Abstract
Fishery-dependent catch per unit effort (CPUE) data have been used as an abundance index (AI) in fish stock assessments. However, fishery-dependent CPUE data are influenced not only by changes in fish abundance but also by other factors, such as the choice or restrictions of fishing grounds to operate. Accordingly, bias may arise in AIs due to a lack of data from unfished or rarely fished areas. To improve the accuracy of AI estimates, spatially arranged CPUE datasets from both trawl fisheries and research vessel surveys in the East China Sea were concurrently analyzed in the present study using a multivariate autoregressive state-space (MARSS) model. Survey datasets complemented information on stock status in the fishing areas where fishery-dependent datasets were limited. As a result, the combined use of datasets from both sources effectively improved the accuracy of estimates of AIs and the spatial distribution of the population density of each fish species.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
