Predicted exotic annual grass abundance in rangelands of the western United States using various precipitation scenarios for 2022
Dates
Publication Date
2023-02-17
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. [...]
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. We achieved strong training accuracy (r= 0.95 and MdAE=2.36 of percent cover) and test accuracy (r= 0.79 and MdAE=4.54 of percent cover). We predicted five versions of EAG percent cover maps for 2022 with different precipitation scenarios, i.e., with the 9-year average, half of the average, three fourth of the average, one and half of the average, and twice the average precipitation. Five versions of spatially explicit EAG percent cover 2022 datasets can provide valuable information to local and regional land managers so they would know what EAG abundance would look like with certain precipitation scenario.
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ExoticAnnualGrass_wPPT_Scenarios.xml Original FGDC Metadata
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PercentCover_ArcGIS_colorramp.lyr
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PercentCover_QGIS_colorramp.qml
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EAG_ppt_normal_2022.zip
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EAG_ppt_normal_2022.tif.ovr
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EAG_ppt_normal_2022.tif.vat.dbf
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EAG_ppt_1pt5_2022.zip
EAG_ppt_1pt5_2022.tif
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EAG_ppt_1pt5_2022.tif.ovr
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EAG_ppt_1pt5_2022.tif.vat.dbf
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EAG_ppt_double_2022.zip
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EAG_ppt_double_2022.tif.ovr
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EAG_ppt_double_2022.tif.vat.dbf
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EAG_ppt_double_2022.tif-ColorRamp.SLD
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EAG_ppt_pt75_2022.zip
EAG_ppt_pt75_2022.tif
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EAG_ppt_pt75_2022.tif.ovr
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EAG_ppt_pt75_2022.tif.vat.dbf
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EAG_ppt_pt75_2022.tif-ColorRamp.SLD
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EAG_ppt_pt5_2022.zip
EAG_ppt_pt5_2022.tif
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EAG_ppt_pt5_2022.tif.ovr
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EAG_ppt_pt5_2022.tif.vat.dbf
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EAG_ppt_pt5_2022.tif-ColorRamp.SLD
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Related External Resources
Type: Related Primary Publication
Dahal, D., Boyte, S.P., and Oimoen, M.J., 2023, Predicting Exotic Annual Grass Abundance in Rangelands of the Western United States Using Various Precipitation Scenarios: Rangeland Ecology & Management, v. 90, p. 221–230, https://doi.org/10.1016/j.rama.2023.04.011.
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.