On the sensitivity of food webs to multiple stressors

Effects of multiple stressors on freshwater food webs: Evidence from a mesocosm experiment

Natural and anthropogenic pressures exert influence on ecosystem structure and function by affecting the physiology and behavior of organisms, as well as the trophic interactions within assemblages. Therefore, understanding how multiple stressors affect aquatic ecosystems can improve our ability to manage and protect these ecosystems and contribute to understanding fundamental ecological principles. Here, we conducted a mesocosm experiment to ascertain the individual and combined effects of multiple stressors on trophic interactions within species in freshwater ecosystems. Furthermore, we investigated how species respond to such changes by adapting their food resources. To mimic a realistic food web, we selected fish and shrimp as top predators, gastropods, zooplankton and zoobenthos as intermediate consumers, with producers (macrophytes, periphyton and phytoplankton) and detritus as basal resources. Twelve different treatments included a control, nutrient loading only, herbicide exposure only, and a combination of nutrient loading and herbicide exposure, each replicated under ambient temperature, constant warming and multiple heat waves to simulate environmental stressors. Our results demonstrated that antagonistic interactions between environmental stressors were widespread in trophic interactions, with a more pronounced and less intense impact observed for the high trophic level species. The responses of freshwater communities to environmental stressors are complex, involving direct effects on individual species as well as indirect effects through species interactions. Moreover, our results confirmed that the combinations of stressors, but not individual stressors, led to a shift to herbivory in top predators, indicating that multiple stressors can be more detrimental to organisms than individual stressors alone. These findings elucidate how changes in the resource utilization of species induced by environmental stressors can potentially influence species interactions and the structural dynamics of food webs in freshwater ecosystems.

High-throughput characterization of bacterial responses to complex mixtures of chemical pollutants

Our understanding of how microbes respond to micropollutants, such as pesticides, is almost wholly based on single-species responses to individual chemicals. However, in natural environments, microbes experience multiple pollutants simultaneously. Here we perform a matrix of multi-stressor experiments by assaying the growth of model and non-model strains of bacteria in all 255 combinations of 8 chemical stressors (antibiotics, herbicides, fungicides and pesticides). We found that bacterial strains responded in different ways to stressor mixtures, which could not be predicted simply from their phylogenetic relatedness. Increasingly complex chemical mixtures were both more likely to negatively impact bacterial growth in monoculture and more likely to reveal net interactive effects. A mixed co-culture of strains proved more resilient to increasingly complex mixtures and revealed fewer interactions in the growth response. These results show predictability in microbial population responses to chemical stressors and could increase the utility of next-generation eco-toxicological assays.

Positive associations fuel soil biodiversity and ecological networks worldwide

Microbial interactions are key to maintaining soil biodiversity. However, whether negative or positive associations govern the soil microbial system at a global scale remains virtually unknown, limiting our understanding of how microbes interact to support soil biodiversity and functions. Here, we explored ecological networks among multitrophic soil organisms involving bacteria, protists, fungi, and invertebrates in a global soil survey across 20 regions of the planet and found that positive associations among both pairs and triads of soil taxa governed global soil microbial networks. We further revealed that soil networks with greater levels of positive associations supported larger soil biodiversity and resulted in lower network fragility to withstand potential perturbations of species losses. Our study provides unique evidence of the widespread positive associations between soil organisms and their crucial role in maintaining the multitrophic structure of soil biodiversity worldwide.

A synthesis of anthropogenic stress effects on emergence-mediated aquatic-terrestrial linkages and riparian food webs

Anthropogenic stress alters the linkage between aquatic and terrestrial ecosystems in various ways. Here, we review the contemporary literature on how alterations in aquatic systems through environmental pollution, invasive species and hydromorphological changes carry-over to terrestrial ecosystems and the food webs therein. We consider both the aquatic insect emergence and flooding as pathways through which stressors can propagate from the aquatic to the terrestrial system. We specifically synthesize and contextualize results on the roles of pollutants in the emergence pathway and their top-down consequences. Our review revealed that the emergence and flooding pathway are only considered in isolation and that the overall effects of invasive species or pollutants on food webs at the water-land interface require further attention. While very few recent studies looked at invasive species, a larger number of studies focused on metal transfer compared to pesticides, pharmaceuticals or PCBs, and multiple stress studies up to now left aquatic-terrestrial linkages unconsidered. Recent research on pollutants and emergence used aquatic-terrestrial mesocosms to elucidate the effects of aquatic stressors such as the mosquito control agent Bti, metals or pesticides to understand the effects on riparian spiders. Quality parameters, such as the structural and functional composition of emergent insect communities, the fatty acid profiles, yet also the composition of pollutants transferred to land prove to be important for the effects on riparian spiders. Process-based models including quality of emergence are useful to predict the resulting top-down directed food web effects in the terrestrial recipient ecosystem. In conclusion, we present and recommend a combination of empirical and modelling approaches in order to understand the complexity of aquatic-terrestrial stressor propagation and its spatial and temporal variation.

