Estimation of seasonal spawning ground locations and ambient sea surface temperatures for eggs and larvae of Pacific saury (Cololabis saira) in the western North Pacific

Since there have been practically no surveys of the eggs of Pacific saury (Cololabis saira) in the western North Pacific (WNP), its spawning ground (SG) distribution has been poorly resolved, based mainly on the larval distribution. This means of estimating SG distribution is imprecise because saury eggs drift for more than a week before they hatch, in a region with intense western boundary currents and their extensions. To improve our understanding of the immature saury, a large number of larvae (body length <25 mm) collected in the WNP during 1993–96 were numerically backtracked to take into account the advection by geostrophic and wind‐forced Ekman currents, and the SG locations and ambient sea surface temperatures (SSTs) for the eggs and larvae on the backtracking trajectories were estimated. The resulting seasonal distributions of SGs indicated that both the locations and the intensities of spawning change from season to season. Moreover, the ambient SSTs for eggs just after fertilization ranged from a high of around 21.5°C in early autumn (September to October) to a low of around 15.0°C in late spring (May to June) with an intermediate of around 20.0°C in winter (January to February). The ambient SSTs showed seasonally different gradients while the individuals developed from eggs to early larvae: the SSTs decreased throughout the autumn (September to December), stayed rather constant in winter (January to February), and increased throughout the spring (March to June). The ambient SSTs for the early larvae were at around 19.0°C in autumn and winter (September to February) and around 16.5°C throughout the spring (March to June).     

Energetics of reproduction and spawning migration for Pacific saury (Cololabis saira)

Pacific saury is a small (up to 35 cm SL) pelagic fish, feeding actively in the food-rich sub-arctic water (10–12 °C) during summer (June – August) and spawning in the food-poor sub-tropical water (20–22 °C) during winter (December – April). Changes in protein, lipid, and energy content for 30 cm standardized fish from August to February were examined. Accumulated body reserves were abundant in the feeding season, most of them being expended during southward migration, and almost exhausted in the active spawning season. Thus it is indicated that most of the allocated energy to egg production was derived from the energy of food consumed during spawning season.     

Oceanographic factors affecting interannual recruitment variability of Pacific saury (Cololabis saira) in the central and western North Pacific

Pacific saury (Cololabis saira) has a short life span of 2 years and tends to exhibit marked population fluctuations. To examine the importance of sea surface temperature (SST) and mixed layer depth (MLD) as oceanographic factors for interannual variability of saury recruitment in early life history, we analyzed the relationship between abundance index (survey CPUE (catch per unit of effort)) of age‐1 fish and the oceanographic factors in the spawning and nursery grounds of the previous year when they were born, for the period of 1979–2006, in the central and western North Pacific. Applying the mixture of two linear regression models, the variability in the survey CPUE was positively correlated with previous year's winter SST in the Kuroshio Recirculation region (KR) throughout the survey period except 1994–2002. In contrast, the survey CPUE was positively correlated with the previous year's spring MLD (a proxy of spring chlorophyll a (Chl‐a) concentration) in the Kuroshio‐Oyashio Transition and Kuroshio Extension (TKE) during 1994–2002. This period is characterized by unusually deep spring MLD during 1994–1997 and anomalous climate conditions during 1998–2002. We suggest that saury recruitment variability was generally driven by the winter SST in the KR (winter spawning/nursery ground), or by the spring Chl‐a concentration (a proxy of prey for saury larvae) in the TKE (spring spawning/nursery ground). These oceanographic factors could be potentially useful to predict abundance trends of age‐1 saury in the future if the conditions leading to the switch between SST and MLD as the key input variable are elucidated further.     

