- Program Areas
- Energy and Water
- Environmental Restoration
- Munitions Response
- Resource Conservation and Climate Change
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Develping Dynamic Reference Models and a Decision Support Framework for Southeastern Ecosystems: An Integrated Approach
Longleaf pine sandhills are well-represented on military lands across the Southeast and provide the primary habitat for the federally endangered red-cockaded woodpecker (RCW). Restoring sandhills, while understanding its impact on the recovery of the RCW and facilitating the military mission, represents the most significant ecological challenge to bases throughout the Southeast. Addressing that challenge has been constrained by lack of understanding of recovery processes in restoration. Defining reference models for measuring ecological recovery continues to be a priority research need for ecological restoration. Often, however, reference forests have been viewed as a static condition that represents the target for restoration (desired future condition). This static view limits management and monitoring in at least two critical ways. First, it ignores the full range of temporal and spatial variation that exists within benchmarks, instead replacing it with a “one size fits all” target. This asserts the mean characteristics of the sandhill ecosystem at the expense of its natural diversity. Secondly, benchmark sites are likely to express dynamics that are difficult to predict due to climate change. This dynamism will constrain recovery assessment unless change is inherently incorporated into how reference ecosystems are viewed and monitoring assessment employs an adaptive, dynamic recovery model.
The objectives of this project are to (1) quantify annual and decadal dynamics of reference longleaf pine sandhills to create dynamic benchmarks for restoration; (2) determine recovery rates of degraded sandhill ecosystems over a 10-15 year period in response to hardwood removal treatments; (3) integrate 1 and 2 above into a longleaf forest dynamic modeling tool that incorporates population response of the federally endangered RCW to changes in forest structure; and (4) integrate 1 and 2 above into a decision support framework that allows the evaluation of landscape-scale ecosystem health through monitoring, while enhancing decision making.
A dynamic ecological reference model will be developed to effectively gauge the impact of common forest management practices (herbicides, fire, and timber harvests) and predict the direction and rate of recovery with respect to ecosystem management objectives. Data inputs for the model will be collected from the following:
1) Resampling six large (81-ha) plots that were intensively studied reference sandhills for a landscape; restoration experiment led by The Nature Conservancy (TNC) from 1993-1997;
2) Sampling all 1-ha plots identified in the extensive monitoring program at Eglin Air Force Base (AFB) that exhibit high-quality reference conditions for sandhill sites; and
3) Resampling 24 81-ha oak removal plots from the original TNC study, documenting recovery over a 15-year time period in longleaf sandhills in response to fire, herbicide, and mechanical treatments.
These data will be used to parameterize a modified RCW population model to simulate RCW population response to dynamic landscape-scale habitat changes through time. Finally, an Oracle-based decision support framework will automate statistical analysis of ecological monitoring data to assess trends in ecological condition relative to dynamic reference conditions and to feed relevant information to the RCW model and Eglin AFB managers.
Reference dynamics and recovery rates of longleaf pine sandhills will be used to address major ecosystem management needs for military installations in the Southeast. Integrating recovery rates and the dynamic reference concept into the RCW population demographics will directly improve management efforts to recover this critical species. By developing a decision support framework to provide automated analyses of ecological condition across the landscape, this project will address a critical shortcoming in ecological monitoring that limits the use of data for adaptive management. (Anticipated Project Completion - 2014)
Symposium & Workshop
FY 2013 New Start Project Selections
Points of Contact
Dr. Robert Mitchell
J. W. Jones Ecological Research Center
Resource Conservation and Climate Change
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