Dear colleagues,
My co-authors and I are pleased to announce the following publication:
James R. Robbins, Anja Brandecker, Michelle Cronin, Mark Jessopp,
Rob McAllen & Ross Culloch (2015):
Handling dolphin detections from C-PODs, with the development of acoustic parameters for verification and the exploration of species identification possibilities. Bioacoustics, DOI: 10.1080/09524622.2015.1125789
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.