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

2020

Article référencé dans le Web of Science WOS:000607111200001

Ploton Pierre, Mortier F., Réjou-Méchain Maxime, Barbier Nicolas, Picard N., Rossi V., Dormann C., Cornu G., Viennois G., Bayol N., Lyapustin A., Gourlet-Fleury S., Pélissier Raphaël

Nature Communications, 2020, 11 (1), 4540 [11 p.] ISSN 2041-1723

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.

Sciences fondamentales / Techniques d'analyse et de recherche [020] ; Sciences du milieu [021] ; Etudes, transformation, conservation du milieu naturel [082]

AFRIQUE CENTRALE

Fonds IRD [F B010080646]

fdi:010080646