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  • 學位論文

目標魚種策略對單位努力漁獲量標準化之影響:以臺灣南太平洋長鰭鮪延繩釣漁業為例

Evaluating the approaches of targeting tactic CPUE standardization: an example for the South Pacific albacore longline fishery of Taiwan

指導教授 : 張以杰

摘要


單位漁獲努力量(catch-per-unit-effort, CPUE)可視為資源豐度的指標之一,然而CPUE容易受到作業時間、漁撈位置和目標魚種漁撈策略變化等因素而影響,因此需要進行標準化,得以作為資源評估中相對資源豐度的指標。臺灣遠洋延繩釣漁業資料為多漁獲魚種CPUE資料,因1970年代超低溫鮪釣漁船之加入,衍生臺灣延繩釣漁業於太平洋海域發展出不同的目標魚種漁撈策略,因此進行CPUE標準化分析時必須考慮漁撈策略改變之情況。本研究利用2007¬-2017年臺灣太平洋遠洋鮪延繩釣作業報表,應用作業組別資訊於GLM之類別解釋變數,並假設其結果為參考相對豐度指標,以此評估四種考慮目標魚種漁撈策略之方法:應用每筐鉤數、集群分析及主成份分析作為 GLM之類別及連續解釋變數,以及有限混合模式(Finite Mixture Modeling)於南太平洋長鰭鮪CPUE標準化。結果顯示四種方法皆消除CPUE資料之變異,但利用偏差度量(bias metric)及平均絕對誤差(mean absolute error, MAE)之結果顯示,主成份分析及有限混和模式與參考相對豐度之結果最為接近。因此本研究建議使用主成份分析或有限混和模式建構南太平洋長鰭鮪歷史豐度資訊,以供後續資源評估及漁業管理使用。

並列摘要


Catch-per-unit-effort (CPUE) is usually assumed to be an index of the relative abundance of a fish stock. However, it is necessary to standardize the CPUE to remove the effects of factors such as the fishing time, location, and change of fishing tactic before to be used as a reliable index of abundance in the stock assessment. Taiwanese distant water longline fishery logbook comprises multiple species CPUE data. Due to the development of the ultra-low temperature tuna longline vessels in the 1970s, Taiwanese distant water longline fisheries have included various fishing tactics in the Pacific Ocean. Therefore, it is necessary to consider the change in fishing tactics when conducting the CPUE standardization. In this study, I used the GLM model with explanatory variable of the targeting tactic derived from the logbook’s targeting group for estimating the reference index of relative abundance of South Pacific albacore based on the Taiwanese distant water longline fisheries logbook data during 2007-2017. I evaluated four approaches: categorical hooks per basket, categorical clusters and continuous principal component scores as GLM explanatory variables, and the finite mixture modeling, by comparing the estimated standardized indices with the reference index. Results showed that all approaches could remove a substantial amount of variation in the CPUE data. However, the principal component analysis and the finite mixture model were identified performing better for estimating the standardized index compared to the reference index based on the bias metric and mean absolute error. This study recommend using the principal component analysis or finite mixture modeling approach for constructing a historical standardized index for the stock assessment and fishery management of South Pacific albacore.

參考文獻


Akaike, H. (1973) Maximum likelihood identification of Gaussian autoregressive moving average models. Biometrika 60:255-265.
Bentley, N., Kendrick, T.H., Starr, P.J., Breen, P.A. (2012) Influence plots and metrics: tools for better understanding fisheries catch-per-unit-effort standardizations. ICES Journal of Marine Science 69:84-88.
Bigelow, K., Hoyle, S. (2008) Standardized CPUE for distant-water fleets targeting south Pacific albacore. WCPFC-SC4-2008/ME-WP-3.
Bigelow, K., Hoyle, S. (2009) Standardized CPUE for distant-water fleets targeting south Pacific albacore. WCPFC-SC5-2009/SA-WP-5.
Bigelow, K., Hoyle, S. (2012) Standardized CPUE for South Pacific albacore. WCPFC‐SC8‐2012/ IP‐14.

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