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Imputing missing data in plant traits: A guide to improve gap‐filling

Joswig, Julia S.; Kattge, Jens; Kraemer, Guido; Mahecha, Miguel D.; Rüger, Nadja; Schaepman, Michael E.; Schrodt, Franziska; Schuman, Meredith C.

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Authors

Julia S. Joswig

Jens Kattge

Guido Kraemer

Miguel D. Mahecha

Nadja Rüger

Michael E. Schaepman

Meredith C. Schuman



Abstract

Aim: Globally distributed plant trait data are increasingly used to understand relationships between biodiversity and ecosystem processes. However, global trait databases are sparse because they are compiled from many, mostly small databases. This sparsity in both trait space completeness and geographical distribution limits the potential for both multivariate and global analyses. Thus, ‘gap‐filling’ approaches are often used to impute missing trait data. Recent methods, like Bayesian hierarchical probabilistic matrix factorization (BHPMF), can impute large and sparse data sets using side information. We investigate whether BHPMF imputation leads to biases in trait space and identify aspects influencing bias to provide guidance for its usage. Innovation: We use a fully observed trait data set from which entries are randomly removed, along with extensive but sparse additional data. We use BHPMF for imputation and evaluate bias by: (1) accuracy (residuals, RMSE, trait means), (2) correlations (bi‐ and multivariate) and (3) taxonomic and functional clustering (valuewise, uni‐ and multivariate). BHPMF preserves general patterns of trait distributions but induces taxonomic clustering. Data set–external trait data had little effect on induced taxonomic clustering and stabilized trait–trait correlations. Main Conclusions: Our study extends the criteria for the evaluation of gap‐filling beyond RMSE, providing insight into statistical data structure and allowing better informed use of imputed trait data, with improved practice for imputation. We expect our findings to be valuable beyond applications in plant ecology, for any study using hierarchical side information for imputation.

Citation

Joswig, J. S., Kattge, J., Kraemer, G., Mahecha, M. D., Rüger, N., Schaepman, M. E., …Schuman, M. C. (2023). Imputing missing data in plant traits: A guide to improve gap‐filling. Global Ecology and Biogeography, 32(8), 1395-1408. https://doi.org/10.1111/geb.13695

Journal Article Type Article
Acceptance Date Apr 14, 2023
Online Publication Date May 16, 2023
Publication Date Aug 1, 2023
Deposit Date Jul 5, 2023
Publicly Available Date Jul 6, 2023
Journal Global Ecology and Biogeography
Print ISSN 1466-822X
Electronic ISSN 1466-8238
Publisher Wiley
Peer Reviewed Peer Reviewed
Volume 32
Issue 8
Pages 1395-1408
DOI https://doi.org/10.1111/geb.13695
Keywords gap‐filling, sparse matrix, Bayesian hierarchical model, matrix factorization, TRY, induced pattern, imputation, machine learning, plant functional trait, sensitivity analysis
Public URL https://nottingham-repository.worktribe.com/output/21098420
Publisher URL https://onlinelibrary.wiley.com/doi/10.1111/geb.13695
Additional Information Received: 2022-03-08; Accepted: 2023-04-14; Published: 2023-05-16

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Publisher Licence URL
https://creativecommons.org/licenses/by-nc/4.0/

Copyright Statement
This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.






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