Climate is one of the main factors driving species distributions and global biodiversity patterns. Obtaining accurate predictions of species' range shifts in response to ongoing climate change has thus become a key issue in ecology and conservation. Correlative species distribution models (cSDMs) have become a prominent tool to this aim in the last decade and have demonstrated good predictive abilities with current conditions, irrespective of the studied taxon. However, cSDMs rely on statistical association between species' presence and environmental conditions and have rarely been challenged on their actual capacity to reflect causal relationships between species and climate. In this study, we question whether cSDMs can accurately identify if climate and species distributions are causally linked, a prerequisite for accurate prediction of range shift in relation to climate change. We compared the performance of cSDMs in predicting the distributions of 132 European terrestrial species, chosen randomly within five taxonomic groups (three vertebrate groups and two plant groups), and of 1,320 virtual species whose distribution is causally fully independent from climate. We found that i) for real species, the performance of cSDMs varied principally with range size, rather than with taxonomic groups and ii) cSDMs did not predict the distributions of real species with a greater accuracy than the virtual ones. Our results unambiguously show that the high predictive power of cSDMs can be driven by spatial autocorrelation in climatic and distributional data and does not necessarily reflect causal relationships between climate and species distributions. Thus, high predictive performance of cSDMs does not ensure that they accurately depict the role of climate in shaping species distributions. Our findings therefore call for strong caution when using cSDMs to provide predictions on future range shifts in response to climate change.

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相关气候生态位模型同样可以很好地预测真实和虚拟物种分布

气候是推动物种分布和全球生物多样性模式的主要因素之一。因此，准确预测物种的范围变化以响应持续的气候变化已成为生态和保护的关键问题。在过去十年中，相关物种分布模型 (cSDM) 已成为实现这一目标的重要工具，并且无论所研究的分类群如何，都已证明对当前条件具有良好的预测能力。然而，cSDM 依赖于物种存在与环境条件之间的统计关联，并且很少受到其反映物种与气候之间因果关系的实际能力的挑战。在这项研究中，我们质疑 cSDM 是否可以准确识别气候和物种分布是否存在因果关系，准确预测与气候变化相关的范围变化的先决条件。我们比较了 cSDM 在预测 132 个欧洲陆地物种的分布方面的性能，这些物种是在五个分类群（三个脊椎动物群和两个植物群）中随机选择的，以及 1,320 个虚拟物种的分布，这些物种的分布在因果关系上完全独立于气候。我们发现 i）对于真实物种，cSDM 的性能主要随范围大小而变化，而不是随着分类群而变化；ii）cSDM 没有比虚拟物种更准确地预测真实物种的分布。我们的结果明确表明，cSDM 的高预测能力可以由气候和分布数据中的空间自相关驱动，并不一定反映气候和物种分布之间的因果关系。因此，cSDM 的高预测性能并不能确保它们准确地描述气候在塑造物种分布中的作用。因此，我们的研究结果要求在使用 cSDM 预测未来因气候变化而发生的范围变化时要非常谨慎。