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.

基于特征的方法为经典分类方法提供了补充意见，这是揭示社区组装，物种相互作用，生态系统功能以及解决重要保护问题的关键一步。这些方法需要大量的采样工作才能提供完整的特征数据集，因此，丢失的数据非常普遍。我们评估了missForest算法的性能，该算法使用随机森林方法通过系统发育信息估算物种特征值。我们模拟了具有不同大小和比例的缺失数据，不同水平的性状保守性和性状相关性的数据集。我们使用missForest算法在没有系统发育信息的情况下测试了性状归因，并使用系统发育特征向量添加了物种之间的系统发育关系。我们的结果表明，性状中的系统发育信号水平及其之间的相关性是影响插补误差测度的主要参数。当性状具有较高的相关性水平并且在系统发育树中保留性状时，错误的度量值会较小。通常，将系统发育矢量作为预测变量包含在missForest算法中可改善对缺失值的估计。但是，系统发育信息对插补过程的重要性取决于缺失条目的比例，性状的系统发育保守性以及性状之间的相关性。missForest算法似乎是一种特质归因的鲁棒方法，可用于估计缺失的特质而无需排除物种。因此，