北斗船位数据提取拖网捕捞努力量算法研究

传统的捕捞努力量统计耗时费力,并且存在延时,不能及时了解宏观的捕捞强度,本研究以象山港拖网渔船为研究对象,选择安装北斗终端的1 508艘渔船,对北斗卫星船位监控系统所获取的渔船船位、航向、航速等信息进行分析挖掘,获得捕捞强度。根据航速统计获得每艘渔船处于捕捞状态的航速阈值,如拖网船300791捕捞状态船位点阈值的航向差最小和最大分别是-50和52°,航速最小和最大分别是0.9和2.0 m/s,两者结合判断捕捞状态点,再采用过滤窗修正,在各渔区格网计算一段时间内渔船捕捞状态点的累计捕捞时间,其值与渔船功率的乘积获得捕捞努力量,物理单位为kW·h,并制作累计捕捞kW·h的格点图和插值图。该方法具有实时、大范围、快速、分辨率高的特点,能够很好地服务于渔业资源保护。     

基于北斗船位数据的拖网捕捞追溯方法研究

水产品安全关系到国计民生,北斗船位数据辅助拖网捕捞追溯,将为水产安全管理增添新的手段。根据航速与航向差的阈值确定拖网渔船捕捞状态,计算累计捕捞时间,作为某区域一段时间的捕捞强度。通过船位追溯渔船、渔场、渔港,掌握水产品的来源与累计捕捞时间,再结合渔场、渔区等信息,实现水产品的追溯。文章研究了作业渔场位置、累计捕捞时间、捕捞努力量等信息的提取方法,实现了渔港的渔船追溯并获得渔港水产品的来源地,可准确到单个捕捞渔场。     

基于神经网络和VMS的渔船捕捞类型辨别

不合理的捕捞方式会导致海洋渔业资源衰退以及海洋生态环境破坏。近年来,渔船监测系统已被用于渔船安全监督、渔业资源管理、海洋生态环境保护等方面。文章以中国近海15艘流刺网渔船、39艘拖网渔船、24艘流动张网渔船共78个样本为研究对象,以BP(back propagation)神经网络为研究模型,借助2014年北斗渔船监测系统(VMS)中对应渔船的航速和航向数据对其作业方式进行辨别。结果显示,基于航速和航向的渔船作业方式辨别正确率分别为93.6%和91%,两者均能较好地对捕捞类型进行分类,且航速的分类精度高于航向。拖网和流动张网渔船分类的正确率在90%以上,而流刺网渔船仅为70%,原因可能是流刺网在空间上的移动较复杂,减弱了航速和航向变化的规律性。     

基于渔船轨迹的阿根廷滑柔鱼捕捞强度空间分析

为了解阿根廷滑柔鱼(Illex argentinus)渔船捕捞行为与空间分布,为其资源管理提供参考,文章采用2018年1-5月的船舶自动识别系统(Automatic Identification System,AIS)数据,通过统计方法分析渔船捕捞行为特征,基于数据挖掘方法识别鱿钓渔船作业轨迹点,统计和绘制鱿鱼渔场捕捞...     

关于闽南、台浅渔场海洋捕捞作业结构调整的探讨

随着闽南、台浅渔场海洋捕捞强度不断增强,捕捞方式和作业结构日趋不合理,渔船效率下降。本文从渔场的渔业资源现状出发,对灯围、拖网等作业规模的总量控制进行探讨,提出加强渔业科学管理,控制拖网,巩固灯围,发展流刺网、深水延绳钓及鱿鱼钓和鱿鱼敷网作业的新路子,以养护和合理利用渔场渔业资源,促进渔业可持续发展。     

基于卫星AIS的中西太平洋金枪鱼延绳钓渔场分布研究

根据2017年7-12月中西太平洋金枪鱼延绳钓渔船卫星AIS数据及海表温度数据,利用全局空间自相关中的莫兰指数及局部空间自相关中的热点分析方法,得到了中西太平洋金枪鱼延绳钓中心渔场位置及变动情况,并提取了中心渔场处海表温度范围。结果表明:1)中西太平洋金枪鱼延绳钓渔船捕捞努力量存在空间自相关并呈显著聚集分布模式,所得热点区域可以作为中西太平洋金枪鱼延绳钓作业中心渔场。2)各月均存在多个中心渔场,其中8月中心渔场范围与7月大致相同,部分中心渔场存在向北扩张或向外扩大的趋势; 9月最大中心渔场东移,出现在180°附近,其它渔场较之前有不同程度的扩散或缩小; 10-12月中心渔场较为集中,主要分布在170°～180°E及155°W附近海域,且单位渔区捕捞努力量大于7-9月。3)中心渔场处不同海表温度范围主要与金枪鱼鱼种有关:155°W、20°N夏威夷群岛附近冷舌区,主要分布着大眼金枪鱼(Thunnus obesus)和黄鳍金枪鱼(Thunnus albacares),海表温度为25. 5～27. 5℃; 10°S～10°N暖池区域,主要为大眼金枪鱼和鲣(Katsuwonus pelamis),海表温度为28. 0～30. 3℃;赤道南部冷暖锋面交汇处,主要为长鳍金枪鱼(Thunnus alalunga),海表温度为25. 0～29. 0℃。     