Occurrence and density of Pacific saury Cololabis saira larvae and juveniles in relation to environmental factors during the winter spawning season in the Kuroshio Current system

The occurrence and density of Pacific saury Cololabis saira larvae and juveniles were examined in relation to environmental factors during the winter spawning season in the Kuroshio Current system, based on samples from extensive surveys off the Pacific coast of Japan in 2003–2012. Dense distributions of larvae and juveniles were observed in areas around and on the offshore side of the Kuroshio axis except during a large Kuroshio meander year (2005). The relationships of larval and juvenile occurrence and density given the occurrence to sea surface temperature (SST), salinity (SSS), and chlorophyll‐a concentration (CHL) were examined by generalized additive models for 10‐mm size classes up to 40 mm. In general, the optimal SST for larval and juvenile occurrence and density given the occurrence was consistently observed at 19–20°C. The patterns were more complex for SSS, but a peak in occurrence was observed at 34.75–34.80. In contrast, there were negative relationships of occurrence and density given the occurrence to CHL. These patterns tended to be consistent among different size classes, although the patterns differed for the smallest size class depending on environmental factors. Synthetically, the window for spawning and larval and juvenile occurrence and density seems to be largely determined by physical factors, in particular temperature. The environmental conditions which larvae and juveniles encounter would be maintained while they are transported. The survival success under the physically favorable but food‐poor conditions of the Kuroshio Current system could be key to their recruitment success.     

北太平洋柔鱼资源与渔场的时空分析

利用相关系数和灰色关联评价方法对1995-2001年北太平洋各海域鱿钓产量及其作业渔场进行时空分析,结果表明主要作业渔场分布在145°E～148°E、153°E～161°E海域,其产量约占各年总渔获量的70%～85%。从作业纬度来看,1999年以前主要产量集中在40°N～43°N海域,而2000和2001年则分布在39°N～41°N和43°N～45°N海域。相关系数分析表明,2000和2001年作业渔场和各海域产量比重均发生了较大的变化,特别是在160°E以西和170°E以东海域,而在1999年以前未发生较大变化。灰色关联评价表明,1998年北太平洋柔鱼资源状况为最好,而2000、2001和1996年较差,1999、1995和1997年处在中间水平。这与实际生产情况和海洋环境条件基本上是相符的。2000和2001年北太平洋资源状况下降,可能与150°E～160°E海域的柔鱼种群资源出现下降有关。     

中西太平洋鲣鱼丰度的时空分布及其与表温的关系

中西太平洋是全球金枪鱼围网的主要海域,鲣鱼(Katsuwonus pelamis)是金枪鱼围网的主要作业对象。本研究利用1983～2007年中西太平洋金枪鱼围网渔获物数据,结合海洋表层温度(SST)数据,分析中西太平洋鲣鱼资源丰度在时间序列和空间位置上的分布规律。研究表明,1983～2002年,各年平均CPUE在时间序列上呈一定的上升趋势,1983～2002年,平均SST在一定范围内上下波动,平均CPUE和平均SST无显著相关性;2003～2007年,平均CPUE和平均SST均呈较大幅度上升,两者呈显著相关。从空间位置分析,鲣鱼资源量集中出现在SST为28～30℃之间的海域,在5°N和10°S附近海域CPUE反映的总体资源量较高,而在0°和5°S的资源量较低。鲣鱼资源量较大区域分布在冷暖水团交汇处。     

不同时空尺度下近海日本鲭栖息地模型比较

根据2003—2011年7—9月近海日本鲭生产数据,结合海洋遥感获取的海表温度(SST)和海面高度数据(SSH),利用作业网次与SST和SSH的关系建立适应性指数(SI)模型,将时间和空间尺度分别划分为3个级别,建立9个不同时空尺度下的栖息地适应性指数(HSI)模型,分析比较得出不同时空尺度下近海日本鲭栖息地适应性指数最优模型,并对最优模型进行验证。结果显示,时间尺度为月,空间尺度为1°×1°是建立近海日本鲭栖息地适应性指数模型的最适时空尺度,当HSI大于0.6时,作业网次比重为75.42%,当HSI小于0.4时,作业网次比重为12.93%。利用2012年7—9月生产数据对最优模型进行验证,结果显示,当HSI大于0.6时,作业网次比重为60.89%,当HSI小于0.4时,作业网次比重为13.88%。研究表明,在建立鱼类栖息地适应性指数模型时,需要考虑海洋环境因子的时空尺度,以便更好地预测中心渔场。     

A new system for the culture and stock enhancement of sea cucumber,Apostichopus japonicus (Selenka),in cofferdams

