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Species trees are an essential tool in conservation and evolutionary biology. In phylogenomics, not only is data choice (e.g. using unlinked orthologs rather than paralogs) an important systematic consideration, but the choice of phylogenetic algorithm is also important. Since individual gene phylogenies can differ from the true species phylogeny, new methods have been proposed for species tree estimation using multiple unlinked genes. Improvements in genome sequencing technologies have increased the amount of data available to researchers and this has increased the utility of multi-locus species tree inference methods. The Bayesian methods BEST and *BEAST that incorporate a coalescent model to account for gene tree and species tree conflict offer promising advances in species tree inference directly from DNA sequences. Methods that infer species trees from gene trees rather than directly from sequence data such as STAR, STEAC, NJst and the likelihood method STEM have been recently developed as computationally efficient alternatives. Bayesian concordance analysis, which has been shown to perform well when horizontal gene transfer is the cause of gene tree and species tree conflict, is also discussed. Furthermore, methods for species delimitation including a non-parametric species tree inference method that does not require a priori species assignments can remove subjectivity from species delimitation. Here, I review the assumptions, required inputs, and the performance of these methods under simulation and in recent empirical studies. Researchers in many disciplines should understand the methods available for phylogenomic species tree inference in order to enhance evolutionary and conservation studies.
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