A growing human population and its needs are putting ecosystems under increasing stress. Pollution from a wide variety of industrial, agricultural, and household chemicals, in combination with a range of other stressors (e.g., global climate change, nutrient inputs, habitat fragmentation), is having undesirable impacts on the environment. Clearly, there is a need to balance the benefits that society derives from ecosystems, in terms of food and fuel, with the other services that ecosystems provide and on which we depend (e.g., clean water and air) . Ecotoxicologists strive to understand how toxic chemicals impact ecological systems and to use this understanding to inform ecological risk assessment . Given the growing extent and complexity of human impact on ecological systems, it has become clear that the simplistic approaches that historically have dominated in ecological risk assessment are insufficient , and new approaches are being developed that incorporate complexity in ways that can inform environmental management more effectively . Species habitat requirements, intra- and interspecific interactions and density dependence, heterogeneous exposure to chemical mixtures and multiple stressors, and long-term sublethal effects on populations are just some of the complexities that routinely have been neglected in ecological risk assessment. This is due mainly to the high resource demands of conducting studies at large spatial or temporal scales and on large numbers of species (some of which would be ethically questionable). Recent years have seen a surge of activity in the development of ecological models—that is, mechanistic effect models—for ecotoxicology and chemical risk assessment [5-8], and it has become obvious that the extrapolative and integrative power of such models can add value [4, 9-11]. Not only are these models useful for extrapolating across levels of biological organization—for example, from individual-level effects to population-level consequences—and across spatial and temporal scales, but they also allow a much more seamless integration with the very elaborate and detailed exposure assessments sometimes used in risk assessment . One of the largest initiatives undertaken to establish the use of mechanistic effect models in ecological risk assessment is the Mechanistic Effect Models for Ecological Risk Assessment of Chemicals (CREAM) project, a Marie Curie Initial Training Network, funded by the European Commission within the 7th Framework Programme , which has focused on training the next generation of researchers with expertise in both modeling and risk assessment.
In this special series on ecological models, we present several contributions that highlight the versatility of modeling approaches and the questions that they can address to demonstrate how the development and application of models can lead to more ecologically relevant and realistic risk assessments. Many, but not all, of the contributions are outputs from the CREAM project (for more CREAM publications, see Grimm and Thorbek ).
Both Gabsi et al.  and Kattwinkel and Liess  considered how intra- and interspecific interactions influence the long-term population impacts of chemical exposure. Gabsi et al.  used an individual-based model (IBM) of Daphnia magna as a virtual laboratory to test how chemicals that target different individual-level endpoints (e.g., reproduction, survival, feeding rates) combined with competition for food and predation impact population resilience. Kattwinkel and Liess  develop a generic 2-species IBM to investigate how recovery after pulsed exposure differs based on the strength of interspecific competition. In both of these contributions, models were used as investigative tools with general and widely applicable results.
The contributions by Barsi et al.  and Galic et al.  focused on specific chemicals, linking chemical exposure concentrations to effects at the individual level through toxicokinetic-toxicodynamic (TKTD) models. Barsi et al.  used an energy–budget model integrated with a TKTD model to analyze the effects of acetone exposure on different endpoints in the pond snail Lymnaea stagnalis. The authors advocate for a toxicity test design that would allow proper parameterization of energy–budget models, which take into account energy acquisition and expenditure of individual organisms. Galic et al.  extrapolated effects at the individual level to long-term population-level consequences by developing an IBM of the freshwater amphipod Gammarus pulex. They explored implications for population recovery brought about by different exposure–effect models. They also integrated the population model separately with a concentration–effect and a detailed TKTD model for 4 pesticides with different modes of action in several simple exposure scenarios and highlight the role of TKTD models that can account for spatial and temporal variability in exposure in current pesticide risk assessment.
The study by Focks et al.  took the realism in exposure to a combination of pesticides a step further, but with simplifications in the exposure–effect link. The authors simulated population recovery of an aquatic macroinvertebrate after exposure to a realistic combination of pesticides used in orchard and tuber crops in the Netherlands. Exposure concentrations were simulated with a pesticide fate model, and a concentration–effect model was implemented to assess effects on individual survival. The results showed that the recovery time after exposure to a realistic pesticide combination is not larger than after exposure to individual compounds.
The contribution with the most realistic landscape representation is that by Topping et al. , in which the current practice of estimating pesticide impacts on populations through simple recovery plots is put under scrutiny. The authors simulated the dynamics of 2 terrestrial arthropods, a spider and a carabid beetle, in a realistic Danish agricultural landscape and under a series of exposure conditions. They demonstrated that source–sink dynamics in typical agricultural systems need to be taken into account in the pesticide risk assessment process.
Meli et al.  investigated how habitat loss and fragmentation, combined with exposure to pesticides, affect biota on a microscale. They simulated the avoidance behavior and population dynamics of soil invertebrates (Collembola) exposed to different combinations of disturbance and spatially heterogeneous pesticide exposure and showed that unexpected population consequences might arise under different disturbance scenarios.
Finally, Baveco et al.  provide a detailed comparison of more complex models, such as IBMs, with simpler models, such as the logistic growth equation. The authors considered how insights from each can contribute to pesticide risk assessment. In several examples in which population recovery after exposure to realistic pesticide concentrations is calculated for 4 freshwater macroinvertebrates, the authors demonstrated how different life histories and dispersal behaviors impact the recovery process in simple and more complex models.
By considering relevant spatial and temporal scales, realistic exposure profiles and exposure–effect links, multiple disturbances, and ecological interactions, contributions in this special section clearly illustrate how mechanistic effect models can add relevance and realism to ecological risk assessment. Because good modeling practice [23, 24] requires decisions about which complexities to include and which to simplify (due to lack of knowledge or inadequate data), these concerns are discussed in our contributions as well. The fact that several contributions are coauthored by researchers from industry shows that using mechanistic effects models for ecological risk assessment is not just an academic exercise, and such models are being considered as additional lines of information in the regulation of chemicals .
We expect that mechanistic effects models will continue to play an increasing role in ecological risk assessment. Developments in this direction will be facilitated by computational advances that can cope with more complex models and ever-increasing amounts of data, as well as the recognition that chemical impacts need to be evaluated in the context of a growing number of other human impacts on ecological systems. Given recent initiatives to articulate environmental protection goals in terms of ecosystem services , a next major challenge for ecological modeling is to link service-providing units (e.g., populations, communities) with ecosystem service provision (e.g., water purification, pollination, pest regulation) both mechanistically and dynamically. As this special section demonstrates, a variety of mechanistic effects models exist to link the impacts of chemicals and other stressors on individual organisms to the population level, but the models need to be linked to the delivery of ecosystem services . Establishing such links will be more challenging for some services (e.g., nutrient cycling) than for others (e.g., pollination), but is necessary if we want to create robust and scientifically sound links between what we measure in ecological risk assessment and what we want to protect.