适用于数据缺乏渔业的资源评估方法研究进展

开展渔业资源评估研究是制定渔业可持续发展策略的重要前提, 而数据有限是全球渔业资源评估面临的普遍挑战。传统资源评估方法具有数据需求量大、要求高等特点, 无法应用于数据缺乏渔业的资源评估中。数据缺乏方法(data-limited method)可结合少量易获得数据和相关历史生物学信息对渔业资源状况、生物学参考点以及资源量等进行评估, 已经成为全球区域性渔业管理组织和资源评估学者的关注热点。本文在分析数据缺乏资源评估方法文献的基础上, 对近几年开发的数据缺乏评估方法模型结构、模型输入输出、假设以及不确定性来源和分析方法等进行了回顾和归纳, 同时对数据缺乏评估方法未来的研究重点和方向进行了展望。分析认为, 数据缺乏渔业资源评估研究仍处于发展阶段, 若渔获量和体长数据均可用, 则应考虑整合了两种类型数据的模型, 如 LIME (length-based integrated mixed effects, 基于体长的综合混合效应)模型和 SSS (simple stock synthesis, 简化资源整合) 模型。建议今后研究中应加强以下几个方面的工作: (1)积极开展长时间、多海域、全覆盖的渔业资源独立科学调查, 以获得具有代表性的样本数据; (2)对现有数据缺乏模型进一步优化, 综合考虑各种因素对评估结果的影响; (3) 进行完整、准确的基础生物学研究, 获得较为准确的历史生物学信息, 从而降低评估结果的不确定性; (4)开展基于渔获量模型和基于体长模型的模拟测试研究, 提高模型对统计偏差和数据质量问题的包容性。     

基于生活史特征的数据有限条件下渔业资源评估方法比较

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。     

运用数据缺乏方法估算印度洋大青鲨可持续渔获量

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。     

印度洋长鳍金枪鱼资源评估的影响因素分析

多个模型被用于印度洋长鳍金枪鱼(Thunnus alalunga)的资源评估,但这些模型的评估结果均存在较大的不确定性,为此,本文对影响印度洋长鳍金枪鱼资源评估的因素进行了分析。分析结果认为:(1)由于渔业数据存在不报、漏报或混报及采样样本数过低、采样协议出现变化等问题,造成印度洋长鳍金枪鱼渔业的渔获量、体长组成或年龄组成数据存在质量问题;(2)尽管对单位捕捞努力渔获量(catch per unit effort,CPUE)进行了标准化,但目标鱼种变化及捕捞努力量空间分布变化仍严重影响了标准化CPUE数据的质量;(3)印度洋长鳍金枪鱼的种群生态学及繁殖生物学研究仍比较薄弱,种群结构、繁殖、生长、自然死亡信息比较缺乏,在资源评估中,相关参数设置需借用其他洋区的研究结果;(4)海洋环境对印度洋长鳍金枪鱼的资源变动与空间分布具有显著影响,但评估模型较少考虑海洋环境的影响。由于上述问题的存在,导致当前评估结果存在较大不确定性。未来,应继续探索提高资源评估质量的方法,同时研究建立管理策略评价框架,以避免渔业资源评估结果的不确定性对该渔业可持续开发的影响。     

石斑鱼生物学及人工繁育研究进展

石斑鱼(Epinephelidae)是名贵的海产鱼类,主要栖息于热带亚热带近岸岩滩和珊瑚礁水域,是岩礁生态系生物群落的重要类群。本文对国内外石斑鱼类生物学、生态学、渔业资源及人工繁育养殖的研究成果进行梳理,综合报道石斑鱼类的形态比较特征,栖息、摄食和繁殖习性,系统演化与分类,种类地理分布,人工繁育等研究进展以及养殖发展概况;报道全球石斑鱼自然资源明显衰退的现状评估以及对石斑鱼资源保护的共识和相关的渔业管理措施。此外,根据作者多年开展的鱼类遗传生物学研究,指出强化我国海域野生石斑鱼资源保护工作,深入开展杂交石斑鱼的基础生物学研究,加强石斑鱼杂交选育行业管理,是我国石斑鱼渔业产业可持续发展的必要保障。     

