Isolation by resistance

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

Are all data types and connectivity models created equal? Validating common connectivity approaches with dispersal data

Aim

There is enormous interest in applying connectivity modelling to resistance surfaces for identifying corridors for conservation action. However, the multiple analytical approaches used to estimate resistance surfaces and predict connectivity across resistance surfaces have not been rigorously compared, and it is unclear what methods provide the best inferences about population connectivity. Using a large empirical data set on puma (Puma concolor), we are the first to compare several of the most common approaches for estimating resistance and modelling connectivity and validate them with dispersal data.

Location

Southern California, USA.

Methods

We estimate resistance using presence‐only data, GPS telemetry data from puma home ranges and genetic data using a variety of analytical methods. We model connectivity with cost distance and circuit theory algorithms. We then measure the ability of each data type and connectivity algorithm to capture GPS telemetry points of dispersing pumas.

Results

We found that resource selection functions based on GPS telemetry points and paths outperformed species distribution models when applied using cost distance connectivity algorithms. Point and path selection functions were not statistically different in their performance, but point selection functions were more sensitive to the transformation used to convert relative probability of use to resistance. Point and path selection functions and landscape genetics outperformed other methods when applied with cost distance; no methods outperformed one another with circuit theory.

Main conclusions

We conclude that path or point selection functions, or landscape genetic models, should be used to estimate landscape resistance for wildlife. In cases where resource limitations prohibit the collection of GPS collar or genetic data, our results suggest that species distribution models, while weaker, may still be sufficient for resistance estimation. We recommend the use of cost distance‐based approaches, such as least‐cost corridors and resistant kernels, for estimating connectivity and identifying functional corridors for terrestrial wildlife.

     

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.     

samc: an R package for connectivity modeling with spatial absorbing Markov chains

Quantifying landscape connectivity is fundamental to better understand and predict how populations respond to environmental change. Currently, popular methods to quantify landscape connectivity emphasize how landscape features provide resistance to movement. While many tools are available to quantify landscape resistance, these do not discern between two fundamentally different sources of resistance: movement behavior and mortality. To address this issue, we developed the samc R package that quantifies landscape connectivity using absorbing Markov chain theory. Within this mathematical framework, movements are represented as transient states in the Markov chain, while mortality is represented by transitions to absorbing states. Not only does this framework explicitly account for these different issues, it provides a probabilistic approach that can incorporate both short-term and long-term dynamics, as well as species distribution and abundance. The package includes functions to quantify life expectancy, long-term visitation rates, and various spatially and temporally explicit measures of mortality and movement at the local and landscape scales. These functions in samc have been optimized to find computationally practical solutions in landscapes comprised of > 2 × 10⁶ cells. Here, we illustrate the workflow of the samc package with publicly available movement and mortality data on the endangered Florida panther Puma concolor coryi. This analysis showed that movement and mortality are generally correlated except for locations near roads (areas of high mortality risk) that are within the dispersal range of source locations. This pattern would have been undetectable with current methods that quantify movement resistance. Overall, the samc package provides a means for implementing spatial absorbing Markov chains that can distinguish between movement behavior and mortality resulting in more reliable landscape connectivity measures.     

Do genetic structure and landscape heterogeneity impact color morph frequency in a polymorphic salamander?

Landscape heterogeneity plays an important role in population structure and divergence, particularly for species with limited vagility. Here, we used a landscape genetic approach to identify how landscape and environmental variables affect genetic structure and color morph frequency in a polymorphic salamander. The eastern red‐backed salamander, Plethodon cinereus, is widely distributed in northeastern North America and contains two common color morphs, striped and unstriped, that are divergent in ecology, behavior, and physiology. To quantify population structure, rates of gene flow, and genetic drift, we amplified 10 microsatellite loci from 648 individuals across 28 sampling localities. This study was conducted in northern Ohio, where populations of P. cinereus exhibit an unusually wide range of morph frequency variation. To test whether genetic distance was more correlated with morph frequency, elevation, canopy cover, waterways, ecological niche or geographic distance, we used resistance distance and least cost path analyses. We then examined whether landscape and environmental variables, genetic distance or geographic distance were correlated with variation in morph frequency. Tests for population structure revealed three genetic clusters across our sampling range, with one cluster monomorphic for the striped morph. Rates of gene flow and genetic drift were low to moderate across sites. Genetic distance was most correlated with ecological niche, elevation and a combination of landscape and environmental variables. In contrast, morph frequency variation was correlated with waterways and geographic distance. Thus, our results suggest that selection is also an important evolutionary force across our sites, and a balance between gene flow, genetic drift and selection interact to maintain the two color morphs.     

