Dealing with uncertainty in landscape genetic resistance models: a case of three co-occurring marsupials

Landscape genetics reveals contrasting patterns of connectivity in two newt species (Lissotriton montandoni and L. vulgaris)

Ecologically distinct species may respond to landscape changes in different ways. In addition to basic ecological data, the extent of the geographic range has been successfully used as an indicator of species sensitivity to anthropogenic landscapes, with widespread species usually found to be less sensitive compared to range-restricted species. In this study, we investigate connectivity patterns of two closely related but ecologically distinct newt species - the range-restricted, Lissotriton montandoni and the widespread, L. vulgaris - using genomic data, a highly replicated setting (six geographic regions per species), and tools from landscape genetics. Our results show the importance of forest for connectivity in both species, but at the same time suggest differential use of forested habitat, with L. montandoni and L. vulgaris showing the highest connectivity at forest-core and forest-edges, respectively. Anthropogenic landscapes (i.e., higher crop- or urban-cover) increased resistance in both species, but the effect was one to three orders of magnitude stronger in L. montandoni than in L. vulgaris. This result is consistent with a view of L. vulgaris as an ecological generalist. Even so, currently, the negative impact of anthropogenic landscapes is mainly seen in connectivity among L. vulgaris populations, which show significantly stronger isolation and lower effective sizes relative to L. montandoni. Overall, this study emphasizes how habitat destruction is compromising genetic connectivity not only in endemic, range-restricted species of conservation concern but also in widespread generalist species, despite their comparatively lower sensitivity to anthropogenic landscape changes.

Biogeographic inferences across spatial and evolutionary scales

The field of biogeography unites landscape genetics and phylogeography under a common conceptual framework. Landscape genetics traditionally focuses on recent-time, population-based, spatial genetics processes at small geographical scales, while phylogeography typically investigates deep past, lineage- and species-based processes at large geographical scales. Here, we evaluate the link between landscape genetics and phylogeographical methods using the western fence lizard (Sceloporus occidentalis) as a model species. First, we conducted replicated landscape genetics studies across several geographical scales to investigate how population genetics inferences change depending on the spatial extent of the study area. Then, we carried out a phylogeographical study of population structure at two evolutionary scales informed by inferences derived from landscape genetics results to identify concordance and conflict between these sets of methods. We found significant concordance in landscape genetics processes at all but the largest geographical scale. Phylogeographical results indicate major clades are restricted to distinct river drainages or distinct hydrological regions. At a more recent timescale, we find minor clades are restricted to single river canyons in the majority of cases, while the remainder of river canyons include samples from at most two clades. Overall, the broad-scale pattern implicating stream and river valleys as key features linking populations in the landscape genetics results, and high degree of clade specificity within major topographic subdivisions in the phylogeographical results, is consistent. As landscape genetics and phylogeography share many of the same objectives, synthesizing theory, models and methods between these fields will help bring about a better understanding of ecological and evolutionary processes structuring genetic variation across space and time.

Genome-wide SNPs detect fine-scale genetic structure in threatened populations of squirrel glider Petaurus norfolcensis

Australian arboreal mammals are experiencing significant population declines, particularly due to land clearing and resulting habitat fragmentation. The squirrel glider, Petaurus norfolcensis, is a threatened species in New South Wales, with a stronghold population in the Lake Macquarie Local Government Area (LGA) where fragmentation due to urbanization is an ongoing problem for the species conservation. Here we report on the use of squirrel glider mitochondrial (385 bp cytochrome b gene, 70 individuals) and nuclear DNA (6,834 SNPs, 87 individuals) markers to assess their population genetic structure and connectivity across 14 locations sampled in the Lake Macquarie LGA. The mitochondrial DNA sequences detected evidence of a historical genetic bottleneck, while the genome-wide SNPs detected significant population structure in the Lake Macquarie squirrel glider populations at scales as fine as one kilometer. There was no evidence of inbreeding within patches, however there were clear effects of habitat fragmentation and biogeographical barriers on gene flow. A least cost path analysis identified thin linear corridors that have high priority for conservation. These areas should be protected to avoid further isolation of squirrel glider populations and the loss of genetic diversity through genetic drift.

