Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1111/gcb.15630 |
Classifying ecosystem stressor interactions: Theory highlights the data limitations of the additive null model and the difficulty in revealing ecological surprises | |
Benjamin J. Burgess; Drew Purves; Georgina Mace; David J. Murrell | |
2021-05-06 | |
发表期刊 | Global Change Biology
![]() |
出版年 | 2021 |
英文摘要 | 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. |
领域 | 气候变化 ; 资源环境 |
URL | 查看原文 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/325838 |
专题 | 气候变化 资源环境科学 |
推荐引用方式 GB/T 7714 | Benjamin J. Burgess,Drew Purves,Georgina Mace,et al. Classifying ecosystem stressor interactions: Theory highlights the data limitations of the additive null model and the difficulty in revealing ecological surprises[J]. Global Change Biology,2021. |
APA | Benjamin J. Burgess,Drew Purves,Georgina Mace,&David J. Murrell.(2021).Classifying ecosystem stressor interactions: Theory highlights the data limitations of the additive null model and the difficulty in revealing ecological surprises.Global Change Biology. |
MLA | Benjamin J. Burgess,et al."Classifying ecosystem stressor interactions: Theory highlights the data limitations of the additive null model and the difficulty in revealing ecological surprises".Global Change Biology (2021). |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论