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

• Published in: Global ecology and biogeography Vol. 24; no. 12; pp. 1510 - 1521
• Main Authors: Schrodt, Franziska, Kattge, Jens, Shan, Hanhuai, Fazayeli, Farideh, Joswig, Julia, Banerjee, Arindam, Reichstein, Markus, Bönisch, Gerhard, Díaz, Sandra, Dickie, John, Gillison, Andy, Karpatne, Anuj, Lavorel, Sandra, Leadley, Paul, Wirth, Christian B, Wright, Ian J, Wright, S. Joseph, Reich, Peter B
• Format: Journal Article
• Language: English
• Published: Oxford Blackwell Science 01.12.2015; Blackwell Publishing Ltd; John Wiley & Sons Ltd; Wiley Subscription Services, Inc
• Subjects: artificial intelligence; Bayesian analysis; Bayesian hierarchical model; biodiversity; biogeography; data collection; gap-filling; imputation; machine learning; MACROECOLOGICAL METHODS; Macroecology; matrix factorization; PFT; Phylogenetics; phylogeny; plant functional trait; prediction; probabilistic models; sparse matrix; spatial extrapolation; TRY; uncertainty
• ISSN: 1466-822X; 1466-8238
• DOI: 10.1111/geb.12335

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 Bayesian 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.