中国边缘海浮游植物群落时空格局与演变趋势
黄邦钦*,肖武鹏,柳 欣

(厦门大学环境与生态学院,近海海洋环境科学国家重点实验室,福建省海陆界面生态环境重点实验室,福建 厦门 361102)

浮游植物; 群落演替; 全球变暖; 气候变化; 人类活动; 中国边缘海

Spatial-temporal distributions and successional patterns of phytoplankton communities in the Chinese marginal seas

(State Key Laboratory of Marine Environmental Science,Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies,College of the Environment & Ecology,Xiamen University,Xiamen 361102,China)

DOI: 10.6043/j.issn.0438-0479.202010011

备注

随着全球变暖和人类活动引起环境变化的加剧,研究边缘海浮游植物群落变化及其驱动因子,以及对未来环境变化的响应成为人们关注的热点.本文总结了近20年来对中国边缘海(南海、东海(包括台湾海峡)和黄海)浮游植物群落时空演替格局和环境调控机制的认识,梳理了浮游植物群落对近岸上升流、冷涡和暖涡等中尺度物理过程的响应模式,汇总了东海和南海浮游植物群落在未来环境变化情景下的可能响应.在此基础上分析了目前中国边缘海浮游植物群落演替研究的不足以及未来的研究重点,指出未来应以典型生态系统的代表性测站为重点,采用多平台多技术手段进行系统、长期、高分辨率的连续观测,并结合大数据分析和模型模拟开展深入研究,以期揭示气候变化和人类活动对边缘海浮游植物群落在年际和年代际尺度上的影响及其效应.
With the aggravation of environmental changes caused by global warming and human activities,the study of variations of phytoplankton communities in marginal seas and their driving factors,as well as their responses to future environmental changes,has become a hot topic.In this paper,the spatial-temporal successional patterns of phytoplankton communities in the Chinese marginal seas(the South China Sea,the East China Sea(including the Taiwan Strait)and the Yellow Sea)were reviewed based on reports in the past two decades.The underlying environmental controlling mechanisms were also discussed.The known knowledge of the responses of phytoplankton communities to coastal upwelling,cyclonic and anti-cyclonic eddies were summarized.Possible responses of phytoplankton communities to future environmental changes in the East China Sea and the South China Sea were also described.The review finally pointed out the deficiencies and the future research focus of phytoplankton ecology in the Chinese marginal seas.In order to reveal the influences and effects of climate change and human activities on the phytoplankton community at interannual and interdecadal scales,future studies should strengthen comprehensive,long-term,and high-resolution observations at fixed locations of typical ecosystems,and take the advantage of big data analysis and model simulation.