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.     

The influence of landscape configuration and environment on population genetic structure in a sedentary passerine: insights from loci located in different genomic regions

The study of the factors structuring genetic variation can help to infer the neutral and adaptive processes shaping the demographic and evolutionary trajectories of natural populations. Here, we analyse the role of isolation by distance (IBD), isolation by resistance (IBR, defined by landscape composition) and isolation by environment (IBE, estimated as habitat and elevation dissimilarity) in structuring genetic variation in 25 blue tit (Cyanistes caeruleus) populations. We typed 1385 individuals at 26 microsatellite loci classified into two groups by considering whether they are located into genomic regions that are actively (TL; 12 loci) or not (NTL; 14 loci) transcribed to RNA. Population genetic differentiation was mostly detected using the panel of NTL. Landscape genetic analyses showed a pattern of IBD for all loci and the panel of NTL, but genetic differentiation estimated at TL was only explained by IBR models considering high resistance for natural vegetation and low resistance for agricultural lands. Finally, the absence for IBE suggests a lack of divergent selection pressures associated with differences in habitat and elevation. Overall, our study shows that markers located in different genomic regions can yield contrasting inferences on landscape‐level patterns of realized gene flow in natural populations.     

Gene flow in complex landscapes: testing multiple hypotheses with causal modeling

Predicting population-level effects of landscape change depends on identifying factors that influence population connectivity in complex landscapes. However, most putative movement corridors and barriers have not been based on empirical data. In this study, we identify factors that influence connectivity by comparing patterns of genetic similarity among 146 black bears (Ursus americanus), sampled across a 3,000-km(2) study area in northern Idaho, with 110 landscape-resistance hypotheses. Genetic similarities were based on the pairwise percentage dissimilarity among all individuals based on nine microsatellite loci (average expected heterozygosity=0.79). Landscape-resistance hypotheses describe a range of potential relationships between movement cost and land cover, slope, elevation, roads, Euclidean distance, and a putative movement barrier. These hypotheses were divided into seven organizational models in which the influences of barriers, distance, and landscape features were statistically separated using partial Mantel tests. Only one of the competing organizational models was fully supported: patterns of genetic structure are primarily related to landscape gradients of land cover and elevation. The alternative landscape models, isolation by barriers and isolation by distance, are not supported. In this black bear population, gene flow is facilitated by contiguous forest cover at middle elevations.     

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.     

Genetic evidence for landscape effects on dispersal in the army ant Eciton burchellii

Inhibited dispersal, leading to reduced gene flow, threatens populations with inbreeding depression and local extinction. Fragmentation may be especially detrimental to social insects because inhibited gene flow has important consequences for cooperation and competition within and among colonies. Army ants have winged males and permanently wingless queens; these traits imply male‐biased dispersal. However, army ant colonies are obligately nomadic and have the potential to traverse landscapes. Eciton burchellii, the most regularly nomadic army ant, is a forest interior species: colony raiding activities are limited in the absence of forest cover. To examine whether nomadism and landscape (forest clearing and elevation) affect population genetic structure in a montane E. burchellii population, we reconstructed queen and male genotypes from 25 colonies at seven polymorphic microsatellite loci. Pairwise genetic distances among individuals were compared to pairwise geographical and resistance distances using regressions with permutations, partial Mantel tests and random forests analyses. Although there was no significant spatial genetic structure in queens or males in montane forest, dispersal may be male‐biased. We found significant isolation by landscape resistance for queens based on land cover (forest clearing), but not on elevation. Summed colony emigrations over the lifetime of the queen may contribute to gene flow in this species and forest clearing impedes these movements and subsequent gene dispersal. Further forest cover removal may increasingly inhibit Eciton burchellii colony dispersal. We recommend maintaining habitat connectivity in tropical forests to promote population persistence for this keystone species.     

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.     

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.     

The gravity of pollination: integrating at‐site features into spatial analysis of contemporary pollen movement

Pollen‐mediated gene flow is a major driver of spatial genetic structure in plant populations. Both individual plant characteristics and site‐specific features of the landscape can modify the perceived attractiveness of plants to their pollinators and thus play an important role in shaping spatial genetic variation. Most studies of landscape‐level genetic connectivity in plants have focused on the effects of interindividual distance using spatial and increasingly ecological separation, yet have not incorporated individual plant characteristics or other at‐site ecological variables. Using spatially explicit simulations, we first tested the extent to which the inclusion of at‐site variables influencing local pollination success improved the statistical characterization of genetic connectivity based upon examination of pollen pool genetic structure. The addition of at‐site characteristics provided better models than those that only considered interindividual spatial distance (e.g. IBD). Models parameterized using conditional genetic covariance (e.g. population graphs) also outperformed those assuming panmixia. In a natural population of Cornus florida L. (Cornaceae), we showed that the addition of at‐site characteristics (clumping of primary canopy opening above each maternal tree and maternal tree floral output) provided significantly better models describing gene flow than models including only between‐site spatial (IBD) and ecological (isolation by resistance) variables. Overall, our results show that including interindividual and local ecological variation greatly aids in characterizing landscape‐level measures of contemporary gene flow.     

