Differences in Spatiotemporal Patterns of Vehicle Collisions with Wildlife and Livestock.

Road ecology research has tended to focus on wildlife-vehicle collisions (WVCs) while omitting or failing to differentiate domestic (i.e., livestock) animal-vehicle collisions (DAVCs). This has limited our understanding of where, when, and how frequently DAVCs occur, and whether these patterns differ from those for WVCs. We used a 10-year collision data set for the U.S. state of Montana to compare temporal and spatial patterns of DAVCs versus WVCs at multiple scales. WVCs exhibited two diel peaks (dawn and dusk) versus only one prominent peak (late evening/early night) for DAVCs. Seasonal patterns of WVCs and DAVCs were broadly similar, but DAVCs exhibited a more pronounced late-fall peak. At the county scale, DAVCs were overrepresented relative to WVCs in most of eastern Montana and underrepresented in most of western Montana. WVC and DAVC hotpots did not show strong overlap at the 1-mile road segment scale. Our results suggest that DAVCs warrant greater attention, and they may represent a high priority for management and mitigation measures in some areas because (1) they can be locally common even when regionally rare, (2) they are more dangerous to motorists on a per-collision basis than WVCs, and (3) they can present a legal liability for livestock owners. Mitigation measures for DAVCs may differ from those for WVCs and require further development and testing. Future data collection efforts should include information not only on the location and timing of animal-vehicle collisions, but also on the species of animals killed.

Ecological and sociopolitical assessment of congressional and presidential designation of federal protected areas.

Protected areas are one of the most effective means by which biodiversity is conserved, but are often criticized for either neglecting the importance of local communities or sacrificing conservation objectives for political expedience. In the United States, federal protected areas can be designated via a democratic legislation process or via executive action, which allows for comparison of the ecological and sociopolitical context of these top-down and bottom-up processes. We compared protected areas resulting from congressional designation vs. presidential designation with respect to their ecological context (using measures of biodiversity and climate refugial potential) and sociopolitical context (using measures of local support for conservation and reliance on natural resource-based industries). We found minimal differences between these designation modes for both ecological and sociopolitical variables. These results suggest that presidentially designated protected areas tend to be no more burdensome to local communities and no less valuable for ecological conservation than more widely accepted federal protected areas such as national parks, and they provide new evidence to inform the current debate over national monuments.

Simulating the spread of selection-driven genotypes using landscape resistance models for desert bighorn sheep.

Landscape genetic studies based on neutral genetic markers have contributed to our understanding of the influence of landscape composition and configuration on gene flow and genetic variation. However, the potential for species to adapt to changing landscapes will depend on how natural selection influences adaptive genetic variation. We demonstrate how landscape resistance models can be combined with genetic simulations incorporating natural selection to explore how the spread of adaptive variation is affected by landscape characteristics, using desert bighorn sheep (Ovis canadensis nelsoni) in three differing regions of the southwestern United States as an example. We conducted genetic sampling and least-cost path modeling to optimize landscape resistance models independently for each region, and then simulated the spread of an adaptive allele favored by selection across each region. Optimized landscape resistance models differed between regions with respect to landscape variables included and their relationships to resistance, but the slope of terrain and the presence of water barriers and major roads had the greatest impacts on gene flow. Genetic simulations showed that differences among landscapes strongly influenced spread of adaptive genetic variation, with faster spread (1) in landscapes with more continuously distributed habitat and (2) when a pre-existing allele (i.e., standing genetic variation) rather than a novel allele (i.e., mutation) served as the source of adaptive genetic variation. The combination of landscape resistance models and genetic simulations has broad conservation applications and can facilitate comparisons of adaptive potential within and between landscapes.

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.