Supplemental material for: Morphological phylogenetics evaluated using novel evolutionary simulations

Evolutionary inferences require reliable phylogenies. Morphological data
has traditionally been analysed using maximum parsimony, but recent
simulation studies have suggested that Bayesian analyses yield more
accurate trees. This debate is ongoing, in part, because of ambiguity over
modes of morphological evolution and a lack of appropriate models. Here we
investigate phylogenetic methods using two novel simulation models – one
in which morphological characters evolve stochastically along lineages and
another in which individuals undergo selection. Both models generate
character data and lineage splitting simultaneously: the resulting trees
are an emergent property, rather than a fixed parameter. Standard
consensus methods for Bayesian searches (Mki) yield fewer incorrect nodes
and quartets than the standard consensus trees recovered using equal
weighting and implied weighting parsimony searches. Distances between the
pool of derived trees (most parsimonious or posterior distribution) and
the true trees – measured using Robinson-Foulds (RF), subtree prune and
regraft (SPR), and tree bisection reconnection (TBR) metrics – demonstrate
that this is related to the search strategy and consensus method of each
technique. The amount and structure of homoplasy in character data differs
between models. Morphological coherence, which has previously not been
considered in this context, proves to be a more important factor for
phylogenetic accuracy than homoplasy. Selection-based models exhibit
relatively lower homoplasy, lower morphological coherence, and higher
inaccuracy in inferred trees. Selection is a dominant driver of
morphological evolution, but we demonstrate that it has a confounding
effect on numerous character properties which are fundamental to
phylogenetic inference. We suggest that the current debate should move
beyond considerations of parsimony versus Bayesian, towards identifying
modes of morphological evolution and using these to build models for
probabilistic search methods.