Spatial validation reveals poor predictive performance of large-scale ecological mapping models.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
11 09 2020
11 09 2020
Historique:
received:
28
01
2020
accepted:
14
08
2020
entrez:
12
9
2020
pubmed:
13
9
2020
medline:
13
9
2020
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 32917875
doi: 10.1038/s41467-020-18321-y
pii: 10.1038/s41467-020-18321-y
pmc: PMC7486894
doi:
Banques de données
figshare
['10.6084/m9.figshare.11865450', '10.6084/m9.figshare.12751628', '10.6084/m9.figshare.12790085']
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
4540Références
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