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

Chemical Mixtures and Multiple Stressors: Same but Different?

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;42:1915-1936. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Quantifying synergistic interactions: a meta-analysis of joint effects of chemical and parasitic stressors

The global biodiversity crisis emphasizes our need to understand how different stressors (climatic, chemical, parasitic, etc.) interact and affect biological communities. We provide a comprehensive meta-analysis investigating joint effects of chemical and parasitic stressors for 1064 chemical-parasitic combinations using the Multiplicative model on mortality of arthropods. We tested both features of the experimental setup (control mortality, stressor effect level) and the chemical mode of action, host and parasite phylogeny, and parasite-host interaction traits as explanatory factors for deviations from the reference model. Synergistic interactions, defined as higher mortality than predicted, were significantly more frequent than no interactions or antagony. Experimental setup significantly affected the results, with studies reporting high (> 10%) control mortality or using low stressor effects (< 20%) being more synergistic. Chemical mode of action played a significant role for synergy, but there was no effects of host and parasite phylogeny, or parasite-host interaction traits. The finding that experimental design played a greater role in finding synergy than biological factors, emphasize the need to standardize the design of mixed stressor studies across scientific disciplines. In addition, combinations testing more biological traits e.g. avoidance, coping, and repair processes are needed to test biology-based hypotheses for synergistic interactions.

Toxic temperatures: Bee behaviours exhibit divergent pesticide toxicity relationships with warming

Climate change and agricultural intensification are exposing insect pollinators to temperature extremes and increasing pesticide usage. Yet, we lack good quantification of how temperature modulates the sublethal effects of pesticides on behaviours vital for fitness and pollination performance. Consequently, we are uncertain if warming decreases or increases the severity of different pesticide impacts, and whether separate behaviours vary in the direction of response. Quantifying these interactive effects is vital in forecasting pesticide risk across climate regions and informing pesticide application strategies and pollinator conservation. This multi-stressor study investigated the responses of six functional behaviours of bumblebees when exposed to either a neonicotinoid (imidacloprid) or a sulfoximine (sulfoxaflor) across a standardised low, mid, and high temperature. We found the neonicotinoid had a significant effect on five of the six behaviours, with a greater effect at the lower temperature(s) when measuring responsiveness, the likelihood of movement, walking rate, and food consumption rate. In contrast, the neonicotinoid had a greater impact on flight distance at the higher temperature. Our findings show that different organismal functions can exhibit divergent thermal responses, with some pesticide-affected behaviours showing greater impact as temperatures dropped, and others as temperatures rose. We must therefore account for environmental context when determining pesticide risk. Moreover, we found evidence of synergistic effects, with just a 3°C increase causing a sudden drop in flight performance, despite seeing no effect of pesticide at the two lower temperatures. Our findings highlight the importance of multi-stressor studies to quantify threats to insects, which will help to improve dynamic evaluations of population tipping points and spatiotemporal risks to biodiversity across different climate regions.

Local stressors mask the effects of warming in freshwater ecosystems

Climate warming is a ubiquitous stressor in freshwater ecosystems, yet its interactive effects with other stressors are poorly understood. We address this knowledge gap by testing the ability of three contrasting null models to predict the joint impacts of warming and a range of other aquatic stressors using a new database of 296 experimental combinations. Despite concerns that stressors will interact to cause synergisms, we found that net impacts were usually best explained by the effect of the stronger stressor alone (the dominance null model), especially if this stressor was a local disturbance associated with human land use. Prediction accuracy depended on stressor identity and how asymmetric stressors were in the magnitude of their effects. These findings suggest we can effectively predict the impacts of multiple stressors by focusing on the stronger stressor, as habitat alteration, nutrients and contamination often override the biological consequences of higher temperatures in freshwater ecosystems.

Are experiment sample sizes adequate to detect biologically important interactions between multiple stressors?

