The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Machine learning-based global maps of ecological variables and the challenge of assessing them
Open accessAbstract
The recent wave of published global maps of ecological variables has caused as much excitement as it has received criticism. Here we look into the data and methods mostly used for creating these maps, and discuss whether the quality of predicted values can be assessed, globally and locally.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Figures
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“If the null hypothesis is not rejected, the algorithm returns a random kfold CV, since it is the appropriate option for random and regular samples (Meyer and Pebesma, 2022;Wadoux et al., 2021;de Bruin et al., 2022).”
From kNNDM CV: k-fold nearest-neighbour distance matching cross-validation for map accuracy estimation (2024) · cites this paper as Meyer and Pebesma, 2022
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“As shown in Meyer and Pebesma (2022), when samples are randomly distributed within the prediction area, the distribution of NNDs during the random 10-fold CV Ĝ * j (r, L) resembles the distribution of NNDs during prediction Ĝij (r), and therefore the value of W is small.”
From kNNDM CV: k-fold nearest-neighbour distance matching cross-validation for map accuracy estimation (2024) · cites this paper as Meyer and Pebesma, 2022
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“In our past work, we argued that the design of a CV method to provide a reliable estimate for map accuracy should be prediction-oriented; i.e. predictive conditions created during CV should resemble the conditions found when the trained model is applied to the prediction area (Milà et al., 2022;Meyer and Pebesma, 2022;Ludwig et al., 2023).”
From kNNDM CV: k-fold nearest-neighbour distance matching cross-validation for map accuracy estimation (2024) · cites this paper as Meyer and Pebesma, 2022
The cited source says what the claim attributes to it.
In the citing paper
“In Milà et al. (2022) we showed that when standard LOO CV is used for reference data randomly distributed within the prediction area, the distribution of NNDs between test and training locations during CV is similar to the distribution of NNDs between prediction and training locations (see Meyer and Pebesma, 2022, for similar results for random kfold CV).”
From kNNDM CV: k-fold nearest-neighbour distance matching cross-validation for map accuracy estimation (2024) · cites this paper as Meyer and Pebesma, 2022
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Very high resolution interpolated climate surfaces for global land areas
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G., & Jarvis, A. (2005) · International Journal of Climatology · doi:10.1002/joc.1276 · Cited by 19,860
Cressie, N. A. C. (1993) · Wiley Series in Probability and Statistics · doi:10.1002/9781119115151 · Cited by 6,507
SoilGrids250m: Global gridded soil information based on machine learning
Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X., Bauer-Marschallinger, B., et al. (2017) · PLOS ONE · doi:10.1371/journal.pone.0169748 · Cited by 4,733
Cross‐validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure
Roberts, D. R., Bahn, V., Ciuti, S., Boyce, M. S., Elith, J., Guillera‐Arroita, G., Hauenstein, S., Lahoz‐Monfort, J. J., Schröder, B., Thuiller, W., et al. (2017) · Ecography · doi:10.1111/ecog.02881 · Cited by 3,689
TRY plant trait database – enhanced coverage and open access
Kattge, J., Bönisch, G., Díaz, S. M., Lavorel, S., Prentice, I. C., Leadley, P., Tautenhahn, S., Werner, G. D. A., Aakala, T., Abedi, M., et al. (2019) · Global Change Biology · doi:10.1111/gcb.14904 · Cited by 2,210
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
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 1,343
Tomislav Hengl, A., Nussbaum, M., Wright, M. N., Heuvelink, G. B. M., & Gräler, B. (2018) · PeerJ · doi:10.7717/peerj.5518 · Cited by 1,177
Chilès, J., & Delfiner, P. (2012) · Wiley series in probability and statistics · doi:10.1002/9781118136188 · Cited by 1,122
Spatial validation reveals poor predictive performance of large-scale ecological mapping models
Ploton, P., Mortier, F., Réjou‐Méchain, M., Barbier, N., Picard, N., Rossi, V., Dormann, C. F., Cornu, G., Viennois, G., Bayol, N., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18321-y · Cited by 1,110
Cited by
Sorted by citations
Herfort, B., Lautenbach, S., de Albuquerque, J. P., Anderson, J., & Zipf, A. (2023) · Nature Communications · doi:10.1038/s41467-023-39698-6 · Cited by 278
Zheng, Q., Ha, T., Prishchepov, A. V., Zeng, Y., Yin, H., & Koh, L. P. (2023) · Nature Communications · doi:10.1038/s41467-023-41837-y · Cited by 231
Challenges in data-driven geospatial modeling for environmental research and practice
Koldasbayeva, D., Tregubova, P., Gasanov, M. E., Zaytsev, A., Petrovskaia, A., & Burnaev, E. V. (2024) · Nature Communications · doi:10.1038/s41467-024-55240-8 · Cited by 254
How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences
Jiang, S., Sweet, L., Blougouras, G., Brenning, A., Li, W., Reichstein, M., Denzler, J., Shangguan, W., Yu, G., Huang, F., et al. (2024) · Earth s Future · doi:10.1029/2024ef004540 · Cited by 209
Ryo, M. (2022) · Artificial Intelligence in Agriculture · doi:10.1016/j.aiia.2022.11.003 · Cited by 203
Metadata
- Authors
- Meyer, H., & Pebesma, E.
- Affiliations
- University of Münster (DE)
- Corresponding author
- Meyer, Hanna
- Pebesma, Edzer
- Venue
- Nature Communications
- Published
- 2022-04-22
- Volume
- 13
- Issue
- 1
- Pages
- 2208-2208
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC9033849
- OpenAlex
- W4224247190
- Cited by
- 310
- References
- 25
- Citations by year
- 2026: 97 · 2025: 61 · 2024: 72 · 2023: 60 · 2022: 19
- FWCI
- 124.96
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Environmental Science › Ecological Modeling › Species Distribution and Climate Change
- Life Sciences › Agricultural and Biological Sciences › Soil Science › Soil Geostatistics and Mapping
- Physical Sciences › Environmental Science › Nature and Landscape Conservation › Ecology and Vegetation Dynamics Studies
- Keywords
- machine learning, uncertainty quantification, spatial prediction, biodiversity data
- MeSH terms
- Machine Learning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
