Community- and ecosystem-level effects of multiple environmental change drivers: Beyond null model testing

Combined stress of an insecticide and heatwaves or elevated temperature induce community and food web effects in a Mediterranean freshwater ecosystem

Ongoing global climate change will shift nature towards Anthropocene's unprecedented conditions by increasing average temperatures and the frequency and severity of extreme events, such as heatwaves. While such climatic changes pose an increased threat for freshwater ecosystems, other stressors like pesticides may interact with warming and lead to unpredictable effects. Studies that examine the underpinned mechanisms of multiple stressor effects are scarce and often lack environmental realism. Here, we conducted a multiple stressors experiment using outdoor freshwater mesocosms with natural assemblages of macroinvertebrates, zooplankton, phytoplankton, macrophytes, and microbes. The effects of the neonicotinoid insecticide imidacloprid (1 µg/L) were investigated in combination with three temperature scenarios representing ambient, elevated temperatures (+4 °C), and heatwaves (+0 to 8 °C), the latter two having similar energy input. We found similar imidacloprid dissipation patterns for all temperature treatments with lowest average dissipation half-lives under both warming scenarios (DT50: 3 days) and highest under ambient temperatures (DT50: 4 days) throughout the experiment. Amongst all communities, only the zooplankton community was significantly affected by the combined treatments. This community demonstrated low chemical sensitivity with lagged and significant negative imidacloprid effects only for cyclopoids. Heatwaves caused early and long-lasting significant effects on the zooplankton community as compared to elevated temperatures, with Polyarthra, Daphnia longispina, Lecanidae, and cyclopoids being the most negatively affected taxa, whereas Ceriodaphnia and nauplii showed positive responses to temperature. Community recovery from imidacloprid stress was slower under heatwaves, suggesting temperature-enhanced toxicity. Finally, microbial and macrofauna litter degradation were significantly enhanced by temperature, whereas the latter was also negatively affected by imidacloprid. A structural equation model depicted cascading food web effects of both stressors with stronger relationships and significant negative stressor effects at higher than at lower trophic levels. Our study highlights the threat of a series of heatwaves compared to elevated temperatures for imidacloprid-stressed freshwaters.

Putting the Asymmetric Response Concept to the test: Modeling multiple stressor exposure and release in a stream food web

Communities in stream ecosystems often respond asymmetrically to increase and release of stressors, as indicated by slow and incomplete recovery. The Asymmetric Response Concept (ARC) posits that this is due to a shift in the relative importance of three mechanisms: tolerance, dispersal, and biotic interactions. In complex natural communities, these mechanisms may produce alternative outcomes through poorly understood indirect effects. To understand how the three mechanisms respond to different temporal stressor scenarios, we studied multiple scenarios using a stream food web model. We asked the following questions: Do groups of species decline as expected on the basis of individual tolerance rankings derived from laboratory experiments when they are embedded in a complex dynamic food web? Does the response of ecosystem function match that of communities? To address these questions, we aggregated data on individual tolerances at the level of functional groups and studied how single and multiple stressors affect food web dynamics and nutrient cycling. Multiple stressor scenarios involved different intensities of salt and temperature increase. Functional groups exhibited a different relative tolerance ranking between the laboratory and dynamic food web contexts. Salt as a single stressor had only minor and transient effects at low level but led to the loss of one or more functional groups at high level. In contrast, high temperature, alone or in combination with salt, caused the loss of functional groups at all tested levels. Patterns often differed between the response of communities and ecosystem function. We discuss our findings with respect to the ARC.

Studying interactions among anthropogenic stressors in freshwater ecosystems: A systematic review of 2396 multiple-stressor experiments

Understanding the interactions among anthropogenic stressors is critical for effective conservation and management of ecosystems. Freshwater scientists have invested considerable resources in conducting factorial experiments to disentangle stressor interactions by testing their individual and combined effects. However, the diversity of stressors and systems studied has hindered previous syntheses of this body of research. To overcome this challenge, we used a novel machine learning framework to identify relevant studies from over 235,000 publications. Our synthesis resulted in a new dataset of 2396 multiple-stressor experiments in freshwater systems. By summarizing the methods used in these studies, quantifying trends in the popularity of the investigated stressors, and performing co-occurrence analysis, we produce the most comprehensive overview of this diverse field of research to date. We provide both a taxonomy grouping the 909 investigated stressors into 31 classes and an open-source and interactive version of the dataset (https://jamesaorr.shinyapps.io/freshwater-multiple-stressors/). Inspired by our results, we provide a framework to help clarify whether statistical interactions detected by factorial experiments align with stressor interactions of interest, and we outline general guidelines for the design of multiple-stressor experiments relevant to any system. We conclude by highlighting the research directions required to better understand freshwater ecosystems facing multiple stressors.

