Optimal pooling of data for the reliable estimation of trait probability distributions

Abstract

Aim: Functional diversity indices that summarize trait variation between organisms within communities are widely used tools in community ecology. Although intraspecific trait variation is often an important component of the total variation, traditional functional diversity indices neglect this component. Recently, Carmona et al. proposed a framework to overcome this limitation based on fitting trait probability distributions (TPDs). Unfortunately, this article does not contain any guidance on pooling data before fitting TPDs. Innovation: Reliable fitting of TPDs needs larger sample size than usually used. This larger sample size can be reached pooling of data comes from different localities. If the TPD is the same in these localities, pooling results in a more reliable estimation. Moreover, if pooled data represent different TPDs, the fitted distribution is an artefact. In this paper, we suggest an algorithm for the automated selection of optimal pooling. It is based on fitting Gaussian mixture models and model selection using Bayes information criterion (BIC). Main conclusions: The new algorithm was able to select the optimal pooling of data, which is illustrated using artificial data. Analysis of field data examples showed that optimal pooling is often not obvious: both merging all data and analysing measurements from each locality separately might result in unreliable estimates.