JH渔网防污剂的研制与应用

经多年研制及应用试验，开发出一种无重金属离了的ＪＨ渔网有机防污剂，用于养殖扇贝笼的防污，安全无地海区无污染，对养殖扇贝无不育影响，延长了网具的使用寿命，提高了扇贝产量。专家鉴定认为，本研究填补了无重离子防污剂的国内空白。     

海水网箱网衣碱性硅酸盐防污涂料的初步研究

本文以碱性硅酸盐为主防污剂,开发了一种防污期长、无毒、环保且高效的海水网箱网衣防污涂料：涂有该种防污涂料的试验网衣于2002年4月开始进行浅海挂网试验．为期七个月;每月一次定期对试验网片进行附着情况检查、拍照及称重。结果证明．与对照组相比,涂有硅酸盐防污涂料的网衣材料的网片增重及被附着的面积百分比均有大幅度的降低;实拍照片也证明涂有防污涂料的网片具有极佳的防污效果：根据试验结果,讨论了影响防污涂料防污效果的诸多因素。     

荞麦粉可抑制渔网上附着的藤壶

<正> 日本三重县水产研究所长期致力于研究渔网防污剂。最近该所研究人员发现,荞麦粉是一种安全有效的渔网防污剂。渔网防污剂过去都使用有机锡化合物等,这类化学物质虽然使用方便,但含有毒性,会污染海水。该研究所因此着重于天然物质中寻找无毒性的渔网防污     

日本利用荞麦粉防止藤壶附着

日本利用荞麦粉防止藤壶附着藤壶等海洋生物在养殖网箱和船底附着，是多年来水产业难以解决的问题。据一直从事防污剂研究的日本三重县水产开发研究所的试验结果，养麦粉有防生物附着效果。他们认为，在环境保护越来越受重视的今天，研究开发安全、无毒的防污剂十分必要。...     

海洋网箱网衣无公害防污技术的初步研究

根据不同的防污机理,采用内填充及涂敷方法,制备了几种不同种类的防污网衣材料,包括添加纳米无机抗菌剂的防污网材料、低表面能防污涂料、硅酸盐防污涂料.各种试验网衣于2002年4月开始进行浅海挂网试验,按编号顺序,将各组实验网片吊挂在实验海区生产网箱之间,吊挂深度为2～3 m,各实验网片之间保持1.5 m左右的间距,地点在浙江省佛渡的网箱养殖生产水域.通过每月吊挂一块新空白网片的方法来调查不同季节附着生物的种类和附着情况;每月1次定期对实验网片进行附着情况检查、拍照及称重,并进行试验海区理化因子的测定.结果证明几种防污材料,均具有一定的海洋防污能力.其中涂有硅酸盐防污涂料的网衣材料,其防污效果最佳.还根据试验结果讨论了这3种防污技术的机理、功效及影响因素.     

中药免疫增强剂在黄鳝养殖上的应用试验初报

近几年来，大量研究资料表明，中草药具有抗微生物病原作用、免疫促进作用、营养平衡作用等多种功效，可应用于水产养殖动物以提高其生产性能和抗病能力。为了进一步了解中草药在黄鳝养殖上应用的可行性，我们用复方渔用中药免疫增强剂(Traditional Chinese Medicinal ImmunoIntensifier，下简称TCMI)在江西省南昌县蒋巷镇的网箱养鳝基地进行了不同添加量的养殖对比试验，现将试验情况报告如下：     

海水网箱网衣防污技术的研究进展

海水网箱养殖中,网衣污损附着物会影响网箱设施安全及其养殖鱼类的健康生长,因此,网衣防污是网箱养殖业备受关注的问题.本文在简述海水网箱网衣分类与特点的基础上,重点介绍了人工清除法、防污涂料法、机械清除法、金属合金网衣防污法、箱体转动防污法、生物防污法、网衣本征防污法等网衣防污技术最新研究进展;结合本课题组在网衣防污方面的...     

无公害海洋防污技术的研究进展

海洋中有2000多种污损生物,多数生活在海岸和海湾等近海海域。污损生物的幼虫和孢子能够漂浮游动,发展到一定阶段后就附着定居下来,污损海上设施,例如船体、浮标、桥墩、码头、网箱及网具等。海洋防污技术是用于防止海洋生物对海上设施的附着污损,起步于舰船防污涂料的研究,并以毒料释放型防污为主要技术途径,通过涂料中可释放的铜、锡、汞、铅等防污剂,在材料周围形成对海洋植物孢子     

