preprints.org > biology and life sciences > ecology, evolution, behavior and systematics > doi: 10.20944/preprints202310.1653.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 24 October 2023 / Approved: 25 October 2023 / Online: 25 October 2023 (12:56:53 CEST)

Detailed evaluation is provided for the likelihood statistics intrinsic to interlocking Sequential Bayes analysis, which allows estimation of evidential support for dendrograms charting the macroevolution of taxa. It involves complexity functions, such as fractal evolution, to generate well-supported evolutionary trees. Required are data on trait changes from ancestral species to descendant species, which is facilitated by reduction of large genera to monothetic groups (one ancestral species each), most conveniently named as separate genera. Assigning each species one Shannon informational bit per new trait, then adding the traits as sequential Bayesian analysis (each posterior probability used as the prior for the next iteration of Bayes Formula), then interpretation with an odds chart, provides a posterior probability that the ancestor-descendant order is correct, that is, entirely follows evolutionary theory involving adaptation and rarity of total trait reversals. The key fact is that the most recently acquired traits of the ancestral species are selectively inviolate and passed on without change to each descendant species. The details of sequential Bayesian analysis were clarified by calculation of likelihood ratios and Bayes factors that compare optimal models with the next most likely model. Such analysis demonstrated that the optimum arrangement of ancestor and descendant species leads to high support values for fitting evolutionary theory, comparable to statistical support levels reported for molecular evolutionary trees. The fact that phylogenetic analysis does not offer an ancestor-descendant model in is found to be conducive of precision at the expense of accuracy, and leads to wrong ancestor-free models with likelihoods explaining data at high credible levels. The number of advanced traits in the outgroup as a Bayesian prior greatly enhances posterior probability. The method is simple, free of special computer analysis, and well-suited to standard taxonomic practice.

adaptation; ancestor-descendant; Bayes factors; bryophytes; complexity; evolution; likelihood ratio; monothetic genus; sequential Bayes; Shannon information bits; Tainoa

Biology and Life Sciences, Ecology, Evolution, Behavior and Systematics

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