ArticleNature communications2020
Spatial validation reveals poor predictive performance of large-scale ecological mapping models.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 115 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
115 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Practical guidelines for validation of supervised machine learning models in accelerometer-based animal behaviour classification.The Journal of animal ecology · 2025Pooled it
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- Canopy functional composition as an indicator of bird traits, taxonomy, and vulnerability in tropical Peruvian forests.Nature communications · 2026Article
- Sequential Soybean Phenotypes for Grain Yield Prediction in Reciprocal Two-Season Validation.Plants (Basel, Switzerland) · 2026Article
- Afforestation and reforestation support coupled gains in soil life and carbon storage worldwide.Nature ecology & evolution · 2026Article
- An interpretable soil-pH suitability index for arbuscular mycorrhizal fungal genera.Mycorrhiza · 2026Article
- Global patterns and feasible improvement potential of phosphorus use efficiency in cereal croplands.Nature food · 2026Article
- Using resurvey data to predict changes in ecosystem functioning across protected and unprotected coastal dunes.Conservation biology : the journal of the Society for Conservation Biology · 2026Article
- Integrating Biosynthetic, Genomic and Ecological Open Data for Medicinal Plant Research: A Leakage-Aware Evidence-Prioritization Framework.International journal of molecular sciences · 2026Article
- Biogeography of community canopy leaf traits and their links to global forest photosynthesis.Science advances · 2026Article
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- A 0.6-meter resolution canopy height model for the contiguous United States.Scientific data · 2026Article
- Climate Warming Will Reduce Boreal Forest Litterfall, but the Response Differs Among Plant Functional Types.Ecology and evolution · 2026Article
- A cloud-computing framework for downscaled global 300 m SIF retrieval from Sentinel-3 and TROPOSIF.International journal of applied earth observation and geoinformation : ITC journal · 2026Article
- Integrating Remote Sensing and Machine Learning to Project Global Habitat Suitability and Productivity of Chinese Fir Under Climate Change.Ecology and evolution · 2026Article
- Four-Decade (1980-2023) Surface Ozone Concentrations across the Contiguous United States: Fine-Resolution Estimates and Health Implications.Environmental science & technology · 2026Article
- Global patterns of urban heat shaped by climate and morphology.Nature communications · 2026Article
55 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.