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Pang H, Yongo E, Lu Z, Li Q, Liu X, Li L, Guo Z. Spatio-temporal dynamics of phytoplankton community structure in the coastal waters of the Southern Beibu Gulf. ENVIRONMENTAL MONITORING AND ASSESSMENT 2024; 196:721. [PMID: 38985365 DOI: 10.1007/s10661-024-12849-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 06/22/2024] [Indexed: 07/11/2024]
Abstract
This study was conducted during October 2021 (autumn) and April 2022 (spring) to explore the phytoplankton community structure, their distribution characteristics, and the influence of environmental factors in the coastal waters of the Southern Beibu Gulf. The 15 sampling sites were grouped based on the difference in offshore distance to analyze the temporal and spatial differences in community structure and environmental driving in the investigated sea area of the coastal waters of the Southern Beibu Gulf. Permutational multivariate analysis of variance was conducted on the sample data in time and space, revealing that there is no significant difference in space (p > 0.05), but there is significant difference in time (p < 0.05). Notably, water pressure, pH, chemical oxygen demand, nitrite, and labile phosphate were higher in autumn, while total ammonia nitrogen, dissolved oxygen, and suspended solids were significantly higher in spring. Additionally, the study identified 87 phytoplankton species belonging to 6 phyla, dominating by Bacillariophyta, followed by Dinophyta and Cyanophyta. The phytoplankton density, Shannon Weiner's diversity index (H'), Pielou's evenness index (J), and Margalef's richness index (D) ranged from 84.88 to 4675.33 cells L-1, 0.56 to 2.58, 0.26 to 0.89, and 1.21 to 3.64, respectively. Permutational multivariate analysis of variance showed non-significant spatial differences in phytoplankton composition (p > 0.05) but seasonal differences (p < 0.05). Furthermore, canonical correspondence analysis (CCA) identified pH, dissolved oxygen, suspended solids, chemical oxygen demand, nitrite, and labile phosphate as key environmental factors influencing the phytoplankton community structure (p < 0.05). In this study, the dynamic changes of phytoplankton community structure and environmental factors in the southern coastal waters of Beibu Gulf were analyzed in detail from two aspects of time and space. The key environmental factors to protect the ecological environment in the southern coastal area of Beibu Gulf were found out. It provides a reference method and theoretical basis for the management and protection of Beibu Gulf and other tropical marine environment.
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Affiliation(s)
- Haipeng Pang
- School of Life and Health Sciences, School of Marine Science and Engineering, State Key Laboratory of Marine Resource Utilization in the South China Sea, Hainan University, Haikou, 570228, China
| | - Edwine Yongo
- School of Life and Health Sciences, School of Marine Science and Engineering, State Key Laboratory of Marine Resource Utilization in the South China Sea, Hainan University, Haikou, 570228, China
- Department of Fisheries and Aquatic Sciences, University of Eldoret, Eldoret, Kenya
| | - Zhiyuan Lu
- School of Life and Health Sciences, School of Marine Science and Engineering, State Key Laboratory of Marine Resource Utilization in the South China Sea, Hainan University, Haikou, 570228, China
| | - Qian Li
- School of Life and Health Sciences, School of Marine Science and Engineering, State Key Laboratory of Marine Resource Utilization in the South China Sea, Hainan University, Haikou, 570228, China
| | - Xiaojin Liu
- School of Life and Health Sciences, School of Marine Science and Engineering, State Key Laboratory of Marine Resource Utilization in the South China Sea, Hainan University, Haikou, 570228, China
| | - Liang Li
- China National Materials Resources and Environment Co., Ltd. Hainan Branch, Haikou, 570228, China.
| | - Zhiqiang Guo
- School of Life and Health Sciences, School of Marine Science and Engineering, State Key Laboratory of Marine Resource Utilization in the South China Sea, Hainan University, Haikou, 570228, China.
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Xu S, Li G, He C, Huang Y, Yu D, Deng H, Tong Z, Wang Y, Dupuy C, Huang B, Shen Z, Xu J, Gong J. Diversity, community structure, and quantity of eukaryotic phytoplankton revealed using 18S rRNA and plastid 16S rRNA genes and pigment markers: a case study of the Pearl River Estuary. MARINE LIFE SCIENCE & TECHNOLOGY 2023; 5:415-430. [PMID: 37637251 PMCID: PMC10449762 DOI: 10.1007/s42995-023-00186-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 06/11/2023] [Indexed: 08/29/2023]
Abstract
Understanding consistencies and discrepancies in characterizing diversity and quantity of phytoplankton is essential for better modeling ecosystem change. In this study, eukaryotic phytoplankton in the Pearl River Estuary, South China Sea were investigated using nuclear 18S rRNA and plastid 16S or 23S rRNA genes and pigment analysis. It was found that 18S abundance poorly explained the variations in total chlorophyll a (Chl-a). However, the ratios of log-transformed 18S abundance to Chl-a in the major phytoplankton groups were generally environment dependent, suggesting that the ratio has potential as an indicator of the physiological state of phytoplankton. The richness of 18S-based operational taxonomic units was positively correlated with the richness of 16S-based amplicon sequence variants of the whole phytoplankton community, but insignificant or weak for individual phytoplankton groups. Overall, the 18S based, rather than the 16S based, community structure had a greater similarity to pigment-based estimations. Relative to the pigment data, the proportion of haptophytes in the 18S dataset, and diatoms and cryptophytes in the 16S dataset, were underestimated. This study highlights that 18S metabarcoding tends to reflect biomass-based community organization of eukaryotic phytoplankton. Because there were lower copy numbers of plastid 16S than 18S per genome, metabarcoding of 16S probably approximates cell abundance-based community organization. Changes in biomass organization of the pigment-based community were sensitive to environmental changes. Taken together, multiple methodologies are recommended to be applied to more accurately profile the diversity and community composition of phytoplankton in natural ecosystems. Supplementary Information The online version contains supplementary material available at 10.1007/s42995-023-00186-x.
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Affiliation(s)
- Shumin Xu
- School of Marine Sciences, Sun Yat-Sen University (Zhuhai Campus), and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519000 China
- Guangdong Provincial Key Laboratory of Marine Resources and Coastal Engineering, Guangzhou, 510006 China
| | - Guihao Li
- School of Marine Sciences, Sun Yat-Sen University (Zhuhai Campus), and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519000 China
| | - Cui He
- School of Marine Sciences, Sun Yat-Sen University (Zhuhai Campus), and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519000 China
| | - Yi Huang
- School of Marine Sciences, Sun Yat-Sen University (Zhuhai Campus), and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519000 China
| | - Dan Yu
- School of Marine Sciences, Sun Yat-Sen University (Zhuhai Campus), and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519000 China
| | - Huiwen Deng
- School of Marine Sciences, Sun Yat-Sen University (Zhuhai Campus), and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519000 China
| | - Zhuyin Tong
- State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, 361102 China
| | - Yichong Wang
- State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, 361102 China
| | - Christine Dupuy
- BIOFEEL, UMRi LIENSs, La Rochelle Université/CNRS, La Rochelle, France
| | - Bangqin Huang
- State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, 361102 China
| | - Zhuo Shen
- School of Marine Sciences, Sun Yat-Sen University (Zhuhai Campus), and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519000 China
| | - Jie Xu
- Centre for Regional Oceans, Department of Civil and Environmental Engineering, Faculty of Science and Technology, University of Macau, Macau, China
| | - Jun Gong
- School of Marine Sciences, Sun Yat-Sen University (Zhuhai Campus), and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519000 China
- Guangdong Provincial Key Laboratory of Marine Resources and Coastal Engineering, Guangzhou, 510006 China
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Wu J, Zhu Z, Waniek JJ, Niu M, Wang Y, Zhang Z, Zhou M, Zhang R. The biogeography and co-occurrence network patterns of bacteria and microeukaryotes in the estuarine and coastal waters. MARINE ENVIRONMENTAL RESEARCH 2023; 184:105873. [PMID: 36628821 DOI: 10.1016/j.marenvres.2023.105873] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 01/02/2023] [Accepted: 01/02/2023] [Indexed: 06/17/2023]
Abstract
Community and diversity shifts of bacteria and microeukaryotes with strong environmental and spatial variations have been unveiled in the Pearl River Estuary (PRE) and northern coastal part of South China Sea (SCS). However, it is not clear what the determining factors shape the microbial community and how the biotic interactions respond to the estuarine and oceanic environment. Here, we established the multiple regression models (MRM) and co-occurrence networks on microbial communities in PRE and SCS habitats. The results showed that there were significant differences of the abiotic factors affecting the bacterial and microeukaryotic communities between PRE and SCS habitats. Salinity explained the largest variations to the microbial community dissimilarities in PRE. Whereas spatial and environmental factors determined the microbial community dissimilarities in SCS. Positive relations between parasitic lineages (e.g. Perkinsea and Cercozoa) and algal taxa (Dinophyceae, Cryptophyta, Chlorophyta and Ochrophyta) dominated in the PRE network. While parasites Syndiniales positively correlated with other Syndiniales and protists in SCS. Strong positive associations among autotrophic and heterotrophic groups were revealed in both niches. Therefore, the biotic interactions are also important and may be responsible for the unexplained variations of the abiotic factors from MRM models. Microbial network in the PRE estuarine water had weakened resistance to environmental disturbances, while the SCS network had greater capacity to maintain network stability. This study shed light on the different mechanisms of abiotic and biotic factors in shaping the compositions of bacteria and microeukaryotes between PRE and SCS niches, and highlights the weakening effect of environmental disturbances on the microbial network stability.
