Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.fishres.2004.08.003
DC FieldValue
dc.titleAnalysing commercial catch and effort data from a Penaeid trawl fishery: A comparison of linear models, mixed models, and generalised estimating equations approaches
dc.contributor.authorBishop, J.
dc.contributor.authorVenables, W.N.
dc.contributor.authorWang, Y.-G.
dc.date.accessioned2014-10-28T05:10:02Z
dc.date.available2014-10-28T05:10:02Z
dc.date.issued2004-12
dc.identifier.citationBishop, J., Venables, W.N., Wang, Y.-G. (2004-12). Analysing commercial catch and effort data from a Penaeid trawl fishery: A comparison of linear models, mixed models, and generalised estimating equations approaches. Fisheries Research 70 (2-3 SPEC. ISS.) : 179-193. ScholarBank@NUS Repository. https://doi.org/10.1016/j.fishres.2004.08.003
dc.identifier.issn01657836
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/105000
dc.description.abstractStatistical methods are often used to analyse commercial catch and effort data to provide standardised fishing effort and/or a relative index of fish abundance for input into stock assessment models. Achieving reliable results has proved difficult in Australia's Northern Prawn Fishery (NPF), due to a combination of such factors as the biological characteristics of the animals, some aspects of the fleet dynamics, and the changes in fishing technology. For this set of data, we compared four modelling approaches (linear models, mixed models, generalised estimating equations, and generalised linear models) with respect to the outcomes of the standardised fishing effort or the relative index of abundance. We also varied the number and form of vessel covariates in the models. Within a subset of data from this fishery, modelling correlation structures did not alter the conclusions from simpler statistical models. The random-effects models also yielded similar results. This is because the estimators are all consistent even if the correlation structure is mis-specified, and the data set is very large. However, the standard errors from different models differed, suggesting that different methods have different statistical efficiency. We suggest that there is value in modelling the variance function and the correlation structure, to make valid and efficient statistical inferences and gain insight into the data. We found that fishing power was separable from the indices of prawn abundance only when we offset the impact of vessel characteristics at assumed values from external sources. This may be due to the large degree of confounding within the data, and the extreme temporal changes in certain aspects of individual vessels, the fleet and the fleet dynamics. © 2004 Published by Elsevier B.V.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.fishres.2004.08.003
dc.sourceScopus
dc.subjectFisheries data quantity
dc.subjectFishing power
dc.subjectIndices of abundance
dc.subjectStandardisation of fishing effort
dc.typeArticle
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.doi10.1016/j.fishres.2004.08.003
dc.description.sourcetitleFisheries Research
dc.description.volume70
dc.description.issue2-3 SPEC. ISS.
dc.description.page179-193
dc.description.codenFISRD
dc.identifier.isiut000225943700004
Appears in Collections:Staff Publications

Show simple item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

36
checked on Aug 17, 2022

WEB OF SCIENCETM
Citations

39
checked on Aug 17, 2022

Page view(s)

102
checked on Aug 18, 2022

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.