Competition matters: species interactions prolong the long-term effects of pulsed toxicant stress on populations

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.

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.

Temperature-dependent competitive advantages of an allelopathic alga over non-allelopathic alga are altered by pollutants and initial algal abundance levels

In the context of climate warming, the dominance of allelopathic algae that cause ecosystem disturbances is an important topic. Although the hypothesis that an increase in temperature will be favorable to the dominance of allelopathic algae has been increasingly supported by many studies, it is still unclear how other factors can affect the influence of temperature. In this study, the effects of copper exposure and initial algal abundance on the competition between Pseudokirchneriella subcapitata (non-allelopathic alga) and Chlorella vulgaris (allelopathic alga) were investigated during temperature changes. The results showed that increased temperatures enhanced the competitive advantage of C. vulgaris only in the absence of copper exposure. Our data confirmed that copper exposure along with increased temperature (20-30 °C) may change the competitive advantage of C. vulgaris from favorable to unfavorable. The initial algal abundance was found to affect competition outcome by controlling copper toxicity. This study suggests that pollutants and initial abundance can alter the effects of increased temperature on the allelopathic interaction. Given the temporal dynamics of algal abundance and the pollutants in natural ecosystems, these findings should be considered in the prediction of temperature influence on an algal community.

Thiacloprid-induced toxicity influenced by nutrients: Evidence from in situ bioassays in experimental ditches

Many studies show that neonicotinoid insecticides cause toxicity to aquatic invertebrates. Some studies report that insecticide toxicity may differ in combination with other agrochemicals under realistic field conditions. To explore such altered toxicity further, we aimed to determine the single and combined effects of environmentally relevant levels of the neonicotinoid thiacloprid and nutrients on different endpoints of 4 aquatic invertebrate species. Animals were exposed to these agrochemicals using a caged experiment within experimental ditches. We observed thiacloprid-induced toxicity for 2 crustaceans, Daphnia magna and Asellus aquaticus, and for 1 out of 2 tested insect species, Cloeon dipterum. We observed no toxic effects for Chironomus riparius at the time-weighted average test concentration of 0.51 μg thiacloprid/L. For D. magna, the observed toxicity, expressed as the lowest-observed-effect concentration (LOEC), on growth and reproduction was present at thiacloprid concentrations that were 2456-fold lower than laboratory-derived LOEC values. This shows that these species, when exposed under natural conditions, may exhibit neonicotinoid-induced toxic stress. Contrary to the low nutrient treatment, such toxicity was often not observed under nutrient-enriched conditions. This was likely attributable to the increased primary production that allowed for compensatory feeding. These findings warrant the inclusion of different feeding regimes in laboratory experiments to retrieve the best estimates of neonicotinoid-induced toxicity in the natural environment. Environ Toxicol Chem 2018;37:1907-1915. © 2018 SETAC.

Defence mechanisms: the role of physiology in current and future environmental protection paradigms

Ecological risk assessments principally rely on simplified metrics of organismal sensitivity that do not consider mechanism or biological traits. As such, they are unable to adequately extrapolate from standard laboratory tests to real-world settings, and largely fail to account for the diversity of organisms and environmental variables that occur in natural environments. However, an understanding of how stressors influence organism health can compensate for these limitations. Mechanistic knowledge can be used to account for species differences in basal biological function and variability in environmental factors, including spatial and temporal changes in the chemical, physical and biological milieu. Consequently, physiological understanding of biological function, and how this is altered by stressor exposure, can facilitate proactive, predictive risk assessment. In this perspective article, existing frameworks that utilize physiological knowledge (e.g. biotic ligand models, adverse outcomes pathways and mechanistic effect models), are outlined, and specific examples of how mechanistic understanding has been used to predict risk are highlighted. Future research approaches and data needs for extending the incorporation of physiological information into ecological risk assessments are discussed. Although the review focuses on chemical toxicants in aquatic systems, physical and biological stressors and terrestrial environments are also briefly considered.

