in topographically complex landscapes occur within watersheds when possible but conclude that dispersal can occur across watersheds. © 2021 The Wildlife Society. This article is a U.S. Government work and is in the public domain in the USA.

Assessment of playa wetland network connectivity for amphibians of the south-central Great Plains (USA) using graph-theoretical, least-cost path, and landscape resistance modelling

Land-cover changes are likely affecting structural and functional connectivity for wetland-associated wildlife among the > 80,000 playa of the Great Plains (USA). We investigated how patterns of land cover from 2008 to 2016 affected structural and functional connectivity among playas under biologically feasible scenarios and a range of vagilities for amphibians. We compared graph-theoretical, least-cost path (LCP), and landscape resistance models. Using land-cover and irrigation data and literature-based estimates of species-specific dispersal, we developed three alternative scenarios with resistance values that differed by land-cover type. Sensitivity analysis examined effects of altering the numeric range of values within models. From a structural perspective, the playa network was highly fragmented, with coalescence distances greater than amphibian dispersal capabilities (> 15 km). The network was insensitive to assigning different resistance values to different land-cover types: 81% of LCP locations overlapped with at least one other modeled scenario, and 57% of LCPs overlapped among all three scenarios. Our findings partially support previous graph-based assessments of playa structural connectivity that indicated high levels of path redundancy within this ecological network. However, although there was extensive path overlap among scenarios, many modelled linkages were at distances well beyond the individual lifetime dispersal maxima for regional amphibians. Thus, functional connectivity would be achieved in this landscape only by spanning scales from daily movements to inter-generational dispersal.

Modelling dispersal in a large parrot: a comparison of landscape resistance models with population genetics and vocal dialect patterns

Identifying the range, core areas and dispersal pathways or barriers in heterogeneous landscapes is important for managing threatened species. Studies of variation in learned vocalisations are a promising complementary tool to traditional landscape genetics studies for identifying potential dispersal barriers. Here we use multiple data sources to inform the conservation of a parrot species. We tested for correlations between landscape resistance models, population genetic structure and vocal variation of parrots to investigate the effects of natural barriers on genetic and behavioural population structure including narrow habitat corridors and a mountain range. We studied palm cockatoos (Probosciger aterrimus) within their Australian distribution. We constructed landscape resistance surfaces restricted to areas of high climatic suitability from a maximum entropy (MAXENT) distribution model. We verified three landscape resistance predictions from CIRCUITSCAPE (isolation by elevation, habitat and distance) using four data sets (individual genetic divergence, acoustic divergence in repertoire and two call types). Landscape resistance models revealed strong effects of isolation by elevation on genetic, repertoire and structural call differentiation. Neither isolation by habitat nor isolation by distance were well supported by differentiation in the data. Our landscape resistance analysis validated by four datasets supports the Great Dividing Range as the main limitation on dispersal and connectivity among palm cockatoo populations. Combined genetic and behavioural approaches can determine landscape-level connectivity of individuals and demonstrate how dispersal barriers influence genetic and behavioural patterns in a large parrot.

Assessing the complex relationship between landscape, gene flow, and range expansion of a Mediterranean carnivore

Landscape resistance is often disregarded in studies of range expansions and population connectivity. To assess those effects, we simulated the expansion of the Egyptian mongoose (Herpestes ichneumon) in relation to landscape resistance through kernel resistance modeling, confronting it with previously published data regarding the observed pattern of expansion and genetic diversity of the population in Portugal. We modeled population expansion as a function of shrub cover and elevation through iterative simulation of a resistance model and a null model. We then performed an overlap analysis to assess the congruence between the observed pattern of expansion and both resistance and null models across 30 years. We also tested whether there is an effect of allelic surfing or the central-marginal hypothesis by correlating observed allelic richness (1) with the number of simulated years that each location with sampled genotypes had been occupied by the mongoose population and (2) with the cumulative resistant kernel density (which is a measure of population centrality). Results indicated a higher similarity between observed range expansion and the simulation using the null model and a marginally significant correlation between observed allelic richness and number of years of the simulated presence of the species in the null model. The pattern of range expansion in this population is most consistent with a neutral model of uniform resistance, and genetic diversity is most correlated with null model as well. This suggests that range expansion and genetic diversity patterns in expanding populations may not always be predicted by landscape resistance models developed through association of observed genetic differentiation with landscape features.

