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The black‐tailed prairie dog (Cynomys ludovicianus) is a keystone species of North American short‐ and mixed‐grass temperate grasslands with critical importance for multiple species of conservation concern. Low population numbers due to habitat and social constraints as well as disease require conservation and management actions. To identify potential habitat and future relocation sites, we evaluated 6 statistical techniques to predict the optimal resource selection function model for black‐tailed prairie dog habitat in 3 Native nations of Montana. Random forest models performed best across our study area and included grass coverage and climatic and topographic variables as the most important predictors of prairie dog occupancy. Using predictive habitat models in conjunction with land‐use designations can refine conservation planning to support wildlife population objectives where there are foundations for long‐term success. Our analysis also serves as a guide for assessing habitat suitability, connectivity, and restoration for additional species dependent or interconnected with black‐tailed prairie dogs. [ABSTRACT FROM AUTHOR]

Understanding spatial and temporal variation in beaver abundance is a central goal for a wide range of management issues, ranging from species reintroductions to mitigation of environmental and economic impacts. Yet due to high costs associated with surveys, many studies are limited to a single regional estimate, or a complete census of a smaller study area extrapolated to the surrounding landscape. We present a survey design that allows for predicting beaver abundance across the broader landscape through interpolation. In October 2019, we conducted an aerial survey in a 15,000 km2 study area around Algonquin Provincial Park in Ontario, Canada. We counted 145 colonies on 73 plots that averaged 4.5 km2 (+/−3.27 SD). Our regional estimate for beaver abundance of 0.55 (95% CI = +/−0.18) colonies/km2 is comparable to historical surveys conducted in the region in the 1970s. We then predicted beaver abundance in unsampled plots using a Poisson generalized additive model (adjusted R2 = 0.81, deviance explained = 55.9%) that included non‐linear responses to elevation (P < 0.001), shoreline complexity (P = 0.003), and availability of shade‐tolerant hardwoods (P = 0.001). Our species distribution model predicted strong east‐west patterns in beaver abundance across the study region associated with spatial patterns in elevation and forest composition. [ABSTRACT FROM AUTHOR]