Detection probability and density estimation of fin whales by a Seaglider

Abstract

A single-hydrophone ocean glider was deployed within a cabled hydrophone array to demonstrate a framework for estimating population density of fin whales (Balaenoptera physalus) from a passive acoustic glider. The array was used to estimate tracks of acoustically active whales. These tracks became detection trials to model the detection function for glider-recorded 360-s windows containing fin whale 20-Hz pulses using a generalized additive model. Detection probability was dependent on both horizontal distance and low-frequency glider flow noise. At the median 40-Hz spectral level of 97 dB re 1 μPa2/Hz, detection probability was near one at horizontal distance zero with an effective detection radius of 17.1 km [coefficient of variation (CV) = 0.13]. Using estimates of acoustic availability and acoustically active group size from tagged and tracked fin whales, respectively, density of fin whales was estimated as 1.8 whales per 1000 km2 (CV = 0.55). A plot sampling density estimate for the same area and time, estimated from array data alone, was 1.3 whales per 1000 km2 (CV = 0.51). While the presented density estimates are from a small demonstration experiment and should be used with caution, the framework presented here advances our understanding of the potential use of gliders for cetacean density estimation.

Document Details

Document Type
Pub Defense Publication
Publication Date
Oct 01, 2022
Source ID
10.1121/10.0014793

Entities

People

  • Brian Matsuyama
  • Danielle Harris
  • David Mellinger
  • Haruyoshi Matsumoto
  • Holger Klinck
  • Jay Barlow
  • Selene Fregosi
  • Stephen W. Martin

Organizations

  • Cornell University
  • National Marine Mammal Foundation
  • National Oceanic and Atmospheric Administration
  • Office of Naval Research
  • Oregon State University
  • University of St Andrews

Tags

Fields of Study

  • Environmental science

Readers

  • Marine Mammal Biology
  • Regression Analysis.
  • Statistical inference.

Technology Areas

  • Autonomy