Data from: Varying dataset resolution alters predictive accuracy of spatially explicit ensemble models for avian species distribution

• Main Authors: Curry, Claire M., Ross, Jeremy D., Contina, Andrea J., Bridge, Eli S.
• Format: Dataset
• Language: unknown
• Published: Data Archiving and Networked Services (DANS) 2019
• Subjects: Life sciences; medicine and health care; Anthropocene; spatiotemporal exploratory models; data resolution; Peucaea cassinii; Bartramia longicauda; Colinus virginianus; Spizella pusilla; Ammodramus savannarum; Random Forest; landscape ecology; Molothrus ater; Machine Learning; Eremophila alpestris; Sturnella magna; Sturnella neglecta; Chondestes grammacus; grassland birds; Holocene; Spiza americana; envir; geo; Jeremy
• Online Access: https://doi.org/10.5061/dryad.7m13q9b

Species distribution models can be made more accurate by use of new “Spatiotemporal Exploratory Models” (STEMs), a type of spatially explicit ensemble model (SEEM) developed at the continental scale that averages regional models pixel by pixel. Although SEEMs can generate more accurate predictions o...