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.     

Landscape Genetics for the Empirical Assessment of Resistance Surfaces: The European Pine Marten (Martes martes) as a Target-Species of a Regional Ecological Network

Coherent ecological networks (EN) composed of core areas linked by ecological corridors are being developed worldwide with the goal of promoting landscape connectivity and biodiversity conservation. However, empirical assessment of the performance of EN designs is critical to evaluate the utility of these networks to mitigate effects of habitat loss and fragmentation. Landscape genetics provides a particularly valuable framework to address the question of functional connectivity by providing a direct means to investigate the effects of landscape structure on gene flow. The goals of this study are (1) to evaluate the landscape features that drive gene flow of an EN target species (European pine marten), and (2) evaluate the optimality of a regional EN design in providing connectivity for this species within the Basque Country (North Spain). Using partial Mantel tests in a reciprocal causal modeling framework we competed 59 alternative models, including isolation by distance and the regional EN. Our analysis indicated that the regional EN was among the most supported resistance models for the pine marten, but was not the best supported model. Gene flow of pine marten in northern Spain is facilitated by natural vegetation, and is resisted by anthropogenic landcover types and roads. Our results suggest that the regional EN design being implemented in the Basque Country will effectively facilitate gene flow of forest dwelling species at regional scale.     

Landscape characteristics influencing the genetic structure of greater sage‐grouse within the stronghold of their range: a holistic modeling approach

Given the significance of animal dispersal to population dynamics and geographic variability, understanding how dispersal is impacted by landscape patterns has major ecological and conservation importance. Speaking to the importance of dispersal, the use of linear mixed models to compare genetic differentiation with pairwise resistance derived from landscape resistance surfaces has presented new opportunities to disentangle the menagerie of factors behind effective dispersal across a given landscape. Here, we combine these approaches with novel resistance surface parameterization to determine how the distribution of high‐ and low‐quality seasonal habitat and individual landscape components shape patterns of gene flow for the greater sage‐grouse (Centrocercus urophasianus) across Wyoming. We found that pairwise resistance derived from the distribution of low‐quality nesting and winter, but not summer, seasonal habitat had the strongest correlation with genetic differentiation. Although the patterns were not as strong as with habitat distribution, multivariate models with sagebrush cover and landscape ruggedness or forest cover and ruggedness similarly had a much stronger fit with genetic differentiation than an undifferentiated landscape. In most cases, landscape resistance surfaces transformed with 17.33‐km‐diameter moving windows were preferred, suggesting small‐scale differences in habitat were unimportant at this large spatial extent. Despite the emergence of these overall patterns, there were differences in the selection of top models depending on the model selection criteria, suggesting research into the most appropriate criteria for landscape genetics is required. Overall, our results highlight the importance of differences in seasonal habitat preferences to patterns of gene flow and suggest the combination of habitat suitability modeling and linear mixed models with our resistance parameterization is a powerful approach to discerning the effects of landscape on gene flow.     

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.     

Effects of slope and riparian habitat connectivity on gene flow in an endangered Panamanian frog, Atelopus varius

Aim Understanding how heterogeneous landscapes shape genetic structure not only sheds light on processes involved in population divergence and speciation, but can also guide management strategies to promote and maintain genetic connectivity of populations of endangered species. This study aimed to (1) identify barriers and corridors for gene flow among populations of the endangered frog, Atelopus varius and (2) assess the relative contributions of alternative landscape factors to patterns of genetic variation among these populations in a hypothesis testing framework. Location This study took place in western Panama and included all nine of the remaining known populations of A. varius at the time of study. Methods The influence of landscape variables on gene flow among populations was examined by testing for correlations between alternative landscape‐resistance scenarios and genetic distance. Fifteen alternative hypotheses about the influence of (1) riparian habitat corridors, (2) steep slopes, and (3) climatic suitability on patterns of genetic structure were tested in a causal modelling framework, using Mantel and partial‐Mantel tests, along with an analysis of molecular variation. Results Only the hypothesis attributing resistance to dispersal across steep slopes (genetic isolation by slope distance) was fully supported by the causal modelling approach. However, the analysis of molecular variance and the paths of least‐slope among populations suggest that riparian habitat connectivity may influence genetic structure as well. Main conclusions These results suggest that patterns of genetic variation among A. varius populations are affected by the slope of the landscape such that areas with steep slopes act as barriers to gene flow. In contrast, areas of low slope, such as streams and mountain ridges, appear to be important corridors for gene flow, especially among high elevation populations. These results engender important considerations for the management of this critically endangered species.     

