Classifying ecosystem stressor interactions: Theory highlights the data limitations of the additive null model and the difficulty in revealing ecological surprises.

Benjamin J Burgess,David J Murrell,Drew Purves,Georgina Mace

doi:10.1111/gcb.15630

Benjamin J Burgess, David J Murrell + Show 2 more

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https://doi.org/10.1111/gcb.15630

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Abstract

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.

Highlights

Ecological communities are being subjected to a wide variety of external stressors (Halpern et al, 2015) that act across terrestrial, freshwater and marine biomes and threaten ecosystems and their services (Scheffers et al, 2016)
For a better comparison with the empirical data, and to test the robustness of the additive null model to observation error, we modelled observation error by taking the 360,000 theoretical interactions from our original analyses and multiplying the density of each trophic level by a random number drawn from a Gaussian distribution with a mean of 1.00 and standard deviation of
We found no strong difference between the classification of stressor interactions from either form of food chain model (Table 1), or between the different lengths of food chains, indicating the frequencies of interaction classifications were robust to these details of the models

Summary

Introduction

Ecological communities are being subjected to a wide variety of external stressors (Halpern et al, 2015) that act across terrestrial, freshwater and marine biomes and threaten ecosystems and their services (Scheffers et al, 2016). These stressors, termed drivers, factors or perturbations (Orr et al, 2020), are frequently anthropogenic in origin (Geldmann et al, 2014; Vörösmarty et al, 2010), but are capable of being abiotic or biotic (Przeslawski et al, 2015), and are able to act at the local to global scales (Ban et al, 2014; França et al, 2020). Knowledge of how stressors interact is important in guiding conservation and management initiatives, and in helping to prevent remediation measures from being ineffective, or even potentially harming those ecosystems they are intended to preserve (Brown et al, 2013; Côté et al, 2016)

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