Integrating age structured and landscape resistance models to disentangle invasion dynamics of a pond-breeding anuran

Modelling population dynamics of invasive species may help to propose effective management countermeasures. Invasion dynamics generally show recursive patterns across species and regions, where initial lag is followed by spread and eventual dominance phases. However, timing and modes of these phases are highly variable, emerging from the interplay between traits of the invader and characteristics of the invaded landscape. Disentangling this interplay is particularly arduous in species with complex life-histories, where an individual passes through different life stages that alter physiology, behaviour and interactions with the environment. Here, we describe an age structured model that can be utilized to simulate population dynamics of invasive pond-breeding anurans. The model follows a spatially structured population approach, each pond representing a discrete habitat patch that exchanges individuals with other similar patches, and simulates change in survival and dispersal behaviour as a function of age. It also integrates dispersal with landscape complexity through landscape resistance modelling to depict functional connectivity across the pond network. Then we apply the model to a case study, the invasion of the guttural toad Sclerophrys gutturalis in Cape Town, first detected in 2000. Age-structured demographic and spatial dynamics of the focal population are reconstructed in a network of 415 ponds embedded in a heterogeneous landscape. Parameterization is conducted through field and laboratory surveys, a literature review and data collected during an ongoing extirpation from 2010. We use the model to explore: i) occurrence and duration of lag phase; ii) whether the spatial spread fits an accelerating or a linear trend; iii) how simulated dynamics match field observations. Additionally we test model sensitivity to demographic and behavioural traits. We found a lag phase in both demographic and spatial dynamics; however the lag duration of these dynamics does not coincide, where invaders start to spread across the pond network five years before the demographic explosion. Also, we found that the spatial spread fits an accelerating trend that causes complete invasion of the network in six years. Such dynamics noticeably match field observations and confirmed patterns previously detected in other invaders characterized by high dispersal abilities. Sensitivity analysis suggests that it would have been preferable to quantify initial propagule size and post-metamorphic survival in the field; both timing and modes of invasion are particularly sensitive to these parameters. We conclude that the model has potential to forecast amphibian invasion dynamics and test management countermeasures.

Simulating the spread of selection-driven genotypes using landscape resistance models for desert bighorn sheep.

Landscape genetic studies based on neutral genetic markers have contributed to our understanding of the influence of landscape composition and configuration on gene flow and genetic variation. However, the potential for species to adapt to changing landscapes will depend on how natural selection influences adaptive genetic variation. We demonstrate how landscape resistance models can be combined with genetic simulations incorporating natural selection to explore how the spread of adaptive variation is affected by landscape characteristics, using desert bighorn sheep (Ovis canadensis nelsoni) in three differing regions of the southwestern United States as an example. We conducted genetic sampling and least-cost path modeling to optimize landscape resistance models independently for each region, and then simulated the spread of an adaptive allele favored by selection across each region. Optimized landscape resistance models differed between regions with respect to landscape variables included and their relationships to resistance, but the slope of terrain and the presence of water barriers and major roads had the greatest impacts on gene flow. Genetic simulations showed that differences among landscapes strongly influenced spread of adaptive genetic variation, with faster spread (1) in landscapes with more continuously distributed habitat and (2) when a pre-existing allele (i.e., standing genetic variation) rather than a novel allele (i.e., mutation) served as the source of adaptive genetic variation. The combination of landscape resistance models and genetic simulations has broad conservation applications and can facilitate comparisons of adaptive potential within and between landscapes.

Host–pathogen evolutionary signatures reveal dynamics and future invasions of vampire bat rabies

Anticipating how epidemics will spread across landscapes requires understanding host dispersal events that are notoriously difficult to measure. Here, we contrast host and virus genetic signatures to resolve the spatiotemporal dynamics underlying geographic expansions of vampire bat rabies virus (VBRV) in Peru. Phylogenetic analysis revealed recent viral spread between populations that, according to extreme geographic structure in maternally inherited host mitochondrial DNA, appeared completely isolated. In contrast, greater population connectivity in biparentally inherited nuclear microsatellites explained the historical limits of invasions, suggesting that dispersing male bats spread VBRV between genetically isolated female populations. Host nuclear DNA further indicated unanticipated gene flow through the Andes mountains connecting the VBRV-free Pacific coast to the VBRV-endemic Amazon rainforest. By combining Bayesian phylogeography with landscape resistance models, we projected invasion routes through northern Peru that were validated by real-time livestock rabies mortality data. The first outbreaks of VBRV on the Pacific coast of South America could occur by June 2020, which would have serious implications for agriculture, wildlife conservation, and human health. Our results show that combining host and pathogen genetic data can identify sex biases in pathogen spatial spread, which may be a widespread but underappreciated phenomenon, and demonstrate that genetic forecasting can aid preparedness for impending viral invasions.

