##  [Individual Identification in Acoustic Recordings](/resource/individual-identification-acoustic-recordings) 

Organization

[Alberta Biodiversity Monitoring Institute (ABMI)](/organization/alberta-biodiversity-monitoring-institute-abmi)

[University of Alberta (UofA)](/organization/university-alberta-uofa)

[Oregon State University](/organization/oregon-state-university)

[United States Department of Agriculture (USDA)](/organization/united-states-department-agriculture-usda)

 

 

Resource Type

[Peer reviewed article](/taxonomy/term/37)

 

 

Author(s)

Elly Knight

Tessa Rhinehart

Devin de Zwaan

Matthew Weldy

Mark Cartwright

Scott Hawley

Jeffery Larkin

Damon Lesmeister

Erin Bayne

Justin Kitzes

 

 

Original Authors

Elly Knight

Tessa Rhinehart

Devin de Zwaan

Matthew Weldy

Mark Cartwright

Scott Hawley

Jeffery Larkin

Damon Lesmeister

Erin Bayne

Justin Kitzes

 

 

Resource Date:

2024

 

 

This resource is available on an external database and may require a paid subscription to access it. It is included on the CCLM to support our goal of capturing and sharing the breadth of all available knowledge pertaining to Boreal Caribou, Wetlands, and Land Management.

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.