This study estimates the carbon use efficiency (CUE) of ecosystems using eddy covariance observations of CO2 flux between ecosystems and the atmosphere. The researchers used a theoretical basis to estimate CUE from these observations, accounting for factors such as temperature, growth respiration, and maintenance respiration. They organized daily flux data into groups of 5 consecutive days and estimated CUE, growth respiration, and maintenance respiration for each group using a Markov Chain Monte Carlo (MCMC) method. The researchers also used a random forest model to estimate global CUE using climate, soil, and vegetation variables. The study found that CUE varies across different plant functional types (PFTs) and is influenced by factors such as temperature, precipitation, and soil nitrogen content. The estimated CUE values were validated using independent datasets, including tree-ring records and soil respiration observations. The study provides a new understanding of the factors controlling CUE and its variation across different ecosystems, which can inform models of ecosystem carbon cycling and help predict the responses of ecosystems to climate change.
Global differences in vegetation’s ability to use carbon efficiently are deduced from eddy covariance measurement data