Hypoxia and salinity constrain the sediment microbiota-mediated N removal potential in an estuary: A multi-trophic interrelationship perspective

Reactive nitrogen (N) enrichment is a common environmental problem in estuarine ecosystems, while the microbial-mediated N removal process is complicated for other multi-environmental factors. Therefore, A systematic investigation is necessary to understand the multi-trophic microbiota-mediated N removal characteristics under various environmental factors in estuaries. Here, we studied how multiple factors affect the multi-trophic microbiota-mediated N removal potential (denitrification and anammox) and N2O emission along a river-estuary-bay continuum in southeastern China using the environmental DNA (eDNA) approach. Results suggested that hypoxia and salinity were the dominant environmental factors affecting multi-trophic microbiota-mediated N removal in the estuary. The synergistic effect of hypoxia and salinity contributed to the loss of taxonomic (MultiTaxa) and phylogenetic (MultiPhyl) diversity across multi-trophic microbiota and enhanced the interdependence among multi-trophic microbiota in the estuary. The N removal potential calculated as the activities of key N removal enzymes was also significantly constrained in the estuary (0.011), compared with the river (0.257) and bay (0.461). Structural equation modeling illustrated that metazoans were central to all sediment N removal potential regulatory pathways. The top-down forces (predation by metazoans) restrained the growth of heterotrophic bacteria, which may affect microbial N removal processes in the sediment. Furthermore, we found that the hypoxia and salinity exacerbated the N2O emission in the estuary. This study clarifies that hypoxia and salinity constrain estuarine multi-trophic microbiota-mediated N removal potential and highlights the important role of multi-trophic interactions in estuarine N removal, providing a new perspective on mitigating estuarine N accumulation.

Stressor fluctuations alter mechanisms of seagrass community responses relative to static stressors

Ecosystems are increasingly affected by multiple anthropogenic stressors that contribute to habitat degradation and loss. Natural ecosystems are highly dynamic, yet multiple stressor experiments often ignore variability in stressor intensity and do not consider how effects could be mediated across trophic levels, with implications for models that underpin stressor management. Here, we investigated the in situ effects of changes in stressor intensity (i.e., fluctuations) and synchronicity (i.e., timing of fluctuations) on a seagrass community, applying the stressors reduced light and physical disturbance to the sediment. We used structural equation models (SEMs) to identify causal effects of dynamic multiple stressors on seagrass shoot density and leaf surface area, and abundance of associated crustaceans. Responses depended on whether stressor intensities fluctuated or remained static. Relative to static stressor exposure at the end of the experiment, shoot density, leaf surface area, and crustacean abundance all declined under in-phase (synchronous; 17, 33, and 30 % less, respectively) and out-of-phase (asynchronous; 11, 28, and 39 % less, respectively) fluctuating treatments. Static treatment increased seagrass leaf surface area and crustacean abundance relative to the control group. We hypothesised that crustacean responses are mediated by changes in seagrass; however, causal analysis found only weak evidence for a mediation effect via leaf surface area. Changes in crustacean abundance, therefore, were primarily a direct response to stressors. Our results suggest that the mechanisms underpinning stress responses change when stressors fluctuate. For instance, increased leaf surface area under static stress could be caused by seagrass acclimating to low light, whereas no response under fluctuating stressors suggests an acclimation response was not triggered. The SEMs also revealed that community responses to the stressors can be independent of one another. Therefore, models based on static experiments may be representing ecological mechanisms not observed in natural ecosystems, and underestimating the impacts of stressors on ecosystems.

A general framework to quantify and compare ecological impacts under temporal dynamics

Biodiversity is diminishing at alarming rates due to multiple anthropogenic drivers. To mitigate these drivers, their impacts must be quantified accurately and comparably across drivers. To enable that, we present a generally applicable framework introducing fundamental principles of ecological impact quantification, including the quantification of interactions between multiple drivers. The framework contrasts biodiversity variables in impacted against those in unimpacted or other reference situations while accounting for their temporal dynamics through modelling. Properly accounting for temporal dynamics reduces biases in impact quantification and comparison. The framework addresses key questions around ecological impacts in global change science, namely, how to compare impacts under temporal dynamics across stressors, how to account for stressor interactions in such comparisons, and how to compare the success of management actions over time.

