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The relationship between insect development and temperature has been well established and has a wide range of uses, including using blow flies for postmortem (PMI) interval estimations in death investigations. To use insects in estimating PMI, we must be able to determine the insect age at the time of discovery and backtrack to time of oviposition. Unfortunately, existing development models of forensically important insects are only linear approximations and do not take into account the curvilinear properties experienced at extreme temperatures. A series of experiments were conducted with two species of forensically important blow flies (Lucilia sericata and Phormia regina) that met the requirements needed to create statistically valid development models.
For each species, experiments were conducted over 11 temperatures (7.5 to 32.5 ° C, at 2.5° C) with a 16:8 L:D cycle. Experimental units contained 20 eggs, 10 g beef liver, and 2.5 cm of pine shavings (L. sericata) or damp sand (P. regina). Each life stage (egg to adult) had five sampling times: at the beginning, one-quarter mark, one-half mark, three-quarter mark, and the end. Each time was replicated four times, for a total of 20 measurements per life stage. For each sampling time, the cups were pulled from the chambers and the stage of each maggot was documented morphologically through posterior spiracle slits and cephalopharyngeal skeletal development.
Data from both species were normally distributed with the later larval stages (L3f, L3m) having the most variation within and transitioning between stages. The biological minimum for both species was between 7.5° C and 10° C, with little egg development and no egg emergence at 7.5° C for either species. Temperature-induced mortality was highest from 10.0 to 17.5° C and 32.5° C for both species.
The development data generated for both species illustrate the advantages of curvilinear models in describing development at environmental temperatures near the biological minima and maxima and the practical significance of curvilinear models over linear approximations.
Advisor: Leon G. Higley