Phylogenetic relationships among the phrynosomatid sand lizards inferred from mitochondrial DNA sequences generated by heterogeneous evolutionary processes

Nucleotide sequences of the mitochondrial protein coding cytochrome b (cyt b; 650 bp) and small-subunit 12S ribosomal RNA (similar to 350 bp) genes we re used in analyses of phylogenetic relationships among extant phrynosomati d sand lizards, including an examination of competing hypotheses regarding the evolution of "earlessness." Sequences were obtained from all currently recognized species of sand lizards as well as representatives of the first and second outgroups and analyzed using both parsimony and likelihood metho ds. The cyt b data offer strong support for relationships that correspond w ith relatively recent divergences and moderate to low support for relations hips reflecting more ancient divergences within the clade. These data suppo rt monophyly of the "earless" taxa, the placement of Uma as the sister taxo n to the other sand lizards, and monophyly of all four taxa traditionally r anked as genera. All well-supported relationships in the 12S phylogeny are completely congruent with well-supported relationships in the cyt b phyloge ny; however, the 12S data alone provide very little support for deeper dive rgences. Phylogenetic relationships within species are concordant with geog raphy and suggest patterns of phylogeographic differentiation, including th e conclusion that at least one currently recognized species (Holbrookia mac ulata) actually consists of more than one species; By independently optimiz ing likelihood model parameters for various subsets of the data, we found t hat nucleotide substitution processes vary widely between genes and among t he structural and functional regions or classes of sites within each gene. Therefore, we compared competing phylogenetic hypotheses, using parameter e stimates specific to those subsets, analyzing the subsets separately and in various combinations. The hypothesis supported by the cyt b data was favor ed over rival hypotheses in all but one of the five comparisons made with t he entire data set, including the set of partitions that best explained the data, although we were unable to confidently reject (P < 0.05) alternative hypotheses. Our results highlight the importance of optimizing models and parameter estimates for different genes or parts thereof-a strategy that ta kes advantages of the strengths of both combining and partitioning data.