The male postabdominal structures of the West Palaearctic species of the genus Tachina are described. A new identification key is given. Characters are illustrated by original pen drawings and deep focus micrographs, some of them for the first time. The results are documented by molecular analyses (based on CO°I, Cyt°b, 12S, and 16S rDNA). This approach solves old taxonomical discrepancies, which resulted in these conclusions: 1) the taxonomic concept ofthe genus was evaluated; 2) the position ofthe present subgenus Tachina s.str. seems to be untenable: T. grossa (Linnaeus, 1758) could be categorized inside existing subgenus Tachina s.str. and a new subgenus could be created for T. magna (Giglio- Tos, 1890); 3) an expected new species from subgenus Eudoromyia was confirmed; 4) T. nigrohirta (Stein, 1924) having been resurrected from synonymy was confirmed as a valid species; 5) some differences between central European and Japanese specimens of T. nupta (Rondani, 1859) were found.
Abstract:The classification methodology based on morphometric data and supervised artificial neural networks (ANN) was tested on five fly species of the parasitoid genera Tachina and Ectophasia (Diptera, Tachinidae). Objects were initially photographed, then digitalized; consequently the picture was scaled and measured by means of an image analyser. The 16 variables used for classification included length of different wing veins or their parts and width of antennal segments. The sex was found to have some influence on the data and was included in the study as another input variable. Better and reliable classification was obtained when data from both the right and left wings were entered, the data from one wing were however found to be sufficient. The prediction success (correct identification of unknown test samples) varied from 88 to 100% throughout the study depending especially on the number of specimens in the training set. Classification of the studied Diptera species using ANN is possible assuming a sufficiently high number (tens) of specimens of each species is available for the ANN training. The methodology proposed is quite general and can be applied for all biological objects where it is possible to define adequate diagnostic characters and create the appropriate database.
Artificial neural networks (ANN) methodology, molecular analyses and comparative morphology of the male postabdomen were used successfully in parallel for species identification and resolution of some taxonomic problems concerning West Palaearctic species of the genus Tachina Meigen, 1803. Supervised feed-forward ANN with back-propagation of errors was applied on morphometric and qualitative characters to solve known taxonomic discrepancies. Background molecular analyses based on mitochondrial markers CO I, Cyt b, 12S and 16S rDNA and study of male postabdominal structures were published separately. All three approaches resolved taxonomic doubts with identical results in the following five cases: case 1, the four presently recognized subgenera of the genus Tachina were confirmed and the description of a new subgenus was recommended; case 2, the validity of a new boreo-alpine species (sp.n.) was confirmed; case 3, the previously supposed presence of T. casta (Rondani, 1859) in central Europe was not supported; case 4, West Palaearctic T. nupta (Rondani, 1859) was contrasted with East Palaearctic specimens from Japan, which seem to represent a valid species not conspecific with central European specimens; T. nupta needs detailed further study; case 5, T. nigrohirta (Stein, 1924) resurrected recently from synonymy with T. ursina Meigen, 1824 was confirmed as a valid species. This parallel application of three alternative methods has enabled the principle of 'polyphasic taxonomy' to be tested and verified using these separate results. For the first time, the value of using the ANN approach in taxonomy was justified by two non-mathematical methods (molecular and morphological).
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