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 pika Ochotona 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.

中文翻译：

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将过去和现在的物种出现与有序物种分布模型相结合，以研究对气候变化的响应


许多生物体会留下它们以前出现的证据，例如粪便、废弃的洞穴、中穴、古老的 eDNA 或化石，这表明一个物种已经消失的区域。然而，在单个建模框架中将这些证据与当代事件相结合仍然具有挑战性。传统的二元物种分布模型将发生率简化为两种时间上的粗略状态（现在/不存在），因此无法利用过去发生的证据的时间序列中固有的信息。相比之下，序数建模可以使用状态的自然时变顺序（例如，从未占用、以前占用与当前占用）来更深入地了解范围偏移。我们展示了序数建模在识别生物地理和气候变量对美国鼠兔（一种气候敏感型哺乳动物）当前和过去居住情况的主要影响方面的能力。在物种最南端的暖边范围限制上进行了五年的采样，我们在 570 个栖息地斑块中测试了这些变量的影响，这些栖息地的发生被归类为二元或有序。这两项分析产生了不同的顶级模型和预测因子——顺序模型强调慢性寒冷是最重要的发生预测因子，而二元模型表明整个夏季的平均温度是首要的。在发生可能性较高的有序模型中，较冷的冬季温度相关，我们假设这反映了绝缘和融水供应积雪的保留时间更长。我们的二进制结果反映了过去采用二进制分析的其他鼠兔研究的结果，其中温暖的温度会降低发生的可能性。 由于顺序分析和二进制分析顶级模型都包含气候和生物地理因素，因此结果构成了气候适应规划的重要考虑因素。物种出现的跨时间证据在评估对气候变化的反应方面仍然没有得到充分利用。与假设所有状态都发生在同一时间段内的多状态占用建模相比，序数模型能够利用物种出现的历史证据来识别驱动物种随时间分布的因素。