Abstract1. Understanding how landscape features affect functional connectivity among populations is a cornerstone of spatial ecology and landscape genetic analyses.However, parameterization of resistance surfaces that best describe connectivity is a challenging and often subjective process. 2.ResistanceGA is an R package that utilizes a genetic algorithm to optimize resistance surfaces based on pairwise genetic data and effective distances calculated using CIRCUITSCAPE, least cost paths or random-walk commute times.Functions in this package allow for the optimization of categorical and continuous resistance surfaces, and simultaneous optimization of multiple resistance surfaces.3. ResistanceGA provides a coherent framework to optimize resistance surfaces without a priori assumptions, conduct model selection, and make inference about the contribution of each surface to total resistance. 4.ResistanceGA fills a void in the landscape genetic toolbox, allowing for unbiased optimization of resistance surfaces and for the simultaneous optimization of multiple resistance surfaces to create novel composite resistance surfaces, but could have broader applicability to other fields of spatial ecological research.

Landscape genetics has seen tremendous advances since its introduction, but parameterization and optimization of resistance surfaces still poses significant challenges. Despite increased availability and resolution of spatial data, few studies have integrated empirical data to directly represent ecological processes as genetic resistance surfaces. In our study, we determine the landscape and ecological factors affecting gene flow in the western slimy salamander (Plethodon albagula). We used field data to derive resistance surfaces representing salamander abundance and rate of water loss through combinations of canopy cover, topographic wetness, topographic position, solar exposure and distance from ravine. These ecologically explicit composite surfaces directly represent an ecological process or physiological limitation of our organism. Using generalized linear mixed-effects models, we optimized resistance surfaces using a nonlinear optimization algorithm to minimize model AIC. We found clear support for the resistance surface representing the rate of water loss experienced by adult salamanders in the summer. Resistance was lowest at intermediate levels of water loss and higher when the rate of water loss was predicted to be low or high. This pattern may arise from the compensatory movement behaviour of salamanders through suboptimal habitat, but also reflects the physiological limitations of salamanders and their sensitivity to extreme environmental conditions. Our study demonstrates that composite representations of ecologically explicit processes can provide novel insight and can better explain genetic differentiation than ecologically implicit landscape resistance surfaces. Additionally, our study underscores the fact that spatial estimates of habitat suitability or abundance may not serve as adequate proxies for describing gene flow, as predicted abundance was a poor predictor of genetic differentiation.

BackgroundSignificant shifts in climate are considered a threat to plants and animals with significant physiological limitations and limited dispersal abilities. The southern Appalachian Mountains are a global hotspot for plethodontid salamander diversity. Plethodontids are lungless ectotherms, so their ecology is strongly governed by temperature and precipitation. Many plethodontid species in southern Appalachia exist in high elevation habitats that may be at or near their thermal maxima, and may also have limited dispersal abilities across warmer valley bottoms.Methodology/Principal FindingsWe used a maximum-entropy approach (program Maxent) to model the suitable climatic habitat of 41 plethodontid salamander species inhabiting the Appalachian Highlands region (33 individual species and eight species included within two species complexes). We evaluated the relative change in suitable climatic habitat for these species in the Appalachian Highlands from the current climate to the years 2020, 2050, and 2080, using both the HADCM3 and the CGCM3 models, each under low and high CO2 scenarios, and using two-model thresholds levels (relative suitability thresholds for determining suitable/unsuitable range), for a total of 8 scenarios per species.Conclusion/SignificanceWhile models differed slightly, every scenario projected significant declines in suitable habitat within the Appalachian Highlands as early as 2020. Species with more southern ranges and with smaller ranges had larger projected habitat loss. Despite significant differences in projected precipitation changes to the region, projections did not differ significantly between global circulation models. CO2 emissions scenario and model threshold had small effects on projected habitat loss by 2020, but did not affect longer-term projections. Results of this study indicate that choice of model threshold and CO2 emissions scenario affect short-term projected shifts in climatic distributions of species; however, these factors and choice of global circulation model have relatively small affects on what is significant projected loss of habitat for many salamander species that currently occupy the Appalachian Highlands.

Geographic isolation substantially contributes to species endemism on oceanic islands when speciation involves the colonisation of a new island. However, less is understood about the drivers of speciation within islands. What is lacking is a general understanding of the geographic scale of gene flow limitation within islands, and thus the spatial scale and drivers of geographical speciation within insular contexts. Using a community of beetle species, we show that when dispersal ability and climate tolerance are restricted, microclimatic variation over distances of only a few kilometres can maintain strong geographic isolation extending back several millions of years. Further to this, we demonstrate congruent diversification with gene flow across species, mediated by Quaternary climate oscillations that have facilitated a dynamic of isolation and secondary contact. The unprecedented scale of parallel species responses to a common environmental driver for evolutionary change has profound consequences for understanding past and future species responses to climate variation.

Environmental gradients are instrumental in shaping the distribution and local abundance of species because at the most fundamental level, an organism’s performance is constrained by the environment it inhabits. In topographically complex landscapes, slope, aspect, and vegetative cover interact to affect solar exposure, creating temperature-moisture gradients and unique microclimates. The significance of the interaction of abiotic gradients and biotic factors such as competition, movement, or physiology has long been recognized, but the scale at which these factors vary on the landscape has generally precluded their inclusion in spatial abundance models. We used fine-scale spatial data relating to surface-soil moisture, temperature, and canopy cover to describe the spatial distribution of abundance of a terrestrial salamander, Plethodon albagula, across the landscape. Abundance was greatest in dense-canopy ravine habitats with high moisture and low solar exposure, resulting in a patchy distribution of abundance. We hypothesize that these patterns reflect the physiological constraints of Plethodontid salamanders. Furthermore, demographic cohorts were not uniformly distributed among occupied plots on the landscape. The probability of gravid female occurrence was nearly uniform among occupied plots, but juveniles were much more likely to occur on plots with lower surface temperatures. The disconnect between reproductive effort and recruitment suggests that survival differs across the landscape and that local population dynamics vary spatially. Our study demonstrates a connection between abundance, fine-scale environmental gradients, and population dynamics, providing a foundation for future research concerning movement, population connectivity, and physiology.