Anticipating global terrestrial ecosystem state change using FLUXNET.

Area of Science:

• Ecology
• Global Change Biology
• Computational Biology

Background:

• Ecosystems are complex systems with dynamic functional and structural states.
• Understanding ecosystem state transitions under changing bioclimatic forcings is crucial for global change biology.
• Process Networks (PN) offer a method to empirically describe ecosystem functional states using time-series data.

Purpose of the Study:

• To empirically describe monthly ecosystem functional states and their couplings using the FLUXNET LaThuile dataset.
• To calculate the elasticity of these functional couplings to seasonal bioclimatic forcings.
• To extrapolate these elasticities globally using artificial neural networks to map ecosystem responses to climate change.

Main Methods:

• Utilized the FLUXNET LaThuile synthesis dataset for 204 terrestrial sites.
• Developed Process Networks (PN) to quantify functional couplings between ecosystem components.
• Calculated elasticity of couplings to air temperature, precipitation, solar radiation, and phenophase.
• Employed artificial neural networks for global extrapolation of elasticity.

Main Results:

• Generated the LaThuile PN version 1.0 database, detailing ecosystem functional couplings.
• Created global maps of functional elasticity to seasonal bioclimatic forcings.
• Identified tropical forests, hot deserts, savannas, and high elevations as highly elastic to climate change.
• Found ecosystem elasticity to seasonal air temperature is significantly higher than to other forcings.

Conclusions:

• The generated elasticity maps provide a novel resource for anticipating ecological state transitions and validating models.
• Ecosystems exhibit unique responses to changing forcings, with significant variation in elasticity.
• Functional elasticity shows moderate relationships with structural state changes across ecosystems.