|Joyce, L. - USFS|
|Pyke, D. - USGS|
Submitted to: Ecological Society of America Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: April 1, 2001
Publication Date: July 15, 2001
Citation: JOYCE, L.A., PYKE, D.A., HERRICK, J.E. MANAGING RANGELANDS: IMPLICATIONS OF A PARADIGM SHIFT IN THE ECOLOGY OF ARID AND SEMIARID ECOSYSTEMS. 86TH ANNUAL MEETING OF THE ECOLOGICAL SOCIETY OF AMERICA. 2001. ABSTRACT P. 21. Technical Abstract: Arid and semi-arid ecosystems often exhibit unanticipated, nonlinear responses to environmental events and to management activities. While the importance of nonlinear dynamics and thresholds has been widely documented, most rangeland management is still based on the classical Clementsian understanding of succession and retrogression. The objectives of this talk kare to review how recent ecological theory has been integrated into resource management, to identify the scientific and nonscientific limitations of this integration and, based on these limitations, to define types of research land managers need to effectively apply modern ecological theory. The concept of multiple states and transitions is being integrated into revisions of the ecological site descriptions of the Natural Resource Conservation Service. The Bureau of Land Management is implementing the concept of thresholds in their qualitative rangeland health assessments. A Amore complete integration into resource management is limited by a poor understanding of states, transitions and thresholds in many arid and semi-arid ecosystems, and the lack of scientifically-based protocols for selecting management options when uncertainty exists about the presence and relationship to one or more thresholds. These limitations are compounded by administrative structures and competing resource demands. Ecological research is needed to clearly define environmental factors that control states, transitions, and thresholds for the variety of rangeland ecosystems. Adaptive management is paramount and will require collaboration among scientists and managers to successfully apply these increasingly complex models.