Although the management and restoration of habitat is the key method to conserve species of interest, local habitat management often fails to elicit desired responses in populations. Landscape features beyond the local habitat scale affect the population dynamics of ring‐necked pheasants (Phasianus colchicus), but the mechanism behind this response is unknown. One possibility is that nest survival, which is primarily reduced by nest predation, is regulating pheasant responses to the landscape. We investigated the extent to which land use affected nest survival by studying 202 artificial nests on 12 Conservation Reserve Program (CRP) fields in Nebraska, USA with varying surrounding land‐use practices. After running a hierarchical analysis of competing models, we found that predicted nest survival increased as the amount of CRP, winter wheat, and pastureland surrounding a CRP field increased, whereas increasing fallow fields was correlated with decreased nest success. Our findings support theoretical and empirical evidence that nest predation rates are shaped by predator search efficacy. Changing the relative availability of nesting habitat that potentially holds alternative prey sources in our study affected nest survival rates, possibly by altering the search area of opportunistic nest predators. The similarities between the landscape relationships that predict nest survival and landscape predictors of pheasant abundance indicate that nest survival may potentially act as the mechanism shaping population dynamics within an ever changing farmland ecosystem. We recommend that managers consider the land use surrounding areas under consideration for habitat improvement to enhance conservation investments. © 2016 The Wildlife Society.

Understanding species-habitat relationships is vital to successful conservation, but the tools used to communicate species-habitat relationships are often poorly suited to the information needs of conservation practitioners. Here we present a novel method for translating a statistical species-habitat model, a regression analysis relating ring-necked pheasant abundance to landcover, into an interactive online tool. The Pheasant Habitat Simulator combines the analytical power of the R programming environment with the user-friendly Shiny web interface to create an online platform in which wildlife professionals can explore the effects of variation in local landcover on relative pheasant habitat suitability within spatial scales relevant to individual wildlife managers. Our tool allows users to virtually manipulate the landcover composition of a simulated space to explore how changes in landcover may affect pheasant relative habitat suitability, and guides users through the economic tradeoffs of landscape changes. We offer suggestions for development of similar interactive applications and demonstrate their potential as innovative science delivery tools for diverse professional and public audiences.

Home range estimation is an important analytical method in applied spatial ecology, yet best practices for addressing the effects of spatial variation in detection probability on home range estimates remain elusive. We introduce the R package "DiagnoseHR," simulation tools for assessing how variation in detection probability arising from landscape, animal behavior, and methodological processes affects home range inference. We demonstrate the utility of simulation methods for home range analysis planning by comparing bias arising from three home range estimation methods under multiple detection scenarios. We simulated correlated random walks in three landscapes that varied in detection probability and constructed home ranges from locations filtered through a range of sampling protocols. Home range estimates were less biased by reduced detection probability when sampling effort was increased, but the effects of sampling day distribution were minimal. Like others, we found that kernel density estimates were the least affected by variation in detection probability, while minimum convex polygons were most affected. Our results illustrate the value of quantifying uncertainty in home range estimates and suggest that field biologists working in environments with low detection may wish to weight sample-size greater than concerns about temporal autocorrelation when designing sampling protocols.

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