Abstract:The understory biomass (Wu) and its relationships with stand structure are paramount important for sustainable management and carbon accounting for forest ecosystem, but until now it remains poorly understood. Therefore we selected Sichuan cypress (Cupressus funebris) plantation forests across Sichuan basin, southwestern China, to explore the correlations of Wu with stand structure and to establish the regional estimation models for Wu and its components-Shrub biomass (Ws) and herbaceous biomass (Wh). We tried to answer two questions: which structural parameters would relate closely to the Wu, Ws and Wh and therefore can be integrated into the biomass estimation models, respectively, and whether the fit estimation models by integrating the overstory parameter could be better to predict Wu across the region. We employed plot methods to survey the stand structure including tree, shrub and herbaceous layers and the Wu, Ws and Wh of below-and above-ground on fourteen Sichuan cypress forests from 12 counties in Sichuan basin. Pearson correlation analysis was applied to explore the relationships of the Wu, Ws and Wh with all 12 structural parameters and five models were selected to simulate and screen the fitted biomass estimation equations. Our data displayed that the Wu and Ws had significant correlations with the percentage cover (Cs), average height (Hs) and the projected volume (Vs =Cs×Hs) of shrubs, it was closer than that with other structural parameters including stand density (Du). Among all fitting equations, the power equation was the best one to estimate the shrub biomass: Ws=0.0005V1.0411s (Ra2=0.762, P < 0.001, n=40), and the multiple linear regression model to the understory biomass: lnWu=0.0158Hs+0.0111Cs-0.5358 (Ra2=0.695, P < 0.001, n=40). We did not find suitable fitting model to estimate the herbaceous biomass across the Sichuan cypress plantation due to the adjusted coefficient no more than 0.410 (Ra2 < 0.410, P < 0.01, n=40). An important finding was that integrating Du into the multiple linear regression model could improve the estimation accuracy for Wu across the region (lnWu=a+bDu+cHs+dCs, Ra2=0.721, P < 0.001, n=40). We concluded that Wu is important and can be better predicted by Cs, Hs or Vs with Du for the Sichuan cypress plantation forests across Sichuan basin, providing new insight to develop the understory biomass estimation models for the regional forest carbon accounting system.