ResDisMapper: An r package for fine‐scale mapping of resistance to dispersal

Management of biological invasions and conservation activity in the fight against habitat fragmentation both require information on how ongoing dispersal of organisms is affected by the environment. However, there are few landscape genetic computer programs that map resistance to dispersal at small spatiotemporal scales. To facilitate such analyses, we present an r package named ResDisMapper for the mapping of resistance to dispersal at small spatiotemporal scales, without the need for prior knowledge on environmental features or intensive computation. Based on the concept of isolation by distance (IBD), ResDisMapper calculates resistance using deviations of each pair of samples from the general IBD trend (IBD residuals). The IBD residuals are projected onto the studied area, which allows construction and visualization of a fine‐scale map of resistance based on spatial accumulation of positive or negative IBD residuals. In this study, we tested ResDisMapper with both simulated and empirical data sets and compared its performance with two other popular landscape genetic programs. Overall, we found that ResDisMapper can map resistance with relatively high accuracy. The latest version of the package and associated documentation are available on Github ( https://github.com/takfung/ResDisMapper ).     

Landscape genetics identifies streams and drainage infrastructure as dispersal corridors for an endangered wetland bird

Anthropogenic alterations to landscape structure and composition can have significant impacts on biodiversity, potentially leading to species extinctions. Population‐level impacts of landscape change are mediated by animal behaviors, in particular dispersal behavior. Little is known about the dispersal habits of rails (Rallidae) due to their cryptic behavior and tendency to occupy densely vegetated habitats. The effects of landscape structure on the movement behavior of waterbirds in general are poorly studied due to their reputation for having high dispersal abilities. We used a landscape genetic approach to test hypotheses of landscape effects on dispersal behavior of the Hawaiian gallinule (Gallinula galeata sandvicensis), an endangered subspecies endemic to the Hawaiian Islands. We created a suite of alternative resistance surfaces representing biologically plausible a priori hypotheses of how gallinules might navigate the landscape matrix and ranked these surfaces by their ability to explain observed patterns in genetic distance among 12 populations on the island of O`ahu. We modeled effective distance among wetland locations on all surfaces using both cumulative least‐cost‐path and resistance‐distance approaches and evaluated relative model performance using Mantel tests, a causal modeling approach, and the mixed‐model maximum‐likelihood population‐effects framework. Across all genetic markers, simulation methods, and model comparison metrics, surfaces that treated linear water features like streams, ditches, and canals as corridors for gallinule movement outperformed all other models. This is the first landscape genetic study on the movement behavior of any waterbird species to our knowledge. Our results indicate that lotic water features, including drainage infrastructure previously thought to be of minimal habitat value, contribute to habitat connectivity in this listed subspecies.     

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.     

rCUR:an R package for CUR matrix decomposition

ABSTRACT: BACKGROUND: Many methods for dimensionality reduction of large data sets such as those generated in microarray studies boil down to the Singular Value Decomposition (SVD). Although singular vectors associated with the largest singular values have strong optimality properties and can often be quite useful as a tool to summarize the data, they are linear combinations of up to all of the data points, and thus it is typically quite hard to interpret those vectors in terms of the application domain from which the data are drawn. Recently, an alternative dimensionality reduction paradigm, CUR matrix decompositions, has been proposed to address this problem and has been applied to genetic and internet data. CUR decompositions are low-rank matrix decompositions that are explicitly expressed in terms of a small number of actual columns and/or actual rows of the data matrix. Since they are constructed from actual data elements, CUR decompositions are interpretable by practitioners of the eld from which the data are drawn. RESULTS: We present an implementation to perform CUR matrix decompositions, in the form of a freely available, open source R-package called rCUR. This package will help users to perform CUR-based analysis on large-scale data, such as those obtained from different high-throughput technologies, in an interactive and exploratory manner. We show two examples that illustrate how CUR-based techniques make it possible to reduce signicantly the number of probes, while at the same time maintaining major trends in data and keeping the same classication accuracy. CONCLUSIONS: The package rCUR provides functions for the users to perform CUR-based matrix decompositions in the R environment. In gene expression studies, it gives an additional way of analysis of differential expression and discriminant gene selection based on the use of statistical leverage scores. These scores, which have been used historically in diagnostic regression analysis to identify outliers, can be used by rCUR to identify the most informative data points with respect to which to express the remaining data points.     

