We are pleased to announce our Machine Learning Virtual Issue is now online.

This collection of MEE articles showcases exciting advances and applications of machine learning (ML) across a wide range of ecological and evolutionary disciplines.
From the analysis of reef structure and tree crowns, to species and individual animal identification, biological overlap, content analysis, biodiversity assessment and counting animals, ML automates the extraction of meaningful information from large digital collections.
Our Associate Editors Arthur Porto, Marta Vidal-Garcia, Miguel Acevedo, Theoni Photopoulou and Sarab Sethi curated this virtual issue by selecting their favourite MEE articles that use machine learning. Find out below why these papers were chosen, and how they are helping to progress research in ecology and evolution.
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