Abstract
The identification of fish species using otolith shape has been common in many fields of the marine science. Different analytical processes can be applied for the morphological discrimination, but reviewing the literature we have found conceptual and statistical limitations in the use of shape indices and wavelets (contour analysis), being specially worrying in the first case due to their widespread routine use. In the present study, 42 species were classified using otolith shape indices and wavelets and applying traditional and machine learning classifiers and performance measures (accuracy, Cohen’s kappa statistic, sensitivity, and precision). Our results were conclusive; wavelets were a more adequate option for the classification of species than shape indices, independently of classifiers and performance measures considered. The artificial neural network and support vector machine provided the highest values for all performance measures using wavelets. In all cases, the measures of sensitivity and precision pointed out a higher confusion between some otolith patterns using shape indices. Therefore, we strongly discourage the routine use of shape indices for the identification of species.
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