试点电子捕捞日志助力海洋渔业可持续发展

<正>捕捞日志又称渔捞日志,是从事捕捞作业的渔船按规定记录生产作业的位置、渔获种类和产量以及环境因子等信息的记录薄。新修订的《渔业捕捞许可管理规定》中,第五十条到五十三条明确指出,海洋大中型渔船从事捕捞活动的应当填写渔捞日志,并且对渔船填写渔捞日志的具体内容、提交方式、管理部门和处罚内容作了进一步明确规定,渔捞日志管理逐渐提上日程。面对众多的渔船,要如何科学地进行信息采集呢?电子捕捞日志数据采集与管理系统的出现将解决这个问题。记者近日从中国水产科学研究院南海水产     

浙江省帆张网捕捞强度分布的提取方法

捕捞强度是渔业资源管理和评估领域的重要参数之一。传统的计算方法无法满足实时、大范围、快速统计时空分布的需要。本文提出根据VMS数据对渔船生产活动划分作业阶段;结合帆张网渔船的作业特点,采用阈值划分和密度聚类算法提取网位,计算各航次的捕捞时长;划分地理格网并累加其范围内的捕捞时长,以各格网平均每平方公里累计捕捞时长(h/km2)作为帆张网捕捞强度的量化依据,并可视化捕捞强度空间分布。本研究以2017年浙江省所属帆张网渔船的北斗VMS船位数据为研究对象,共提取有效作业航次733个,网位6021个,累计捕捞时长736 761.78h。在121.6E~126.5E,27.6N~33.9N范围内划分0.1°×0.1°地理格网,2017年上半年帆张网捕捞相对分散,各格网平均捕捞强度11.26 h/km2;下半年相对集中,平均捕捞强度12.83 h/km2;全年平均14.76 h/km2。其中,在125.4E~126.1E,31.2N~32.1N范围内捕捞强度最大,平均28.51 h/km2。本文设计的网位提取、捕捞时长、捕捞强度空间分布,为分析帆张网渔船作业状态和捕捞强度量化提供新的研究思路。     

灯光罩网渔船兼作金枪鱼延绳钓捕捞试验

2015年3月~4月利用"粤电渔42212"灯光罩网渔船在南沙北部海域开展了金枪鱼延绳钓捕捞试验。延绳钓试验以不影响灯光罩网的正常作业为前提。试验期间,灯光罩网作业40晚,放网297次,平均渔获率336.4kg·h-1,总渔获质量129.6 t,鸢乌贼(Sthenoteuthis oualaniensis)占85.31%,黄鳍金枪鱼(Thunnus albacaes)占0.21%。延绳钓作业7次,放钩2 700枚,总渔获质量1 281.6 kg,剑鱼(Xiphias gladius)和黄鳍金枪鱼分别占72.88%和11.16%;延绳钓平均上钩率和CPUE分别为25.74尾·千钩-1和427.48 kg·千钩-1,其中黄鳍金枪鱼为1.90尾·千钩-1、52.74 kg·千钩-1,剑鱼为3.76尾·千钩-1、291.86 kg·千钩-1。试验证明,金枪鱼延绳钓和灯光罩网的渔场分布恰好一致,作业时间没有冲突。相比发展专业钓船,罩网渔船兼作延绳钓具有投资金额少、生产成本低的优势。建议通过探捕拓展和延长外海渔期,并研究解决金枪鱼保鲜问题。     