Population genomics and geographic dispersal in Chagas disease vectors: Landscape drivers and evidence of possible adaptation to the domestic setting

Accurate prediction of vectors dispersal, as well as identification of adaptations that allow blood-feeding vectors to thrive in built environments, are a basis for effective disease control. Here we adopted a landscape genomics approach to assay gene flow, possible local adaptation, and drivers of population structure in Rhodnius ecuadoriensis, an important vector of Chagas disease. We used a reduced-representation sequencing technique (2b-RADseq) to obtain 2,552 SNP markers across 272 R. ecuadoriensis samples from 25 collection sites in southern Ecuador. Evidence of high and directional gene flow between seven wild and domestic population pairs across our study site indicates insecticide-based control will be hindered by repeated re-infestation of houses from the forest. Preliminary genome scans across multiple population pairs revealed shared outlier loci potentially consistent with local adaptation to the domestic setting, which we mapped to genes involved with embryogenesis and saliva production. Landscape genomic models showed elevation is a key barrier to R. ecuadoriensis dispersal. Together our results shed early light on the genomic adaptation in triatomine vectors and facilitate vector control by predicting that spatially-targeted, proactive interventions would be more efficacious than current, reactive approaches.

Re-infestation of recently insecticide-treated houses by wild/secondary triatomine, their potential adaptation to this new environment and capabilities to geographically disperse across multiple human communities jeopardise sustainable Chagas disease control. This is the first study in Chagas disease vectors that identifies genomic regions possibly linked to adaptations to the built environment and describes landscape drivers for accurate prediction of geographic dispersal. We sampled multiple domestic and wild Rhodnius ecuadoriensis population pairs across a mountainous terrain in southern Ecuador. We evidenced that triatomine movement from forest to built enviroments does occur at a high rate. In these highly connected population pairs we detected loci possibly linked to local adaptation among the genomic makers we evaluated and in doing so we pave the way for future triatomine genomic research. We highlighted that current haphazardous vector control in the zone will be hindered by reinfestation of triatomines from the forest. Instead, we recommend frequent and spatially-targeted vector control and provided a landacape genomic model that identifies highly connected and isolated triatomine populations to facilitate efficient vector control.

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.

Using environmental and geographic data to optimize ex situ collections and preserve evolutionary potential

Maintenance of biodiversity through seed banks and botanical gardens, where the wealth of species' genetic variation may be preserved ex situ, is a major goal of conservation. However, challenges can persist in optimizing ex situ collections if trade-offs exist among cost, effort, and conserving species evolutionary potential, particularly when genetic data are not available. We evaluated the genetic consequences of population preservation informed by geographic (isolation by distance [IBD]) and environmental (isolation by environment [IBE]) distance for ex situ collections for which population provenance is available. We used 19 genetic and genomic data sets from 15 plant species to assess the proportion of population genetic differentiation explained by geographic and environmental factors and to simulate ex situ collections prioritizing source populations based on pairwise geographic distance, environmental distance, or both. Specifically, we tested the impact prioritizing sampling based on these distances may have on the capture of neutral, functional, or putatively adaptive genetic diversity and differentiation. Individually, IBD and IBE explained limited population genetic differences across all 3 genetic marker classes (IBD, 10-16%; IBE, 1-5.5%). Together, they explained a substantial proportion of population genetic differences for functional (45%) and adaptive (71%) variation. Simulated ex situ collections revealed that inclusion of IBD, IBE, or both increased allelic diversity and genetic differentiation captured among populations, particularly for loci that may be important for adaptation. Thus, prioritizing population collections based on environmental and geographic distance data can optimize genetic variation captured ex situ. For the vast majority of plant species for which there is no genetic information, these data are invaluable to conservation because they can guide preservation of genetic variation needed to maintain evolutionary potential within collections.

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.

The use and misuse of regression models in landscape genetic analyses

The field of landscape genetics has been rapidly evolving, adopting and adapting analytical frameworks to address research questions. Current studies are increasingly using regression-based frameworks to infer the individual contributions of landscape and habitat variables on genetic differentiation. This paper outlines appropriate and inappropriate uses of multiple regression for these purposes, and demonstrates through simulation the limitations of different analytical frameworks for making correct inference. Of particular concern are recent studies seeking to explain genetic differences by fitting regression models with effective distance variables calculated independently on separate landscape resistance surfaces. When moving across the landscape, organisms cannot respond independently and uniquely to habitat and landscape features. Analyses seeking to understand how landscape features affect gene flow should model a single conductance or resistance surface as a parameterized function of relevant spatial covariates, and estimate the values of these parameters by linking a single set of resistance distances to observed genetic dissimilarity via a loss function. While this loss function may involve a regression-like step, the associated nuisance parameters are not interpretable in terms of organismal movement and should not be conflated with what is actually of interest: the mapping between spatial covariates and conductance/resistance. The growth and evolution of landscape genetics as a field has been rapid and exciting. It is the goal of this paper to highlight past missteps and demonstrate limitations of current approaches to ensure that future use of regression models will appropriately consider the process being modeled, which will provide clarity to model interpretation.

