[Show abstract][Hide abstract] ABSTRACT: Context The ability of landscapes to impede species’ movement or gene flow may be quantified by resistance models. Few studies have assessed the performance of resistance models parameterized by expert opinion. In addition, resistance models differ in terms of spatial and thematic resolution as well as their focus on the ecology of a particular species or more generally on the degree of human modification of the landscape (i.e. landscape integrity). The effect of these design decisions on model accuracy is poorly understood. Objectives We sought to understand the influence of expert parameterization, resolution, and specificity (i.e. species-specific or landscape integrity) on the fit of resistance model predictions to empirical landscape patterns. Methods With genetic and observational data collected from Greater Sage-Grouse (Centrocercus urophasianus) in Washington State, USA, we used landscape genetic analysis and logistic regression to evaluate a range of resistance models in terms of their ability to predict empirical patterns of genetic differentiation and lek occupancy. Results We found that species-specific, fine resolution resistance models generally had stronger relationships to empirical patterns than coarse resolution or landscape integrity models, and that the expert models were less predictive than alternative parameterizations. Conclusions Our study offers an empirical framework to validate expert resistance models, suggests the need to match the grain of the data to the scale at which the species responds to landscape heterogeneity, and underscores the limitations of landscape integrity models when the species under study does not meet their assumptions.
[Show abstract][Hide abstract] ABSTRACT: Effective population size (Ne) is an important parameter in conservation genetics because it quantifies a population’s capacity to resist loss of genetic diversity due to inbreeding and drift. The classical approach to estimate Ne from genetic data involves grouping sampled individuals into discretely defined subpopulations assumed to be panmictic. Importantly, this assumption does not capture the continuous nature of populations genetically isolated by distance. Alternative approaches based on Wright’s genetic neighborhood concept quantify the local number of breeding individuals (NS) in a continuous population (as opposed to the global Ne). However, they do not reflect the potential for NS to vary spatially nor do they account for the resistance of a heterogeneous landscape to gene flow (isolation by resistance). Here, we describe an application of Wright’s neighborhood concept that provides spatially-explicit estimates of local NS from genetic data in continuous populations isolated by distance or resistance. We delineated local neighborhoods surrounding each sampled individual based on sigma (), a measure of the local extent of breeding. When was known, the linkage disequilibrium method applied to local neighborhoods produced unbiased estimates of NS that were highly variable across the landscape. NS near the periphery or areas surrounded by high resistance was as much as an order of magnitude lower compared to the center, raising the potential for a spatial component to extinction vortex dynamics in continuous populations. When is not known, it may be estimated from genetic data, but two methods we evaluated identified analysis extents that produced considerable bias or error in the estimate of NS. When is known or accurately estimated, and the assumptions of Wright’s neighborhood are met, the method we describe provides spatially explicit information regarding short-term genetic processes that may inform conservation genetic analyses and management.
[Show abstract][Hide abstract] ABSTRACT: Behavioral and genetic adaptations to spatiotemporal variation in habitat conditions allow species to maximize their biogeographic range and persist over time in dynamic environments. An understanding of these local adaptations can be used to guide management and conservation of populations over broad extents encompassing diverse habitats. This understanding is often achieved by identifying covariates related to species' occurrence in multiple independent studies conducted in relevant habitats and seasons. However, synthesis across studies is made difficult by differences in the model covariates evaluated and analytical frameworks employed. Furthermore, inferences may be confounded by spatiotemporal variation in which habitat attributes are limiting to the species' ecological requirements. In this study, we sought to quantify spatiotemporal variation in resource selection by the American marten (Martes americana) in forest ecosystems of the Pacific Northwest, USA. We developed resource selection functions for both summer and winter based on occurrence data collected in mesic and xeric forest habitats. Use of a consistent analytical framework facilitated comparisons. Habitat attributes predicting marten occurrence differed strongly between the two study areas, but not between seasons. Moreover, the spatial scale over which covariates were calculated greatly influenced their predictive power. In the mesic environment, marten resource selection was strongly tied to riparian habitats, whereas in the xeric environment, marten responded primarily to canopy cover and forest fragmentation. These differences in covariates associated with marten occurrence reflect differences in which factors were limiting to marten ecology in each study area, as well as local adaptations to habitat variability. Our results highlight the benefit of controlled metareplication studies in which analyses of multiple study areas and seasons at varying spatial scales are integrated into a single framework.
