Currently, species identification of stranded marine mammals mostly relies on morphological features, which has inherent challenges. The use of genetic information for marine mammal species identification remains limited, therefore, new approaches that can contribute to a better monitoring of stranded species are needed. In that context, the ISSR-HRM method we have proposed offers a new approach for marine mammal species identification. Consequently, new approaches need to be developed to identify individuals at the species level. Eight primers of the ISSR markers were chosen for HRM analysis resulting in ranges of accuracy of 56.78–75.50% and 52.14–75.93% in terms of precision, while a degree of sensitivity of more than 80% was recorded when each single primer was used. The ISSR-HRM primer combinations revealed a success rate of 100% in terms of discrimination for all marine mammals included in this study. Furthermore, ISSR-HRM analysis was successfully employed in determining marine mammal discrimination among varying marine mammal species. Thus, ISSR-HRM analysis could serve as an effective alternative tool in the species identification process. This option would offer researchers a heightened level of convenience in terms of its performance and success rate. It would also offer field practice to veterinarians, biologists and other field-related people a greater degree of ease with which they could interpret results when effectively classifying stranded marine mammals. However, further studies with more samples and with a broader geographical scope will be required involving distinct populations to account for the high degree of intraspecific variability in cetaceans and to demonstrate the range of applications of this approach.
Publisher Correction: Deep neural networks for automated detection of marine mammal species
