单 位：
农业部大洋渔业开发重点实验室;上海海洋大学海洋科学学院
摘 要：
运用数据缺乏方法,即基于资源衰减的可持续渔获量估算模型(DCAC),结合Monte Carol模拟,对印度洋大青鲨(Prionace glauca)的可持续渔获量进行估计。结果表明,若大青鲨资源衰减比率(Δ)为正值,当自然死亡系数M增大或最大可持续产量对应的捕捞死亡系数(F_(MSY))与M的比值c增大时,可持续渔获量估算值(Y_(sust))增大;若Δ接近零甚至为负值,当M或c增大时,Y_(sust)呈减小趋势。资源丰度指数的选择对DCAC结果有较大影响,基于日本延绳钓渔业1998—2014年和2001—2014年两个时间序列的丰度指数得到的Y_(sust)结果可靠,且与其他模型估算的MSY值接近。2014年印度洋大青鲨的年渔获量正好处在或略高于最大可持续产量(MSY)水平,但该结果仍具有一定的不确定性。本研究表明运用DCAC方法估算印度洋大青鲨可持续渔获量是可行的,但对其他鲨鱼种类的适用性仍需进一步研究,该结果可为数据缺乏方法在大洋和中国近海渔业中的应用提供参考。
译 名：
Estimate of sustainable yield of blue shark(Prionace glauca) in the Indian Ocean using data-poor approach
作 者：
GENG Zhe;ZHU Jiangfeng;XIA Meng;MA Lulu;College of Marine Sciences, Shanghai Ocean University;Key Laboratory of Exploitation of Oceanic Fisheries, Ministry of Agriculture;
关键词：
Prionace glauca;;data-poor approach;;stock assessment;;Indian Ocean
摘 要：
Sharks occupy the top trophic level in the marine community and play an important role in maintaining ecosystem stability and diversity. The stock status of shark species is often difficult to assess by formal stock assessment methods due to limited fishery data. Blue shark(Prionace glauca) is the most widely distributed pelagic shark species in tropical and temperate oceanic waters. This species is often caught as bycatch in oceanic longline fisheries that target billfishes and tunas, and also in the artisanal longline fisheries that operate in coastal areas such as Chile. Because of its slow growth and late maturity, the blue shark is defined as "Near Threatened" globally in the IUCN species list. Determining the stock status of Indian Ocean blue shark using a data-poor approach has been assigned as a high research priority by the Indian Ocean Tuna Commission. In this study, we assessed the Indian Ocean blue shark stock status using the depletion-corrected average catch(DCAC) approach and Monte Carlo simulation. DCAC is a data-poor approach that only needs basic biological information(natural mortality, M), catch data, and an abundance index. M was estimated by the Hoeing method, resulting in a mean M of 0.193 y-1 and a standard error of ln M of 0.05. In addition to the annual catch data, the application of DCAC also needs the means and standard errors of the following parameters: depletion of the biomass(⊿) and F_(MSY)/M. First, we estimated the sustainable yield(Y_(sust)) of blue shark using abundance indices(standardized catch per unit effort [CPUE] time series) derived from different longline fleets(i.e., Spain, Portugal, Japan, and Taiwan, China). Second, we evaluated the sensitivity of DCAC by considering multiple combinations of different levels of M and F_(MSY)/M, CPUE indices, and lengths of time series of data. Lastly, we summarized the estimated Y_(sust) values and compared our estimates with the results from other assessment approaches for this species. The results showed that Y_(sust) increased with M or F_(MSY)/M when⊿ was positive. However, Y_(sust) decreased with M or F_(MSY)/M when⊿ was close to zero or negative. The results were sensitive to the CPUE index. The estimated Y_(sust) was reliable and close to the maximum sustainable yield(estimated from other assessment models) when the Japanese longline CPUE index(1998–2014 or 2001–2014) was used. The current(2014) annual catch of blue shark might be at or just above the estimated maximum sustainable yield, although the estimate is subject to uncertainties. This study suggests that DCAC is suitable for estimating the Y_(sust) of Indian Ocean blue shark using catch data and CPUE indices as the main sources. This study also provides guidelines for the application of data-poor approaches in domestic fisheries of China.
