Title:

Resetting the baseline: Machine learning predicted meadows for 60 watersheds in the Sierra Nevada

Author(s):

Cummings, Adam K.; Pope, Karen L.

Publication Year:

2023

How to Cite:

These data were collected using funding from the U.S. Government and can be used without additional permissions or fees. If you use these data in a publication, presentation, or other research product please use the following citation:

Cummings, Adam K.; Pope, Karen L. 2023. Resetting the baseline: Machine learning predicted meadows for 60 watersheds in the Sierra Nevada. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2023-0029

Abstract:

This data publication contains the geospatial data layers generated from machine learning models. Random forest models were developed to identify potential historical meadow habitats in 60 watersheds of the Sierra Nevada, California in 2023. The models were trained using over 11,000 mapped extant meadow polygons from the Sierra Nevada MultiSource Meadow Polygons data. Geospatial predictor variables representing topographic position, relative elevation, flow accumulation, snowpack, and distance to stream channels were used to train the models to predict locations with similar hydrogeomorphic characteristics to modern meadows. This data publication includes prediction rasters representing continuous meadow probability values from 0-1 for each watershed generated by both local watershed-scale models and a Sierra Nevada-wide model. Polygon layers representing aggregated high probability meadow areas for each watershed from the local models and Sierra Nevada model are also provided. These polygons were generated by selecting contiguous pixels with values greater than 0.5 in the prediction rasters and converting to vector polygons. The provided data layers can be used to identify potential areas for meadow restoration that could increase groundwater storage, floodplain connectivity, biodiversity, and resilience to wildfire and climate change across the Sierra Nevada mountain range. The mapped historical meadow habitats greatly expand the known extent of meadows in the region.

Keywords:

environment; Ecology, Ecosystems, & Environment; Hydrology, watersheds, sedimentation; Landscape ecology; Inventory, Monitoring, & Analysis; Resource inventory; Natural Resource Management & Use; Restoration; forest encroachment; groundwater; headwater streams; random forest; Sierra Nevada; watershed planning; California; Sierra Nevada Mountains

Related publications:

• Cummings, Adam K.; Pope, Karen L. 2023. Recovering the lost potential of meadows to help mitigate challenges facing California’s forests and water supply. California Fish and Wildlife Journal. 109(1): e3. https://doi.org/10.51492/cfwj.109.3 https://research.fs.usda.gov/treesearch/66206
• Cummings, Adam K.; Pope, Karen L.; Mak, Gilbert. 2023. Resetting the baseline: Using machine learning to find lost meadows. Landscape Ecology. 38: 2639–2653. https://doi.org/10.1007/s10980-023-01726-7 https://research.fs.usda.gov/treesearch/66484

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