Advances in diapriid (Hymenoptera: diapriidae) systematics, with contributions to cybertaxonomy and the analysis of rRNA sequence data
MetadataShow full item record
Diapriids (Hymenoptera: Diapriidae) are small parasitic wasps. Though found throughout the world they are relatively unknown. A framework for advancing diapriid systematics is developed by introducing a new web-based application/database capable of storing a broad range of systematic data, and the first molecular phylogeny specifically focused at examining intrafamilial relationships. In addition to these efforts, a description of a new taxon is provided. Several advantages of digital description, including linking descriptions to an ontology of morphological terms, are highlighted. The functionality of the database is further illustrated in the production of a catalog of diapriid host associations. The hosts database currently holds over 450 association records, for over 500 named taxa (parasitoids and hosts), and over 180 references. Diapriids are found to be primarily endoparasitoids of Diptera emerging from the host pupa. Phylogenetic inference for a molecular dataset of 28S and 18S rRNA sequence data, derived from a diverse selection of diapriids, is accomplished with a new suite of tools developed for handling complex rRNA datasets. Several parsimony-based methodologies, including an alignment-free method of analyzing multiple sequences, are reviewed and applied using the new software tools. Diapriid phylogenetic relationships are shown to be broadly congruent with existing morphology-based classifications. Methods for analyzing typically excluded sequence data are shown to recover phylogenetic signal that would otherwise be lost and the alignment-free method performed remarkably well in this regard. Empirically, phylogenetic approaches that incorporate structural data were not notably different than those that did not.
Yoder, Matthew Jon (2007). Advances in diapriid (Hymenoptera: diapriidae) systematics, with contributions to cybertaxonomy and the analysis of rRNA sequence data. Doctoral dissertation, Texas A&M University. Available electronically from