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Title: Threshold models linked to land classification and indicators as guides to restoration – prospects and pitfalls

item Bestelmeyer, Brandon
item Herrick, Jeffrey - Jeff
item BRISKE, DAVID - Texas A&M University
item FERNANDEZ-GIMENEZ, MARIA - Colorado State University
item WU, X BEN - Texas A&M University
item BROWN, JOEL - Natural Resources Conservation Service (NRCS, USDA)
item Havstad, Kris

Submitted to: Ecology Symposium
Publication Type: Abstract Only
Publication Acceptance Date: 7/1/2009
Publication Date: 8/16/2009
Citation: Bestelmeyer, B.T., Herrick, J.E., Briske, D.D., Fernandez-Gimenez, M., Wu, X., Brown, J., Havstad, K.M. 2009. Threshold models linked to land classification and indicators as guides to restoration – prospects and pitfalls [abstract]. 10th International Congress of Ecology: Ecology in a Changing Climate, August 16-19, 2009, Brisbane, Queensland, Australia.

Interpretive Summary:

Technical Abstract: Threshold (or regime shift) models are useful for restoration because they match actions to conditions where benefits are likely to be maximized. The procedures by which threshold models should be applied, however, are in the early stages of development. Here, we describe ecological concepts and derivative elements of an approach to applying threshold models including 1) combining experiments, simulations, and local ecological knowledge to develop conceptual state-and-transition models representing specific geographic areas; 2) linking the models to soil, plant, and landscape process-based indicators detected in ground-based or remotely-sensed assessment; 3) recognizing model contingency with respect to varying land-use, soils and geomorphology, and climate; and 4) cooperative planning of restoration actions integrated with model-based experimentation and monitoring. We describe several tools that have been used for each element and limitations of the tools and concepts revealed in applications in the United States and Mongolia. We suggest that the integration of threshold/resilience concepts with scale-specific models, process-based indicators, spatial data on the biophysical and social environment, and restoration experiments is becoming an increasingly common solution to connecting abstract ideas with real-world restoration applications.