Indo-Pacific humpback dolphin occurrence north of Lantau Island, Hong Kong, based on year-round passive acoustic monitoring

Temporal variation of the underwater soundscape in Jiaotou Bay, an Indo-Pacific humpback dolphin (Sousa chinensis) habitat off Hainan Island, China

The underwater soundscape is an important ecological element affecting numerous aquatic animals, in particular dolphins, which must identify salient cues from ambient ocean noise. In this study, temporal variations in the soundscape of Jiaotou Bay were monitored from February 2016 to January 2017, where a population of Indo-Pacific humpback dolphins (Sousa chinensis) has recently been a regular sighting. An autonomous acoustic recorder was deployed in shallow waters, and 1/3-octave band sound pressure levels (SPLs) were calculated with central frequencies ranging from 25 Hz to 40 kHz, then were grouped into 3 subdivided bands via cluster analysis. SPLs at each major band showed significant differences on a diel, fishing-related period, seasonal, and tidal phase scale. Anthropogenic noise generated by passing ships and underwater explosions were recorded in the study area. The fish and dolphin acoustic activities both exhibited diel and seasonal variations, but no tidal cycle patterns. A negative significant relationship between anthropogenic sound detection rates and dolphin detection rates were observed, and fish detection rates showed no effect on dolphin detection rates, indicating anthropogenic activity avoidance and no forced foraging in dolphins in the study area. The results provide fundamental insight into the acoustic dynamics of an important Indo-Pacific humpback dolphin habitat within a coastal area affected by a rapid increase in human activity, and demonstrate the need to protect animal habitat from anthropogenic noises.

Influence of acoustic habitat variation on Indo-Pacific humpback dolphin (Sousa chinensis) in shallow waters of Hainan Island, China

The Indo-Pacific humpback dolphin (IPHD, Sousa chinensis) is a coastal species inhabiting tropical and warm-temperate waters. The presence of this vulnerable dolphin was recently discovered in shallow waters southwest of Hainan Island, China. The influence of the acoustic habitat on the distribution and behavior of IPHD was investigated using an array of passive acoustic platforms (n = 6) that spanned more than 100 km of coastline during a 75-day monitoring period. Its presence was assessed within 19 215 five-min recordings by classifying echolocation clicks using machine learning techniques. Spectrogram analysis was applied to further investigate the acoustic behavior of IPHD and to identify other prominent sound sources. The variation in the ambient noise levels was also measured to describe the spatiotemporal patterns of the acoustic habitat among the different sampling sites. Social and feeding sounds of IPHD (whistles and click-series of pulsed sounds) were identified together with other biological sources (finless porpoise, soniferous fishes, and snapping shrimps) and anthropogenic activities (ship noise, explosions, and sonars). Distribution, acoustic behavior, and habitat use of this nearshore dolphin species were strongly influenced by the abundance of soniferous fishes, and under similar conditions, the species was more acoustically active in locations with lower noise levels.

Classification of producer characteristics in primate long calls using neural networks

Primate long calls are high-amplitude vocalizations that can be critical in maintaining intragroup contact and intergroup spacing, and can encode abundant information about a call's producer, such as age, sex, and individual identity. Long calls of the wild emperor (Saguinus imperator) and saddleback (Leontocebus weddelli) tamarins were tested for these identity signals using artificial neural networks, machine-learning models that reduce subjectivity in vocalization classification. To assess whether modelling could be streamlined by using only factors which were responsible for the majority of variation within networks, each series of networks was re-trained after implementing two methods of feature selection. First, networks were trained and run using only the subset of variables whose weights accounted for ≥50% of each original network's variation, as identified by the networks themselves. In the second, only variables implemented by decision trees in predicting outcomes were used. Networks predicted dependent variables above chance (≥58.7% for sex, ≥69.2 for age class, and ≥38.8% for seven to eight individuals), but classification accuracy was not markedly improved by feature selection. Findings are discussed with regard to implications for future studies on identity signaling in vocalizations and streamlining of data analysis.