|
| 引用本文: | 韦梦琳,李法云,洪天宇,吴海鹏,刘天依,赵坤.上海鹦鹉洲湿地与外围河道浮游植物群落时空差异及其影响因子.湖泊科学,2025,37(2):429-445. DOI:10.18307/2025.0216 |
| Wei Menglin,Li Fayun,Hong Tianyu,Wu Haipeng,Liu Tianyi,Zhao Kun.Spatio-temporal differences of phytoplankton communities and their driving factors in the Yingwuzhou Wetland and its surrounding canals, Shanghai. J. Lake Sci.2025,37(2):429-445. DOI:10.18307/2025.0216 |
|
| |
|
|
| 本文已被：浏览 546次 下载 280次 |
码上扫一扫！ |
|
|
| 上海鹦鹉洲湿地与外围河道浮游植物群落时空差异及其影响因子 |
|
韦梦琳1，2，李法云1，2，洪天宇1，吴海鹏3，刘天依1，赵坤1，4
|
|
1.上海应用技术大学生态技术与工程学院，上海 201418 ；2.美丽中国与生态文明研究院（上海高校智库），上海 201418 ；3.华东师范大学生态与环境科学学院，上海 200241 ；4.上海城市路域生态工程技术研究中心，上海 201418
|
|
| 摘要: |
| 滨海湿地是全球气候变化与人类活动的敏感区和脆弱区。为探究滨海湿地浮游植物群落时空差异及影响因子，本研究在上海鹦鹉洲湿地与外围城市河道共设置14个采样点分4个季节进行浮游植物样品采集，共鉴定浮游植物7门97种，硅藻门和绿藻门种类最为丰富。鹦鹉洲湿地与外围河道共有种有61种（占总种类的62.9%），湿地特有种19种、外围河道特有种17种。种类数的季节差异显著，表现为夏季（89种）＞冬季（68种）＞秋季（49种）＞春季（42种），四季共有种23种，夏季特有种12种，其他季节特有种仅2~3种。从生物量来看，鹦鹉洲湿地浮游植物群落类型比外围河道表现出更多样的季节演替：湿地从春季到冬季表现为硅藻-蓝|裸|甲藻-隐藻-裸|绿藻的演替规律，而外围河道则表现为隐|硅藻-蓝藻-隐|硅藻-隐|硅藻的季节演替规律。鹦鹉洲湿地浮游植物主要优势种为细小隐球藻（Aphanocapsa elachista）、尖尾蓝隐藻（Chroomonas acuta）；外围河道主要优势种为啮蚀隐藻（Cryptomonas erosa）、尖尾蓝隐藻、细小隐球藻、歪头颤藻（Oscillatoria curviceps）以及颗粒直链藻（Melosira granulata），优势种季节更替明显。水温、溶解氧、透明度、盐度及营养盐是影响上海鹦鹉洲湿地及外围河道浮游植物群落分布最主要的环境因子，其中盐度是区分湿地与河道浮游植物群落的关键因子，水温与氨氮是区分四季浮游植物群落的关键因子。变差分解显示，环境因子对功能离散度（FDiv）的解释率（18.8%）显著高于对Shannon-Wiener多样性的解释率（10%），表明环境对物种生态位的筛选强于对物种个体的筛选。受环境影响不显著的功能均匀度（FEve）却受时间因子的显著影响，可能与季节更替过程中气象条件、水体生态系统营养级结构的变化、浮游生物群落季节性演替过程中种间关系等因素有关。功能丰富度（FRic）表明秋季是四季中浮游植物群落抗干扰能力最弱的季节，且湿地的抗干扰能力显著强于河道。环境因子对FRic的解释量（41.2%）显著高于对物种丰富度的解释量（16%），表明FRic的环境敏感性比物种丰富度高。本研究将对滨海区域生物多样性保护、生态系统功能恢复与管理提供科学依据。 |
| 关键词: 浮游植物 群落类型 功能多样性 分类回归树 变差分解 鹦鹉洲湿地 |
| DOI：10.18307/2025.0216 |
| 分类号: |
| 基金项目:长江三角洲河口湿地生态系统教育部/上海市野外科学观测研究站开放项目（K202204）；国家自然科学基金青年项目（32001153）；上海市城市化生态过程与生态恢复重点实验室开放项目（SHUES2022A07）；上海应用技术大学引进人才科研启动经费（YJ2022-39）联合资助 |
|
| Spatio-temporal differences of phytoplankton communities and their driving factors in the Yingwuzhou Wetland and its surrounding canals, Shanghai |
|
Wei Menglin1，2，Li Fayun1，2，Hong Tianyu1，Wu Haipeng3，Liu Tianyi1，Zhao Kun1，4
|
|
1.School of Ecological Technology and Engineering, Shanghai Institute of Technology, Shanghai 201418 , P.R.China ；2.Research Institution of Beautiful China and Ecological Civilization, University Think Tank of Shanghai Municipality, Shanghai 201418 , P.R.China ；3.School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200241 , P.R.China ；4.Shanghai Engineering Research Center of Urban Road Ecological Technology, Shanghai 201418 , P.R.China
|
| Abstract: |
| Coastal wetlands are sensitive and vulnerable areas to global climate change and anthropogenic pressures. To study the spatio-temporal differences and driving factors of phytoplankton communities in coastal wetlands, we collected phytoplankton samples from 14 sites for four seasons in the Yingwuzhou Wetland (YWW) and its surrounding urban canals (SUC). A total of 97 phytoplankton species belonging to 7 phyla were identified during the study period, most of which are Bacillariophyta and Chlorophyta. A total of 61 phytoplankton species (62.9% of total species) were identified in both