Body density and diving gas volume of the northern bottlenose whale (Hyperoodon ampullatus)

Abstract

Diving lung volume and tissue density, reflecting lipid store volume, are important physiological parameters which have only been estimated for a few breath-hold diving species. We outfitted 12 northern bottlenose whales with data loggers which recorded depth, 3-axis acceleration and speed either with a fly-wheel or from change of depth corrected by pitch angle. We fitted measured values of the change in speed during 5s descent and ascent glides to a hydrodynamic model of drag and buoyancy forces using a Bayesian estimation framework. The resulting estimate of diving gas volume was 27.4±4.2 (95% credible interval, CI) ml kg−1, closely matching the measured lung capacity of the species. Dive-by-dive variation in gas volume did not correlate with dive depth or duration. Estimated body densities of individuals ranged from 1028.4 to 1033.9 kg m−3 at the sea surface, indicating overall negative tissue buoyancy of this species in seawater. Body density estimates were highly precise with ±95% credible intervals ranging from 0.1-0.4 kg m−3, which would equate to a precision of <0.5% of lipid content based upon extrapolation from the elephant seal. Six whales tagged near Jan Mayen (Norway, 71° N) had lower body density and were closer to neutral buoyancy than six whales tagged in the Gully (Nova Scotia, Canada, 44° N), a difference which was consistent with the amount of gliding observed during ascent versus descent phases in these animals. Implementation of this approach using longer-duration tags could be used to track longitudinal changes in body density and lipid-store body condition of free-ranging cetaceans.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jan 01, 2016
Source ID
10.1242/jeb.137349

Entities

People

  • Kagari Aoki
  • Katsufumi Sato
  • Patrick Miller
  • Saana Isojunno
  • Sophie Smout
  • Tomoko Narazaki

Organizations

  • Strategic Environmental Research and Development Program
  • University of St Andrews
  • University of Tokyo

Tags

Fields of Study

  • Environmental science

Readers

  • Atmospheric Science / Meteorology, specifically Wind Wave Turbulence.
  • Marine Mammal Biology
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference