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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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Liu H, He J, Xu J, Yin K. A novel indicator of anthropogenic influence on the fluctuability and stability of phytoplankton community composition. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 947:174570. [PMID: 38977105 DOI: 10.1016/j.scitotenv.2024.174570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Revised: 06/26/2024] [Accepted: 07/05/2024] [Indexed: 07/10/2024]
Abstract
Marine community composition is expected to be relatively stable in a natural environment over time but shift under increasing anthropogenic disturbances. In coastal waters, diatoms and dinoflagellates are two dominant phytoplankton functional groups. In this study, we developed an areal phytoplankton community composition index (APCI) that is based on the area of a scatter plot of dinoflagellate abundance (y-axis) vs diatom abundance (x-axis) using a time window of 1 year, 2 years or 3 years data. An APCI allows an ecological interpretation: it represents the fluctuability of a community composition within a time window and a temporal change between two neighbouring APCIs in a time series represents the stability of the composition. We used a 28-yr time series of monthly data on diatom and dinoflagellate abundance at four stations in Tolo Harbour and Channel (Tolo), Hong Kong to test the hypothesis that temporal changes in APCIs indicate environmental disturbances and to examine the applicability of APCI to indicate changes in nutrient conditions. We calculated the area (APCI) of a scatter plot of monthly data for 1-year, 2-year and 3-year windows, referred to as APCI-1y, -2y and -3y, respectively. The results show that, the fluctuability, is larger in APCI-3y than in APCI-1y, while the stability is stronger as temporal changes between neighbouring APCI-3y are smaller than between APCI-1ys. Temporal trends of APCIs are significantly correlated with those of dissolved inorganic nitrogen and phosphate concentration, which have declined after the implementation of a sewage diversion management plan in 1998. Hence, the APCI method is likely a robust indicator to assess a response of the phytoplankton community composition in a water body to environmental disturbances.
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Affiliation(s)
- Haozhen Liu
- School of Marine Sciences/Guangdong Key Laboratory of Marine Resources and Coastal Engineering, Sun Yat-sen University and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, China
| | - Jianzhang He
- School of Marine Sciences/Guangdong Key Laboratory of Marine Resources and Coastal Engineering, Sun Yat-sen University and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, China
| | - Jie Xu
- Department of Ocean Science and Technology and Centre for Regional Oceans, Faculty of Science and Technology, University of Macau, Taipa, Macau.
| | - Kedong Yin
- School of Marine Sciences/Guangdong Key Laboratory of Marine Resources and Coastal Engineering, Sun Yat-sen University and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, China.
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Li L, Xia R, Dou M, Zhang K, Chen Y, Jia R, Li X, Dou J, Li X, Hu Q, Zhang H, Zhong N, Yan C. Integrated machine learning reveals aquatic biological integrity patterns in semi-arid watersheds. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 359:121054. [PMID: 38728982 DOI: 10.1016/j.jenvman.2024.121054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 01/28/2024] [Accepted: 04/29/2024] [Indexed: 05/12/2024]
Abstract
Semi-arid regions present unique challenges for maintaining aquatic biological integrity due to their complex evolutionary mechanisms. Uncovering the spatial patterns of aquatic biological integrity in these areas is a challenging research task, especially under the compound environmental stress. Our goal is to address this issue with a scientifically rigorous approach. This study aims to explore the spatial analysis and diagnosis method of aquatic biological based on the combination of machine learning and statistical analysis, so as to reveal the spatial differentiation patterns and causes of changes of aquatic biological integrity in semi-arid regions. To this end, we have introduced an innovative approach that combines XGBoost-SHAP and Fuzzy C-means clustering (FCM), we successfully identified and diagnosed the spatial variations of aquatic biological integrity in the Wei River Basin (WRB). The study reveals significant spatial variations in species number, diversity, and aquatic biological integrity of phytoplankton, serving as a testament to the multifaceted responses of biological communities under the intricate tapestry of environmental gradients. Delving into the depths of the XGBoost-SHAP algorithm, we discerned that Annual average Temperature (AT) stands as the pivotal driver steering the spatial divergence of the Phytoplankton Integrity Index (P-IBI), casting a positive influence on P-IBI when AT is below 11.8 °C. The intricate interactions between hydrological variables (VF and RW) and AT, as well as between water quality parameters (WT, NO3-N, TP, COD) and AT, collectively sculpt the spatial distribution of P-IBI. The fusion of XGBoost-SHAP with FCM unveils pronounced north-south gradient disparities in aquatic biological integrity across the watershed, segmenting the region into four distinct zones. This establishes scientific boundary conditions for the conservation strategies and management practices of aquatic ecosystems in the region, and its flexibility is applicable to the analysis of spatial heterogeneity in other complex environmental contexts.
