Publication Details |
| Category | Text Publication |
| Reference Category | Journals |
| DOI | 10.1111/geb.13695 |
Licence  |
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| Title (Primary) | Imputing missing data in plant traits: A guide to improve gap-filling |
| Author | Joswig, J.S.; Kattge, J.; Kraemer, G.; Mahecha, M.D.; Rüger, N.; Schaepman, M.E.; Schrodt, F.; Schuman, M.C. |
| Source Titel | Global Ecology and Biogeography |
| Year | 2023 |
| Department | iDiv; RS |
| Volume | 32 |
| Issue | 8 |
| Page From | 1395 |
| Page To | 1408 |
| Language | englisch |
| Topic | T5 Future Landscapes |
| Data and Software links | https://doi.org/10.17871/TRY.96 |
| Supplements | https://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1111%2Fgeb.13695&file=geb13695-sup-0001-AppendixS1.zip |
| Keywords | Bayesian hierarchical model; gap-filling; imputation; induced pattern; machine learning; matrix factorization; plant functional trait; sensitivity analysis sparse matrix TRY |
| Abstract |
AimGlobally 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. InnovationWe 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 ConclusionsOur 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. |
| Persistent UFZ Identifier | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=27091 |
| Joswig, J.S., Kattge, J., Kraemer, G., Mahecha, M.D., Rüger, N., Schaepman, M.E., Schrodt, F., Schuman, M.C. (2023): Imputing missing data in plant traits: A guide to improve gap-filling Glob. Ecol. Biogeogr. 32 (8), 1395 - 1408 10.1111/geb.13695 |
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