拉式(鱼岁)网箱养殖试验

<正>目前，在我国北方地区，拉氏(鱼岁)已成为新兴的水产养殖品种。网箱养殖是当今世界水产养殖集约化生产的一项重要技术，具有提高鱼产品的品质、产量高、缩短养殖周期、易捕捞、病害少以及养殖成本低等优点。本文开展了网箱养殖拉氏(鱼岁)商品鱼试验，研究其生长性能和网箱养殖技术，为拉氏(鱼岁)网箱养殖技术推广提供依据。     

铜台金网衣网箱健康养殖产业联盟成立

11月6日,在第十七届中国国际渔业博览会上,由国际铜业协会携手中国水产科学研究院东海水产研究所、大连天正实业有限公司等科研机构以及国内大型海水养殖企业共同发起的“铜合金网衣网箱健康养殖产业联盟”正式宣布成立。该联盟以“大力发展高效、健康、环保的海洋养殖,提高中国海洋渔业长期产能”为宗旨,其目的在于进一步推动铜合金网衣网箱在中国海洋养殖产业中的实际应用。铜合金网衣网箱以“高效、健康、环保”为主要特性：一是更高效,铜合金网衣网箱容积保持率大,允许更高的养殖密度,且鱼类生长速度快、个体重量增加,促进了海水养殖企业的产能显著提升;二是更健康,因为铜的天然抑菌性能和抗腐蚀性,避免了海洋生物附着物在养殖网中生长,从而为鱼类养殖创造更清洁、健康的条件,同时也减少了因使用抗生素而对人类健康造成的间接危害,另外,因减少了清洁、更换渔网的次数,降低了因食肉动物的袭击或鱼类脱逃而造成的损失;三是更环保,铜合金网衣网箱100％可回收利用,低碳环保。     

Managing non‐target,data‐poor species using catch limits: lessons from the Alaskan groundfish fishery

Abstract The 2006 reauthorisation of the Magnuson‐Stevens Fishery Conservation and Management Act requires annual catch limits for all target and non‐target species within federally managed fisheries in the United States. In Alaska, both target and non‐target species in the Alaska groundfish fisheries have been managed using catch limits since the early 1990s. Non‐target species that are caught incidentally in a fishery require monitoring to ensure that the population is not negatively impacted by commercial fishing. Resource assessment scientists have been challenged with obtaining sufficient data to recommend an acceptable catch level for management of these species. This paper reviews three case studies where a catch limit is determined for non‐target species when certain data are limited: (1) varying levels of biomass and catch data for all species within a species group or complex; (2) adequate catch data but no biomass data; (3) emerging target fishery of data‐poor species, plus an example of how a complex of ecosystem component species is managed.     

Use of individual types of fishing effort in analyzing catch and effort data by use of a generalized linear model

Fishing effort is a function of many (continuous) variables which fishers can manipulate. However, when catch and fishing effort data are analysed using a generalized linear model, individual types of fishing effort usually enter as a composite quantity. But not all quantities can be combined into a composite quantity. Use of such data this way generally leads to a loss of information and incurs a model bias. In this paper, I analyse catch and effort data for the blue swimmer crab off South Australia by a direct use of individual types of fishing effort to extract a relative index of biomass, and use the concept of homogeneous functions to present some of the results. I also give formulae for choosing a combination of different types of fishing effort to effect a specified level of catch in both absolute and relative terms. Assuming that catch follows an independent gamma, normal, negative binomial, or Poisson distribution, fitting of a generalized linear model with a log-link function to the commercial catch and effort data suggests that: (1) the exploitable biomass remained relatively constant from 1 July 1983 to 30 June 1996; (2) the relative instantaneous rate of fishing mortality of a particular sex and age (if gear selectivity was constant over time) slightly increased over time; (3) a 1% increase in the number of days fished gave about 0.85% increase in catch whereas a 1% increase in the number of people on a boat led to only about a 0.45% increase in catch. This implies that use of a composite measure of fishing effort such as boat days and man days when analysing catch and effort data is inappropriate for this fishery. Although a generalized linear model may be a reasonable first-order approximation, catch and effort data are best interpreted through a process model.     

Estimating stock depletion level from patterns of catch history

The degree to which a stock is depleted is one of the most important quantities in fisheries management because it is used to quantify the success of management and to inform management responses. However, stock depletion is extremely difficult to estimate, particularly with limited data. Using the RAM Legacy database, we developed a boosted regression tree (BRT) model to correlate depletion with a range of predictors calculated from catch data, making the model usable for many fisheries worldwide. The most important predictors were found to be catch trends obtained from linear regressions of scaled catch on time, including regression coefficients for the whole catch time series, the subseries before and after the maximum catch, and in recent years. Eight predictors explain about 80% of variation in depletion. There is a correlation of .5 between measured levels of depletion and the predictions of the BRT model. Predictions are less biased when the stock is fished down below half of the carrying capacity. The BRT model outperforms comparable existing catch‐based depletion estimators and could be used to provide priors for depletion for data‐poor stock assessment methods, or used more directly to provide estimates of the probability that depletion is below a given threshold value.     

