Applying an interpretable machine learning approach to assess intraspecific trait variation under landscape-scale population differentiation

Abstract

Premise: Here we demonstrate the application of interpretable machine learning methods to investigate intraspecific functional trait divergence using diverse genotypes of the wide-ranging sunflower Helianthus annuus occupying populations across contrasting ecoregions - the Great Plains versus the North American Deserts. Methods: Recursive feature elimination was applied to functional trait data from the HeliantHome database, followed by the application of Boruta to detect traits most predictive of ecoregion. Random Forest and Gradient Boosting Machine classifiers were then trained and validated, with results visualized using accumulated local effects plots. Key Results: The most ecoregion-predictive functional traits span categories of leaf economics, plant architecture, reproductive phenology, and floral and seed morphology. Relative to the Great Plains, genotypes from the North American Deserts exhibit shorter stature, fewer leaves, higher leaf nitrogen, and longer average length of phyllaries. Conclusions: This approach readily identifies traits predictive of ecoregion origin, and thus functional traits most likely to be responsible for contrasting ecological strategies across the landscape. This type of approach can be used to parse large plant trait datasets in a wide range of contexts, including explicitly testing the applicability of interspecific paradigms at intraspecific scales.