Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, London, UK.
DeepMind, London, UK.
Glob Chang Biol. 2021 Jul;27(13):3052-3065. doi: 10.1111/gcb.15630. Epub 2021 May 6.
Understanding how multiple co-occurring environmental stressors combine to affect biodiversity and ecosystem services is an on-going grand challenge for ecology. Currently, progress has been made through accumulating large numbers of smaller-scale empirical studies that are then investigated by meta-analyses to detect general patterns. There is particular interest in detecting, understanding and predicting 'ecological surprises' where stressors interact in a non-additive (e.g. antagonistic or synergistic) manner, but so far few general results have emerged. However, the ability of the statistical tools to recover non-additive interactions in the face of data uncertainty is unstudied, so crucially, we do not know how well the empirical results reflect the true stressor interactions. Here, we investigate the performance of the commonly implemented additive null model. A meta-analysis of a large (545 interactions) empirical dataset for the effects of pairs of stressors on freshwater communities reveals additive interactions dominate individual studies, whereas pooling the data leads to an antagonistic summary interaction class. However, analyses of simulated data from food chain models, where the underlying interactions are known, suggest both sets of results may be due to observation error within the data. Specifically, we show that the additive null model is highly sensitive to observation error, with non-additive interactions being reliably detected at only unrealistically low levels of data uncertainty. Similarly, plausible levels of observation error lead to meta-analyses reporting antagonistic summary interaction classifications even when synergies co-dominate. Therefore, while our empirical results broadly agree with those of previous freshwater meta-analyses, we conclude these patterns may be driven by statistical sampling rather than any ecological mechanisms. Further investigation of candidate null models used to define stressor-pair interactions is essential, and once any artefacts are accounted for, the so-called 'ecological surprises' may be more frequent than was previously assumed.
理解多种共存的环境胁迫因子如何共同影响生物多样性和生态系统服务是生态学目前面临的一个重大挑战。目前，该领域的进展主要是通过积累大量小规模的实证研究，然后通过元分析来检测一般模式。人们特别感兴趣的是检测、理解和预测“生态惊喜”，即胁迫因子以非加性（例如拮抗或协同）的方式相互作用，但到目前为止，很少有普遍的结果出现。然而，统计工具在面对数据不确定性时恢复非加性相互作用的能力尚未得到研究，因此至关重要的是，我们不知道实证结果在多大程度上反映了真实的胁迫因子相互作用。在这里，我们研究了常用的加性零模型的性能。对大量（545 个相互作用）关于胁迫因子对淡水群落影响的实证数据集的元分析表明，加性相互作用主导了单个研究，而汇总数据则导致拮抗的综合相互作用类。然而，对食物链模型中已知潜在相互作用的模拟数据的分析表明，这两种结果都可能是由于数据中的观测误差造成的。具体来说，我们表明加性零模型对观测误差非常敏感，只有在不切实际的低数据不确定性水平下才能可靠地检测到非加性相互作用。同样，合理水平的观测误差会导致元分析报告拮抗的综合相互作用分类，即使协同作用占主导地位。因此，虽然我们的实证结果与之前的淡水元分析结果大致一致，但我们得出的结论是，这些模式可能是由统计抽样而不是任何生态机制驱动的。进一步调查用于定义胁迫因子对相互作用的候选零模型是必要的，一旦考虑到任何人为因素，所谓的“生态惊喜”可能比以前假设的更为频繁。