大西洋中上层鲨鱼资源状况的初步探讨

据1994—2001年4个航次在金枪鱼延绳钓渔船对热带大洋性中上层鲨鱼资源进行调查，并分析国际大西洋金枪鱼资源保护委员会提供的关于中上层鲨鱼的渔业数据。结果表明：延绳钓渔业共兼捕13种鲨鱼，其中，尖吻鲭鲨和大青鲨是优势种类。尖吻鲭鲨在第1、第2和第4航次的CPUE分别为每千钩0．3502、0．1754和0．0642尾，呈下降趋势。大青鲨在第1～3航次的的CPUE达到每千钩5～7尾，而在第4航次下降为每千钩0．8尾。研究报告了中国金枪鱼船队自1993年开始在大西洋兼捕尖吻鲭鲨和大青鲨的渔获量，并根据捕捞死亡系数和大西洋总渔获量数据，初步探讨了尖吻鲭鲨和大青鲨年平均资源量，影响CPUE的因素和大西洋中上层资源状况。     

运用生产力-易捕率指数对10种热带太平洋鲨鱼种群的研究

热带太平洋是全球产量最高的金枪鱼渔场,大洋性鲨鱼种群遭受金枪鱼渔业的影响受到国际社会的高度关注。由于缺少渔业统计资料,一般难以运用标准的资源评估方法对这些兼捕的种类进行评估。笔者运用种群生产力-易捕率分析（productivity-susceptibility analysis,PSA）方法,对热带太平洋10种鲨鱼遭受金枪鱼延绳钓渔业影响的风险程度进行比较分析,并计算风险指数（vulnerability）。风险指数从低到高的种类依次为锤头双髻鲨（Sphyrna zygaena）、路氏双髻鲨（S.lewini）、无沟双髻鲨（S.mokarran）、尖吻鲭鲨（Isurus oxyrinchus）、狐形长尾鲨（Alopias vulpinus）、长鳍真鲨（Carcharhinus longimanus）、大青鲨（Prionace glauca）、镰状真鲨（C.falciformis）、浅海长尾鲨（A.pelagicus）、大眼长尾鲨（A.superciliosus）,表明大眼长尾鲨种群受延绳钓渔业影响而遭受过度捕捞的潜在风险最高,垂头双髻鲨的风险最低。该研究结果可以为热带太平洋金枪鱼延绳钓渔业的管理和生态系统保护提供科学参考。     

西德的渔业研究现状

西德的渔业研究是由西德渔业研究所负责的，其研究范围很广，包括从资源评估到网具研究和食用技术等。然而，渔业研究所的首要任务是监测可开发鱼群，调查捕鱼活动对鱼群的影响，以及鱼群的各种生物学和环境学变动，以这些研究的结果为基础．来制定国家渔业政策和开展国际讨论，确定捕捞限额和资源保护政策。     

广东渔业资源数据采集与分析系统建设及应用分析

正渔业资源监测是在渔业开发的同时,持续地对渔业活动和资源的变动进行调查、跟踪、评估和预测的过程。开展渔业资源监测,完善渔业资源监测评估体系,有利于全面了解近海渔业资源状况,及时掌握重要渔业资源种群变动趋势和渔业生产效益,为科学实施渔业资源开发利用、渔业资源养护和渔业资源管理提供数据基础和科学依据。2017年6月,广东省启动广东渔业资源数据采集与分析系统建设工作。至今,广东省建     

基于CMSY和BSM的东海区重要渔业种类资源评估

有限数据方法(data-limited method)可结合少量易获得数据和相关生物学信息对渔业资源状况、生物学参考点以及生物量等进行评估,已经成为全球区域性渔业管理组织和资源评估学者的关注热点。本研究采用基于渔获量的最大可持续渔获量(catch-based maximum sustainable yield,CMSY)和基于贝叶斯状态空间的Schaefer产量模型(Bayesian Schaefer production model,BSM)评估了东海区19个重要经济种类的资源状况,并提出了基于最大可持续渔获量(maximum sustainable yield,MSY)的渔业管理建议。结果显示,19个种类中有1个种类衰竭,3个种类严重衰退,5个种类过度捕捞,5个种类轻度过度捕捞,5个种类健康。种群状态长期评估结果表明,处于生物可持续水平的鱼类种群占比已由1980年的95%下降至2019年的26%。同时对CMSY和BSM方法的结果进行了比较,整合单位捕捞努力量渔获量(catch per unit effort,CPUE)数据的BSM方法导致了置信区间较宽,并调节了生物量轨迹的变化形态...     

