Handling dolphin detections from C-PODs, with the development of acoustic parameters for verification and the exploration of species identification possibilities. Issue 2 (3rd May 2016)

• Record Type: Journal Article
• Authors: Robbins, James R.; Brandecker, Anja; Cronin, Michelle; Jessopp, Mark; McAllen, Rob; Culloch, Ross
• Is Part Of: Bioacoustics. Volume 25:Issue 2(2016:May)
• Page Start: 99
• Page End: 110
• Publication Date: 2016-05-03
• Subjects: C-POD -- dolphin -- passive acoustic monitoring -- acoustic characteristics -- species identification; Bioacoustics -- Periodicals; Sound production by animals -- Periodicals; Animal sounds -- Periodicals; Sound recordings -- Periodicals; 591.59405
• DOI: 10.1080/09524622.2015.1125789
• Languages: English
• ISSNs: 0952-4622

Abstract

Abstract: C-PODs are static passive acoustic monitoring devices used to detect odontocete vocalizations in the range of 20–160 kHz. However, falsely classified detections may be an issue, particularly with broadband species (i.e. many dolphin species) due to anthropogenic and other noise occurring at the same frequency. While porpoise detections are verified using species-specific acoustic parameters, the equivalent does not currently exist for verifying dolphin detections. Development of such parameters would increase the accuracy of dolphin detections and eliminate the need for additional monitoring techniques or devices, reducing the cost of monitoring programmes. Herein, we present parameters based on acoustic characteristics of bottlenose ( n = 29), common ( n = 19) and Risso's ( n = 99) dolphin click trains, sighted within 1 km of C-PODs during land-based surveys, for in-software verification. Overlap of click train parameters among dolphin species prevented robust species identification; therefore, parameters were devised for these dolphin species collectively using frequency, inter-click interval and click train duration. A data set of 4898 Detection Positive Hours was visually verified using these parameters. The temporal and spatial patterns in the visually verified data were similar to land-based observations, suggesting the parameters operate at an acceptable accuracy. However, 68% of high-, moderate- and low-quality KERNO detections were false-positive. Our results suggest that the accuracy of classifiers and quality class weightings are site-specific, and we highlight the importance of data exploration to make the most appropriate software choices based on the aims of a study.