R3 PM Application of Spatially Explicit Techniques in Ecological Risk Assessment
Thursday, 17 November 2005: 1:50 PM - 5:30 PM in Ballroom 3

(BUR-1117-831866) Programming Perspectives on Wildlife Exposure Modeling: SEEM and FR.

Burmistrov, D¹, Wickwire, WT¹, Stackelberg, K von¹, Menzie, CA¹, Johnson, MS², Bridges, TS³, Dortch, MS³, ¹ Menzie-Cura & Associates, Inc., Winchester, MA, USA² Health Effects Research Program, US Army Center for Health Promotion and Preventive Medicine, Aberdeen Proving Ground, MD, USA³ United States Engineer Research and Development Center, Us Army Corps of Engineers, Vicksburg, MS, USA

ABSTRACT- Increasingly sophisticated analytical tools offer the risk assessment community the ability to improve the realism of wildlife exposure estimates. New models offer to account for the intersection of spatially distributed contamination and wildlife habitat and to record the impact that the intersection has on exposure estimates, e.g. high quality habitat intersecting with low contaminant levels. The project team is developing two spatially explicit exposure models: the Spatially Explicit Exposure Model (SEEM) for terrestrial wildlife and FishRand (FR) for aquatic wildlife. Both models are currently being integrated into the U.S. Army Risk Assessment Modeling System (ARAMS). Because a model is an interpretation of reality and cannot be a perfect representation of nature, we make a number of simplified assumptions regarding wildlife behavior. Understanding the applied modeling algorithms is important in order to understand the impact of the assumptions and the uncertainties that emerge from the assumptions on the final model outputs. Both SEEM and FR are Monte Carlo models. They use different modeling approaches in recognition of behavior differences between terrestrial and aquatic ecosystems. Both incorporate habitat suitability as a characteristic that increases the likelihood of exposure, and employ movement-based individual foraging. Movement rules are used to capture different foraging strategies. Markov Chain Monte Carlo is used to create a habitat quality weighted, though randomized, movement/foraging pattern for each individual (SEEM) or group of individuals (FR). Both models are powerful tools that provide assessors with the opportunity to explore wildlife exposure from the perspective of populations with exposure impacted by habitat availability and suitability. With an understanding of the underlying mathematics and assumptions, users can avoid the common model application pitfall of applying results without a full understanding of the assumptions used to create the model.

Key words: ecological, risk, spatial, modeling