Multiscale assessment of functional connectivity: Landscape genetics of eastern indigo snakes in an anthropogenically fragmented landscape in central Florida.

Landscape features can strongly influence gene flow and the strength and direction of these effects may vary across spatial scales. However, few studies have evaluated methodological approaches for selecting spatial scales in landscape genetics analyses, in part because of computational challenges associated with optimizing landscape resistance surfaces (LRS). We used the federally threatened eastern indigo snake (Drymarchon couperi) in central Florida as a case study with which to compare the importance of landscape features and their scales of effect in influencing gene flow. We used genetic algorithms (ResistanceGA) to empirically optimize LRS using categorical land cover surfaces, multiscale resource selection surfaces (RSS), and four combinations of landscape covariates measured at multiple spatial scales (multisurface multiscale LRS). We compared LRS where scale was selected using pseudo- and full optimization. Multisurface multiscale LRS received more empirical support than LRS optimized from categorical land cover surfaces or RSS. Multiscale LRS with scale selected using full optimization generally outperformed those with scale selected using pseudo-optimization. Multiscale LRS with large spatial scales (1200-1800 m) received the most empirical support. Our results highlight the importance of considering landscape features across multiple spatial scales in landscape genetic analyses, particularly broad scales relative to species movement potential. Different effects of scale on home range-level movements and dispersal could explain weak associations between habitat suitability and gene flow in other studies. Our results also demonstrate the importance of large tracts of undeveloped upland habitat with heterogenous vegetation communities and low urbanization for promoting indigo snake connectivity.

Evaluation of the R package 'resistancega': A promising approach towards the accurate optimization of landscape resistance surfaces.

Understanding how landscape features affect gene flow has implications for numerous fields including molecular and evolutionary ecology. Despite this, modelling landscape resistance surfaces has remained a significant challenge. The R package resistancega was developed to provide a framework for optimizing landscape resistance surfaces. In this study, we assessed ResistanceGA's ability to recover the true resistance surface under a variety of scenarios, including when the underlying surface: (a) had different levels of spatial autocorrelation and (b) was transformed into a resistance surface using different functional transformations. These scenarios were evaluated with regard to varying sample size and varying levels of variance in the measure of genetic distance. We also assessed the ability of ResistanceGA to identify the true resistance surface among alternative correlated surfaces. In univariate simulations, correlation between the true and optimized resistance surfaces remained high with increased variance in genetic distance, but only when sample size was moderate to high (≥50). Model selection error was also driven by sample size with low type I error when simulations had moderate to high sample sizes, even with moderate to high variance in genetic distance and correlated alternative surfaces. ResistanceGA also performed well in multivariate simulationsbut had more difficulty identifying the true data generating surfaces when genetic data were simulated using an agent-based approach (especially with individual-based genetic data). Overall, our simulations highlight the ability of ResistanceGA to accurately optimize resistance surfaces but also underscore challenges in optimizing landscape resistance surfaces, especially with highly stochastic individual-based data.

Multiscale resistant kernel surfaces derived from inferred gene flow: An application with vernal pool breeding salamanders.

The importance of assessing spatial data at multiple scales when modelling species-environment relationships has been highlighted by several empirical studies. However, no landscape genetics studies have optimized landscape resistance surfaces by evaluating relevant spatial predictors at multiple spatial scales. Here, we model multiscale/layer landscape resistance surfaces to estimate resistance to inferred gene flow for two vernal pool breeding salamander species, spotted (Ambystoma maculatum) and marbled (A. opacum) salamanders. Multiscale resistance surface models outperformed spatial layers modelled at their original spatial scale. A resistance surface with forest land cover at a 500-m Gaussian kernel bandwidth and normalized vegetation index at a 100-m Gaussian kernel bandwidth was the top optimized resistance surface for A. maculatum, while a resistance surface with traffic rate and topographic curvature, both at a 500-m Gaussian kernel bandwidth, was the top optimized resistance surface for A. opacum. Species-specific resistant kernels were fit at all vernal pools in our study area with the optimized multiscale/layer resistance surface controlling kernel spread. Vernal pools were then evaluated and scored based on surrounding upland habitat (local score) and connectivity with other vernal pools on the landscape, with resistant kernels driving vernal pool connectivity scores. As expected, vernal pools that scored highest were in areas within forested habitats and with high vernal pool densities and low species-specific landscape resistance. Our findings highlight the success of using a novel analytical approach in a multiscale framework with applications beyond vernal pool amphibian conservation.

The Influence of Sex and Season on Conspecific Spatial Overlap in a Large, Actively-Foraging Colubrid Snake.

