Towards a unified study of multiple stressors: divisions and common goals across research disciplines

Abstract

Anthropogenic environmental changes, or ‘stressors’, increasingly threaten biodiversity and ecosystem functioning worldwide. Multiple-stressor research is a rapidly expanding field of science that seeks to understand and ultimately predict the interactions between stressors. Reviews and meta-analyses of the primary scientific literature have largely been specific to either freshwater, marine or terrestrial ecology, or ecotoxicology. In this cross-disciplinary study, we review the state of knowledge within and among these disciplines to highlight commonality and division in multiple-stressor research. Our review goes beyond a description of previous research by using quantitative bibliometric analysis to identify the division between disciplines and link previously disconnected research communities. Towards a unified research framework, we discuss the shared goal of increased realism through both ecological and temporal complexity, with the overarching aim of improving predictive power. In a rapidly changing world, advancing our understanding of the cumulative ecological impacts of multiple stressors is critical for biodiversity conservation and ecosystem management. Identifying and overcoming the barriers to interdisciplinary knowledge exchange is necessary in rising to this challenge. Division between ecosystem types and disciplines is largely a human creation. Species and stressors cross these borders and so should the scientists who study them.

Why scaling up uncertain predictions to higher levels of organisation will underestimate change

“…an additive model) at one level (e.g., individuals, populations) being used to build multiple-stressor predictions at higher levels of biological organisation (e.g. communities, ecosystems), an approach for which there is growing interest (Orr et al, 2020;Thompson, MacLennan, & Vinebrooke, 2018;Kroeker, Kordas, & Harley, 2017;Côté et al, 2016). To be clear, scaling up predictions is not equivalent to simply scaling up investigations; our theory does not predict greater synergism at higher levels of organisation.…”

Ecological theory predicts ecosystem stressor interactions in freshwater ecosystems, but highlights the strengths and weaknesses of the additive null model

“…The choice of null model is hotly debated within ecological stressor research (Schäfer & Piggott 2018), and it has been argued that null models should be able to accurately predict the combined effects of stressors (Orr et al 2020). However, our work does add some cautionary notes to this view since it is clear that the additive null model for stressor interactions is very sensitive to sampling variation, and for likely realistic levels of sampling variation it is hard to correctly reject the null model ( Figure 2).…”

“…Galic et al 2018). Fourthly, there is a profusion of null models and classification schemes for stressor interactions (Schäfer & Piggott 2018;Orr et al 2020), making comparisons between studies very difficult, especially when we do not know the relationships between different null models. For example, under the same dataset, when should we expect synergistic and antagonistic interactions to be reclassified when we move from, say, the additive null model, to the multiplicative null model?…”

“…Ecosystems are being subjected to a wide variety of external stressors (Halpern et al 2015), acting across terrestrial, freshwater, and marine biomes (Scheffers et al 2016). Stressors, also termed drivers, factors, or perturbations (Orr et al 2020), are frequently anthropogenic in origin (Vörösmarty et al 2010;Geldmann et al 2014), but are capable of being abiotic or biotic (Przeslawski et al 2015), and are able to act at any scale, from local to global (Ban et al 2014;França et al 2020). While individual stressors, (e.g.…”

“…While distinctions are increasingly being made for various forms of antagonistic interactions (e.g. Jackson et al 2016), there exists a range of other classification schemes (Orr et al 2020), implemented across a number of studies (e.g. Travers-Trolet et al 2014;Piggott et al 2015a).…”