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Developing predictive systems models to address complexity and relevance for ecological risk assessment.
1School of Biological Sciences, University of Nebraska-Lincoln, Lincoln, Nebraska 68588-0118, USA. vforbes3@unl.edu
Integrated Environmental Assessment and Management
|April 25, 2013
Summary
Ecological risk assessments (ERAs) can be improved using predictive systems models (PSMs). PSMs offer a cost-effective way to incorporate ecological complexity and provide decision-relevant risk information for better environmental management.
Area of Science:
- Environmental Science
- Ecology
- Risk Assessment
Background:
- Ecological risk assessments (ERAs) are underutilized in risk management due to a lack of ecological relevance and complexity.
- Current ERAs often fail to align with protection goals and provide decision-makers with easily interpretable risk metrics.
Purpose of the Study:
- To propose predictive systems models (PSMs) as a cost-effective solution for enhancing ecological relevance and complexity in ERAs.
- To demonstrate how PSMs can generate value-relevant outputs for improved risk management decision-making.
Main Methods:
- Utilizing predictive systems models (PSMs) to capture ecological complexities and relevance.
- Designing PSMs to provide outputs in terms of value-relevant effects modulated against exposure.
Main Results:
- PSMs offer a practical approach to incorporate necessary ecological complexities cost-effectively.
- PSM outputs provide a better basis for decision-making compared to arbitrary ratios or threshold values used in traditional ERAs.
Conclusions:
- Predictive systems models (PSMs) can significantly improve the utility and application of ecological risk assessments.
- Implementing PSMs can lead to more informed and effective environmental risk management strategies.
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