Diurnal distribution of phytoplankton in large shallow lakes based on time series clustering
Phytoplankton blooms, which occur over short to seasonal time scales, threaten freshwater ecosystems. However, short-term changes in phytoplankton distributions are often overlooked, leading to underestimations in predictions and difficulties in lake management. Considering that potential informatio...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125002596 |
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| author | Yanhong Chen Haibin Cai Yiqing Gong Kun Lu Jingqiao Mao Weiyu Chen Kang Wang Huan Gao Mingming Tian |
| author_facet | Yanhong Chen Haibin Cai Yiqing Gong Kun Lu Jingqiao Mao Weiyu Chen Kang Wang Huan Gao Mingming Tian |
| author_sort | Yanhong Chen |
| collection | DOAJ |
| description | Phytoplankton blooms, which occur over short to seasonal time scales, threaten freshwater ecosystems. However, short-term changes in phytoplankton distributions are often overlooked, leading to underestimations in predictions and difficulties in lake management. Considering that potential information from abundant automatic monitoring datasets has not been fully explored, we developed an automated recognition method to identify diurnal variations in phytoplankton via time series clustering. The temporal and spatial characteristics of the diurnal patterns of the chlorophyll-a (Chl-a) concentration in Taihu Lake were explored. Additionally, the contributions of environmental factors to the phytoplankton distribution were analysed. The results revealed that (1) the diurnal changes in the Chl-a concentration in Taihu Lake could be divided into four main patterns, each with unique potential hotspots that reflect different ecological responses; (2) these patterns revealed high spatiotemporal heterogeneity across the lake, highlighting the complex ecological dynamics; and (3) notably, strong winds, an increase in temperature, and abrupt environmental fluctuations at the day scale were identified as critical drivers of diurnal phytoplankton patterns. This study demonstrates the potential of employing unsupervised clustering algorithms for identifying the diurnal dynamics of the phytoplankton distribution under complex influences, which can aid in optimizing the management and monitoring of shallow lakes. |
| format | Article |
| id | doaj-art-0f8c5ece8ae04750abe408288069cb27 |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-0f8c5ece8ae04750abe408288069cb272025-08-20T05:05:19ZengElsevierEcological Informatics1574-95412025-12-019010325010.1016/j.ecoinf.2025.103250Diurnal distribution of phytoplankton in large shallow lakes based on time series clusteringYanhong Chen0Haibin Cai1Yiqing Gong2Kun Lu3Jingqiao Mao4Weiyu Chen5Kang Wang6Huan Gao7Mingming Tian8College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, ChinaCollege of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, ChinaInstitute of Water Science and Technology, Hohai University, Nanjing 210098, ChinaSu Yi Design Group Co., Ltd, Nanjing 210012, ChinaCollege of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China; Corresponding author.School of Civil Engineering Architecture, Jiangsu Open University, Nanjing 210000, ChinaCollege of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, ChinaCollege of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, ChinaCollege of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, ChinaPhytoplankton blooms, which occur over short to seasonal time scales, threaten freshwater ecosystems. However, short-term changes in phytoplankton distributions are often overlooked, leading to underestimations in predictions and difficulties in lake management. Considering that potential information from abundant automatic monitoring datasets has not been fully explored, we developed an automated recognition method to identify diurnal variations in phytoplankton via time series clustering. The temporal and spatial characteristics of the diurnal patterns of the chlorophyll-a (Chl-a) concentration in Taihu Lake were explored. Additionally, the contributions of environmental factors to the phytoplankton distribution were analysed. The results revealed that (1) the diurnal changes in the Chl-a concentration in Taihu Lake could be divided into four main patterns, each with unique potential hotspots that reflect different ecological responses; (2) these patterns revealed high spatiotemporal heterogeneity across the lake, highlighting the complex ecological dynamics; and (3) notably, strong winds, an increase in temperature, and abrupt environmental fluctuations at the day scale were identified as critical drivers of diurnal phytoplankton patterns. This study demonstrates the potential of employing unsupervised clustering algorithms for identifying the diurnal dynamics of the phytoplankton distribution under complex influences, which can aid in optimizing the management and monitoring of shallow lakes.http://www.sciencedirect.com/science/article/pii/S1574954125002596Shallow lakesDiurnal variationPhytoplankton distributionEnvironmental contributionsTime series clustering |
| spellingShingle | Yanhong Chen Haibin Cai Yiqing Gong Kun Lu Jingqiao Mao Weiyu Chen Kang Wang Huan Gao Mingming Tian Diurnal distribution of phytoplankton in large shallow lakes based on time series clustering Ecological Informatics Shallow lakes Diurnal variation Phytoplankton distribution Environmental contributions Time series clustering |
| title | Diurnal distribution of phytoplankton in large shallow lakes based on time series clustering |
| title_full | Diurnal distribution of phytoplankton in large shallow lakes based on time series clustering |
| title_fullStr | Diurnal distribution of phytoplankton in large shallow lakes based on time series clustering |
| title_full_unstemmed | Diurnal distribution of phytoplankton in large shallow lakes based on time series clustering |
| title_short | Diurnal distribution of phytoplankton in large shallow lakes based on time series clustering |
| title_sort | diurnal distribution of phytoplankton in large shallow lakes based on time series clustering |
| topic | Shallow lakes Diurnal variation Phytoplankton distribution Environmental contributions Time series clustering |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125002596 |
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