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Biting midges are widespread around the world and play an essential role in the epidemiology of over 100 veterinary and medical diseases. For taxonomists, it is difficult to correctly identify species because of affinities among cryptic species and species complexes. In this study, species boundaries were examined for  C. clastrieri  and  C. festivipennis  and compared with six other  Culicoides  species .  The classifiers are partial least squares discriminant analysis (PLS-DA) and sparse partial least squares discriminant analysis (sPLS-DA).The performance of the proposed method was evaluated using four models: (i) geometric morphometrics applied to wings; (ii) morphological wing characters, (iii) " Full wing " (landmarks and morphological characters) and (iv)  " Full model " (morphological characters—wing, head, abdomen, legs—and wing landmarks). Double cross-validation procedures were used to validate the predictive ability of PLS-DA and sPLS-DA models. The AUC (area under the ROC curve) and the balanced error rate showed that the sPLS-DA model performs better than the PLS-DA model. Our final sPLS-DA analysis on the full wing and full model, with nine and seven components respectively, managed to perfectly classify our specimens. The  C. clastrieri  and  C. festivipennis  sequences, containing both COI and 28S genes, revealed our markers’ weak discrimination power, with an intraspecific and interspecific divergence of 0.4% and 0.1% respectively. Moreover,  C. clastrieri  and  C. festivipennis  are grouped in the same clade. The morphology and wing patterns of  C. clastrieri  and  C. festivipennis  can be used to clearly distinguish them. Our study confirms  C. clastrieri  and  C. festivipennis  as two distinct species. Our results show that caution should be applied when relying solely on DNA barcodes for species identification or discovery.

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Comprehensive characterisation of Culicoides clastrieri and C. festivipennis (Diptera: Ceratopogonidae) according to morphological and morphometric characters using a multivariate approach and DNA barcode