Continuous Plant Trait Vectors using Generative AI

Plant morphological and physiological trait combinations exist on an almost continuous spectrum across varying climate conditions and geographic locations. Currently, a dominant but limiting approach to capturing this diversity, for example within Earth System Models (ESMs), is to discretize into few largely arbitrary Plant Functional Type (PFT) categories (e.g. tropical broad-leaved deciduous, C3 grass, temperate needle-leaved evergreen, etc.) based on broad functional similarities and responses to the environment, leading to much information loss.

Given recent advances in generative Artificial Intelligence (AI), it is now possible to develop Deep Learning (DL) models that can learn the distribution of plant trait vectors conditioned under varying environmental factors. This work explores using generative modelling approaches like conditional variational autoencoders / flow matching to train a Neural Network (NN) to learn the joint distribution of 26 plant traits as in the TRY Plant Trait Database under different environmental conditions across the globe. Generation is conditioned on climate variables from the ERA5-Land reanalysis dataset and Copernicus Digital Elevation Model fetched via Google Earth Engine alongside soil properties obtained from the ISRIC WISE30sec dataset.

Outputs of such a trained model can contribute towards downscaling and gap-filling approaches, as well as studies trying to understand plant responses under changing climate conditions. Furthermore, trained hidden layer output embeddings, being Continuous Plant Trait Vectors (CPTVs), better capture the spectrum of varying trait combinations. Such information-rich CPTVs have the potential to  be viable alternatives to PFT classes w.r.t parameterization of Earth System Functions within ESMs. The model itself serves as a tool for furthering understanding of plant functional adaptations through exploration of the learned trait space via cluster analysis, enabling the identification of latent structure, relationships, and patterns, as well as supporting hypothesis generation and comparative analysis across populations or conditions.