The extrapolation problem and how population modeling can help.
We argue that population modeling can add value to ecological risk assessment by reducing uncertainty when extrapolating from ecotoxicological observations to relevant ecological effects. We review other methods of extrapolation, ranging from application factors to species sensitivity distributions to suborganismal (biomarker and "-omics") responses to quantitative structure-activity relationships and model ecosystems, drawing attention to the limitations of each. We suggest a simple classification of population models and critically examine each model in an extrapolation context. We conclude that population models have the potential for adding value to ecological risk assessment by incorporating better understanding of the links between individual responses and population size and structure and by incorporating greater levels of ecological complexity. A number of issues, however, need to be addressed before such models are likely to become more widely used. In a science context, these involve challenges in parameterization, questions about appropriate levels of complexity, issues concerning how specific or general the models need to be, and the extent to which interactions through competition and trophic relationships can be easily incorporated.
Forbes
Valery E
VE
Centre for Integrated Population Ecology, Department of Environmental, Social and Spatial Change, Roskilde University, DK-4000, Roskilde, Denmark. vforbes@ruc.dk
Calow
Peter
P
Sibly
Richard M
RM
D003627:Data Interpretation, Statistical; D054750:Ecotoxicology / Q000706:statistics & numerical data*; D004785:Environmental Pollutants / Q000633:toxicity*; D004787:Environmental Pollution / Q000706:statistics & numerical data; D015233:Models, Statistical; D011153:Population; D021281:Quantitative Structure-Activity Relationship
D004785:Environmental Pollutants