Replicated landscape genetic and network analyses reveal wide variation in functional connectivity for American pikas.

Landscape connectivity is essential for maintaining viable populations, particularly for species restricted to fragmented habitats or naturally arrayed in metapopulations and facing rapid climate change. The importance of assessing both structural connectivity (physical distribution of favorable habitat patches) and functional connectivity (how species move among habitat patches) for managing such species is well understood. However, the degree to which functional connectivity for a species varies among landscapes, and the resulting implications for conservation, have rarely been assessed. We used a landscape genetics approach to evaluate resistance to gene flow and, thus, to determine how landscape and climate-related variables influence gene flow for American pikas (Ochotona princeps) in eight federally managed sites in the western United States. We used empirically derived, individual-based landscape resistance models in conjunction with predictive occupancy models to generate patch-based network models describing functional landscape connectivity. Metareplication across landscapes enabled identification of limiting factors for dispersal that would not otherwise have been apparent. Despite the cool microclimates characteristic of pika habitat, south-facing aspects consistently represented higher resistance to movement, supporting the previous hypothesis that exposure to relatively high temperatures may limit dispersal in American pikas. We found that other barriers to dispersal included areas with a high degree of topographic relief, such as cliffs and ravines, as well as streams and distances greater than 1-4 km depending on the site. Using the empirically derived network models of habitat patch connectivity, we identified habitat patches that were likely disproportionately important for maintaining functional connectivity, areas in which habitat appeared fragmented, and locations that could be targeted for management actions to improve functional connectivity. We concluded that climate change, besides influencing patch occupancy as predicted by other studies, may alter landscape resistance for pikas, thereby influencing functional connectivity through multiple pathways simultaneously. Spatial autocorrelation among genotypes varied across study sites and was largest where habitat was most dispersed, suggesting that dispersal distances increased with habitat fragmentation, up to a point. This study demonstrates how landscape features linked to climate can affect functional connectivity for species with naturally fragmented distributions, and reinforces the importance of replicating studies across landscapes.

Identification of the “source” and “sink” patterns influencing non-point source pollution in the Three Gorges Reservoir Area

Non-point source pollution is one of the primarily ecological issues affecting the Three Gorges Reservoir Area. In this paper, landscape resistance and motivation coefficient, which integrated various landscape elements, such as land use, soil, hydrology, topography, and vegetation, was established based on the effects of large-scale resistance and motivation on the formation of non-point source pollution. In addition, cost models of the landscape resistance and motivation coefficients were constructed based on the distances from the landscape units to the sub-basin outlets in order to identify the “source” and “sink” patterns affecting the formation of non-point source pollution. The results indicated that the changes in the landscape resistance and motivation coefficients of the 16 sub-basins exhibited inverse relationships to their spatial distributions. The landscape resistance and motivation cost curves were more volatile than the landscape resistance and motivation coefficient curves. The landscape resistance and motivation cost trends of the 16 sub-basins became increasingly apparent along the flow of the Yangtze River. The landscape resistance and motivation cost models proposed in this paper could be used to identify large-scale non-point source pollution “source” and “sink” patterns. Moreover, the proposed model could be used to describe the large-scale spatial characteristics of non-point source pollution formation based on “source” and “sink” landscape pattern indices, spatial localization, and landscape resistance and motivation coefficients.

