Get reliable rangeland science

The use of inventory data and previous scientific results to develop ecological site classifications and state-and-transition models (STMs) can be difficult. Here we draw on an example from a short-term project in the Calden region of central Argentina to illustrate a straightforward, logical approach to the use of data and prior results to develop a general STM. We used stratified random sampling (based on a general soil map) and opportunistic, targeted sampling of vegetation and soils at 47 points within the Caldenal region as a basis for classifying ecological sites and states. Cluster analysis on vegetation combined with nonparametric tests of cluster-environment relationships were used to evaluate patterns controlling the distribution of states in the landscape. Cluster identity was consistent with existing, informal concepts and state and transition concepts applied to clusters were based on existing studies from the range management and ecological literature. Our data supported the utility of three ecological sites and five plant communities/states in the region. While the details of analysis necessarily vary among cases, this general approach can be useful in most settings. Two important insights emerge from our work in the Calden region and elsewhere. First, multivariate analysis using functional groups of plants, rather than complete species lists, can produce more interpretable classifications. Second, the occurrence of states tends to covary with environmental factors, such that sites and states cannot easily be disentangled. Thus, it may be useful to define state-and-transition models more broadly and to define the effect of environmental gradients on reference states and state transitions within general models.