基于北斗卫星数据的拖网渔船状态与网次提取

拖网网次是捕捞努力量计算的重要参数,拖网渔船捕捞在放网、拖网、起网过程中速度变化非常明显,北斗船位数按3 min间隔记录渔船的经纬度位置、航速、航向、时间等信息,通过航速可以判断渔船状态。本文对浙嵊渔10201、浙嵊渔10243双拖网调查船的航速进行统计,划分拖网捕捞状态的航速阈值为0.8~1.6m/s,借助北斗船用终端记录的航速,判断渔船状态,提取出全部处于拖网作业的网次,并与现场记录的起放网时间和位置进行了比较验证,放网时人工记录与北斗记录时间差平均为4.3 min,起网时人工记录与北斗记录时间差平均为7.2 min,放网的GPS位置与北斗位置距离差值平均为0.35 nmi,起网的GPS位置与北斗位置距离差值平均为0.24 nmi。     

金枪鱼延绳钓钓具的最适浸泡时间

根据2010年10月—2011年1月金枪鱼延绳钓海上调查数据,分两种起绳方式,建立每次作业每一根支绳的浸泡时间计算模型。将钓具的浸泡时间以1 h为间隔分别统计每个区间的支绳数量及大眼金枪鱼(Thunnus obesus)、黄鳍金枪鱼(Thunnus albacores)的渔获尾数,并计算其钓获率(CPUE)。结果表明:1)大眼金枪鱼和黄鳍金枪鱼的CPUE都随浸泡时间的增加呈现先增后减的趋势,这是由于饵料的诱引效果变化及渔获的丢失引起的;2)二次曲线可拟合浸泡时间与大眼金枪鱼和黄鳍金枪鱼CPUE的关系;3)大眼金枪鱼和黄鳍金枪鱼CPUE最高的浸泡时间分别为9.9 h和10.1 h。建议:1)今后在金枪鱼延绳钓作业中,保证每一根支绳在水中的浸泡时间为9.5~10.5 h,以提高捕捞效率并减少副渔获物;2)可把延绳钓钓具的浸泡时间作为有效捕捞努力量,并用于CPUE的标准化。研究结果可用于提高捕捞效率并减少副渔获物的技术方案制订,并为渔业生产和CPUE的标准化提供科学参考。     

Alternative error distribution models for standardization of catch rates of non-target species from a pelagic longline fishery: billfish species in the Venezuelan tuna longline fishery

Alternative error distributions were evaluated for calculating indices of relative abundance for non-target species using catch and effort data from commercial fisheries. A general procedure is presented for testing the underlying assumptions of different error distributions. Catch rates, from an observer program, of billfish caught mainly as bycatch in a pelagic tuna longline fishery in the Western Central Atlantic were standardized. Although catches of billfishes are not common in pelagic tuna longline fisheries, these fisheries are one of the main sources of fishing mortality for these stocks in the central Atlantic due to the magnitude and spatial extent of longline fishing effort. Billfish CPUE data are highly skewed with a large proportion of zero observations. Delta distribution models can accommodate this type of data, and involve modeling the probability of a non-zero observation and the catch rate given that the catch is non-zero separately. Three different Delta models were compared against other error distributions, including the lognormal, log-gamma, and Poisson. Diagnostic checks and deviance table analyses were performed to identify the best error distribution and the set of factors and interactions that most adequately explained the observed variability. The results indicated that the Delta-lognormal model (a binomial error distribution for the probability of a non-zero catch and lognormal error for the positive catch rates) complied best with the underlying characteristics of the data set. Analyses of catch rates for blue marlin, white marlin and sailfish confirmed the spatio-temporal nature of their distribution in the central Atlantic and Caribbean Sea. Also, the analyses indicated that catch rates of billfish differed among fishing vessel types; larger vessels had a higher probability of catching blue marlin, the more oceanic-oriented species, and lower probabilities of catching the more coastal-oriented species white marlin and sailfish. Standardized catch rates indicated in general a lower relative abundance for blue and white marlin in the most recent years, although estimated confidence intervals overlap through the years especially for white marlin.     

Application of a habitat-based model to estimate effective longline fishing effort and relative abundance of Pacific bigeye tuna (Thunnus obesus)

