Validation of the accuracy of household reporting of subsistence fishing catch and effort: a Fijian case study

Abstract  The accuracy of household reporting of subsistence fishing catch and effort and seafood consumption on the Fijian island of Ono-i-Lau was studied. A creel survey was carried out concurrently to validate the household survey data. Reported estimates of fishing participation, effort, and fish consumption were not significantly different to the creel survey estimates. Householder's appeared to overestimate the number of fish from the most abundant family Lethrinidae, and underestimate rarer fish. However, there was no significant difference between the reported and observed contribution of the majority of finfish families and invertebrate taxa. The reported and observed catch rate estimates were not significantly different, indicating that Ono-i-Lau villagers are relatively accurate in their estimation of the number and size of fish from recalled catches.     

长江口中华绒螯蟹(Eriocheirsinensis)蟹苗汛期预报的研究

报导了对长江口中华绒螯蟹 (河蟹 ,Eriocheirsinensis)蟹苗汛期预报的研究结果。蟹苗汛期决定于河蟹胚胎及溞状幼体发育的生物学因素、水温条件和潮汐?そ诘暮有芳性?2月底 3月初交配抱卵 ,5月初孵出溞状幼体。 1970～ 1981年的资料表明 ,从 3月 1日河蟹开始交配抱卵至大眼幼体 (蟹苗 )回归淡水 ,约需 12 77度日。按这一标准估算 ,在± 2d的变差下 ,12年中有 11年的发汛起始日在估算范围内 ,而若允许将变差时间提高到± 3d ,则 12年中的发汛起始日全部在预测范围内。长江口的大眼幼体要入江 ,必须借助潮汐的动力。而在潮汐半月周期的潮位曲线上 ,只有从起汛期至大汛期 (农历廿七到初三 ;十二到十八 ) ,潮位逐日上升 ,蟹苗才有可能从河口浅海进入江段。综合考虑度日和潮汐两个因素 ,即可预报蟹苗发汛的起始日     

智利外海渔场竹筴鱼资源分布特征

根据在智利 2 0 0海里专属经济区外海的渔场周年探捕调查 ,对智利竹鱼 (Trachurusmur phyi)单位努力量渔获量 (CPUE)的构成和季节变化及其资源分布特征进行了初步探讨。结果显示 ,竹鱼在智利外海分布广 ,30°～ 43°S ,78°～ 87°W海区均可形成拖网作业渔场。南半球冬季竹鱼密集分布区较偏南 (38°～43°S) ,8月密集分布区向北偏移至 35°～ 40°S,春季鱼群继续向北洄游至 30°～35°S ,并开始分散索饵 ,集群性较差 ,到翌年秋季再集群向南洄游 ,在 38°～ 43°S ,78°～ 85°W形成越冬场。CPUE以冬季最高 ,春、秋季次之 ,夏季最低。冬季以 6月份平均CPUE最高 ,达 1 5 .1 8t/h ,夏季以 3月份平均CPUE最低 ,仅 1 .1 2t/h。     

长江江苏江段刀鲚的研究进展(英文)

刀鲚为洄游性鱼类,每年2～3月成熟的个体,成群由海入江,沿江而上进行生殖洄游。长江是我国第一大河,地理位置优越,流域面积辽阔,具有饵料丰富、生物种类繁多等得天独厚的条件,构成了渔业资源赖以生长、繁衍、生息、所需的饵料、温度、空间等优良自然水域生态环境。笔者以长江江苏江段刀鲚捕捞日志的相关数据为研究对象,采用数据库、统计软件和GIS软件科学形象的表达长江刀鲚江苏江段的分析结果。结果表明:2008年到2011年,江苏江段的刀鲚捕捞船只数和作业天数基本稳定,而单船捕捞量和经济收益波动范围比较大,尤其2010年单船捕捞量和经济收益有了大幅度提升。本研究旨在了解长江刀鲚的资源变动、产量变化、生物学概况等,为长江刀鲚的合理捕捞及管理提供科学依据。     

福建省休闲渔业暨“水乡渔村”品牌建设浅析

21世纪以来,中国休闲渔业发展迅速、效益良好、潜力巨大,是我国旅游业发展的一种新态势,传统渔业发展出现新契机。福建位于海峡西岸经济区,是我国东南海洋大省,更是渔业大省,发展休闲渔业条件十分优越。休闲渔业样板"水乡渔村"将与"森林人家"、"农家乐"共同打造海西农业休闲旅游的三大品牌。从福建省休闲渔业现状出发,对水乡渔村品牌发展进行分析,提出保障措施。     

塔里木河流域淡水养殖业可持续发展思考

淡水养殖业发展一直很稳定,其在渔业经济中的地位不可取代,但随着社会经济的发展,养殖环境和条件也随之发生变化,出现了许多妨碍发展的新情况、新问题。文章就塔里木河流域淡水养殖业的发展状况与存在问题,从加强养殖环境整治和养殖容量研究、实施良种工程和科技兴渔战略、建设病害防治体系、严格规范养殖行为等方面,提出几点有针对性的对策。     