A new multilayer, plate‐type system for the culture and stock enhancement of sea cucumbers in cofferdam was developed. To optimize and evaluate the system, four experimental designs were implemented using polyethylene (PE)‐corrugated sheets of various colours, interval spacing and shapes/styles. Results showed that a system equipped with black PE‐corrugated sheets attracted more animals than either blue, green, transparent or a selection of mixed sheets (six transparent sheets in the upper layer and five black sheets in the lower layer) (P<0.05). Also, more animals gathered in the system with oblique‐angled sheets (30° to the base plate) than either a wavy (the bottom and every second sheet was at an angle of 10° to the base plate) or parallel arrangement (P<0.05), and more animals assembled in the system with 2 cm between sheets than spacings of 3, 4 or 5 cm (P<0.05). As expected, the upper layers of the systems attracted more animals than lower layers in most cases except for those with transparent and mixed oblique‐angled sheets with a 3 cm spacing (P<0.05). Thus, a system with black, oblique‐angled‐corrugated sheets and 2 cm spacing is recommended for Apostichopus japonicus (Selenka) culture and stock enhancement in cofferdams or ponds.     

中西太平洋金枪鱼围网黄鳍金枪鱼产量的时空分布及与表温的关系

黄鳍金枪鱼是中西太平洋金枪鱼围网渔业中的重要捕捞种类之一。本文根据2003年中西太平洋金枪鱼围网生产统计及其表温数据,利用频次统计分析和地理信息软件Marine Explorer 4.0对黄鳍金枪鱼产量和单位日产量(CPUE)的时空分布进行分析,探讨其与海水表温的关系。结果显示,产量和CPUE最高的是2月份,其次是9月份,5月份为最低。高产量的范围为140~160°E、0°~5°S;CPUE高值区分布在130°E、0°~15°S,140°~160°E、0°~15°S和175°W、0°~15°S;产量经纬度重心分别为150°30′E和3°48′S。产量主要分布在海表温为28~31℃的海域,产量比重高达95.45%,其中29~30℃产量为最高,占69.54%。     

Environmental and spatial effects on the distribution of blue marlin (Makaira nigricans) as inferred from data for longline fisheries in the Pacific Ocean

Blue marlin is distributed throughout tropical and temperate waters in the Pacific Ocean. However, the preference of this species for particular habitats may impact its vulnerability to being caught. The relationship between spatio‐temporal patterns of blue marlin abundance and environmental factors is examined using generalized additive models fitted to catch and effort data from longline fisheries. The presence of blue marlin, and the catch rate given presence, are modeled separately. Latitude, longitude, and sea‐surface temperature explain the greatest proportion of the deviance. Spatial distributions of relative density of blue marlin, based on combining the probability of presence and relative density given presence, indicate that there is seasonal variation in the distribution of blue marlin, and that the highest densities occur in the tropics. Seasonal patterns in the relative density of blue marlin appear to be related to shifts in SST. The distribution and relative abundance of blue marlin are sufficiently heterogeneous in space and time that the results of analyses of catch and effort data to identify ‘hotspots’ could be used as the basis for time‐area management to reduce the amount of blue marlin bycaught in longline fisheries.     

Cluster analysis of linear model coefficients under contiguity constraints for identifying spatial and temporal fishing effort patterns

For fisheries management purposes, it is essential to take into account spatial and seasonal characteristics of fishing activities to allow a reliable assessment of fishing impact on resource. This paper presents a novel technique for describing spatial and temporal patterns in fishing effort. The spatial and seasonal fishing activity patterns of the French trawler fleet in the Celtic Sea during the period 1991–1998 were analysed by modelling fishing effort (fishing time) with generalised linear models. The linear model for fishing effort included fixed effects for both spatial (statistical rectangles) and temporal units (months). In addition, spatial correlations in any given month were modelled by an exponentially decreasing function. Temporal correlations were included using the previous month's fishing effort for a given spatial unit as predictor. A method based on cluster analysis of estimated model coefficients of spatial or temporal fixed effects is proposed for identifying groups of similar spatial and temporal units. A contiguity constraint is imposed in the clustering algorithm, ensuring that only neighbouring spatial units or consecutive temporal units are grouped. The cluster analysis identified 22 spatial and 9 temporal groups. Winter and spring months stood out as being more variable than the remaining months. Spatial groups were of varying size, and generally larger offshore. The proposed method is generic and could for example be used to analyse temporal and spatial patterns in catch or catch rate data.