[Show abstract][Hide abstract] ABSTRACT: The marbled murrelet (Brachyramphus marmoratus) is a seabird in the family Alcidae that forages in nearshore waters of the Pacific Northwest, and nests in adjacent older-forest conifers within 80 km of shore. The species is of conservation concern due to habitat loss and declining numbers, and is listed as threatened in British Columbia, Canada and in the United States portion of its range south of Canada. Recent monitoring in the United States indicated that murrelet numbers continued to decline there, especially in the waters of Washington State. To better understand this decline, and to inform conservation planning for the species, we evaluated how terrestrial and marine factors influence the distribution and abundance of the murrelet in coastal waters, including whether at-sea hotspots of murrelet abundance exist. Murrelet at-sea abundance and distribution were determined by surveys conducted annually from 2000 to 2012 in coastal waters from the United States-Canada border south to San Francisco Bay. We summarized mean and variance of murrelet density at the scale of 5-km segments of coastal waters throughout this area. We used a boosted regression tree analysis to investigate the contributions of a suite of marine and terrestrial attributes to at-sea murrelet abundance in each segment. We observed several regional hotspots of higher murrelet abundance at sea. Terrestrial attributes made the strongest contribution, especially the amount and cohesiveness of suitable nesting habitat in proximity to each segment, whereas marine attributes explained less of the spatial and temporal variation in murrelet abundance. At-sea hotspots of murrelet abundance therefore reflect not only suitable marine foraging habitat but primarily the proximity of suitable inland nesting habitat.
Journal of Marine Systems 06/2014; DOI:10.1016/j.jmarsys.2014.06.010 · 2.51 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The predominant analytical approach to associate landscape patterns with gene flow processes is based on the association of cost distances with genetic distances between individuals. Mantel and partial Mantel tests have been the dominant statistical tools used to correlate cost distances and genetic distances in landscape genetics. However, the inherent high correlation among alternative resistance models results in a high risk of spurious correlations using simple Mantel tests. Several refinements, including causal modeling, have been developed to reduce the risk of affirming spurious correlations and to assist model selection. However, the evaluation of these approaches has been incomplete in several respects. To demonstrate the general reliability of the causal modeling approach with Mantel tests, it must be shown to be able to correctly identify a wide range of landscape resistance models as the correct drivers relative to alternative hypotheses. The objectives of this study were to (1) evaluate the effectiveness of the originally published causal modeling framework to support the correct model and reject alternative hypotheses of isolation by distance and isolation by barriers and to (2) evaluate the effectiveness of causal modeling involving direct competition of all hypotheses to support the correct model and reject all alternative landscape resistance models. We found that partial Manteltests have very low Type II error rates, but elevated Type I error rates. This leads to frequent identification of support for spurious correlations between alternative resistance hypotheses and genetic distance, independent of the true resistance model. The frequency in which this occurs is directly related to the degree of correlation between true and alternative resistance models. We propose an improvement based on the relative support of the causal modeling diagnostic tests.
[Show abstract][Hide abstract] ABSTRACT: Population connectivity is mediated by the movement of organisms or propagules through landscapes. However, little is known about how variation in the pattern of landscape mosaics affects the detectability of landscape genetic relationships. The goal of this paper is to explore the impacts of limiting factors on landscape genetic processes using simulation modeling. We used spatially explicit, individual-based simulation modeling to quantify the effects of habitat area, fragmentation and the contrast in resistance between habitat and non-habitat on the apparent strength and statistical detectability of landscape genetic relationships. We found that landscape genetic effects are often not detectable when habitat is highly connected. In such situations landscape structure does not limit gene flow. We also found that contrast in resistance values between habitat and non-habitat interacts with habitat extensiveness and fragmentation to affect detectability of landscape genetic relationships. Thus, the influence of landscape features critical to providing connectivity may not be detectable if gene flow is not limited by spatial patterns or resistance contrast of these features. We developed regression equations that reliably predict whether or not isolation by resistance will be detected independently of isolation by distance as a function of habitat fragmentation and contrast in resistance between habitat and non-habitat.
