Machine learning to classify animal species in camera trap images: applications in ecology

Tabak and colleagues trained and evaluated deep-learning models on large North American camera-trap datasets, achieving high species-classification accuracy and demonstrating practical workflows for ecologists. They tested how well models generalize to new locations and projects, an important question because performance often drops when cameras move to unfamiliar backgrounds and species mixes. The paper provided accessible guidance, code, and a trained model that practitioners could apply to their own images, lowering the barrier to automated classification. By framing machine learning as a usable tool rather than a research novelty, it accelerated adoption across wildlife studies. It is a widely cited applied reference for camera-trap image classification and helped establish best practices for evaluating model transferability.