Development of Microplitis croceipes as a biological sensor
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Classical conditioning, a form of associative learning, was first described in the vertebrate literature by Pavlov, but has since been documented for a wide variety of insects. Our knowledge of associative learning by insects began with Karl vonFrisch explaining communication among honeybees, Apis mellifera L. (Hymenoptera: Apidae). Since then, the honey bee has provided us with much of what we understand about associative learning in insects and how we relate the theories of learning in vertebrates to insects. Fruit flies, moths, and parasitic wasps are just a few examples of other insects that have been documented with the ability to learn. A novel direction in research on this topic attempts to harness the ability of insects to learn for the development of biological sensors. Parasitic wasps, especially Microplitis croceipes (Cresson) (Hymenoptera: Braconidae), have been conditioned to detect the odors associated with explosives, food toxins, and cadavers. Honeybees and moths have also been associatively conditioned to several volatiles of interest in forensics and national security. In some cases, handheld devices have been developed to harness the insects and observe conditioned behavioral responses to air samples in an attempt to detect target volatiles. Current research on the development of biological sensors with insects is focusing on factors that influence the learning and memory ability of arthropods as well as potential mathematical techniques for improving the interpretation of the behavioral responses to conditioned stimuli. Chemical detection devices using arthropod-based sensing could be used in situations where trained canines cannot be used (such as toxic environments) or are unavailable, electronic devices are too expensive and/or not of sufficient sensitivity, and when conditioning to target chemicals must be done within minutes of detection. The purpose of this article is to provide a brief review of the development of M. croceipes as a model system for exploring associative learning for the development of biological sensors. © 2008 The Authors.
author list (cited authors)
Tomberlin, J. K., Rains, G. C., & Sanford, M. R.