"Is there any reason, other than esprit de corps, to explain why, in many journals, it became almost impossible to publish a paper using parsimony as the only method?"
Pablo A. Goloboff, Ambrosio Torres and J. Salvador Arias. 2017. Weighted parsimony outperforms other methods of phylogenetic inference under models appropriate for morphology. Cladistics
Aqui esta el resumen.
One of the lasting controversies in phylogenetic inference is the degree to which speciﬁc evolutionary models should inﬂuence
the choice of methods. Model-based approaches to phylogenetic inference (likelihood, Bayesian) are defended on the premise
that without explicit statistical models there is no science, and parsimony is defended on the grounds that it provides the best
rationalization of the data, while refraining from assigning speciﬁc probabilities to trees or character-state reconstructions.
Authors who favour model-based approaches often focus on the statistical properties of the methods and models themselves,
but this is of only limited use in deciding the best method for phylogenetic inference—such decision also requires considering
the conditions of evolution that prevail in nature. Another approach is to compare the performance of parsimony and model-
based methods in simulations, which traditionally have been used to defend the use of models of evolution for DNA sequences.
Some recent papers, however, have promoted the use of model-based approaches to phylogenetic inference for discrete morpho-
logical data as well. These papers simulated data under models already known to be unfavourable to parsimony, and modelled
morphological evolution as if it evolved just like DNA, with probabilities of change for all characters changing in concert along
tree branches. The present paper discusses these issues, showing that under reasonable and less restrictive models of evolution
for discrete characters, equally weighted parsimony performs as well or better than model-based methods, and that parsimony
under implied weights clearly outperforms all other methods.
© The Willi Hennig Society 2017.