A machine learning approach to study plant functional trait divergence

• Published in: Applications in plant sciences Vol. 12; no. 5; pp. e11576 - n/a
• Main Authors: Majumder, Sambadi, Mason, Chase M.
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.09.2024; John Wiley and Sons Inc; Wiley
• Subjects: Application; Diversification; ecophysiology; Environmental conditions; evolution; feature selection; Flowers & plants; genus; gradient boosting machine; Helianthus; Interspecific; intraspecific variation; Learning algorithms; Machine learning; Morphology; multidimensional; Observational learning; Phenotypic divergence; phenotypic variation; Phenotypic variations; Physiology; random forest; Seeds; species; gradient boosting machine; random forest; ecophysiology; multidimensional; Helianthus; feature selection
• ISSN: 2168-0450; 2168-0450
• DOI: 10.1002/aps3.11576

Premise Plant functional traits are often used to describe the spectra of ecological strategies used by different species. Here, we demonstrate a machine learning approach for identifying the traits that contribute most to interspecific phenotypic divergence in a multivariate trait space. Methods De...