Intraspecific demographic and trait responses to environmental change drivers are linked in two species of ciliate

BACKGROUND

Over the past decade, theory and observations have suggested intraspecific variation, trait-based differences within species, as a buffer against biodiversity loss from multiple environmental changes. This buffering effect can only occur when different populations of the same species respond differently to environmental change. More specifically, variation of demographic responses fosters buffering of demography, while variation of trait responses fosters buffering of functioning. Understanding how both responses are related is important for predicting biodiversity loss and its consequences. In this study, we aimed to empirically assess whether population-level trait responses to multiple environmental change drivers are related to the demographic response to these drivers. To this end, we measured demographic and trait responses in microcosm experiments with two species of ciliated protists. For three clonal strains of each species, we measured responses to two environmental change drivers (climate change and pollution) and their combination. We also examined if relationships between demographic and trait responses existed across treatments and strains.

RESULTS

We found different demographic responses across strains of the same species but hardly any interactive effects between the two environmental change drivers. Also, trait responses (summarized in a survival strategy index) varied among strains within a species, again with no driver interactions. Demographic and trait responses were related across all strains of both species tested in this study: Increasing intrinsic growth and self-limitation were associated with a shift in survival strategy from sit-and-wait towards flee.

CONCLUSIONS

Our results support the existence of a link between a population's demographic and trait responses to environmental change drivers in two species of ciliate. Future work could dive deeper into the specifics of phenotypical trait values, and changes therein, related to specific life strategies in different species of ciliate and other zooplankton grazers.

The impact of multifactorial stress combination on plants, crops, and ecosystems: how should we prepare for what comes next?

The complexity of environmental conditions encountered by plants in the field, or in nature, is gradually increasing due to anthropogenic activities that promote global warming, climate change, and increased levels of pollutants. While in the past it seemed sufficient to study how plants acclimate to one or even two different stresses affecting them simultaneously, the complex conditions developing on our planet necessitate a new approach of studying stress in plants: Acclimation to multiple stress conditions occurring concurrently or consecutively (termed, multifactorial stress combination [MFSC]). In an initial study of the plant response to MFSC, conducted with Arabidopsis thaliana seedlings subjected to an MFSC of six different abiotic stresses, it was found that with the increase in the number and complexity of different stresses simultaneously impacting a plant, plant growth and survival declined, even if the effects of each stress involved in such MFSC on the plant was minimal or insignificant. In three recent studies, conducted with different crop plants, MFSC was found to have similar effects on a commercial rice cultivar, a maize hybrid, tomato, and soybean, causing significant reductions in growth, biomass, physiological parameters, and/or yield traits. As the environmental conditions on our planet are gradually worsening, as well as becoming more complex, addressing MFSC and its effects on agriculture and ecosystems worldwide becomes a high priority. In this review, we address the effects of MFSC on plants, crops, agriculture, and different ecosystems worldwide, and highlight potential avenues to enhance the resilience of crops to MFSC.

A process framework for integrating stressor-response functions into cumulative effects models

Stressor-response (SR) functions quantify ecological responses to natural environmental variation or anthropogenic stressors. They are also core drivers of cumulative effects (CE) models, which are increasingly recognized as essential management tools to grapple with the diffuse footprint of human impacts. Here, we provide a process framework for the identification, development, and integration of SR functions into CE models, and highlight their consequential properties, behaviour, criteria for selecting appropriate stressors and responses, and general approaches for deriving them. Management objectives (and causal effect pathways) will determine the ultimate stressor and target response variables of interest (i.e., individual growth/survival, population size, community structure, ecosystem processes), but data availability will constrain whether proxies need to be used for the target stressor or response variables. Available data and confidence in underlying mechanisms will determine whether empirical or mechanistic (theoretical) SR functions are optimal. Uncertainty in underlying SR functions is often the primary source of error in CE modelling, and monitoring outcomes through adaptive management to iteratively refine parameterization of SR functions is a key element of model application. Dealing with stressor interactions is an additional challenge, and in the absence of known or suspected interaction mechanisms, controlling main effects should remain the primary focus. Indicators of suspected interaction presence (i.e., much larger or smaller responses to stressor reduction than expected during monitoring) should be confirmed through adaptive management cycles or targeted stressor manipulations. Where possible, management decisions should selectively take advantage of interactions to strategically mitigate stressor impacts (i.e., by using antagonisms to suppress stressor impacts, and by using synergisms to efficiently reduce them).

