Available species-specific allometric aboveground biomass (AGB) models for tree ferns occurring in the Atlantic forest are based on small calibration datasets, potentially leading to increased uncertainty in model parameter estimates. This study constructed common AGB models for Cyathea delgadii Sternb. and Dicksonia sellowiana Hook. using data from 75 individuals destructively sampled in the subtropical Atlantic forest. Three power-law models linking AGB to a single compound variable were calibrated. Incorporating a species effect into the scaling parameter through a dummy variable substantially improved prediction accuracy. Nonetheless, incorporating a species effect into the allometric - exponent resulted in weak parameter identifiability, suggesting that the two species can share a common allometric exponent. The selected AGB model achieved prediction accuracy comparable to available species-specific models, albeit with smaller uncertainty in model parameter estimates. Pooling data from species with similar allometric relationships was shown to be an efficient strategy for improving AGB models.