让动物个体识别更符合生态实际，提升实用性与可信度。
Centering Ecological Goals in Automated Identification of Individual Animals
- 以生态问题为导向设计识别方法，而非只追求算法精度。
- 强调识别结果需匹配实际研究问题和数据条件。
- 适合生态学家与算法开发者共同参与的跨学科研究。
长期追踪个体动物对生态学与保护生物学中的种群数量、存活率、迁徙模式及社会结构研究至关重要。近年来，基于图像甚至声学数据的自动化识别技术取得进展，有望大幅提升效率，但其在实际生态研究中的应用仍受限。我们指出，主要障碍并非算法性能不足，而是方法开发与评估方式与真实生态数据采集、处理及使用流程存在脱节。未来突破的关键不在于单纯提升算法，而在于将生态背景置于核心：识别的有效性取决于研究问题、可用数据以及何种错误具有实际意义。唯有以此为出发点，才能实现既准确又生态实用、透明可信的个体识别系统。
原文摘要 · Abstract (English)
Recognizing individual animals over time is central to many ecological and conservation questions, including estimating abundance, survival, movement, and social structure. Recent advances in automated identification from images and even acoustic data suggest that this process could be greatly accelerated, yet their promise has not translated well into ecological practice. We argue that the main barrier is not the performance of the automated methods themselves, but a mismatch between how those methods are typically developed and evaluated, and how ecological data is actually collected, processed, reviewed, and used. Future progress, therefore, will depend less on algorithmic gains alone than on recognizing that the usefulness of automated identification is grounded in ecological context: it depends on what question is being asked, what data are available, and what kinds of mistakes matter. Only by centering these questions can we move toward automated identification of individuals that is not only accurate but also ecologically useful, transparent, and trustworthy.
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