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Cited by 315Species Distribution and Climate ChangeSoil Geostatistics and MappingEcology and Vegetation Dynamics Studies

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 access
Meyer, Hanna, Pebesma, Edzer·2022·Nature Communications
DOI 10.1038/s41467-022-29838-9PMC PMC9033849
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Abstract

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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Fig. 1 Spatial distance distributions in global mapping studies. Spatial distribution (left; equal Earth projection) and distribution of nearest neighbor distances (right; sample-to-sample distance in pink, prediction-location-to-sample distance in blue) for three different publicly available datasets: cation exchange capacity in the soil from the WoSIS database23 as used for global soil mapping3 (A), specific leaf area from the Try database24 as used for the global mapping in Moreno-Martinez et al. (2018)25 (B), and the nematodes dataset compiled by Van den Hoogen et al. (2019)2 (C). For comparison, the fourth dataset is a simulated completely spatially random sample of the same size as the nematode dataset (D). Distance distributions were calculated and visualized using the R package “CAST”26.
Fig. 1 Spatial distance distributions in global mapping studies. Spatial distribution (left; equal Earth projection) and distribution of nearest neighbor distances (right; sample-to-sample distance in pink, prediction-location-to-sample distance in blue) for three different publicly available datasets: cation exchange capacity in the soil from the WoSIS database23 as used for global soil mapping3 (A), specific leaf area from the Try database24 as used for the global mapping in Moreno-Martinez et al. (2018)25 (B), and the nematodes dataset compiled by Van den Hoogen et al. (2019)2 (C). For comparison, the fourth dataset is a simulated completely spatially random sample of the same size as the nematode dataset (D). Distance distributions were calculated and visualized using the R package “CAST”26.CC BY · PubMed Central

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Metadata

Authors
Meyer, H., & Pebesma, E.
Affiliations
University of Münster (DE)
Corresponding author
  • Meyer, Hanna
  • Pebesma, Edzer
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2022-04-22
Volume
13
Issue
1
Pages
2208-2208
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-022-29838-9
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
Licence
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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