Using Bayesian Model Averaging to Predict Tree Aboveground Biomass in Tropical Moist Forests

With the growing interest in estimating carbon stocks in forests, available allometric equations have been compiled. These compilations often reveal contrasting models for the same species and site. Rather than choosing a model with the risk of not selecting the best available one, Bayesian model averaging (BMA) offers a way to combine different allometric equations into a single predictive model. In the deterministic version of BMA, existing models with known coefficients are combined. In the statistical version, competing models are at the same time fitted and combined. Using the BMA of deterministic models, we combined three existing multispecies pan-tropical biomass equations for tropical moist forests. The resulting model brought a relatively minor although consistent improvement of the predictions of the aboveground dry biomass of trees. These three models were particular cases of a family of models that were subsequently combined using the BMA of statistical models. Again, the resulting model was able to capture features in the biomass response to diameter that no single model was able to fit. BMA thus is an alternative to model selection that allows integrating the biomass response from different models. (Resume d'auteur)