title: Developing predictive systems models to address complexity and relevance for ecological risk assessment.
abstract: Ecological risk assessments (ERAs) are not used as well as they could be in risk management. Part of the problem is that they often lack ecological relevance; that is, they fail to grasp necessary ecological complexities. Adding realism and complexity can be difficult and costly. We argue that predictive systems models (PSMs) can provide a way of capturing complexity and ecological relevance cost-effectively. However, addressing complexity and ecological relevance is only part of the problem. Ecological risk assessments often fail to meet the needs of risk managers by not providing assessments that relate to protection goals and by expressing risk in ratios that cannot be weighed against the costs of interventions. Once more, PSMs can be designed to provide outputs in terms of value-relevant effects that are modulated against exposure and that can provide a better basis for decision making than arbitrary ratios or threshold values. Recent developments in the modeling and its potential for implementation by risk assessors and risk managers are beginning to demonstrate how PSMs can be practically applied in risk assessment and the advantages that doing so could have.
authors.affiliation: School of Biological Sciences, University of Nebraska-Lincoln, Lincoln, Nebraska 68588-0118, USA. vforbes3@unl.edu
authors.forename: Valery E
authors.initials: VE
authors.lastname: Forbes
authors.forename: Peter
authors.initials: P
authors.lastname: Calow
mesh_terms: D004781:Environmental Exposure; D004784:Environmental Monitoring / Q000379:methods*; D058735:Environmental Policy / Q000331:legislation & jurisprudence; D004785:Environmental Pollutants / Q000633:toxicity*; D056890:Eukaryota / Q000187:drug effects; D005062:European Union; D033161:Government Regulation; D008962:Models, Theoretical; D018570:Risk Assessment / Q000379:methods
chemical_list: D004785:Environmental Pollutants
keywords: Ecological relevance; Predictive systems models; Risk management; Risk quotients; Valuation