title: Population models in pesticide risk assessment: lessons for assessing population-level effects, recovery, and alternative exposure scenarios from modeling a small mammal.
abstract: In the last few years, the interest in using ecological population models as a tool for pesticide risk assessment has increased rapidly. Practical guidance, however, on how to perform a risk assessment with a population model is still lacking. It is still unclear which endpoint (population density, population growth, etc.) is the most sensitive indicator of population-level effects and how risk can be evaluated at the population level. Moreover, a main added value of model-based risk assessments, which is an understanding of the mechanisms involved in alternative exposure scenarios, so far has received little attention. We therefore used an example model to compare commonly used endpoints and alternative exposure scenarios. The model is a structurally realistic, but relatively simple, individual-based, spatially explicit model for the common shrew (Sorex araneus), which was selected because it has been tested and validated extensively. We show that population density is more sensitive for detecting population-level effects in the short term (months) than population growth rate. Population viability measured by extinction risk can also be a relevant endpoint, because it is especially sensitive for small populations. We show that landscape structure and the timing of pesticide application (population structure at the time of application) can have a great impact on population recovery, and we analyze statistical tests for use in population-level risk assessments. Our results demonstrate which factors and insights should be taken into account in population-level risk assessments.
authors.affiliation: Magnus Wang, RIFCON GmbH, Im Neuenheimer Feld 517, 69120 Heidelberg, Germany. magnus.wang@rifcon.de
authors.forename: Magnus
authors.initials: M
authors.lastname: Wang
authors.forename: Volker
authors.initials: V
authors.lastname: Grimm
mesh_terms: D000818:Animals; D023421:Models, Animal; D008954:Models, Biological; D010575:Pesticides / Q000032:analysis* / Q000633:toxicity*; D018570:Risk Assessment; D012788:Shrews
chemical_list: D010575:Pesticides