The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
Spatial validation reveals poor predictive performance of large-scale ecological mapping models
Open accessAbstract
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.
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Cites
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WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas
Fick, S. E., & Hijmans, R. J. (2017) · International Journal of Climatology · doi:10.1002/joc.5086 · Cited by 16,088
Spatial Autocorrelation: Trouble or New Paradigm?
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Cross‐validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure
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Self-citationF. Dormann, C., M. McPherson, J., B. Araújo, M., Bivand, R., Bolliger, J., Carl, G., G. Davies, R., Hirzel, A., Jetz, W., Daniel Kissling, W., et al. (2007) · Ecography · doi:10.1111/j.2007.0906-7590.05171.x · Cited by 3,433
Conditional variable importance for random forests
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Benchmark map of forest carbon stocks in tropical regions across three continents
Saatchi, S. S., Harris, N. L., Brown, S., Lefsky, M., Mitchard, E. T. A., Salas, W., Zutta, B. R., Buermann, W., Lewis, S. L., Hagen, S., et al. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1019576108 · Cited by 2,353
The global tree restoration potential
Bastin, J.-F., Finegold, Y., Garcia, C., Mollicone, D., Rezende, M., Routh, D., Zohner, C. M., & Crowther, T. W. (2019) · Science · doi:10.1126/science.aax0848 · Cited by 2,197
Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps
Baccini, A., Goetz, S. J., Walker, W. S., Laporte, N. T., Sun, M., Sulla-Menashe, D., Hackler, J., Beck, P. S. A., Dubayah, R., Friedl, M. A., et al. (2012) · Nature Climate Change · doi:10.1038/nclimate1354 · Cited by 1,788
Soil nematode abundance and functional group composition at a global scale
van den Hoogen, J., Geisen, S., Routh, D., Ferris, H., Traunspurger, W., Wardle, D. A., de Goede, R. G. M., Adams, B. J., Ahmad, W., Andriuzzi, W. S., et al. (2019) · Nature · doi:10.1038/s41586-019-1418-6 · Cited by 1,292
Cited by
Global stocks and capacity of mineral-associated soil organic carbon
Georgiou, K., Jackson, R. B., Vindušková, O., Abramoff, R. Z., Ahlström, A., Feng, W., Harden, J. W., Pellegrini, A. F. A., Polley, H. W., Soong, J. L., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-31540-9 · Cited by 979
Duncanson, L., Kellner, J. R., Armston, J., Dubayah, R., Minor, D. M., Hancock, S., Healey, S. P., Patterson, P. L., Saarela, S., Marselis, S., et al. (2022) · Remote Sensing of Environment · doi:10.1016/j.rse.2021.112845 · Cited by 495
Self-citationSpatial cross-validation is not the right way to evaluate map accuracy
Wadoux, A. M. J.-C., Heuvelink, G. B. M., de Bruin, S., & Brus, D. J. (2021) · Ecological Modelling · doi:10.1016/j.ecolmodel.2021.109692 · Cited by 313
Machine learning-based global maps of ecological variables and the challenge of assessing them
Meyer, H., & Pebesma, E. (2022) · Nature Communications · doi:10.1038/s41467-022-29838-9 · Cited by 310
Lang, N., Kalischek, N., Armston, J., Schindler, K., Dubayah, R., & Wegner, J. D. (2022) · Remote Sensing of Environment · doi:10.1016/j.rse.2021.112760 · Cited by 291
Metadata
- Authors
- Ploton, P., Mortier, F., Réjou-Méchain, M., Barbier, N., Picard, N., Rossi, V., Dormann, C., Cornu, G., Viennois, G., Bayol, N., Lyapustin, A., Gourlet-Fleury, S., & Pélissier, R.
- Venue
- Nature Communications
- Published
- 2020-09-11
- Volume
- 11
- Issue
- 1
- Pages
- 4540-4540
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W3085784695
- Cited by
- 870
- References
- 54
- FWCI
- 26.42
- Type
- journal article
- Language
- en
- Open access
- gold
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- Ploton, P., Mortier, F., Réjou-Méchain, M., Barbier, N., Picard, N., Rossi, V., Dormann, C., Cornu, G., Viennois, G., Bayol, N., Lyapustin, A., Gourlet-Fleury, S., & Pélissier, R. (2020). Spatial validation reveals poor predictive performance of large-scale ecological mapping models. Nature Communications. https://doi.org/10.1038/s41467-020-18321-y
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