Over the past few decades, geometric morphometric methods have become increasingly popular and powerful tools to describe morphological data while over the same period artificial neural networks have had a similar rise in the classification of specimens to preconceived groups. However, there has been little research into how well these two systems operate together, particularly in comparison to preexisting techniques. In this study, geometric morphometric data and multilayer perceptrons, a style of artificial neural network, were used to classify shark teeth from the genus Carcharhinus to species. Three datasets of varying size and species differences were used. We compared the performance of this combination with geometric morphometric data in a linear discriminate function analysis, linear measurements in a linear discriminate function analysis, and a preexisting methodology from the literature that incorporates linear measurements and a two-layered discriminate function analysis. Across datasets, geometric morphometric data in a multilayer perceptron tended to yield modest accuracies but accuracies that varied less across species whereas other methods were able to achieve higher accuracies in some species at the expense of lower accuracies in others. Further, the performance of the two-layered discriminate function analysis illustrates that constraining what material is classified can increase the accuracy of a method. Based on this tradeoff, the best methodology will then depend on the scope of the study and the amount of material available. J. Morphol. 278:131-141, 2017. ©© 2016 Wiley Periodicals,Inc.
Animals Neural Networks, Computer