全球变化研究组知识库

The annual gross primary productivity (AGPP) forms the foundation for terrestrial carbon sink. AGPP trends stem from the combined impacts of environmental factors and ecosystem responses to those factors. These responses include model structure modifications altering both the structural properties (represented by leaf area index) and the functional properties (represented by per leaf AGPP). Understanding the contributions of ecosystem responses to AGPP trends is crucial for accurately assessing spatiotemporal variations in AGPP, which aids in effective carbon management. Based on eddy covariance measurements of AGPP in China, we generated AGPP during 2000-2020 by constructing yearly AGPP mapping schemes from site measurements and analyzed AGPP trends. The roles of ecosystem responses and their pathways were further elucidated by employing analysis of variance. Results showed that the total AGPP in China was 7.39 +/- 0.62 PgC yr(-1), with an increasing trend of 0.095 PgC yr(-2). This increase primarily sourced from the total AGPP in Subtropical Evergreen Needleleaf Forest. AGPP trends spatially decreased from east to west, with most regions showing positive values. Structural and functional properties contributed similarly to AGPP trends, with varying regional contributions. The structural properties played a dominant role in north regions while the functional properties were more important in south and west regions. Ecosystem responses similarly determined the trends of ecosystem properties, which decreased from west to east. Ecosystem responses thus dominated AGPP trends through the structural properties in north regions but through the functional properties in south and west regions. Therefore, Chinese AGPP exhibited a spatially varied increasing trend during 2000-2020. Ecosystem responses dominated AGPP trends through divergent properties across regions. Our results highlight the significanc of ecosystem responses in AGPP trends and their divergent pathways across regions. This also provides independent data support for quantifying regional carbon budgets.