Are Plant Functional Types Fit for Purpose?

• Published in: Geophysical research letters 2024-01, Vol.51 (1), p.n/a
• Main Authors: Cranko Page, Jon, Abramowitz, Gab, De Kauwe, Martin. G., Pitman, Andy J.
• Format: Article
• Language: English
• Subjects: Analysis; Biosphere; Carbon sequestration; Classification; Climate; Climate change; Climate models; Climate prediction; Coniferous forests; Datasets; Fluctuations; Grasslands; Land surface models; Machine learning; Missing data; Modelling; plant functional traits; plant functional types; Plants; Soils; Terrestrial ecosystems; terrestrial fluxes; Time series; Variables; Vegetation
• ISSN: 0094-8276; 1944-8007
• DOI: 10.1029/2023GL104962

For over 40 years, Plant Functional Types (PFTs) have been used to discretize the ∼400,000 species of terrestrial plants into “similar” classes. Within Earth System Models (ESMs), PFTs simplify terrestrial biosphere modeling in combination with soil information and other site characteristics. However, in flux analysis studies, PFT schemes are often implemented as the sole analytical lens to clarify complex behavior. This usage assumes that PFTs adequately enable a mapping between climate inputs and flux outputs. Here, we show that random forest models, trained using aggregated climate and flux measurements from 245 eddy‐covariance sites, cannot accurately predict PFT groupings, regardless of the nature of the PFT scheme. Similarly, PFTs provide negligible benefit when using site climate to predict site flux regimes and vice versa. While use of PFT classifications is convenient, our results suggest they do not aid analytical skill, which has important implications for future terrestrial flux studies. Plain Language Summary To understand how the land surface behaves, we often divide plants into a small number (20 or less) of ”similar” groups, such as evergreen forests, or grasslands, known as Plant Functional Types (PFTs). The idea is that landscapes with similar large‐scale characteristics will behave in the same way. In land surface models, these PFT groups determine how the simulated plants react to the climate in combination with soil information and other characteristics, yet analysis of observations often use PFT groups alone to try to explain variations in results between different experimental sites. We use machine learning to show that while PFTs might be visually compelling, they do not necessarily represent behavior groupings and might actually hide real world behavior if used for analysis. As such, we suggest that future studies instead try to look at more specific site characteristics when trying to explain analysis results. Key Points Plant Functional Types (PFTs), as often used in land flux studies, are not easily empirically associated with site climate and/or flux regimes A broad selection of alternative vegetation/land cover classifications do not offer greater predictability The disconnect between PFTs and climate/flux regimes has implications for modeling and analysis of terrestrial systems