Structural asymmetry in biotic interactions as a tool to understand and predict ecological persistence

A universal feature of ecological systems is that species do not interact with others with the same sign and strength. Yet, the consequences of this asymmetry in biotic interactions for the short- and long-term persistence of individual species and entire communities remains unclear. Here, we develop a set of metrics to evaluate how asymmetric interactions among species translate to asymmetries in their individual vulnerability to extinction under changing environmental conditions. These metrics, which solve previous limitations of how to independently quantify the size from the shape of the so-called feasibility domain, provide rigorous advances to understand simultaneously why some species and communities present more opportunities to persist than others. We further demonstrate that our shape-related metrics are useful to predict short-term changes in species' relative abundances during 7 years in a Mediterranean grassland. Our approach is designed to be applied to any ecological system regardless of the number of species and type of interactions. With it, we show that is possible to obtain both mechanistic and predictive information on ecological persistence for individual species and entire communities, paving the way for a stronger integration of theoretical and empirical research.

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.

Thermal Performance Curves in a Polluted World: Too Cold and Too Hot Temperatures Synergistically Increase Pesticide Toxicity

Ecotoxicological studies typically cover only a limited part of the natural thermal range of populations and ignore daily temperature fluctuations (DTFs). Therefore, we may miss important stressor interaction patterns and have poor knowledge on how pollutants affect thermal performance curves (TPCs), which is needed to improve insights into the fate of populations to warming in a polluted world. We tested the single and combined effects of pesticide exposure and DTFs on the TPCs of low- and high-latitude populations of Ischnura elegans damselfly larvae. While chlorpyrifos did not have any effect at the intermediate mean temperatures (20-24 °C), it became toxic (reflecting synergisms) at lower (≤16 °C, reduced growth) and especially at higher (≥28 °C, reduced survival and growth) mean temperatures, resulting in more concave-shaped TPCs. Remarkably, these toxicity patterns were largely consistent at both latitudes and hence across a natural thermal gradient. Moreover, DTFs magnified the pesticide-induced survival reductions at 34 °C. The TPC perspective allowed us to identify different toxicity patterns and interaction types (mainly additive vs synergistic) across the thermal gradient. This highlights the importance of using thermal gradients to make more realistic predictions about the impact of pesticides in a warming world and of warming in a polluted world.

Trait adaptation enhances species coexistence and reduces bistability in an intraguild predation module

Disentangling how species coexist in an intraguild predation (IGP) module is a great step toward understanding biodiversity conservation in complex natural food webs. Trait variation enabling individual species to adjust to ambient conditions may facilitate coexistence. However, it is still unclear how coadaptation of all species within the IGP module, constrained by complex trophic interactions and trade-offs among species-specific traits, interactively affects species coexistence and population dynamics. We developed an adaptive IGP model allowing prey and predator species to mutually adjust their species-specific defensive and offensive strategies to each other. We investigated species persistence, the temporal variation of population dynamics, and the occurrence of bistability in IGP models without and with trait adaptation along a gradient of enrichment represented by carrying capacity of the basal prey for different widths and speeds of trait adaptation within each species. Results showed that trait adaptation within multiple species greatly enhanced the coexistence of all three species in the module. A larger width of trait adaptation facilitated species coexistence independent of the speed of trait adaptation at lower enrichment levels, while a sufficiently large and fast trait adaptation promoted species coexistence at higher enrichment levels. Within the oscillating regime, increasing the speed of trait adaptation reduced the temporal variability of biomasses of all species. Finally, species coadaptation strongly reduced the presence of bistability and promoted the attractor with all three species coexisting. These findings resolve the contradiction between the widespread occurrence of IGP in nature and the theoretical predictions that IGP should only occur under restricted conditions and lead to unstable population dynamics, which broadens the mechanisms presumably underlying the maintenance of IGP modules in nature. Generally, this study demonstrates a decisive role of mutual adaptation among complex trophic interactions, for enhancing interspecific diversity and stabilizing food web dynamics, arising, for example, from intraspecific diversity.

