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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.
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“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.
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“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
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“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
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“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
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Citations
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How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences
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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
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