As most ecosystems are being challenged by multiple, co-occurring stressors, an important challenge is to understand and predict how stressors interact to affect biological responses. A popular approach is to design factorial experiments that measure biological responses to pairs of stressors and compare the observed response to a null model expectation. Unfortunately, we believe experiment sample sizes are inadequate to detect most non-null stressor interaction responses, greatly hindering progress. Using both real and simulated data, we show sample sizes typical of many experiments (<6) can (i) only detect very large deviations from the additive null model, implying many important non-null stressor-pair interactions are being missed, and (ii) potentially lead to mostly statistical outliers being reported. Computer code that simulates data under either additive or multiplicative null models is provided to estimate statistical power for user-defined responses and sample sizes, and we recommend this is used to aid experimental design and interpretation of results. We suspect that most experiments may require 20 or more replicates per treatment to have adequate power to detect nonadditive. However, estimates of power need to be made while considering the smallest interaction of interest, i.e., the lower limit for a biologically important interaction, which is likely to be system-specific, meaning a general guide is unavailable. We discuss ways in which the smallest interaction of interest can be chosen, and how sample sizes can be increased. Our main analyses relate to the additive null model, but we show similar problems occur for the multiplicative null model, and we encourage similar investigations into the statistical power of other null models and inference methods. Without knowledge of the detection abilities of the statistical tools at hand or the definition of the smallest meaningful interaction, we will undoubtedly continue to miss important ecosystem stressor interactions.

Are experiment sample sizes adequate to detect biologically important interactions between multiple stressors?

As most ecosystems are being challenged by multiple, co‐occurring stressors, an important challenge is to understand and predict how stressors interact to affect biological responses. A popular approach is to design factorial experiments that measure biological responses to pairs of stressors and compare the observed response to a null model expectation. Unfortunately, we believe experiment sample sizes are inadequate to detect most non‐null stressor interaction responses, greatly hindering progress. Using both real and simulated data, we show sample sizes typical of many experiments (<6) can (i) only detect very large deviations from the additive null model, implying many important non‐null stressor‐pair interactions are being missed, and (ii) potentially lead to mostly statistical outliers being reported. Computer code that simulates data under either additive or multiplicative null models is provided to estimate statistical power for user‐defined responses and sample sizes, and we recommend this is used to aid experimental design and interpretation of results. We suspect that most experiments may require 20 or more replicates per treatment to have adequate power to detect nonadditive. However, estimates of power need to be made while considering the smallest interaction of interest, i.e., the lower limit for a biologically important interaction, which is likely to be system‐specific, meaning a general guide is unavailable. We discuss ways in which the smallest interaction of interest can be chosen, and how sample sizes can be increased. Our main analyses relate to the additive null model, but we show similar problems occur for the multiplicative null model, and we encourage similar investigations into the statistical power of other null models and inference methods. Without knowledge of the detection abilities of the statistical tools at hand or the definition of the smallest meaningful interaction, we will undoubtedly continue to miss important ecosystem stressor interactions.

Responses to simultaneous anthropogenic and biological stressors were mixed in an experimental saltmarsh ecosystem

Coastal ecosystems are essential for absorbing and bouncing back from the impacts of climate change, yet accelerating climate change is causing anthropogenically-derived stressors in these ecosystems to grow. The effects of stressors are more difficult to foresee when they act simultaneously, however, predicting these effects is critical for understanding ecological change. Spartina alterniflora (Spartina), a foundational saltmarsh plant key to coastal resilience, is subject to biological stress such as herbivory, as well as anthropogenic stress such as chemical pollution. Using saltmarsh mesocosms as a model system in a fully factorial experiment, we tested whether the effects of herbivory and two chemicals (oil and dispersant) were mediated or magnified in combination. Spartina responded to stressors asynchronously; ecophysiology responded negatively to oil and herbivores in the first 2-3 weeks of the experiment, whereas biomass responded negatively to oil and herbivores cumulatively throughout the experiment. We generally found mixed multi-stressor effects, with slightly more antagonistic effects compared to either synergistic or additive effects, despite significant reductions in Spartina biomass and growth from both chemical and herbivore treatments. We also observed an indirect positive effect of oil on Spartina, via a direct negative effect on insect herbivores. Our findings suggest that multi-stressor effects in our model system, 1) are mixed but can be antagonistic more often than expected, a finding contrary to previous assumptions of primarily synergistic effects, 2) can vary in duration, 3) can be difficult to discern a priori, and 4) can lead to ecological surprises through indirect effects with implications for coastal resilience. This leads us to conclude that understanding the simultaneous effects of multiple stressors is critical for predicting foundation-species persistence, discerning ecosystem resilience, and managing and mitigating impacts on ecosystem services.