%0 Journal Article
%T Individual identification in acoustic recordings.
%A Knight E
%A Rhinehart T
%A de Zwaan DR
%A Weldy MJ
%A Cartwright M
%A Hawley SH
%A Larkin JL
%A Lesmeister D
%A Bayne E
%A Kitzes J
%J Trends Ecol Evol
%V 0
%N 0
%D 2024 Jun 10
%M 38862357
%F 20.589
%R 10.1016/j.tree.2024.05.007
%X Recent advances in bioacoustics combined with acoustic individual identification (AIID) could open frontiers for ecological and evolutionary research because traditional methods of identifying individuals are invasive, expensive, labor-intensive, and potentially biased. Despite overwhelming evidence that most taxa have individual acoustic signatures, the application of AIID remains challenging and uncommon. Furthermore, the methods most commonly used for AIID are not compatible with many potential AIID applications. Deep learning in adjacent disciplines suggests opportunities to advance AIID, but such progress is limited by training data. We suggest that broadscale implementation of AIID is achievable, but researchers should prioritize methods that maximize the potential applications of AIID, and develop case studies with easy taxa at smaller spatiotemporal scales before progressing to more difficult scenarios.