Ecological forecasting models play an increasingly important role for managing natural resources and assessing our fundamental knowledge of processes driving ecological dynamics. As global environmental change pushes ecosystems beyond their historical conditions, the utility of these models may depend on their transferability to novel conditions. Because species interactions can alter resource use, timing of reproduction, and other aspects of a species' realized niche, changes in biotic conditions, which can arise from community reorganization events in response to environmental change, have the potential to impact model transferability. Using a long‐term experiment on desert rodents, we assessed model transferability under novel biotic conditions to better understand the limitations of ecological forecasting. We show that ecological forecasts can be less accurate when the models generating them are transferred to novel biotic conditions and that the extent of model transferability can depend on the species being forecast. We also demonstrate the importance of incorporating uncertainty into forecast evaluation with transferred models generating less accurate and more uncertain forecasts. These results suggest that how a species perceives its competitive landscape can influence model transferability and that when uncertainties are properly accounted for, transferred models may still be appropriate for decision making. Assessing the extent of the transferability of forecasting models is a crucial step to increase our understanding of the limitations of ecological forecasts.

中文翻译：

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在长期实验研究中，生态预测模型向新型生物条件的可转移性


生态预测模型在管理自然资源和评估我们驱动生态动态过程的基本知识方面发挥着越来越重要的作用。随着全球环境变化将生态系统推向超出其历史条件，这些模型的效用可能取决于它们向新条件的可转移性。由于物种相互作用可以改变资源利用、繁殖时间以及物种已实现生态位的其他方面，因此生物条件的变化（可能由响应环境变化的群落重组事件引起）有可能影响模型的可转移性。使用对沙漠啮齿动物的长期实验，我们评估了模型在新型生物条件下的可转移性，以更好地了解生态预测的局限性。我们表明，当生成生态预测的模型被转移到新的生物条件时，生态预测的准确性可能较低，并且模型可转移性的程度可能取决于被预测的物种。我们还证明了将不确定性纳入预测评估的重要性，转移的模型生成的预测不太准确和更不确定。这些结果表明，一个物种如何看待其竞争格局会影响模型的可转移性，并且当不确定性得到适当考虑时，转移的模型可能仍然适合于决策。评估预测模型的可转移性程度是增加我们对生态预测局限性的理解的关键一步。