Using multilevel models to identify drivers of landscape‐genetic structure among management areas

Landscape genetics offers a powerful approach to understanding species' dispersal patterns. However, a central obstacle is to account for ecological processes operating at multiple spatial scales, while keeping research outcomes applicable to conservation management. We address this challenge by applying a novel multilevel regression approach to model landscape drivers of genetic structure at both the resolution of individuals and at a spatial resolution relevant to management (i.e. local government management areas: LGAs) for the koala (Phascolartos cinereus) in Australia. Our approach allows for the simultaneous incorporation of drivers of landscape‐genetic relationships operating at multiple spatial resolutions. Using microsatellite data for 1106 koalas, we show that, at the individual resolution, foliage projective cover (FPC) facilitates high gene flow (i.e. low resistance) until it falls below approximately 30%. Out of six additional land‐cover variables, only highways and freeways further explained genetic distance after accounting for the effect of FPC. At the LGA resolution, there was significant variation in isolation‐by‐resistance (IBR) relationships in terms of their slopes and intercepts. This was predominantly explained by the average resistance distance among LGAs, with a weaker effect of historical forest cover. Rates of recent landscape change did not further explain variation in IBR relationships among LGAs. By using a novel multilevel model, we disentangle the effect of landscape resistance on gene flow at the fine resolution (i.e. among individuals) from effects occurring at coarser resolutions (i.e. among LGAs). This has important implications for our ability to identify appropriate scale‐dependent management actions.     

Geographic patterns of (genetic, morphologic, linguistic) variation: how barriers can be detected by using Monmonier's algorithm

When sampling locations are known, the association between genetic and geographic distances can be tested by spatial autocorrelation or regression methods. These tests give some clues to the possible shape of the genetic landscape. Nevertheless, correlation analyses fail when attempting to identify where genetic barriers exist, namely, the areas where a given variable shows an abrupt rate of change. To this end, a computational geometry approach is more suitable because it provides the locations and the directions of barriers and because it can show where geographic patterns of two or more variables are similar. In this frame we have implemented Monmonier's (1973) maximum difference algorithm in a new software package to identify genetic barriers. To provide a more realistic representation of the barriers in a genetic landscape, we implemented in the software a significance test by means of bootstrap matrices analysis. As a result, the noise associated with genetic markers can be visualized on a geographic map and the areas where genetic barriers are more robust can be identified. Moreover, this multiple matrices approach can visualize the patterns of variation associated with different markers in the same overall picture. This improved Monmonier's method is highly reliable and can be applied to nongenetic data whenever sampling locations and a distance matrix between corresponding data are available.     

phylin: an r package for phylogeographic interpolation

phylin is a package for the r programming environment which offers different methods to spatially interpolate genetic information from phylogeographic data. These interpolations can be used to predict the spatial occurrence of different lineages within a phylogeny using a modified method of kriging, which allows the usage of a genetic distance matrix to derive a model of spatial dependence. phylin improves the available methods to generate interpolated surfaces from a phylogenetic trees by assessing the autocorrelation structure of the genetic information, interpolating the genetic data based on a statistical model, estimating the uncertainty of the predictions and identifying lineage occurrence and contact zones probability without projection of pairwise genetic distances into mid‐points between sample locations. The package also includes methods to plot interpolation surfaces and provide summary tables from the generated data and models. We provide an example of the usefulness of this tool by inferring the spatial occurrence of distinct historical evolutionary lineages of the Lataste's viper (Vipera latastei Boscá, 1878) in the Iberian Peninsula and identifying potential contact areas. The maps of phylogenetic patterns obtained with these methods provide a spatial context to test hypotheses related to processes underlying the geographic distribution of genetic diversity and to inform conservation planning.     

