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Quantifying both the lethal and non-lethal (or “risk”) effects of predation has emerged as a major research focus in carnivore-ungulate systems. While numerous studies have examined predation risk and risk effects in recent decades, a lack of standardization in approaches has impeded progress in the field. We provide an overview of five major study design considerations involved in assessing predation risk and responses of prey in carnivore-ungulate systems, highlighting how different design choices can impact the strength and scope of inference. First, we stress the importance of distinguishing measures of predation risk (probability of being killed) from measures of risk effects (costs of antipredator behaviors in response to risk). Second, we recommend explicit consideration of spatial and temporal scales using a standardized framework to facilitate cross-study comparisons. Third, ungulates use visual, auditory, and olfactory sensory pathways to evaluate predation risk. Experiments that manipulate signals of risk (e.g., auditory playbacks or application of predator scent) can be powerful approaches, but the dosages and types of cues need to be carefully considered. Fourth, ungulates usually face threats from multiple predators simultaneously, and we highlight the potential for remote cameras and structural equation modeling to help address this challenge. Fifth, emerging technologies may substantially improve our ability to assess risk. We discuss several promising technologies, such as animal-borne video, unmanned aerial vehicles, and physiological sensors. We conclude with general recommendations for study design, which may improve the utility of predation risk research for the conservation and management of carnivore-ungulate systems.

Conservation strategies are hindered by a lack of accurate maps of important habitat for many wildlife species, but especially for species inhabiting managed forest landscapes. Prioritizing restoration efforts on Alaska’s Tongass National Forest from past extensive clearcut logging is extremely challenging given the difficulty in accurately mapping its remote, rugged temperate rainforest landscapes. We tested the application of airborne light detection and ranging (LiDAR) technology to build a winter habitat model for Sitka black-tailed deer (Odocoileus hemionus sitkensis), the primary herbivore in the coastal temperate rainforest. We analyzed the importance of geomorphometric and forest structure characteristics as predictors of deer winter habitat selection using Random Forest applied to a 3-year GPS relocation dataset collected from 40 adult female deer. The LiDAR-based habitat model had a predictive performance of 94% (Out-of-bag error = 6%), a 10% lower model error compared to air-photo interpreted polygons and modeled plot data. Random Forest also outperformed analogous resource selection function models based on a comprehensive k-fold cross-validation. Deer habitat selection patterns in the LiDAR-based model were nonlinear across geomorphometric and forest structure predictive variables, and generally supported existing studies of deer habitat selection. Besides improving deer conservation and management on the Tongass National Forest, our approach could greatly enhance the accuracy and resolution of habitat maps used for conservation and restoration planning across large managed forest landscapes.