Geometric morphometrics and machine learning as tools for the identification of sibling mosquito species of the Maculipennis complex (Anopheles)

Geometric morphometrics allows researchers to use the specific software to quantify and to visualize morphological differences between taxa from insect wings. Our objective was to assess wing geometry to distinguish four Anopheles sibling species of the Maculipennis complex, An. maculipennis s. s., An. daciae sp. inq., An. atroparvus and An. melanoon, found in Northern Italy. We combined the geometric morphometric approach with different machine learning alghorithms: support vector machine (SVM), random forest (RF), artificial neural network (ANN) and an ensemble model (EN). Centroid size was smaller in An. atroparvus than in An. maculipennis s. s. and An. daciae sp. inq. Principal component analysis (PCA) explained only 33% of the total variance and appeared not very useful to discriminate among species, and in particular between An. maculipennis s. s. and An. daciae sp. inq. The performance of four different machine learning alghorithms using procrustes coordinates of wing shape as predictors was evaluated. All models showed ROC-AUC and PRC-AUC values that were higher than the random classifier but the SVM algorithm maximized the most metrics on the test set. The SVM algorithm with radial basis function allowed the correct classification of 83% of An. maculipennis s. s. and 79% of An. daciae sp. inq. ROC-AUC analysis showed that three landmarks, 11, 16 and 15, were the most important procrustes coordinates in mean wing shape comparison between An. maculipennis s. s. and An. daciae sp. inq. The pattern in the three-dimensional space of the most important procrustes coordinates showed a clearer differentiation between the two species than the PCA. Our study demonstrated that machine learning algorithms could be a useful tool combined with the wing geometric morphometric approach.