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Mapped Beaver Ponds, Clusters, and Predictor Variables for Surface Water Accumulation in the Western U.S.
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Type: | Resource | |
Storage: | The size of this resource is 8.1 MB | |
Created: | Jul 03, 2025 at 11:31 p.m. (UTC) | |
Last updated: | Jul 03, 2025 at 11:45 p.m. (UTC) | |
Citation: | See how to cite this resource | |
Content types: | Geographic Feature Content CSV Content |
Sharing Status: | Discoverable (Accessible via direct link sharing) |
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Abstract
This dataset supports the manuscript “Factors influencing surface water accumulation in beaver pond complexes across the Western United States” (Wan et al., 2025). It includes three key data products derived from high-resolution aerial imagery and spatial analysis across 11 study areas in four western U.S. states:
- Mapped Beaver Ponds (pond_dam_area_all.shp): A polygon shapefile of individual beaver ponds delineated from aerial imagery. Attributes include CustomID (unique pond identifier) and Aream2 (pond area in square meters).
- Beaver Pond Clusters (All_cluster_updated.shp): A polygon shapefile of 87 beaver pond clusters aggregated from individual ponds. Attributes include ClusterID (unique cluster identifier) and Aream2 (cluster area in square meters).
- Predictor Variables for GAM Analysis (merged_factors.csv): A CSV file containing 15 environmental and landscape predictor variables, along with ClusterID, total dam length, and pond area per cluster (response variables). These were used in Generalized Additive Models (GAMs) to identify key factors influencing surface water accumulation within beaver pond complexes.
These datasets provide spatial and environmental context for analyzing how beaver pond complexes contribute to surface water retention and landscape hydrology.
For questions regarding this dataset, please contact Dr. Luwen Wan at luwenwan@stanford.edu.
Subject Keywords
Coverage
Spatial
Content
Credits
Funding Agencies
This resource was created using funding from the following sources:
Agency Name | Award Title | Award Number |
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Stanford Institute for Human-Centered Artificial Intelligence (HAI) | ||
Stanford Woods Institute for the Environment |
Contributors
People or Organizations that contributed technically, materially, financially, or provided general support for the creation of the resource's content but are not considered authors.
Name | Organization | Address | Phone | Author Identifiers |
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Emily Fairfax | California State University Channel Islands | California, US | ||
Katharine Maher | Stanford University | CA, US |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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