Herbivory and elevated levels of CO2 and nutrients separately, rather than synergistically, impacted biomass production and allocation in invasive and native plant species

Large parts of the Earth are experiencing environmental change caused by alien plant invasions, rising atmospheric concentration of carbon dioxide (CO2 ), and nutrient enrichments. Elevated CO2 and nutrient concentrations can separately favour growth of invasive plants over that of natives but how herbivory may modulate the magnitude and direction of net responses by the two groups of plants to simultaneous CO2 and nutrient enrichments remains unknown. In line with the enemy release hypothesis, invasive plant species should reallocate metabolites from costly anti-herbivore defences into greater growth following escape from intense herbivory in the native range. Therefore, invasive plants should have greater growth than native plants under simultaneous CO2 and nutrient enrichments in the absence of herbivory. To test this prediction, we grew nine congeneric pairs of invasive and native plant species that naturally co-occurred in grasslands in China under two levels each of nutrient enrichment (low-nutrient vs. high-nutrient), herbivory (with herbivory vs. without herbivory) and under ambient (412.9 ± 0.6 ppm) and elevated (790.1 ± 6.2 ppm) levels of CO2 concentrations in open top chambers in a common garden. Elevated CO2 and nutrient enrichment separately increased total plant biomass, while herbivory reduced it regardless of the plant invasive status. High-nutrient treatment caused the plants to allocate a significantly lower proportion of total biomass to roots, while herbivory induced an opposite pattern. Herbivory suppressed total biomass production more strongly in native plants than invasive plants. The plants exhibited significant interspecific and intergeneric variation in their responses to the various treatment combinations. Overall, these results suggest that elevated CO2 and nutrients and herbivory may separately, rather than synergistically, impact productivity of the invasive and co-occurring native plant species in our study system. Moreover, interspecific variation in resource-use strategies was more important than invasive status in determining plant responses to the various treatment combinations.

A review of plants strategies to resist biotic and abiotic environmental stressors

Plants exposed to a variety of abiotic and biotic stressors including environmental pollution and global warming pose significant threats to biodiversity and ecosystem services. Despite substantial literature documenting how plants adapt to distinct stressors, there still is a lack of knowledge regarding responses to multiple stressors and how these affects growth and development. Exposure of plants to concurrent biotic and abiotic stressors such as cadmium and drought, leads to pronounced inhibition in above ground biomass, imbalance in oxidative homeostasis, nutrient assimilation and stunted root growth, elucidating the synergistic interactions of multiple stressors culminating in adverse physiological outcomes. Impact of elevated heavy metal and water deficit exposure extends beyond growth and development, influencing the biodiversity of the microenvironment including the rhizosphere nutrient profile and microbiome. These findings have significant implications for plant-stress interactions and ecosystem functioning that prompt immediate action in order to eliminate effect of pollution and address global environmental issues to promote sustainable tolerance for multiple stress combinations in plants. Here, we review plant tolerance against stress combinations, highlighting the need for interdisciplinary approaches and advanced technologies, such as omics and molecular tools, to achieve a comprehensive understanding of underlying stress tolerance mechanisms. To accelerate progress towards developing stress-tolerance in plants against multiple environmental stressors, future research in plant stress tolerance should adopt a collaborative approach, involving researchers from multiple disciplines with diverse expertise and resources.

Mean species responses predict effects of environmental change on coexistence

Environmental change research is plagued by the curse of dimensionality: the number of communities at risk and the number of environmental drivers are both large. This raises the pressing question if a general understanding of ecological effects is achievable. Here, we show evidence that this is indeed possible. Using theoretical and simulation-based evidence for bi- and tritrophic communities, we show that environmental change effects on coexistence are proportional to mean species responses and depend on how trophic levels on average interact prior to environmental change. We then benchmark our findings using relevant cases of environmental change, showing that means of temperature optima and of species sensitivities to pollution predict concomitant effects on coexistence. Finally, we demonstrate how to apply our theory to the analysis of field data, finding support for effects of land use change on coexistence in natural invertebrate communities.

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.

