瓯江口春、秋季网采浮游植物分布及其影响因素

Distribution of net-collected phytoplankton in relation to environmental factors in spring and autumn in Oujiang River estuary

浮游植物作为初级生产者在河口生态系统物质、能量循环中起着至关重要的作用.通过测定2015年4月(春季)、11月(秋季)两个航次对瓯江口海域浮游植物的拖网调查及环境因子,研究其群落结构、空间分布及影响因子.结果如下:1)两季共鉴定出浮游植物8门96属208种,其中硅藻门60属157种、甲藻门17属29种,其余门类(绿藻门、裸藻门、蓝藻门、隐藻门、金藻门和定鞭藻门)偶有检出.秋季浮游植物平均丰度(2 443.05个/L)明显高于春季(160.35个/L).2)蛇目圆筛藻(Coscinodiscus argus)和琼氏圆筛藻(Coscinodiscus jonesianus)为两季绝对优势种,此外春季主要季节性优势种为弯菱形藻(Nitzschia sigma)和伏氏海毛藻(Thalassiothrix frauenfeldii),秋季为红海束毛藻(Trichodesmium erythraeum).3)冗余分析结果表明,春季影响浮游植物群落的主要环境因子为透明度、氮磷比、溶解无机氮、硅酸盐和温度,秋季为溶解无机氮、溶解无机磷、盐度、透明度和氮磷比.这些影响因素主要受控于台湾暖流、闽浙沿岸流及瓯江径流.4)聚类分析、多维尺度排序和相似性分析结果表明,春、秋两季浮游植物群落组成在时间、空间上均呈显著差异.5)结合历史资料分析得到瓯江口海域浮游植物赤潮藻种旺发和群落结构改变与富营养化密切相关.因此,本次调查中瓯江口春、秋季网采浮游植物群落主要受环流变化等的影响,季节和区域差异显著,但其响应过程还有待结合水文、地理和化学等多学科数据进一步论证.

The distinctive estuary hydrodynamics, salinity gradient, and nutrient system make the estuary ecosystem play a key role in marine ecosystems. Oujiang River estuary （ORE）, located at the south coast of Zhejiang Province, is one of the main estuaries in China. As a basic and critical part of the food chain, phytoplankton are responsible for most of the primary production in the ecosystem. However, the comprehensive effects of water masses, physicochemical factors, and eutrophication on the phytoplankton community in ORE remain poorly documented. The objective of this work was to understand the response of phytoplankton to environmental factors. Two cruises were carried out in the study area in the spring and autumn of 2015 to collect samples for net-phytoplankton and physical-chemical analyses. With the survey data, a redundancy analysis （RDA）, analysis of similarities （ANOSIM）, and other analytical methods were used to explain the relationship between environmental factors and the phytoplankton community. 1） Based on 35 net phytoplankton samples collected from ORE, a total of 8 phyla, 96 genera, and 208 species were identified, including 157 species of diatoms, 29 dinoflagellates, and other rare species belonging to Chlorophyta, Euglenophyta, Cyanophyta, Cryptophyta, Chrysophyta, and Prymnesiophyta. The phytoplankton abundance in the autumn （2 443.05 cells/L） was generally higher than that in the spring （160.35 cells/L）. 2） There were 15 dominant species, consisting of 14 diatoms and 1 Euglenophyceae. Coscinodiscus argus and Coscinodiscus jonesianus were the absolute dominant species for both seasons. Other seasonal dominant species included Nitzschia sigma and Thalassionthrix frauenfeldii in the spring, and Trichodesmium erythraeum in the autumn. 3） RDA indicated that transparency, N/P, DSi, salinity, and temperature were the main factors controlling the phytoplankton community in spring, whereas DIN, DIP, salinity, transparency, and N/P were the main contributing factors in autumn. 4） The results of the cluster analysis, non-metric multi-dimensional scaling analysis, and the ANOSIM all showed that the structure of the phytoplankton community was significantly different between spring and autumn in the temporal scale. In addition, the plankton community was profoundly controlled by the Taiwan Warm Current （TWC）, Fujian-Zhejiang Coastal Current （FZCC）, and the runoffs from Oujiang River （OR）. 5） The succession of dominant species and the propagation of red-tide species were closely related to the eutrophication exacerbation in ORE caused by marine exploitations and the process of coastal urbanization. The variations in phytoplankton structure and distribution in ORE are highly correlated with the seasonal and spatial variability in physicochemical factors （particularly the transparency and nutrients along the salinity gradient）, which are in turn controlled by TWC, FZCC, and runoff from the OR. The eutrophication in ORE is increasing to induce the reproduction of red-tide species more or less. In conclusion, the variation of net-phytoplankton in ORE was mainly controlled by circulation changes with obvious seasonal and regional differences, but the specific response process remains to be solved through the combination of hydrological, physical, and chemical data.