@article {JRN55379,
	title = {An inductive approach to developing ecological site concepts with existing monitoring data},
	journal = {Rangeland Ecology \& Management},
	volume = {83},
	year = {2022},
	month = {7/2022},
	pages = {133-148},
	abstract = {Ecological sites comprise a land classification system that represents potential vegetation states and their management needs for different soils and climates. In the Rio Grande del Norte National Monument (RGdNNM) in northern New Mexico, uncertainty about the patterns and drivers of vegetation states impedes sustainable land management. Similar challenges are ubiquitous across terrestrial ecosystems and in particular landscapes with high spatial variability in soils and climate. Lack of suitable data has been a barrier to large-scale ecological site development based on quantitative observations. We used data from existing federal monitoring programs alongside spatial, environmental, and land use data to test for the role of climate, geomorphology, soils, and land use history on vegetation communities in RGdNNM. The monitoring dataset was collected with standardized monitoring methods implemented by the Bureau of Land Management{\textquoteright}s Assessment, Inventory, and Monitoring program and the Natural Resources Conservation Service{\textquoteright}s Landscape Monitoring Framework program. Eleven ecological site concepts and paired vegetation communities were identified using multivariate fuzzy clustering and classification tree analysis to determine the influence of abiotic variables on vegetation communities. The ecological site and vegetation community concepts developed for RGdNNM demonstrate how existing monitoring data can be used to interpret the structural and functional characteristics of landscapes. A workflow for applying monitoring data to landscape classification is presented to support the broader framework for ecological site development.},
	keywords = {climates, ecological site concepts, inductive approach, management, monitoring data, soils, vegetation states},
	doi = {https://doi.org/10.1016/j.rama.2022.03.009},
	url = {files/bibliography/22-010.pdf},
	author = {A. Heller and Nicholas Webb and Brandon T Bestelmeyer and Colby Brungard and Zoe Davidson}
}
