Comparison of standardized abundance index of bigeye tuna (Thunnus abesus) in the Indian Ocean by three general linear models
Date Issued
2007
Date
2007
Author(s)
Ma, Hui-Shan
DOI
zh-TW
Abstract
Indian bigeye tuna are mainly exploited by Taiwanese and Japanese longline and French and Spanish purse seiners. Total annual catch in creased from about 100,000 mt in 1993 to about 150,000 in 1995 and stayed at around 119,000 mt averaged between 2000 and 2004. Among the catch, Taiwan fleets took about 30%, which is one of the major fishing countries. In order to exploit the stock sustainably, standardized catch-per-unit-effort (CPUE) is the most common method used as an index to reflect stock abundance. To obtain the actual abundance trend, possible factors influencing the catch rate need to be removed by using standardized because CPUE may differ across time, space, and fishing stratege…etc. In this study, these statistical models, generalized linear models (GLM), generalized linear mixed models (GLMM), and generalized additive models (GAM), were applied to standaedize the common fishery catch effort data. Furthermore, model selection and comparison were conducted among the three models using Akaike information criterion (AIC), Bayesian information criterion (BIC), and Consistant Akaike’s information criterion (CAIC). These results indicated that the GAM with the smallest information criterion was selected as the best model to standardize CPUE for Indian bigeue tuna. The standardized CPUE decreased from the early 1980s to the lowest level in the early 1990s, increased to the peak level in the mid of 1990s, then declined to the 1990 level in 2000, and increased again in 2002, the second high level over past decade. After that, the trends of standardized CPUE were different. CPUE estimated by GLM and GLMM sharply dropped to the historical low level in 2004, but it tended to slowly decline by GAM.
Subjects
印度洋大目鮪
泛線性模式
泛線性混合模式
泛線性加乘模式
Akaike information criterion
Bayesian information criterion
Indian bigeye tuna
Generalized linear model
Generalized linear mixed model
Generalized additive model
AIC
BIC
CAIC
SDGs
Type
thesis