Distribution patterns and population structure of the blue shark (Prionace glauca) in the Atlantic and Indian Oceans

The blue shark (Prionace glauca) is the most frequently captured shark in pelagic oceanic fisheries, especially pelagic longlines targeting swordfish and/or tunas. As part of cooperative scientific efforts for fisheries and biological data collection, information from fishery observers, scientific projects and surveys, and from recreational fisheries from several nations in the Atlantic and Indian Oceans was compiled. Data sets included information on location, size and sex, in a total of 478,220 blue shark records collected between 1966 and 2014. Sizes ranged from 36 to 394 cm fork length. Considerable variability was observed in the size distribution by region and season in both oceans. Larger blue sharks tend to occur in equatorial and tropical regions, and smaller specimens in higher latitudes in temperate waters. Differences in sex ratios were also detected spatially and seasonally. Nursery areas in the Atlantic seem to occur in the temperate south‐east off South Africa and Namibia, in the south‐west off southern Brazil and Uruguay, and in the north‐east off the Iberian Peninsula and the Azores. Parturition may occur in the tropical north‐east off West Africa. In the Indian Ocean, nursery areas also seem to occur in temperate waters, especially in the south‐west Indian Ocean off South Africa, and in the south‐east off south‐western Australia. The distributional patterns presented in this study provide a better understanding of how blue sharks segregate by size and sex, spatially and temporally, and improve the scientific advice to help adopt more informed and efficient management and conservation measures for this cosmopolitan species.     

运用SS3评估东太平洋大眼金枪鱼(Thunnus obesus)资源——复杂模型和简化模型的比较

大眼金枪鱼(Thunnus obesus)是东太平洋最重要的商业性金枪鱼鱼种,其资源评估采用的是结构复杂的Stock Synthesis 3模型(SS3).模型简化是提高资源评估效率的必要手段,但对大眼金枪鱼简化模型的效果尚未开展研究.本研究尝试从渔业数据结构的角度,将SS3复杂模型的23个渔业简化为仅含围网和延绳钓2个渔业,从而比较简化模型的评估能力.结果显示,简化模型能较为准确地描述大眼金枪鱼补充量、亲体量、捕捞死亡系数等主要时间序列的历史动态变化,对传统生物学参考点FMSY的估计也较为准确,且受陡度和自然死亡系数的影响较小,但对其他参考点的估算误差较大.陡度参数对简化模型基于Kobe图判断资源状态的准确性有重要影响,陡度较低时,简化模型能较为准确地判断资源状态.研究表明,权衡模型的评估能力和降低模型结构的复杂性,是大眼金枪鱼资源评估今后需要重点研究的任务之一.此外,对模型简化的效果评价,与采用的生物学参考点和资源状况判断标准的选择有关.     

Evaluation of a spatially sex‐specific assessment method incorporating a habitat preference model for blue marlin (Makaira nigricans) in the Pacific Ocean

Blue marlin, widely distributed throughout the Pacific Ocean, are sexually dimorphic, have certain preferred habitats, and migrate seasonally. These characteristics have been ignored in previous stock assessment models. A population dynamics model that includes spatial structure, and sex and age structure was therefore constructed and fitted to fisheries data for blue marlin, along with information on the relative density of the population over space derived from a habitat preference model that uses the oceanographic and biological variables sea‐surface temperature, mixed layer depth, sea‐surface height anomaly, and chlorophyll‐a concentration. Monte Carlo simulation was then used to examine the estimation performance of the stock assessment method. Estimates of management‐related quantities including current spawning stock biomass are substantially biased when the assessment method ignores seasonal movement and sexual dimorphism. We also found that (i) uncertainty about the relationship between catch rate and abundance influences estimation performance to a larger extent than uncertainty in catches, (ii) the outcomes of the assessment are sensitive to the values assumed for natural mortality and stock‐recruitment steepness, and (iii) the ratio of current spawning stock biomass to that at pre‐exploitation equilibrium appears to be the most robust among the quantities considered. We conclude that assessment methods for blue marlin in the Pacific Ocean need to take account of seasonal migration and sex structure to improve stock assessments.     

