南海西北部蓝圆鲹时空分布及其与环境因子的关系

Spatio-temporal distribution of Decapterus maruadsi and its relationship with environmental factors in the northwestern South China Sea

关键词:  南海西北部  蓝圆鲹  广义可加模型  CPUE

Key words:  Northwestern South China Sea  Decapterus maruadsi  Generalized additive model  CPUE

摘要:

蓝圆鲹(Decapterus maruadsi)是中国东南近海重要的经济鱼类之一。本研究根据2012—2018年南海西北部捕捞产量数据和海洋环境遥感数据，分析了该海域蓝圆鲹季节平均单位捕捞努力量渔获量(catch per unit effort, CPUE)的时空分布特征，并运用广义可加模型(generalized additive model, GAM)探究了CPUE与环境因子的关系。结果显示，蓝圆鲹的CPUE具有明显的季节性：夏季最高，CPUE达0.848 kg/(kW·d)；冬季最低，CPUE为0.087 kg/(kW·d)。2016年CPUE的异常增加可能是受到2015—2016年超强厄尔尼诺事件的影响。GAM分析显示，该海域蓝圆鲹CPUE与经度、海表温度(sea surface temperature, SST)、叶绿素a (chlorophyll a, Chl-a) 浓度、海水深度、海表盐度(sea surface salinity, SSS)、涌浪波向、风浪波向及其周期显著相关。相对较高CPUE海域范围为110.5°~114°E，SST为26~30℃，Chl-a为0.2~1.0 mg/m3，海水深度<120 m，SSS为33.4~33.8，涌浪波向为75°~120°、150°~175°，风浪波向为50°~75°、120°~135°、175°~190°，风浪周期为3.0~4.5 s；其中，风浪波向对CPUE贡献最高，涌浪波向其次，然后是SST。南海西北部蓝圆鲹的资源丰度变化和其洄游特性与季风变化等引起的环境因子的变动有关。

Abstract:

The marine environment has distinct seasonal characteristics in the northwestern South China Sea, under the influence of monsoons, tides, wind stress, and coastal runoff. Decapterus maruadsi is an economically important pelagic fish along the southeast coast and is highly sensitive to the external environment. It is mainly distributed along the coast of the Beibu Gulf and in the waters of western and eastern Guangdong, with abundant resources. To understand the environmental driving mechanisms of D. maruadsi in the northwestern South China Sea, the in-situ fishery data and marine environmental remote sensing data in the northwestern South China Sea from 2012 to 2018 were used to analyze the spatio-temporal distribution of the seasonal average catch per unit effort (CPUE) of D. maruadsi using a generalized additive model. The results showed that the CPUE of D. maruadsi was related to the longitude, sea surface temperature (SST), chlorophyll a (Chl-a) concentration, depth of water, sea surface salinity (SSS), mean direction of total swell, mean direction of wind waves, and mean period of wind waves. The mean direction of wind waves contributed the most to CPUE, followed by the mean direction of the total swell and SST. D. maruadsi occurred mainly in a small area, from longitude 110.5°~114°E, SST 26~30℃, Chl-a 0.2~1.0 mg/m3, depth of water <120 m, SSS 33.4~33.8, mean direction of total swell 70°~120°, 150°~175°, mean direction of wind waves 50°~75°, 120°~135°, 175°~190° and mean period of wind waves 3.0~4.5 s. Moreover, the SST with high CPUE changed significantly withinseasons, contrary to Chl-a. The CPUE of D. maruadsi had obvious seasonal characteristics, with the highest value of 0.848 kg/(kW·d) in summer and the lowest value of 0.087 kg/(kW·d) in winter. The abnormal increase in CPUE in 2016 may have been due to the impact of a super strong El Niño event in 2015–2016. The months in which the sea surface temperature anomaly (SSTA) exceeding 2.5℃ lasted from October 2015 to January 2016, and the spawning period of D. maruadsi is mainly winter and spring, which explains why the CPUE of D. maruadsi began to increase in the winter of 2015. Thus, the super strong El Niño had a positive impact on the replenishment of D. maruadsi in 2016. In this study, the effects of the wind waves and total swell were included in the model for the first time, and we found that wind waves and swells in a certain direction were conducive to the aggregation of D. maruadsi. This may have been due to the south and southwest winds and swells associated with upwelling activity in the northern South China Sea. The changes in the marine environment of the South China Sea are closely related to the ocean dynamics caused by monsoons. To further understand the relationship between the spatio-temporal distribution of D. maruadsi and the marine environment, and to provide a scientific basis for the conservation and adaptive management of D. maruadsi in the northern South China Sea, we can consider adding more representative ocean dynamic factors (such as sea surface and bottom velocities, mixed layer depths, and vertical velocities) to the model, to characterize different dynamic processes and to study the changes in abundance of this fish.