KELCHNER, SCOT A. Centre for Plant Biodiversity Research, CSIRO Plant Industry, Canberra, Australia; School of Botany and Zoology, the Australian National University, Canberra, Australia. - Heterogeneous models for phylogeny estimation: combining data and partitions for Myoporaceae.
Most phylogenetic analyses involve limited data (usually much less
than 10,000 characters) evolving under a mosaic of selective
constraints. In such cases, phylogeny estimation can be particularly
susceptible to random and systematic error. Even when data sets can be
justifiably combined to increase total character number, vagaries of
mutation dynamics among subsets of characters can mislead phylogeny
estimation. One possible solution is to create an heterogeneous
mutation model for phylogenetic analysis, one that can accommodate
observed mutational differences between sets of characters. Here I
describe such a model for phylogeny estimation in Myoporaceae using
two chloroplast sequence data sets - the rpl16 intron and the
trnT-trnL intergenic spacer - and morphological cladistic
characters. The method uses data partitioning, likelihood ratio tests,
bayesian inference, and a conversion of likelihood parameter values
into weighted parsimony step matrices. The heterogeneous model
encompasses five mutational categories for characters and generates
parsimony trees that can be assessed by conventional bootstrap and
Bremer support methods. Comparative results are given for outcomes of
independent character partitions, combined equal-weight parsimony
analysis, and the combined heterogeneous weighted parsimony analysis.
The feasibility of such models for phylogeny estimation and their
potential benefits and limitations are discussed.
Key words: rpl16 intron, trnT-trnL spacer, bayesian inference, heterogeneous models, maximum likelihood, weighted parsimony