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Estimating effective detection area of static passive acoustic data loggers from playback experiments with cetacean vocalisations

Hanna Nuuttila, Katharina Brundiers, Michael Dähne, Jens C. Koblitz, Len Thomas, Winnie Courtene‐Jones, Peter G. H. Evans, John R. Turner, Jim D. Bennell, Jan G. Hiddink

Methods in Ecology and Evolution, Volume: 9, Issue: 12, Pages: 2362 - 2371

Swansea University Author: Hanna Nuuttila

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DOI (Published version): 10.1111/2041-210X.13097

Abstract

Passive acoustic monitoring (PAM) is used for many vocal species. However, few studies have quantified the fraction of vocalisations captured, and how animal distance and sound source level affect detection probability. Quantifying the detection probability or effective detection area (EDA) of a rec...

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Published in: Methods in Ecology and Evolution
ISSN: 2041-210X 2041-210X
Published: 2018
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URI: https://cronfa.swan.ac.uk/Record/cronfa46127
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Quantifying the detection probability or effective detection area (EDA) of a recorder is a prerequisite for designing and implementing monitoring studies, and essential for estimating absolute density and abundance from PAM data. We tested the detector performance of cetacean click loggers (C‐PODs) using artificial and recorded harbour porpoise clicks played at a range of distances and source levels. Detection rate of individual clicks and click sequences (or click trains) was calculated. A Generalised Additive Model (GAM) was used to create a detection function and estimate the effective detection radius (EDR) and EDA for both types of signals.Source level and distance from logger influenced the detection probability. Whilst differences between loggers were evident, detectability was influenced more by the deployment site than within‐logger variability. Maximum distance for detecting real recorded porpoise clicks was 566 m. Mean EDR for artificial signals with source level 176 dB re 1 μPa @ 1m was 187 m., and for a recorded vocalisation with source level up to 182 dB re 1 μPa was 188 m. For detections classified as harbour porpoise click sequences the mean EDR was 72 m. The analytical methods presented are a valid technique for estimating the EDA of any logger used in abundance estimates. We present a practical way to obtain data with a cetacean click logger, with the caveat that artificial playbacks cannot mimic real animal behaviour and are at best able to account for some of the variability in detections between sites, removing logger and propagation effects so that what remains is density and behavioural differences. If calibrated against real‐world EDAs (e.g., from tagged animals) it is possible to estimate site‐specific detection area and absolute density. 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spelling v2 46127 2018-11-28 Estimating effective detection area of static passive acoustic data loggers from playback experiments with cetacean vocalisations 0302aad4bf64c26334e2a44a7e8e8f13 Hanna Nuuttila Hanna Nuuttila true false 2018-11-28 FGSEN Passive acoustic monitoring (PAM) is used for many vocal species. However, few studies have quantified the fraction of vocalisations captured, and how animal distance and sound source level affect detection probability. Quantifying the detection probability or effective detection area (EDA) of a recorder is a prerequisite for designing and implementing monitoring studies, and essential for estimating absolute density and abundance from PAM data. We tested the detector performance of cetacean click loggers (C‐PODs) using artificial and recorded harbour porpoise clicks played at a range of distances and source levels. Detection rate of individual clicks and click sequences (or click trains) was calculated. A Generalised Additive Model (GAM) was used to create a detection function and estimate the effective detection radius (EDR) and EDA for both types of signals.Source level and distance from logger influenced the detection probability. Whilst differences between loggers were evident, detectability was influenced more by the deployment site than within‐logger variability. Maximum distance for detecting real recorded porpoise clicks was 566 m. Mean EDR for artificial signals with source level 176 dB re 1 μPa @ 1m was 187 m., and for a recorded vocalisation with source level up to 182 dB re 1 μPa was 188 m. For detections classified as harbour porpoise click sequences the mean EDR was 72 m. The analytical methods presented are a valid technique for estimating the EDA of any logger used in abundance estimates. We present a practical way to obtain data with a cetacean click logger, with the caveat that artificial playbacks cannot mimic real animal behaviour and are at best able to account for some of the variability in detections between sites, removing logger and propagation effects so that what remains is density and behavioural differences. If calibrated against real‐world EDAs (e.g., from tagged animals) it is possible to estimate site‐specific