Predicting effects of multiple interacting global change drivers across trophic levels

Global change encompasses many co-occurring anthropogenic drivers, which can act synergistically or antagonistically on ecological systems. Predicting how different global change drivers simultaneously contribute to observed biodiversity change is a key challenge for ecology and conservation. However, we lack the mechanistic understanding of how multiple global change drivers influence the vital rates of multiple interacting species. We propose that reaction norms, the relationships between a driver and vital rates like growth, mortality, and consumption, provide insights to the underlying mechanisms of community responses to multiple drivers. Understanding how multiple drivers interact to affect demographic rates using a reaction-norm perspective can improve our ability to make predictions of interactions at higher levels of organization-that is, community and food web. Building on the framework of consumer-resource interactions and widely studied thermal performance curves, we illustrate how joint driver impacts can be scaled up from the population to the community level. A simple proof-of-concept model demonstrates how reaction norms of vital rates predict the prevalence of driver interactions at the community level. A literature search suggests that our proposed approach is not yet used in multiple driver research. We outline how realistic response surfaces (i.e., multidimensional reaction norms) can be inferred by parametric and nonparametric approaches. Response surfaces have the potential to strengthen our understanding of how multiple drivers affect communities as well as improve our ability to predict when interactive effects emerge, two of the major challenges of ecology today.

The cumulative impacts of anthropogenic stressors vary markedly along environmental gradients

Understanding the cumulative effects of multiple stressors on biodiversity is key to managing their impacts. Stressor interactions are often studied using an additive/antagonistic/synergistic typology, aimed at identifying situations where individual stressor effects are reduced or amplified when they act in combination. Here, we analysed variation in the family richness of stream macroinvertebrates in the groups Ephemeroptera, Plecoptera and Trichoptera (EPT) at 4658 sites spanning a 32° latitudinal range in eastern Australia in relation to two largely human-induced stressors, salinity and turbidity, and two environmental gradients, temperature and slope. The cumulative and interactive effect of salinity and turbidity on EPT family richness varied across the landscape and by habitat (edge or riffle) such that we observed additive, antagonistic and synergistic outcomes depending on the environmental context. Our findings highlight the importance of understanding the consistency of multiple stressor impacts, which will involve higher-order interactions between multiple stressors and environmental factors.

Three perspectives on the prediction of chemical effects in ecosystems

The increasing production, use and emission of synthetic chemicals into the environment represents a major driver of global change. The large number of synthetic chemicals, limited knowledge on exposure patterns and effects in organisms and their interaction with other global change drivers hamper the prediction of effects in ecosystems. However, recent advances in biomolecular and computational methods are promising to improve our capacity for prediction. We delineate three idealised perspectives for the prediction of chemical effects: the suborganismal, organismal and ecological perspective, which are currently largely separated. Each of the outlined perspectives includes essential and complementary theories and tools for prediction but captures only part of the phenomenon of chemical effects. Links between the perspectives may foster predictive modelling of chemical effects in ecosystems and extrapolation between species. A major challenge for the linkage is the lack of data sets simultaneously covering different levels of biological organisation (here referred to as biological levels) as well as varying temporal and spatial scales. Synthesising the three perspectives, some central aspects and associated types of data seem particularly necessary to improve prediction. First, suborganism- and organism-level responses to chemicals need to be recorded and tested for relationships with chemical groups and organism traits. Second, metrics that are measurable at many biological levels, such as energy, need to be scrutinised for their potential to integrate across levels. Third, experimental data on the simultaneous response over multiple biological levels and spatiotemporal scales are required. These could be collected in nested and interconnected micro- and mesocosm experiments. Lastly, prioritisation of processes involved in the prediction framework needs to find a balance between simplification and capturing the essential complexity of a system. For example, in some cases, eco-evolutionary dynamics and interactions may need stronger consideration. Prediction needs to move from a static to a real-world eco-evolutionary view.

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.

Impact of multiple small and persistent threats on extinction risk

Many species may face multiple distinct and persistent drivers of extinction risk, yet theoretical and empirical studies tend to focus on the single largest driver. This means that existing approaches potentially underestimate and mischaracterize future risks to biodiversity. We synthesized existing knowledge on how multiple drivers of extinction can interact to influence a species' overall extinction probability in a probabilistic model of extinction risk that incorporated the impacts of multiple drivers of extinction risk, their interactions, and their accumulative effects through time. We then used this model framework to explore how different threats, interactions between them, and time trends may affect a species' overall extinction probability. Multiple small threats together had potential to pose a large cumulative extinction risk; for example, 10 individual threats posed a 1% extinction risk each and cumulatively posed a 9.7% total extinction risk. Interactions among drivers resulted in escalated risk in some cases, and persistent threats with a small (1%) extinction risk each decade ultimately posed large extinction risk over 100 (9.6% total extinction risk) to 200 years (18.2% total extinction risk). By estimating long-term extinction risk posed by several different factors and their interactions, this approach provides a framework to identify drivers of extinction risk that could be proactively targeted to help prevent species currently of least concern from becoming threatened with extinction.

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.