Understanding the factors influencing the degree of spatial overlap among conspecifics is important for understanding multiple ecological processes. Compared to terrestrial carnivores, relatively little is known about the factors influencing conspecific spatial overlap in snakes, although across snake taxa there appears to be substantial variation in conspecific spatial overlap. In this study, we described conspecific spatial overlap of eastern indigo snakes (Drymarchon couperi) in peninsular Florida and examined how conspecific spatial overlap varied by sex and season (breeding season vs. non-breeding season). We calculated multiple indices of spatial overlap using 6- and 3-month utilization distributions (UD) of dyads of simultaneously adjacent telemetered snakes. We also measured conspecific UD density values at each telemetry fix and modeled the distribution of those values as a function of overlap type, sex, and season using generalized Pareto distributions. Home range overlap between males and females was significantly greater than overlap between individuals of the same sex and male home ranges often completely contained female home ranges. Male home ranges overlapped little during both seasons, whereas females had higher levels of overlap during the non-breeding season. The spatial patterns observed in our study are consistent with those seen in many mammalian carnivores, in which low male-male overlap and high inter-sexual overlap provides males with greater access to females. We encourage additional research on the influence of prey availability on conspecific spatial overlap in snakes as well as the behavioral mechanisms responsible for maintaining the low levels of overlap we observed.

Effects of Photo and Genotype-Based Misidentification Error on Estimates of Survival, Detection and State Transition using Multistate Survival Models.

We simulated multistate capture histories (CHs) by varying state survival (ϕ), detection (p) and transition (ψ), number of total capture occasions and releases per capture occasion and then modified these scenarios to mimic false rejection error (FRE), a common misidentification error, resulting from the failure to match samples of the same individual. We then fit a multistate model and estimated accuracy, bias and precision of state-specific ϕ, p and ψ to better understand the effects of FRE on different simulation scenarios. As expected, ϕ, and p, decreased in accuracy with FRE, with lower accuracy when CHs were simulated under a shorter-term study and a lower number of releases per capture occasion (lower sample size). Accuracy of ψ estimates were robust to FRE except in those CH scenarios simulated using low sample size. The effect of FRE on bias was not consistent among parameters and differed by CH scenario. As expected, ϕ was negatively biased with increased FRE (except for the low ϕ low p CH scenario simulated with a low sample size), but we found that the magnitude of bias differed by scenario (high p CH scenarios were more negatively biased). State transition was relatively unbiased, except for the low p CH scenarios simulated with a low sample size, which were positively biased with FRE, and high p CH scenarios simulated with a low sample size. The effect of FRE on precision was not consistent among parameters and differed by scenario and sample size. Precision of ϕ decreased with FRE and was lowest with the low ϕ low p CH scenarios. Precision of p estimates also decreased with FRE under all scenarios, except the low ϕ high p CH scenarios. However, precision of ψ increased with FRE, except for those CH scenarios simulated with a low sample size. Our results demonstrate how FRE leads to loss of accuracy in parameter estimates in a multistate model with the exception of ψ when estimated using an adequate sample size.

Use of abundance of one species as a surrogate for abundance of others.

Indicator species concepts have a long history in conservation biology. Arguments in favor of these approaches generally stress expediency and assume efficacy. We tested the premise that the abundance patterns of one species can be used to infer those of other species. Our data consisted of 72,495 bird observations on 55 species across 1046 plots distributed across 30 sub basins. We analyzed abundance patterns at two spatial scales (plot and sub basin) and for empirical and a priori grouping. There were few significant indicator relationships at either scale or under either grouping rule, and those few we found did not explain a substantial portion of the abundance of other species. Coupled with the lack of proven efficacy for species surrogacy in the literature, our results indicate the utility of indicators and similar types of surrogate approaches must be demonstrated rather than assumed.

A resistant-kernel model of connectivity for amphibians that breed in vernal pools.

Pool-breeding amphibian populations operate at multiple scales, from the individual pool to surrounding upland habitat to clusters of pools. When metapopulation dynamics play a role in long-term viability, conservation efforts limited to the protection of individual pools or even pools with associated upland habitat may be ineffective over the long term if connectivity among pools is not maintained. Connectivity becomes especially important and difficult to assess in regions where suburban sprawl is rapidly increasing land development, road density, and traffic rates. We developed a model of connectivity among vernal pools for the four ambystomatid salamanders that occur in Massachusetts and applied it to the nearly 30,000 potential ephemeral wetlands across the state. The model was based on a modification of the kernel estimator (a density estimator commonly used in home range studies) that takes landscape resistance into account. The model was parameterized with empirical migration distances for spotted salamanders (Ambystoma maculatum), dispersal distances for marbled salamanders (A. opacum), and expert-derived estimates of landscape resistance. The model ranked vernal pools in Massachusetts by local, neighborhood, and regional connectivity and by an integrated measure of connectivity, both statewide and within ecoregions. The most functionally connected pool complexes occurred in southeastern and northeastern Massachusetts, areas with rapidly increasing suburban development. In a sensitivity analysis estimates of pool connectivity were relatively insensitive to uncertainty in parameter estimates, especially at the local and neighborhood scales. Our connectivity model could be used to prioritize conservation efforts for vernal-pool amphibian populations at broader scales than traditional pool-based approaches.