Effects of changing temperature on benthic marine life in Britain and Ireland

• 1. The coastal waters surrounding Britain and Ireland became warmer during the 20th century and, according to the UK Climate Impact Programme 2002 scenarios of change and other sources, average annual seawater temperatures may rise a further 2°C or more by the 2050s. This warming is part of a global rise in sea‐ and air‐surface temperatures that will cause changes in the distribution and abundance of species.
• 2. Initially, there will not be a wholesale movement northwards of southern species or retreat northwards of northern species, because many additional factors will influence the responses of the different organisms. Such factors include the hydrodynamic characteristics of water masses, the presence of hydrographical and geographical barriers to spread and the life history characteristics (reproductive mode, dispersal capability and longevity) of species. Survey data over the past century show how organisms react to changes of the order of 0.5°C, and in the last two decades, when sea temperatures have risen by as much as 1°C, there have been significant local changes in the distribution of intertidal organisms. These past changes provide a clue to more extensive changes expected in the future if global warming develops as predicted.
• 3. Where species affected by climate change are dominant or key structural or functional species in biotopes, there may be a change in the extent and distribution of those biotopes. Some, dominated by predominantly northern species such as the horse mussel Modiolus modiolus, may decline and reduce their value as rich habitats for marine life. Others, characterized by southern species, for example the sea fan Eunicella verrucosa and the alcyonacean Alcyonium glomeratum, may increase in extent.
• 4. Using information on the life history characteristics of species, their present distribution and other factors, a key supported by a decision tree has been constructed to identify ‘types’ of organism according to their likely response to temperature rise. Conspicuous and easily identified rocky substratum species are good candidates to track change. Using the key, many species are shown as likely to increase their range northwards significantly. In contrast, fewer will decline in abundance and extent in the north. If, as anticipated, global warming continues, then species with distributions already accurately mapped, or being mapped at present, will provide baseline data to test forecasts.

Copyright © 2004 John Wiley & Sons, Ltd.     

大西洋海域大眼金枪鱼年龄与生长的初步研究

根据2001年6~10月在大西洋海域金枪鱼延绳钓渔业中采集的89 ind大眼金枪鱼样本,对其叉长、体重进行测定,并以脊椎骨作为年龄鉴定材料。结果表明,叉长组成为85~186 cm,体重组成为11.5~132.5kg,年龄为2~6龄。体重与叉长关系式为W=4.5026×10-5×FL2.8200。利用一般Von Bertalanffy生长方程来拟合,叉长和体重生长方程为:FL=257.90×(1-e-0.1960(t+3.7919))2.5933,Wt=284.28×[(1-e-0.1960(t+3.7919))2.5933]2.8200。叉长和体重的生长拐点分别为1.07龄和5.75龄。     

印度洋东部黄鳍金枪鱼的渔业生物学

根据在印度洋东部海域的延绳钓生产实践 ,对所渔获的黄鳍金枪鱼 (Thunnus albacares)群体的构成、繁殖等基本生物学特征进行了初步探讨。结果显示 ,渔获黄鳍金枪鱼纯重范围为 10～75 kg,叉长范围为 80～ 15 0 cm,纯重、叉长关系为 W=1.5 2 7× 10 -5L3,渔获构成月间变化明显 ,不同年龄个体性成熟与繁殖节律差异显著     

Climatic oscillations and tuna catch rates in the Indian Ocean: a wavelet approach to time series analysis

We analysed the influence of climatic oscillations [based on the Indian Oscillation Index (IOI)] on monthly catch rates of two tropical tuna species in the equatorial Indian Ocean. We carried out wavelet analysis, an efficient method of time series analysis to study non‐stationary data. Catch per unit of effort (CPUE) of bigeye tuna was computed from Japanese longline statistics from 1955 to 2002 in the equatorial Indian Ocean and CPUE of yellowfin tuna was derived from industrial purse seine statistics from 1984 to 2003 in the Western Indian Ocean. Wavelet analyses allowed us to quantify both the pattern of variability in the time series and non‐stationary associations between tuna and climatic signals. Phase analyses were carried out to investigate dependency between the two signals. We reported strong associations between tuna and climate series for the 4‐ and 5‐yr periodic modes, i.e. the periodic band of the El Niño Southern Oscillation signal propagation in the Indian Ocean. These associations were non‐stationary, evidenced from 1970 to 1990 for bigeye, and from 1984 to 1991 and then from 1993 to 2001 for yellowfin. Warm episodes (low negative IOI values) matched increases of longline catch rates of bigeye during the 1970–1990 time frame, whereas the strong 1997–1998 warm event matched a decrease of purse seine catch rates of yellowfin. We discussed these results in terms of changes in catchability for purse seine and longline.     

西北太平洋柔鱼渔情速报系统的开发

The neon flying squid, Ommastrephes bartrami, is one of the most important jig fisheries in the northwest Pacific Ocean. In order to understand the movement of O. bartrami fishing-ground better and supply O. bartrami fishing-ground information for Chinese fishing boats in the northwest Pacific ocean, the fishing condition analysis and forecasting system of O. bartrami was developed successfully. The system was based on established comprehensive database, which included the catch data of O. bartrami (total yields, count of total fishing boats, fishing position etc. ) and oceanic environmental information (SST, SST gradient etc. ). Artificial intelligent technology about case-based reasoning was also combined with GIS component technology successfully in the system. The process and function of system establishment are composed of four parts: setting up of case database for central fishing-ground and its environmental factors, knowledge reasoning of fishery information, GIS visualization analyzing as well as trend forecasting of central fishing-ground and information production mapping. At last as an example of the results, an experimental central fishing-ground forecasting of O. bartrami from 9 to 15 in July 2002 in the northwest Pacific Ocean was given in the paper. The results showed that through three class similar searching forecasting central fishing-ground would move west, and indicating that forecasting of the system for O. bartrami central fishing-ground was correct by comparing to real fishing-ground from 16 to 22 in July 2002. Consequently, artificial intelligent expert system technology about case-based reasoning is a useful method for fishing condition and fishing-ground forecasting.     