Climate change-associated multifactorial stress combination: A present challenge for our ecosystems

Humans negatively influence Earth ecosystems and biodiversity causing global warming, climate change as well as man-made pollution. Recently, the number of different stress factors have increased, and when impacting simultaneously, the multiple stress conditions cause dramatic declines in plant and ecosystem health. Although much is known about how plants and ecosystems are affected by each individual stress, recent research efforts have diverted into how these biological systems respond to several of these stress conditions applied together. Studies of such "multifactorial stress combination" concept have reported a severe decrease in plant survival and microbiome biodiversity along the increasing number of factors in a consistent directional trend. In addition, these results are in concert with studies about how ecosystems and microbiota are affected by natural conditions imposed by climate change. Therefore, all this evidence should serve as an important warning in order to decrease pollutants, create strategies to deal with global warming, and increase the tolerance of plants to multiple stressful factors in combination. Here we review recent studies focused on the impact of abiotic stresses on plants, agrosystems and different ecosystems including forests and microecosystems. In addition, different strategies to mitigate the impact of climate change in ecosystems are discussed.

A framework to understand the role of biological time in responses to fluctuating climate drivers

Understanding biological responses to environmental fluctuations (e.g. heatwaves) is a critical goal in ecology. Biological responses (e.g. survival) are usually measured with respect to different time reference frames, i.e. at specific chronological times (e.g. at specific dates) or biological times (e.g. at reproduction). Measuring responses on the biological frame is central to understand how environmental fluctuation modifies fitness and population persistence. We use a framework, based on partial differential equations (PDEs) to explore how responses to the time scale and magnitude of fluctuations in environmental variables (= drivers) depend on the choice of reference frame. The PDEs and simulations enabled us to identify different components, responsible for the phenological and eco-physiological effects of each driver on the response. The PDEs also highlight the conditions when the choice of reference frame affects the sensitivity of the response to a driver and the type of join effect of two drivers (additive or interactive) on the response. Experiments highlighted the importance of studying how environmental fluctuations affect biological time keeping mechanisms, to develop mechanistic models. Our main result, that the effect of the environmental fluctuations on the response depends on the scale used to measure time, applies to both field and laboratory conditions. In addition, our approach, applied to experimental conditions, can helps us quantify how biological time mediates the response of organisms to environmental fluctuations.

Interactive effects of multiple stressors vary with consumer interactions, stressor dynamics and magnitude

Predicting the impacts of multiple stressors is important for informing ecosystem management but is impeded by a lack of a general framework for predicting whether stressors interact synergistically, additively or antagonistically. Here, we use process-based models to study how interactions generalise across three levels of biological organisation (physiological, population and consumer-resource) for a two-stressor experiment on a seagrass model system. We found that the same underlying processes could result in synergistic, additive or antagonistic interactions, with interaction type depending on initial conditions, experiment duration, stressor dynamics and consumer presence. Our results help explain why meta-analyses of multiple stressor experimental results have struggled to identify predictors of consistently non-additive interactions in the natural environment. Experiments run over extended temporal scales, with treatments across gradients of stressor magnitude, are needed to identify the processes that underpin how stressors interact and provide useful predictions to management.

Anthropogenic pressures enhance the deleterious effects of extreme storms on rocky shore communities

Climate change is not only changing the mean values of environmental parameters that modulate ecosystems, but also the regime of disturbances. Among them, extreme events have a key role in structuring biological communities. Ecosystems are frequently suffering multiple anthropogenic pressures which can cause effects that are not additive. Thus, the effects of extreme events need to be studied in combination with other pressures to adequately evaluate their consequences. We performed a manipulative approach in two rocky shores in the Mediterranean with contrasting levels of anthropogenic pressure (mainly eutrophication) simulating storms with different disturbance regimes in the intertidal and subtidal zones. In the short-term, an extreme storm had a greater impact on the species assemblage than other disturbance regimes, being especially notable in the area suffering from a high anthropogenic pressure. In this area, the species assemblages that suffered from an extreme storm took a longer time to recover than the ones affected by other disturbance regimes and were generally more affected after the disturbance. The intertidal zone, having more variable environmental conditions than the subtidal zone, was more resistant and able to recover from extreme storms. Our results suggest that the effects of extreme events on biological communities could be strengthened when co-occurring with anthropogenic pressures, especially ecosystems adapted to less variable environmental conditions. Thus, limiting other anthropogenic pressures that ecosystems are suffering is crucial to maintain the natural resistance and recovery capacity of ecosystems towards extreme events such as storms.

