S. A. Cushman

Quantifying the lag time to detect barriers in landscape genetics

Understanding how spatial genetic patterns respond to landscape change is crucial for advancing the emerging field of landscape genetics. We quantified the number of generations for new landscape barrier signatures to become detectable and for old signatures to disappear after barrier removal. We used spatially explicit, individual‐based simulations to examine the ability of an individual‐based statistic [Mantel’s r using the proportion of shared alleles’ statistic (Dps)] and population‐based statistic (FST) to detect barriers. We simulated a range of movement strategies including nearest neighbour dispersal, long‐distance dispersal and panmixia. The lag time for the signal of a new barrier to become established is short using Mantel’s r (1–15 generations). FST required approximately 200 generations to reach 50% of its equilibrium maximum, although G’ST performed much like Mantel’s r. In strong contrast, FST and Mantel’s r perform similarly following the removal of a barrier formerly dividing a population. Also, given neighbour mating and very short‐distance dispersal strategies, historical discontinuities from more than 100 generations ago might still be detectable with either method. This suggests that historical events and landscapes could have long‐term effects that confound inferences about the impacts of current landscape features on gene flow for species with very little long‐distance dispersal. Nonetheless, populations of organisms with relatively large dispersal distances will lose the signal of a former barrier within less than 15 generations, suggesting that individual‐based landscape genetic approaches can improve our ability to measure effects of existing landscape features on genetic structure and connectivity.

Identifying future research needs in landscape genetics: Where to from here?

Landscape genetics is an emerging interdisciplinary field that combines methods and concepts from population genetics, landscape ecology, and spatial statistics. The interest in landscape genetics is steadily increasing, and the field is evolving rapidly. We here outline four major challenges for future landscape genetic research that were identified during an international landscape genetics workshop. These challenges include (1) the identification of appropriate spatial and temporal scales; (2) current analytical limitations; (3) the expansion of the current focus in landscape genetics; and (4) interdisciplinary communication and education. Addressing these research challenges will greatly improve landscape genetic applications, and positively contribute to the future growth of this promising field.

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

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

CDPOP: A spatially explicit cost distance population genetics program

Spatially explicit simulation of gene flow in complex landscapes is essential to explain observed population responses and provide a foundation for landscape genetics. To address this need, we wrote a spatially explicit, individual‐based population genetics model (cdpop). The model implements individual‐based population modelling with Mendelian inheritance and k‐allele mutation on a resistant landscape. The model simulates changes in population and genotypes through time as functions of individual based movement, reproduction, mortality and dispersal on a continuous cost surface. This model will be a valuable tool for the study of landscape genetics by increasing our understanding about the effects of life history, vagility and differential models of landscape resistance on the genetic structure of populations in complex landscapes.

A simulation-based evaluation of methods for inferring linear barriers to gene flow

Different analytical techniques used on the same data set may lead to different conclusions about the existence and strength of genetic structure. Therefore, reliable interpretation of the results from different methods depends on the efficacy and reliability of different statistical methods. In this paper, we evaluated the performance of multiple analytical methods to detect the presence of a linear barrier dividing populations. We were specifically interested in determining if simulation conditions, such as dispersal ability and genetic equilibrium, affect the power of different analytical methods for detecting barriers. We evaluated two boundary detection methods (Monmonier’s algorithm and WOMBLING), two spatial Bayesian clustering methods (TESS and GENELAND), an aspatial clustering approach (STRUCTURE), and two recently developed, non‐Bayesian clustering methods [PSMIX and discriminant analysis of principal components (DAPC)]. We found that clustering methods had higher success rates than boundary detection methods and also detected the barrier more quickly. All methods detected the barrier more quickly when dispersal was long distance in comparison to short‐distance dispersal scenarios. Bayesian clustering methods performed best overall, both in terms of highest success rates and lowest time to barrier detection, with GENELAND showing the highest power. None of the methods suggested a continuous linear barrier when the data were generated under an isolation‐by‐distance (IBD) model. However, the clustering methods had higher potential for leading to incorrect barrier inferences under IBD unless strict criteria for successful barrier detection were implemented. Based on our findings and those of previous simulation studies, we discuss the utility of different methods for detecting linear barriers to gene flow.

Simulating the effects of climate change on population connectivity of American marten (Martes americana) in the northern Rocky Mountains, USA

We utilize empirically derived estimates of landscape resistance to assess current landscape connectivity of American marten (Martes americana) in the northern Rocky Mountains, USA, and project how a warming climate may affect landscape resistance and population connectivity in the future. We evaluate the influences of five potential future temperature scenarios involving different degrees of warming. We use resistant kernel dispersal models to assess population connectivity based on full occupancy of suitable habitat in each of these hypothetical future resistance layers. We use the CDPOP model to simulate gene exchange among individual martens in each of these hypothetical future climates. We evaluate: (1) changes in the extent, connectivity and pattern of marten habitat, (2) changes in allelic richness and expected heterozygosity, and (3) changes in the range of significant positive genetic correlation within the northern Idaho marten population under each future scenario. We found that even moderate warming scenarios resulted in very large reductions in population connectivity. Calculation of genetic correlograms for each scenario indicates that climate driven changes in landscape connectivity results in decreasing range of genetic correlation, indicating more isolated and smaller genetic neighborhoods. These, in turn, resulted in substantial loss of allelic richness and reductions in expected heterozygosity. In the U.S. northern Rocky Mountains, climate change may extensively fragment marten populations to a degree that strongly reduces genetic diversity. Our results demonstrate that for species, such as the American marten, whose population connectivity is highly tied to climatic gradients, expected climate change can result in profound changes in the extent, pattern, connectivity and gene flow of populations.