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Affiliation(s)
- Jinnan Wu
- School of Oceanography, Shanghai Jiao Tong University, 200030, Shanghai, China
| | - Zhu Zhu
- School of Oceanography, Shanghai Jiao Tong University, 200030, Shanghai, China.
| | - Joanna J Waniek
- Leibniz Institute for Baltic Sea Research Warnemünde, Seestrasse 15, 18119, Rostock, Germany
| | - Mingyang Niu
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 200030, Shanghai, China
| | - Yuntao Wang
- State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, 310000, Hangzhou, Zhejiang, China
| | - Zhaoru Zhang
- School of Oceanography, Shanghai Jiao Tong University, 200030, Shanghai, China
| | - Meng Zhou
- School of Oceanography, Shanghai Jiao Tong University, 200030, Shanghai, China
| | - Ruifeng Zhang
- School of Oceanography, Shanghai Jiao Tong University, 200030, Shanghai, China.
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Phytoplankton Communities and Their Relationship with Environmental Factors in the Waters around Macau. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19137788. [PMID: 35805446 PMCID: PMC9265806 DOI: 10.3390/ijerph19137788] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Revised: 06/16/2022] [Accepted: 06/21/2022] [Indexed: 11/29/2022]
Abstract
An investigation of the waters around Macau collected 43 phytoplankton species belonging to 29 genera and 5 phyla, including 32 species from 22 genera of Bacillariophyta, 7 species from 3 genera of Pyrrophyta, 2 species from 2 genera of Cyanophyta, and 1 genus and 1 species from both Euglenophyta and Chromophyta. The dominant phytoplankton species in the study areas were Skeletonema costatum (Greville) Cleve, Aulacoseira granulata (Ehrenberg) Simonsen, Thalassiothrix frauenfeidii Grunow, and Thalassionema nitzschioides Grunow. The phytoplankton abundance in the waters around Macau was between 46,607.14 and 1,355,000 cells/m3, with the highest abundance noted in station S8. Diatoms were the main contributor to phytoplankton abundance in station S8, accounting for 96.2% of the total abundance. Station S4 exhibited the lowest phytoplankton abundance of 46,607.1 cells/m3, with diatoms and Chromophytaaccounting for 58.6% and 29.9% of the total phytoplankton abundance, respectively. Biodiversity analysis results showed that the phytoplankton richness index was 1.18−3.61, the uniformity index was 0.24−0.78, and the Shannon−Wiener index was 0.94−3.41. Correlation analysis revealed that ammonia nitrogen was significantly negatively correlated with the phytoplankton richness, uniformity, and Shannon−Wiener indices. Nitrite nitrogen, nitrate nitrogen, inorganic nitrogen, salinity, turbidity, and pH were positively correlated with the phytoplankton evenness index and Shannon−Wiener index. Cluster and non-metric multidimensional scaling analyses demonstrated that the phytoplankton community structure in the waters around Macau could be divided into three groups, with A. granulata, S. costatum, T. frauenfeidii, T. nitzschioides, Chaetoceros curvisetus Cleve, and Chaetoceros diadema (Ehrenberg) Gran being predominant in different grouping communities (contribution% > 10%). Biota-Environment Stepwise Analysis (BIOENV) showed a significant correlation between the phytoplankton community and nitrite nitrogen content in the waters around Macau (correlation: 0.5544, Mantel test: statistic 0.4196, p = 0.009), which was consistent with the results of the canonical correspondence analysis.
