Study of acoustic characteristics of Ganges river dolphin calf using ehcolocation clicks recorded during long-term in-situ observation

Abstract

For understanding the Ganges river dolphin's (Platanista gangetica) echolocation strategies and underwater behavior for a long-term span, targeting 15-18 Ganges river dolphins including calves, the annual long-term in-situ monitoring using the passive acoustic system (a 6-hydrophone array) has been conducted for 4 consecutive seasons, i.e. four monitoring phases, since November 2008. Recorded data was uploaded to the database for further analysis. Visual observations were periodically made by WWF-India through the four monitoring phases. During the monitoring phase 4, in addition, active acoustic monitoring using the imaging sonar (DIDSON: standard model: Detection mode 1.1MHz Identification mode 1.8 MHz) was initially conducted along with the passive acoustic monitoring for 5 days during 13-17 November 2011. In phase 4, from the visual observations, it was found that one or two calves having body length between 0.6m to 0.8m were seen accompanied by other dolphins, having body length over 0.8m within 50m range from the array. Based on the body length analysis using data recorded by the imaging sonar and visual observations, the Ganges river dolphin was classified into three groups, i.e. group A (adult and young adult), group B (calf under one-year old) and group C (calf less than 6 month old). Acoustic characteristics of both dolphin calf groups were revealed by ICI (Inter-click Interval) based analysis. Similar to adult/young adult dolphin, ICI of group B calf varies between 10 to 100msecs. A click sequence of three echolocation phases is also observed. On the other hand, ICI of group C calf is always less than 10msec through the click train and has no sequential changes of three echolocation phases. Taking advantage of ICI characteristics difference between group C calf and other groups, we succeeded in detecting each group C calf from data uploaded to the database using ICI histogram based analysis.