Assessment of Bias in Pan-Tropical Biomass Predictions

Above-ground biomass (AGB) is an essential descriptor of forests, of use in ecological and climate-related research. At tree- and stand-scale, destructive but direct measurements of AGB are replaced with predictions from allometric models characterizing the correlational relationship between AGB, and predictor variables including stem diameter, tree height and wood density. These models are constructed from harvested calibration data, usually via linear regression. Here, we assess systematic error in out-of-sample predictions of AGB introduced during measurement, compilation and modeling of in-sample calibration data. Various conventional bivariate and multivariate models are constructed from open access data of tropical forests. Metadata analysis, fit diagnostics and cross-validation results suggest several model misspecifications: chiefly, unaccounted for inconsistent measurement error in predictor variables between in- and out-of-sample data. Simulations demonstrate conservative inconsistencies can introduce significant bias into tree- and stand-scale AGB predictions. When tree height and wood density are included as predictors, models should be modified to correct for bias. Finally, we explore a fundamental assumption of conventional allometry, that model parameters are independent of tree size. That is, the same model can provide predictions of consistent trueness irrespective of size-class. Most observations in current calibration datasets are from smaller trees, meaning the existence of a size dependency would bias predictions for larger trees. We determine that detecting the absence or presence of a size dependency is currently prevented by model misspecifications and calibration data imbalances. We call for the collection of additional harvest data, specifically under-represented larger trees.