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Title: Whistle classification of sympatric false killer whale populations in Hawaiian waters yields low accuracy rates

Cetaceans are ecologically important marine predators, and designating individuals to distinct populations can be challenging. Passive acoustic monitoring provides an approach to classify cetaceans to populations using their vocalizations. In the Hawaiian Archipelago, three genetically distinct, sympatric false killer whale (Pseudorca crassidens) populations coexist: a broadly distributed pelagic population and two island-associated populations, an endangered main Hawaiian Islands (MHI) population and a Northwestern Hawaiian Islands (NWHI) population. The mechanisms that sustain the genetic separation between these overlapping populations are unknown but previous studies suggest that the acoustic diversity between populations may correspond to genetic differences. Here, we investigated whether false killer whale whistles could be correctly classified to population based on their characteristics to serve as a method of identifying populations when genetic or photographic-identification data are unavailable. Acoustic data were collected during line-transect surveys using towed hydrophone arrays. We measured 50 time and frequency parameters from whistles in 16 false killer whale encounters identified to population and used those measures to train and test random forest classification models. Random forest models that included three populations correctly classified 42% of individual whistles overall and resulted in a low kappa coefficient, κ = 0.15, indicating low agreement between models, and the true population. Whistles from the MHI population showed the highest correct classification rate (52%) compared to pelagic and NWHI whistles (42 and 36%, respectively). Pairwise random forest models classifying pelagic and MHI whistles proved slightly more accurate (62% accuracy, κ = 0.24), though a similar pelagic-NWHI model did not (56% accuracy, κ = 0.12). Results suggest that the time-frequency whistle characteristics are not suitable to confidently classify encounters to a specific false killer whale population, although certain features of whistles produced by the endangered MHI population allow for overall higher classification accuracy. Inclusion of other vocalization types, such as echolocation clicks, and alternative whistle variables may improve correct classification success for these sympatric populations.

Other Citation Details:

Yvonne Barkley, Erin M. Oleson, Julie N. Oswald, Erik C. Franklin

DOI (Digital Object Identifier):


Theme Keywords

Thesaurus Keyword
None cetaceans
None false killer whale
None machine learning
None passive acoustic monitoring
None population classification

Spatial Keywords

Thesaurus Keyword
None Hawaiian archipelago

Physical Location

Organization: Pacific Islands Fisheries Science Center
City: Honolulu
State/Province: HI
Country: USA

Document Information

Document Type: Journal article
Format: Acrobat Portable Document Format
Status Code: Published

Support Roles


CC ID: 847404
Date Effective From: 2019-06-03
Date Effective To:
Contact (Person): Barkley, Yvonne
Email Address:


CC ID: 847403
Date Effective From: 2019-06-03
Date Effective To:
Contact (Person): Oleson, Erin M
Address: 1845 Wasp Blvd.
Honolulu, HI 96818
Email Address:
Phone: (808)725-5712
Business Hours: 9:00 a.m. - 5:00 p.m.



CC ID: 996141
URL Type:
Online Resource

Catalog Details

Catalog Item ID: 56590
GUID: gov.noaa.nmfs.inport:56590
Metadata Record Created By: Marie C Hill
Metadata Record Created: 2019-06-03 20:48+0000
Metadata Record Last Modified By: SysAdmin InPortAdmin
Metadata Record Last Modified: 2022-08-09 17:11+0000
Metadata Record Published: 2020-11-25
Owner Org: PIFSC
Metadata Publication Status: Published Externally
Do Not Publish?: N
Metadata Last Review Date: 2020-11-25
Metadata Review Frequency: 1 Year
Metadata Next Review Date: 2021-11-25