Should tree biomass allometry be restricted to power models?

• Non-power allometry better fit tree biomass data than power model.
• Power model for tree biomass could be segmented into two pieces.
• Diameter and height as separated predictors improved the biomass prediction.
• Collinearity between diameter and height was not an issue to predict biomass.
• Growth theories should be developed to explain biomass allometry.

The increasing number of model types that are used to predict tree biomass from diameter, height and wood density has brought questioning about the biological relevance of complex allometries (i.e. non-power models). Statistical issues such as collinearity among predictors and unreliable coefficient estimates have also been associated with complex allometric models. Using a data set of 225 trees from central Africa, we assessed the relevance of simple allometry (i.e. power model) versus complex allometry to predict tree biomass. A complex allometric model of biomass was developed based on a model of resource partition between dbh and height growths. Although being a good model for biomass prediction, the power model was outperformed by the complex allometric model. A careful examination showed that the power model could be segmented into two pieces of power models. Using tree diameter and height as separated predictors improved the biomass prediction, irrespective of the collinearity between these two predictors. A critical value of 25% for the PRSE statistic used to assess the reliability of coefficient estimates corresponded to a significance level of 10-510-5–10-410-4 and was thus unreasonably low. We conclude that growth theories should be developed to explain allometric models, but that the arbitration between these models should ultimately rely on observed data, not on theories.