Cuticular hydrocarbons for identifying Sarcophagidae (Diptera)

Abstract

The composition and quantity of insect cuticular hydrocarbons (CHCs) can be species-specific as well as sexually dimorphic within species. CHC analysis has been previously used for identification and ageing purposes for several insect orders including true flies (Diptera). Here, we analysed the CHC chemical profiles of adult males and females of eleven species of flesh flies belonging to the genus Sarcophaga Meigen (Sarcophagidae), namely Sarcophaga africa (Wiedemann), S. agnata Rondani, S. argyrostoma Robineau-Desvoidy, S. carnaria (Linnaeus), S. crassipalpis Macquart, S. melanura Meigen, S. pumila Meigen, S. teretirostris Pandellé, S. subvicina Rohdendorf, S. vagans Meigen and S. variegata (Scopoli). Cuticular hydrocarbons extracted from pinned specimens from the collections of the Natural History Museum, London using a customised extraction technique were analysed using Gas Chromatography–Mass Spectrometry. Time of preservation prior to extraction ranged between a few weeks to over one hundred years. CHC profiles (1) allowed reliable identification of a large majority of specimens, (2) differed between males and females of the same species, (3) reliably associated males and females of the same species, provided sufficient replicates (up to 10) of each sex were analysed, and (4) identified specimens preserved for up to over one hundred years prior to extraction.

Cuticular hydrocarbons for the identification and geographic assignment of empty puparia of forensically important flies

“…Chemotaxonomy within the field of entomology has been around for many years, and it is widely accepted that CHC analysis provides an excellent means of species identification across a broad range of insect orders like Hymenoptera [15,[23][24][25][26][27][28][29][30][31][32], Isoptera [14,33], Blattodea [31,34,35] and Coleoptera [28][29][30]. CHC analysis has also been used for Diptera [7,22,31,[36][37][38][39], providing a complementary technique when the taxonomical identification is ambiguous or even not feasible, which could be due to the damaged physical condition or DNA degradation, or quite simply because the morphology between particular species is too similar to identify them [22,40].…”

“…A number of papers have begun to explore the potential of using CHC for species identification or population assignment and ageing various life stages of forensically important Calliphoridae [22,36,[43][44][45][46][47].…”

Creating a Searchable Chromatographic Database with the NIST Mass Spectral Search Program

“…A second test database was created using chromatographic data from a previously published study. Classification of Sarcophaga insects based on the chromatographic analysis of cuticular hydrocarbons was described in a separate publication . Retention time aligned chromatograms from this study were exported into a single Excel spreadsheet with retention time and peak area information for each component.…”

Cuticular hydrocarbon variation among Rhagoletis fruit flies (Diptera: Tephritidae): implications for premating reproductive isolation and ecological speciation

“…Using a similar approach to a number of recent CHC studies (e.g., Moore et al ., 2021), we implemented a machine learning technique known as a neural net classifier (NNC) to further explore differences between species and among sexes for the above‐mentioned levels of biological organisation. In these analyses, our objective was to determine if CHC profiles could accurately predict species and sex identity.…”

“…We then fit a neural network with one hidden layer to the training dataset using the nnet (Venables & Ripley, 2002) and caret (Kuhn et al ., 2020) packages in R with 10‐fold cross validation repeated 10 times similar to the methods of Moore et al . (2021), to ‘classify’ each taxa and/or sex based on their CHC profiles and determine statistical significance. The best‐fit model, which determined the accuracy at which variation in CHCs distinguished among Rhagoletis taxa and between males and females, was then evaluated with a confusion matrix, using a one‐sided t ‐test to statistically compare the testing dataset generated in the cross‐validation process to the ‘no information rate’ training dataset.…”