RT Journal Article SR Electronic T1 Estimating rates and patterns of diversification with incomplete sampling: A case study in the rosids JF bioRxiv FD Cold Spring Harbor Laboratory SP 749325 DO 10.1101/749325 A1 Miao Sun A1 Ryan A. Folk A1 Matthew A. Gitzendanner A1 Robert P. Guralnick A1 Pamela S. Soltis A1 Zhiduan Chen A1 Douglas E. Soltis YR 2019 UL http://biorxiv.org/content/early/2019/08/29/749325.abstract AB Premise of the Study Recent advances in generating large-scale phylogenies enable broad-scale estimation of species diversification rates. These now-common approaches typically (1) are characterized by incomplete coverage without explicit sampling methodologies, and/or (2) sparse backbone representation, and usually rely on presumed phylogenetic placements to account for species without molecular data. Here we use an empirical example to examine effects of incomplete sampling on diversification estimation and provide constructive suggestions to ecologists and evolutionists based on those results.Methods We used a supermatrix for rosids, a large clade of angiosperms, and its well-sampled subclade Cucurbitaceae, as empirical case studies. We compared results using this large phylogeny with those based on a previously inferred, smaller supermatrix and on a synthetic tree resource with complete taxonomic coverage. Finally, we simulated random and representative taxon sampling and explored the impact of sampling on three commonly used methods, both parametric (RPANDA, BAMM) and semiparametric (DR).Key Results We find the impact of sampling on diversification estimates is idiosyncratic and often strong. As compared to full empirical sampling, representative and random sampling schemes either depress or exaggerate speciation rates depending on methods and sampling schemes. No method was entirely robust to poor sampling, but BAMM was least sensitive to moderate levels of missing taxa.Conclusions We (1) urge caution in use of summary backbone trees containing only higher-level taxa, (2) caution against uncritical modeling of missing taxa using taxonomic data for poorly sampled trees, and (3) stress the importance of explicit sampling methodologies in macroevolutionary studies.