Centering Ecological Goals in Automated Identification of Individual Animals

TL;DR

This paper emphasizes that ecological relevance and context are crucial for developing effective automated animal identification methods, beyond just improving algorithmic accuracy.

Contribution

It highlights the importance of aligning automated identification development with ecological data collection, questions, and error considerations.

Findings

Automated identification methods often mismatch ecological data collection practices.

Ecological usefulness depends on context, data, and error types, not just accuracy.

Centering ecological goals improves transparency and trustworthiness of identification methods.

Abstract

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.