Biodiversity promotes ecosystem functioning despite environmental change

Three decades of research have demonstrated that biodiversity can promote the functioning of ecosystems. Yet, it is unclear whether the positive effects of biodiversity on ecosystem functioning will persist under various types of global environmental change drivers. We conducted a meta‐analysis of 46 factorial experiments manipulating both species richness and the environment to test how global change drivers (i.e. warming, drought, nutrient addition or CO₂ enrichment) modulated the effect of biodiversity on multiple ecosystem functions across three taxonomic groups (microbes, phytoplankton and plants). We found that biodiversity increased ecosystem functioning in both ambient and manipulated environments, but often not to the same degree. In particular, biodiversity effects on ecosystem functioning were larger in stressful environments induced by global change drivers, indicating that high‐diversity communities were more resistant to environmental change. Using a subset of studies, we also found that the positive effects of biodiversity were mainly driven by interspecific complementarity and that these effects increased over time in both ambient and manipulated environments. Our findings support biodiversity conservation as a key strategy for sustainable ecosystem management in the face of global environmental change.

Stressor richness intensifies productivity loss but mitigates biodiversity loss

Ecosystems are subject to a multitude of anthropogenic environmental changes. Experimental research in the field of multiple stressors has typically involved varying the number of stressors, here termed stressor richness, but without controlling for total stressor intensity. Mistaking stressor intensity effects for stressor richness effects can misinform management decisions when there is a trade-off between mitigating these two factors. We incorporate multiple stressors into three community models and show that, at a fixed total stressor intensity, increasing stressor richness aggravates joint stressor effects on ecosystem functioning, but reduces effects on species persistence and composition. In addition, stressor richness weakens the positive selection and negative complementarity effects on ecosystem function. We identify the among-species variation of stressor effects on traits as a key determinant of the resulting community-level stressor effects. Taken together, our results unravel the mechanisms linking multiple environmental changes to biodiversity and ecosystem function.

Refocusing multiple stressor research around the targets and scales of ecological impacts

Ecological communities face a variety of environmental and anthropogenic stressors acting simultaneously. Stressor impacts can combine additively or can interact, causing synergistic or antagonistic effects. Our knowledge of when and how interactions arise is limited, as most models and experiments only consider the effect of a small number of non-interacting stressors at one or few scales of ecological organization. This is concerning because it could lead to significant underestimations or overestimations of threats to biodiversity. Furthermore, stressors have been largely classified by their source rather than by the mechanisms and ecological scales at which they act (the target). Here, we argue, first, that a more nuanced classification of stressors by target and ecological scale can generate valuable new insights and hypotheses about stressor interactions. Second, that the predictability of multiple stressor effects, and consistent patterns in their impacts, can be evaluated by examining the distribution of stressor effects across targets and ecological scales. Third, that a variety of existing mechanistic and statistical modelling tools can play an important role in our framework and advance multiple stressor research.

A state-space approach to understand responses of organisms, populations and communities to multiple environmental drivers

Understanding the response of biotic systems to multiple environmental drivers is one of the major concerns in ecology. The most common approach in multiple driver research includes the classification of interactive responses into categories (antagonistic, synergistic). However, there are situations where the use of classification schemes limits our understanding or cannot be applied. Here, we introduce and explore an approach that allows us to better appreciate variability in responses to multiple drivers. We then apply it to a case, comparing effects of heatwaves on performance of a cold-adapted species and a warm-adapted competitor. The heatwaves had a negative effect on the native (but not on the exotic) species and the approach highlighted that the exotic species was less responsive to multivariate environmental variation than the native species. Overall, we show how the proposed approach can enhance our understanding of variation in responses due to different driver intensities, species, genotypes, ontogeny, life-phases or among spatial scales at any level of biological organization.

Giménez et al. explore a “state-space” approach (SSEA) to examine variation in effects of multiple environmental drivers on biological systems. They illustrate the SSEA with a case study where larvae of an exotic crab were less responsive to an experimental heatwave than those of a native species.

On the sensitivity of food webs to multiple stressors

Evaluating the effects of multiple stressors on ecosystems is becoming increasingly vital with global changes. The role of species interactions in propagating the effects of stressors, although widely acknowledged, has yet to be formally explored. Here, we conceptualise how stressors propagate through food webs and explore how they affect simulated three-species motifs and food webs of the Canadian St. Lawrence System. We find that overlooking species interactions invariably underestimate the effects of stressors, and that synergistic and antagonistic effects through food webs are prevalent. We also find that interaction type influences a species' susceptibility to stressors; species in omnivory and tri-trophic food chain interactions in particular are sensitive and prone to synergistic and antagonistic effects. Finally, we find that apex predators were negatively affected and mesopredators benefited from the effects of stressors due to their trophic position in the St. Lawrence System, but that species sensitivity is dependent on food web structure. In conceptualising the effects of multiple stressors on food webs, we bring theory closer to practice and show that considering the intricacies of ecological communities is key to assess the net effects of stressors on species.

