The charismatic predatory insect group of praying mantises (order Mantodea) includes over 2,400 described species that exhibit an incredibly rich diversity of morphology and life history strategies. Despite the long tradition of ecological, behavioral, and physiological research focused on the group, mantises have received limited taxonomic attention and the current classification scheme does not reflect genetically based, evolutionary relationships. This project will unify traditional and modern morphological and genetically based systematic methods to reconstruct the evolutionary relationships for all praying mantises, which will guide the construction of a natural classification that reflects true evolutionary groups. This research will also produce two species-level taxonomic monographs (representing about 21% of the praying mantis diversity) while building new museum collections, describing numerous new species, producing online, interactive identification keys for mantis genera, and producing a publicly accessible database of taxonomic information and scientific literature references.
This project will represent a significant first step in modernizing the systematics of these insects that are often used to control such pests as aphids. The project will also provide a model for future revisions of other insect groups. This research will impact the broader community by: 1) producing high-quality images of mantises and their morphology, organized in an evolutionary framework that will facilitate community identifications through online, interactive taxonomic keys; 2) opening annually scheduled workshops to include university students, high school teachers, and the public, which will provide information and training in praying mantis taxonomy, morphology, and biology; 3) providing new scientific training at the post-doctoral, graduate, and undergraduate student levels; and 4) immediately complementing current comparative research in mantis sexual ecology, prey recognition physiology, and auditory evolution by providing a stable and predictive classification.
