Abstract:
We adjusted generic models for species-rich forest types and specific models for 15 species. Regression assumptions, lack of fitness and goodness of fit and comparison between models were assessed analytically. Generic models produced estimates not less reliable than species-specific models. Logarithmic models presented the best results of adjustment and evenness of residual variance. Assessment of dendrometric variables is important to obtain accurate estimates of stand attributes as biomass and carbon stock estimates. Some of them, as tree height and stem volume, are difficult and expensive to measure; volume models, calibrated on large datasets in tropical and subtropical forests, are rare. This study aimed to construct stem volume models for native tree species in three forest types in southern Brazil, to select models with best fitness, to assess agreement between measured and predicted datasets and to compare species-specific and generic models. Data from 418 sample plots were used to adjust generic models for forest types and specific models for 15 species. Regression assumptions, modelling efficiency, lack of fitness, goodness of fit and comparison between species-specific and generic models were assessed by analytical methods. Logarithmic models presented the best results of adjustment and evenness of residual variance. Lack of fit F test showed acceptable adjust quality for nearly all speciesspecific and generic models; R2adj* and modelling efficiency measure presented values close to 1 for all fitted models; model identity F test showed differences between specific and generic models in some cases. Since regression assumptions were satisfied and because of their quality of fit, the fitted models compose useful tools for predicting total stem volume (with bark) for forest remnants in southern Brazil. Stratification of datasets by forest type for model fitting showed to be necessary, but, commonly, generic models for forest types produced estimates not less reliable than species-specific models.
