Understanding biotic interactions is a crucial goal in community ecology and species distribution modelling, and large strides have been made towards improving multivariate computational methods with the aim of quantifying biotic interactions and improving predictions of species occurrence. Yet, while considerable attention has been given to computational approaches and the interpretation of these quantitative tools, the importance of sampling design to reveal these biotic interactions has received little consideration. This study explores the influential role of priority effects, that is, the order of habitat colonisation, in shaping our ability to detect biotic interactions. Using a simple set of simulations, we demonstrate that commonly used cross‐sectional co‐occurrence data alone cannot be used to make reliable inferences on asymmetric biotic interactions, even if they perform well in predicting the occurrence of species. We then show how sampling designs that consider priority effects can recover the asymmetric effects that are lost when priority effects are ignored. Based on these findings, we urge for caution when drawing inferences on biotic interactions from cross‐sectional binary co‐occurrence data, and provide guidance on sampling designs that may provide the necessary data to tackle this longstanding challenge.

中文翻译：

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如果不考虑优先效应，就无法推断不对称生物相互作用


了解生物相互作用是群落生态学和物种分布建模的一个重要目标，并且在改进多元计算方法方面已经取得了长足的进步，其目的是量化生物相互作用和改进对物种发生的预测。然而，尽管人们对计算方法和这些定量工具的解释给予了相当多的关注，但很少有人考虑到抽样设计揭示这些生物相互作用的重要性。这项研究探讨了优先效应（即栖息地殖民顺序）在塑造我们检测生物相互作用的能力方面的影响作用。使用一组简单的模拟，我们证明了常用的横截面共现数据不能单独用于对不对称生物相互作用做出可靠的推断，即使它们在预测物种的出现方面表现良好。然后，我们展示考虑优先效应的抽样设计如何恢复忽略优先效应时丢失的不对称效应。基于这些发现，我们敦促在从横截面二元共现数据中推断生物相互作用时要谨慎，并提供抽样设计指导，以提供必要的数据来应对这一长期存在的挑战。