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Soil, geologic, geomorphic, climate, and vegetation data from long-term monitoring plots (2009 - 2018) in Arches, Canyonlands, and Capitol Reef National Parks, Utah, USA

Published by U.S. Geological Survey | Department of the Interior | Metadata Last Checked: July 16, 2025 | Last Modified: 20221121
These data (all data tables for the data release) represent a suite of biotic and abiotic variables that characterized plant communities and the geologic, geomorphic, edaphic, climatic, and land use history context in which distinct plant communities occur. In 2009, the National Park Service's Inventory and Monitoring program for the Northern Colorado Plateau Network (NCPN) began measuring vegetation cover and site characteristics at monitoring plots stratified across different vegetation types within national parks on the Colorado Plateau. NCPN biologists remeasured vegetation cover at these plots in a rotating panel over the following decade. In 2019, U. S. Geological Survey geologists and soil scientists collected/compiled soil pit, geologic/geomorphic setting, climate, and livestock grazing history data at a subset of 135 of the NCPN plots. We examined correlations between soil properties and geologic/geomorphic variables to understand the influence of geologic and geomorphic setting on soil formation. We identified distinct plant communities from the vegetation cover data using hierarchical cluster analysis and nonmetric multidimensional scaling (NMDS). We then assessed relationships among vegetation clustering and NMDS results, soils, geology/geomorphology, and climate using random forest models to determine the abiotic conditions associated with specific plant communities. These data can be used to analyze landscape-scale relationships among plant communities and the abiotic contexts in which they occur; assess the distribution of native and non-native plant species across abiotic gradients; and examine relationships among geologic, geomorphic, and edaphic properties. The observational nature of these data and high correlation among some abiotic variables may complicate efforts to determine causal relationships within this data set.

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