ABSTRACT
Abundance estimation of marine mammals requires matching of detection of an animal or a group of animal by two independent means. A multimodal detection model using visual and acoustic cues (surfacing and phonation) that enables abundance estimation of dolphins is proposed. The method does not require a specific time window to match the cues of both means for applying mark-recapture method. The proposed model was evaluated using data obtained in field observations of Ganges River dolphins and Irrawaddy dolphins, as examples of dispersed and condensed distributions of animals, respectively. The acoustic detection probability was approximately 80%, 20% higher than that of visual detection for both species, regardless of the distribution of the animals in present study sites. The abundance estimates of Ganges River dolphins and Irrawaddy dolphins fairly agreed with the numbers reported in previous monitoring studies. The single animal detection probability was smaller than that of larger cluster size, as predicted by the model and confirmed by field data. However, dense groups of Irrawaddy dolphins showed difference in cluster sizes observed by visual and acoustic methods. Lower detection probability of single clusters of this species seemed to be caused by the clumped distribution of this species.
Subject(s)
Acoustics , Dolphins/physiology , Environmental Monitoring/methods , Marine Biology/methods , Vocalization, Animal , Algorithms , Animals , Cues , Dolphins/classification , Dolphins/psychology , Humans , Models, Theoretical , Oceans and Seas , Phonation , Population Density , Reproducibility of Results , Signal Processing, Computer-Assisted , Sound Spectrography , Species Specificity , Visual Perception , Vocalization, Animal/classificationABSTRACT
In this paper, a sperm whale click analysis scheme is proposed in order to calculate the position of individual sperm whales in a group using data received at two arrays deployed near the surface. The proposed method mainly consists of two parts: short baseline (SBL) with classification and long baseline (LBL) with class matching. In SBL with classification, a click is automatically detected, and its direction of arrival is calculated. The clicks are then classified based on their direction vectors. The class data are then sent together with direction data and matched to the other array's class data. LBL with class matching is used for localization. The classification algorithm can be used to estimate the number of whales clicking and to list potential candidates for LBL matching. As a result, the proposed method is able to localize the positions of the whales in a group. The performance of the proposed method is evaluated using data recorded off Ogasawara islands with two arrays near the surface. The three-dimensional underwater trajectories of six sperm whales are extracted to demonstrate the capability of the proposed method.
