Fin whale density and distribution estimation using acoustic bearings derived from sparse arrays

Danielle V. Harris, Jennifer L. Miksis-Olds, Julia A. Vernon, Len Thomas

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)
2 Downloads (Pure)

Abstract

Passive acoustic monitoring of marine mammals is common, and it is now possible to estimate absolute animal density from acoustic recordings. The most appropriate density estimation method depends on how much detail about animals' locations can be derived from the recordings. Here, a method for estimating cetacean density using acoustic data is presented, where only horizontal bearings to calling animals are estimable. This method also requires knowledge of call signal-to-noise ratios, as well as auxiliary information about call source levels, sound propagation, and call production rates. Results are presented from simulations, and from a pilot study using recordings of fin whale (Balaenoptera physalus) calls from Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) hydrophones at Wake Island in the Pacific Ocean. Simulations replicating different animal distributions showed median biases in estimated call density of less than 2%. The estimated average call density during the pilot study period (December 2007-February 2008) was 0.02 calls hr-1 km2 (coefficient of variation, CV: 15%). Using a tentative call production rate, estimated average animal density was 0.54 animals/1000 km2 (CV: 52%). Calling animals showed a varied spatial distribution around the northern hydrophone array, with most detections occurring at bearings between 90 and 180 degrees.

Original languageEnglish
Pages (from-to)2980-2993
Number of pages14
JournalJournal of the Acoustical Society of America
Volume143
Issue number5
Early online date18 May 2018
DOIs
Publication statusPublished - May 2018

Fingerprint

Dive into the research topics of 'Fin whale density and distribution estimation using acoustic bearings derived from sparse arrays'. Together they form a unique fingerprint.

Cite this