Interactions among multiple stressors vary with exposure duration and biological response

Abstract

Coastal ecosystems are exposed to multiple anthropogenic stressors. Effective management actions would be better informed from generalized predictions of the individual, combined and interactive effects of multiple stressors; however, few generalities are shared across different meta-analyses. Using an experimental study, we present an approach for analysing regression-based designs with generalized additive models that allowed us to capture nonlinear effects of exposure duration and stressor intensity and access interactions among stressors. We tested the approach on a globally distributed marine diatom, using 72 h photosynthesis and growth assays to quantify the individual and combined effects of three common water quality stressors; photosystem II-inhibiting herbicide exposure, dissolved inorganic nitrogen (DIN) enrichment and reduced light (due to excess suspended sediment). Exposure to DIN and reduced light generally resulted in additivity, while exposure to diuron and reduced light resulted in additive, antagonistic or synergistic interactions, depending on the stressor intensity, exposure period and biological response. We thus find the context of experimental studies to be a primary driver of interactions. The experimental and modelling approaches used here bridge the gap between two-way designs and regression-based studies, which provides a way forward to identify generalities in multiple stressor interactions.

Fluctuating fortunes: Stressor synchronicity and fluctuating intensity influence biological impacts

“…The interaction between light and herbicide at the highest stressor intensities for biomass leans towards an antagonism when introduced in‐ and out‐of‐phase. High bias statistics for both in‐and out‐of‐phase treatments also suggested the potential for stronger stressor interactions at higher intensities, a finding consistent with previous theoretical predictions and empirical experiments (King et al, 2022 ; Turschwell et al, 2022 ). For example, a stronger synergistic effect of reduced light and herbicide contamination on algal growth inhibition was observed at the highest stressor intensities (King et al, 2022 ).…”

“…High bias statistics for both in‐and out‐of‐phase treatments also suggested the potential for stronger stressor interactions at higher intensities, a finding consistent with previous theoretical predictions and empirical experiments (King et al, 2022 ; Turschwell et al, 2022 ). For example, a stronger synergistic effect of reduced light and herbicide contamination on algal growth inhibition was observed at the highest stressor intensities (King et al, 2022 ). Therefore, we suggest that extreme synergisms and antagonisms can occur at higher stressor intensities, amplifying or reducing the impacts of in‐ and out‐of‐phase fluctuations than those hypothesised in our conceptual model.…”

“…However, photosynthetic impacts increase at higher stressor intensities (e.g. Du et al, 2021 ; King et al, 2022 ), and in this study, critical thresholds might have been surpassed and mechanisms for physiological compensation overwhelmed, resulting in no differences across variable intensities and synchronicities. Additionally, differences in effects could be masked due to impacts on physiological endpoints not measured here that can affect photosynthetic capacity (e.g.…”

Variability of functional and biodiversity responses to perturbations is predictable and informative

“…Since two community metrics can observe opposite interactions between the same stressors, it is unwise to think of stressor interactions at the community level as “intrinsic” to the stressors themselves. As there is growing interest in the existence of context dependency of stressor interactions (Turschwell et al, 2022; King et al, 2022), development of general theory is sorely needed. Our work offers a mechanistic explanation for one of the drivers of this context dependency: observations at the community level.…”

Predicting effects of multiple interacting global change drivers across trophic levels

“…On the other hand, it was also found that rapid evolution can create novel synergisms between multiple drivers ( Orr et al, 2022 ). Besides evolutionary processes, phenotypic plasticity may influence the response to multiple drivers over shorter time scales and hence the duration of the exposure is an important consideration ( King et al, 2022 ). However, given that both evolution and phenotypic plasticity will shape the reaction norms of vital rates, our approach readily allows us to account for these processes occurring over time.…”