This paper has two complementary purposes: first, to present a method to perform multiple regression on distance matrices, with permutation testing appropriate for path-length matrices representing evolutionary trees, and then, to apply this method to study the joint evolution of brain, behavior and other characteristics in marsupials. To understand the computation method, consider that the dependent matrix is unfolded as a vector y; similarly, consider X to be a table containing the independent matrices, also unfolded as vectors. A multiple regression is computed to express y as a function of X. The parameters of this regression (R and partial regression coefficients) are tested by permutations, as follows. When the dependent matrix variable y represents a simple distance or similarity matrix, permutations are performed in the same manner as the Mantel permutational test. When it is an ultrametric matrix representing a dendrogram, we use the double-permutation method (Lapointe and Legendre 1990, 1991). When it is a path-length matrix representing an additive tree (cladogram), we use the triple-permutation method (Lapointe and Legendre 1992). The independent matrix variables in X are kept fixed with respect to one another during the permutations. Selection of predictors can be accomplished by forward selection, backward elimination, or a stepwise procedure. A phylogenetic tree, derived from marsupial brain morphology data (28 species), is compared to trees depicting the evolution of diet, sociability, locomotion, and habitat in these animals, as well as their taxonomy and geographical relationships. A model is derived in which brain evolution can be predicted from taxonomy, diet, sociability and locomotion (R = 0.75). A new tree, derived from the "predicted" data, shows a lot of similarity to the brain evolution tree. The meaning of the taxonomy, diet, sociability, and locomotion predictors are discussed and conclusions are drawn about the evolution of brain and behavior in marsupials.

We review past DNA-hybridisation studies of marsupials and present a reanalysis of the data, utilising results from our and additional studies to formulate and rationalise a new classification of Marsupialia. In the reanalysis, 13 individual DNA-hybridisation matrices, many lacking some pairwise comparisons, were sutured in stages to provide the basis for generating a tree of 101 marsupials plus an outgroup eutherian; a fourteenth matrix provided data for a tree including eight additional eutherians and a monotreme. Validation was achieved by jackknifing on taxa for each matrix as well as on tables combining two or more matrices generated during assembly of the 102-taxon data set. The results are consistent with most conclusions from the individual studies and dramatise the unevenness of hierarchical levels in current classifications of marsupials. In particular, the affinities of the American marsupial Dromiciops gliroides with, and the distinctness of marsupial bandicoots from, Australasian metatherians are reaffirmed, while opossums are shown to be as internally divergent as are most members of the order Diprotodontia. Calibration of the 102-taxon tree and dating of the major dichotomies suggest that no extant marsupial lineage originated before the latest Cretaceous, and that all of them together with most South American and all Australasian fossils should be recognised as a monophyletic group contrasting with a largely Laurasian (if possibly paraphyletic) taxon. These inferences, together with the details of the phylogeny, mandate that the misleading ‘Australian’ v. ‘American’ distinction be abandoned, even as a geographic convenience.

This paper has two complementary purposes: first, to present a method to perform multiple regression on distance matrices, with permutation testing appropriate for path-length matrices representing evolutionary trees, and then, to apply this method to study the joint evolution of brain, behavior and other characteristics in marsupials. To understand the computation method, consider that the dependent matrix is unfolded as a vector y; similarly, consider X to be a table containing the independent matrices, also unfolded as vectors. A multiple regression is computed to express y as a function of X. The parameters of this regression (R and partial regression coefficients) are tested by permutations, as follows. When the dependent matrix variable y represents a simple distance or similarity matrix, permutations are performed in the same manner as the Mantel permutational test. When it is an ultrametric matrix representing a dendrogram, we use the double-permutation method (Lapointe and Legendre 1990, 1991). When it is a path-length matrix representing an additive tree (cladogram), we use the triple-permutation method (Lapointe and Legendre 1992). The independent matrix variables in X are kept fixed with respect to one another during the permutations. Selection of predictors can be accomplished by forward selection, backward elimination, or a stepwise procedure. A phylogenetic tree, derived from marsupial brain morphology data (28 species), is compared to trees depicting the evolution of diet, sociability, locomotion, and habitat in these animals, as well as their taxonomy and geographical relationships. A model is derived in which brain evolution can be predicted from taxonomy, diet, sociability and locomotion (R = 0.75). A new tree, derived from the "predicted" data, shows a lot of similarity to the brain evolution tree. The meaning of the taxonomy, diet, sociability, and locomotion predictors are discussed and conclusions are drawn about the evolution of brain and behavior in marsupials.

Background: The concept of a tree of life is prevalent in the evolutionary literature. It stems from attempting to obtain a grand unified natural system that reflects a recurrent process of species and lineage splittings for all forms of life. Traditionally, the discipline of systematics operates in a similar hierarchy of bifurcating (sometimes multifurcating) categories. The assumption of a universal tree of life hinges upon the process of evolution being tree-like throughout all forms of life and all of biological time. In multicellular eukaryotes, the molecular mechanisms and species-level population genetics of variation do indeed mainly cause a tree-like structure over time. In prokaryotes, they do not. Prokaryotic evolution and the tree of life are two different things, and we need to treat them as such, rather than extrapolating from macroscopic life to prokaryotes. In the following we will consider this circumstance from philosophical, scientific, and epistemological perspectives, surmising that phylogeny opted for a single model as a holdover from the Modern Synthesis of evolution.

Comparative phylogeography has emerged as a means of understanding the spatial patterns of genetic divergence of codistributed species. However, researchers are often frustrated because of the lack of appropriate statistical tests to assess concordancy of multiple phylogeographic trees. We develop a method for testing congruence across multiple species and synthesizing the data into a regional supertree. Nine phylogeographic data sets of species with different life histories and ecologies were statistically compared using maximum agreement subtrees (MAST) and showed a high degree of concordancy. A supertree combining the different phylogeographic trees was then computed using matrix representation with parsimony, and the groups defined by this supertree were tested against climatic data to investigate a potential mechanism driving divergence. Our data suggest that species and genetic lineages in California are shaped by climatic regimes. The supertree method in combination with MAST represents a new approach to test congruence hypotheses and detect common geographic signals in comparative phylogeography.