臺灣魩鱙漁業資源評估:時序資料長度、地區及漁法對最大可持續生產量估值的效應

Abstract

合理並永續利用漁業資源是漁業管理及的重點。臺灣位處亞熱帶地區，海洋漁業資源豐富，其中，魩鱙漁業是重要且傳統的沿岸漁業之一，自1977年從日本引進效率較高的捕撈漁具後魩鱙的產量也隨之提高，也同時面臨其他經濟性魚類仔魚遭混獲的挑戰，導致相關管理規範陸續建立。本研究使用2009-2021年魩鱙漁業資料，以共變數合併系群產量模型(a stock production model incorporating covariate，簡稱ASPIC)估算魩鱙漁業的最大可持續生產量(maxima sustainable yield，簡稱MSY)，該模型為傳統之Schaefer剩餘生產量模型以非平衡的方式加以落實模式參數的估計，並進一步評估時序資料長短(5年、10年、13年)、不同地區(西北部、西南部、東北部)、不同漁法(大目袖網、流袋網)及不同努力量對最大可持續生產量的變化影響。研究結果指出，在時序資料長度比較上，使用5年份資料所估出的最大可持續生產量數值最高，使用13年份資料則估值最低；在不同地區的結果比較上，東北部地區所估出的最大可持續生產量數值最高，西北部地區所估出的數值最低，可能和東北部及西北部分別是三個地區中捕獲量最高及最低的地區有關；在不同漁法的結果比較上，大目袖網所估出的最大可持續生產量數值在不同努力量下均低於流袋網，但需注意，當只使用單一漁法的資料進行估計時，將會高估魩鱙漁業的最大可持續生產量。整體來看，使用不同時序資料、不同地區、不同使用漁法及不同努力量的資料確實會影響資源量評估的結果。在未來進行魩鱙漁業資源量評估時，應盡可能地使用較長時間年份的資料，並針對不同地區的每個漁法資料逐一分別進行估計，以更能因地制宜制定適合的捕撈規範。在管理面，建議除了將評估出的MSY作為依據，增加利益相關者對該漁業的了解，並保持良好的溝通管道與信任等，都是在管理上不可或缺的重要因素。

An essential goal of fishery management is to harvest the fishery resources rationally with the assistance of stock assessment. Located in the subtropical zone, Taiwan has abundant fisheries resources, and larval fisheries are some among the abundance. However, threat level raised when a high-efficiency fishing gear imported from Japan in 1977, because the gear significantly increased the total catch in Taiwanese area. Moreover, the fisheries faced the challenge of bycatching other economic fish larvae. In this study, I estimated the maxima sustainable yield (MSY) of larval fisheries from 2009-2021 in Taiwanese waters using a stock production model incorporating covariate (ASPIC). Furthermore, we evaluated the effect of different lengths of time-series data (5, 10, and 13 years), different fishing regions (NW, SW, and NE), different fishing types (the two boat trawling nets and the flow bag net), and different fishing efforts on the MSY values. The results showed that in the different time-series, the largest MSY value was estimated by using the 5-year data, and the smallest value by the 13-year data. In the different fishing areas, the largest MSY value was exhibited in the NE area, and the smallest value in the NW area, in which the result may related to the fact that the NE and NW are the regions with the highest and lowest catches, respectively. In the different fishing methods, the MSY value was larger when using the flow bag net’s data than the two boat trawling nets, however, it should be noted that the MSY value may be overestimated by using data from only one type of fishing method. Overall, my findings indicate that MSY estimates are sensitive to various type of input dataset, such as the length of time-series data, the subset of regional data, and the representative data of various fishing types. It is suggested to use data from each fishing types in different fishing regions with longer time-series data for future analysis. Moreover, increasing stakeholders’ understanding of the fishery and maintaining good communication are all important in management.