Improving ecological inference and uncertainty quantification from camera trap data through the fusion of AI confidences and manual annotations

2026-05-13

AI Summary

Although camera trap image data are abundant, manual annotation is costly, and AI-based predictions are often unsuitable for direct ecological inference or uncertainty quantification. This study proposes the first data fusion model that integrates AI identification confidence with human annotations within a unified Bayesian hierarchical framework, substantially improving both the accuracy of ecological parameter estimates and the reliability of their associated uncertainties. Applied to body condition analysis of white-tailed deer, the method reveals that males exhibit better body condition during the rutting season and that this enhanced condition is significantly associated with green, open habitats—findings that outperform those obtained using conventional approaches.

Abstract

Camera traps have become a core 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. Yet this data richness introduces a new bottleneck when translating the images into usable information due to time and effort required for human annotation. Recently, artificial intelligent (AI) has been integrated into the workflow to improve this efficiency. However, the data procured from AI approaches are of a different nature, necessitating new statistical methods in order to obtain inference, make predictions, and quantify uncertainty. We propose a new Bayesian hierarchical data-fusion model which 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 prediction power, which we demonstrate using a simulation study. 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. We find that bucks in rut have higher body condition than other deer and that green, open habitats are correlated with high body condition. Our new model derived novel ecological inference compared to a traditional approach using the same data.

Problem

• ecological inference
• uncertainty quantification
• camera trap data
• AI confidence
• data fusion

Innovation

• Bayesian hierarchical model
• data fusion
• camera trap
• uncertainty quantification
• AI confidence