Mediterranean scrubland and elevation drive gene flow of a Mediterranean carnivore, the Egyptian mongooseHerpestes ichneumon(Herpestidae)

Identifying the environmental features affecting gene flow across a species range is of extreme importance for conservation planning. We investigated the genetic structure of the Egyptian mongoose (Herpestes ichneumon) in Western Iberian Peninsula by analyzing the correlations between genetic distances and landscape resistance models. We evaluated several functional relationships between elevation, vegetation cover, temperature, and genetic differentiation under the original and reciprocal causal modelling approaches. Additionally, we assessed evidence of isolation-by-distance (IBD) in the mongoose population. Original causal modelling identified IBD as the best model explaining genetic patterns in the mongoose population. By contrast, reciprocal causal modelling supported high shrub cover at middle elevations as the best model explaining species gene flow. The results from reciprocal causal modelling demonstrate that the Egyptian mongoose is dependent of ecosystems dominated by Mediterranean shrub cover. Recent land-use changes related to rural abandonment promoted the growth of shrub areas, especially at middle elevations, facilitating genetic connectivity in the mongoose population in those areas, where anthropogenic activities are less intense. The present study should be considered as a model for landscape genetics studies of Mediterranean carnivores in the Iberian range with the aim of better understanding how recent land-use changes affect a broad guild of species.

A novel landscape genetic approach demonstrates the effects of human disturbance on the Udzungwa red colobus monkey (Procolobus gordonorum)

A comprehensive understanding of how human disturbance affects tropical forest ecosystems is critical for the mitigation of future losses in global biodiversity. Although many genetic studies of tropical forest fragmentation have been conducted to provide insight into this issue, relatively few have incorporated landscape data to explicitly test the effects of human disturbance on genetic differentiation among populations. In this study, we use a newly developed landscape genetic approach that relies on a genetic algorithm to simultaneously optimize resistance surfaces to investigate the effects of human disturbance in the Udzungwa Mountains of Tanzania, which is an important part of a universally recognized biodiversity hotspot. Our study species is the endangered Udzungwa red colobus monkey (Procolobus gordonorum), which is endemic to the Udzungwa Mountains and a known indicator species that thrives in large and well-protected blocks of old growth forest. Population genetic analyses identified significant population structure among Udzungwa red colobus inhabiting different forest blocks, and Bayesian cluster analyses identified hierarchical structure. Our new method for creating composite landscape resistance models found that the combination of fire density on the landscape and distance to the nearest village best explains the genetic structure observed. These results demonstrate the effects that human activities are having in an area of high global conservation priority and suggest that this ecosystem is in a precarious state. Our study also illustrates the ability of our novel landscape genetic method to detect the impacts of relatively recent landscape features on a long-lived species.

Using network theory to prioritize management in a desert bighorn sheep metapopulation

Connectivity models using empirically-derived landscape resistance maps can predict potential linkages among fragmented animal and plant populations. However, such models have rarely been used to guide systematic decision-making, such as identifying the most important habitat patches and dispersal corridors to protect or restore in order to maximize regional connectivity. Combining resistance models with network theory offers one means of prioritizing management for connectivity, and we applied this approach to a metapopulation of desert bighorn sheep (Ovis canadensis nelsoni) in the Mojave Desert of the southwestern United States. We used a genetic-based landscape resistance model to construct network models of genetic connectivity (potential for gene flow) and demographic connectivity (potential for colonization of empty habitat patches), which may differ because of sex-biased dispersal in bighorn sheep. We identified high-priority habitat patches and corridors and found that the type of connectivity and the network metric used to quantify connectivity had substantial effects on prioritization results, although some features ranked highly across all combinations. Rankings were also sensitive to our empirically-derived estimates of maximum effective dispersal distance, highlighting the importance of this often-ignored parameter. Patch-based analogs of our network metrics predicted both neutral and mitochondrial genetic diversity of 25 populations within the study area. This study demonstrates that network theory can enhance the utility of landscape resistance models as tools for conservation, but it is critical to consider the implications of sex-biased dispersal, the biological relevance of network metrics, and the uncertainty associated with dispersal range and behavior when using this approach.

Re-Evaluating Causal Modeling with Mantel Tests in Landscape Genetics

The predominant analytical approach to associate landscape patterns with gene flow processes is based on the association of cost distances with genetic distances between individuals. Mantel and partial Mantel tests have been the dominant statistical tools used to correlate cost distances and genetic distances in landscape genetics. However, the inherent high correlation among alternative resistance models results in a high risk of spurious correlations using simple Mantel tests. Several refinements, including causal modeling, have been developed to reduce the risk of affirming spurious correlations and to assist model selection. However, the evaluation of these approaches has been incomplete in several respects. To demonstrate the general reliability of the causal modeling approach with Mantel tests, it must be shown to be able to correctly identify a wide range of landscape resistance models as the correct drivers relative to alternative hypotheses. The objectives of this study were to (1) evaluate the effectiveness of the originally published causal modeling framework to support the correct model and reject alternative hypotheses of isolation by distance and isolation by barriers and to (2) evaluate the effectiveness of causal modeling involving direct competition of all hypotheses to support the correct model and reject all alternative landscape resistance models. We found that partial Mantel tests have very low Type II error rates, but elevated Type I error rates. This leads to frequent identification of support for spurious correlations between alternative resistance hypotheses and genetic distance, independent of the true resistance model. The frequency in which this occurs is directly related to the degree of correlation between true and alternative resistance models. We propose an improvement based on the relative support of the causal modeling diagnostic tests.

