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State-and-Transition Models for Heterogeneous Landscapes: A Strategy for Development and Application

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Interpretation of assessment and monitoring data requires information about how reference conditions and ecological resilience vary in space and time. Reference conditions used as benchmarks are often specified via potential-based land classifications (e.g., ecological sites) that describe the plant communities potentially observed in an area based on soil and climate. State-and-transition models (STMs) coupled to ecological sites specify indicators of ecological resilience and thresholds. Although general concepts surrounding STMs and ecological sites have received increasing attention, strategies to apply and quantify these concepts have not. In this paper, we outline concepts and a practical approach to potential-based land classification and STM development. Quantification emphasizes inventory techniques readily available to natural resource professionals that reveal processes interacting across spatial scales. We recommend a sequence of eight steps for the co-development of ecological sites and STMs, including 1) creation of initial concepts based on literature and workshops; 2) extensive, low-intensity traverses to refine initial concepts and to plan inventory; 3) development of a spatial hierarchy for sampling based on climate, geomorphology, and soils; 4) stratified medium-intensity inventory of plant communities and soils across a broad extent and with large sample sizes; 5) storage of plant and soil data in a single database; 6) model-building and analysis of inventory data to test initial concepts; 7) support and/or refinement of concepts; and 8) high-intensity characterization and monitoring of states. We offer a simple example of how data assembled via our sequence are used to refine ecological site classes and STMs. The linkage of inventory to expert knowledge and site-based mechanistic experiments and monitoring provides a powerful means for specifying management hypotheses and, ultimately, promoting resilience in grassland, shrubland, savanna, and forest ecosystems.
Bestelmeyer, Brandon T. , Tugel, Arlene J. , Peacock, George L. Jr. , Robinett, Daniel G. , Shaver, Pat L. , Brown, Joel R. , Herrick, Jeffrey E. , Sanchez, Homer , Havstad, Kris M.
rangelands , landscapes , spatial variation , simulation models , plant communities , vegetation , environmental monitoring , environmental assessment , quantitative analysis , land classification , land management , grasslands , shrublands , savannas , forests , ecosystems
p. 1-15.
Includes references
Rangeland ecology & management 2009 Jan., v. 62, issue no. 1
Journal Articles, USDA Authors, Peer-Reviewed
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