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A comparison of different characters and methods of analysis for phylogenetic reconstruction in the genus Taenia

Lachberg, Sylvia (1992) A comparison of different characters and methods of analysis for phylogenetic reconstruction in the genus Taenia. PhD thesis, Murdoch University.

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Members of the family Taeniidae are responsible for considerable economic loss and human morbidity, but there is a good deal of controversy surrounding the taxonomy of this group. Although much recent work has centred on strain variation in pathogenic species, there has been little attempt to answer more fundamental questions about the phylogeny of the group as a whole. This study aims to provide a rational basis for determining phylogenetic relationships among species of the genus Taenia by evaluating the relative value of 1) traditional and molecular characters and 2) commonly used methods of analysis.

Complete data sets for five species of Taenia and Echinococcus granulosus were obtained for eight biological, nine morphological, 63 isoenzyme, 65 mitochondrial DNA (mtDNA) and 97 ribosomal DNA (rDNA) characters. From these data sets, phylogenetic relationships among the species were reconstructed using phenetic analysis (UPGMA clustering), distance phylogenetic analysis (Fitch-Margoliash) and character state analysis (maximum parsimony).

Virtually identical trees were produced by both distance and character state analyses of the DNA data sets. The same tree was also produced by distance analysis of the biological data and character state analysis of the morphological data, and by character state analysis of the entire data set (total evidence). The primacy of character selection in phylogenetic reconstruction is discussed in relation to these findings.

Item Type: Thesis (PhD)
Murdoch Affiliation(s): School of Veterinary Studies
Notes: Note to the author: If you would like to make your thesis openly available on Murdoch University Library's Research Repository, please contact: Thank you.
Supervisor(s): Lymbery, Alan and Thompson, Andrew
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