AbstractSpecies are frequently responding to contemporary climate change by shifting to higher elevations and poleward to track suitable climate space. However, depending on local conditions and species’ sensitivity, the nature of these shifts can be highly variable and difficult to predict. Here, we examine how the American pika (Ochotona princeps), a philopatric, montane lagomorph, responds to climatic gradients at three spatial scales. Using mixed‐effects modeling in an information‐theoretic approach, we evaluated a priori model suites regarding predictors of site occupancy, relative abundance, and elevational‐range retraction across 760 talus patches, nested within 64 watersheds across the Northern Rocky Mountains of North America, during 2017–2020. The top environmental predictors differed across these response metrics. Warmer temperatures in summer and winter were associated with lower occupancy, lower relative abundances, and greater elevational retraction across watersheds. Occupancy was also strongly influenced by habitat patch size, but only when combined with climate metrics such as actual evapotranspiration. Using a second analytical approach, acute heat stress and summer precipitation best explained retraction residuals (i.e., the relative extent of retraction given the original elevational range of occupancy). Despite the study domain occurring near the species’ geographic‐range center, where populations might have higher abundances and be at lower risk of climate‐related stress, 33.9% of patches showed evidence of recent extirpations. Pika‐extirpated sites averaged 1.44℃ warmer in summer than did occupied sites. Additionally, the minimum elevation of pika occupancy has retracted upslope in 69% of watersheds (mean: 281 m). Our results emphasize the nuance associated with evaluating species’ range dynamics in response to climate gradients, variability, and temperature exceedances, especially in regions where species occupy gradients of conditions that may constitute multiple range edges. Furthermore, this study highlights the importance of evaluating diverse drivers across response metrics to improve the predictive accuracy of widely used, correlative models.