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

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Machine learning-based global maps of ecological variables and the challenge of assessing them

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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.CC BY · PubMed Central

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Authors: Meyer, H., & Pebesma, E.
Affiliations: University of Münster (DE)
Corresponding author: Meyer, Hanna
Pebesma, Edzer
ORCID: Meyer, Hanna
Pebesma, Edzer
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: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
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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Machine learning-based global maps of ecological variables and the challenge of assessing them

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