|Title||State-and-Transition models for heterogeneous landscapes: A strategy for development and application|
|Publication Type||Journal Article|
|Year of Publication||2009|
|Authors||Bestelmeyer BT, Tugel A.J., Peacock G.L., Robinett D., Shaver P.L., Brown J., Herrick JE, Sanchez H., Havstad K|
|Journal||Rangeland Ecology and Management|
|ARIS Log Number||230656|
|Keywords||assessment, ecological resilience, monitoring, quantitative, state-and-transition|
Interpretation of assessment and monitoring data requires information about reference conditions and ecological resilience. Reference conditions used as benchmarks can be 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 can specify indicators of ecological resilience and thresholds. Although general concepts surrounding STMs and ecological sites have received increasing attention, strategies for their data-driven development 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 and that can reveal processes interacting across spatial scales. We recommend a sequence of 8 steps for the co-development of land classes and STMs including 1) creation of initial ecological site concepts and STMs based on literature and workshops; 2) extensive, low-intensity traverses to assist in generating 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 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. Second, we offer a simple example of how data assembled via our sequence can be 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.