Site fidelity, residency and habitat use of the Risso's dolphin Grampus griseus in the Gulf of Taranto (Northern Ionian Sea, Central-eastern Mediterranean Sea) by photo-identification

A photo-identification tool was applied to identify and create a catalogue of photo from Risso's dolphins providing evidence of its site fidelity, residency and habitat use in the Gulf of Taranto (Northern Ionian Sea, Central-eastern Mediterranean Sea). Sightings carried out from 2013 to 2016, occurred in a depth range from 436 to 1000 m with a mean depth value of 762 +/- 217 m. A total of 215 specimens of G. griseus were observed including 206 adults and juveniles, 7 calves and 2 new-borns. The evidence of calving and nursering females was inferred from calf association without any other sex information. Group size varied between 2 and 30 dolphin specimens, with a mean value of 13 +/- 7 specimens. A total of 54 Risso's dolphins were identified as distinctly marked. Among these, 34 specimens (63%) were sighted only once and 20 specimens (37%) were re-sighted up to 4 times. Rates of re-sighting within single year were relatively high, reaching 23, 18 and 43% during 2013 (3 re-sighted/13 dolphins), 2014 (4 re-sighted/22 dolphins) and 2016 (12 re-sighted/28 dolphins), respectively. The site fidelity reached 0.50, with a mean value of 0.13 0.18. The monthly residency rate varied between 0.11 and 0.44, with a mean value of 0.17 0.09. Among a total of 9 Risso's dolphins considered as "multi-year residents", 7 specimens were re-sighted in two consecutive years and 2 were re-sighted in a period of two non-consecutive years. Although results suggest the need for further studies implementing the photo-identification catalogue for the Risso's dolphin and allowing possible reconstruction of its migration patterns on the Mediterranean and global scale, the Taranto Valley canyon system seems to be a critical habitat to enforce specific conservation measures for the effective protection of the species

Emission rate of acoustic signals for the common bottlenose and striped dolphins in the gulf of taranto (Northern Ionian Sea, Central-Eastern Mediterranean Sea)

Dolphins emit a large variety of acoustic signals on which they rely for navigation, communication and searching for food. Nevertheless, each species uses quantitative and qualitative species-specific characteristics for tonal and pulsed signals, and the different behavioral contexts may affect the use of these signals. The main aim of this work is to evaluate if the emission rate of acoustic signals can be used as a cue to recognize species, and as an intra-specific feature for recognizing behavioral contexts. We collected acoustic and behavioral data during 27 sightings of bottlenose and striped dolphins in the Gulf of Taranto, (Northern Ionian Sea, Central-Eastern Mediterranean Sea). 396.31minutes of recordings were considered for analysis. The emission rate, calculated as the number of vocalizations/minutes of recording per animals within the group, was compared among species and behaviors. A higher emission rate was recorded for bottlenose dolphins clicks during traveling compared to striped dolphins involved in the same behavior, and for striped dolphins whistles during socializing compared to other behaviors of the same species. However, we must consider that smaller groups were analyzed for bottlenose dolphins. Results suggest that dolphins share click echoes in larger groups and increase communication signals during social activities. Our preliminary results can help inform management and conservation actions, in the framework of EU regulations, in this high impacted area

Convolutional Neural Networks for Risso’s Dolphins Identification

Photo-identication is one of the best practices to estimate the abundance of cetaceans and, as such, it can help to obtain the biological information necessary to decision-making and actions to preserve the marine environment and its biodiversity. The Risso's dolphin is one of the least-known cetacean species on a global scale, and the distinctive scars on its dorsal n proved to be extremely useful to photo-identify single individuals. The main novelty of this paper is the development of a newmethod based on deep learning, called Neural Network Pool (NNPool), and specically devoted to the photo-identication of Risso's dolphins. This new method also includes the unique function of recognizing unknown vs known dolphins in large datasets with no interaction by the user. Moreover, the new version of DolFin catalogue, collecting Risso's dolphins data and photos acquired between 2013-2018 in the Northern Ionian Sea (Central-eastern Mediterranean Sea), is presented and used here to carry out the experiments. Results have been validated using a further data set, containing new images of Risso's dolphins from the Northern Ionian Sea and the Azores, acquired in 2019. The performance of the NNPool appears satisfying and increases proportionally to the number of images available, thus highlighting the importance of building large-scale data set for the application at hand