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

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.

Global Warming, Climate Change, and Environmental Pollution: Recipe for a Multifactorial Stress Combination Disaster

Global warming, climate change, and environmental pollution present plants with unique combinations of different abiotic and biotic stresses. Although much is known about how plants acclimate to each of these individual stresses, little is known about how they respond to a combination of many of these stress factors occurring together, namely a multifactorial stress combination. Recent studies revealed that increasing the number of different co-occurring multifactorial stress factors causes a severe decline in plant growth and survival, as well as in the microbiome biodiversity that plants depend upon. This effect should serve as a dire warning to our society and prompt us to decisively act to reduce pollutants, fight global warming, and augment the tolerance of crops to multifactorial stress combinations.

Physiological basis of interactive responses to temperature and salinity in coastal marine invertebrate: Implications for responses to warming

Developing physiological mechanistic models to predict species' responses to climate-driven environmental variables remains a key endeavor in ecology. Such approaches are challenging, because they require linking physiological processes with fitness and contraction or expansion in species' distributions. We explore those links for coastal marine species, occurring in regions of freshwater influence (ROFIs) and exposed to changes in temperature and salinity. First, we evaluated the effect of temperature on hemolymph osmolality and on the expression of genes relevant for osmoregulation in larvae of the shore crab Carcinus maenas. We then discuss and develop a hypothetical model linking osmoregulation, fitness, and species expansion/contraction toward or away from ROFIs. In C. maenas, high temperature led to a threefold increase in the capacity to osmoregulate in the first and last larval stages (i.e., those more likely to experience low salinities). This result matched the known pattern of survival for larval stages where the negative effect of low salinity on survival is mitigated at high temperatures (abbreviated as TMLS). Because gene expression levels did not change at low salinity nor at high temperatures, we hypothesize that the increase in osmoregulatory capacity (OC) at high temperature should involve post-translational processes. Further analysis of data suggested that TMLS occurs in C. maenas larvae due to the combination of increased osmoregulation (a physiological mechanism) and a reduced developmental period (a phenological mechanisms) when exposed to high temperatures. Based on information from the literature, we propose a model for C. maenas and other coastal species showing the contribution of osmoregulation and phenological mechanisms toward changes in range distribution under coastal warming. In species where the OC increases with temperature (e.g., C. maenas larvae), osmoregulation should contribute toward expansion if temperature increases; by contrast in those species where osmoregulation is weaker at high temperature, the contribution should be toward range contraction.

Multiple stressors determine river ecological status at the European scale: Towards an integrated understanding of river status deterioration

The biota of European rivers are affected by a wide range of stressors impairing water quality and hydro-morphology. Only about 40% of Europe's rivers reach 'good ecological status', a target set by the European Water Framework Directive (WFD) and indicated by the biota. It is yet unknown how the different stressors in concert impact ecological status and how the relationship between stressors and status differs between river types. We linked the intensity of seven stressors to recently measured ecological status data for more than 50,000 sub-catchment units (covering almost 80% of Europe's surface area), which were distributed among 12 broad river types. Stressor data were either derived from remote sensing data (extent of urban and agricultural land use in the riparian zone) or modelled (alteration of mean annual flow and of base flow, total phosphorous load, total nitrogen load and mixture toxic pressure, a composite metric for toxic substances), while data on ecological status were taken from national statutory reporting of the second WFD River Basin Management Plans for the years 2010-2015. We used Boosted Regression Trees to link ecological status to stressor intensities. The stressors explained on average 61% of deviance in ecological status for the 12 individual river types, with all seven stressors contributing considerably to this explanation. On average, 39.4% of the deviance was explained by altered hydro-morphology (morphology: 23.2%; hydrology: 16.2%), 34.4% by nutrient enrichment and 26.2% by toxic substances. More than half of the total deviance was explained by stressor interaction, with nutrient enrichment and toxic substances interacting most frequently and strongly. Our results underline that the biota of all European river types are determined by co-occurring and interacting multiple stressors, lending support to the conclusion that fundamental management strategies at the catchment scale are required to reach the ambitious objective of good ecological status of surface waters.

