Towards automated long-term acoustic monitoring of endangered river dolphins: a case study in the Brazilian Amazon floodplains

Abstract

Abstract Using passive acoustic monitoring (PAM) and convolutional neural networks (CNN), we monitored the movements of the two endangered Amazon River dolphin species, the boto (Inia geoffrensis) and the tucuxi (Sotalia fluviatilis) from main rivers to floodplain habitats (várzea) in the Mamirauá Reserve (Amazonas, Brazil). We detected dolphin presence in four main areas based on the classification of their echolocation clicks. Using the same method, we automatically detected boat passages to estimate a possible interaction between boat and dolphin presence. Performance of the CNN classifier was high with an average precision of 0.95 and 0.92 for echolocation clicks and boats, respectively. Peaks of acoustic activity were detected synchronously at the river entrance and channel, corresponding to dolphins seasonally entering the várzea. Additionally, the river dolphins were regularly detected inside the flooded forest, suggesting a wide dispersion of their populations inside this large area, traditionally understudied and particularly important for boto females and calves. Boats overlapped with dolphin presence 9% of the time. PAM and recent advances in classification methods bring a new insight of the river dolphins’ use of várzea habitats, which will contribute to conservation strategies of these species.