Spatial validation reveals poor predictive performance of large-scale ecological mapping models

Pierre Ploton (), Frédéric Mortier, Maxime Réjou-Méchain, Nicolas Barbier, Nicolas Picard, Vivien Rossi, Carsten Dormann, Guillaume Cornu, Gaëlle Viennois, Nicolas Bayol, Alexei Lyapustin, Sylvie Gourlet-Fleury and Raphaël Pélissier
Additional contact information
Pierre Ploton: AMAP, Univ Montpellier, IRD, CNRS, INRAE, CIRAD
Frédéric Mortier: UPR Forêts et Sociétés
Maxime Réjou-Méchain: AMAP, Univ Montpellier, IRD, CNRS, INRAE, CIRAD
Nicolas Barbier: AMAP, Univ Montpellier, IRD, CNRS, INRAE, CIRAD
Nicolas Picard: Via della Sforzesca 1
Vivien Rossi: CIRAD, UPR Forêts et Sociétés
Carsten Dormann: University of Freiburg
Guillaume Cornu: UPR Forêts et Sociétés
Gaëlle Viennois: AMAP, Univ Montpellier, IRD, CNRS, INRAE, CIRAD
Nicolas Bayol: Forêt Ressources Management Ingénierie
Alexei Lyapustin: NASA Goddard Space Flight Center
Sylvie Gourlet-Fleury: UPR Forêts et Sociétés
Raphaël Pélissier: AMAP, Univ Montpellier, IRD, CNRS, INRAE, CIRAD

Nature Communications, 2020, vol. 11, issue 1, 1-11

Abstract: Abstract 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.

Date: 2020
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DOI: 10.1038/s41467-020-18321-y

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