Automatic classification of whistles from coastal dolphins of the southern African subregion.

Passive acoustic monitoring (PAM) is commonly used to generate information on the distribution, abundance, and behavior of cetacean species. In African waters, the utilization of PAM lags behind most other continents. This study examines whether the whistles of three coastal delphinid species (Delphinus delphis, Tursiops truncatus, and Tursiops aduncus) commonly encountered in the southern African subregion can be readily distinguished using both statistical analysis of standard whistle parameters and the automated detection and classification software PAMGuard. A first account of whistles recorded from D. delphis from South Africa is included. Using PAMGuard, classification to species was high with an overall mean correct classification rate of 87.3%. Although lower, high rates of correct classification were also found (78.4%) when the two T. aduncus populations were included separately. Classification outcomes reflected patterns observed in standard whistle parameters. Such acoustic discrimination may be useful for confirmation of morphologically similar species in the field. Classification success was influenced by training and testing the classifier with data from different populations, highlighting the importance of locally collected acoustic data to inform classifiers. The small number of sampling populations may have inflated the classification success, therefore, classification trials using a greater number of species are recommended.

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

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.

Marine mammal acoustic detections in the Greenland and Barents Sea, 2013 - 2014 seasons.

While the Greenland and Barents Seas are known habitats for several cetacean and pinniped species there is a lack of long-term monitoring data in this rapidly changing environment. Moreover, little is known of the ambient soundscapes, and increasing off-shore anthropogenic activities can influence the ecosystem and marine life. Baseline acoustic data is needed to better assess current and future soundscape and ecosystem conditions. The analysis of a year of continuous data from three passive acoustic monitoring devices revealed species-dependent seasonal and spatial variation of a large variety of marine mammals in the Greenland and Barents Seas. Sampling rates were 39 and 78 kHz in the respective locations, and all systems were operational at a duty cycle of 2 min on, 30 min off. The research presents a description of cetacean and pinniped acoustic detections along with a variety of unknown low-frequency tonal sounds, and ambient sound level measurements that fall within the scope of the European Marine Strategy Framework (MSFD). The presented data shows the importance of monitoring Arctic underwater biodiversity for assessing the ecological changes under the scope of climate change.