Predicting catch per unit effort from a multispecies commercial fishery in Port Phillip Bay, Australia
Karina L. Ryan
A C and Denny Meyer B
+ Author Affiliations
- Author Affiliations
A Department of Primary Industries and Regional Development, Western Australian Fisheries and Marine Research Laboratories, PO Box 20, North Beach, WA 6920, Australia.
B Faculty of Health, Arts and Design, Swinburne University of Technology, PO Box 218, Hawthorn, Vic. 3122, Australia.
C Corresponding author. Email: karina.ryan@dpird.wa.gov.au
Marine and Freshwater Research 71(4) 542-556 https://doi.org/10.1071/MF18286
Submitted: 3 August 2018 Accepted: 26 May 2019 Published: 10 September 2019
Abstract
Quantitative models that predict stock abundance can inform stock assessments and adaptive management that allows for less stringent controls when abundance is high and environmental conditions are suitable, or tightening controls when abundance is low and environmental conditions are least suitable. Absolute estimates of stock abundance are difficult and expensive to obtain, but data from routine reporting in commercial fisheries logbooks can provide an indicator of stock status. Autoregressive integrated moving average (ARIMA) models were constructed using catch per unit effort (CPUE) from commercial fishing in Port Phillip Bay from 1978–79 to 2009–10. Univariate and multivariate models were compared for short-lived species (Sepioteuthis australis), and species represented by 1–2 year-classes (Sillaginodes punctatus) and 5–6 year-classes (Chrysophrys auratus). Simple transfer models incorporating environmental variables produced the best predictive models for all species. Multivariate ARIMA models are dependent on the availability of an appropriate time series of explanatory variables. This study demonstrates an application of time series methods to predict monthly CPUE that is relevant to fisheries for species that are short lived or vulnerable to fishing during short phases in their life history or where high intra-annual variation in stock abundance occurs through environmental variability.
Additional keywords: Box–Jenkins, cross-correlation, seasonal pattern, time series analysis, transfer function, trend.
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