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INTEGRATING MULTIPLE SOURCES OF DATA TO CONSTRUCT A TIME SERIES OF RECREATIONAL CATCH AND EFFORT FOR THE WEST COAST BIOREGION OF WESTERN AUSTRALIA

Published online by Cambridge University Press:  02 December 2022

EVA KWOK MAN LAI*
Affiliation:
School of Science, Edith Cowan University, Joondalup, Western Australia 6027, Australia
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Abstract

Type
PhD Abstract
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of Australian Mathematical Publishing Association Inc.

There is growing attention on reconstructing recreational catch to quantify the impacts of recreational fishing [Reference Cooke, Venturelli, Twardek, Lennox, Brownscombe, Skov, Hyder, Suski, Diggles, Arlinghaus and Danylchuk1, Reference Radford, Hyder, Zarauz, Mugerza, Ferter, Prellezo, Strehlow, Townhill, Lewin and Weltersbach6]. In the absence of quantitative catch and effort data, a time series of reconstructed catch and effort is determined from sporadic data sources [Reference Freire, Belhabib, Espedido, Hood, Kleisner, Lam, Machado, Mendonça, Meeuwig, Moro, Motta, Palomares, Smith, Teh, Zeller, Zylich and Pauly2, Reference Pauly and Zeller5]. The aim of this study was to reconstruct a time series of recreational catches for key species from a boat-based recreational fishery in the West Coast Bioregion of Western Australia from 1993/94 to 2017/18, based on data collected by various survey approaches [Reference Lai, Mueller, Hyndes and Ryan3, Reference Ryan, Hall, Lai, Smallwood, Tate, Taylor and Wise7]. Prior to the reconstruction, there was a need to develop a thorough understanding of the survey methods and the data collected to ensure scientific credibility and stakeholder acceptance of reconstruction results. Thus, preparatory aspects of the reconstruction included a statistical comparison of catch and effort estimates from four Access Point surveys between 1996/97 and 2009/10, which highlighted the need to monitor recreational fisheries at appropriate intervals, particularly when the impacts of management changes on fish populations need to be considered [Reference Lai, Mueller, Hyndes and Ryan3]. A further aspect was an assessment of biases in survey design and estimation methods by corroborating independent survey methods involving an off-site Phone-Diary and on-site Bus-route Access Point and Remote Camera surveys conducted concurrently over a 12-month period in 2011/12 [Reference Lai, Ryan, Mueller and Hyndes4]. It was found that the relatively low cost and availability of a licensing database as a sampling frame favoured the Phone-Diary survey as a long-term monitoring tool for this boat-based recreational fishery. These two studies provided necessary information for developing the reconstruction method and understanding the limitation of the data and difficulties of the reconstruction. To reconstruct a time series of recreational catches, a method was developed to integrate data gathered from aperiodic Access Point surveys between 1996/97 and 2009/10, and biennial Phone-Diary surveys from 2011/12 to 2017/18. The catch reconstruction models included parameters for participation (number of fishers) based on the Estimated Residential Population and number of Recreational Boat Fishing Licences, effort (number of days fished per fisher per year) and catch (number of fish per fisher per day) based on the probability of a catch and nonzero catch rate. For many of the species considered, reconstructed annual catch was variable and aligned with estimates from the Access Point and Phone-Diary surveys. Catch information from periodic Phone-Diary surveys provided the necessary information for catch reconstructions, which in turn provided an approach to fill in the gaps between surveys. The reconstruction of catch from recreational fishing provided a time series that can be compared against the commercial sector and used in stock assessment modelling.

Footnotes

Thesis submitted to Edith Cowan University in April 2022; degree approved on 22 April 2022; supervisors Ute Mueller and Glenn Hyndes.

References

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