Enrico Di Minin

This study identifies shared spatial predictors of human-wildlife conflict across diverse ecological and geographic contexts using ~11,000 conflict incidents.

This paper introduces a multimodal machine learning framework for detecting wildlife trade on YouTube by integrating text and visual analysis, identifying 3,715 posts featuring 226 species, including many threatened ones.

The paper evaluates a community-based conservation model in Kenya, finding that cattle grazing supports wildlife but requires management to control threats like sheep/goat grazing and infrastructure proximity.

The paper examines factors driving public and scientific interest in bird species using Wikipedia pageviews and Web of Knowledge articles, finding that charisma, familiarity, and Red List status influence attention.