Bighorn sheep (Ovis canadensis) can live in extremely harsh environments and subsist on submaintenance diets for much of the year. Under these conditions, energy stored as body fat serves as an essential reserve for supplementing dietary intake to meet metabolic demands of survival and reproduction. We developed equations to predict ingesta-free body fat in bighorn sheep using ultrasonography and condition scores in vivo and carcass measurements postmortem. We then used in vivo equations to investigate the relationships between body fat, pregnancy, overwinter survival, and population growth in free-ranging bighorn sheep in California and Nevada. Among 11 subpopulations that included alpine winter residents and migrants, mean ingesta-free body fat of lactating adult females during autumn ranged between 8.8% and 15.0%; mean body fat for nonlactating females ranged from 16.4% to 20.9%. In adult females, ingesta-free body fat > 7.7% during January (early in the second trimester) corresponded with a > 90% probability of pregnancy and ingesta-free body fat > 13.5% during autumn yielded a probability of overwinter survival > 90%. Mean ingesta-free body fat of lactating females in autumn was positively associated with finite rate of population increase (λ) over the subsequent year in bighorn sheep subpopulations that wintered in alpine landscapes. Bighorn sheep with ingesta-free body fat of 26% in autumn and living in alpine environments possess energy reserves sufficient to meet resting metabolism for 83 days on fat reserves alone. We demonstrated that nutritional condition can be a pervasive mechanism underlying demography in bighorn sheep and characterizes the nutritional value of their occupied ranges. Mountain sheep are capital survivors in addition to being capital breeders, and because they inhabit landscapes with extreme seasonal forage scarcity, they also can be fat reserve obligates. Quantifying nutritional condition is essential for understanding the quality of habitats, how it underpins demography, and the proximity of a population to a nutritional threshold.

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.

Abstract. Loss of migratory corridors has been identified as an important ecological issue among species that exhibit long-distance migration worldwide. Increased mineral exploration and development has raised the level of concern over the protection of terrestrial migration routes for ungulates. Mineral exploration and other types of development may adversely affect migratory corridors for large herbivores, but little is known about functional effects on migratory behavior and resource selection. To address these important questions we examined movement patterns and resource selection to understand the effects of an operating gold mine on migratory pathways of mule deer (Odocoileus hemionus). We captured and applied radio collars to female mule deer (n ¼ 43) on the migratory pathway and in the proximity of an active mine in the Ruby Mountains of eastern Nevada. We used Brownian Bridge Movement Models to delineate stopover sites for each individual during both the autumn and spring migrations. We calculated linear efficiency of movement along the migration path and movement rate between stopover locations outside and within the mining area to determine the effects of the mine on movement patterns. We also used resource selection functions to determine if mule deer avoided areas with extensive excavation and disturbance of the land surface when navigating through the mine complex. Our results indicated greater linear efficiency of movement along the migration path and movement rates between stopover locations outside the mine when compared with movement through the mine complex. Additionally, mule deer that migrated through the mine complex avoided the highest disturbance levels by spending the majority of their time in undisturbed habitat patches. These results suggest an increase in energy expenditure of mule deer navigating through highly disturbed areas, which may have fitness consequences for migratory animals. Such increases in energy expenditure during migration may decrease survival or productivity of migratory populations of large, terrestrial mammals.