Incorporating phylogenetic conservatism and trait collinearity into machine learning frameworks can better predict macroinvertebrate traits
In the face of rapid environmental changes, understanding and monitoring biological traits and functional diversity are crucial for effective biomonitoring. However, when it comes to freshwater macroinvertebrates, a significant dearth of biological trait data poses a major challenge. In this opinion...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-08-01
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| Series: | Frontiers in Ecology and Evolution |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fevo.2023.1260173/full |
| _version_ | 1797732701877305344 |
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| author | Shuyin Li Qingyi Luo Qingyi Luo Ruiwen Li Bin Li |
| author_facet | Shuyin Li Qingyi Luo Qingyi Luo Ruiwen Li Bin Li |
| author_sort | Shuyin Li |
| collection | DOAJ |
| description | In the face of rapid environmental changes, understanding and monitoring biological traits and functional diversity are crucial for effective biomonitoring. However, when it comes to freshwater macroinvertebrates, a significant dearth of biological trait data poses a major challenge. In this opinion article, we put forward a machine-learning framework that incorporates phylogenetic conservatism and trait collinearity, aiming to provide a better vision for predicting macroinvertebrate traits in freshwater ecosystems. By adopting this proposed framework, we can advance biomonitoring efforts in freshwater ecosystems. Accurate predictions of macroinvertebrate traits enable us to assess functional diversity, identify environmental stressors, and monitor ecosystem health more effectively. This information is vital for making informed decisions regarding conservation and management strategies, especially in the context of rapidly changing environments. |
| first_indexed | 2024-03-12T12:17:17Z |
| format | Article |
| id | doaj.art-0a680ce1824a472a9d03520fa5ad0c90 |
| institution | Directory Open Access Journal |
| issn | 2296-701X |
| language | English |
| last_indexed | 2024-03-12T12:17:17Z |
| publishDate | 2023-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Ecology and Evolution |
| spelling | doaj.art-0a680ce1824a472a9d03520fa5ad0c902023-08-30T07:22:29ZengFrontiers Media S.A.Frontiers in Ecology and Evolution2296-701X2023-08-011110.3389/fevo.2023.12601731260173Incorporating phylogenetic conservatism and trait collinearity into machine learning frameworks can better predict macroinvertebrate traitsShuyin Li0Qingyi Luo1Qingyi Luo2Ruiwen Li3Bin Li4Yangtze River Basin Ecological Environment Monitoring and Scientific Research Center, Yangtze River Basin Ecological Environment Supervision and Administration Bureau, Ministry of Ecology and Environment, Wuhan, ChinaState Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, ChinaCollege of Advanced Agricultural Sciences, University of Chinese Academy of Sciences, Beijing, ChinaYangtze River Basin Ecological Environment Monitoring and Scientific Research Center, Yangtze River Basin Ecological Environment Supervision and Administration Bureau, Ministry of Ecology and Environment, Wuhan, ChinaInstitute of Environment and Ecology, Shandong Normal University, Jinan, ChinaIn the face of rapid environmental changes, understanding and monitoring biological traits and functional diversity are crucial for effective biomonitoring. However, when it comes to freshwater macroinvertebrates, a significant dearth of biological trait data poses a major challenge. In this opinion article, we put forward a machine-learning framework that incorporates phylogenetic conservatism and trait collinearity, aiming to provide a better vision for predicting macroinvertebrate traits in freshwater ecosystems. By adopting this proposed framework, we can advance biomonitoring efforts in freshwater ecosystems. Accurate predictions of macroinvertebrate traits enable us to assess functional diversity, identify environmental stressors, and monitor ecosystem health more effectively. This information is vital for making informed decisions regarding conservation and management strategies, especially in the context of rapidly changing environments.https://www.frontiersin.org/articles/10.3389/fevo.2023.1260173/fullbiodiversityglobal changesustainable developmentphylogenetic treetrait |
| spellingShingle | Shuyin Li Qingyi Luo Qingyi Luo Ruiwen Li Bin Li Incorporating phylogenetic conservatism and trait collinearity into machine learning frameworks can better predict macroinvertebrate traits Frontiers in Ecology and Evolution biodiversity global change sustainable development phylogenetic tree trait |
| title | Incorporating phylogenetic conservatism and trait collinearity into machine learning frameworks can better predict macroinvertebrate traits |
| title_full | Incorporating phylogenetic conservatism and trait collinearity into machine learning frameworks can better predict macroinvertebrate traits |
| title_fullStr | Incorporating phylogenetic conservatism and trait collinearity into machine learning frameworks can better predict macroinvertebrate traits |
| title_full_unstemmed | Incorporating phylogenetic conservatism and trait collinearity into machine learning frameworks can better predict macroinvertebrate traits |
| title_short | Incorporating phylogenetic conservatism and trait collinearity into machine learning frameworks can better predict macroinvertebrate traits |
| title_sort | incorporating phylogenetic conservatism and trait collinearity into machine learning frameworks can better predict macroinvertebrate traits |
| topic | biodiversity global change sustainable development phylogenetic tree trait |
| url | https://www.frontiersin.org/articles/10.3389/fevo.2023.1260173/full |
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