Land use, climate, and landscape context jointly determine the occurrence of state transitions in terrestrial ecosystems. State-and-transition models (STM) are used to clarify the roles of drivers, and ecological sites (climoedaphic land units) represent the effects of landscape context. On the Taos Plateau in northern New Mexico, uncertainty about the patterns and drivers of vegetation state transitions impedes sustainable land management. There is evidence of a shift from grass-dominated to shrub-dominated systems within the region; however, the efficacy of restoration treatments is highly variable, likely due to unrecognized variation in climate and soils. Similar challenges are ubiquitous across terrestrial ecosystems and in particular landscapes with high spatial variability in soils. We used data from federal vegetation monitoring programs and spatial, environmental, and land use data to test for the role of climate, geomorphology, soils, and land use history on restoration success on the Taos Plateau. The large dataset comprises a suite of recently-established core monitoring methods that are consistent across agencies and provide scalable estimates of resource distribution and land change trends across the western U.S. We used a suite of multivariate methods to characterize vegetation states and their relationships to environmental variables to test propositions in conceptual STMs.
Results/Conclusions
Preliminary analysis indicates the existence of six distinct climoedaphic units (ecological sites) that vary in either reference conditions or transition likelihood. Average precipitation, elevation, soil depth to restrictive layer, and soil clay content were useful in predicting vegetation community functional groups. Plant functional group abundance and vegetation structure varied within ecological sites based on management history, indicating that multiple vegetation states are present that are related to land use legacies. We developed a key to ecological sites and alternative states that allow land managers to predict restoration responses. A workflow for using multivariate analysis of core methods data to inform ecological site and STM concept development using the R software will be enable other areas of the U.S. to replicate our approach.