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In this article we propose a new method, which we name 'quartet neighborjoining', or 'quartet-NJ', to infer an unrooted species tree on a given set oftaxa T from empirical distributions of unrooted quartet gene trees on allfour-taxon subsets of T. In particular, quartet-NJ can be used to estimate aspecies tree on T from distributions of gene trees on T. The quartet-NJalgorithm is conceptually very similar to classical neighbor joining, and itsstatistical consistency under the multispecies coalescent model is proved by avariant of the classical 'cherry picking'-theorem. In order to demonstrate thesuitability of quartet-NJ, coalescent processes on two different species trees(on five resp. nine taxa) were simulated, and quartet-NJ was applied to thesimulated gene tree distributions. Further, quartet-NJ was applied to quartetdistributions obtained from multiple sequence alignments of 28 proteins of nineprokaryotes.

Estimating Species Trees from Quartet Gene Tree Distributions under the Coalescent Model