Cited by 206Ecology and Vegetation Dynamics StudiesSpecies Distribution and Climate ChangeResearch Data Management Practices

BHPMF – a hierarchical B ayesian approach to gap‐filling and trait prediction for macroecology and functional biogeography

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Schrodt, Franziska, Kattge, Jens, Shan, Hanhuai, Fazayeli, Farideh, Joswig, Julia, Banerjee, Arindam, Reichstein, Markus, Bönisch, Gerhard +10 more·2015·Global Ecology and Biogeography

Abstract

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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  • Using the species mean of every attribute as initial values in matrix completion improves the accuracy of predicting plant traits over traditional methods.

    “This approach has been shown to provide significant improvements in the accuracy of predicting plant traits over traditional methods [47].”

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  1. van Buuren, S. & Groothuis-Oudshoorn, K. (2011) mice: multi- ple imputation by chained equations in r. Journal of Statistical Software, 45, 1-67.
  2. Kazakou, E., Violle, C., Roumet, C., Navas, M.L., Vile, D., Kattge, J. & Garnier, E. (2014) Are trait-based species rankings con- sistent across datasets and spatial scales? Journal of Vegetation Science, 25, 235-237.
  3. Salakhutdinov, S. & Mnih, A. (2008) Probabilistic matrix fac- torization. Advances in Neural Information Processing Systems 20 (NIPS 07). Available at: http://www.cs.toronto.edu/ ∼rsalakhu/papers/nips07_pmf.pdf

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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
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 · 2014: 2
FWCI
12.46
Type
journal article
Language
en
Open access
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

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