Bayesian model predicts the aboveground biomass of Caragana microphylla in sandy lands better than OLS regression models

Abstract

Aims: In forest ecosystems, different types of regression models have been frequently used for the estimation of aboveground biomass, where Ordinary Least Squares (OLS) regression models are the most common prediction models. Yet, the relative performance of Bayesian and OLS models in predicting aboveground biomass of shrubs, especially multi-stem shrubs, has relatively been less studied in forests. Methods: In this study, we developed the biomass prediction models for Caragana microphylla Lam. which is a widely distributed multi-stems shrub, and contributes to the decrease of wind erosion and the fixation of sand dunes in the Horqin Sand Land, one of the largest sand lands in China. We developed six types of formulations under the framework of the regression models, and then, selected the best model based on specific criteria. Consequently, we estimated the parameters of the best model with OLS and Bayesian methods with training and test data under different sample sizes with the bootstrap method. Lastly, we compared the performance of the OLS and Bayesian models in predicting the aboveground biomass of C. microphylla. Important Findings: The performance of the allometric equation (power = 1) was best among six types of equations, even though all of those models were significant. The results showed that mean squared error of test data with non-informative prior Bayesian method and the informative prior Bayesian method was lower than with the OLS method. Among the tested predictors (i.e. plant height and basal diameter), we found that basal diameter was not a significant predictor either in OLS or Bayesian methods, indicating that suitable predictors and well-fitted models should be seriously considered. This study highlights that Bayesian methods, the bootstrap method and the type of allometric equation could help to improve the model accuracy in predicting shrub biomass in sandy lands.