Artificial Intelligence–Enhanced Tropical Forest Coexistence Modeling

Harnessing machine learning to enhance plant coexistence in a vegetation demographic model.

Harmonious plant coexistence in a vibrant tropical forest ecosystem.

[Courtesy Justin Clark on Unsplash.]

The Science

Tropical forests are critical components of global carbon, water, and energy cycles with the highest biodiversity on Earth. However, modeling the coexistence of different plant types—a key feature of biodiversity—in these forests remains challenging. Researchers used a vegetation demographic model, the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), integrated with the Energy Exascale Earth System Model (E3SM) Land Model (ELM) to improve modeling plant coexistence. The team employed advanced machine learning (ML) techniques to optimize key trait parameters in FATES, remarkably enhancing plant coexistence simulations. The ML approach also improved the accuracy of FATES simulations of water, energy, and carbon fluxes and aboveground biomass.

The Impact

By harnessing the power of ML, this study significantly enhanced scientists’ models of different plant types’ coexistence in tropical forests. Artificial intelligence–enhanced ecosystem models could accurately predict the effects of environmental changes on diverse ecosystems, fostering effective strategies for sustainable development, carbon sequestration, and achieving carbon-neutral and net-zero emissions goals. Moreover, this research highlights the need for advancing vegetation demographic models to refine coexisting plant simulations to capture intricate ecosystem interactions.


A research team employed two approaches to optimize trait parameters in FATES: leveraging field-based plant trait relationships and utilizing ML surrogate models. Ensembles of FATES experiments were conducted on a tropical forest site near Manaus, Brazil, in the Amazon basin. The ML-based surrogate models were used to optimize trait parameters in FATES to improve plant functional type (PFT): plants that have similar environmental responses, ecosystem roles, and coexistence and achieve better model-observation agreements.

Considering only observed trait relationships improved the water, energy, and carbon simulations but degraded PFT coexistence in ELM-FATES simulations. The ML approach significantly enhanced PFT coexistence in the FATES experiments, increasing its occurrence from 21 to 73%. After applying observation constraints to identify small simulation biases, the ML-guided simulations retained 33% of the coexistence experiments, showing a 23.6-fold increase in PFT coexistence compared to the default experiments. The ML approach also improved FATES simulations of water, energy, and carbon fluxes, as well as aboveground biomass. Based on these results, researchers propose a reproducible ML method to improve model fidelity and PFT coexistence in vegetation demography models. This research highlights the potential of using ML in Earth system modeling of ecosystem dynamics and their response and feedback to climate change impacts.

Principal Investigator

Ruby Leung
Pacific Northwest National Laboratory
[email protected]

Program Manager

Brian Benscoter
U.S. Department of Energy, Biological and Environmental Research (SC-33)
Environmental System Science
[email protected]


This research was conducted at Pacific Northwest National Laboratory, operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under contract DE-AC05-76RL01830. This study was supported by DOE’s Biological and Environmental Research (BER) program through the Next-Generation Ecosystem Experiments (NGEE)-Tropics project. Funding was also provided by the European Union’s Horizon 2020 (H2020) research and innovation program under grant agreement nos. 101003536 (ESM2025–Earth System Models for the Future) and 821003 (4C, Climate–Carbon Interactions in the Coming Century).

Related Links


Li, L., et al. "A Machine Learning Approach Targeting Parameter Estimation for Plant Functional Type Coexistence Modeling Using ELM-FATES (v2.0)." Geoscientific Model Development 16 4017–40  (2023).