We applied passive acoustic monitoring and Hierarchical Density-Based Spatial Clustering of Applications with Noise clustering to define spatiotemporally cohesive groupings of franciscana dolphin (Pontoporia blainvillei) echolocation click trains. This unsupervised, objective method identified biologically relevant click train clusters, offering rare insights into the species' social organization. The observed structure revealed consistent intra-cluster cohesion and inter-cluster separation, supporting the effectiveness of the approach. Our findings demonstrate that clustering acoustic detections can serve as a robust framework for delineating social groups and can be integrated into future density estimation protocols, enhancing the ecological understanding and conservation potential for this cryptic and vulnerable species.
Mura et al. (Fri,) studied this question.