Abstract
Monitoring how plant biomass changes with environmental conditions is critical for evaluating ecosystem carbon storage and understanding the underlying mechanisms of carbon dynamics. Usually destructive methods require a lot of time and cost. And the non-destructive methods to repeatedly estimate the biomass of herbaceous plants across large scale environmental conditions are still limited. Leymus chinensis, as an important constructive species, constitutes a unique community type in the eastern Eurasian steppe across the semi-humid, semi-arid and arid areas in the middle temperate and warm temperate zones. Therefore, it is necessary to analyze the driving factors and establish a unified estimation equation of the aboveground biomass (AGB) of L. chinensis across large scale. In this study, for the first time, we combined climatic factors and plant characteristics to analyze the individual AGB of L. chinensis in northern China, and established a non-destructive prediction model that can accurately quantify the AGB of L. chinensis across large scale in China. 33 sites across different climate conditions with L. chinensis as the constructive species in China were sampled to measure plant traits of L. chinensis individual, including leaf thickness (LT), stem diameter (SD), stem length (SL), plant height (PH), etc. We found that plant traits (PH,SD) were the main factors driving the AGB of L. chinensis individuals, and climate factors mainly affected AGB of L. chinensis individuals indirectly by affecting plant traits. Then, we established the AGB estimation equation of L. chinensis individuals across large scale for the first time. The equation was: AGB = −0.638 + 0.405*SD + 0.017*PH, which had a high predictive ability. The result is not only helpful to quickly and conveniently measure the AGB of L. chinensis individuals in non-destructive experiments, but also important to evaluate the productivity and carbon storage of L. chinensis community across large scale. At the same time, it also laid the foundation for the subsequent non-destructive estimation of the aboveground biomass of individual plants.
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