Divergent landscape effects on population connectivity in two co‐occurring amphibian species

The physical and environmental attributes of landscapes often shape patterns of population connectivity by influencing dispersal and gene flow. Landscape effects on movement are typically evaluated for single species. However, inferences from multiple species are required for multi-species management strategies increasingly being applied in conservation. In this study, I compared the spatial genetic patterns of two amphibian species across the northeastern United States and estimated the influence of specific landscape features on the observed genetic structure. The spotted salamander (Ambystoma maculatum) and wood frog (Rana sylvatica) share many ecological attributes related to habitat use, phenology and site fidelity. However, I hypothesized that important differences in their movement patterns and life history would create distinct genetic patterns for each species. Using 14 microsatellite loci, I tested for differences in the level of genetic differentiation between the two species across 22 breeding ponds. The effects of eight landscape features were also estimated by evaluating 32 landscape resistance models. Spotted salamanders exhibited significantly higher genetic differentiation than wood frogs. Different landscape features were also identified as potential drivers of the genetic patterns in each species, with little overlap in model support between species. Collectively, these results provide strong evidence that these two amphibian species interact with the landscape in measurably different ways. The distinct genetic patterns observed are consistent with key differences in movement ability and life history between A.maculatum and R.sylvatica. These results highlight the importance of considering more than one species when assessing the impacts of the landscape matrix on population connectivity, even for ecologically similar species within the same habitats.

Adaptation with gene flow across the landscape in a dune sunflower

Isolation by adaptation increases divergence at neutral loci when natural selection against immigrants reduces the rate of gene flow between different habitats. This can occur early in the process of adaptive divergence and is a key feature of ecological speciation. Despite the ability of isolation by distance (IBD) and other forms of landscape resistance to produce similar patterns of neutral divergence within species, few studies have used landscape genetics to control for these other forces. We have studied the divergence of Helianthus petiolaris ecotypes living in active sand dunes and adjacent non-dune habitat, using landscape genetics approaches, such as circuit theory and multiple regression of distance matrices, in addition to coalescent modelling. Divergence between habitats was significant, but not strong, and was shaped by IBD. We expected that increased resistance owing to patchy and unfavourable habitat in the dunes would contribute to divergence. Instead, we found that landscape resistance models with lower resistance in the dunes performed well as predictors of genetic distances among subpopulations. Nevertheless, habitat class remained a strong predictor of genetic distance when controlling for isolation by resistance and IBD. We also measured environmental variables at each site and confirmed that specific variables, especially soil nitrogen and vegetation cover, explained a greater proportion of variance in genetic distance than did landscape or the habitat classification alone. Asymmetry in effective population sizes and numbers of migrants per generation was detected using coalescent modelling with Bayesian inference, which is consistent with incipient ecological speciation being driven by the dune habitat.

Inferring landscape effects on gene flow: a new model selection framework

Populations in fragmented landscapes experience reduced gene flow, lose genetic diversity over time and ultimately face greater extinction risk. Improving connectivity in fragmented landscapes is now a major focus of conservation biology. Designing effective wildlife corridors for this purpose, however, requires an accurate understanding of how landscapes shape gene flow. The preponderance of landscape resistance models generated to date, however, is subjectively parameterized based on expert opinion or proxy measures of gene flow. While the relatively few studies that use genetic data are more rigorous, frameworks they employ frequently yield models only weakly related to the observed patterns of genetic isolation. Here, we describe a new framework that uses expert opinion as a starting point. By systematically varying each model parameter, we sought to either validate the assumptions of expert opinion, or identify a peak of support for a new model more highly related to genetic isolation. This approach also accounts for interactions between variables, allows for nonlinear responses and excludes variables that reduce model performance. We demonstrate its utility on a population of mountain goats inhabiting a fragmented landscape in the Cascade Range, Washington.