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Predicted exotic annual grass abundance in rangelands of the western United States using various precipitation scenarios for 2022

Dates

Publication Date
Time Period
2022

Citation

Dahal, D., Boyte, S.P., and Oimoen, M.J., 2023, Predicted exotic annual grass abundance in rangelands of the western United States using various precipitation scenarios for 2022: U.S. Geological Survey data release, https://doi.org/10.5066/P9X84TAN.

Summary

Invasion of exotic annual grass (EAG), such as cheatgrass (Bromus tectorum), red brome (Bromus rubens), and medusahead (Taeniatherum caput-medusae), could have irreversible degradation impact to arid and semiarid rangeland ecosystems in the western United States. The distribution and abundance of these EAG species are highly influenced by weather variables such as temperature and precipitation. We set out to develop a machine learning modelling approach using a lightGBM algorithm to predict how changes in annual and immediate past precipitation regimes impact the abundance of EAG in the study area. The predictive model primarily utilized edaphic and weather variables and a seed source proxy from previous years to make the predictions. [...]

Contacts

Attached Files

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Purpose

The purpose for this release is to provide land managers and researchers spatially explicit percent cover datasets of predicted exotic annual grasses based on various precipitation scenarios for 2022. Appropriate use of the data should be defined by the user; however, this data comes with caveats. First, these predictions should be viewed as relative abundances. Second, comparing this dataset to similar datasets with different spatial resolutions or different dates can lead to substantial differences between dataset values.

Additional Information

Identifiers

Type Scheme Key
DOI https://www.sciencebase.gov/vocab/category/item/identifier doi:10.5066/P9X84TAN

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