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

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 surve...

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Autores principales:	Elizabeth Znidersic, Michael W. Towsey, Christine Hand, David M. Watson
Formato:	article
Lenguaje:	EN
Publicado:	Resilience Alliance 2021
Materias:	acoustic monitoring autonomous recording unit black rail call recognizer long-duration false-color spectrogram marsh bird Plant culture SB1-1110 Environmental sciences GE1-350 Plant ecology QK900-989
Acceso en línea:	https://doaj.org/article/f7fc9446294d40a4beb295ae067cc46f
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spelling	oai:doaj.org-article:f7fc9446294d40a4beb295ae067cc46f2021-11-15T16:40:14ZEastern Black Rail detection using semi-automated analysis of long-duration acoustic recordings1712-6568https://doaj.org/article/f7fc9446294d40a4beb295ae067cc46f2021-06-01T00:00:00Zhttps://www.ace-eco.org/vol16/iss1/art9/https://doaj.org/toc/1712-6568Detecting 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.Elizabeth ZnidersicMichael W. TowseyChristine HandDavid M. WatsonResilience Alliancearticleacoustic monitoringautonomous recording unitblack railcall recognizerlong-duration false-color spectrogrammarsh birdPlant cultureSB1-1110Environmental sciencesGE1-350Plant ecologyQK900-989ENAvian Conservation and Ecology, Vol 16, Iss 1, p 9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	acoustic monitoring autonomous recording unit black rail call recognizer long-duration false-color spectrogram marsh bird Plant culture SB1-1110 Environmental sciences GE1-350 Plant ecology QK900-989
spellingShingle	acoustic monitoring autonomous recording unit black rail call recognizer long-duration false-color spectrogram marsh bird Plant culture SB1-1110 Environmental sciences GE1-350 Plant ecology QK900-989 Elizabeth Znidersic Michael W. Towsey Christine Hand David M. Watson Eastern Black Rail detection using semi-automated analysis of long-duration acoustic recordings
description	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.
format	article
author	Elizabeth Znidersic Michael W. Towsey Christine Hand David M. Watson
author_facet	Elizabeth Znidersic Michael W. Towsey Christine Hand David M. Watson
author_sort	Elizabeth Znidersic
title	Eastern Black Rail detection using semi-automated analysis of long-duration acoustic recordings
title_short	Eastern Black Rail detection using semi-automated analysis of long-duration acoustic recordings
title_full	Eastern Black Rail detection using semi-automated analysis of long-duration acoustic recordings
title_fullStr	Eastern Black Rail detection using semi-automated analysis of long-duration acoustic recordings
title_full_unstemmed	Eastern Black Rail detection using semi-automated analysis of long-duration acoustic recordings
title_sort	eastern black rail detection using semi-automated analysis of long-duration acoustic recordings
publisher	Resilience Alliance
publishDate	2021
url	https://doaj.org/article/f7fc9446294d40a4beb295ae067cc46f
work_keys_str_mv	AT elizabethznidersic easternblackraildetectionusingsemiautomatedanalysisoflongdurationacousticrecordings AT michaelwtowsey easternblackraildetectionusingsemiautomatedanalysisoflongdurationacousticrecordings AT christinehand easternblackraildetectionusingsemiautomatedanalysisoflongdurationacousticrecordings AT davidmwatson easternblackraildetectionusingsemiautomatedanalysisoflongdurationacousticrecordings
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Eastern Black Rail detection using semi-automated analysis of long-duration acoustic recordings

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