Otolith morphology of four mackerel species (Scomberomorus spp.) in Australia: species differentiation and prediction for fisheries monitoring and assessment

Zischke, Mitchell T., Litherland, Lenore, Tilyard, Benjamin R., Stratford, Nicholas J., Jones, Ebony L. and Wang, You-Gan (2016) Otolith morphology of four mackerel species (Scomberomorus spp.) in Australia: species differentiation and prediction for fisheries monitoring and assessment. Fisheries Research, 176 39-47. doi:10.1016/j.fishres.2015.12.003


Author Zischke, Mitchell T.
Litherland, Lenore
Tilyard, Benjamin R.
Stratford, Nicholas J.
Jones, Ebony L.
Wang, You-Gan
Title Otolith morphology of four mackerel species (Scomberomorus spp.) in Australia: species differentiation and prediction for fisheries monitoring and assessment
Formatted title
Otolith morphology of four mackerel species (Scomberomorus spp.) in Australia: species differentiation and prediction for fisheries monitoring and assessment
Journal name Fisheries Research   Check publisher's open access policy
ISSN 0165-7836
1872-6763
Publication date 2016-04-01
Year available 2015
Sub-type Article (original research)
DOI 10.1016/j.fishres.2015.12.003
Open Access Status Not Open Access
Volume 176
Start page 39
End page 47
Total pages 9
Place of publication Amsterdam, Netherlands
Publisher Elsevier
Language eng
Formatted abstract
Four species of large mackerels (Scomberomorus spp.) co-occur in the waters off northern Australia and are important to fisheries in the region. State fisheries agencies monitor these species for fisheries assessment; however, data inaccuracies may exist due to difficulties with identification of these closely related species, particularly when specimens are incomplete from fish processing. This study examined the efficacy of using otolith morphometrics to differentiate and predict among the four mackerel species off northeastern Australia. Seven otolith measurements and five shape indices were recorded from 555 mackerel specimens. Multivariate modelling including linear discriminant analysis (LDA) and support vector machines, successfully differentiated among the four species based on otolith morphometrics. Cross validation determined a predictive accuracy of at least 96% for both models. An optimum predictive model for the four mackerel species was an LDA model that included fork length, feret length, feret width, perimeter, area, roundness, form factor and rectangularity as explanatory variables. This analysis may improve the accuracy of fisheries monitoring, the estimates based on this monitoring (i.e. mortality rate) and the overall management of mackerel species in Australia.
Keyword Otolith shape
S. commerson
S. munroi
S. queenslandicus
S. semifasciatus
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ
Additional Notes Published online 22 December 2015

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mathematics and Physics
School of Geography, Planning and Environmental Management Publications
Official 2016 Collection
 
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