Next-generation ecological risk assessment: Predicting risk from molecular initiation to ecosystem service delivery

Ecological risk assessment is the process of evaluating how likely it is that the environment may be impacted as the result of exposure to one or more chemicals and/or other stressors. It is not playing as large a role in environmental management decisions as it should be. A core challenge is that risk assessments often do not relate directly or transparently to protection goals. There have been exciting developments in in vitro testing and high-throughput systems that measure responses to chemicals at molecular and biochemical levels of organization, but the linkage between such responses and impacts of regulatory significance - whole organisms, populations, communities, and ecosystems - are not easily predictable. This article describes some recent developments that are directed at bridging this gap and providing more predictive models that can make robust links between what we typically measure in risk assessments and what we aim to protect.

Modeling Macroinvertebrate Community Dynamics in Stream Mesocosms Contaminated with a Pesticide

Modeling community dynamics of aquatic invertebrates is an important but challenging task, in particular in ecotoxicological risk assessment. Systematic parameter estimation and rigorous assessment of model uncertainty are often lacking in such applications. We applied the mechanistic food web model Streambugs to investigate the temporal development of the macroinvertebrate community in an ecotoxicological mesocosm experiment with pulsed contaminations with the insecticide thiacloprid. We used Bayesian inference to estimate parameters and their uncertainty. Approx. 85% of all experimental observations lie within the 90% uncertainty intervals indicating reasonably good fits of the calibrated model. However, a validation with independent data was not possible due to lacking data. Investigation of vital rates and limiting factors in the model yielded insights into recovery dynamics. Inclusion of the emergence process and sub-lethal effects turned out to be potentially relevant model extensions. Measurements of food source dynamics, individual body size (classes), and additional knowledge on sub-lethal effects would support more accurate modeling. This application of a process-based, ecotoxicological community model with uncertainty assessment by Bayesian inference increased our process understanding of toxicant effects in macroinvertebrate communities and helped identifying potential improvements in model structure and experimental design.

Theoretically exploring direct and indirect chemical effects across ecological and exposure scenarios using mechanistic fate and effects modelling

Predicting ecosystem response to chemicals is a complex problem in ecotoxicology and a challenge for risk assessors. The variables potentially influencing chemical fate and exposure define the exposure scenario while the variables determining effects at the ecosystem level define the ecological scenario. In absence of any empirical data, the objective of this paper is to present simulations by a fugacity-based fate model and a differential equation-based ecosystem model to theoretically explore how direct and indirect effects on invertebrate shallow pond communities vary with changing ecological and exposure scenarios. These simulations suggest that direct and indirect effects are larger in mesotrophic systems than in oligotrophic systems. In both trophic states, interaction strength (quantified using grazing rates) was suggested a more important driver for the size and recovery from direct and indirect effects than immigration rate. In general, weak interactions led to smaller direct and indirect effects. For chemicals targeting mesozooplankton only, indirect effects were common in (simple) food-chains but rare in (complex) food-webs. For chemicals directly affecting microzooplankton, the dominant zooplankton group in the modelled community, indirect effects occurred both in food-chains and food-webs. We conclude that the choice of the ecological and exposure scenarios in ecotoxicological modelling efforts needs to be justified because of its influence on the prevalence and magnitude of the predicted effects. Overall, more work needs to be done to empirically test the theoretical expectations formulated here.

Modelling the impact of the environmental scenario on population recovery from chemical stress exposure: a case study using Daphnia magna

Recovery of organisms is an important attribute for evaluating the acceptability of chemicals' effects in ecological risk assessment in Europe. Recovery in the field does not depend on the chemical's properties and type of exposure only, but it is strongly linked to important environmental variables and biological interactions as well. Yet, these remain only marginally considered in the European risk assessment of chemicals. Here, we use individual-based modelling to investigate how the environmental scenario affects Daphnia magna population recovery from chemical exposure. Simulation experiments were performed for chemicals with lethality levels ranging from 40% to 90% at different food and temperature conditions. The same toxicity levels were then tested in combination with biological interactions including predation or competition. Results show that for the same chemical effect strength, populations often exhibited different recovery times in a different environmental context. The interactions between the chemical and the environmental variables were the strongest determinants of population recovery. Most important, biotic interactions even induced opposite effects on recovery at low and at high mortality levels. Results of this study infer that no specific role can be attributed to any abiotic or biotic variable in isolation. We conclude that unless the complex interactive mechanisms between the different factors constituting the full environmental scenario are taken into account in risk assessment, we cannot achieve a complete understanding of recovery processes from chemical effects.