The response of terrestrial vegetation carbon use efficiency to global environmental changes

Terrestrial vegetation carbon use efficiency (CUE) is a key measure for assessing carbon transfer from the atmosphere to terrestrial biomass, crucial for understanding carbon cycling and allocation in ecosystems. CUE provides valuable insights into how terrestrial ecosystems respond to environmental changes. In this study, we utilized satellite datasets (MODIS and GLASS), MsTMIP models, and TRENDY models to analyze the spatiotemporal distribution characteristics of global vegetation CUE. We found that the average CUE for global land vegetation is 0.44 ± 0.06, with a slight annual increase and significant spatial heterogeneity, characterized by latitude gradients and vegetation types. High-latitude regions demonstrated higher CUE values compared to low-latitude regions. Further employing an integrated attribution approach, we identified the response mechanisms of vegetation CUE to global changes. The comprehensive response of vegetation CUE to climate change, land use change, atmospheric CO₂, and nitrogen deposition was found to fluctuate and increase, with a model-averaged CUE increase of approximately 0.01. Land use change was identified as the largest contributor to the annual trend of overall global CUE (48.8%), while climate change was the main factor influencing the interannual variability (IAV) of global CUE (91.7%). Regarding global distribution, the IAV of vegetation CUE is mainly influenced by climate change. CUE annual trends in more regions were influenced by climate change, with 65% and 73% of the ensemble mean of the MsTMIP and TRENDY models, respectively. The results of the MsTMIP and TRENDY models consistently show that, globally, land use change affects about a quarter of the total annual trend of CUE. Land use change affected CUE annual trends to a greater extent than climate change. In addition, the vegetation type most affected by climate change is the deciduous needleleaf forests, and the CUE annual trend of cropland is most affected by land use change. Our findings reveal global patterns and drivers of CUE variability, highlighting the significant impact of climate change and land use change.