| 摘要: |
| 于2006 年5—11 月对东海泉州湾赤潮监控区四个监测站位开展赤潮常规监测。根据监测结果, 分别以各站位23 项水质理化生物环境因子指标为自变量, 相应赤潮藻类优势种的细胞密度为因变量, 进行多元逐步回归分析, 建立了各站位优势种中肋骨条藻(Skeletonema costatum)、太平洋海链藻(Thalassiosira pacifica)、微小原甲藻(Prorocentrum minimum)、尖刺拟菱形藻(Pseudo-nitzschia pungens)、丹麦细柱藻(Leptocylindrus danicus)和旋链角毛藻(Chaetoceros curvisetus)等的细胞密度多参量回归方程。结果表明, 所有回归方程的复相关系数都接近于1, 方差分析的结果均为回归极显著,表明所建立的回归方程可作为相应赤潮优势种细胞密度预报方程的高度有效性, 将对今后泉州湾的赤潮预报提供良好的指导作用。
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| 关键词: 泉州湾, 赤潮生物, 逐步回归分析, 回归方程, 赤潮预报 |
| DOI：10.11693/hyhz201003007007 |
| 分类号: |
| 基金项目:福建省海洋与渔业局重点项目赤潮监测专项经费资助, 闽海渔涵200677 号; NSFC-云南联合基金：滇池蓝藻水华时空演替及驱动机制研究项目资助, U0833604 号; 中国科学院重大交叉项目：富营养化水体蓝藻水华发生与致灾的关键过程及其监测指标体系项目资助, KZCX1-YW-14-1 号。 |
附件 |
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| FORECAST EQUATIONS FOR CELL DENSITY OF THE DOMINANT RED-TIDE ALGAE AT THE QUANZHOU BAY |
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JIANG Xing-Long1,2, SONG Li-Rong1
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1.Institute of Hydrobiology, Chinese Academy of Sciences;2.Jimei University
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| Abstract: |
| Red-tide routine monitoring at the Quanzhou Bay, East Sea, was implemented at four sampling stations from May to November, 2006. There were six types of dominant red-tide algae: Skeletonema costatum, Thalassiosira pacifica, Prorocentrum minimum, Pseudo-nitzschia pungens, Leptocylindrus danicus and Chaetoceros curvisetus. Based on the red-tide routine monitoring results, multi-parameter regression equations were established by stepwise regression analysis, with totally 23 kinds of environmental, physical, chemical and biological factors as the independent variables, and cell densities of individual dominant red-tide algae as the dependant variables. All multiple correlation coefficients of these multi-parameter regression equations were close to 1.0000, and the results of variance analysis for each of regression equations showed that the correlation was significant. Therefore, these multi-parameter regression equations will be useful forecast equations for forecasting cell densities of the dominant red-tide algae at the Quanzhou Bay in the future.
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| Key words: Quanzhou Bay, Red-tide organisms, Stepwise regression analysis, Regression equations, Red-tide forecast |