Scaling migrations to communities: An empirical case of migration network in the Arctic

Seasonal migrants transport energy, nutrients, contaminants, parasites and diseases, while also connecting distant food webs between communities and ecosystems, which contributes to structuring meta-communities and meta-ecosystems. However, we currently lack a framework to characterize the structure of the spatial connections maintained by all migratory species reproducing or wintering in a given community. Here, we use a network approach to represent and characterize migratory pathways at the community level and provide an empirical description of this pattern from a High-Arctic terrestrial community. We define community migration networks as multipartite networks representing different biogeographic regions connected with a focal community through the seasonal movements of its migratory species. We focus on the Bylot Island High-Arctic terrestrial community, a summer breeding ground for several migratory species. We define the non-breeding range of each species using tracking devices, or range maps refined by flyways and habitat types. We show that the migratory species breeding on Bylot Island are found across hundreds of ecoregions on several continents during the non-breeding period and present a low spatial overlap. The migratory species are divided into groups associated with different sets of ecoregions. The non-random structure observed in our empirical community migration network suggests evolutionary and geographic constraints as well as ecological factors act to shape migrations at the community level. Overall, our study provides a simple and generalizable framework as a starting point to better integrate migrations at the community level. Our framework is a far-reaching tool that could be adapted to address the seasonal transport of energy, contaminants, parasites and diseases in ecosystems, as well as trophic interactions in communities with migratory species.

Ranking species based on sensitivity to perturbations under non-equilibrium community dynamics

Managing ecological communities requires fast detection of species that are sensitive to perturbations. Yet, the focus on recovery to equilibrium has prevented us from assessing species responses to perturbations when abundances fluctuate over time. Here, we introduce two data-driven approaches (expected sensitivity and eigenvector rankings) based on the time-varying Jacobian matrix to rank species over time according to their sensitivity to perturbations on abundances. Using several population dynamics models, we demonstrate that we can infer these rankings from time-series data to predict the order of species sensitivities. We find that the most sensitive species are not always the ones with the most rapidly changing or lowest abundance, which are typical criteria used to monitor populations. Finally, using two empirical time series, we show that sensitive species tend to be harder to forecast. Our results suggest that incorporating information on species interactions can improve how we manage communities out of equilibrium.

Fluctuating fortunes: Stressor synchronicity and fluctuating intensity influence biological impacts

Ecosystems remain under enormous pressure from multiple anthropogenic stressors. Manipulative experiments evaluating stressor interactions and impacts mostly apply stressors under static conditions without considering how variable stressor intensity (i.e. fluctuations) and synchronicity (i.e. timing of fluctuations) affect biological responses. We ask how variable stressor intensity and synchronicity, and interaction type, can influence how multiple stressors affect seagrass. At the highest intensities, fluctuating stressors applied asynchronously reduced seagrass biomass 36% more than for static stressors, yet no such difference occurred for photosynthetic capacity. Testing three separate hypotheses to predict underlying drivers of differences in biological responses highlighted alternative modes of action dependent on how stressors fluctuated over time. Given that environmental conditions are constantly changing, assessing static stressors may lead to inaccurate predictions of cumulative effects. Translating multiple stressor experiments to the real world, therefore, requires considering variability in stressor intensity and the synchronicity of fluctuations.

Perceived multiple stressor effects depend on sample size and stressor gradient length

Multiple stressors are continuously deteriorating surface waters worldwide, posing many challenges for their conservation and restoration. Combined effect types of multiple stressors range from single-stressor dominance to complex interactions. Identifying prevalent combined effect types is critical for environmental management, as it helps to prioritise key stressors for mitigation. However, it remains unclear whether observed single and combined stressor effects reflect true ecological processes unbiased by sample size and length of stressor gradients. Therefore, we examined the role of sample size and stressor gradient lengths in 158 paired-stressor response cases with over 120,000 samples from rivers, lakes, transitional and marine ecosystems around the world. For each case, we split the overall stressor gradient into two partial gradients (lower and upper) and investigated associated changes in single and combined stressor effects. Sample size influenced the identified combined effect types, and stressor interactions were less likely for cases with fewer samples. After splitting gradients, 40 % of cases showed a change in combined effect type, 30 % no change, and 31 % showed a loss in stressor effects. These findings suggest that identified combined effect types may often be statistical artefacts rather than representing ecological processes. In 58 % of cases, we observed changes in stressor effect directions after the gradient split, suggesting unimodal stressor effects. In general, such non-linear responses were more pronounced for organisms at higher trophic levels. We conclude that observed multiple stressor effects are not solely determined by ecological processes, but also strongly depend on sampling design. Observed effects are likely to change when sample size and/or gradient length are modified. Our study highlights the need for improved monitoring programmes with sufficient sample size and stressor gradient coverage. Our findings emphasize the importance of adaptive management, as stress reduction measures or further ecosystem degradation may change multiple stressor-effect relationships, which will then require associated changes in management strategies.

