Using Phylogenetic Information To Impute Missing Functional Trait Values In Ecological Databases

Trait-based approaches offer complementary views to the classic taxonomic approach, which is a crucial step forward to unveil mechanisms of community assembly, species interactions, ecosystem functioning, and tackling important conservation issues. These approaches require an enormous sampling effort to provide complete trait datasets, consequently, missing data are very common. We evaluated the performance of the missForest algorithm, that uses the Random Forest method to impute species traits values using phylogenetic information. We simulated datasets with different sizes and proportions of missing data, different levels of trait conservatism, and trait correlation. We tested trait imputation using the missForest algorithm without phylogenetic information and adding the phylogenetic relationship among species, using phylogenetic eigenvector. Our results show that the level of phylogenetic signal in traits and the correlation among them are the main parameters that influence the measures of imputation error. The measures of errors are smaller when traits have higher levels of correlation and when traits are conserved in the phylogenetic tree. In general, the inclusion of phylogenetic vectors as predictors in the missForest algorithm improves the estimation of missing values. However, the importance of phylogenetic information to the imputation process depends on the proportion of missing entries, phylogenetic conservatism of traits, and the correlation among traits. The missForest algorithm seems to be a robust method for trait imputation, and it can be used to estimate missing traits without the exclusion of species. Thus, we expect to have contributed with a new step to guide methodological choices to impute entire databases of traits with the goal of decreasing uncertainties and bias in the interpretation of ecological patterns and processes at different levels of ecological organization.