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Validation of temperature methods for the estimation of pre-appearance interval in carrion insects

The pre-appearance interval (PAI) is an interval preceding appearance of an insect taxon on a cadaver. It decreases with an increase in temperature in several forensically-relevant insects. Therefore, forensic entomologists developed temperature methods for the estimation of PAI. In the current stud...

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Detalles Bibliográficos
Autores principales: Matuszewski, Szymon, Mądra-Bielewicz, Anna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4752966/
https://www.ncbi.nlm.nih.gov/pubmed/26820285
http://dx.doi.org/10.1007/s12024-015-9735-z
Descripción
Sumario:The pre-appearance interval (PAI) is an interval preceding appearance of an insect taxon on a cadaver. It decreases with an increase in temperature in several forensically-relevant insects. Therefore, forensic entomologists developed temperature methods for the estimation of PAI. In the current study these methods were tested in the case of adult and larval Necrodes littoralis (Coleoptera: Silphidae), adult and larval Creophilus maxillosus (Coleoptera: Staphylinidae), adult Necrobia rufipes (Coleoptera: Cleridae), adult Saprinus semistriatus (Coleoptera: Histeridae) and adult Stearibia nigriceps (Diptera: Piophilidae). Moreover, factors affecting accuracy of estimation and techniques for the approximation and correction of predictor temperature were studied using results of a multi-year pig carcass study. It was demonstrated that temperature methods outperform conventional methods. The accuracy of estimation was strongly related to the quality of the temperature model for PAI and the quality of temperature data used for the estimation. Models for larval stage performed better than models for adult stage. Mean temperature for the average seasonal PAI was a good initial approximation of predictor temperature. Moreover, iterative estimation of PAI was found to effectively correct predictor temperature, although some pitfalls were identified in this respect. Implications for the estimation of PAI are discussed.