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Globally, many species’ distributions are shifting in response to contemporary climate change. However, the direction and rate of shifts remain difficult to predict, impeding managers’ abilities to optimize resource allocation. Here, we developed a new approach for forecasting species range‐limit shifts that requires only abundance data along environmental (for example, elevational) gradients. We posited that the distribution of species’ abundances could offer insights into the potential for future range‐limit shifts. We then tested this prediction using data from several transect studies that compared historical and contemporary distributions. Consistent with our prediction, we found that strong asymmetry in abundance distributions (that is, “leaning” distributions) indeed preceded species’ lower‐limit range shifts. Accordingly, surveying abundances along environmental gradients may represent a promising, cost‐effective method for forecasting local shifts. Ideally, this approach will be incorporated by practitioners into species‐specific management planning and will inform on‐the‐ground conservation efforts. 

Many organisms leave evidence of their former occurrence, such as scat, abandoned burrows, middens, ancient eDNA or fossils, which indicate areas from which a species has since disappeared. However, combining this evidence with contemporary occurrences within a single modeling framework remains challenging. Traditional binary species‐distribution modeling reduces occurrence to two temporally coarse states (present/absent), so thus cannot leverage the information inherent in temporal sequences of evidence of past occurrence. In contrast, ordinal modeling can use the natural time‐varying order of states (e.g. never occupied versus previously occupied versus currently occupied) to provide greater insights into range shifts. We demonstrate the power of ordinal modeling for identifying the major influences of biogeographic and climatic variables on current and past occupancy of the American pikaOchotona princeps, a climate‐sensitive mammal. Sampling over five years across the species' southernmost, warm‐edge range limit, we tested the effects of these variables at 570 habitat patches where occurrence was classified either as binary or ordinal. The two analyses produced different top models and predictors – ordinal modeling highlighted chronic cold as the most‐important predictor of occurrence, whereas binary modeling indicated primacy of average summer‐long temperatures. Colder wintertime temperatures were associated in ordinal models with higher likelihood of occurrence, which we hypothesize reflect longer retention of insulative and meltwater‐provisioning snowpacks. Our binary results mirrored those of other past pika investigations employing binary analysis, wherein warmer temperatures decrease likelihood of occurrence. Because both ordinal‐ and binary‐analysis top models included climatic and biogeographic factors, results constitute important considerations for climate‐adaptation planning. Cross‐time evidences of species occurrences remain underutilized for assessing responses to climate change. Compared to multi‐state occupancy modeling, which presumes all states occur in the same time period, ordinal models enable use of historical evidence of species' occurrence to identify factors driving species' distributions more finely across time.