【摘要】 根据2014—2017年南海北部200m等深线以浅海域渔业资源调查的短尾大眼鲷(Priacanthusmacracanthus)数据,结合遥感获得的海表温度(seasurfacetemperature,SST)数据,首次将渔场水深(D)与SST的乘积(SSTD)引入作为新的变量,采用灰色关联度方法筛选与渔场关联度最高的变量,将关联度最高的变量与标准化后的单位捕捞努力量(catch per unit effect, CPUE)采用一元非线性回归建立模型,对模型理论CPUE与实际CPUE的时空变化进行分析。结果表明,南海北部短尾大眼鲷渔场随季节变化明显,各季节CPUE随SST呈先增大后减小的趋势;不同季节CPUE最高的海域水深不同,春季为160 m,夏季为140 m,秋季为60 m,冬季为140 m; CPUE与SST、D、SSTD进行灰色关联度分析发现,各季节CPUE与SSTD关联度最高且关联度均超过0.5;将SSTD作为变量, CPUE作为表征渔场好坏的指标值,建立环境因子与CPUE的关系模型,模型理论CPUE高值区有明显的季节变化,且实际CPUE高值区的分布与理论CPUE高值区分布一致,以上结果 P值均小于0.05,模型预测准确。更多还原

【Abstract】 In recent years, with the change of the structure of catches, the proportion of Priacanthus macracanthus in near-shore fishing is increasing. It is one of the main economic species in the coastal waters of the South China Sea, with high economic value and ecology significance. This was the first study based on the Priacanthus macracanthus data from the shallow sea fishery resources survey from the 200 m isobath in the northern part of the South China Sea from 2014 to 2017, combined with the sea surface temperature(SST) data obtained by remote sensing. The product of water depth(D) and SST(SSTD) was introduced as a new variable and represented the change of temperature inside the ocean. This variable can more succinctly reflect the relationship between fisheries and SST and water depth. The grey correlation method was used to screen the variables with the highest correlation with the fishery. The most relevant variables and the standardized Catch per Unit Effect(CPUE) were modeled by one-dimensional nonlinear regression. The modeled CPUEi and the actual temporal and spatial changes of the CPUE were analyzed. The results showed that the fishery of the big-tailed bigeye fish in the northern South China Sea changed significantly with season. The CPUE increased first and then decreased with the increase of SST in all seasons. The SST of the spring CPUE was 26℃, and the highest CPUE in summer was at an SST of 28℃. The SST at the highest CPUE in the fall was 25℃, and the SST at the highest CPUE in winter was 20℃. In the spring, the CPUE had an increasing trend with the depth of water. The CPUE was the largest at 160 m. The CPUE changed with water depth in summer, and reached a maximum at 140 m. The CPUE increased first and then decreased with the water depth in autumn, at a water depth of 60 m. The maximum was reached when the CPUE increased with the increase of water depth in winter, and reached the maximum at 140 m. The high value of CPUE in spring was mainly distributed in the southeast side of Hainan Island and the sea area outside Shantou. The summer CPUE high-value area was mainly distributed in the southeast side of Hainan Island and the coastal waters of Guangdong Province. The high CPUE area in autumn was mainly distributed in the outer sea area of the Pearl River Estuary, and the lower, high-value areas were mainly distributed in the southern part of the Beibu Gulf and the offshore waters of Shantou. The gray correlation analysis between CPUE and SST, and D and SSTD showed that CPUE and SSTD had the highest correlation in each season. Based on the one-dimensional nonlinear regression, a relationship model between SSTD and CPUE was established. The modeled theoretical CPUEi high-value area had obvious seasonal changes. For the actual CPUE, the distribution of the high-value area was consistent with the theoretical CPUEi high value area distribution, and the average accuracy of the model was 75%. The fisheries for Priacanthus macracanthus in different seasons showed different distributional trends. The distribution of fisheries in summer and autumn was more dispersed than those in winter and spring, and the CPUE was significantly higher than those in winter and spring, which may be related to the biological characteristics of spawning in summer. In addition, the large-eyed fishery in the northern part of the South China Sea had an obvious plaque-like distribution, which was quite different from the traditionally assumed fishery. The distribution of CPUE high-value areas tended to be concentrated, whereas the CPUE of most sea areas was low and had certain clustering features. From the spatial location of the fishery distribution, the winter and spring fishing grounds were mostly distributed in the offshore waters, which were farther away from the shore in summer and autumn. This may be related to the seasonal variation of temperature and bio-distribution of the bait; also, the temperature of the sea in winter and spring is low. The distribution of the main feed bio-shrimp, cephalopod larvae, and chlorophyll was closer to the shore, so the CPUE high-value area was mainly concentrated in the coastal waters; whereas the summer and autumn seawater temperature rises, and the chlorophyll high-value area is offshore. The direction moves, so the CPUE high-value area was mainly distributed in the offshore waters with deep-water depth.更多还原