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Clade support measures and their adequacy
Author(s) -
Grant Taran,
Kluge Arnold G.
Publication year - 2008
Publication title -
cladistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.323
H-Index - 92
eISSN - 1096-0031
pISSN - 0748-3007
DOI - 10.1111/j.1096-0031.2008.00231.x
Subject(s) - mathematics , statistics , explanatory power , statistical power , econometrics , bayesian probability , function (biology) , statistical inference , biology , philosophy , epistemology , evolutionary biology
In addition to hypothesis optimality, the evaluation of clade (group, edge, split, node) support is an important aspect of phylogenetic analysis. Here we clarify the logical relationship between support and optimality and formulate adequacy conditions for support measures. Support, S , and optimality, O , are both empirical knowledge claims about the strength of hypotheses, h 1 , h 2 , … h n , in relation to evidence, e , given background knowledge, b . Whereas optimality refers to the absolute strength of hypotheses, support refers to the relative strength of hypotheses. Consequently, support and optimality are logically related such that they vary in direct proportion to each other, S ( h | e,b ) ∝ O ( h | e,b ). Furthermore, in order for a support measure to be objective it must quantify support as a function of explanatory power. For example, Goodman–Bremer support and ratio of explanatory power (REP) support satisfy the adequacy requirement S ( h | e,b ) ∝ O ( h | e,b ) and calculate support as a function of explanatory power. As such, these are adequate measures of objective support. The equivalent measures for statistical optimality criteria are the likelihood ratio (or log‐likelihood difference) and likelihood difference support measures for maximum likelihood and the posterior probability ratio and posterior probability difference support measures for Bayesian inference. These statistical support measures satisfy the adequacy requirement S ( h | e,b ) ∝ O ( h | e,b ) and to that extent are internally consistent; however, they do not quantify support as a function of explanatory power and therefore are not measures of objective support. Neither the relative fit difference (RFD; relative GB support) nor any of the parsimony (bootstrap and jackknife character resampling) or statistical [bootstrap character resampling, Markov chain Monte Carlo (MCMC) clade frequencies] support measures based on clade frequencies satisfy the adequacy condition S ( h | e,b ) ∝ O ( h | e,b ) or calculate support as a function of explanatory power. As such, they are not adequate support measures. © The Willi Hennig Society 2008.