Abstract
Antarctic krill are subject to precautionary catch limits, based on
biomass estimates, to ensure human activities do not adversely impact
their important ecological role. Accurate target strength models of
individual krill underpin biomass estimates. These models are scaled
using measured and estimated distributions of length and orientation.
However, while the length distribution of a krill swarm is accessible
from net samples, there is currently limited consensus on the method for
estimating krill orientation distribution. This leads to a limiting
factor in biomass calculations. In this work, we consider geometric
shape as a variable in target strength calculations and describe a
practical method for generating a catalog of krill shapes. A catalog of
shapes produces a more variable target strength response than an
equivalent population of a scaled generic shape. Furthermore, using a
shape catalog has the greatest impact on backscattering cross-section
(linearized target strength) where the dominant scattering mechanism is
mie scattering, irrespective of orientation distribution weighting. We
suggest that shape distributions should be used in addition to length
and orientation distributions to improve the accuracy of krill biomass
estimates.
Original language | English |
---|---|
Article number | 658384 |
Number of pages | 14 |
Journal | Frontiers in Marine Science |
Volume | 8 |
DOIs | |
Publication status | Published - 7 Apr 2021 |
Keywords
- Antarctic krill
- Euphasia superba
- Morphometrics
- Target strength
- Acoustic scattering
Fingerprint
Dive into the research topics of 'Improving the accuracy of krill target strength using a shape catalog'. Together they form a unique fingerprint.Datasets
-
Improving the accuracy of krill target strength using a shape catalogue (dataset)
Bairstow, F. J. (Creator), Gastauer, S. (Creator), Finley, L. (Creator), Edwards, T. (Creator), Brown, C. T. A. (Creator), Kawaguchi, S. (Creator) & Cox, M. J. (Creator), University of St Andrews, 8 Apr 2021
DOI: 10.17630/1cb66301-633a-47f1-9e58-e813e4c7561e
Dataset
File