Using the best of two worlds: A bio-economic stock assessment (BESA) method using catch and price data

Reliable stock assessments are essential for successful and sustainable fisheries management. Advanced stock assessment methods are expensive, as they require age- or length-structured catch and detailed fishery-independent data, which prevents their widespread use, especially in developing regions. Furthermore, modern fisheries management increasingly includes socio-economic considerations. Integrated ecological-economic advice can be provided by bio-economic models, but this requires the estimation of economic parameters. To improve accuracy of data-limited stock assessment while jointly estimating biological and economic parameters, we propose to use price data, in addition to catches, in a new bio-economic stock assessment (‘BESA’) approach for de-facto open access stocks. Price data are widely available, also in the Global South. BESA is based on a state-space approach and uncovers biomass dynamics by use of the extended Kalman filter in combination with Bayesian estimation. We show that estimates for biological and economic parameters can be obtained jointly, with reliability gains for the stock assessment from the additional information inherent in price data, compared to alternative assessment methods for data-poor stocks. In a real-world application to Barents Sea shrimp (Pandalus borealis, Pandalidae), we show that BESA benchmarks well also against advanced stock assessment results. BESA can thus be both a stand-alone approach for currently unassessed stocks as well as a complement to other available methods by providing bio-economic information for advanced fisheries management.     

Developing robust frequentist and Bayesian fish stock assessment methods

Errors in fitting models to data are usually assumed to follow a normal (or log normal) distribution in fisheries. This assumption is usually used in formulating likelihood functions often required in frequentist and Bayesian stock assessment modelling. Fisheries data are commonly subject to atypical errors, resulting in outliers in stock assessment modelling. Because most stock assessment models are nonlinear and contain multiple variables, it is difficult, if not impossible, to identify outliers by plotting fisheries data alone. Commonly used normal distribution‐based frequentist and Bayesian stock assessment methods are sensitive to outliers, resulting in biased estimates of model parameters that are vital in defining the dynamics of fish stocks and evaluating alternative strategies for fisheries management. Because of the high likelihood of having outliers in fisheries data, frequentist or Bayesian methods robust to outliers are more desirable in fisheries stock assessment. This study reviews three approaches that can be used to develop robust frequentist or Bayesian stock assessment methods. Using simulated fisheries as examples, we demonstrate how these approaches can be used to develop the frequentist and Bayesian stock assessment approaches that are robust to outliers in fisheries data and compare the robust approaches with the commonly used normal distribution‐based approach. The proposed robust approaches provide alternative ways to developing frequentist or Bayesian stock assessment methods.     

Integrating recreational fisheries data into stock assessment: implications for model performance and subsequent harvest strategies

Understanding the impacts of recreational fishing on commercially fished stocks is becoming increasingly relevant for fisheries managers. However, data from recreational fisheries are not commonly included in stock assessments of commercially fished stocks. Simulation models of two assessment methods employed in Australia's Commonwealth fisheries were used to explore how recreational fishery data can be included, and the likely consequences for management. In a data‐poor management strategy for blue eye trevalla, Hyperoglyphe antarctica (Carmichael), temporal trends in recreational catch most affected management outcomes. In a data‐rich age‐structured stock assessment for striped marlin, Kajikia audax (Philippi), estimates of stock status were biased when recreational catches were large or when the recreational fishery targeted different size classes than the commercial fishery and these data were not integrated into the assessment. Including data from recreational fishing can change perceptions of stock status and impact recommendations for harvest strategies and management action. An understanding of recreational fishery dynamics should be prioritised for some species.