BHPMF – a hierarchical B ayesian approach to gap‐filling and trait prediction for macroecology and functional biogeography
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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].”
From Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data (2017) · cites this paper as [47]
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PalmTraits 1.0, a species-level functional trait database of palms worldwide
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Advancing an interdisciplinary framework to study seed dispersal ecology
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A simple approach for maximizing the overlap of phylogenetic and comparative data
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Self-citationHigh exposure of global tree diversity to human pressure
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Self-citationLate Quaternary climate legacies in contemporary plant functional composition
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Self-citationLeaf metabolic traits reveal hidden dimensions of plant form and function
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Self-citationIsoprene emission structures tropical tree biogeography and community assembly responses to climate
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Realizing the potential of trait‐based aquatic ecology: New tools and collaborative approaches
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Challenges and opportunities for biogeography—What can we still learn from von Humboldt?
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Self-citationRaunkiæran shortfalls: Challenges and perspectives in trait‐based ecology
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Leaf‐IT: An Android application for measuring leaf area
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Transcending data gaps: a framework to reduce inferential errors in ecological analyses
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The leaf economic and plant size spectra of European forest understory vegetation
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A global analysis of avian island diversity–area relationships in the Anthropocene
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Potential and limitations of inferring ecosystem photosynthetic capacity from leaf functional traits
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Self-citationFunctional diversity of the Australian flora: Strong links to species richness and climate
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Self-citationClimate-trait relationships exhibit strong habitat specificity in plant communities across Europe
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Self-citationAssessing among‐lineage variability in phylogenetic imputation of functional trait datasets
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Pitfalls of ignoring trait resolution when drawing conclusions about ecological processes
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Advancing causal inference in ecology: Pathways for biodiversity change detection and attribution
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Phylogeny and the prediction of tree functional diversity across novel continental settings
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Traits of dominant plant species drive normalized difference vegetation index in grasslands globally
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ETIB‐T: An Equilibrium Theory of Island Biogeography for plant traits
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Global functional variation in alpine vegetation
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Self-citationGlobal multifaceted biodiversity patterns, centers, and conservation needs in angiosperms
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Physics-Guided Deep Learning for Improved Production Forecasting in Unconventional Reservoirs
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Matos, I. S., Boakye, M., Niewiadomski, I., Antonio, M., Carlos, S., Johnson, B. C., Chu, A., Echevarria, A., Fontao, A., Garcia, L., et al. (2024) · New Phytologist · doi:10.1111/nph.20037 · Cited by 20 · see citing works
Beyond species means – the intraspecific contribution to global wood density variation
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Benchmarking imputation methods for categorical biological data
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Revealing the functional traits linked to hidden environmental factors in community assembly
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Using large language models to extract plant functional traits from unstructured text
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AVOTREX : A Global Dataset of Extinct Birds and Their Traits
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Similar factors underlie tree abundance in forests in native and alien ranges
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Self-citationLand‐Use Impacts on Plant Functional Diversity Throughout Europe
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The importance of oxygen for explaining rapid shifts in a marine fish
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Self-citationCombining agent-based, trait-based and demographic approaches to model coral-community dynamics
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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
- 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 · 2014: 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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