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BHPMF - a hierarchical Bayesian approach to gap-filling and trait prediction for macroecology and functional biogeography: Gap-filling in trait databases


Jens Kattge

Hanhuai Shan

Farideh Fazayeli

Julia Joswig

Arindam Banerjee

Markus Reichstein


Sandra Diaz

John Dickie

Andy Gillison

Anuj Karpatne

Sandra Lavorel

Paul Leadley

Christian B. Wirth

Ian J. Wright

S. Joseph Wright

Peter B. Reich



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.


For this purpose we introduce BHPMF, a ierarchical Bayesian extension of probabilistic matrix actorization (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 frompoint 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.


Schrodt, F., Kattge, J., Shan, H., Fazayeli, F., Joswig, J., Banerjee, A., …Reich, P. B. (2015). BHPMF - a hierarchical Bayesian approach to gap-filling and trait prediction for macroecology and functional biogeography: Gap-filling in trait databases. Global Ecology and Biogeography, 24(12), 1510-1521.

Journal Article Type Article
Acceptance Date Jun 1, 2015
Online Publication Date Jul 14, 2015
Publication Date 2015-12
Deposit Date Oct 31, 2017
Publicly Available Date Oct 31, 2017
Journal Global Ecology and Biogeography
Print ISSN 1466-822X
Electronic ISSN 1466-8238
Publisher Wiley
Peer Reviewed Peer Reviewed
Volume 24
Issue 12
Pages 1510-1521
Keywords Bayesian hierarchical model; Gap-filling; Imputation; Machine learning; Matrix factorization, PFT; Plant functional trait; Sparse matrix; Spatial extrapolation; TRY
Public URL
Publisher URL


Schrodt_et_al-2015-Global_Ecology_and_Biogeography.pdf (811 Kb)

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