@conference {JRN54588,
	title = {Data support for a state-and-transition model: What have we learned?},
	booktitle = {Society for Range Management, 65th Annual Meeting},
	year = {2012},
	month = {01/2012},
	publisher = {Society for Range Management},
	organization = {Society for Range Management},
	address = {Spokane, WA},
	abstract = {<p>State-and-transition models (STMs) were conceived as a means to synthesize knowledge about alternative plant communities and the processes that lead to transitions among them for specific land areas. STMs that have been developed over the past decade have often been limited by 1) a lack of detail on ecological mechanisms and management effects and 2) an over-reliance on expert knowledge and casual observations that are seldom critically evaluated. We used a state-and-transition model developed 10 years ago, in consultation with local experts, as a basis to examine how a model{\textquoteright}s structure would be changed via changes to model concepts, inclusion of a broad-scale, data-rich inventory dataset, and new experiments and monitoring. We found that elements of the initial model (Sandy ecological site, Chihuahuan Desert, Major Land Resource Area 42.2) were supported by the data, yet others were not. We found that the reference state was more resilient, an eroded shrubland state could attain higher grass cover, and that grasses and shrubs coexist over a wider range of values than previously assumed. We also found that the model could be simplified by reducing the number of states and quantitative criteria for ecological states and community phases were developed. This assessment suggests that a combination of local/expert knowledge and different kinds of data can be brought together to produce improved STMs. Even if this level of effort is not possible for all STMs in area, it should be possible for widespread or {\textquotedblleft}benchmark{\textquotedblright} ecological sites in most Major Land Resource Areas.</p>
},
	author = {Brandon T Bestelmeyer and D.D. Briske and Maria Fern{\'a}ndez-Gim{\'e}nez and Wu, X.B.}
}
