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Joint Control of Net Primary Productivity by Climate and Soil Nitrogen in the Forests of Eastern China
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The nature and extent of climate and soil nutrient controls in Chinese forests remain poorly resolved. Here, we synthesized the data on carbon–climate–soil in eastern China, and litter N was firstly taken into consideration, to examine the variation of net primary productivity (NPP) and its driving forces. Results showed that NPP had significant latitude pattern and varied substantially across climate zones. Bivariate analyses indicated that mean annual temperature (MAT), mean annual precipitation (MAP), soil N content (Nsoil), and annual litter N (Nre) were the main controlling factors in spatial pattern of forest NPP. Notably, partial general linear model analysis revealed that MAT, MAP, and Nre jointly explained 84.8% of the spatial variation of NPP. Among the three major factors, Nre explained more variation of forest NPP than the other two factors, and MAT and MAP affected NPP mainly through the change of litter N rather than via themselves, highlighting the importance of litter N in estimating forest NPP. However, to accurately describe the pattern of forest NPP in China, more detailed field measurements and methodologies on NPP and relevant confounding factors should be addressed in future studies.
Title: Joint Control of Net Primary Productivity by Climate and Soil Nitrogen in the Forests of Eastern China
Description:
The nature and extent of climate and soil nutrient controls in Chinese forests remain poorly resolved.
Here, we synthesized the data on carbon–climate–soil in eastern China, and litter N was firstly taken into consideration, to examine the variation of net primary productivity (NPP) and its driving forces.
Results showed that NPP had significant latitude pattern and varied substantially across climate zones.
Bivariate analyses indicated that mean annual temperature (MAT), mean annual precipitation (MAP), soil N content (Nsoil), and annual litter N (Nre) were the main controlling factors in spatial pattern of forest NPP.
Notably, partial general linear model analysis revealed that MAT, MAP, and Nre jointly explained 84.
8% of the spatial variation of NPP.
Among the three major factors, Nre explained more variation of forest NPP than the other two factors, and MAT and MAP affected NPP mainly through the change of litter N rather than via themselves, highlighting the importance of litter N in estimating forest NPP.
However, to accurately describe the pattern of forest NPP in China, more detailed field measurements and methodologies on NPP and relevant confounding factors should be addressed in future studies.
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