Effects Of Biotic And Abiotic Factors On Biomass Conversion And Expansion Factors Of Natural White Birch Forest In Northeast China

Forest ecosystems play a crucial role in the global carbon balance,and forest biomass is one of the basic characteristics of forest ecosystems and the main factor for estimating forest carbon stocks and judging the capacity of forest carbon sinks.Therefore,the accurate estimation of biomass of various forest types is of great scientific significance for the global terrestrial ecosystem carbon cycle and carbon stocks.Betula platyphylline Suk.,as one of the tree types with great natural regeneration capacity,has become a major component species of forest vegetation in northeastern China.The study of its biomass and carbon stock has received much attention from scholars.Biomass conversion and expansion factors(BCEFs)can convert stockpiles to biomass or carbon stocks,and it is widely used in national and regional biomass estimation and greenhouse gas reporting.To quantify the regional-scale variation in BCEFs,1035 permanent sample plots(PSPs)of natural white birch forest in Northeast China were used to compile a dataset,along with climatic and topographic variables.Natural white birches BCEFs study the influencing factors and model building.Firstly,the correlation between 5 biological factors and each component BCEFs was determined by Pearson test.The biological factor with the strongest correlation was selected as the basic variable,and the four basic models were fitted,and the power function with the best fitting effect was selected as the basic model form of BCEFs.Second,we used the power function as the basic model and generalized it to incorporate climate and topography by parameterization.The individual contribution rate of biotic and abiotic factors to each component BCEFs was analyzed by analytic hierarchy process.Then,considering the influence of region(forestry and forestry bureau)on BCEFs,this study introduced region as a random effect into the generalized BCEFs model,and constructed a nonlinear mixed-effects model for each component BCEFs based on the generalized BCEFs model to reflect the influence of changes between different forest management units on each component BCEFs.In addition,a random sampling strategy was used to calibrate the random effects in the mixed-effects model.In this study,we analyzed the effects of biotic and abiotic factors on the BCEFs of each component.The results showed that(1)Generalized models with environmental predictors outperformed basic models without environmental variables,indicating that environmental factors explained the variation in BCEFs for biomass components of natural white birch forest.However,the results showed that abiotic factors were relatively more explained for BCEFst and BCEFto,while biotic factors were relatively more explained for BCEFbr,BCEFfol and BCEFro.In conclusion,biotic and abiotic factors together explain the variation of the components BCEFs in natural white birch forests.(2)The stocking volume(M)exhibited a negative proportional relationship in the stem BCEFs(BCEFst),the root BCEFs(BCEFro)and the total tree BCEFs(BCEFto)models.The quadratic mean diameter(Dq)exhibited a positive proportional relationship in the branch BCEFs(BCEFbr)and the foliage BCEFs(BCEFfol)models.Considering the effects of abiotic variables on the BCEF of each component,the results showed that BCEFst and BCEFto decreased as the mean annual precipitation(MAP)increased,BCEFbr increased as the annual heat(AHM)increased,BCEFfol gradually decreased as the elevation(ELV)increased,and BCEFro first increased with increasing mean annual temperature(MAT)and then declined.(3)Although the generalized model with abiotic predictors fitted better than the basic model,the mixed-effects model was superior,and the model was better fitted and tested using the mixed-effects model for each component of the BCEFs.(4)In addition,the prediction accuracy of the mixed-effects model increased gradually with increasing sample size,and the selection of 8 plots for calibration and making predictions with the mixed-effects model was the best sampling strategy in this study of natural white birch forest.The prediction models constructed in this paper have good prediction effects for each component of BCEFs.The results of this study not only provide a basis for accurate prediction of each component of BCEFs in natural birch forests in northeastern,but also can accurately evaluate the forest quality and provide a certain degree of support for carbon emission regulation and carbon trading.