Using transfer learning with a convolutional neural network to detect African manatee (Trichechus senegalensis) vocalizations.

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).

Estimating Florida manatee (Trichechus manatus latirostris) abundance using passive acoustic methods.

Manatees are difficult to detect, particularly cryptic populations that inhabit areas with limited water clarity. The effectiveness of using vocal detections to estimate manatee abundance was evaluated in a clear water spring where manatees congregate seasonally. Vocalizations were extracted by a detection classifier that clustered sounds with similar spectral properties.

The influence of variations in background noise on Florida manatee (Trichechus manatus latirostris) detection of boat noise and vocalizations.

A manatee's primary modality to detect a vessel on a possible collision course is hearing as underwater visibility is limited in many manatee habitats and their visual acuity is poor. We estimate a Florida manatee's ability to detect the sound of an approaching boat and vocalizations in four different soundscapes in Sarasota Bay, FL. Background noise samples were collected every 5 minutes for a two-week period during winter and summer at each location (2019 or 2020).

First characterization of vocalizations and passive acoustic monitoring of the vulnerable African manatee (Trichechus senegalensis).

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.

Passive acoustic listening stations (PALS) show rapid onset of ecological effects of harmful algal blooms in real time.

Monitoring ecological changes in marine ecosystems is expensive and time-consuming. Passive acoustic methods provide continuous monitoring of soniferous species, are relatively inexpensive, and can be integrated into a larger network to provide enhanced spatial and temporal coverage of ecological events. We demonstrate how these methods can be used to detect changes in fish populations in response to a Karenia brevis red tide harmful algal bloom by examining sound spectrum levels recorded by two land-based passive acoustic listening stations (PALS) deployed in Sarasota Bay, Florida, before and during a red tide event.