Improving ecological inference and uncertainty quantification from camera trap data through the fusion of AI confidences and manual annotations
Camera traps have become an important tool in ecological research, enabling large-scale, noninvasive monitoring of wildlife populations and behavior. By automatically recording animals as they pass within view, these devices generate massive image datasets with minimal field effort. This data richness introduces a new bottleneck when translating the images into usable information due to time and effort required for human annotation. Artificial intelligence (AI) has recently been integrated into the workflow to improve efficiency. However, the data procured from AI approaches are of a different nature, necessitating new statistical methods. We develop a new Bayesian hierarchical data-fusion model that combines the strengths of human annotations and AI predictions. The benefits of our approach are an ability to provide uncertainty quantification as well as improved inference and predictive power, which we demonstrate through simulation. We apply our model to an AI analysis of the body condition of white-tailed deer (Odocoileus virginianus) from camera trap images from North Carolina to study the relationship between health and their environment. Our analysis derived novel ecological inference compared to a more traditional approach using the same data. We find that bucks in rut have higher (healthier) body condition than other deer and that green, open habitats are correlated with high body condition.