Whistle classification ofsympatric false killer whale populations in Hawaiian waters yields low accuracy rates

• Published in: Frontiers in Marine Science
• Main Authors: Barkley, Yvonne, Oleson, Erin M., Oswald, Julie N., Franklin, Erik C.
• Other Authors: University of St Andrews. School of Biology, University of St Andrews. Scottish Oceans Institute
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2019
• Subjects: Cetaceans; False killer whale; Passive acoustic monitoring; Population classification; Hawaiian archipelago; Machine Learning; QH301 Biology; NDAS; SDG 14 - Life Below Water; QH301; Pacific; Killer Whale
• Online Access: http://hdl.handle.net/10023/18775; https://doi.org/10.3389/fmars.2019.00645

Funding for passive acoustic data collection during the shipboard cetacean line-transect surveys was provided by PIFSC, SWFSC, NOAA Fisheries Pacific Islands Regional Office, and NOAA Fisheries Office of Protected Resources (OPR) for HICEAS 2010, PIFSC for PICEAS, PACES and HITEC, and PIFSC, OPR, NOAA Fisheries Office of Science and Technology, Chief of Naval Operation Environmental Readiness Division and Pacific Fleet, and Bureau of Ocean Energy Management for HICEAS 2017. Funding for passive acoustic data analysis was provided by PIFSC and the National Science Foundation Graduate Research Fellowships Program. 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 ...