Use and misuse of trait imputation in ecology: the problem of using out‐of‐context imputed values

Despite the progress in the measurement and accessibility of plant trait information, acquiring sufficiently complete data from enough species to answer broad‐scale questions in plant functional ecology and biogeography remains challenging. A common way to overcome this challenge is by imputation, or ‘gap‐filling' of trait values. This has proven appropriate when focusing on the overall patterns emerging from the database being imputed. However, some applications force the imputation procedure out of its original scope, using imputed values independently from the imputation context, and specific trait values for a given species are used as input for computing new variables. We tested the performance of three widely used imputation methods (Bayesian hierarchical probabilistic matrix factorization, multiple imputation by chained equations with predictive mean matching, and Rphylopars) on a database of tropical tree and shrub traits. By applying a leave‐one‐out procedure, we assessed the accuracy and precision of the imputed values and found that out‐of‐context use of imputed values may bias the estimation of different variables. We also found that low redundancy (i.e. low predictability of a new value on the basis of existing values) in the dataset, not uncommon for empirical datasets, is likely the main cause of low accuracy and precision in the imputed values. We therefore suggest the use of a leave‐one‐out procedure to test the quality of the imputed values before any out‐of‐context application of the imputed values, and make practical recommendations to avoid the misuse of imputation procedures. Furthermore, we recommend not publishing gap‐filled datasets, publishing instead only the empirical data, together with the imputation method applied and the corresponding script to reproduce the imputation. This will help avoid the spread of imputed data, whose accuracy, precision, and source are difficult to assess and track, into the public domain.

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Publication Details

Journal
Ecography
Published
2025-02-03
DOI
https://doi.org/10.1111/ecog.07520
Citations
6
Primary Topic
Ecology and Vegetation Dynamics Studies
Type
article
Field-Weighted Citation Impact
13.88

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article

Use and misuse of trait imputation in ecology: the problem of using out‐of‐context imputed values

Fernanda C. Souza, Nathan G. Swenson, Ted R. Feldpausch, Wesley Jonatar Alves da Cruz et al.
6 citations
Ecography
Ecology and Vegetation Dynamics Studies
13.88
article

Use and misuse of trait imputation in ecology: the problem of using out‐of‐context imputed values

Fernanda C. Souza, Nathan G. Swenson, Ted R. Feldpausch, Wesley Jonatar Alves da Cruz, Sabina Cerruto Ribeiro, Gerhard Boenisch, Yadvinder Malhi, Gonzalo Rivas‐Torres, Oliver Lawrence Phillips, Gerardo Flores Llampazo, Mathias Disney, Jesús Aguirre‐Gutiérrez, Nayane Cristina Candida dos Santos Prestes, David Galbraith, Renato A. Ferreira de Lima, Esteban Álvarez‐Dávila, Lucas D. Gorné, Emilio Vilanova, Sandra Díaz, Anthony Di Fiore, Paulo S. Morandi, Rubens Manoel dos Santos, Ben Hur Marimon-Junior, Nathan J. B. Kraft, Beatriz Schwantes Marimon, Timothy R. Baker, Abel Monteagudo Mendoza, Hirma Ramírez‐Angulo, Alejandro Araujo‐Murakami
article en
6 citations

Abstract

Despite the progress in the measurement and accessibility of plant trait information, acquiring sufficiently complete data from enough species to answer broad‐scale questions in plant functional ecology and biogeography remains challenging. A common way to overcome this challenge is by imputation, or ‘gap‐filling' of trait values. This has proven appropriate when focusing on the overall patterns emerging from the database being imputed. However, some applications force the imputation procedure out of its original scope, using imputed values independently from the imputation context, and specific trait values for a given species are used as input for computing new variables. We tested the performance of three widely used imputation methods (Bayesian hierarchical probabilistic matrix factorization, multiple imputation by chained equations with predictive mean matching, and Rphylopars) on a database of tropical tree and shrub traits. By applying a leave‐one‐out procedure, we assessed the accuracy and precision of the imputed values and found that out‐of‐context use of imputed values may bias the estimation of different variables. We also found that low redundancy (i.e. low predictability of a new value on the basis of existing values) in the dataset, not uncommon for empirical datasets, is likely the main cause of low accuracy and precision in the imputed values. We therefore suggest the use of a leave‐one‐out procedure to test the quality of the imputed values before any out‐of‐context application of the imputed values, and make practical recommendations to avoid the misuse of imputation procedures. Furthermore, we recommend not publishing gap‐filled datasets, publishing instead only the empirical data, together with the imputation method applied and the corresponding script to reproduce the imputation. This will help avoid the spread of imputed data, whose accuracy, precision, and source are difficult to assess and track, into the public domain.

EcographyVol. 2025(6)
Gabriel René Moreno Autonomous University (BO), University of Notre Dame (US), Centre National de la Recherche Scientifique (FR), University of Leeds (GB), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (FR), Universidade Federal de Lavras (BR), Universidade Federal do Acre (BR), Wildlife Conservation Society (US), Max Planck Society (DE), Consejo Nacional de Investigaciones Científicas y Técnicas (AR), University of California, Los Angeles (US), Universidad Nacional de Córdoba (AR), Universidade de São Paulo (BR), Universidad de Los Andes (VE), University of Exeter (GB), Leverhulme Trust (GB), Universidade do Estado de Mato Grosso (BR), National University of Saint Anthony the Abbot in Cuzco (PE), University of Oxford (GB), Museo de Historia Natural Noel Kempff Mercado (BO), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), Instituto de Investigaciones de la Amazonía Peruana (PE), Evolutionary Genomics (United States) (US), UMR Botanique et Modélisation de l’Architecture des Plantes et des végétations (FR), National Centre for Earth Observation (GB), Universidad Nacional Abierta y a Distancia (CO), Max Planck Institute for Biogeochemistry (DE), Institut de Recherche pour le Développement (FR), University College London (GB), Universidad San Francisco de Quito (EC), The University of Texas at Austin (US)
Sight Research UK, University of Oxford, Fundação de Amparo à Pesquisa do Estado de São Paulo, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Natural Environment Research Council
Life in Land
Openalex Percentile: Top 1%
Ecology and Vegetation Dynamics Studies
13.88
DOIOpen Access PDFOpenAlex
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