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

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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.

Keywords: allometric equation; biomass equation; ensemble of models; multimodel inference; tropical forest

Document Type: Research Article


Publication date: 2012-02-01

More about this publication?
  • Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
    Forest Science is published bimonthly in February, April, June, August, October, and December.

    2015 Impact Factor: 1.702
    Ranking: 16 of 66 in forestry

    Average time from submission to first decision: 62.5 days*
    June 1, 2016 to Feb. 28, 2017

    Also published by SAF:
    Journal of Forestry
    Other SAF Publications
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