The effect of gene flow from unsampled demes in landscape genetic analysis

An assumption of correlative landscape genetic methods is that genetic differentiation at neutral markers arises solely from the degree to which the intervening landscape between individuals or populations resists gene flow. However, this assumption is violated when gene flow occurs into the sampled population from an unsampled, differentiated deme. This may happen when sampling within only a portion of a population's extent or when closely related species hybridize with the sampled population. In both cases, violation of the modelling assumptions has the potential to reduce landscape genetic model selection accuracy and result in poor inferences. We used individual-based population genetic simulations in complex landscapes within a model selection framework to explore the potential confounding effect of gene flow from unsampled demes. We hypothesized that as gene flow from outside the sampling extent increased, model selection accuracy would decrease due to the formation of a hybrid zone where allele frequencies were perturbed in a way that was not correlated with effective distances between sampled individuals. Surprisingly, we found this expectation was unfounded, because the reduced accuracy due to admixture was counteracted by an increase in allelic diversity as alleles spread from the unsampled deme into the sampled population. These new alleles increased the power to detect landscape genetic relationships and even slightly improving model selection accuracy overall. This is a reassuring result, suggesting that sampling the full extent of a population or related species that may hybridize may be unnecessary, as long as other well-established sampling requirements are met.

The Influence of Environmental Variation on the Genetic Structure of a Poison Frog Distributed Across Continuous Amazonian Rainforest

Biogeographic barriers such as rivers have been shown to shape spatial patterns of biodiversity in the Amazon basin, yet relatively little is known about the distribution of genetic variation across continuous rainforest. Here, we characterize the genetic structure of the brilliant-thighed poison frog (Allobates femoralis) across an 880-km-long transect along the Purus-Madeira interfluve south of the Amazon river, based on 64 individuals genotyped at 7609 single-nucleotide polymorphism (SNP) loci. A population tree and clustering analyses revealed 4 distinct genetic groups, one of which was strongly divergent. These genetic groups were concomitant with femoral spot coloration differences, which was intermediate within a zone of admixture between two of the groups. The location of these genetic groups did not consistently correspond to current ecological transitions between major forest types. A multimodel approach to quantify the relative influence of isolation-by-geographic distance (IBD) and isolation-by-environmental resistance (IBR) nevertheless revealed that, in addition to a strong signal of IBD, spatial genetic differentiation was explained by IBR primarily linked to dry season intensity (r2 = 8.4%) and canopy cover (r2 = 6.4%). We show significant phylogenetic divergence in the absence of obvious biogeographical barriers and that finer-scaled measures of genetic structure are associated with environmental variables also known to predict the density of A. femoralis.

Population genetics of Anopheles koliensis through Papua New Guinea: New cryptic species and landscape topography effects on genetic connectivity

New Guinea is a topographically and biogeographically complex region that supports unique endemic fauna. Studies describing the population connectivity of species through this region are scarce. We present a population and landscape genetic study on the endemic malaria-transmitting mosquito, Anopheles koliensis (Owen). Using mitochondrial and nuclear sequence data, as well as microsatellites, we show the evidence of geographically discrete population structure within Papua New Guinea (PNG). We also confirm the existence of three rDNA ITS2 genotypes within this mosquito and assess reproductive isolation between individuals carrying different genotypes. Microsatellites reveal the clearest population structure and show four clear population units. Microsatellite markers also reveal probable reproductive isolation between sympatric populations in northern PNG with different ITS2 genotypes, suggesting that these populations may represent distinct cryptic species. Excluding individuals belonging to the newly identified putative cryptic species (ITS2 genotype 3), we modeled the genetic differences between A. koliensis populations through PNG as a function of terrain and find that dispersal is most likely along routes with low topographic relief. Overall, these results show that A. koliensis is made up of geographically and genetically discrete populations in Papua New Guinea with landscape topography being important in restricting dispersal.

Circuit-theory applications to connectivity science and conservation

Conservation practitioners have long recognized ecological connectivity as a global priority for preserving biodiversity and ecosystem function. In the early years of conservation science, ecologists extended principles of island biogeography to assess connectivity based on source patch proximity and other metrics derived from binary maps of habitat. From 2006 to 2008, the late Brad McRae introduced circuit theory as an alternative approach to model gene flow and the dispersal or movement routes of organisms. He posited concepts and metrics from electrical circuit theory as a robust way to quantify movement across multiple possible paths in a landscape, not just a single least-cost path or corridor. Circuit theory offers many theoretical, conceptual, and practical linkages to conservation science. We reviewed 459 recent studies citing circuit theory or the open-source software Circuitscape. We focused on applications of circuit theory to the science and practice of connectivity conservation, including topics in landscape and population genetics, movement and dispersal paths of organisms, anthropogenic barriers to connectivity, fire behavior, water flow, and ecosystem services. Circuit theory is likely to have an effect on conservation science and practitioners through improved insights into landscape dynamics, animal movement, and habitat-use studies and through the development of new software tools for data analysis and visualization. The influence of circuit theory on conservation comes from the theoretical basis and elegance of the approach and the powerful collaborations and active user community that have emerged. Circuit theory provides a springboard for ecological understanding and will remain an important conservation tool for researchers and practitioners around the globe.

