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 functional diversity are crucial for effective biomonitoring. However, when it comes to freshwater macroinvertebrates, a significant dearth trait data poses major challenge. this opinion article, we put forward machine-learning framework that incorporates phylogenetic conservatism collinearity, aiming provide better vision predicting macroinvertebrate in ecosystems. By adopting proposed framework, can advance biomonitoring efforts Accurate predictions enable us assess diversity, identify stressors, monitor ecosystem health more effectively. This information is vital making informed decisions regarding conservation management strategies, especially context rapidly changing environments.