Influence of landscape heterogeneity on the functional connectivity of Allegheny woodrats (<Emphasis Type="Italic">Neotoma magister</Emphasis>) in Virginia

Allegheny woodrats (Neotoma magister) exist as groups of metapopulations due to their dependence on naturally disjunct rocky outcrops in the eastern United States. Severe demographic declines of Allegheny woodrats have occurred in many parts of the range due to a myriad of interacting processes, therefore identifying factors that help maintain the integrity of metapopulations is needed to guide conservation efforts. One factor considered critical to metapopulation persistence is maintaining functional connectivity. Therefore, our objective was to identify landscape factors that influence gene flow in Allegheny woodrats. We sampled 159 individuals from throughout the distribution in Virginia, genotyped them at 22 microsatellite loci, and modeled effects of land cover, roads, large rivers, and elevation on gene flow. The model that parameterized areas?≤?750 m in elevation as a barrier to gene flow performed best among the tested models, which indicates that functional connectivity is highest in high elevation habitats. We found little evidence for anthropogenic factors influencing gene flow, but continued study across extant metapopulations is needed in more peripheral areas to evaluate if anthropogenic barriers impact functional connectivity. Overall, our study emphasizes the importance of considering elevation for maintaining functional connectivity of Allegheny woodrats in the face of ongoing demographic declines in the eastern United States.     

Landscape genetic inferences vary with sampling scenario for a pond‐breeding amphibian

A critical decision in landscape genetic studies is whether to use individuals or populations as the sampling unit. This decision affects the time and cost of sampling and may affect ecological inference. We analyzed 334 Columbia spotted frogs at 8 microsatellite loci across 40 sites in northern Idaho to determine how inferences from landscape genetic analyses would vary with sampling design. At all sites, we compared a proportion available sampling scheme (PASS), in which all samples were used, to resampled datasets of 2–11 individuals. Additionally, we compared a population sampling scheme (PSS) to an individual sampling scheme (ISS) at 18 sites with sufficient sample size. We applied an information theoretic approach with both restricted maximum likelihood and maximum likelihood estimation to evaluate competing landscape resistance hypotheses. We found that PSS supported low‐density forest when restricted maximum likelihood was used, but a combination model of most variables when maximum likelihood was used. We also saw variations when AIC was used compared to BIC. ISS supported this model as well as additional models when testing hypotheses of land cover types that create the greatest resistance to gene flow for Columbia spotted frogs. Increased sampling density and study extent, seen by comparing PSS to PASS, showed a change in model support. As number of individuals increased, model support converged at 7–9 individuals for ISS to PSS. ISS may be useful to increase study extent and sampling density, but may lack power to provide strong support for the correct model with microsatellite datasets. Our results highlight the importance of additional research on sampling design effects on landscape genetics inference.     

Main roads and land cover shaped the genetic structure of a Mediterranean island wild boar population

Patterns of genetic differentiation within and among animal populations might vary due to the simple effect of distance or landscape features hindering gene flow. An assessment of how landscape connectivity affects gene flow can help guide management, especially in fragmented landscapes. Our objective was to analyze population genetic structure and landscape genetics of the native wild boar (Sus scrofa meridionalis) population inhabiting the island of Sardinia (Italy), and test for the existence of Isolation‐by‐Distance (IBD), Isolation‐by‐Barrier (IBB), and Isolation‐by‐Resistance (IBR). A total of 393 Sardinian wild boar samples were analyzed using a set of 16 microsatellite loci. Signals of genetic introgression from introduced non‐native wild boars or from domestic pigs were revealed by a Bayesian cluster analysis including 250 reference individuals belonging to European wild populations and domestic breeds. After removal of introgressed individuals, genetic structure in the population was investigated by different statistical approaches, supporting a partition into five discrete subpopulations, corresponding to five geographic areas on the island: north‐west (NW), central west (CW), south‐west (SW), north‐central east (NCE), and south‐east (SE). To test the IBD, IBB, and IBR hypotheses, we optimized resistance surfaces using genetic algorithms and linear mixed‐effects models with a maximum likelihood population effects parameterization. Landscape genetics analyses revealed that genetic discontinuities between subpopulations can be explained by landscape elements, suggesting that main roads, urban settings, and intensively cultivated areas are hampering gene flow (and thus individual movements) within the Sardinian wild boar population. Our results reveal how human‐transformed landscapes can affect genetic connectivity even in a large‐sized and highly mobile mammal such as the wild boar, and provide crucial information to manage the spread of pathogens, including the African Swine Fever virus, endemic in Sardinia.     

