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.
This project had two complementary sets of objectives. The first set of objectives was to make several improvements and additions to the Effective Area Model (EAM) toolkit developed through SERDP project Predicting the Effects of Ecosystem Fragmentation and Restoration: Management Models for Animal Populations (RC-1100). The EAM is a habitat model where landscape context, in the form of explicit consideration of habitat edges, is incorporated into predictions of habitat quality. The EAM toolbox required the development of an assortment of programs, scripts, and guiding documents to help researchers and managers implement a program of large-scale, long-term management for multiple species. The second set of objectives focused on continuing work with managers at Fort Hood, Texas and Fort Benning, Georgia to apply this project’s landscape modeling approach to their management challenges.
This project relied on the vast stores of spatial and ecological data that are available to differing degrees on military bases, in this case at Fort Benning and Fort Hood. Therefore, no field work was performed. At each base, available data were used to implement each step in the modeling process. At the same time, automated tools were developed, including the EAM and R packages and conceptual tools, such as the edge effects model, for scientists and managers to use in applying the modeling framework. The overall workflow that was used to apply the modeling framework over the three-year project period at both installations included six steps: (1) identify management needs; (2) develop scenario maps; (3) develop edge response functions; (4) run scenarios through the EAM; (5) process, visualize, and analyze EAM output; and (6) develop management recommendations.
The study on the scaling-up of edge effects showed that small-scale effects integrate up to the landscape in ways that are often surprising. For instance, in some cases where strong divergences in the predictions are expected of the EAM and null models, differences in modeled outcomes were negligible. In cases where the magnitude of edge effects was variable, the distance to which the edge response extended into adjoining habitats had surprisingly minimal effects on predicted density. However, simply changing the symmetry of the edge response across the edge (i.e., shifting the response completely into one habitat or another while keeping its shape constant) had the largest overall impact on landscape-scale distributions. Thus, factors that influence exactly where edge-sensitive species reach maximum or minimum abundance are of interest.
Results also identified cases where simple metrics may suffice for understanding responses to landscape-scale changes in context and structure, as well as when more complex metrics become necessary. In fact, the research results suggested that simple metrics, such as distance to the closest edge, may often be sufficient for the most habitat-sensitive species, as long as the species is known to always avoid edges within their preferred habitat(s).
This work suggested that large site-to-site variability in edge responses, widely noted in the literature, may in part be explained by differences in patch structure and context. Other factors, however, such as connectivity and local patch quality, also may be important.
To access end-user products developed through this research, please visit the Species Management section on the RC Tools and Training page.
This project advanced the understanding of landscape context and edge effects by providing a powerful framework for quantifying and modeling species-level impacts of landscape-scale changes resulting from different management actions. Further, it provides an enhanced set of tools for analyzing the potential impacts of edges on multiple species, while helping to shift focus from single-species management to a larger community of organisms. The results presented, combined with other recent developments in landscape ecology and the study of edge effects, indicate a rapid expansion of predictive capacity regarding this previously confounding set of issues. Further, improved understanding and advances in modeling approaches, including those presented here, are likely to enhance the ability to design managed landscapes for improved conservation outcomes. Resource managers can use information from the EAM to help plan management actions, shape thinking regarding large-scale dynamics, or implementing large-scale, long term management plans to help maintain the health of ecological communities.