[Show abstract][Hide abstract] ABSTRACT: Climate change is likely to alter population connectivity, particularly for species associated with higher elevation environments. The goal of this study is to predict the potential effects of future climate change on population connectivity and genetic diversity of American marten populations across a 30.2 million hectare region of the in the US northern Rocky Mountains. We use a landscape resistance model validated from empirical landscape genetics modeling to predict the current and expected future extent and fragmentation of American marten dispersal habitat under five climate change scenarios, corresponding to climatic warming of between 0.7 and 3.3 °C, consistent with expected climate change by year 2080. We predict the regions of the current and future landscapes where gene flow is expected to be governed by isolation by distance and the regions where population fragmentation is expected to limit gene flow. Finally, we predict changes in the strength and location of predicted movement corridors, fracture zones and the location of dispersal barriers across the study area in each scenario. We found that under the current climate, gene flow is predicted to be limited primarily by distance (isolation), and landscape structure does not significantly limit gene flow, resulting in very high genetic diversity over most of the study area. Projected climatic warming substantially reduces the extent and increases the fragmentation of marten populations in the western and northwestern parts of the study area. In contrast, climate change is not predicted to fragment the extensive higher elevation mountain massifs in central Idaho, the northern U.S. continental divide, and Greater Yellowstone Ecosystem. In addition, we show locations in the study area that are important corridors in the current landscape that remain intact across the climate change scenarios.
[Show abstract][Hide abstract] ABSTRACT: Little is known about how variation in landscape mosaics affects genetic differentiation. The goal of this paper is to quantify
the relative importance of habitat area and configuration, as well as the contrast in resistance between habitat and non-habitat,
on genetic differentiation. We hypothesized that habitat configuration would be more influential than habitat area in influencing
genetic differentiation. Population size is positively related to habitat area, and therefore habitat area should affect genetic
drift, but not gene flow. In contrast, differential rates and patterns of gene flow across a landscape should be related to
habitat configuration. Using spatially explicit, individual-based simulation modeling, we found that habitat configuration
had stronger relationships with genetic differentiation than did habitat area, but there was a high degree of confounding
between the effects of habitat area and configuration. We evaluated the predictive ability of six widely used landscape metrics
and found that patch cohesion and correlation length of habitat are among the strongest individual predictors of genetic differentiation.
Correlation length, patch density and clumpy are the most parsimonious set of variables to predict the magnitude of genetic
differentiation in complex landscapes.
KeywordsLandscape genetics–Area–Configuration–Fragmentation–Limiting factors–CDPOP–Simulation–Thresholds
[Show abstract][Hide abstract] ABSTRACT: 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.
[Show abstract][Hide abstract] ABSTRACT: Anthropogenic landscape changes have greatly reduced the population size, range and migration rates of many terrestrial species. The small local effective population size of remnant populations favours loss of genetic diversity leading to reduced fitness and adaptive potential, and thus ultimately greater extinction risk. Accurately quantifying genetic diversity is therefore crucial to assessing the viability of small populations. Diversity indices are typically calculated from the multilocus genotypes of all individuals sampled within discretely defined habitat patches or larger regional extents. Importantly, discrete population approaches do not capture the clinal nature of populations genetically isolated by distance or landscape resistance. Here, we introduce spatial Genetic Diversity (sGD), a new spatially explicit tool to estimate genetic diversity based on grouping individuals into potentially overlapping genetic neighbourhoods that match the population structure, whether discrete or clinal. We compared the estimates and patterns of genetic diversity using patch or regional sampling and sGD on both simulated and empirical populations. When the population did not meet the assumptions of an island model, we found that patch and regional sampling generally overestimated local heterozygosity, inbreeding and allelic diversity. Moreover, sGD revealed fine-scale spatial heterogeneity in genetic diversity that was not evident with patch or regional sampling. These advantages should provide a more robust means to evaluate the potential for genetic factors to influence the viability of clinal populations and guide appropriate conservation plans.
[Show abstract][Hide abstract] ABSTRACT: 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.