Landscape influences on dispersal behaviour: a theoretical model and empirical test using the fire salamander,Salamandra infraimmaculata

When populations reside within a heterogeneous landscape, isolation by distance may not be a good predictor of genetic divergence if dispersal behaviour and therefore gene flow depend on landscape features. Commonly used approaches linking landscape features to gene flow include the least cost path (LCP), random walk (RW), and isolation by resistance (IBR) models. However, none of these models is likely to be the most appropriate for all species and in all environments. We compared the performance of LCP, RW and IBR models of dispersal with the aid of simulations conducted on artificially generated landscapes. We also applied each model to empirical data on the landscape genetics of the endangered fire salamander, Salamandra infraimmaculata, in northern Israel, where conservation planning requires an understanding of the dispersal corridors. Our simulations demonstrate that wide dispersal corridors of the low-cost environment facilitate dispersal in the IBR model, but inhibit dispersal in the RW model. In our empirical study, IBR explained the genetic divergence better than the LCP and RW models (partial Mantel correlation 0.413 for IBR, compared to 0.212 for LCP, and 0.340 for RW). Overall dispersal cost in salamanders was also well predicted by landscape feature slope steepness (76 %), and elevation (24 %). We conclude that fire salamander dispersal is well characterised by IBR predictions. Together with our simulation findings, these results indicate that wide dispersal corridors facilitate, rather than hinder, salamander dispersal. Comparison of genetic data to dispersal model outputs can be a useful technique in inferring dispersal behaviour from population genetic data.     

Using a genetic network to parameterize a landscape resistance surface for fishers, Martes pennanti

Knowledge of dispersal-related gene flow is important for addressing many basic and applied questions in ecology and evolution. We used landscape genetics to understand the recovery of a recently expanded population of fishers (Martes pennanti) in Ontario, Canada. An important focus of landscape genetics is modelling the effects of landscape features on gene flow. Most often resistance surfaces in landscape genetic studies are built a priori based upon nongenetic field data or expert opinion. The resistance surface that best fits genetic data is then selected and interpreted. Given inherent biases in using expert opinion or movement data to model gene flow, we sought an alternative approach. We used estimates of conditional genetic distance derived from a network of genetic connectivity to parameterize landscape resistance and build a final resistance surface based upon information-theoretic model selection and multi-model averaging. We sampled 657 fishers from 31 landscapes, genotyped them at 16 microsatellite loci, and modelled the effects of snow depth, road density, river density, and coniferous forest on gene flow. Our final model suggested that road density, river density, and snow depth impeded gene flow during the fisher population expansion demonstrating that both human impacts and seasonal habitat variation affect gene flow for fishers. Our approach to building landscape genetic resistance surfaces mitigates many of the problems and caveats associated with using either nongenetic field data or expert opinion to derive resistance surfaces.     

Effects of sample size,number of markers,and allelic richness on the detection of spatial genetic pattern

The influence of study design on the ability to detect the effects of landscape pattern on gene flow is one of the most pressing methodological gaps in landscape genetic research. To investigate the effect of study design on landscape genetics inference, we used a spatially‐explicit, individual‐based program to simulate gene flow in a spatially continuous population inhabiting a landscape with gradual spatial changes in resistance to movement. We simulated a wide range of combinations of number of loci, number of alleles per locus and number of individuals sampled from the population. We assessed how these three aspects of study design influenced the statistical power to successfully identify the generating process among competing hypotheses of isolation‐by‐distance, isolation‐by‐barrier, and isolation‐by‐landscape resistance using a causal modelling approach with partial Mantel tests. We modelled the statistical power to identify the generating process as a response surface for equilibrium and non‐equilibrium conditions after introduction of isolation‐by‐landscape resistance. All three variables (loci, alleles and sampled individuals) affect the power of causal modelling, but to different degrees. Stronger partial Mantel r correlations between landscape distances and genetic distances were found when more loci were used and when loci were more variable, which makes comparisons of effect size between studies difficult. Number of individuals did not affect the accuracy through mean equilibrium partial Mantel r, but larger samples decreased the uncertainty (increasing the precision) of equilibrium partial Mantel r estimates. We conclude that amplifying more (and more variable) loci is likely to increase the power of landscape genetic inferences more than increasing number of individuals.