Realizing the Potential of the Effective Area Model: Refining the Software and Incorporating Recent Advances to Maximize Usefulness on Military Installations
Abstract
Landscape structure is increasingly recognized as a factor that can greatly impact habitat quality. Despite this, the tools to understand how landscape context impacts habitat quality, largely felt through edge effects, have been slow to develop. Yet research suggests that observed edge responses are increasingly predictable and offer an avenue to understand landscape-scale responses to management actions for both individual species and communities of organisms. We developed a series of tools to harness either field data or basic natural history information about local ecological communities in order to predict responses to changes in landscape structure. The cornerstone of this toolkit is the Effective Area Model (EAM). The EAM takes information about local edge effects and extrapolates them over landscapes, offering the user predictions of species' responses or useful metrics on landscape structure. In addition to this tool, we developed R-packages to characterize edge responses from field data and also to help users process and visualize output from the EAM. We have implemented this approach to management by demonstrating its use on two bases: Ft. Benning, GA and Ft. Hood, TX. Our results show how managers can use information from the EAM to help plan management actions or shape thinking regarding large-scale dynamics. We further show how simple metrics may sometimes suffice to understand responses to landscape changes, but also when more nuanced metrics may be necessary to help managers grapple with complex responses across a community of organisms. Finally, we present an approach to incorporating output from the EAM (or other models) into installation maps, offering a straight-forward approach to landscape-scale management.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 01, 2010
- Accession Number
- ADA540031
Entities
People
- Leslie Ries
- Thomas D. Sisk
Organizations
- University of Maryland