Effects of Biotic and Abiotic Factors on Biomass Conversion and Expansion Factors of Natural White Birch Forest (Betula platyphylla Suk.) in Northeast China

Abstract

Biomass conversion and expansion factors (BCEFs) are widely utilized in national and regional biomass estimates and greenhouse gas reporting, as they can be used to directly transform the stocking volume into biomass. In this study, the power function was used as the basic model form with biotic variables, and abiotic variables were considered to improve the fitting results. Then, the random effects parameters were also introduced into the models to describe the variation of BCEFs among different forest management units. Random sampling strategies were applied to calibrate the random effects. The results showed that the stocking volume exhibited a negative proportional relationship in the stem BCEF (BCEFst), the root BCEF (BCEFro) and the total tree BCEF (BCEFto) models, and the quadratic mean diameter exhibited a positive proportional relationship in the branch BCEF (BCEFbr) and the foliage BCEF (BCEFfol) models. In addition, the fitting effect of generalized models with abiotic predictors was superior to that of the basic models. Considering the effects of abiotic variables on the BCEFs of each component, the results showed that BCEFst and BCEFto decreased as the mean annual precipitation increased; BCEFbr increased as the annual temperature increased; BCEFfol gradually decreased as the elevation increased; and BCEFro first increased with increasing mean annual temperature and then declined. In conclusion, abiotic factors explained the variation in BCEFs for the biomass components of the natural white birch forest. Although the fitting effect of generalized models with abiotic predictors was superior to that of the basic models, the mixed-effects model was preferable for modeling the BCEFs of each component. In addition, the prediction precision of the mixed-effects models enhanced gradually with increasing sample size, and the selection of eight plots for calibration and prediction based on the mixed-effects model was the best sampling strategy in this study of a natural white birch forest.