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作者:史登福1 2  张魁1  陈作志1 
单位:1. 中国水产科学研究院南海水产研究所, 农业农村部外海渔业开发重点实验室, 广东 广州 510300;
2. 上海海洋大学海洋科学学院, 上海 201306
关键词:渔业资源 评估模型 数据缺乏 生活史特征 北大西洋大青鲨渔业 
渔业资源评估是开展渔业资源管理,维系渔业可持续发展的基础工作。传统的渔业资源评估方法需要统计产量、资源丰度指数甚至年龄结构等大量数据,由于调查经费和数据的缺乏,全球仅1%的鱼种进行过系统性的资源评估。近年来,在数据有限(data-limited)条件下如何开展资源评估已日益成为学术界的关注热点。本文将基于生活史特征的评估方法分为仅需要生活史参数,需要产量数据和生活史参数,需要产量数据、生活史参数及体长或年龄数据等3大类,分别从方法、数据要求、输出结果及局限性进行了系统回顾分析,提供了关于生活史特征参数的常见估算方法,并就其中两种模型对北大西洋大青鲨(Prionace glauca)的可持续渔获量进行了初步评估与比较。最后,对数据缺乏模型的使用及模型在中国近海渔业资源评估中的运用提出了建议。
Fishery stock assessment is a basic component of modern management, required to maintain sustainable fishery development. Traditional methods require a large amount of statistical data assessing yield, abundance index, and age structure. Due to limited funding and data for such surveys, only 1% of fish stocks have systematic assessments conducted. Therefore, it is difficult to assess maximum sustainable yield (MSY) or determine allowable catch for most fishery resources using traditional methods. In recent years, stock assessment using limited available data has become a focus of increasing academic research. A good assessment model based on incomplete data would allow managers to assess the risk of overexploitation, current population biomass, sustainable yield, optimal fishing mortality, and population status relative to reference points such as current total catch limits. These parameters can then be used to determine appropriate fishing limits for the target population. Such models use different assumptions and have different limitations. Therefore, it is necessary to select an appropriate model that will minimize error in the results when evaluating target resources. Where more than one model is available, they can be compared to assess which obtains the best results. In assessment of fishery resources using data-poor methods, more and more attention is being paid to characteristic life history parameters such as intrinsic growth rate, natural mortality coefficient, and so on. Under conditions that combine the yield of the evaluated population with the corresponding life cycle parameters, a more reliable MSY value or sustainable yield can be obtained. In this paper, assessment models based on life-history characteristics are divided into three categories:(1) models that only use life history parameters; (2) models that incorporate catch data and life-history parameters; (3) models that incorporate catch data, life-history parameters, and lifespan or age data. The introductions, data requirements, output results, and limitations of each model is reviewed and systematically analyzed. In addition, several common life-history parameter estimation methods are provided. A simple preliminary assessment of sustainable catch was conducted for North Atlantic blue shark (Prionace glauca) using Catch-MSY and DCAC models, and results were compared. Results calculated using the DCAC model are similar to those obtained with the Catch-MSY model. Maximum sustainable yield of blue shark was about 3.0×104 tons. This paper also provides suggestions on use of data-limited models and applications to assessment of offshore fishery resources in China. The current survey of Chinese offshore fishery resources started late, and recorded data are relatively few, therefore most fisheries had difficulty estimating by traditional methods. The natural mortality coefficient of fish off China's coasts is generally >0.2; thus, assessment errors using DCAC and DB-SRA models will be large. The catch-MSY model can fit the present resource situation well, and is often used to evaluate fishery resources off China's coasts. In view of the low reliability of the upper limit estimate used by the Catch-MSY model to estimate environmental capacity, the SS model can be compared with it in future resource assessments to provide more theoretical support for protection and scientific assessment of offshore fishery resources.
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