Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. AbstractModelling populations on an individual-by-individual basis has proven to be a fruitful approach. Many complex patterns that are observed on the population level have been shown to arise from simple interactions between individuals. However, a major problem with these models is that the typically large number of individuals needed requires impractically large computation times. The common solution, reduction of the number of individuals in the model, can lead to loss of variation, irregular dynamics, and large sensitivity to the value of random generator seeds. As a solution to these problems, we propose to add an extra variable feature to each model individual, namely the number of real individuals it actually represents. This approach allows zooming from a real individual-by-individual model to a cohort representation or ultimately an all-animals-are-equal view without changing the model formulation. Therefore, the super-individual concept offers easy possibilities to check whether the observed behaviour is an artifact of following a limited number of individuals or of lumping individuals, and also to verify whether individual variability is indeed an essential ingredient for the observed behaviour. In addition the approach offers arbitrarily large computational advantages. As an example the super-individual approach is applied to a generic model of the dynamics of a size-distributed consumer cohort as well as to an elaborate applied simulation model of the recruitment of striped bass.

Whereas current chemical risk assessment (RA) schemes within the European Union (EU) focus mainly on toxicity and bioaccumulation of chemicals in individual organisms, most protection goals aim at preserving populations of nontarget organisms rather than individuals. Ecological models are tools rarely recommended in official technical documents on RA of chemicals, but are widely used by researchers to assess risks to populations, communities and ecosystems. Their great advantage is the relatively straightforward integration of the sensitivity of species to chemicals, the mode of action and fate in the environment of toxicants, life-history traits of the species of concern, and landscape features. To promote the usage of ecological models in regulatory risk assessment, this study tries to establish whether existing, published ecological modeling studies have addressed or have the potential to address the protection aims and requirements of the chemical directives of the EU. We reviewed 148 publications, and evaluated and analyzed them in a database according to defined criteria. Published models were also classified in terms of 5 areas where their application would be most useful for chemical RA. All potential application areas are well represented in the published literature. Most models were developed to estimate population-level responses on the basis of individual effects, followed by recovery process assessment, both in individuals and at the level of metapopulations. We provide case studies for each of the proposed areas of ecological model application. The lack of clarity about protection goals in legislative documents made it impossible to establish a direct link between modeling studies and protection goals. Because most of the models reviewed here were not developed for regulatory risk assessment, there is great potential and a variety of ecological models in the published literature.

Several European directives and regulations address the environmental risk assessment of chemicals. We used the protection of freshwater ecosystems against plant protection products, biocidal products, human and veterinary pharmaceuticals, and other chemicals and priority substances under the Water Framework Directive as examples to explore the potential of ecological effect models for a refined risk assessment. Our analysis of the directives, regulations, and related guidance documents lead us to distinguish the following 5 areas for the application of ecological models in chemical risk assessment: 1) Extrapolation of organism-level effects to the population level: The protection goals are formulated in general terms, e.g., avoiding "unacceptable effects" or "adverse impact" on the environment or the "viability of exposed species." In contrast, most of the standard ecotoxicological tests provide data only on organism-level endpoints and are thus not directly linked to the protection goals which focus on populations and communities. 2) Extrapolation of effects between different exposure profiles: Especially for plant protection products, exposure profiles can be very variable and impossible to cover in toxicological tests. 3) Extrapolation of recovery processes: As a consequence of the often short-term exposures to plant protection products, the risk assessment is based on the community recovery principle. On the other hand, assessments under the other directives assume a more or less constant exposure and are based on the ecosystem threshold principle. 4) Analysis and prediction of indirect effects: Because effects on 1 or a few taxa might have consequences on other taxa that are not directly affected by the chemical, such indirect effects on communities have to be considered. 5) Prediction of bioaccumulation within food chains: All directives take the possibility of bioaccumulation, and thus secondary poisoning within the food chain, into account.

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