Calibrating individual tree biomass models for contrasting tropical species at an uneven-aged site in the native Atlantic Forest of Brazil: A direct comparison of alternative approaches, sample sizes, and sample selection methods

Tree biomass equations are important yet difficult, time-intensive, and expensive to develop. However, the calibration of previously developed, species-specific models could be a viable alternative, particularly for highly diverse and protected forests like the Atlantic Forest of Brazil. Consequently, the primary research goal of this study was to conduct a comprehensive evaluation of the potential to calibrate an existing individual tree aboveground biomass model for a new species and/or site by using linear mixed-effects. Specific research objectives were to determine the optimal approach for effective calibration by allowing sample selection method, sample size, and range of tree sizes sampled to vary. In particular, a certain set of species was used as a primary dataset to fit both generalized and species-specific biomass models, that were then calibrated for a secondary dataset a a different site and location. Both similar and divergent species at the secondary site were used to calibrate and evaluate the previous models. Our results suggested that species-level calibration was efficient for the majority of the species or individuals examined that can greatly improve the performance at much lower sample sizes required to develop a new equation, especially for the larger trees in the stand. In general, one to three randomly selected trees were sufficient to effectively calibrate a biomass model for a new species. We expect the combination of model calibration for abundant species associated with the use of the previous developed generalized model for less abundant species can drastically reduce the need for destructive sampling and improve predictions, which is important for highly threatened forests like the Atlantic Forest in Brazil. Overall, the results highlight the potential of model calibration to significantly improve both biomass and carbon estimates in species-rich forests like those in the tropics.