Reliable estimates of beta diversity with incomplete sampling

Beta diversity, the compositional variation among communities or assemblages, is crucial to understanding the principles of diversity assembly. The mean pairwise proportional dissimilarity expresses overall heterogeneity of samples in a data set and is among the most widely used and most robust measures of beta diversity. Obtaining a complete list of taxa and their abundances requires substantial taxonomic expertise and is time consuming. In addition, the information is generally incomplete due to sampling biases. Based on the concept of the ecological significance of dominant taxa, we explore whether determining proportional dissimilarity can be simplified based on dominant species. Using simulations and six case studies, we assess the correlation between complete community compositional data and reduced subsets of a varying number of dominant species. We find that gross beta diversity is usually depicted accurately when only the 80th percentile or five of the most abundant species of each site is considered. In data sets with very high evenness, at least the 10 most abundant species should be included. Focusing on dominant species also maintains the rank-order of beta diversity among sites. Our new approach will allow ecologists and paleobiologists to produce a far greater amount of data on diversity patterns with less time and effort, supporting conservation studies and basic science.