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{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T04:39:51Z","timestamp":1692333591708},"reference-count":40,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2023,5,16]],"date-time":"2023-05-16T00:00:00Z","timestamp":1684195200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["US NSF 20\u2010508"]},{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["RU1536\/3\u20101"]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Global Ecol Biogeogr"],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Aim<\/jats:title><jats:p>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, \u2018gap\u2010filling\u2019 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Innovation<\/jats:title><jats:p>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\u2010 and multivariate) and (3) taxonomic and functional clustering (valuewise, uni\u2010 and multivariate). BHPMF preserves general patterns of trait distributions but induces taxonomic clustering. Data set\u2013external trait data had little effect on induced taxonomic clustering and stabilized trait\u2013trait correlations.<\/jats:p><\/jats:sec><jats:sec><jats:title>Main Conclusions<\/jats:title><jats:p>Our study extends the criteria for the evaluation of gap\u2010filling 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.<\/jats:p><\/jats:sec>","DOI":"10.1111\/geb.13695","type":"journal-article","created":{"date-parts":[[2023,5,17]],"date-time":"2023-05-17T04:20:52Z","timestamp":1684297252000},"page":"1395-1408","update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Imputing missing data in plant traits: A guide to improve gap\u2010filling"],"prefix":"10.1111","volume":"32","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-7786-1728","authenticated-orcid":false,"given":"Julia S.","family":"Joswig","sequence":"first","affiliation":[{"name":"Department of Geography University of Zurich Z\u00fcrich Switzerland"},{"name":"Max Planck Institute for Biogeochemistry Jena Germany"}]},{"given":"Jens","family":"Kattge","sequence":"additional","affiliation":[{"name":"Max Planck Institute for Biogeochemistry Jena Germany"},{"name":"German Centre for Integrative Biodiversity Research (iDiv) Halle\u2010Jena\u2010Leipzig Leipzig Germany"}]},{"given":"Guido","family":"Kraemer","sequence":"additional","affiliation":[{"name":"Max Planck Institute for Biogeochemistry Jena Germany"},{"name":"German Centre for Integrative Biodiversity Research (iDiv) Halle\u2010Jena\u2010Leipzig Leipzig Germany"
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