Spatiotemporal dynamics drive synergism of land use and climatic extreme events in insect meta-populations

Streib, Lucas; Juvigny‐Khenafou, Noel; Heer, H. de; Kattwinkel, Mira; Schäfer, Ralf B.

doi:10.1016/j.scitotenv.2021.152602

Cited by 5 publications

(10 citation statements)

References 62 publications

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“…Previous studies have called for regression-based experimental designs to predict interaction types among multiple stressors [12,14], because interaction types will vary with experimental context such as stressor intensity and the exposure duration at which responses are measured [12,16,62]. This study showed how GAMs can be applied to translate nonlinear stressor effects into a common interaction classification, to illustrate the importance of experimental context when determining interaction types.…”

Section: Discussionmentioning

confidence: 94%

Interactions among multiple stressors vary with exposure duration and biological response

et al. 2022

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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.

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Section: Discussionmentioning

confidence: 94%

Interactions among multiple stressors vary with exposure duration and biological response

et al. 2022

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“…The main criticism is that the classification of effects does not provide mechanistic understanding and that rather mechanistic models should be developed (as discussed in Process‐based models for predicting ecosystem effects of multiple stressors and chemical mixtures section). Many studies have shown that the outcome of the classification depends on the study context including (1) the study design, in particular the chosen stressor intensities in factorial designs, (2) the level of biological organization, where, for example, additive effects on the population level become nonadditive through species interactions in a community (Thompson et al, 2018), and (3) the time point of classification because effect sizes and directions can change in dynamic systems (Baas et al, 2007; Brooks & Crowe, 2019; e.g., Streib et al, 2022). Thus, a nonadditive classification does not imply a mechanistic effect interaction.…”

Section: General Mechanisms and Identification Of Interactions With N...mentioning

confidence: 99%

“…Similar to the temporal dimension, the spatial dimension of multiple stressors and chemical mixture effects has received little attention. Most studies have been conducted using spatially restrictive microcosms and mesocosms, thereby ignoring species dispersal (Nyström & Folke, 2001; Ryser et al, 2021; Schiesari et al, 2018; Streib et al, 2022). However, dispersal can be important because it may sustain populations in patches affected by stressors and thereby reduce the extinction risk, a phenomenon described in the source‐sink concept and called the rescue effect (Furrer & Pasinelli, 2016; Gotelli, 1991; Pulliam, 1988).…”

Section: Experimental Design In Chemical Mixture and Multiple Stresso...mentioning

confidence: 99%

“…The relevance of the spatial dimension for the propagation of stressors and their effects depends on the connectivity of habitats (Heino et al, 2021). The connectivity of habitats in a landscape is determined by the dispersal ability of organisms, the distance between habitat patches, and the influence of the landscape structure on dispersal, including dispersal mortality (Amarasekare, 2004; Streib et al, 2022). Physical structures in the landscape such as dams or roads can strongly constrain the movement of resources and organisms, thereby reducing habitat connectivity (Lange et al, 2018).…”

Section: Experimental Design In Chemical Mixture and Multiple Stresso...mentioning

confidence: 99%

“…While we have so far discussed the spatial dimension from the perspective of organisms, the stressors themselves often have different spatial profiles that determine the local co‐occurrence in a habitat patch: while some stressors are very local (e.g., emission of chemical), others occur at the scale of landscapes or regions (e.g., heatwave, drought; Boyd et al, 2018; Brown et al, 2014; Morris et al, 2022; Streib et al, 2022). Chemicals can create spatial gradients of exposure in a landscape through a combination of transport processes and diffusion as well as processes decreasing their bioavailability such as degradation or binding to matter (e.g., soil, sediment; Schiesari et al, 2019).…”

Section: Experimental Design In Chemical Mixture and Multiple Stresso...mentioning

confidence: 99%

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Chemical Mixtures and Multiple Stressors: Same but Different?

Schäfer

Jackson

Juvigny‐Khenafou

et al. 2023

Enviro Toxic and Chemistry

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Ecosystems are strongly influenced by multiple anthropogenic stressors, including a wide range of chemicals and their mixtures. Studies on the effects of multiple stressors have largely focussed on nonchemical stressors, whereas studies on chemical mixtures have largely ignored other stressors. However, both research areas face similar challenges and require similar tools and methods to predict the joint effects of chemicals or nonchemical stressors, and frameworks to integrate multiple chemical and nonchemical stressors are missing. We provide an overview of the research paradigms, tools, and methods commonly used in multiple stressor and chemical mixture research and discuss potential domains of cross‐fertilization and joint challenges. First, we compare the general paradigms of ecotoxicology and (applied) ecology to explain the historical divide. Subsequently, we compare methods and approaches for the identification of interactions, stressor characterization, and designing experiments. We suggest that both multiple stressor and chemical mixture research are too focused on interactions and would benefit from integration regarding null model selection. Stressor characterization is typically more costly for chemical mixtures. While for chemical mixtures comprehensive classification systems at suborganismal level have been developed, recent classification systems for multiple stressors account for environmental context. Both research areas suffer from rather simplified experimental designs that focus on only a limited number of stressors, chemicals, and treatments. We discuss concepts that can guide more realistic designs capturing spatiotemporal stressor dynamics. We suggest that process‐based and data‐driven models are particularly promising to tackle the challenge of prediction of effects of chemical mixtures and nonchemical stressors on (meta‐)communities and (meta‐)food webs. We propose a framework to integrate the assessment of effects for multiple stressors and chemical mixtures. Environ Toxicol Chem 2023;00:1–22. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

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A conceptual framework for landscape-based environmental risk assessment (ERA) of pesticides

Tarazona,

de Alba-Gonzalez,

Bedos

et al. 2024

Environment International

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Spatiotemporal dynamics drive synergism of land use and climatic extreme events in insect meta-populations

Cited by 5 publications

References 62 publications

Interactions among multiple stressors vary with exposure duration and biological response

Interactions among multiple stressors vary with exposure duration and biological response

Chemical Mixtures and Multiple Stressors: Same but Different?

A conceptual framework for landscape-based environmental risk assessment (ERA) of pesticides

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