A global assessment of BirdNET performance: Differences among continents, biomes, and species
David Funosas; Esther Sebastián‐González; Jon Morant; Oscar Humberto Marín‐Gómez; Irene Mendoza; Miguel A. Mohedano-Muñoz; Eduardo Santamaría-García; Giulia Bastianelli · 2026 · Ecological Indicators
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Abstract Recent advances in machine learning have accelerated automated species detection across diverse ecological domains, enabling large-scale, non-invasive monitoring of biodiversity. In ornithological research, coupling passive acoustic monitoring (PAM) with rapidly-developing novel identification tools such as BirdNET—a deep learning–based sound recognition algorithm—offers new opportunities for surveying vocally active bird communities. Yet, BirdNET performance across diverse ecological and biogeographic contexts remains to be quantified. Here, we present the first worldwide evaluation
Abstract by David Funosas; Esther Sebastián‐González; Jon Morant; Oscar Humberto Marín‐Gómez; Irene Mendoza; Miguel A. Mohedano-Muñoz; Eduardo Santamaría-García; Giulia Bastianelli, Ecological Indicators (2026) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1016/j.ecolind.2025.114550