Mapping aboveground forest biomass is central for assessing the global carbon balance. However, current large-scale maps show strong disparities, despite good validation statistics of their underlying models. Here, we attribute this contradiction to a flaw in the validation methods, which ignore spatial autocorrelation (SAC) in data, leading to overoptimistic assessment of model predictive power. To illustrate this issue, we reproduce the approach of large-scale mapping studies using a massive forest inventory dataset of 11.8 million trees in central Africa to train and validate a random forest model based on multispectral and environmental variables. A standard nonspatial validation method suggests that the model predicts more than half of the forest biomass variation, while spatial validation methods accounting for SAC reveal quasi-null predictive power. This study underscores how a common practice in big data mapping studies shows an apparent high predictive power, even when predictors have poor relationships with the ecological variable of interest, thus possibly leading to erroneous maps and interpretations.

Summary: 1. Functional traits are posited to explain interspecific differences in performance, but these relationships are difficult to describe for long‐lived organisms such as trees, which exhibit strong ontogenetic changes in demographic rates. Here, we use a size‐dependent model of tree growth to test the extent to which of 17 functional traits related to leaf and stem economics, adult stature and seed size predict the ontogenetic trajectory of tree growth.2. We used a Bayesian modelling framework to parameterize and contrast three size‐dependent diameter growth models using 16 years of census data from 5524 individuals of 50 rain forest tree species: a size‐dependent model, a size‐dependent model with species‐specific parameters and a size‐dependent model based on functional traits.3. Most species showed clear hump‐shaped ontogenetic growth trajectories and, across species, maximum growth rate varied nearly tenfold, from 0.58 to 5.51 mm year⁻¹. Most species attained their maximum growth at 60% of their maximum size, whereas the magnitude of ontogenetic changes in growth rate varied widely among species.4. The Trait‐Model provided the best compromise between explained variance and model parsimony and needed considerably fewer parameters than the model with species terms.5. Stem economics and adult stature largely explained interspecific differences in growth strategy. Maximum absolute diameter growth rates increased with increasing adult stature and leaf δ¹³C and decreased with increasing wood density. Species with light wood had the greatest potential to modulate their growth, resulting in hump‐shaped ontogenetic growth curves. Seed size and leaf economics, generally thought to be of paramount importance for plant performance, had no significant relationships with the growth parameters.6. Synthesis. Our modelling approach offers a promising way to link demographic parameters to their functional determinants and hence to predict growth trajectories in species‐rich communities with little parameter inflation, bridging the gap between functional ecology and population demography.