A new habitat‐based model is developed to improve estimates of relative abundance of Pacific bigeye tuna (Thunnus obesus). The model provides estimates of `effective' longline effort and therefore better estimates of catch‐per‐unit‐of‐effort (CPUE) by incorporating information on the variation in longline fishing depth and depth of bigeye tuna preferred habitat. The essential elements in the model are: (1) estimation of the depth distribution of the longline gear, using information on gear configuration and ocean currents; (2) estimation of the depth distribution of bigeye tuna, based on habitat preference and oceanographic data; (3) estimation of effective longline effort, using fine‐scale Japanese longline fishery data; and (4) aggregation of catch and effective effort over appropriate spatial zones to produce revised time series of CPUE. Model results indicate that effective effort has increased in both the western and central Pacific Ocean (WCPO) and eastern Pacific Ocean (EPO). In the WCPO, effective effort increased by 43% from the late 1960s to the late 1980s due primarily to the increased effectiveness of effort (deeper longline sets) rather than to increased nominal effort. Over the same period, effective effort increased 250% in the EPO due primarily to increased nominal effort. Nominal and standardized CPUE indices in the EPO show similar trends – a decline during the 1960s, a period of stability in the 1970s, high values during 1985–1986 and a decline thereafter. In the WCPO, nominal CPUE is stable over the time‐series; however, standardized CPUE has declined by ～50%. If estimates of standardized CPUE accurately reflect relative abundance, then we have documented substantial reductions of bigeye tuna abundance for some regions in the Pacific Ocean. A decline in standardized CPUE in the subtropical gyres concurrent with stability in equatorial areas may represent a contraction in the range of the population resulting from a decline in population abundance. The sensitivity of the results to the habitat (temperature and oxygen) assumptions was tested using Monte Carlo simulations.     

南海及临近海域黄鳍金枪鱼渔场时空分布与海表温度的关系

为得到南海及临近海域黄鳍金枪鱼(Thunnus albacores)渔场最适宜栖息海表温度(SST)范围,基于美国国家海洋大气局(NOAA)气候预测中心月平均海表温度(SST)资料,结合中西太平洋渔业委员会(WCPFC)发布的南海及临近海域金枪鱼延绳钓渔业数据,绘制了月平均SST和月平均单位捕捞努力量渔获量(CPUE)的空间叠加图,用于分析南海及临近海域黄鳍金枪鱼渔场CPUE时空分布和SST的关系。结果表明,南海及临近海域黄鳍金枪鱼CPUE在16℃~31℃均有分布。在春季和夏季(3~8月),位于10°~20°N的大部分渔区CPUE较高,其南北侧CPUE较低;而到了秋季和冬季(9月到次年2月),高产渔场区域会向南拓宽。CPUE在各SST区间的散点图呈现出明显的负偏态分布,高CPUE主要集中在26℃~30℃,最高值出现在29℃附近;在22℃~26℃范围内CPUE散点分布较为零散,但在这个范围也会出现相当数量的高CPUE;在22℃以下的CPUE几乎属于低CPUE和零CPUE;零CPUE的平均SST为26.7℃(±3.2℃),低CPUE的平均SST为27.8℃(±2.1℃),高CPUE的平均SST为28.4℃(±1.5℃),高CPUE在各SST区间的分布要比零CPUE和低CPUE更为集中。采用频次分析和经验累积分布函数计算其最适SST范围,得到南海及临近海域黄鳍金枪鱼最适SST为26.9℃~29.4℃。本研究初步得到南海及临近海域黄鳍金枪鱼中心渔场时空分布特征及SST适宜分布区间,可为开展南海及临近海域金枪鱼渔情预报工作提供理论依据和参考。     

Global marine yield halved as fishing intensity redoubles

There is widespread concern and debate about the state of global marine resources and the ecosystems supporting them, notably global fisheries, as catches now generally stagnate or decline. Many fisheries are not assessed by standard stock assessment methods including many in the world's most biodiverse areas. Though simpler methods using widely available catch data are available, these are often discounted largely because data on fishing effort that contributed to the changes in catches are mostly not considered. We analyse spatial and temporal patterns of global fishing effort and its relationship with catch to assess the status of the world's fisheries. The study reveals that fleets now fish all of the world's oceans and have increased in power by an average of 10‐fold (25‐fold for Asia) since the 1950s. Significantly, for the equivalent fishing power expended, landings from global fisheries are now half what they were a half‐century ago, indicating profound changes to supporting marine environments. This study provides another dimension to understand the global status of fisheries.     

Catch rate standardization for yellowfin tuna (Thunnus albacares) in Taiwan's distant-water longline fishery in the Western and Central Pacific Ocean,with consideration of target change

Catch per unit of effort (CPUE) needs to be standardized to remove the effects of factors such as fishing time and location, before it can be used as an index of abundance in fish stock assessments. One of the most substantial effects arises from a change of target species. This is particularly important for the Taiwanese distant-water longline fishery, which has a long history of fishery data from two fleets that target various tuna species across three oceans. We review the development of the Taiwanese distant-water longline fishery and compare five designs for standardizing the catch rate of yellowfin tuna (Thunnus albacares) in the western and central Pacific Ocean, using generalized linear models with lognormal and delta-lognormal error assumptions. Two approaches to address targeting effects were tested: separating fishing fleet data based on observer records, and including four target indicators calculated from catch data. Four statistical regions (relating to major fishing grounds) were treated as a single factor in the first three cases and were treated separately for the last two (one independent run for each region). The last case, which involved independent analyses for each fishing fleet for each region, and using the delta-lognormal approach, was considered to provide the most informative standardized CPUE trends for yellowfin tuna.     

