The advent of geometric morphometrics and the revitalization of artificial neural networks have created powerful new tools to classify morphological structures to groups. Although these two approaches have already been combined, there has been less attention on how such combinations perform relative to more traditional methods. Here we use geometric morphometric data and neural networks to identify from which species upper-jaw teeth from carcharhiniform sharks in the genus Carcharhinus originated, and these results are compared to more traditional classification methods. In addition to the methodological applications of this comparison, an ability to identify shark teeth would facilitate the incorporation of shark teeth's vast fossil record into evolutionary studies. Using geometric morphometric data originating from Naylor and Marcus (1994), we built two types of neural networks, multilayer perceptrons and radial basis function neural networks to classify teeth from C. acronotus, C. leucas, C. limbatus, and C. plumbeus, as well as classifying the teeth using linear discriminate analysis. All classification schemes were trained using the right upper-jaw teeth of 15 individuals. Between these three methods, the multilayer perceptron performed the best, followed by linear discriminate analysis, and then the radial basis function neural network. All three classification systems appear to be more accurate than previous efforts to classify Carcharhinus teeth using linear distances between landmarks and linear discriminate analysis. In all three classification systems, misclassified teeth tended to originate either near the symphysis or near the jaw angle, though an additional peak occurred between these two structures. To assess whether smaller training sets would lead to comparable accuracies, we used a multilayer perceptron to classify teeth from the same species but now based on a training set of right upper-jaw teeth from only five individuals. Although not as accurate as the network based on 15 individuals, the network performed favorably. As a final test, we built a multilayer perceptron to classify teeth from C. altimus, C. obscurus, and C. plumbeus, which have more similar upper-jaw teeth than the original four species, based on training sets of five individuals. Again, the classification system performed better than a system that combines linear measurements and discriminate function analysis. Given the high accuracies for all three systems, it appears that the use of geometric morphometric data has a great impact on the accuracy of the classification system, whereas the exact method of classification tends to make less of a difference. These results may be applicable to other systems and other morphological structures.