Relationships between migration rates and landscape resistance assessed using individual-based simulations

Linking landscape effects on gene flow to processes such as dispersal and mating is essential to provide a conceptual foundation for landscape genetics. It is particularly important to determine how classical population genetic models relate to recent individual‐based landscape genetic models when assessing individual movement and its influence on population genetic structure. We used classical Wright–Fisher models and spatially explicit, individual‐based, landscape genetic models to simulate gene flow via dispersal and mating in a series of landscapes representing two patches of habitat separated by a barrier. We developed a mathematical formula that predicts the relationship between barrier strength (i.e., permeability) and the migration rate (m) across the barrier, thereby linking spatially explicit landscape genetics to classical population genetics theory. We then assessed the reliability of the function by obtaining population genetics parameters (m, FST) using simulations for both spatially explicit and Wright–Fisher simulation models for a range of gene flow rates. Next, we show that relaxing some of the assumptions of the Wright–Fisher model can substantially change population substructure (i.e., FST). For example, isolation by distance among individuals on each side of a barrier maintains an FST of ∼0.20 regardless of migration rate across the barrier, whereas panmixia on each side of the barrier results in an FST that changes with m as predicted by classical population genetics theory. We suggest that individual‐based, spatially explicit modelling provides a general framework to investigate how interactions between movement and landscape resistance drive population genetic patterns and connectivity across complex landscapes.

Limiting factors and landscape connectivity: the American marten in the Rocky Mountains

In mobile animals, movement behavior can maximize fitness by optimizing access to critical resources and minimizing risk of predation. We sought to evaluate several hypotheses regarding the effects of landscape structure on American marten foraging path selection in a landscape experiencing forest perforation by patchcut logging. We hypothesized that in the uncut pre-treatment landscape marten would choose foraging paths to maximize access to cover types that support the highest density of prey. In contrast, in the post-treatment landscapes we hypothesized marten would choose paths primarily to avoid crossing openings, and that this would limit their ability to optimally select paths to maximize foraging success. Our limiting factor analysis shows that different resistant models may be supported under changing landscape conditions due to threshold effects, even when a species’ response to landscape variables is constant. Our results support previous work showing forest harvest strongly affects marten movement behavior. The most important result of our study, however, is that the influence of these features changes dramatically depending on the degree to which timber harvest limits available movement paths. Marten choose foraging paths in uncut landscapes to maximize time spent in cover types providing the highest density of prey species. In contrast, following landscape perforation by patchcuts, marten strongly select paths to avoid crossing unforested areas. This strong response to patch cutting reduces their ability to optimize foraging paths to vegetation type. Marten likely avoid non-forested areas in fragmented landscapes to reduce risk of predation and to benefit thermoregulation in winter, but in doing so they may suffer a secondary cost of decreased foraging efficiency.

Simulating Pattern-Process Relationships to Validate Landscape Genetic Models

Landscapes may resist gene flow and thereby give rise to a pattern of genetic isolation within a population. The mechanism by which a landscape resists gene flow can be inferred by evaluating the relationship between landscape models and an observed pattern of genetic isolation. This approach risks false inferences because researchers can never feasibly test all plausible alternative hypotheses. In this paper, rather than infer the process of gene flow from an observed genetic pattern, we simulate gene flow and determine if the simulated genetic pattern is related to the observed empirical genetic pattern. This is a form of inverse modeling and can be used to independently validate a landscape genetic model. In this study, we used this approach to validate a model of landscape resistance based on elevation, landcover, and roads that was previously related to genetic isolation among mountain goats (Oreamnos americanus) inhabiting the Cascade Range, Washington (USA). The strong relationship between the empirical and simulated patterns of genetic isolation we observed provides independent validation of the resistance model and demonstrates the utility of this approach in supporting landscape genetic inferences.

Simulating natural selection in landscape genetics

Linking landscape effects to key evolutionary processes through individual organism movement and natural selection is essential to provide a foundation for evolutionary landscape genetics. Of particular importance is determining how spatially‐explicit, individual‐based models differ from classic population genetics and evolutionary ecology models based on ideal panmictic populations in an allopatric setting in their predictions of population structure and frequency of fixation of adaptive alleles. We explore initial applications of a spatially‐explicit, individual‐based evolutionary landscape genetics program that incorporates all factors – mutation, gene flow, genetic drift and selection – that affect the frequency of an allele in a population. We incorporate natural selection by imposing differential survival rates defined by local relative fitness values on a landscape. Selection coefficients thus can vary not only for genotypes, but also in space as functions of local environmental variability. This simulator enables coupling of gene flow (governed by resistance surfaces), with natural selection (governed by selection surfaces). We validate the individual‐based simulations under Wright‐Fisher assumptions. We show that under isolation‐by‐distance processes, there are deviations in the rate of change and equilibrium values of allele frequency. The program provides a valuable tool (cdpop v1.0; http://cel.dbs.umt.edu/software/CDPOP/) for the study of evolutionary landscape genetics that allows explicit evaluation of the interactions between gene flow and selection in complex landscapes.