Perspectives in machine learning for wildlife conservation

Tuia and colleagues offered a broad, forward-looking synthesis of how machine learning can advance wildlife conservation, with camera-trap image analysis as a central example alongside acoustic, satellite, and bio-logging data. They surveyed achievements such as automated species recognition and individual identification, and discussed persistent challenges including dataset bias, poor generalization to new regions, the need for interpretable models, and the importance of close collaboration between ecologists and computer scientists. The paper articulated a roadmap for responsible, effective deployment of artificial intelligence in conservation. As a high-profile cross-disciplinary perspective, it shaped discourse on the role of machine learning in biodiversity monitoring. It is widely cited as a reference framing the opportunities and pitfalls of AI for wildlife.