Assessing the Phylogenetic Utility of DNA Barcoding Using the New Zealand Cicada Genus Kikihia

DNA Barcoding (Hebert et al. 2003) has the potential to revolutionize the process of identifying and cataloguing biodiversity; however, significant controversy surrounds some of the proposed applications. In the seven years since DNA barcoding was introduced, the Web of Science records more than 600 studies that have weighed the pros and cons of this procedure. Unfortunately, the scientific community has been unable to come to any consensus on what threshold to use to differentiate species or even whether the barcoding region provides enough information to serve as an accurate species identification tool. The purpose of my thesis is to analyze mitochondrial DNA (mtDNA) barcoding’s potential to identify known species and provide a well-resolved phylogeny for the New Zealand cicada genus Kikihia. In order to do this, I created a phylogenetic tree for species in the genus Kikihia based solely on the barcoding region and compared it to a phylogeny previously created by Marshall et al. (2008) that benefits from information from other mtDNA and nuclear genes as well as species-specific song data. I determined how well the barcoding region delimits species that have been recognized based on morphology and song. In addition, I looked at the effect of sampling on the success of barcoding studies. I analyzed subsets of a larger, more densely sampled dataset for the Kikihia Muta Group to determine which aspects of my sampling strategy led to the most accurate identifications. Since DNA barcoding would by definition have problems in diagnosing hybrid individuals, I studied two species (K. “murihikua” and K. angusta) that are known to hybridize. Individuals that were not obvious hybrids (determined by morphology) were selected for the case study. Phylogenetic analysis of the barcoding region revealed insights into the reasons these two species could not be successfully differentiated using barcoding alone.

Polytomies and bayesian phylogenetic inference

Bayesian phylogenetic analyses are now very popular in systematics and molecular evolution because they allow the use of much more realistic models than currently possible with maximum likelihood methods. There are, however, a growing number of examples in which large Bayesian posterior clade probabilities are associated with very short edge lengths and low values for non-Bayesian measures of support such as nonparametric bootstrapping. For the four-taxon case when the true tree is the star phylogeny, Bayesian analyses become increasingly unpredictable in their preference for one of the three possible resolved tree topologies as data set size increases. This leads to the prediction that hard (or near-hard) polytomies in nature will cause unpredictable behavior in Bayesian analyses, with arbitrary resolutions of the polytomy receiving very high posterior probabilities in some cases. We present a simple solution to this problem involving a reversible-jump Markov chain Monte Carlo (MCMC) algorithm that allows exploration of all of tree space, including unresolved tree topologies with one or more polytomies. The reversible-jump MCMC approach allows prior distributions to place some weight on less-resolved tree topologies, which eliminates misleadingly high posteriors associated with arbitrary resolutions of hard polytomies. Fortunately, assigning some prior probability to polytomous tree topologies does not appear to come with a significant cost in terms of the ability to assess the level of support for edges that do exist in the true tree. Methods are discussed for applying arbitrary prior distributions to tree topologies of varying resolution, and an empirical example showing evidence of polytomies is analyzed and discussed.

Polytomies and bayesian phylogenetic inference

Bayesian phylogenetic analyses are now very popular in systematics and molecular evolution because they allow the use of much more realistic models than currently possible with maximum likelihood methods. There are, however, a growing number of examples in which large Bayesian posterior clade probabilities are associated with very short edge lengths and low values for non-Bayesian measures of support such as nonparametric bootstrapping. For the four-taxon case when the true tree is the star phylogeny, Bayesian analyses become increasingly unpredictable in their preference for one of the three possible resolved tree topologies as data set size increases. This leads to the prediction that hard (or near-hard) polytomies in nature will cause unpredictable behavior in Bayesian analyses, with arbitrary resolutions of the polytomy receiving very high posterior probabilities in some cases. We present a simple solution to this problem involving a reversible-jump Markov chain Monte Carlo (MCMC) algorithm that allows exploration of all of tree space, including unresolved tree topologies with one or more polytomies. The reversible-jump MCMC approach allows prior distributions to place some weight on less-resolved tree topologies, which eliminates misleadingly high posteriors associated with arbitrary resolutions of hard polytomies. Fortunately, assigning some prior probability to polytomous tree topologies does not appear to come with a significant cost in terms of the ability to assess the level of support for edges that do exist in the true tree. Methods are discussed for applying arbitrary prior distributions to tree topologies of varying resolution, and an empirical example showing evidence of polytomies is analyzed and discussed.