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This document summarizes a study that compared 16 species distribution modeling methods using occurrence data from 226 species in 6 regions. The study found that novel machine learning and community modeling methods, which are well-suited for noisy occurrence data from museums and herbaria, consistently outperformed more established methods like general additive models and BIOCLIM. The results suggest that occurrence data can effectively model species distributions and novel methods improve predictions, ben

Author

Anahi Vargas

Language

EN