eprintid: 10091968 rev_number: 27 eprint_status: archive userid: 608 dir: disk0/10/09/19/68 datestamp: 2020-02-24 14:32:06 lastmod: 2021-09-22 22:26:14 status_changed: 2020-02-24 14:32:06 type: article metadata_visibility: show creators_name: Disney, M title: Assessment of bias in pan-tropical biomass predictions ispublished: pub divisions: UCL divisions: B03 divisions: C03 divisions: F26 keywords: tropical forests, above-ground biomass, allometry, prediction, error, uncertainty note: © 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. abstract: 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. date: 2020-02 date_type: published publisher: Royal Society, The official_url: https://doi.org/10.3389/ffgc.2020.00012 oa_status: green full_text_type: pub language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 1633361 doi: 10.3389/ffgc.2020.00012 lyricists_name: Disney, Mathias lyricists_name: Gomez-Dans, Jose lyricists_name: Lewis, Philip lyricists_name: Lewis, Simon lyricists_id: MIDIS56 lyricists_id: JLGOM54 lyricists_id: PELEW26 lyricists_id: SLLEW24 actors_name: Jayawardana, Anusha actors_id: AJAYA51 actors_role: owner full_text_status: public publication: Frontiers in Forests and Global Change volume: 3 article_number: 12 issn: 1364-503X citation: Disney, M; (2020) Assessment of bias in pan-tropical biomass predictions. Frontiers in Forests and Global Change , 3 , Article 12. 10.3389/ffgc.2020.00012 <https://doi.org/10.3389/ffgc.2020.00012>. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10091968/1/Disney_ffgc-03-00012.pdf