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Quantitative traits have long been hypothesized to affect speciation and extinction rates. For example, smaller body size or increased specialization may be associated with increased rates of diversification. Here, I present a phylogenetic likelihood-based method (quantitative state speciation and extinction [QuaSSE]) that can be used to test such hypotheses using extant character distributions. This approach assumes that diversification follows a birth–death process where speciation and extinction rates may vary with one or more traits that evolve under a diffusion model. Speciation and extinction rates may be arbitrary functions of the character state, allowing much flexibility in testing models of trait-dependent diversification. I test the approach using simulated phylogenies and show that a known relationship between speciation and a quantitative character could be recovered in up to 80% of the cases on large trees (500 species). Consistent with other approaches, detecting shifts in diversification due to differences in extinction rates was harder than when due to differences in speciation rates. Finally, I demonstrate the application of QuaSSE to investigate the correlation between body size and diversification in primates, concluding that clade-specific differences in diversification may be more important than size-dependent diversification in shaping the patterns of diversity within this group. [ABSTRACT FROM PUBLISHER]

Species traits may influence rates of speciation and extinction, affecting both the patterns of diversification among lineages and the distribution of traits among species. Existing likelihood approaches for detecting differential diversification require complete phylogenies; that is, every extant species must be present in a well-resolved phylogeny. We developed 2 likelihood methods that can be used to infer the effect of a trait on speciation and extinction without complete phylogenetic information, generalizing the recent binary-state speciation and extinction method. Our approaches can be used where a phylogeny can be reasonably assumed to be a random sample of extant species or where all extant species are included but some are assigned only to terminal unresolved clades. We explored the effects of decreasing phylogenetic resolution on the ability of our approach to detect differential diversification within a Bayesian framework using simulated phylogenies. Differential diversification caused by an asymmetry in speciation rates was nearly as well detected with only 50% of extant species phylogenetically resolved as with complete phylogenetic knowledge. We demonstrate our unresolved clade method with an analysis of sexual dimorphism and diversification in shorebirds (Charadriiformes). Our methods allow for the direct estimation of the effect of a trait on speciation and extinction rates using incompletely resolved phylogenies. [ABSTRACT FROM PUBLISHER]

Determining whether speciation and extinction rates depend on the state of a particular character has been of long-standing interest to evolutionary biologists. To assess the effect of a character on diversification rates using likelihood methods requires that we be able to calculate the probability that a group of extant species would have evolved as observed, given a particular model of the character's effect. Here we describe how to calculate this probability for a phylogenetic tree and a two-state (binary) character under a simple model of evolution (the "BiSSE" model, binary-state speciation and extinction). The model involves six parameters, specifying two speciation rates (rate when the lineage is in state 0; rate when in state 1), two extinction rates (when in state 0; when in state 1), and two rates of character state change (from 0 to 1, and from 1 to 0). Using these probability calculations, we can do maximum likelihood inference to estimate the model's parameters and perform hypothesis tests (e.g., is the rate of speciation elevated for one character state over the other?). We demonstrate the application of the method using simulated data with known parameter values. [ABSTRACT FROM AUTHOR]