基于海洋遥感的西北印度洋鸢乌贼渔场形成机制的初步分析

2003年9-11月中国鱿钓船对西北印度洋海域鸢乌贼渔场进行生产性探捕调查，发现在16°N、61°E附近海域存在中心渔场，平均日产达5 t以上，但渔场10 d后迅速消失。本文结合本次调查生产数据和卫星遥感资料对这一渔场进行了分析。渔场形成内在动力在于上升流的存在，它使深海缺氧、营养丰富的海水上涌到表层，上升流影响区域SST低，表层藻类 的繁盛，海表面叶绿素a（Chl-a）浓度值高。溶解氧缺乏，驱使鸢乌贼朝溶解氧比较丰富的区域即向SST高值和Chl-a低值的区域聚集，但鸢乌贼也有朝食物丰富区域即Chl-a高和SST低值区域觅食习性。鱼群易集中在SST梯度较大且Chl-a梯度较大的狭长区域。同时，当该海域附近上升流发生后，低压扰动有利于较大的SST梯度及Chl－a梯度的出现和维持，渔场海洋环境复杂，鱼群迅速聚集，形成中心渔场；而当低压扰动消失，风速增大，风向单一，渔场海洋环境单一均匀，鱼群会很快分散，中心渔场消失。     

Naturally occurring bacteraemia in American lobsters, Homarus americanus Milne-Edwards, in Long Island Sound

The health status of the American lobster, Homarus americanus Milne-Edwards, in Long Island Sound (LIS) has been in decline, with seasonal mortality events occurring since 1998. In order to assess the potential effects of environmental conditions on lobster health via haemolymph analysis, lobsters collected from various sites in LIS were examined and sampled while concurrent environmental data (water temperature, salinity and dissolved oxygen) were recorded. The pH of the haemolymph of each lobster was tested, followed by a collection of haemolymph for serum biochemistry analysis and bacterial culture. This report focuses on the results of the bacterial sampling. The majority of bacteria cultured were opportunistic pathogens commonly found in the environment, including some that are associated with sewage and pollution. The prevalence of bacteraemia was correlated with the site of collection, the month in which the lobsters were sampled, and water temperature.     

应用栖息地指数对印度洋大眼金枪鱼分布模式研究

运用分位数回归方法对温度、温差、氧差与印度洋大眼金枪鱼延绳钓钓获率进行二次回归分析,找出最佳上界方程,以最佳上界方程拟合的数值来建立栖息地指数(HSI)模型,从而揭示印度洋大眼金枪鱼栖息地的分布模式。研究表明,温度、温差、氧差与印度洋大眼金枪鱼延绳钓钓获率的最佳上界分位数回归方程分别为HR_(T0.9)=-44.803 7.685T_(0.9)-0.255T_(0.9)~2,HR_(dT_(0.9))=6.234 0.953dT_(0.9)-0.026dT_(0.9)~2和HR_(dO_(0.88))=7.422 4.25dO_(0.88)-0.727dO_(0.88)~2。10°N～10°S间印度洋海域大眼金抢鱼HSI指数达到0.9以上;10°N以北的波斯湾及10°S～15°S海域的HSI指数为0.8～0.9;15°S～40°S之间海域HSI指数介于0.7～0.8,其中50°E～90°E、15°S～25°S间存在一片季节性HSI指数<0.7的区域;40°S以南的海域HSI指数<0.6。     

Considerations of stock structure of yellowfin tuna (Thunnus albacares) in the Indian Ocean based on fishery data

Yellowfin stock structure in the Indian Ocean was studied by using industrial tuna longline fishery data. Three types of test variables were used to detect stock structure, i.e., CPUE, age-specific CPUE, and coefficient of variation for size. Time-series data of test variables were compiled for six sub-areas that were arranged by dividing the whole region systematically along longitude lines every 20 degrees. Then time-series data were smoothed by moving averages, and regressed by simple models. Patterns of time-series trends were graphically and statistically compared to classify homogeneous sub-area groups. Two assumptions were (a) that homogeneous stocks exist longitudinally and overlap in adjacent waters, and (b) that test variables within homogeneous sub-area groups are equally affected, and hence patterns of the time-series trends are similar. After graphical screening for significant sub-area groups, analysis of covariance was applied to test homogeneity of regression parameters representing patterns of the time-series trends. By classifying homogeneous sub-area groups, stock structures were determined at the P <0.05 and P <0.50 levels. The P<0.50 level was recognized as a useful criterion for ‘weak’ test variables since masked or vague structures at the P <0.05 level were likely cleared at this level in many cases. Results of this study and past stock structure studies were reviewed and compared. It was concluded that there are two major and two minor stocks of yellowfin tuna. The two major stocks (the western and the eastern) are located at 40^o-90^oE and 70^o-130^oE respectively. The minor stocks are the far western and the far eastern stocks (the latter possibly being a part of the Pacific stock), which are located westward of 40^oE and eastward of 110^oE respectively. Neighboring stocks are intermingled in adjacent waters.