Consequences of Different Null Models on the Tree Shape Bias of Supertree Methods

Supertree methods are widely applied and give rise to new conclusions about phylogenies (e.g., BinindaEmonds et al. 2007). Although several desiderata for supertree methods exist (Wilkinson, Thorley, et al. 2004), only few of them have been studied in greater detail, examples include shape bias (Wilkinson et al. 2005) or pareto properties (Wilkinson et al. 2007). Here I look more closely at two matrix representation methods, matrix representation with compatibility (MRC) and matrix representation with parsimony (MRP). Different null models of random data are studied and the resulting tree shapes are investigated. Thereby I consider unrooted trees and a bias in tree shape is determined by a tree balance measure. The measure for unrooted trees is a modification of a tree balance measure for rooted trees. I observe that depending on the underlying null model of random data, the methods may resolve conflict in favor of more balanced tree shapes. The analyses refer only to trees with the same taxon set, also known as the consensus setting (e.g., Wilkinson et al. 2007), but I will be able to draw conclusions on how to deal with missing data.