Classifying ecosystem stressor interactions: Theory highlights the data limitations of the additive null model and the difficulty in revealing ecological surprises.
Lotka-Volterra
environmental drivers
food chain
freshwater
meta-analysis
multiple stressors
observation error
theoretical ecology
Journal
Global change biology
ISSN: 1365-2486
Titre abrégé: Glob Chang Biol
Pays: England
ID NLM: 9888746
Informations de publication
Date de publication:
07 2021
07 2021
Historique:
revised:
05
03
2021
received:
11
08
2020
accepted:
23
03
2021
pubmed:
9
4
2021
medline:
7
8
2021
entrez:
8
4
2021
Statut:
ppublish
Résumé
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.
Types de publication
Journal Article
Meta-Analysis
Langues
eng
Sous-ensembles de citation
IM
Pagination
3052-3065Informations de copyright
© 2021 The Authors. Global Change Biology published by John Wiley & Sons Ltd.
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