Resource selection functions (RSFs) are tremendously valuable for ecologists and resource managers because they quantify spatial patterns in resource utilization by wildlife, thereby facilitating identification of critical habitat areas and characterizing specific habitat features that are selected or avoided. RSFs discriminate between known‐use resource units (e.g., telemetry locations) and available (or randomly selected) resource units based on an array of environmental features, and in their standard form are performed using logistic regression. As generalized linear models, standard RSFs have some notable limitations, such as difficulties in accommodating nonlinear (e.g., humped or threshold) relationships and complex interactions. Increasingly, ecologists are using flexible machine‐learning methods (e.g., random forests, neural networks) to overcome these limitations. Herein, we investigate the seasonal resource selection patterns of mule deer (Odocoileus hemionus) by comparing a logistic regression framework with random forest (RF), a popular machine‐learning algorithm. Random forest (RF) models detected nonlinear relationships (e.g., optimal ranges for slope and elevation) and complex interactions which would have been very challenging to discover and characterize using standard model‐based approaches. Compared with standard RSF models, RF models exhibited improved predictive skill, provided novel insights about resource selection patterns of mule deer, and, when projected across a relevant geographic space, manifested notable differences in predicted habitat suitability. We recommend that wildlife researchers harness the strengths of machine‐learning tools like RF in addition to “classical” tools (e.g., mixed‐effects logistic regression) for evaluating resource selection, especially in cases where extensive telemetry data sets are available.

Climate models predict that shifts in temperature and precipitation patterns are likely to occur across the globe. Changing climate will likely have strong effects on arid environments as a result of increased temperatures, increasing frequency and intensity of droughts, and less consistent pulses of rainfall. Therefore, understanding the link between patterns of precipitation, temperature, and population performance of species occupying these environments will continue to increase in importance as climatic shifts occur within these natural ecosystems. We sought to evaluate how individual, maternal, population, and environmental, particularly temperature and precipitation, level factors influence population performance of a large herbivore in an arid environment. We used mule deer (Odocoileus hemionus) as a representative species and quantified juvenile survival to test hypotheses about effects of environmental factors on population performance. Precipitation events occurring in mid‐ to late‐pregnancy (January–April) leading to spring green‐up, as indexed by normalized difference in vegetation index, had the strongest positive effect on juvenile survival and recruitment. In addition, larger neonates had an increased probability of survival. Our findings indicate that timing and amount of precipitation prior to parturition have strong influences on maternal nutritional condition, which was passed on to young. These results have important implications for understanding how animal populations may benefit from timing of precipitation during spring and prior to parturition, especially in arid environments.

Large herbivores exhibit relatively slow-paced life histories, and allocate resources toward maintaining high rates of adult survival, while juvenile survival has greater variability. Maternal females make decisions throughout life stages of reproduction to meet their nutritional demands while simultaneously ensuring survival and recruitment of young to maximize fitness. We investigated tradeoffs associated with resource selection by mule deer (Odocoileus hemionus) surrounding stages of reproduction in Mojave National Preserve, CA, United States. To understand potential tradeoffs associated with offspring survival and maternal nutritional condition, we measured differences in patterns of resource selection among pre-parturient females, females provisioning young, and females following the loss of young. The third trimester of gestation and lactation are considered the most nutritionally demanding stages of reproduction. We hypothesized that energetic costs would change rapidly throughout those stages of reproduction, especially after the loss of an offspring. Further, we hypothesized that lactating females would balance the acquisition of nutritional sources with safety of young. We used radio-collar and randomly generated locations to model resource selection in a hierarchical approach utilizing machine learning algorithms and traditional resource selection functions (RSFs). We also monitored recruitment of young born to GPS-collared females using VHF radio-collars equipped with mortality indicators. During all three stages of reproduction, adult females selected greater NDVI, less rugged terrain, areas close to water (especially while provisioning offspring), and higher elevations. Selection for greater levels of NDVI was stronger pre-parturition and following the loss of offspring compared to when females were provisioning offspring. We also observed high variation toward the selection of NDVI among individual females while provisioning young, which was less pronounced during the other reproductive stages. Offspring survival during our study was positively associated with females that selected greater levels of NDVI. Further, we were not able to detect a tradeoff between safety of young