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.

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.

Rapid environmental degradation in a subarctic ecosystem influences resource use of a keystone avian herbivore.

1. Environmental degradation can change resource use strategies of animals and thereby affect survival and fitness. Arctic herbivores may be especially susceptible to the effects of such environmental change because their rapid growth rates demand high-quality forage, which may be limited as environmental conditions deteriorate. We studied the consequences of a trophic cascade, driven by Lesser Snow Goose (Chen caerulescens caerulescens) overgrazing on the south-west coast of Hudson Bay, Canada, which has caused tidal marsh (TM) degradation and the reduction in high-quality forage plants, on gosling growth and resource use. 2. We compared resource use and body size of goslings that inhabited tidal and freshwater marsh (FM) to determine how current foraging strategies influence growth and to test the hypothesis that during early growth goslings require and so consume high-quality TM plants, but that during later growth they may switch to foraging in lower-quality FM. 3. To investigate gosling resource use throughout growth, we measured once a week for 28 days the body size of goslings as well as stable isotope ratios (δ(34) S, δ(15) N and δ(13) C) in multiple tissues of goslings that were collected from both TM and nearby FM. We also measured the stable isotope ratios in forage plants sampled along transects and from gosling foreguts. We used an isotope-mixing model to determine the contribution of FM plants to gosling tissues. 4. Contrary to the proposed hypothesis, goslings inhabiting FM or TM primarily consumed FM plants during early growth. Furthermore, goslings that foraged extensively in FM had similar growth rates and grew to a similar size and body mass, as goslings that foraged in the degraded TM. However, goslings that currently inhabit freshwater or TM were significantly smaller than goslings that inhabited TM in the 1980s prior to habitat degradation. 5. Consequences of smaller overall body size include decreased survival and fecundity for arctic-nesting geese. The ability of phenotypically plastic responses to sustain persistence is limited by reaction norms and the extent of environmental change. Current research is assessing whether those limits have been reached in this system.

An adaptive framework for selecting environmental monitoring protocols to support ocean renewable energy development.

Offshore renewable energy developments (OREDs) are projected to become common in the United States over the next two decades. There are both a need and an opportunity to guide efforts to identify and track impacts to the marine ecosystem resulting from these installations. A monitoring framework and standardized protocols that can be applied to multiple types of ORED would streamline scientific study, management, and permitting at these sites. We propose an adaptive and reactive framework based on indicators of the likely changes to the marine ecosystem due to ORED. We developed decision trees to identify suites of impacts at two scales (demonstration and commercial) depending on energy (wind, tidal, and wave), structure (e.g., turbine), and foundation type (e.g., monopile). Impacts were categorized by ecosystem component (benthic habitat and resources, fish and fisheries, avian species, marine mammals, and sea turtles) and monitoring objectives were developed for each. We present a case study at a commercial-scale wind farm and develop a monitoring plan for this development that addresses both local and national environmental concerns. In addition, framework has provided a starting point for identifying global research needs and objectives for understanding of the potential effects of ORED on the marine environment.