Eastern Black Rail detection using semi-automated analysis of long-duration acoustic recordings

Avian Conservation and Ecology

article

Elizabeth Znidersic¹  Michael W. Towsey¹  Christine Hand³  David M. Watson¹ 

[1] Institute for Land, Water and Society, Charles Sturt University;QUT Ecoacoustics Research Group, Science and Engineering Faculty, Queensland University of Technology;South Carolina Department of Natural Resources

关键词: acoustic monitoring;    autonomous recording unit;    Black Rail;    call recognizer;    long-duration false-color spectrogram;    marsh bird;   

DOI  :  10.5751/ACE-01773-160109

学科分类：口腔科学

来源: Resilience Alliance Publications

【 摘 要 】

Detecting presence and inferring absence are both critical in species monitoring and management. False-negatives in any survey methodology can have significant consequences when conservation decisions are based on incomplete results. Marsh birds are notoriously difficult to detect, and current survey methods rely on traditional labor-intensive methods, and, more recently, passive acoustic monitoring. We investigated the efficiency of passive acoustic monitoring as a survey tool for the cryptic and poorly understood Eastern Black Rail (Laterallus jamaicensis jamaicensis) analyzing data from two sites collected at the Tom Yawkey Wildlife Center, South Carolina, USA. We demonstrate two new techniques to automate the reviewing and analysis of long-duration acoustic monitoring data. First, we used long-duration false-color spectrograms to visualize the 20 days of recording and to confirm presence of Black Rail "kickee-doo" calls. Second, we used a machine learning model (Random Forest in regression mode) to automate the scanning of 480 consecutive hours of acoustic recording and to investigate spatial and temporal presence. Detection of the Black Rail call was confirmed in the long-duration false-color spectrogram and the call recognizer correctly predicted Black Rail in 91% of the first 316 top-ranked predictions at one site. From ten days of continuous acoustic recordings, Black Rail calls were detected on only four consecutive days. Long-duration false-color spectrograms were effective for detecting Black Rail calls because their tendency to vocalize over consecutive minutes leaves a visible trace in the spectrogram. The call recognizer performed effectively when the Black Rail call was the dominant acoustic activity in its frequency band. We demonstrate that combining false-color spectrograms with a machine-learned recognizer creates a more efficient monitoring tool than a stand-alone species-specific call recognizer, with particular utility for species whose vocalization patterns and occurrence are unpredictable or unknown.

【 授权许可】

CC BY