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.

Net effect of environmental fluctuations in multiple global-change drivers across the tree of life

Jensen's inequality predicts that the response of any given system to average constant conditions is different from its average response to varying ones. Environmental fluctuations in abiotic conditions are pervasive on Earth; yet until recently, most ecological research has addressed the effects of multiple environmental drivers by assuming constant conditions. One could thus expect to find significant deviations in the magnitude of their effects on ecosystems when environmental fluctuations are considered. Drawing on experimental studies published during the last 30 years reporting more than 950 response ratios (n = 5,700), we present a comprehensive analysis of the role that environmental fluctuations play across the tree of life. In contrast to the predominance of interactive effects of global-change drivers reported in the literature, our results show that their cumulative effects were additive (58%), synergistic (26%), and antagonistic (16%) when environmental fluctuations were present. However, the dominant type of interaction varied by trophic level (autotrophs: interactive; heterotrophs: additive) and phylogenetic group (additive in Animalia; additive and positive antagonism in Chromista; negative antagonism and synergism in Plantae). In addition, we identify the need to tackle how complex communities respond to fluctuating environments, widening the phylogenetic and biogeographic ranges considered, and to consider other drivers beyond warming and acidification as well as longer timescales. Environmental fluctuations must be taken into account in experimental and modeling studies as well as conservation plans to better predict the nature, magnitude, and direction of the impacts of global change on organisms and ecosystems.

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.

Food web rewiring drives long-term compositional differences and late-disturbance interactions at the community level

Multiple anthropogenic disturbances affect the structure and functioning of communities. Recent evidence highlighted that, after pulse disturbance, the functioning a community performs may be recovered fast due to functional redundancy, whereas community multivariate composition needs a longer time. Yet, the mechanisms that drive the different community recovery times have not been quantified empirically. We use quantitative food-web analysis to assess the influence of species interactions on community recovery. We found species-interactions strength to be the main mechanism driving differences between structural and functional recovery. Additionally, we show that interactions between multiple disturbances appear in the long term only when both species-interaction strength and food-web architecture change significantly.

Ecological communities are constantly exposed to multiple natural and anthropogenic disturbances. Multivariate composition (if recovered) has been found to need significantly more time to be regained after pulsed disturbance compared to univariate diversity metrics and functional endpoints. However, the mechanisms driving the different recovery times of communities to single and multiple disturbances remain unexplored. Here, we apply quantitative ecological network analyses to try to elucidate the mechanisms driving long-term community-composition dissimilarity and late-stage disturbance interactions at the community level. For this, we evaluate the effects of two pesticides, nutrient enrichment, and their interactions in outdoor mesocosms containing a complex freshwater community. We found changes in interactions strength to be strongly related to compositional changes and identified postdisturbance interaction-strength rewiring to be responsible for most of the observed compositional changes. Additionally, we found pesticide interactions to be significant in the long term only when both interaction strength and food-web architecture are reshaped by the disturbances. We suggest that quantitative network analysis has the potential to unveil ecological processes that prevent long-term community recovery.

Predators mitigate the destabilising effects of heatwaves on multitrophic stream communities

Amidst the global extinction crisis, climate change will expose ecosystems to more frequent and intense extreme climatic events, such as heatwaves. Yet, whether predator species loss-a prevailing characteristic of the extinction crisis-will exacerbate the ecological consequences of extreme climatic events remains largely unknown. Here, we show that the loss of predator species can interact with heatwaves to moderate the compositional stability of ecosystems. We exposed multitrophic stream communities, with and without a dominant predator species, to realistic current and future heatwaves and found that heatwaves destabilised algal communities by homogenising them in space. However, this happened only when the predator was absent. Additional heatwave impacts on multiple aspects of stream communities, including changes to the structure of algal and macroinvertebrate communities, as well as total algal biomass and its temporal variability, were not apparent during heatwaves and emerged only after the heatwaves had passed. Taken together, our results suggest that the ecological consequences of heatwaves can amplify over time as their impacts propagate through biological interaction networks, but the presence of predators can help to buffer such impacts. These findings underscore the importance of conserving trophic structure, and highlight the potential for species extinctions to amplify the effects of climate change and extreme events.