Assessing the Molluscan Hypothesis Serialia (Monoplacophora + Polyplacophora) Using Novel Molecular Data
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Wilson, Nerida G.
Rouse, Greg W.
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CitationWilson, Nerida G., Greg W. Rouse, and Gonzalo Giribet. 2010. Assessing the Molluscan Hypothesis Serialia (Monoplacophora + Polyplacophora) Using Novel Molecular Data. Molecular Phylogenetics and Evolution 54, no. 1: 187–193.
AbstractA consensus on molluscan relationships has yet to be achieved, largely because of conflicting morphological and molecular hypotheses. Monoplacophora show marked seriality of ctenidia, atria, muscles and nephridia and this has been interpreted as plesiomorphic for Mollusca, reflecting a segmented ancestry. More recently this seriality, also partly seen in Polyplacophora, has been seen as a derived condition. Analysis of the first published monoplacophoran DNA sequence from Laevilipilina antarctica Warén & Hain, 1992 [Giribet, G., Okusu, A., Lindgren, A.R., Huff, S., Schrödl, M., Nishiguchi, M.K., 2006. Evidence for a clade composed of molluscs with serially repeated structures: Monoplacophorans are related to chitons. Proc. Natl. Acad. Sci. USA 103, 7723–7728. 10.1073/pnas.0602578103], showed Monoplacophora inside Polyplacophora. These taxa were then grouped under the name Serialia, reflecting the hypothesis that their seriality is a synapomorphy. Subsequent examination revealed that part of the L. antarctica published sequence was the result of contamination with Polyplacophora (Giribet, Supplementary Material S1). We collected and sequenced another monoplacophoran, Laevipilina hyalina McLean, 1979, resulting in the first multi-gene dataset representing all molluscan classes. Our analyses did not show unambiguous support for Serialia. Model-based approaches strongly supported Serialia as a clade, however, parsimony analyses under dynamic and static homology did not resolve the position of Monoplacophora. Although our study provides support for Serialia and none for Conchifera, it appears that further resolution of molluscan relationships will require large increases of data.
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