Reducing sea turtle by-catch in pelagic longline fisheries

Reducing by‐catch of sea turtles in pelagic longline fisheries, in concert with activities to reduce other anthropogenic sources of mortality, may contribute to the recovery of marine turtle populations. Here, we review research on strategies to reduce sea turtle by‐catch. Due to the state of management regimes in most longline fisheries, strategies to reduce turtle interactions must not only be effective but also must be commercially viable. Because most research has been initiated only recently, many results are not yet peer‐reviewed, published or readily accessible. Moreover, most experiments have small sample sizes and have been conducted over only a few seasons in a small number of fisheries; many study designs preclude drawing conclusions about the independent effect of single factors on turtle by‐catch and target catch rates; and few studies consider effects on other by‐catch species. In the US North Atlantic longline swordfish fishery, 4.9‐cm wide circle hooks with fish bait significantly reduced sea turtle by‐catch rates and the proportion of hard‐shell turtles that swallowed hooks vs. being hooked in the mouth compared to 4.0‐cm wide J hooks with squid bait without compromising commercial viability for some target species. But these large circle hooks might not be effective or economically viable in other longline fisheries. The effectiveness and commercial viability of a turtle avoidance strategy may be fishery‐specific, depending on the size and species of turtles and target fish and other differences between fleets. Testing of turtle avoidance methods in individual fleets may therefore be necessary. It is a priority to conduct trials in longline fleets that set gear shallow, those overlapping the most threatened turtle populations and fleets overlapping high densities of turtles such as those fishing near breeding colonies. In addition to trials using large 4.9‐cm wide circle hooks in place of smaller J and Japan tuna hooks, other fishing strategies are under assessment. These include: (i) using small circle hooks (≤ 4.6‐cm narrowest width) in place of smaller J and Japan tuna hooks; (ii) setting gear below turtle‐abundant depths; (iii) single hooking fish bait vs. multiple hook threading; (iv) reducing gear soak time and retrieval during daytime; and (v) avoiding by‐catch hotspots through fleet communication programmes and area and seasonal closures.     

基于贝叶斯概率的印度洋大眼金枪鱼渔场预报

本文采用贝叶斯概率为模型基础框架,利用来自印度洋金枪鱼管理委员会(IOTC)的大眼金枪鱼延绳钓历史渔获统计数据和美国国家海洋大气管理局(NOAA)的海温最优插值再分析数据,进行适用于印度洋金枪鱼延绳钓渔场的模型参数估算与预报模型构建。模型回报精度验证结果表明,印度洋大眼金枪鱼延绳钓渔场综合预报的准确率达到了65.96%。模型预报结果用概率百分比来表示,符合渔业资源分布的客观特点。利用中分辨率成像光谱仪MODIS提供的SST产品进行业务化运行的渔场预报,利用模型结果每周生成印度洋大眼金枪鱼延绳钓渔场概率预报图,用不同大小的圆形来表示渔场概率的高低,可以为印度洋区域的远洋渔业生产提供信息支持。     

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。     

大眼金枪鱼渔场与环境关系的研究进展

大眼金枪鱼是金枪鱼远洋渔业的主要捕捞对象。本文从大眼金枪鱼适宜环境因子、大眼金枪鱼渔场变动、资源丰度及其与环境因子间关系的研究方法等几方面总结了大眼金枪鱼渔场与环境关系的研究进展。大眼金枪鱼种群资源丰度的指标主要是CPUE和标准化后的CPUE,CPUE标准化的方法主要是GLM模型和GLM/HBM模型;目前,分析大眼金枪鱼资源变化与环境间关系的研究方法主要有聚类分析法、G IS软件定性分析法和栖息地指数模型。其中,聚类分析适用于研究大眼金枪鱼的渔场变动,包括系统聚类分析法、动态聚类分析法和灰色星座分析法,利用G IS软件定性分析适用于分析单个环境因子对渔场产生的影响;而栖息地指数模型能综合多个环境因子,分析它们共同对渔场产生的影响。