Elevation-dependent pattern of net CO₂ uptake across China

Area of Science:

• Ecology
• Environmental Science
• Climate Change Research

Background:

• Elevation gradients significantly influence terrestrial ecosystem structure and function.
• Understanding elevation-dependent patterns of carbon uptake, specifically net ecosystem productivity (NEP), is crucial but underexplored.

Purpose of the Study:

• To investigate the elevation-dependent patterns of net ecosystem productivity (NEP) across China.
• To analyze the factors shaping NEP along elevation gradients.
• To assess the temperature sensitivity and temporal changes of NEP in different elevation zones.

Main Methods:

• Analysis of data from 203 eddy covariance sites across China.
• Statistical analysis of elevation-dependent patterns of NEP.
• Assessment of temperature sensitivity using observed data.
• Utilizing model ensemble and satellite observations for temporal trend analysis.
• Application of machine learning for future NEP change predictions.

Main Results:

• A negative linear elevation-dependent pattern of NEP was identified across China.
• Hydrothermal factors, nutrient supply, and ecosystem types collectively shape this pattern.
• Net ecosystem productivity (NEP) exhibits higher temperature sensitivity in high-elevation environments (3000-5000 m) compared to lower elevations (<3000 m).
• High-elevation environments show more rapid relative changes in NEP over the past four decades.
• Machine learning predicts a stronger relative increase in NEP in high-elevation areas.

Conclusions:

• Terrestrial ecosystems in China exhibit a varying elevation-dependent pattern of net ecosystem productivity (NEP).
• High-elevation ecosystems are more sensitive to temperature and undergoing faster changes in carbon uptake.
• Future climate change may lead to disproportionately larger shifts in NEP in high-elevation regions.