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Cascading predictions from common to uncommon species improves species distribution models for plants

Species distribution models (SDMs) traditionally rely on abiotic factors like climate and topography to predict plant species distributions. While effective at broad scales, these models often fail at finer spatial resolutions due to their inability to capture localized environmental conditions and...

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Principais autores: Jiexun Xu, Kimberly Low, Christophe Botella, Benjamin Deneu, Dennis Shasha, Alexis Joly
Format: Artigo
Sprog:Inglês
Udgivet: Elsevier 2026-02-01
Serier:Ecological Informatics
Fag:
Species distribution model
Dominant species
Uncommon species
Cascade
Machine learning
Online adgang:http://www.sciencedirect.com/science/article/pii/S1574954125004339
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