Even among the understudied sirenians, African manatees (Trichechus senegalensis) are a poorly understood, elusive, and vulnerable species that is difficult to detect. We used passive acoustic monitoring in the first effort to acoustically detect African manatees and provide the first characterization of their vocalizations. Within two 3-day periods at Lake Ossa, Cameroon, at least 3367 individual African manatee vocalizations were detected such that most vocalizations were detected in the middle of the night and at dusk. Call characteristics such as fundamental frequency, duration, harmonics, subharmonics, and emphasized band were characterized for 289 high-quality tonal vocalizations with a minimum signal-to-noise ratio of 4.5 dB. African manatee vocalizations have a fundamental frequency of 4.65 ± 0.700 kHz (mean ± SD), duration of 0.181 ± 0.069 s, 97% contained harmonics, 21% contained subharmonics, and 27% had an emphasized band other than the fundamental frequency. Altogether, the structure of African manatee vocalizations is similar to other manatee species. We suggest utilizing passive acoustic monitoring to fill in the gaps in understanding the distribution and biology of African manatees.
The African manatee (Trichechus senegalensis) is an elusive, data-deficient, and endangered species which inhabits marine and freshwater systems throughout Western and Central Africa. A major challenge in understanding the species ecology and distribution is the difficulty in detecting it using traditional visual surveys. The recent invasion of Giant Salvinia (Salvinia molesta) at the most important site for the species in Cameroon further limits their detectability and may restrict their movements and habitat use. To investigate methods’ effectiveness in detecting African manatees, we conducted monthly vessel surveys from which visual point scans, 360° sonar scans, and passive acoustic monitoring were conducted simultaneously at ten locations and over 12 months in Lake Ossa, Cameroon. Manatee detection frequency was calculated for each method and the influence of some environmental conditions on the methods’ effectiveness and manatee detection likelihood was assessed by fitting a binary logistic regression to our data. Detection frequencies were significantly different between methods (p < 0.01) with passive acoustics being the most successful (24.17%; n = 120), followed by the 360° sonar scan (11.67%; n = 120), and the visual point scan (3.33%; n = 120). The likelihood of detecting manatees in Lake Ossa was significantly influenced by water depth (p = 0.02) and transparency (p < 0.01). It was more likely to detect manatees in shallower water depths and higher water transparency. Passive acoustic detections were more effective in uninvaded areas of the Lake. We recommend using passive acoustics to enhance African manatee detections in future surveys.
African manatees ( Trichechus senegalensis) are vulnerable, understudied, and difficult to detect. Areas where African manatees are found were acoustically sampled and deep learning techniques were used to develop the first African manatee vocalization detector. A transfer learning approach was used to develop a convolutional neural network (CNN) using a pretrained CNN (GoogLeNet). The network was highly successful, even when applied to recordings collected from a different location. Vocal detections were more common at night and tended to occur within less than 2 min of one another.
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