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BHPMF – a hierarchical Bayesian approach to gap‐filling and trait prediction for macroecology and functional biogeography
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Aim: Functional traits of organisms are key to understanding and predicting biodiversity and ecological change, which motivates continuous collection of traits and their integration into global databases. Such trait matrices are inherently sparse, severely limiting their usefulness for further analyses. On the other hand, traits are characterized by the phylogenetic trait signal, trait–trait correlations and environmental constraints, all of which provide information that could be used to statistically fill gaps. We propose the application of probabilistic models which, for the first time, utilize all three characteristics to fill gaps in trait databases and predict trait values at larger spatial scales. Innovation: For this purpose we introduce BHPMF, a hierarchical B ayesian extension of probabilistic matrix factorization (PMF). PMF is a machine learning technique which exploits the correlation structure of sparse matrices to impute missing entries. BHPMF additionally utilizes the taxonomic hierarchy for trait prediction and provides uncertainty estimates for each imputation. In combination with multiple regression against environmental information, BHPMF allows for extrapolation from point measurements to larger spatial scales. We demonstrate the applicability of BHPMF in ecological contexts, using different plant functional trait datasets, also comparing results to taking the species mean and PMF. Main conclusions: Sensitivity analyses validate the robustness and accuracy of BHPMF: our method captures the correlation structure of the trait matrix as well as the phylogenetic trait signal – also for extremely sparse trait matrices – and provides a robust measure of confidence in prediction accuracy for each missing entry. The combination of BHPMF with environmental constraints provides a promising concept to extrapolate traits beyond sampled regions, accounting for intraspecific trait variability. We conclude that BHPMF and its derivatives have a high potential to support future trait‐based research in macroecology and functional biogeography.
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“This approach has been shown to provide significant improvements in the accuracy of predicting plant traits over traditional methods [47].”
From Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data (2017) · cites this paper as [47]
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Metadata
- Authors
- Schrodt, F., Kattge, J., Shan, H., Fazayeli, F., Joswig, J., Banerjee, A., Reichstein, M., Bönisch, G., Díaz, S., Dickie, J., Gillison, A., Karpatne, A., Lavorel, S., Leadley, P., Wirth, C. B., Wright, I. J., Wright, S. J., & Reich, P. B.
- Affiliations
- University of Minnesota (US)
- German Centre for Integrative Biodiversity Research (DE)
- Microsoft (United States) (US)
- Max Planck Institute for Biogeochemistry (DE)
- Consejo Nacional de Investigaciones Científicas y Técnicas (AR)
- Royal Botanic Gardens, Kew (GB)
- Center for Biodiversity Management Yungaburra 4884 Queensland Australia
- Centre National de la Recherche Scientifique (FR)
- Université Paris-Sud (FR)
- Macquarie University (AU)
- Smithsonian Tropical Research Institute (PA)
- Corresponding author
- Schrodt, Franziska
- ORCID
- Venue
- Global Ecology and Biogeography
- Publisher
- Wiley
- Published
- 2015-07-14
- Volume
- 24
- Issue
- 12
- Pages
- 1510-1521
- ISSN
- 1466-822X (ISSN-L)
- 1466-8238
- OpenAlex
- W2117380102
- Cited by
- 206
- References
- 48
- Citations by year
- 2026: 26 · 2025: 27 · 2024: 21 · 2023: 25 · 2022: 16 · 2021: 22 · 2020: 15 · 2019: 17 · 2018: 16 · 2017: 8 · 2016: 9 · 2015: 2
- FWCI
- 12.46
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Global Ecology and Biogeography (publishedVersion)
- Repository@Nottingham (University of Nottingham) (submittedVersion, cc-by)
- Nottingham ePrints (University of Nottingham) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Smithsonian Digital Repository (Smithsonian Institution) (submittedVersion)
- Funders
- University of Minnesota
- Institute on the Environment
- NSF (IIS-0812183, IIS-0916750, IIS-1029711, IIS-1017647)
- NSF CAREER award (IIS-0953274)
- Topics
- Physical Sciences › Environmental Science › Nature and Landscape Conservation › Ecology and Vegetation Dynamics Studies
- Physical Sciences › Environmental Science › Ecological Modeling › Species Distribution and Climate Change
- Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
- Keywords
- Bayesian hierarchical model, probabilistic matrix factorization, functional traits, phylogenetic signal, trait prediction, taxonomic hierarchy, environmental constraints, plant functional traits, intraspecific trait variation, uncertainty estimation, macroecology, functional biogeography
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.
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