Critical classification parameters linking species to Plant Functional Type in African ecosystems.

Area of Science:

• Ecology
• Computational Biology
• Earth System Science

Background:

• Accurate African ecosystem simulation in land surface models (LSMs) is hindered by scarce ecological data, particularly plant traits.
• Existing plant trait databases have limited applicability for LSMs requiring specific Plant Functional Types (PFTs).

Purpose of the Study:

• To systematically classify African plant species into PFTs compatible with the JULES LSM.
• To improve the parameterization of PFTs in LSMs for better African ecosystem representation.

Main Methods:

• Utilized the TRY plant trait database, encompassing 2,082 African plant species.
• Assigned classification parameters (growth form, leaf type, phenology, photosynthetic pathway, climate zone) using multiple data sources.
• Developed a lookup table for integrating plant trait data into LSM PFT parameterizations.

Main Results:

• Increased the number of plant species mapped to PFT classes sixfold (from 265 to 1603).
• Expanded usable trait observations fivefold across 27 evaluated traits.
• Successfully classified species from 137 plant families.

Conclusions:

• The developed lookup table significantly enhances the integration of plant trait data into LSMs.
• Improved PFT parameterization will lead to more accurate simulations of African ecosystems.
• This work supports better large-scale ecological modeling and climate change impact assessments in Africa.