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Morphological changes of larvae and pupae of Lucilia sericata (Diptera: Calliphoridae) reared at two temperatures and on three food types.

Tharindu Bandara BambaradeniyaPaola Annarosa MagniIan Robert Dadour
Published in: Journal of medical entomology (2024)
Determining the minimum postmortem interval (minPMI) from an entomological perspective relies mainly on development data recorded for various species of flies collected from a crime scene or suspicious death. This study focused on the larval and pupal development of Lucilia sericata (Meigen), with an emphasis on the changes of the external morphology of the puparium and its pupal content throughout the duration of metamorphosis. Colonies of L. sericata were reared on 3 types of swine tissue (skeletal muscle, liver tissue, and heart tissue) at 2 different temperature regimes; 24 ± 1 °C and 30 ± 1 °C. The overall developmental time, larval width and length, and inner and outer pupal morphology changes were observed and recorded. The results show that: (i) temperature significantly influenced overall development time, as well as changes in larval width and length, but this effect was not dependent on tissue type; (ii) larval development duration was longest on heart tissue, and shortest on skeletal muscle for both temperatures; and (iii) pupation was longest for larvae reared on skeletal muscle at 24 ± 1 °C, and on liver tissue at 30 ± 1 °C, while those larvae reared on liver tissue at 24 ± 1 °C and heart tissue at 30 ± 1 °C had the shortest pupation period. A seven-character checklist plus 4 landmark stages were developed comprising the external morphology of the puparium and pupal content changes of L. sericata. In conclusion, the study provides larval and pupal development timetables, as well as checklists and photo guides for pupal character development that may be useful for future postmortem determinations.
Keyphrases
  • skeletal muscle
  • aedes aegypti
  • drosophila melanogaster
  • heart failure
  • atrial fibrillation
  • risk assessment
  • machine learning
  • adipose tissue
  • deep learning