太平洋黄鳍金枪鱼延绳钓渔获分布及渔场水温浅析

根据收集到的渔获量数据、海水表层温度数据和有关文献资料,应用GIS技术对太平洋黄鳍金枪鱼延绳钓渔业进行了定量及定性综合分析。结果表明:太平洋延绳钓黄鳍金枪鱼渔场主要分布在20°N—20°S之间的热带太平洋海域,具纬向分布特征。对渔获产量同海表温度的分月统计显示:太平洋黄鳍金枪鱼渔场最适月平均表层水温约28℃～29℃,渔场出现频次为偏态分布型。最后,结合有关文献综合讨论了海表温度、溶解氧含量、海流等环境因子与金枪鱼渔场分布和形成机制的关系。     

风味蛋白酶水解金枪鱼(Eleotridae)碎肉蛋白的动力学模型研究

本文对风味蛋白酶水解金枪鱼碎肉蛋白的特性进行了研究, 采用测定不同底物浓度下的瞬时速率的方法来测定米氏常数Km值, 探讨了水解时间、底物浓度与水解速率及水解度的关系, 并拟合出水解时间与水解度之间关系的数学模型。结果表明: 该酶解反应的米氏常数Km=0.0098, 酶解过程的动力学方程为DH = 8.621 ln (1 + 0.053t?0.0003[S]t)。通过动力学方程求得当酶浓度为200U/g时, 最大临界初始底物浓度[S]为176.81g/L。验证实验表明此模型的可信度高(R2=0.99), 实际值与拟合值基本吻合, 可以用于模拟风味蛋白酶水解金枪鱼碎肉蛋白制备活性肽的反应过程和酶解条件的工艺优化。     

基于改进ConvLSTM网络太平洋长鳍金枪鱼时空分布趋势预测

渔场资源与位置的变动由空间与环境因子共同驱动,远洋渔场时空演变信息的精准预测是远洋捕捞的关键支撑。该研究考虑渔业生产统计数据,并兼顾同期海洋环境数据包括海表面温度（Sea surface temperature, SST）、海表面盐度（Sea surface salinity, SSS）、初级生产力（primary productivity, PP）和溶解氧浓度（dissolved oxygen concentration, O2）,提出了一种融合卷积长短期记忆网络（ConvLSTM）和卷积神经网络（CNN）的渔场时空分布预测模型。首先对时空因子进行编码,提取高层时空特征;其次采用CNN提取海洋环境变量的抽象特征,并基于ConvLSTM提取渔业数据的时空特征,最后融合高层时空关联信息对渔场时空演变趋势进行预测。以1995－2018年太平洋海域的延绳钓生产数据对模型进行验证,模型的根均方误差为0.1036,实验对比发现较传统渔场预报模型的预测误差降低15%~40%,预测的高产渔区与实际作业的高渔获量区匹配度高。该研究构建的渔场时空预测模型能够准确地预测出太平洋长鳍金枪鱼的时空分布,为太平洋长鳍金枪鱼的延绳钓渔业提供科学参考依据。     

Habitat mapping of the Atlantic bluefin tuna derived from satellite data: Its potential as a tool for the sustainable management of pelagic fisheries

The feeding and spawning habitats of the overfished Atlantic bluefin tuna (BFT) are mapped in the Mediterranean Sea and used in the present proposal for selecting restricted fishing grounds. The feeding habitat is mainly traced by oceanic fronts of satellite-derived temperature and chlorophyll while the spawning habitat is mostly characterized by an important heating of surface waters. The proposal recommends opening the fishery in feeding areas in case the BFT stock is low (current situation). Only spawning areas at its latest stage could be opened once the stock has recovered to its optimum yield. Due to the possible concentration of fishing vessels if fishing areas are restricted (e.g. four-fold increase with a 1/16th restriction of the Mediterranean Sea) the inspection activities could be better targeted. Identified spawning grounds, opened or closed to fishing, could also be particularly monitored by control operations. Within the authorized areas, the habitat maps would guide fishermen to the favourable habitat reducing their costs. The habitat guided management could be able to adapt the spatial and temporal distribution of the effort to the requirements of both the fisheries’ control and the resource. Its implementation is likely to protect the stock (a) by apparently decreasing illegal fishing which accounts in the recent years for more than 1/3rd of total catches, (b) by protecting the spawners to ensure a suitable recruitment and (c) by distributing the effort to respect the population structure. The first species studied is the emblematic bluefin tuna which is at high risk of collapse due to overfishing. The approach is a priori transposable to other epipelagic species of commercial importance.     

