The 2008 Joint Meeting of the Society for Range Management and the America Forage and Grassland Council. |
Tuesday, January 29, 2008 - 10:40 AM
Resilience-Based Application of State-and-Transition Models
David D. Briske, Department of Ecosystem Science and Management, Texas A&M University, 2138 TAMU, College Station, TX 77843, Brandon T. Bestelmeyer, USDA-ARS Jornada Experimental Range, MSC 3JER Box 30003, New Mexico State University, Las Cruces, NM 88003, Tamzen K. Stringham, Department of Rangeland Ecology and Management, Oregon State University, 202 Strand Agricultural Hall, Corvallis, OR 97331, and Pat L. Shaver, West National Technology Support Center, USDA-NRCS, 1201 NE Lloyd Blvd. Suite 100, Portland, OR 97232.
We recommend that several conceptual modifications be incorporated into the state-and-transition model (STM) framework to: 1) explicitly link this framework to the concept of ecological resilience, 2) direct management attention away from thresholds and toward the maintenance of state resilience, and 3) enhance the ability of STMs to capture a broader set of relevant ecological information to support ecosystem management. Ecological resilience describes the amount of change or disruption that is required to transform a system from being maintained by one set of mutually reinforcing processes and structures to a different set of processes and structures (e.g., alternative stable state). Effective ecosystem management must focus on the adoption of management practices and policies that maintain or enhance ecological resilience to prevent stable states from exceeding potential thresholds. In this context, resilience management does not focus on thresholds per se, but rather on within-state dynamics that influence resilience and state proximity and vulnerability to thresholds. Resilience-based ecosystem management provides greater opportunities to incorporate adaptive management than does threshold-based management because thresholds specifically define the limits of state resilience, rather than the conditions that determine the likelihood that these limits will be surpassed. We recommend that the STM framework incorporate triggers, at-risk communities, feedback mechanisms, and restoration pathways and develop process-specific indicators that enable managers to identify at-risk plant communities and potential restoration pathways.