The consequences of null model selection for predicting mortality from multiple stressors

Many ecological systems are now exposed to multiple stressors, and ecosystem management increasingly requires consideration of the joint effects of multiple stressors on focal populations, communities and ecosystems. In the absence of empirical data, ecosystem managers could use null models based on the combination of independently acting stressors to estimate the joint effects of multiple stressors. Here, we used a simulation study and a meta-analysis to explore the consequences of null model selection for the prediction of mortality resulting from exposure to two stressors. Comparing five existing null models, we show that some null models systematically predict lower mortality rates than others, with predicted mortality rates up to 67.5% higher or 50% lower than the commonly used Simple Addition model. However, the null model predicting the highest mortality rate differed across parameter sets, and therefore there is no general ‘precautionary null model’ for multiple stressors. Using a multi-model framework, we re-analysed data from two earlier meta-analyses and found that 54% of the observed joint effects fell within the range of predictions from the suite of null models. Furthermore, we found that most null models systematically underestimated the observed joint effects, with only the Stressor Addition model showing a bias for overestimation. Finally, we found that the intensity of individual stressors was the strongest predictor of the magnitude of the joint effect across all null models. As a result, studies characterizing the effects of individuals stressors are still required for accurate prediction of mortality resulting from multiple stressors.

How Do Indirect Effects of Contaminants Inform Ecotoxicology? A Review

Indirect effects in ecotoxicology are defined as chemical- or pollutant-induced alterations in the density or behavior of sensitive species that have cascading effects on tolerant species in natural systems. As a result, species interaction networks (e.g., interactions associated with predation or competition) may be altered in such a way as to bring about large changes in populations and/or communities that may further cascade to disrupt ecosystem function and services. Field studies and experimental outcomes as well as models indicate that indirect effects are most likely to occur in communities in which the strength of interactions and the sensitivity to contaminants differ markedly among species, and that indirect effects will vary over space and time as species composition, trophic structure, and environmental factors vary. However, knowledge of indirect effects is essential to improve understanding of the potential for chemical harm in natural systems. For example, indirect effects may confound laboratory-based ecological risk assessment by enhancing, masking, or spuriously indicating the direct effect of chemical contaminants. Progress to better anticipate and interpret the significance of indirect effects will be made as monitoring programs and long-term ecological research are conducted that facilitate critical experimental field and mesocosm investigations, and as chemical transport and fate models, individual-based direct effects models, and ecosystem/food web models continue to be improved and become better integrated.

Biodiversity mediates the effects of stressors but not nutrients on litter decomposition

Understanding the consequences of ongoing biodiversity changes for ecosystems is a pressing challenge. Controlled biodiversity-ecosystem function experiments with random biodiversity loss scenarios have demonstrated that more diverse communities usually provide higher levels of ecosystem functioning. However, it is not clear if these results predict the ecosystem consequences of environmental changes that cause non-random alterations in biodiversity and community composition. We synthesized 69 independent studies reporting 660 observations of the impacts of two pervasive drivers of global change (chemical stressors and nutrient enrichment) on animal and microbial decomposer diversity and litter decomposition. Using meta-analysis and structural equation modeling, we show that declines in decomposer diversity and abundance explain reduced litter decomposition in response to stressors but not to nutrients. While chemical stressors generally reduced biodiversity and ecosystem functioning, detrimental effects of nutrients occurred only at high levels of nutrient inputs. Thus, more intense environmental change does not always result in stronger responses, illustrating the complexity of ecosystem consequences of biodiversity change. Overall, these findings provide strong evidence that the consequences of observed biodiversity change for ecosystems depend on the kind of environmental change, and are especially significant when human activities decrease biodiversity.

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

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.

Towards a general framework for the assessment of interactive effects of multiple stressors on aquatic ecosystems: Results from the Making Aquatic Ecosystems Great Again (MAEGA) workshop

A workshop was held in Wageningen, The Netherlands, in September 2017 to collate data and literature on three aquatic ecosystem types (agricultural drainage ditches, urban floodplains, and urban estuaries), and develop a general framework for the assessment of multiple stressors on the structure and functioning of these systems. An assessment framework considering multiple stressors is crucial for our understanding of ecosystem responses within a multiply stressed environment, and to inform appropriate environmental management strategies. The framework consists of two components: (i) problem identification and (ii) impact assessment. Both assessments together proceed through the following steps: 1) ecosystem selection; 2) identification of stressors and quantification of their intensity; 3) identification of receptors or sensitive groups for each stressor; 4) identification of stressor-response relationships and their potential interactions; 5) construction of an ecological model that includes relevant functional groups and endpoints; 6) prediction of impacts of multiple stressors, 7) confirmation of these predictions with experimental and monitoring data, and 8) potential adjustment of the ecological model. Steps 7 and 8 allow the assessment to be adaptive and can be repeated until a satisfactory match between model predictions and experimental and monitoring data has been obtained. This paper is the preface of the MAEGA (Making Aquatic Ecosystems Great Again) special section that includes three associated papers which are also published in this volume, which present applications of the framework for each of the three aquatic systems.