南海北部湾2012年捕捞产量估算

南海北部湾是广西、广东和海南三省区渔民的传统重要渔场之一,准确掌握北部湾捕捞产量对于搞好北部湾渔业生产管理意义重大。文章采用2012年南海捕捞信息动态采集网络的广西渔船生产数据,参考广西相关渔业调查资料,分别推算2012年广西拖网、围网、刺网、钓具、定置网和其他杂渔具在北部湾的捕捞产量,统计广西全年在北部湾的捕捞产量为39．2×10^4t,进而推算中国渔船2012年在北部湾的产量为65．7×10^4t。在此基础上,再采用相关文献中有关越南渔船在北部湾的产量,估算2012年北部湾的捕捞产量约为85．7×10^4t。     

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.     

Evaluation of observer‐ and industry‐based catch data in a recreational charter fishery

There is international recognition for greater inclusion of recreational fisheries catch data in species, fisheries and ecosystem assessments. Recreational charter fisheries provide important social services and contribute to total species catches. This study compares and validates industry logbook catch and effort data (1,357 trips) against observer data (154 trips) across six ports in a recreational charter fishery in eastern Australia. The mean numbers of clients and fishing effort (hours) per trip varied inconsistently between data sources and among ports. Logbooks did not adequately report released catches, and the mean number of species retained per trip was consistently underestimated in logbooks compared to observer data. For both data sources, catch rates of total individuals and key species displayed similar trends across different units of effort; catch per hour, client, client/hour and trip. The mean catch rates of total individuals and most key species, except those retained for bait, were similar across data sources, as were estimates of total fleet harvests. The length compositions of retained catches of some key species displayed truncation of larger organisms in the observer data whereas other species did not. Despite the shortcomings of the logbook data, future fishery and species monitoring strategies could include industry and observer data sources.     

Standardizing catch and effort data: a review of recent approaches

The primary indices of abundance for many of the world's most valuable species (e.g. tunas) and vulnerable species (e.g. sharks) are based on catch and effort data collected from commercial and recreational fishers. These indices can, however, be misleading because changes over time in catch rates can occur because of factors other than changes in abundance. Catch-effort standardization is used to attempt to remove the impact of these factors. This paper reviews the current state of the art in the methods for standardizing catch and effort data. It outlines the major estimation approaches being applied, the methods for dealing with zero observations, how to identify and select appropriate explanatory variables, and how standardized catch rate data can be used when conducting stock assessments.     

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.     

Estimation of yellowfin tuna (Thunnus albacares) habitat in waters adjacent to Australia’s East Coast: making the most of commercial catch data

The physical environment directly influences the distribution, abundance, physiology and phenology of marine species. Relating species presence to physical ocean characteristics to determine habitat associations is fundamental to the management of marine species. However, direct observation of highly mobile animals in the open ocean, such as tunas and billfish, is challenging and expensive. As a result, detailed data on habitat preferences using electronic tags have only been collected for the large iconic, valuable or endangered species. An alternative is to use commercial fishery catch data matched with historical ocean data to infer habitat associations. Using catch information from an Australian longline fishery and Bayesian hierarchical models, we investigate the influence of environmental variables on the catch distribution of yellowfin tuna (Thunnus albacares). The focus was to understand the relative importance of space, time and ocean conditions on the catch of this pelagic predator. We found that pelagic regions with elevated eddy kinetic energy, a shallow surface mixed layer and relatively high concentrations of chlorophyll a are all associated with high yellowfin tuna catch in the Tasman Sea. The time and space information incorporated in the analysis, while important, were less informative than oceanic variables in explaining catch. An inspection of model prediction errors identified clumping of errors at margins of ocean features, such as eddies and frontal features, which indicate that these models could be improved by including representations of dynamic ocean processes which affect the catch of yellowfin tuna.     

CPUE standardisation and the construction of indices of stock abundance in a spatially varying fishery using general linear models

Construction of annual indices of stock abundance based on catch and effort data remains central to many fisheries’ assessments. While the use of more advanced statistical methods has helped catch rates to be standardised against many explanatory variables, the changing spatial characteristics of most fisheries data sets provide additional challenges for constructing reliable indices of stock abundance. After reviewing the use of general linear models to construct indices of annual stock abundance, potential biases which can arise due to the unequal and changing nature of the spatial distribution of fishing effort are examined and illustrated through the analysis of simulated data. Finally, some options are suggested for modelling catch rates in unfished strata and for accounting for the uncertainties in the stock and fishery dynamics which arise in the interpretation of spatially varying catch rate data.