Assessment of bias in pan-tropical biomass predictions
- 1Department of Geography, University College London, United Kingdom
- 2CAVElab - Computational & Applied Vegetation Ecology, Ghent University, Belgium
- 3Environment Department, University of York, United Kingdom
- 4National Centre for Earth Observation, Department of Physics and Astronomy, University of Leicester, United Kingdom
- 5Ecology and Global Change, School of Geography, University of Leeds, United Kingdom
- 6Environmental Change Institute, School of Geography and the Environment, University of Oxford, United Kingdom
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 characterising 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 modelling 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 consistently true predictions 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.
Keywords: tropical forests, above-ground biomass, Allometry, prediction, error, uncertainty
Received: 25 Aug 2019;
Accepted: 27 Jan 2020.
Copyright: © 2020 Burt, Calders, Cuni-Sanchez, Gómez-Dans, Lewis, Lewis, Malhi, Phillips and Disney. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Dr. Andrew Burt, University College London, Department of Geography, London, WC1E 6BT, England, United Kingdom, a.burt@ucl.ac.uk