Forest cover mediates genetic connectivity of northwestern cougars

Population structure, connectivity, and dispersal success of individuals can be challenging to demonstrate for solitary carnivores with low population densities. Though the cougar (Puma concolor) is widely distributed throughout North America and is capable of dispersing long distances, populations can be geographically structured and genetic isolation has been documented in some small populations. We described genetic structure and explored the relationship between landscape resistance and genetic variation in cougars in Washington and southern British Columbia using allele frequencies of 17 microsatellite loci for felids. We evaluated population structure of cougars using the Geneland clustering algorithm and spatial principal components analysis. We then used Circuitscape to estimate the landscape resistance between pairs of individuals based on rescaled GIS layers for forest canopy cover, elevation, human population density and highways. We quantified the effect of landscape resistance on genetic distance using multiple regression on distance matrices and boosted regression tree analysis. Cluster analysis identified four populations in the study area. Multiple regression on distance matrices and boosted regression tree models indicated that only forest canopy cover and geographic distance between individuals had an effect on genetic distance. The boundaries between genetic clusters largely corresponded with breaks in forest cover, showing agreement between population structure and genetic gradient analyses. Our data indicate that forest cover promotes gene flow for cougars in the Pacific Northwest, which provides insight managers can use to preserve or enhance genetic connectivity.     

introgress: a software package for mapping components of isolation in hybrids

A new software package (introgress) provides functions for analysing introgression of genotypes between divergent, hybridizing lineages, including estimating genomic clines from multi-locus genotype data and testing for deviations from neutral expectations. The software works with co-dominant, dominant and haploid marker data, and does not require fixed allelic differences between parental populations for the sampled genetic markers. Permutation and parametric procedures generate neutral expectations for introgression and provide a basis for significance tests of observed genomic clines. The software also implements maximum likelihood estimates of hybrid index from genotypic data and a number of graphical analyses. The package is an extension of the R statistical software, is written in the R language and is freely available through the Comprehensive R Archive Network (CRAN; http://cran.r-project.org/). In this study, we describe introgress and demonstrate its use with a sample data set.     

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.     

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.     

pedantics: an r package for pedigree-based genetic simulation and pedigree manipulation, characterization and viewing

Analyses of pedigrees and pedigree-derived parameters (e.g. relatedness and fitness) provide some of the most informative types of studies in evolutionary biology. The r package pedantics implements tools to facilitate power and sensitivity analyses of pedigree-related studies of natural populations. Functions are available to permute pedigree data in various ways with the goal of mimicking patterns of pedigree errors and missingness that occur in studies of natural populations. Another set of functions simulates genetic and phenotypic data based on arbitrary pedigrees. Finally, functions are also available with which visual and numerical representations of pedigree structure can be generated.     

hzar: hybrid zone analysis using an R software package

We present a new software package (hzar ) that provides functions for fitting molecular genetic and morphological data from hybrid zones to classic equilibrium cline models using the Metropolis–Hastings Markov chain Monte Carlo (MCMC) algorithm. The software applies likelihood functions appropriate for different types of data, including diploid and haploid genetic markers and quantitative morphological traits. The modular design allows flexibility in fitting cline models of varying complexity. To facilitate hypothesis testing, an autofit function is included that allows automated model selection from a set of nested cline models. Cline parameter values, such as cline centre and cline width, are estimated and may be compared statistically across clines. The package is written in the R language and is available through the Comprehensive R Archive Network (CRAN; http://cran.r-project.org/ ). Here, we describe hzar and demonstrate its use with a sample data set from a well‐studied hybrid zone in western Panama between white‐collared (Manacus candei) and golden‐collared manakins (M. vitellinus). Comparisons of our results with previously published results for this hybrid zone validate the hzar software. We extend analysis of this hybrid zone by fitting additional models to molecular data where appropriate.