sites. Besides, 19 species were only identified in the YWW, while 17 species were specific to SUC. The richness of phytoplankton species exhibited significantly seasonal changes, with species numbers ranked in summer (89 species)＞winter (68 species)＞fall(49 species)＞spring (42 species). There are 23 shared species in all seasons, 12 species only identified in summer, and 2-3 typical species identified in other seasons. More diverse seasonal succession was found in YWW than those in SUC in terms of biomass in phytoplankton community types. The communities in YWW showed a succession from Bacillariophyta type(spring) to Cyanophyta-Euglenophyta-Chlorophyta type (summer), to Cryptophyta type (fall), and to Euglenophyta-Chlorophyta type (winter). While in SUC communities showed the other pattern from Cryptophyta-Bacillariophyta type (spring), to Cyanophyta-Euglenophyta-Pyrrophyta type (summer), to Cryptophyta-Bacillariophyta type (fall), and to Cryptophyta-Bacillariophyta type (winter). Seasonal dominant species varied significantly in both YWW and SUC. The dominant species of phytoplankton community in YWW were Aphanocapsa elachista and Chroomonas acuta, while the main dominant species of phytoplankton community in SUC were Cryptomonas erosa, Chroomonas acuta, Aphanocapsa elachista, Oscillatoria curviceps, and Melosira granulata. Statistical analyses revealed water temperature（WT）, dissolved oxygen, secchi depth, salinity, and nutrients were the main physicochemical factors regulating the distribution of phytoplankton communities in this area. Salinity was the key factor in distinguish the phytoplankton community in the YWW from SUC. WT and NH3-N were the key factors in differentiating phytoplankton communities in different seasons. Variance partitioning analysis showed that the explained variance by environmental factors to the functional divergence (18.8%) was significantly higher than to the Shannon-Wiener diversity index (10%), which indicated that environmental filtering on niches was stronger than filtering on individuals. The functional evenness was not significantly influenced by the environmental factors but was significantly influenced by the temporal factors, which were possibly related to climate change, trophic structure shift of aquatic ecosystems, or biological interaction alter of seasonal plankton communities. The functional richness of both sites indicated that fall was most vulnerable to the disturbance, and the resilience of the wetland was significantly higher than that of its surrounding canals. The explained variance by environmental factors to the functional richness (41.2%) was significantly higher than to the species richness (16%), which indicated that functional richness was more sensitive to environmental changes than species richness. This study may provide scientific references for biodiversity conservation, ecosystem function restoration, and management in coastal areas. |
| Key words: Phytoplankton community types functional diversity classification and regression tree variance partitioning analysis Yingwuzhou Wetland |
|
|
|
|
