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Hunt, Luke J.H.; Fair, Julie; Odland, Maxwell

doi:10.1111/1752-1688.12829

Cited by 2 publications

(1 citation statement)

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“…Therefore, meadow restoration efforts are thought to be more bene cial at the site level rather than for their ability to affect broader landscapes. For example, debate exists whether meadow restoration can substantially increase water storage to affect downstream base ow (Hunt et al 2018, Nash et al 2018, Hunt et al 2020, Nash et al 2020). The rationale is that meadows have too small of a capacity to store and release water to have a signi cant effect on hydrological processes (Nash et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Resetting the Baseline: Using Machine Learning to Find Lost Meadows

Cummings

Pope

Mak

2023

Preprint

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Context. Mountain meadows occur in specific geomorphological conditions where low-gradient topography promotes fine sediment accumulation and high groundwater tables. Over 150 years of human-caused hydrological degradation of meadows along with fire suppression has resulted in decreased groundwater elevations and encroachment of upland vegetation, greatly diminishing the ecological value of meadows for water storage, baseflow, sediment capture, wildfire resistance, wildlife habitat, and carbon storage. Objectives. We aimed to understand where and how frequently meadows historically occurred to reset the baseline condition and provide insight into their restoration potential. We trained machine learning algorithms to identify potential meadow areas with similar hydrogeomorphic conditions to extant meadows while ignoring their unique vegetative characteristics since we hypothesized that vegetation would change but geomorphology would remain. Methods. We used a publicly available dataset of over 11,000 hand-digitized meadow polygons occurring within a 25,300 km2, 60-watershed region in the Sierra Nevada, California USA to train random forest models to detect meadow-like hydrogeomorphic conditions. Predictor variables represented topographical position, flow accumulation, climate, and topographical relief at differing scales. We assessed model performance and produced maps delineating high probability meadow polygons. Results. Our findings showed that there is 2.6 to 8.3 times more potential meadow habitat than currently documented. The predicted area includes a mixture of existing but undocumented meadows, non-meadow habitats that may have converted from meadows due to lost function and forest encroachment, and areas with meadow-like geomorphology that may never have been meadows. The polygons encompassing predicted meadows often expanded existing meadows habitats into adjacent areas with continuous topography, but with upland vegetation and incised channels. Conclusions. Using readily available data and accessible statistical techniques, we demonstrate the accuracy of a tool to detect about five times more historical meadows than currently recognized within a complex, mountainous landscape. This “found” area greatly increases the potential area that could be subject to meadow restoration with benefits for biodiversity, wildfire management, carbon sequestration, and water storage.

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Section: Introductionmentioning

confidence: 99%

Resetting the Baseline: Using Machine Learning to Find Lost Meadows

Cummings

Pope

Mak

2023

Preprint

View full text Add to dashboard Cite

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Resetting the baseline: using machine learning to find lost meadows

Cummings,

Pope,

Mak

2023

Landsc Ecol

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Context Mountain meadows occur in specific geomorphological conditions where low-gradient topography promotes fine sediment accumulation and high groundwater tables. Over 150 years of human-caused hydrological degradation of meadows along with fire suppression has resulted in decreased groundwater elevations and encroachment of upland vegetation, greatly diminishing the ecological value of meadows for water storage, baseflow, sediment capture, wildfire resistance, wildlife habitat, and carbon storage. Objectives We aimed to understand where and how frequently meadows historically occurred to reset the baseline condition and provide insight into their restoration potential. We trained machine learning algorithms to identify potential meadow areas with similar hydrogeomorphic conditions to extant meadows while ignoring their unique vegetative characteristics because we hypothesized that vegetation would change but geomorphology would remain. Methods We used a publicly available dataset of over 11,000 hand-digitized meadow polygons occurring within a 25,300 km2, 60-watershed region in the Sierra Nevada, California USA to train random forest models to detect meadow-like hydrogeomorphic conditions. Predictor variables represented topographical position, flow accumulation, snowpack, and topographical relief at differing spatial scales. We assessed model performance and produced maps delineating high probability meadow polygons. Results Our findings showed that there is nearly three times more potential meadow habitat than currently documented. The predicted area includes a mixture of existing but undocumented meadows, non-meadowlands that may have converted from meadows due to lost function and forest encroachment, and areas with meadow-like geomorphology that may never have been meadow. The polygons encompassing predicted meadows often expanded existing meadows habitats into adjacent areas with continuous topography, but with upland vegetation and incised channels. Conclusions Using readily available data and accessible statistical techniques, we demonstrated the accuracy of a tool to detect about three times more historical meadows than currently recognized within a complex, mountainous landscape. This “found” area greatly increased the potential area that could be subject to meadow restoration with benefits for biodiversity, wildfire management, carbon sequestration, and water storage.

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Cited by 2 publications

References 6 publications

Resetting the Baseline: Using Machine Learning to Find Lost Meadows

Resetting the Baseline: Using Machine Learning to Find Lost Meadows

Resetting the baseline: using machine learning to find lost meadows

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