Land managers of forested landscapes face increased difficulty in projecting how various management techniques impact ecological processes, such as carbon sequestration potential. Given the large use of prescribed fire on U.S. Department of Defense lands, there is a need to assess landscape carbon flux in response to different fire frequencies and management regimes. Ecosystem process models provide a robust solution to informing management decisions in an uncertain future. Model selection depends on the geographic location of interest, the overall goals and objectives of management, and the inherent biotic and abiotic disturbances that affect ecosystem dynamics (e.g., vegetation response and interactions with climate and climate-driven disturbances).
The project objectives were as follows:
The project team synthesized two forested data rich study sites (Jones Center at Ichauway in southern Georgia and Harvard Forest in Massachusetts) and provided them to ESTCP and other chosen ESTCP teams, while continuing to support those teams and ESTCP. These long-term datasets included information on soil, vegetation, biomass, carbon flux, and management. The project team also calibrated a landscape class, the Landscape Disturbance and Succession II (LANDIS-II) model, and a global class, the Ecosystem Demography (ED) model for the Jones Center at Ichauway. The project team assessed the strengths and weaknesses of each model including validating for Net Ecosystem Exchange with eddy-covariance flux tower data. The models are now available for any additional scenarios involving southeastern U.S. pinelands.
The project team found that both models predicted similar carbon projections and general species dynamics at short and long fire return intervals but differed at intermediate ranges due to inherent species representation (individual species versus plant functional types) within each model. Both models supported an aggressive prescribed fire regime in southeastern U.S. pinelands to maintain ecosystem carbon stability. The project team provided a detailed assessment of how, why, and when to use either model.
The project team's cost assessment is primarily labor based. The models are free to access and use, however, a data technician(s) and a modeling expert(s) are needed to parameterize, calibrate, and validate the models. Subsequent studies after these steps may be less labor intensive with varying costs.
No implementation issues were encountered.