FiSSE: A simple nonparametric test for the effects of a binary character on lineage diversification rates

It is widely assumed that phenotypic traits can influence rates of speciation and extinction, and several statistical approaches have been used to test for correlations between character states and lineage diversification. Recent work suggests that model-based tests of state-dependent speciation and...

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Bibliographic Details
Published in:Evolution 2017-06, Vol.71 (6), p.1432-1442
Main Authors: Rabosky, Daniel L., Goldberg, Emma E.
Format: Article
Language:English
Subjects:
BiSSE
Complement
Computer simulation
Correlation
Diversification
Electric power distribution
Extinction
key innovation
macroevolution
Nonparametric statistics
ORIGINAL ARTICLE
Phylogeny
Speciation
species selection
Statistics
Test procedures
Tips
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Summary:It is widely assumed that phenotypic traits can influence rates of speciation and extinction, and several statistical approaches have been used to test for correlations between character states and lineage diversification. Recent work suggests that model-based tests of state-dependent speciation and extinction are sensitive to model inadequacy and phylogenetic pseudoreplication. We describe a simple nonparametric statistical test (“FiSSE”) to assess the effects of a binary character on lineage diversification rates. The method involves computing a test statistic that compares the distributions of branch lengths for lineages with and without a character state of interest. The value of the test statistic is compared to a null distribution generated by simulating character histories on the observed phylogeny. Our tests show that FiSSE can reliably infer trait-dependent speciation on phylogenies of several hundred tips. The method has low power to detect trait-dependent extinction but can infer state-dependent differences in speciation even when net diversification rates are constant. We assemble a range of macroevolutionary scenarios that are problematic for likelihood-based methods, and we find that FiSSE does not show similarly elevated false positive rates. We suggest that nonparametric statistical approaches, such as FiSSE, provide an important complement to formal process-based models for trait-dependent diversification.
ISSN:0014-3820
1558-5646
DOI:10.1111/evo.13227