BHPMF – a hierarchical Bayesian approach to gap-filling and trait prediction for macroecology and functional biogeography

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BHPMF – a hierarchical Bayesian approach to gap-filling and trait prediction for macroecology and functional biogeography

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 analy...

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Bibliographic Details
Main Authors:	Schrodt, Franziska, Kattge, Jens, Shan, Hanhuai, Fazayeli, Farideh, Joswig, Julia, Banerjee, Arindam, Reichstein, Markus, Bönisch, Gerhard, Diaz, Sandra, Dickie, John, Gillison, Andy, Karpatne, Anuj, Lavorel, Sandra, Leadley, Paul, Wirth, Christian B., Wright, Ian J., Wright, S. Joseph, Reich, Peter B.
Format:	Article
Published:	Wiley 2015
Subjects:	Bayesian hierarchical model; Gap-filling; Imputation; Machine learning; Matrix factorization PFT; Plant functional trait; Sparse matrix; Spatial extrapolation; TRY
Online Access:	https://eprints.nottingham.ac.uk/47674/

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