Regional variation in drivers of connectivity for two frog species (Rana pretiosa and R. luteiventris) from the U.S. Pacific Northwest

Comparative landscape genetics has uncovered high levels of variability in which landscape factors affect connectivity among species and regions. However, the relative importance of species traits versus environmental variation for predicting landscape patterns of connectivity is unresolved. We provide evidence from a landscape genetics study of two sister taxa of frogs, the Oregon spotted frog (Rana pretiosa) and the Columbia spotted frog (Rana luteiventris) in Oregon and Idaho, USA. Rana pretiosa is relatively more dependent on moisture for dispersal than R. luteiventris, so if species traits influence connectivity, we predicted that connectivity among R. pretiosa populations would be more positively associated with moisture than R. luteiventris. However, if environmental differences are important drivers of gene flow, we predicted that connectivity would be more positively related to moisture in arid regions. We tested these predictions using eight microsatellite loci and gravity models in two R. pretiosa regions and four R. luteiventris regions (n = 1,168 frogs). In R. pretiosa, but not R. luteiventris, connectivity was positively related to mean annual precipitation, supporting our first prediction. In contrast, connectivity was not more positively related to moisture in more arid regions. Various temperature metrics were important predictors for both species and in all regions, but the directionality of their effects varied. Therefore, the pattern of variation in drivers of connectivity was consistent with predictions based on species traits rather than on environmental variation.

Environmental and geographic variables are effective surrogates for genetic variation in conservation planning

Protected areas buffer species from anthropogenic threats and provide places for the processes that generate and maintain biodiversity to continue. However, genetic variation, the raw material for evolution, is difficult to capture in conservation planning, not least because genetic data require considerable resources to obtain and analyze. Here we show that freely available environmental and geographic distance variables can be highly effective surrogates in conservation planning for representing adaptive and neutral intraspecific genetic variation. We obtained occurrence and genetic data from the IntraBioDiv project for 27 plant species collected over the European Alps using a gridded sampling scheme. For each species, we identified loci that were potentially under selection using outlier loci methods, and mapped their main gradients of adaptive and neutral genetic variation across the grid cells. We then used the cells as planning units to prioritize protected area acquisitions. First, we verified that the spatial patterns of environmental and geographic variation were correlated, respectively, with adaptive and neutral genetic variation. Second, we showed that these surrogates can predict the proportion of genetic variation secured in randomly generated solutions. Finally, we discovered that solutions based only on surrogate information secured substantial amounts of adaptive and neutral genetic variation. Our work paves the way for widespread integration of surrogates for genetic variation into conservation planning.

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.

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.

Applying landscape genetics to the microbial world

Landscape genetics, which explicitly quantifies landscape effects on gene flow and adaptation, has largely focused on macroorganisms, with little attention given to microorganisms. This is despite overwhelming evidence that microorganisms exhibit spatial genetic structuring in relation to environmental variables. The increasing accessibility of genomic data has opened up the opportunity for landscape genetics to embrace the world of microorganisms, which may be thought of as 'the invisible regulators' of the macroecological world. Recent developments in bioinformatics and increased data accessibility have accelerated our ability to identify microbial taxa and characterize their genetic diversity. However, the influence of the landscape matrix and dynamic environmental factors on microorganism genetic dispersal and adaptation has been little explored. Also, because many microorganisms coinhabit or codisperse with macroorganisms, landscape genomic approaches may improve insights into how micro- and macroorganisms reciprocally interact to create spatial genetic structure. Conducting landscape genetic analyses on microorganisms requires that we accommodate shifts in spatial and temporal scales, presenting new conceptual and methodological challenges not yet explored in 'macro'-landscape genetics. We argue that there is much value to be gained for microbial ecologists from embracing landscape genetic approaches. We provide a case for integrating landscape genetic methods into microecological studies and discuss specific considerations associated with the novel challenges this brings. We anticipate that microorganism landscape genetic studies will provide new insights into both micro- and macroecological processes and expand our knowledge of species' distributions, adaptive mechanisms and species' interactions in changing environments.

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.