DNA-based and taxonomic identification of forensically important Sarcophagidae (Diptera) in southeastern Spain.

Studying dipterans at the scene of a death can provide essential information for interpreting the evidence and help to reconstruct the events happened to a corpse in the past. Molecular tools have been employed for identification at specific levels in the cases of cryptic species or poorly conserved specimens. Identification of specimens is essential in forensic entomology since each species has a specific growth rate, which determines the calculation of the minimum post mortem interval (minPMI). In addition, phylogeographic reconstruction within a species can help to differentiate the haplotypes from a geographic area, thereby helping to clarify the possible relocation of a corpse. The morphological identification of Sarcophagidae species is often difficult, especially for the females. This is an important Diptera family since some of its species are among the first to reach a corpse, especially in warm areas. In this study, we compared the sarcophagids found in human corpses in forensic cases in Alicante (southeast of Spain) with specimens collected from baited traps in the same area and surrounding provinces. In total, 189 specimens were collected, comprising 72 from forensic cases and 117 from baited traps. Molecular identification was conducted by sequencing the cox1 mitochondrial gene and analyzing the sequences using ABGD, GMYC, and BIN species delimitation methods. The median joining algorithm in the PopART program was used to construct phylogeographic networks. Eight species in the family Sarcophagidae were identified. The most widely collected species were Sarcophaga argyrostoma and Sarcophaga tibialis. The haplotype networks obtained for these species did not indicate a clear geographic distribution of haplotypes. The S. argyrostoma samples from Alcoy were clearly isolated. The results demonstrated that this method is useful for identifying Sarcophagidae samples in forensic investigations and it can be employed for minPMI estimation.