Abstract Detail


Caetano , Daniel S [1], O'Meara, Brian [2], Beaulieu, Jeremy [1].

A new hope: hidden state models improve the adequacy of state-dependent diversification approaches using empirical trees.

The state-dependent speciation and extinction models (SSE) have recently been criticized due to their high rates of “false positive” results. In other words, when rates of diversification vary independently from the trait of interest, the standard “null” model of homogeneous diversification rates is rejected in favor of an equally incorrect alternative model of trait-dependent diversification. Since then, many researchers have advocated avoiding SSE models in favor of other “non-parametric” or “semi-parametric” approaches. The hidden Markov modeling (HMM) approach provides a partial solution to the issues of model adequacy detected with SSE models. The inclusion of “hidden states” incorporates the rate heterogeneity observed in empirical phylogenies and makes it possible to distinguish between true signals of state-dependent diversification and instances of diversification shifts independent of the trait of interest. While HMM models have clear statistical benefits, their adoption into other classes of SSE models has been hampered by the interpretational challenges of what exactly a “hidden state” represents. We show that HMM models in combination with a model-averaging approach naturally account for the hidden majority of traits when examining the meaningful impact of a suspected “driver” of diversification. We also demonstrate this concept by extending the HMM to the geographic state-dependent speciation and extinction (GeoSSE) model. We test the efficacy of our “hiGeoSSE” extension with both simulations and a data set of conifers that examines the connection between biogeographic movements between Northern and Southern Hemisphere and their diversification rates. On the whole, we show that hidden states are a general framework that, when applied to SSE models, can, in fact, properly distinguish heterogeneous effects of diversification attributed to a focal character when present. We emphasize, however, that they should not be treated as a separate class of SSE models, but instead viewed as complementary and should be included as part of a set of models under evaluation. They also represent a straightforward approach to incorporating different types of unobserved heterogeneity in phylogenetic trees than a simple single rate category model is able to explain.

1 - University of Arkansas, Department of Biological Sciences, SCEN Room 735, Fayetteville, AR, 72703, US
2 - 8424 Mecklenburg Ct., Knoxville, TN, 37923, United States

model testing
state dependent speciation and extinction.

Presentation Type: Oral Paper
Abstract ID:893
Candidate for Awards:None

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