Key traits in functional trait networks can identify the temporal structure of fish communities in coastal waters
Traditional analyses of fish community structure based on species composition and abundance ignore many functional attributes of fish species. The composition of fish functional traits can reflect the attributes and functions of the fish community. However, little is known about which functional tra...
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Elsevier
2025-05-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125000263 |
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| author | Jianyu Zou Xiaozhuang Zhang Yupeng Ji Ying Xue Chongliang Zhang Yiping Ren Binduo Xu |
| author_facet | Jianyu Zou Xiaozhuang Zhang Yupeng Ji Ying Xue Chongliang Zhang Yiping Ren Binduo Xu |
| author_sort | Jianyu Zou |
| collection | DOAJ |
| description | Traditional analyses of fish community structure based on species composition and abundance ignore many functional attributes of fish species. The composition of fish functional traits can reflect the attributes and functions of the fish community. However, little is known about which functional traits can reflect the temporal structure of fish communities. In this study, we employed trait network analysis, principal coordinates analysis, cluster analysis, and Spearman correlation analysis to construct a functional trait network, identify key traits, examine interannual changes in functional trait composition, and explore changes in the temporal structure and functions of fish communities in Haizhou Bay and its adjacent waters, Yellow Sea, China, from 2013 to 2022 based on species data collected from bottom trawl surveys. The composition of functional traits has changed significantly over the past decade, indicating significant alterations in the attributes and functions of the fish community within Haizhou Bay. All 95 strong correlations in the functional trait network were positive, suggesting that competitive interaction was very weak in the entire community assembly. The temporal structure of the fish community was determined based on 10 key traits, and it was consistent with that identified via species-based analysis. The highly significant positive correlations between the similarity matrices of the key traits and the species similarity matrix indicated that interannual differences in key traits could well represent discrepancies in the fish community structure in different years. Our study revealed that key traits in the functional trait network could be used to identify the temporal structure of fish communities and revealed differences in the functions of different temporal components. |
| format | Article |
| id | doaj-art-cbf8b131e82340bab6c88fd55e2c217f |
| institution | OA Journals |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
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| series | Ecological Informatics |
| spelling | doaj-art-cbf8b131e82340bab6c88fd55e2c217f2025-08-20T02:03:46ZengElsevierEcological Informatics1574-95412025-05-018610301710.1016/j.ecoinf.2025.103017Key traits in functional trait networks can identify the temporal structure of fish communities in coastal watersJianyu Zou0Xiaozhuang Zhang1Yupeng Ji2Ying Xue3Chongliang Zhang4Yiping Ren5Binduo Xu6College of Fisheries, Ocean University of China, Qingdao 266003, ChinaCollege of Fisheries, Ocean University of China, Qingdao 266003, ChinaCollege of Fisheries, Ocean University of China, Qingdao 266003, China; Field Observation and Research Station of Haizhou Bay Fishery Ecosystem, Ministry of Education, Qingdao 266003, ChinaCollege of Fisheries, Ocean University of China, Qingdao 266003, China; Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao Marine Science and Technology Center, Qingdao 266237, China; Field Observation and Research Station of Haizhou Bay Fishery Ecosystem, Ministry of Education, Qingdao 266003, ChinaCollege of Fisheries, Ocean University of China, Qingdao 266003, China; Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao Marine Science and Technology Center, Qingdao 266237, China; Field Observation and Research Station of Haizhou Bay Fishery Ecosystem, Ministry of Education, Qingdao 266003, ChinaCollege of Fisheries, Ocean University of China, Qingdao 266003, China; Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao Marine Science and Technology Center, Qingdao 266237, China; Field Observation and Research Station of Haizhou Bay Fishery Ecosystem, Ministry of Education, Qingdao 266003, ChinaCollege of Fisheries, Ocean University of China, Qingdao 266003, China; Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao Marine Science and Technology Center, Qingdao 266237, China; Field Observation and Research Station of Haizhou Bay Fishery Ecosystem, Ministry of Education, Qingdao 266003, China; Corresponding author at: 5 Yushan Road, Shinan District, Qingdao 266003, Shandong Province, China.Traditional analyses of fish community structure based on species composition and abundance ignore many functional attributes of fish species. The composition of fish functional traits can reflect the attributes and functions of the fish community. However, little is known about which functional traits can reflect the temporal structure of fish communities. In this study, we employed trait network analysis, principal coordinates analysis, cluster analysis, and Spearman correlation analysis to construct a functional trait network, identify key traits, examine interannual changes in functional trait composition, and explore changes in the temporal structure and functions of fish communities in Haizhou Bay and its adjacent waters, Yellow Sea, China, from 2013 to 2022 based on species data collected from bottom trawl surveys. The composition of functional traits has changed significantly over the past decade, indicating significant alterations in the attributes and functions of the fish community within Haizhou Bay. All 95 strong correlations in the functional trait network were positive, suggesting that competitive interaction was very weak in the entire community assembly. The temporal structure of the fish community was determined based on 10 key traits, and it was consistent with that identified via species-based analysis. The highly significant positive correlations between the similarity matrices of the key traits and the species similarity matrix indicated that interannual differences in key traits could well represent discrepancies in the fish community structure in different years. Our study revealed that key traits in the functional trait network could be used to identify the temporal structure of fish communities and revealed differences in the functions of different temporal components.http://www.sciencedirect.com/science/article/pii/S1574954125000263Fish communityFunctional traitsTrait networkKey traitsTemporal structure |
| spellingShingle | Jianyu Zou Xiaozhuang Zhang Yupeng Ji Ying Xue Chongliang Zhang Yiping Ren Binduo Xu Key traits in functional trait networks can identify the temporal structure of fish communities in coastal waters Ecological Informatics Fish community Functional traits Trait network Key traits Temporal structure |
| title | Key traits in functional trait networks can identify the temporal structure of fish communities in coastal waters |
| title_full | Key traits in functional trait networks can identify the temporal structure of fish communities in coastal waters |
| title_fullStr | Key traits in functional trait networks can identify the temporal structure of fish communities in coastal waters |
| title_full_unstemmed | Key traits in functional trait networks can identify the temporal structure of fish communities in coastal waters |
| title_short | Key traits in functional trait networks can identify the temporal structure of fish communities in coastal waters |
| title_sort | key traits in functional trait networks can identify the temporal structure of fish communities in coastal waters |
| topic | Fish community Functional traits Trait network Key traits Temporal structure |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125000263 |
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