四川柏木人工林林下植被生物量与林分结构的关系

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 Wuwith stand structure and to establish the regional estimation models for Wuand its components- Shrub biomass( Ws) and herbaceous biomass( Wh). We tried to answer two questions: which structural parameters would relate closely to the Wu,Wsand Whand 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 Wuacross the region. We employed plot methods to survey the stand structure including tree,shrub and herbaceous layers and the Wu,Wsand Whof 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,Wsand Whwith all 12 structural parameters and five models were selected to simulate and screen the fitted biomass estimation equations. Our data displayed that the Wuand Wshad 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( R2a= 0. 762,P 0. 001,n = 40),and the multiple linear regression model to the understory biomass: ln Wu= 0.0158Hs+ 0.0111Cs- 0. 5358( R2a= 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( R2a 0.410,P0.01,n = 40). An important finding was that integrating Du into the multiple linear regression model could improve the estimation accuracy for Wuacross the region( ln Wu= a + b Du+c Hs+d Cs,R2a= 0.721,P0.001,n = 40). We concluded that Wuis important and can be better predicted by Cs,Hsor Vs with Dufor 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.