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

Metadata Updated: October 29, 2023

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.

Access & Use Information

Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

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Dates

Metadata Created Date June 1, 2023
Metadata Updated Date October 29, 2023

Metadata Source

Harvested from DOI EDI

Additional Metadata

Resource Type Dataset
Metadata Created Date June 1, 2023
Metadata Updated Date October 29, 2023
Publisher U.S. Geological Survey
Maintainer
@Id http://datainventory.doi.gov/id/dataset/79639daaa68453bf6a2702287ac84004
Identifier USGS:63b45ea9d34e92aad3ca9d12
Data Last Modified 20230217
Category geospatial
Public Access Level public
Bureau Code 010:12
Metadata Context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
Metadata Catalog ID https://datainventory.doi.gov/data.json
Schema Version https://project-open-data.cio.gov/v1.1/schema
Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
Harvest Object Id b7cf6c3c-d2ea-48b5-9974-dd41b0f87c52
Harvest Source Id 52bfcc16-6e15-478f-809a-b1bc76f1aeda
Harvest Source Title DOI EDI
Metadata Type geospatial
Old Spatial -124.94,31.17,-109.0,49.0
Publisher Hierarchy White House > U.S. Department of the Interior > U.S. Geological Survey
Source Datajson Identifier True
Source Hash 51104818bc0aeceacdfdf15f05f7f9fe6ca6aee03f82dff3db7dff18ca7546cc
Source Schema Version 1.1
Spatial {"type": "Polygon", "coordinates": -124.94, 31.17, -124.94, 49.0, -109.0, 49.0, -109.0, 31.17, -124.94, 31.17}

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