detection area and absolute density. We highlight the importance of accounting for both biological and environmental factors affecting vocalisations so that accurate estimates of detection area can be determined, and effective monitoring regimes implemented. Journal Article Methods in Ecology and Evolution 9 12 2362 2371 2041-210X 2041-210X cetacean, acoustic monitoring, passive acoustics, SAM, C-POD, harbour porpoise, Phocoena phocoena, density estimation 14 12 2018 2018-12-14 10.1111/2041-210X.13097 COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University 2023-06-23T15:49:53.2467443 2018-11-28T13:19:18.0298626 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Biosciences Hanna Nuuttila 1 Katharina Brundiers 2 Michael Dähne 3 Jens C. Koblitz 4 Len Thomas 5 Winnie Courtene‐Jones 6 Peter G. H. Evans 7 John R. Turner 8 Jim D. Bennell 9 Jan G. Hiddink 10
title Estimating effective detection area of static passive acoustic data loggers from playback experiments with cetacean vocalisations
spellingShingle Estimating effective detection area of static passive acoustic data loggers from playback experiments with cetacean vocalisations
Hanna Nuuttila
title_short Estimating effective detection area of static passive acoustic data loggers from playback experiments with cetacean vocalisations
title_full Estimating effective detection area of static passive acoustic data loggers from playback experiments with cetacean vocalisations
title_fullStr Estimating effective detection area of static passive acoustic data loggers from playback experiments with cetacean vocalisations
title_full_unstemmed Estimating effective detection area of static passive acoustic data loggers from playback experiments with cetacean vocalisations
title_sort Estimating effective detection area of static passive acoustic data loggers from playback experiments with cetacean vocalisations
author_id_str_mv 0302aad4bf64c26334e2a44a7e8e8f13
author_id_fullname_str_mv 0302aad4bf64c26334e2a44a7e8e8f13_***_Hanna Nuuttila
author Hanna Nuuttila
author2 Hanna Nuuttila
Katharina Brundiers
Michael Dähne
Jens C. Koblitz
Len Thomas
Winnie Courtene‐Jones
Peter G. H. Evans
John R. Turner
Jim D. Bennell
Jan G. Hiddink
format Journal article
container_title Methods in Ecology and Evolution
container_volume 9
container_issue 12
container_start_page 2362
publishDate 2018
institution Swansea University
issn 2041-210X
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doi_str_mv 10.1111/2041-210X.13097
college_str Faculty of Science and Engineering
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hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
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department_str School of Biosciences, Geography and Physics - Biosciences{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Biosciences, Geography and Physics - Biosciences
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description Passive acoustic monitoring (PAM) is used for many vocal species. However, few studies have quantified the fraction of vocalisations captured, and how animal distance and sound source level affect detection probability. Quantifying the detection probability or effective detection area (EDA) of a recorder is a prerequisite for designing and implementing monitoring studies, and essential for estimating absolute density and abundance from PAM data. We tested the detector performance of cetacean click loggers (C‐PODs) using artificial and recorded harbour porpoise clicks played at a range of distances and source levels. Detection rate of individual clicks and click sequences (or click trains) was calculated. A Generalised Additive Model (GAM) was used to create a detection function and estimate the effective detection radius (EDR) and EDA for both types of signals.Source level and distance from logger influenced the detection probability. Whilst differences between loggers were evident, detectability was influenced more by the deployment site than within‐logger variability. Maximum distance for detecting real recorded porpoise clicks was 566 m. Mean EDR for artificial signals with source level 176 dB re 1 μPa @ 1m was 187 m., and for a recorded vocalisation with source level up to 182 dB re 1 μPa was 188 m. For detections classified as harbour porpoise click sequences the mean EDR was 72 m. The analytical methods presented are a valid technique for estimating the EDA of any logger used in abundance estimates. We present a practical way to obtain data with a cetacean click logger, with the caveat that artificial playbacks cannot mimic real animal behaviour and are at best able to account for some of the variability in detections between sites, removing logger and propagation effects so that what remains is density and behavioural differences. If calibrated against real‐world EDAs (e.g., from tagged animals) it is possible to estimate site‐specific detection area and absolute density. We highlight the importance of accounting for both biological and environmental factors affecting vocalisations so that accurate estimates of detection area can be determined, and effective monitoring regimes implemented.
published_date 2018-12-14T15:49:48Z
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