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Diao C, Jia H, Guo S, Hou G, Xian W, Zhang H. Biodiversity exploration in autumn using environmental DNA in the South China sea. ENVIRONMENTAL RESEARCH 2022; 204:112357. [PMID: 34774836 DOI: 10.1016/j.envres.2021.112357] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 10/30/2021] [Accepted: 11/06/2021] [Indexed: 06/13/2023]
Abstract
The South China Sea (SCS) is an important part of the Indo-Pacific convergence zone, with high biodiversity and abundant marine resources. Traditional methods are primarily used to monitor biodiversity. However, a few studies have used environmental DNA (eDNA) metabarcoding to research the assemblage structure of the SCS. This study used eDNA metabarcoding to survey the SCS assemblage and its relationship with environmental factors over a month-long time-series (August 30th to September 30th, 2020) of seawater samples from the central part of the SCS (9°-20°86' N, 113°-118°47' E). 32 stations were divided into six groups (A, B, C, D, E, F) according to longitude. We collected water samples, extracted eDNA, and amplified 18S rRNA gene V4 region (18S V4), 18S rRNA gene V9 region (18S V9), and 12S rRNA gene (12S). Krona diagrams were used to show species composition. We identified 192 phytoplankton, 104 invertebrate, and 61 fish species from 18S V4, 18S V9, and 12S, respectively. Generally, the three assemblage structures exhibited an increase in species diversity with increasing longitude. Group E had the highest fish diversity. Groups F and C had the highest phytoplankton and invertebrate diversity, respectively. Canonical correspondence analysis showed that four factors (chlorophyll a, depth, salinity, and temperature) were correlated with assemblage structure. Chlorophyll a was the main environmental factor that affected fish, phytoplankton, and invertebrate assemblage structures; salinity was strongly correlated with fish and invertebrate assemblage structures; temperature was a key factor that impacted fish and invertebrate assemblage structures; and depth was strongly correlated with invertebrate assemblage structure. Our results revealed that eDNA metabarcoding is a powerful tool for improving detection rate and using multiple markers is an effective approach for monitoring biodiversity. This study provided information that can be used to enhance biodiversity protection efforts in the SCS.
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Affiliation(s)
- Caoyun Diao
- CAS Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, 266071, China; Laboratory for Marine Ecology and Environmental Science, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao, 266237, China; Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, 266071, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Hui Jia
- CAS Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, 266071, China; Laboratory for Marine Ecology and Environmental Science, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao, 266237, China; Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, 266071, China; China School of Marine Science and Engineering, Qingdao Agricultural University, Qingdao, 266109, China
| | - Shujin Guo
- Jiaozhou Bay National Marine Ecosystem Research Station, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, 266071, China
| | - Gang Hou
- College of Fisheries, Guangdong Ocean University, Zhanjiang, 524000, China
| | - Weiwei Xian
- CAS Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, 266071, China; Laboratory for Marine Ecology and Environmental Science, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao, 266237, China; Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, 266071, China.
| | - Hui Zhang
- CAS Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, 266071, China; Laboratory for Marine Ecology and Environmental Science, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao, 266237, China; Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, 266071, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
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Ubiquitous Occurrence of a Biogenic Sulfonate in Marine Environment. SUSTAINABILITY 2022. [DOI: 10.3390/su14031240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The biogenic sulfonate 2,3-dihydroxypropane-1-sulfonate (DHPS) is a vital metabolic currency between phytoplankton and bacteria in marine environments. However, the occurrence and quantification of DHPS in the marine environment has not been well-characterized. In this study, we used targeted metabolomics to determine the concentration of DHPS in the Pearl River Estuary, an in situ costal mesocosm ecosystem and a hydrothermal system off Kueishantao Island. The results suggested that DHPS occurred ubiquitously in the marine environment, even in shallow-sea hydrothermal systems, at a level comparable to that of dimethylsulfoniopropionate. The concentration of DHPS was closely related to phytoplankton community composition and was especially associated with the abundance of diatoms. Epsilonproteobacteria were considered as the most likely producers of DHPS in shallow-sea hydrothermal systems. This work expands current knowledge on sulfonates and presents a new viewpoint on the sulfur cycle in hydrothermal systems.
