Abstract
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.
| Original language | English |
|---|---|
| Article number | e2023GL104962 |
| Journal | Geophysical Research Letters |
| Volume | 51 |
| Issue number | 1 |
| Early online date | 27 Dec 2023 |
| DOIs | |
| Publication status | Published - 16 Jan 2024 |
Bibliographical note
Funding Information:JCP, GA, MGDK, and AJP were supported by the Australian Research Council Centre of Excellence for Climate Extremes (CE170100023). MGDK was additionally supported by the ARC Discovery Grants (Grant DP190102025 and DP190101823), the NSW Research Attraction and Acceleration Program, and acknowledges funding from the UK Natural Environment Research Council (NE/W010003/1). We thank the National Computational Infrastructure at the Australian National University, an initiative of the Australian Government for access to supercomputer resources. The authors would also like to thank Daniel Falster at UNSW for his insightful comments and discussions, and the site PIs at ICOS for providing the data on the flux products. Open access publishing facilitated by University of New South Wales, as part of the Wiley - University of New South Wales agreement via the Council of Australian University Librarians.
Funding Information:
JCP, GA, MGDK, and AJP were supported by the Australian Research Council Centre of Excellence for Climate Extremes (CE170100023). MGDK was additionally supported by the ARC Discovery Grants (Grant DP190102025 and DP190101823), the NSW Research Attraction and Acceleration Program, and acknowledges funding from the UK Natural Environment Research Council (NE/W010003/1). We thank the National Computational Infrastructure at the Australian National University, an initiative of the Australian Government for access to supercomputer resources. The authors would also like to thank Daniel Falster at UNSW for his insightful comments and discussions, and the site PIs at ICOS for providing the data on the flux products. Open access publishing facilitated by University of New South Wales, as part of the Wiley ‐ University of New South Wales agreement via the Council of Australian University Librarians.
Publisher Copyright:
© 2023. The Authors.
Keywords
- machine learning
- plant functional traits
- plant functional types
- terrestrial fluxes
