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Published on: July 17, 2019
Sexual size dimorphism in three species of forensically important blowflies (Diptera: Calliphoridae) and its
Marcos Patrício Macedo1, Luciano C Arantes2, Rosana Tidon3
1Criminalistics Institute, Federal District Police Department, Brasília (DF), Brazil; Evolutionary Biology Laboratory, Institute of Biological Sciences, Universidade of Brasilia, Brazil.
Abstract:
Forensic entomologists rely on insects present in human remains to establish a minimum postmortem interval (PMImin). Blowflies have been widely used in these estimates because they lay eggs on the victim's body shortly after death and, within hours, larvae hatch and grow at predictable rates. Hence, isomegalen diagrams based on larval size and local temperature are considered good models to estimate PMImin. Still, most professional do not account for size sexual dimorphism in blowflies, which add uncertainty to predictive models by two mechanisms: (1) males and females might grow up to different sizes, and; (2) males and females might grow at different rates. In this study, we investigate sexual dimorphism and biases on the prediction of adult size in three species of blowflies (Lucilia sericata, Calliphora vicina and Calliphora vomitoria) reared under different larval densities. Estimated size range, stablished with and without sex discrimination, showed that females were larger than males in the three species. The ultimate size of adult stage, however, was more difficult to predict. C. vicina and C. vomitoria decrease as density raises but at different rates, and even males and females of the same species react differently to density increase. Adult size of L. sericata, in contrast, shows a slight increase with density. Except for C. vomitoria females, estimated size ranges are lower when species are divided by sex. Our results show that sex is an important factor to consider in PMImin estimates. Scenarios for all three species shorten their estimated size ranges when compared to databases with no sex identification. Therefore, computing data by sex raises accuracy in size based predictive models.
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