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.

翻译：相机陷阱已成为生态学研究的核心工具，能够实现大规模、非侵入性的野生动物种群和行为监测。通过自动记录经过视野的动物，这些设备以最少的野外工作量生成海量图像数据集。然而，当将图像转化为可用信息时，这种数据丰富性带来了新的瓶颈——人类标注所需的时间和精力。近年来，人工智能（AI）被整合到工作流程中以提升效率。然而，通过AI方法获取的数据具有不同性质，需要新的统计方法以实现推断、预测和不确定性量化。我们提出了一种新的贝叶斯层次数据融合模型，结合了人工标注和AI预测的优势。该方法的优点是能够提供不确定性量化，并提升推断和预测能力，我们通过模拟研究对此进行了证明。我们将模型应用于北卡罗来纳州相机陷阱图像中白尾鹿（Odocoileus virginianus）体况的AI分析，以研究健康与环境之间的关系。我们发现，处于发情期的雄鹿体况优于其他鹿，且绿色开放栖息地与高体况相关。相比使用相同数据的传统方法，我们的新模型得出了新颖的生态推断。