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Affiliation(s)
- Lina Li
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China; School of Water Conservancy Science and Engineering, Zhengzhou University, Zhengzhou, 450001, China
| | - Rui Xia
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China.
| | - Ming Dou
- School of Water Conservancy Science and Engineering, Zhengzhou University, Zhengzhou, 450001, China
| | - Kai Zhang
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - Yan Chen
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China; Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
| | - Ruining Jia
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China; Northwest University College of Urban and Environmental Sciences, Northwest University, Xi'an, 710127, China
| | - Xiaoxuan Li
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - Jinghui Dou
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China; Northwest University College of Urban and Environmental Sciences, Northwest University, Xi'an, 710127, China
| | - Xiang Li
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - Qiang Hu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - Hui Zhang
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China; School of Information Technology & Management, University of International Business and Economics, 100029, China
| | - Nixi Zhong
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China; Northwest University College of Urban and Environmental Sciences, Northwest University, Xi'an, 710127, China
| | - Chao Yan
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China; Northwest University College of Urban and Environmental Sciences, Northwest University, Xi'an, 710127, China
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Guo S, Sun X, Zhang J, Yao Q, Wei C, Wang F. Unveiling the evolution of phytoplankton communities: Decades-long insights into the southern Yellow Sea, China (1959-2023). MARINE POLLUTION BULLETIN 2024; 201:116179. [PMID: 38394795 DOI: 10.1016/j.marpolbul.2024.116179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 02/11/2024] [Accepted: 02/16/2024] [Indexed: 02/25/2024]
Abstract
We obtained historical and observational data on phytoplankton communities from 1959 to 2023 to explore the responses of the phytoplankton community structure to long-term environmental changes in the southern Yellow Sea (SYS), China. The results revealed a decrease in the proportions of diatom cell abundance within the phytoplankton community by 8 %, accompanied by a corresponding increase in that of dinoflagellates. Dominant phytoplankton species were mainly chain-forming diatoms before 2000, and large dinoflagellate species from the genera Tripos and Noctiluca increased their dominance after 2000. Warm-water phytoplankton species have increased in dominance over the study period. Correlation analysis revealed that the ocean warming and alterations in nutrient structure (N/P and Si/N ratios) were mostly responsible for the long-term evolution trend, and these changes may result in an increase in dinoflagellate harmful algal blooms, reduced efficiency of the biological carbon pump, and heightened hypoxia in the future, which should draw our attention.
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Affiliation(s)
- Shujin Guo
- Jiaozhou Bay National Marine Ecosystem Research Station, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, PR China; Laboratory for Marine Ecology and Environmental Science, Qingdao Marine Science and Technology Center, Qingdao 266237, PR China; Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, PR China
| | - Xiaoxia Sun
- Jiaozhou Bay National Marine Ecosystem Research Station, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, PR China; Laboratory for Marine Ecology and Environmental Science, Qingdao Marine Science and Technology Center, Qingdao 266237, PR China; Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China.
| | - Jian Zhang
- National Marine Data and Information Service, Tianjin 300171, PR China
| | - Qingzhen Yao
- Frontiers Science Center for Deep Ocean Multispheres and Earth System, and Key Laboratory of Marine Chemistry Theory and Technology, Ministry of Education, Ocean University of China, Qingdao 266100, PR China
| | - Chuanjie Wei
- Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, PR China; Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, PR China
| | - Feng Wang
- Jiaozhou Bay National Marine Ecosystem Research Station, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China
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Li S, Dong Y, Sun X, Zhao Y, Zhao L, Zhang W, Xiao T. Seasonal and spatial variations of Synechococcus in abundance, pigment types, and genetic diversity in a temperate semi-enclosed bay. Front Microbiol 2024; 14:1322548. [PMID: 38274747 PMCID: PMC10808157 DOI: 10.3389/fmicb.2023.1322548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 12/29/2023] [Indexed: 01/27/2024] Open
Abstract
Synechococcus is abundant and globally widespread in various marine environments. Seasonal and spatial variations in Synechococcus abundance, pigment types, and genetic diversity were investigated based on flow cytometric analysis and high-throughput sequencing of cpcBA operon (encoding phycocyanin) and rpoC1 gene (encoding RNA polymerase) in a temperate semi-enclosed bay. Synechococcus abundance exhibited seasonal variations with the highest value in summer and the lowest value in winter, which was consistent with temperature variation. Three pigment types of Synechococcus type 1, type 2, and type 3 were distinguished based on cpcBA operon, which displayed obvious variations spatially between the inner and the outer bay. Freshwater discharge and water turbidity played important roles in regulating Synechococcus pigment types. Synechococcus assemblages were phylogenetically diverse (12 different lineages) based on rpoC1 gene and dominated by three core lineages S5.1-I, S5.1-IX, and S5.2-CB5 in different seasons. Our study demonstrated that Synechococcus abundance, pigment types, and genetic diversity displayed variations seasonally and spatially by different techniques, which were mainly driven by temperature, salinity, nutrients, and turbidity. The combination of more technical means provides more information for studying Synechococcus distribution. In this study, three pigment types of Synechococcus were discriminated simultaneously by dual lasers flow cytometer for the first time.
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Affiliation(s)
- Suheng Li
- CAS Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China
- Laboratory for Marine Ecology and Environmental Science, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China
- Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yi Dong
- CAS Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China
- Laboratory for Marine Ecology and Environmental Science, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China
- Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, China
| | - Xiaoxia Sun
- CAS Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China
- Laboratory for Marine Ecology and Environmental Science, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China
- Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, China
- Jiaozhou Bay Marine Ecosystem Research Station, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China
| | - Yuan Zhao
- CAS Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China
- Laboratory for Marine Ecology and Environmental Science, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China
- Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, China
| | - Li Zhao
- CAS Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China
- Laboratory for Marine Ecology and Environmental Science, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China
- Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, China
| | - Wuchang Zhang
- CAS Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China
- Laboratory for Marine Ecology and Environmental Science, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China
- Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, China
| | - Tian Xiao
- CAS Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China
- Laboratory for Marine Ecology and Environmental Science, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China
- Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, China
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