钦州湾浮游植物群落结构变化及其影响因素分析

发布时间：2018-11-10 12:14

【摘要】：本文基于2013-11、2014-04、2014-08和2014-12钦州湾海域4个月份的现场调查数据,研究了浮游植物的群落结构特征及其与环境的适应,并采用PRIMER6进行多元统计分析,结果如下:调查期间,钦州湾共鉴定出浮游植物187种(包括变型和变种),其中硅藻门126种,占浮游植物出现种数的67.38%;甲藻门54种,占28.88%;金藻门2种,黄藻门、蓝藻门、定鞭藻门、裸藻门和隐藻门各1种。其中网采浮游植物6门54属134种;水采浮游植物8门71属148种。优势种季节间有明显的演替现象,除了球形棕囊藻外,其余均为硅藻,并多为广温性种。不同季节的浮游植物细胞丰度差异明显,网采与水采浮游植物在平面分布和季节变化上也不完全一致。网采丰度2014-08最高,2013-11最低;水采2014-12因棕囊藻丰度较大导致该月丰度最高。除2014-12月,其他3个月份浮游植物群落多样性指数及均匀度指数趋势基本一致且较高,物种较丰富,群落结构稳定性较高。2014-12棕囊藻爆发,对钦州湾浮游植物群落结构和生态系统稳定性造成影响。营养盐中DIN春夏季含量较低,平面分布总体南低北高;无机磷分布趋势较为规律,高值区主要分布在近岸附近,在2014-04春季出现高值区向外海转移的现象。使用PRIMER 6对钦州湾浮游植物群落进行多元统计分析,对四个月份进行CA聚类分析和MDS多维标度分析,结果均显示四个月份浮游植物群落结构存在差异。同时对群落差异性进行ANOSIM检验,显著性水平均为0.01,说明不同季节浮游植物群落差异极其显著。在成对检验中,各季节月之间浮游植物群落也差异显著。SIMPER分析中发现差异性种均是一些广温广盐种或暖温带近岸种,适宜钦州湾环境,同时网采和水采结果中引起差异的主要种基本一致。对各个月份环境因子PCA分析中,不同月份环境因子系数的差异表明不同时间环境因子表现出来的贡献率和作用不同。BIOENV分析发现,浮游植物细胞丰度与环境因子的相关系数大部分月份较高,说明丰度与环境因子之间有良好的相关性。四个月份与浮游植物生长相关性较高的是氮含量,以2013-11秋和2014-12初冬较为明显;无机磷在2014-04也体现出与丰度的相关性;温度、盐度与丰度相关性主要体现在2014-08夏季。
[Abstract]:Based on the field survey data in Qinzhou Bay in 2013-11, 2014-04, 2014-08 and 2014-12, the characteristics of phytoplankton community structure and their adaptation to the environment were studied. PRIMER6 was used to carry out multivariate statistical analysis. The results are as follows: during the investigation, 187 species of phytoplankton (including forms and varieties) were identified in Qinzhou Bay, among which 126 species were diatoms, accounting for 67.38 species of phytoplankton. There are 54 species of Prorophyta, 2 species of Chlorella, 1 species of Xanthophyta, 1 species of Cyanophyta, 1 species of Phaeophyta and 1 species of Cryptophyta. Among them, there are 134 species of phytoplankton of 6 phyla, 54 genera, and 148 species of phytoplankton of 8 phyla, 71 genera. The dominant species showed obvious succession between seasons, except for Phaeocystis globosa, the other species were diatoms, and most of them were wide temperature species. The abundance of phytoplankton was different in different seasons, and the distribution and seasonal variation of phytoplankton in water and net were not completely consistent. The net harvest abundance was the highest in 2014-08, the lowest in 2013-11, and the highest in water harvesting 2014-12 due to the large abundance of Phaeocystis. Except for 2014-12, the trend of diversity index and evenness index of phytoplankton community in the other three months were basically the same and higher, the species were abundant, and the community structure was stable. Effects on phytoplankton community structure and ecosystem stability in Qinzhou Bay. The content of DIN in nutrient is lower in spring and summer, and the plane distribution is lower in the south and higher in the north, the distribution trend of inorganic phosphorus is more regular, the high value area mainly distributes near the shore, and the high value area transfers to the offshore in 2014-04 spring. Using PRIMER 6, the phytoplankton community in Qinzhou Bay was analyzed by multivariate statistical analysis, CA cluster analysis and MDS multidimensional scale analysis in four months. The results showed that there were differences in phytoplankton community structure in four months. At the same time, the difference of phytoplankton community was tested by ANOSIM, and the significant level was 0.01, which indicated that the difference of phytoplankton community in different seasons was extremely significant. In paired tests, phytoplankton communities were also significantly different in different seasons and months. SIMPER analysis showed that the different species were some wide temperature and wide salt species or warm temperate coastal species, which were suitable for Qinzhou Bay environment. At the same time, the results of net mining and water extraction caused the difference between the main species is basically the same. In the PCA analysis of environmental factors in different months, the difference of environmental factor coefficients in different months indicates that the contribution rate and function of environmental factors are different in different time. The correlation coefficient between phytoplankton cell abundance and environmental factors was higher in most months, indicating that there was a good correlation between abundance and environmental factors. Nitrogen content was significantly correlated with phytoplankton growth in four months, especially in the autumn of 2013-11 and the early winter of 2014-12, and inorganic phosphorus also showed the correlation with abundance in 2014-04. The correlation between temperature, salinity and abundance is mainly reflected in 2014-08 summer.
【学位授予单位】：国家海洋局第一海洋研究所
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：Q948.8

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