Incorporating phylogenetic conservatism and trait collinearity into machine learning frameworks can better predict macroinvertebrate traits

Frontiers in Ecology and Evolution

Ecology and Evolution

Bin Li¹  Qingyi Luo²  Shuyin Li³  Ruiwen Li³ 

[1] Institute of Environment and Ecology, Shandong Normal University, Jinan, China;State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, China;College of Advanced Agricultural Sciences, University of Chinese Academy of Sciences, Beijing, China;Yangtze River Basin Ecological Environment Monitoring and Scientific Research Center, Yangtze River Basin Ecological Environment Supervision and Administration Bureau, Ministry of Ecology and Environment, Wuhan, China;

关键词: biodiversity;    global change;    sustainable development;    phylogenetic tree;    trait;   

DOI  :  10.3389/fevo.2023.1260173

 received in 2023-07-17, accepted in 2023-08-11,  发布年份 2023

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

Unknown   
Copyright © 2023 Li, Luo, Li and Li