南海不同海域鸢乌贼生长与死亡参数比较

根据2011-2015年南海海域灯光罩网渔船捕获的鸢乌贼样品,利用Fisat II软件对南海鸢乌贼群体的生长死亡系数进行估算。结果显示：南海没有大型群鸢乌贼个体,且雌性个体（12.05cm）比雄性个体（10.91cm）大;南海北部和南部的鸢乌贼均呈正异速生长,胴长和体质量关系b值分别为3.470~3.605和3.379~3.773;生长参数K反映了南海北部更适合于雄性鸢乌贼生长;南海南部海域鸢乌贼雌性（1.84）和雄性（1.76）总死亡系数相差不大,开发较为均衡;南海北部海域鸢乌贼雌性（0.60）总死亡系数远小于雄性（4.83）,显示该海域鸢乌贼未能充分开发。从渔业资源开发的角度,渔业政策偏向于开发南海南部鸢乌贼资源,使得北部鸢乌贼资源未能充分开发,不利于南海鸢乌贼资源的可持续利用。     

基于VMS的张网渔船捕捞努力量与网位坐标提取方法

捕捞努力量是渔业资源管理和评估领域的重要参数之一,传统捕捞努力量计算方法无法满足实时、大范围、快速统计的需要。以我国近海作业的某张网渔船为研究对象,采用BP(back propagation)神经网络模型,对张网船155在2016年和2017年北斗渔船监控系统所获取的若干连续航次的经纬度坐标、航速和航向等信息进行分析和判断,提取各航次作业的网位坐标,通过阈值筛选渔船布网位置和时间,计算放网时长,把网口迎流面积与放网时长的乘积作为网次的捕捞努力量。结合BP神经网络和阈值分析的判断结果,网位判断准确率为82%,4个航次累计捕捞时长3562.62 h,累计捕捞努力量712524(m²·h)。设计的张网渔船状态判断、确定网位、放网时长提取和捕捞努力量计算方法为张网作业分析和其捕捞强度量化提供新的研究思路。     

Catch per unit effort standardization of the eastern Bering Sea walleye pollock (Theragra chalcogramma) fleet

A general linear model (GLM) was used to standardize catch per unit effort (CPUE) data for Alaska walleye pollock (Theragra chalcogramma) from the Bering Sea fleet for the years 1995–1999. Data were stratified temporally by year and season and spatially by area using either Alaska Department of Fish and Game (ADF&G) or National Marine Fisheries Service (NMFS) reporting areas. Four factors were used: vessel identification (ID) number, vessel speed, percentage of pollock by weight in the haul (a measure of targeting), and whether most of the haul took place before or after sunset. At least 29 combinations of main effects, quadratic covariates, and interactions were tested for each year/area/season stratum. GLM models explained from 31 to 48% of the total sums of squares. Vessel identification number was included in all models and explained the most variability. Of the remaining factors, the square of the percentage of pollock in the haul was included in most models, following an F-test to determine parsimony. Analysis of the vessel identification number coefficients indicated that larger vessels tended to have higher CPUEs; and that this relationship differed between dedicated catcher vessels and offshore catcher processors. Coefficient estimates and response surfaces generally indicated increased CPUEs with the percentage of pollock in the haul and showed mixed results with vessel speed. The vessel identification number incorporated most vessel characteristics, leaving vessel speed primarily as a fitting variable with less biological meaning. The year/area/season stratification procedure was found to be necessary due to the unbalanced design, which otherwise would have factor levels with no data in a large combined model. In addition, the stratification procedure reduced the variability in CPUE substantially.     

Filtering method for obtaining stock indices by shark species from species-combined logbook data in tuna longline fisheries

ABSTRACT:   A method of filtering logbook data to obtain estimates of catch per unit effort (CPUE) for sharks has been proposed which simultaneously addresses the issues of under-reporting and the lack of species-specific catch records in historical data. Logbook catch data from vessels with high shark reporting rates are used to represent catch rates for the abundant blue shark Prionace glauca and low reporting vessels' data are used for the main commercially valuable species, the shortfin mako Isurus oxyrinchus . Logbook reporting rate filter (RRF) levels are evaluated through analytical and model-based comparisons to species-specific logbook records available since 1994 and shipboard observer data. At the high reporting rates, the ≥ 80% filter avoids large numbers of false zeros and provides the best fit to observer data for blue shark. At the low reporting rates, the ≤ 40% filter best matches the observer data for makos, but this filter level includes large numbers of false zeros and sharks of other species. The ≤ 20% filter produces a dataset that is better targeted to mako catches but considerably under-estimates CPUE relative to observer records. For these reasons, other means of estimating mako catch rates are suggested for further research.