Explainable AI Enhances Biodiversity Monitoring and Ecological Image Analysis.

Artificial intelligence is rapidly transforming biodiversity monitoring by automating the analysis of vast amounts of ecological imagery from various sources like camera traps, drones, and satellites. While these AI tools significantly enhance the scale and speed of conservation assessments, many computer vision models operate as "black boxes," making it difficult to discern the basis of their predictions. This opacity raises concerns about whether models rely on ecologically meaningful signals or on spurious correlations and biases, which could undermine conservation decisions. The authors argue that Explainable AI (XAI) should become an essential part of ecological model validation. Conservation professionals need to understand not just a model's accuracy, but also the reasoning behind its outputs. The paper offers practical advice for applying XAI techniques to common ecological computer vision tasks, including image classification, object detection, and image segmentation. Two case studies, involving harbor seal detection and cetacean anatomical segmentation from aerial imagery, illustrate how XAI can support model auditing, refinement, and deployment. These examples demonstrate XAI's ability to identify biologically relevant cues, expose false positives caused by background noise or shape confounds, uncover edge effects, and guide strategies for data collection, augmentation, and model retraining. Ultimately, XAI helps ensure that AI-supported ecological evidence is transparent, reliable, and actionable for biodiversity conservation efforts.