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Data from: Combining past and contemporary species occurrences with ordinal species distribution modeling to investigate responses to climate change

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Jan 10, 2025 version files 48.49 KB

Abstract

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 present-day occurrences within a single modeling framework remains challenging. Traditional binary species distribution modeling reduces occurrence to two temporally vague 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 vs. occupied in the past vs. 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 pika (Ochotona princeps), a climate-sensitive mammal. Sampling over 5 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 several other past pika investigations employing binary analysis. 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 are common, yet 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.