A spatial ecosystem and populations dynamics model (SEAPODYM) – Modeling of tuna and tuna-like populations

An enhanced version of the spatial ecosystem and population dynamics model SEAPODYM is presented to describe spatial dynamics of tuna and tuna-like species in the Pacific Ocean at monthly resolution over 1° grid-boxes. The simulations are driven by a bio-physical environment predicted from a coupled ocean physical–biogeochemical model. This new version of SEAPODYM includes expanded definitions of habitat indices, movements, and natural mortality based on empirical evidences. A thermal habitat of tuna species is derived from an individual heat budget model. The feeding habitat is computed according to the accessibility of tuna predator cohorts to different vertically migrating and non-migrating micronekton (mid-trophic) functional groups. The spawning habitat is based on temperature and the coincidence of spawning fish with presence or absence of predators and food for larvae. The successful larval recruitment is linked to spawning stock biomass. Larvae drift with currents, while immature and adult tuna can move of their own volition, in addition to being advected by currents. A food requirement index is computed to adjust locally the natural mortality of cohorts based on food demand and accessibility to available forage components. Together these mechanisms induce bottom-up and top-down effects, and intra- (i.e. between cohorts) and inter-species interactions. The model is now fully operational for running multi-species, multi-fisheries simulations, and the structure of the model allows a validation from multiple data sources. An application with two tuna species showing different biological characteristics, skipjack (Katsuwonus pelamis) and bigeye (Thunnus obesus), is presented to illustrate the capacity of the model to capture many important features of spatial dynamics of these two different tuna species in the Pacific Ocean. The actual validation is presented in a companion paper describing the approach to have a rigorous mathematical parameter optimization [Senina, I., Sibert, J., Lehodey, P., 2008. Parameter estimation for basin-scale ecosystem-linked population models of large pelagic predators: application to skipjack tuna. Progress in Oceanography]. Once this evaluation and parameterization is complete, it may be possible to use the model for management of tuna stocks in the context of climate and ecosystem variability, and to investigate potential changes due to anthropogenic activities including global warming and fisheries pressures and management scenarios.     

吉尔伯特群岛海域大眼金枪鱼(Thunnus obesus) 栖息环境综合指数

根据2010年10月—2011年1月在吉尔伯特群岛海域利用金枪鱼延绳钓调查所取得的32个站点的大眼金枪鱼渔获数据,以及测得的温度、盐度、叶绿素、溶解氧浓度、水平海流及垂直海流数据,采用分位数回归的方法研究了各水层(80—240m,每40m为一层)中各环境因子与大眼金枪鱼渔获率的关系,建立"栖息环境综合指数(integrated habitat index,IHI)模型",并利用另外8个站点的数据验证研究结果。结果表明,(1)IHI模型的预测能力较好;(2)不同的水层影响大眼金枪鱼分布的环境因子不同,在较浅的水层(80—200m),大眼金枪鱼的渔获率与溶解氧浓度和海流相关,而在较深水层(>200m)则仅与温度相关;(3)大眼金枪鱼较适宜的栖息水层为120—160m;(4)大眼金枪鱼IHI指数分布较高的两个海域分别为2—3°S,169—175°E与1—3°S,178—180°E,建议在上述两个海域作业时,尽可能使钓具沉降到120—160m的水层,以达到减少兼捕渔获物,同时提高生产效率。     

金枪鱼心血管系统的特性与功能研究进展

金枪鱼是一种具备快速游泳能力的洄游性经济鱼类,其心血管系统存在血管逆流热交换器,并具备高心率、高心输出量、高血氧输送速率等特点。同时,作为区域恒温动物,金枪鱼对低温、低氧等环境有着独特的适应能力。为了系统地了解金枪鱼心血管系统的结构与功能特性及其环境适应性,本文综合国内外相关研究进展,阐述了金枪鱼独特的心血管系统组成与结构特点,分析了其高心输出量、高心率和高血氧输送速率的结构基础,并重点论述其支持高代谢率、调节体温、强低氧耐受能力等心血管功能,旨在为金枪鱼心血管系统方面的研究提供参考。     

基于不同环境因子的中西太平洋鲣鱼资源丰度灰色预测模型构建

鲣Katsuwonus pelamis广泛分布于各大洋热带和亚热带海域,其中以中西太平洋资源量最为丰富。综合评价环境因子对鲣鱼资源量的影响,构建科学的资源预报模型可为我国可持续合理开发该鱼种提供参考。本研究利用1998—2013年中西太平洋渔获量数据,以单位捕捞努力量渔获量(CPUE)为资源相对丰度指标,利用灰色关联方法分析鲣鱼资源相对丰度与环境因子之间的关联度,选取合适的环境因子,并基于不同环境因子构建不同的灰色预测模型对鲣鱼资源相对丰度进行预测,比较选择最优模型。结果表明, 中西太平洋鲣鱼的产量逐年递增,而CPUE在年间有着较大的波动。灰色关联分析认为,海表面温度与CPUE的平均关联度最大,其次为Nino3.4区海表温度距平值,其他的环境因子与CPUE的关联度较小。基于多环境因子的预测模型中,包含所有因子(海表面温度、海表面高度、叶绿素质量浓度a和Nino3.4区海表温度距平值)的模型M1有着最佳的拟合效果,实际值与预测值的相对误差为6.475 2,相关系数为0.687 4;而基于单一环境因子的预测模型中,去除11月SST数据的模型S2有着最佳的拟合效果,实际值与预测值的相对误差为7.419 2,相关系数为0.791 0。相比多环境因子的预测模型,单一环境因子预测模型有着较高的稳定性,实际值与预测值直接相关性也较高,可以作为中西太平洋鲣鱼资源相对丰度预报的最优模型。