ISEM, CNRS, Univ Montpellier, IRD , Montpellier, France.
Santa Fe Institute, 1399 Hyde Park Road , Santa Fe, NM 87501, USA.
Proc Biol Sci. 2024 Oct;291(2032):20240930. doi: 10.1098/rspb.2024.0930. Epub 2024 Oct 9.
Predicting how ecological communities will respond to disturbances is notoriously challenging, especially given the variability in species' responses within the same community. Focusing solely on aggregate responses may obscure extinction risks for certain species owing to compensatory effects, emphasizing the need to understand the drivers of the response variability at the species level. Yet, these drivers remain poorly understood. Here, we reveal that despite the typical complexity of biotic interaction networks, species' responses follow a discernible pattern. Specifically, we demonstrate that the species whose population abundances are most reduced by biotic interactions-which are not always the rarest species-are those that exhibit the strongest responses to disturbances. This insight enables us to pinpoint sensitive species within communities without requiring precise information about biotic interactions. Our novel approach introduces avenues for future research aimed at identifying sensitive species and elucidating their impacts on entire communities.
预测生态群落将如何对干扰做出反应是一项极具挑战性的任务,尤其是考虑到同一群落中物种的反应具有变异性。仅仅关注总体反应可能会掩盖某些物种的灭绝风险,因为存在补偿效应,这强调了需要了解物种层面反应变异性的驱动因素。然而,这些驱动因素仍未被很好地理解。在这里,我们揭示了尽管生物相互作用网络通常具有复杂性,但物种的反应遵循一种可识别的模式。具体来说,我们证明了那些种群丰度受生物相互作用影响最大的物种——这些物种并不总是最稀有的物种——是对干扰反应最强的物种。这一观点使我们能够在不要求有关生物相互作用的精确信息的情况下,确定群落内的敏感物种。我们的新方法为未来的研究提供了途径,旨在识别敏感物种并阐明它们对整个群落的影响。