In this study, we present for the first time a model for the social structure of Risso’s dolphins ( Grampus griseus Cuvier, 1812). Over the period 2004–2006, 1028 Risso’s dolphins were identified at Pico island, Azores. Individuals sighted on 10 or more occasions were included in the analysis of social structure (n = 183). High resighting rates indicate strong site fidelity for at least part of the population. We found that individuals form stable, long-term bonds organised in pairs or in clusters of 3–12 individuals. Social structure is stratified based on age and sex classes, with strong associations between adult males and between adult females. We suggest that clusters form the basic units of Risso’s dolphin society. Thirteen pods consisting solely of adults, likely males, and 3 pods consisting of mother–calf pairs were identified. Males are organised in stable, long-term associations of varying size that occur throughout the complete range of behavioural states observed. For females, associations can be of similar strength, but the time scale may vary depending on the presence of nursing calves. As subadults, associations also occur (pair formation), but are less stable than those observed for adults. We propose a new model for Risso’s dolphin societies known as a stratified social organisation, which differs from the fission–fusion and matrilineal society models.

Relationships between social status and relative position of group-living animals have been described in a variety of species. For wild cetaceans, who spend most of their time underwater, collecting detailed, continuous data to assess such relationships depends highly on group size, formation, shyness of animals and observation platform. We test a new method for focal group sampling using an Unmanned Aerial Vehicle (UAV), focusing on one long-term followed group of 13 male Risso’s dolphins (Grampus griseus) in the Azores, Portugal. We aim to assess the usefulness of a UAV in delivering robust data to evaluate sociality in relation to relative position. Our analysis is based on recordings of synchronous breathing events, which are taken as an indicator of association strength. Twenty-one separate UAV flights were performed during seven surveys in July–August 2017, recording 2,886 breathing events and 571 synchronous dyads. Results showed strong differences in sociality between individuals and identified two strongly associated pairs, one strongly associated trio and six less associated individuals within the group. We subsequently created continuous time series of relative positions by interpolation of the positions recorded with the UAV at breathing events, and applied the Dynamic Time Warping method to assess associations based on relative position. This analysis identified more detailed association patterns than the synchrony analysis, and revealed a correlation between measures of sociality and relative position, at an individual and sub-cluster level, which may indicate dominant relationships. We compared results with those obtained with Photo-ID to assess any observation bias related to using a UAV. We found that 37% more breathing events were recorded with the UAV, and 21% more synchronous dyads detected, compared with Photo-ID, collected over the same observation periods, but, based on synchrony data, both methods yielded very similar results. We conclude that using a UAV for focal group follows of Risso’s dolphins enables a more granular study of association patterns than Photo-ID, by taking into account the relative position of individuals. The correlation found between measures of sociality and relative position holds promise for using UAVs in future studies of dominant relationships in Risso’s dolphins and other cetacean species.

One of Risso’s dolphin’s distinctive characteristics is the tendency to “lighten” with age due to the accumulation of unpigmented scars. These accumulated scars may provide an indication of age. Photographic skin recaptures gathered from 61 free-ranging animals over a period of 15 years were analyzed to develop a skin classification model in 6 skin stages. Classification of photographic skin captures following this model was tested by 15 experts and 13 nonexpert rankers, with a general probability of agreement of 79%. The duration of each skin stage was estimated using a statistical model based on the recorded dates in which individual animals were known to have entered and/or exited a given stage. A Bayesian approach was used to combine available photographic skin recapture data using expert knowledge as prior to predict the duration for each skin stage and thus the mean age at each stage. Results suggest that animals may live more than 45 years, which is in agreement with published information based on dental layers. The proposed skin stages can be correlated with reproduction, with the transition to stage 3 linked to the onset of maturity. Adult females are less scarified than males and were not observed in the whiter skin classes. The proposed skin stage model is noninvasive and easy to apply and could be a valuable tool in further studies of population structure and dynamics of Risso’s dolphins.

Photo-identification is a widely used non-invasive technique in biological studies for understanding if a specimen has been seen multiple times only relying on specific unique visual characteristics. This information is essential to infer knowledge about the spatial distribution, site fidelity, abundance or habitat use of a species. Today there is a large demand for algorithms that can help domain experts in the analysis of large image datasets. For this reason, it is straightforward that the problem of identify and crop the relevant portion of an image is not negligible in any photo-identification pipeline. This paper approaches the problem of automatically cropping cetaceans images with a hybrid technique based on domain analysis and deep learning. Domain knowledge is applied for proposing relevant regions with the aim of highlighting the dorsal fins, then a binary classification of fin vs. no-fin is performed by a convolutional neural network. Results obtained on real images demonstrate the feasibility of the proposed approach in the automated process of large datasets of Risso’s dolphins photos, enabling its use on more complex large scale studies. Moreover, the results of this study suggest to extend this methodology to biological investigations of different species.