期刊
EVOLUTION
卷 72, 期 11, 页码 2308-2324出版社
WILEY
DOI: 10.1111/evo.13602
关键词
Biogeography; BiSSE; GeoSSE; hidden Markov; HiSSE; model averaging
资金
- Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES) [1093/12-6]
The state-dependent speciation and extinction (SSE) models have recently been criticized due to their high rates of false positive results. Many researchers have advocated avoiding SSE models in favor of other nonparametric or semiparametric 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 can account for rate heterogeneity observed in empirical phylogenies and allows for reliable detection of state-dependent diversification or diversification shifts independent of the trait of interest. However, the adoption of HMM has been hampered by the interpretational challenges of what exactly a hidden state represents, which we clarify herein. We show that HMMs in combination with a model-averaging approach naturally account for hidden traits when examining the meaningful impact of a suspected driver of diversification. We also extend the HMM to the geographic state-dependent speciation and extinction (GeoSSE) model. We test the efficacy of our GeoHiSSE extension with both simulations and an empirical dataset. On the whole, we show that hidden states are a general framework that can distinguish heterogeneous effects of diversification attributed to a focal character.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据