Enhancing Chub Mackerel Catch Per Unit Effort (CPUE) Standardization through High-Resolution Analysis of Korean Large Purse Seine Catch and Effort Using AIS Data

Solomon Amoah Owiredu, Shem Otoi Onyango, Eun-A Song, Kwang-Il Kim (), Byung-Yeob Kim and Kyoung-Hoon Lee
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Solomon Amoah Owiredu: Department of Marine Industrial & Maritime Police, College of Ocean Sciences, Jeju National University, Jeju 63243, Republic of Korea
Shem Otoi Onyango: Department of Marine Industrial & Maritime Police, College of Ocean Sciences, Jeju National University, Jeju 63243, Republic of Korea
Eun-A Song: Department of Marine Industrial & Maritime Police, College of Ocean Sciences, Jeju National University, Jeju 63243, Republic of Korea
Kwang-Il Kim: Department of Marine Industrial & Maritime Police, College of Ocean Sciences, Jeju National University, Jeju 63243, Republic of Korea
Byung-Yeob Kim: Department of Marine Industrial & Maritime Police, College of Ocean Sciences, Jeju National University, Jeju 63243, Republic of Korea
Kyoung-Hoon Lee: Division of Marine Production System Management, Pukyong National University, Busan 48513, Republic of Korea

Sustainability, 2024, vol. 16, issue 3, 1-19

Abstract: Accurate determination of fishing effort from Automatic Identification System (AIS) data improves catch per unit effort (CPUE) estimation and precise spatial management. By combining AIS data with catch information, a weighted distribution method is applied to allocate catches across various fishing trajectories, accounting for temporal dynamics. A Generalized Linear Model (GLM) and Generalized Additive Model (GAM) were used to examine the influence of spatial–temporal and environmental variables (year, month, Sea Surface Temperature (SST), Sea Surface Salinity (SSS), current velocity, depth, longitude, and latitude) and assess the quality of model fit for these effects on chub mackerel CPUE. Month, SST, and year exhibited the strongest relationship with CPUE in the GLM model, while the GAM model emphasizes the importance of month and year. CPUE peaked within specific temperature and salinity ranges and increased with longitude and specific latitudinal bands. Month emerged as the most influential variable, explaining 38% of the CPUE variance, emphasizing the impact of regulatory measures on fishery performance. The GAM model performed better, explaining 69.9% of the nominal CPUE variance. The time series of nominal and standardized indices indicated strong seasonal cycles, and the application of fine-scale fishing effort improved nominal and standardized CPUE estimates and model performance.

Keywords: chub mackerel; CPUE standardization; generalized linear model; generalized additive model; automatic identification system; total allowable catch; South Korea (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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