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Cheang CC, Lee BY, Ip BHY, Yiu WH, Tsang LM, Ang PO. Fish and crustacean biodiversity in an outer maritime estuary of the Pearl River Delta revealed by environmental DNA. MARINE POLLUTION BULLETIN 2020; 161:111707. [PMID: 33065394 DOI: 10.1016/j.marpolbul.2020.111707] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 09/13/2020] [Accepted: 09/21/2020] [Indexed: 06/11/2023]
Abstract
Understanding the faunal community structure in the estuary would be crucial in assessing the health of the ecosystem. The poor visibility in the estuarine area due to the outflow from the Pearl River hinders the conventional visual census in assessing the megafaunal biodiversity. In this study, the fish and crustacean biodiversity of Hong Kong's western waters, i.e. the outer maritime estuary of the PRD, were studied through the metabarcoding of environmental DNA (eDNA). eDNA from the seawater and sediment samples was extracted from five sites in the region. After testing the performance of two genetic markers, amplicons of the cytochrome oxidase I, amplified by polymerase chain reaction, were subjected to Illumina high-throughput sequencing (MiSeq) analysis. A total of 22 fish species from 17 families and 34 crustacean species from 27 families were identified by blasting the sequences against the NCBI GenBank database, demonstrating segregation between samples from different sites. This study provides insight on the detail distribution of fish assembly in PRD, when compared with a previous eDNA study in the inner brackish PRD.
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Affiliation(s)
- Chi-Chiu Cheang
- Department of Science and Environmental Studies, The Education University of Hong Kong, Taipo, N.T., Hong Kong, China.
| | - Bo-Yee Lee
- Department of Science and Environmental Studies, The Education University of Hong Kong, Taipo, N.T., Hong Kong, China
| | - Brian Ho-Yeung Ip
- Department of Science and Environmental Studies, The Education University of Hong Kong, Taipo, N.T., Hong Kong, China
| | - Wai-Hong Yiu
- Department of Science and Environmental Studies, The Education University of Hong Kong, Taipo, N.T., Hong Kong, China
| | - Ling-Ming Tsang
- School of Life Sciences, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China
| | - Put O Ang
- Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China
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Liu X, Sen B, Zhao Y, Bai M, He Y, Xie Y, Li J, Wang G. Gradients of three coastal environments off the South China Sea and their impacts on the dynamics of heterotrophic microbial communities. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 659:499-506. [PMID: 31096379 DOI: 10.1016/j.scitotenv.2018.12.405] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Revised: 12/24/2018] [Accepted: 12/26/2018] [Indexed: 06/09/2023]
Abstract
Heterotrophic fungus-like marine protists are recognized to contribute significantly to the coastal carbon cycling largely due to their high biomass and ability to decompose recalcitrant organic matter. Yet, little is known about their dynamics at polluted coastal environments in the context of heterotrophic microbial communities. Here, we present the dynamics of these protists relative to their heterotrophic counterparts in three different environments, namely Pearl River Estuary (ZJK), Shenzhen Bay (SZW) and Daya Bay (DYW) along the coastline of South China Sea. ZJK and SZW were characterized by low salinity and high N levels with large variations, unlike DYW. However, the average abundance of fungus-like protists did not differ significantly (P > 0.05) among these environments, except that it increased in August (422 ± 264 cells/mL, P < 0.01) over March, May and October. Correlation analysis revealed association of their abundance to different environmental factors, namely dissolved organic N in ZJK (rho = -0.87); NH4+ (rho = 0.64) and Chl a (rho = 0.73) in SZW; and salinity (rho = 0.46), DO (rho = 0.57) and total P (rho = 0.48) in DYW, suggesting distinct influence of trophic conditions. Analysis of their abundance relative to other heterotrophic protists (HP) shows that fungus-like protists display selective advantage over HP in the environment (DWY) with low N levels. Further, the similar biomass fraction (ZJK: 5.97 ± 6.23%, SZW: 5.97 ± 5.28%, and DYW: 12.1 ± 11.4%; P > 0.05) of fungus-like protists relative to heterotrophic bacteria, suggest their invariable contribution to carbon cycling. Thus, dynamics of fungus-like protists in relation to their heterotrophic counterparts is largely regulated by the trophic conditions of coastal environments.
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Affiliation(s)
- Xianhua Liu
- Center of Marine Environmental Ecology, School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China
| | - Biswarup Sen
- Center of Marine Environmental Ecology, School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China
| | - Yue Zhao
- Center of Marine Environmental Ecology, School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China
| | - Mohan Bai
- Center of Marine Environmental Ecology, School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China
| | - Yaodong He
- Center of Marine Environmental Ecology, School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China
| | - Yunxuan Xie
- Center of Marine Environmental Ecology, School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China
| | - Jianyang Li
- Center of Marine Environmental Ecology, School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China
| | - Guangyi Wang
- Center of Marine Environmental Ecology, School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China; Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China.
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