@article{fdi:010065348, title = {{S}hould tree biomass allometry be restricted to power models ?}, author = {{P}icard, {N}. and {R}utishauser, {E}. and {P}loton, {P}ierre and {N}gomanda, {A}. and {H}enry, {M}.}, editor = {}, language = {{ENG}}, abstract = {{T}he 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). {S}tatistical issues such as collinearity among predictors and unreliable coefficient estimates have also been associated with complex allometric models. {U}sing a data set of 225 trees from central {A}frica, 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. {A}lthough 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. {U}sing 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(-5)-10(-4) and was thus unreasonably low. {W}e 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.}, keywords = {{A}llometry ; {C}arbon allocation ; {P}ower model ; {S}egmented regression ; {T}ree ; biomass ; {T}ropical forest}, booktitle = {}, journal = {{F}orest {E}cology and {M}anagement}, volume = {353}, numero = {}, pages = {156--163}, ISSN = {0378-1127}, year = {2015}, DOI = {10.1016/j.foreco.2015.05.035}, URL = {https://www.documentation.ird.fr/hor/fdi:010065348}, }