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BHPMF – a hierarchical Bayesian approach to gap‐filling and trait prediction for macroecology and functional biogeography
Open accessAbstract
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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Cites
R: A Language and Environment for Statistical Computing
R Core Team (2000) · doi:10.32614/r.manuals · Cited by 355,893
Multiple Imputation for Nonresponse in Surveys
Rubin, D. B. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316696 · Cited by 20,701
Very high resolution interpolated climate surfaces for global land areas
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G., & Jarvis, A. (2005) · International Journal of Climatology · doi:10.1002/joc.1276 · Cited by 19,860
mice: Multivariate Imputation by Chained Equations in R
Buuren, S. V., & Groothuis-Oudshoorn, K. (2011) · Journal of Statistical Software · doi:10.18637/jss.v045.i03 · Cited by 13,870
Missing data: our view of the state of the art
Schafer, J. L., & Graham, J. W. (2002) · Psychological Methods · doi:10.1037/1082-989x.7.2.147 · Cited by 11,638
Matrix Factorization Techniques for Recommender Systems
Koren, Y., Bell, R. M., & Volinsky, C. (2009) · Computer · doi:10.1109/mc.2009.263 · Cited by 11,232
The worldwide leaf economics spectrum
Wright, I. J., Reich, P. B., Westoby, M., Ackerly, D. D., Baruch, Z., Bongers, F., Cavender-Bares, J., Chapin, T., Cornelissen, J. H. C., Diemer, M., et al. (2004) · Nature · doi:10.1038/nature02403 · Cited by 9,021
Self-citationProbabilistic Matrix Factorization
Mnih, A., & Salakhutdinov, R. (2007) · neural information processing systems · Cited by 3,538
Towards a worldwide wood economics spectrum
Chave, J., Coomes, D. A., Jansen, S., Lewis, S. L., Swenson, N. G., & Zanne, A. E. (2009) · Ecology Letters · doi:10.1111/j.1461-0248.2009.01285.x · Cited by 3,354
TRY – a global database of plant traits
KATTGE, J., DÍAZ, S., LAVOREL, S., PRENTICE, I. C., LEADLEY, P., BÖNISCH, G., GARNIER, E., WESTOBY, M., REICH, P. B., WRIGHT, I. J., et al. (2011) · Global Change Biology · doi:10.1111/j.1365-2486.2011.02451.x · Cited by 2,638
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Research works printed but not linked: 2
- ArcGIS Desktop E., 2011, Release 10.1
- Salakhutdinov S., 2008, Advances in Neural Information Processing Systems 20 (NIPS 07)
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Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423
“The studies in [375–382] were carried out in diverse fields, showcasing the potential of this new paradigm for data science.”
The global spectrum of plant form and function
Díaz, S., Kattge, J., Cornelissen, J. H. C., Wright, I. J., Lavorel, S., Dray, S., Reu, B., Kleyer, M., Wirth, C., Prentice, I. C., et al. (2015) · Nature · doi:10.1038/nature16489 · Cited by 3,516
Self-citationTRY plant trait database – enhanced coverage and open access
Kattge, J., Bönisch, G., Díaz, S. M., Lavorel, S., Prentice, I. C., Leadley, P., Tautenhahn, S., Werner, G. D. A., Aakala, T., Abedi, M., et al. (2019) · Global Change Biology · doi:10.1111/gcb.14904 · Cited by 2,210
Self-citationTheory-Guided Data Science: A New Paradigm for Scientific Discovery from Data
Karpatne, A., Atluri, G., Faghmous, J. H., Steinbach, M., Banerjee, A., Ganguly, A., Shekhar, S., Samatova, N., & Kumar, V. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2720168 · Cited by 1,752
Self-citationGlobal trait–environment relationships of plant communities
Bruelheide, H., Dengler, J., Purschke, O., Lenoir, J., Jiménez-Alfaro, B., Hennekens, S. M., Botta-Dukát, Z., Chytrý, M., Field, R., Jansen, F., et al. (2018) · Nature Ecology & Evolution · doi:10.1038/s41559-018-0699-8 · Cited by 614
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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
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