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

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

doi:10.1111/2041-210x.13097

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

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

^*Corresponding author for this work

School of Biological and Marine Sciences

Research output: Contribution to journal › Article › peer-review

Abstract

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 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.

Original language	English
Pages (from-to)	2362-2371
Number of pages	0
Journal	Methods in Ecology and Evolution
Volume	9
Issue number	12
Early online date	22 Oct 2018
DOIs	https://doi.org/10.1111/2041-210x.13097
Publication status	Published - Dec 2018

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Nuuttila, H. K., Brundiers, K., Dähne, M., Koblitz, J. C., Thomas, L., Courtene‐Jones, W., Evans, P. G. H., Turner, J. R., Bennell, J. D., & Hiddink, J. G. (2018). Estimating effective detection area of static passive acoustic data loggers from playback experiments with cetacean vocalisations. Methods in Ecology and Evolution, 9(12), 2362-2371. https://doi.org/10.1111/2041-210x.13097

@article{d15ee540e9d84ccaa57f34cc242de56e,

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

abstract = "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 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. ",

author = "Nuuttila, {Hanna K.} and Katharina Brundiers and Michael D{\"a}hne and Koblitz, {Jens C.} and Len Thomas and W Courtene‐Jones and Evans, {Peter G.H.} and Turner, {John R.} and Bennell, {Jim D.} and Hiddink, {Jan G.}",

year = "2018",

month = dec,

doi = "10.1111/2041-210x.13097",

language = "English",

volume = "9",

pages = "2362--2371",

journal = "Methods in Ecology and Evolution",

issn = "2041-210X",

publisher = "John Wiley & Sons Inc.",

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Nuuttila, HK, Brundiers, K, Dähne, M, Koblitz, JC, Thomas, L, Courtene‐Jones, W, Evans, PGH, Turner, JR, Bennell, JD & Hiddink, JG 2018, 'Estimating effective detection area of static passive acoustic data loggers from playback experiments with cetacean vocalisations', Methods in Ecology and Evolution, vol. 9, no. 12, pp. 2362-2371. https://doi.org/10.1111/2041-210x.13097

TY - JOUR

T1 - Estimating effective detection area of static passive acoustic data loggers from playback experiments with cetacean vocalisations

AU - Nuuttila, Hanna K.

AU - Brundiers, Katharina

AU - Dähne, Michael

AU - Koblitz, Jens C.

AU - Thomas, Len

AU - Courtene‐Jones, W

AU - Evans, Peter G.H.

AU - Turner, John R.

AU - Bennell, Jim D.

AU - Hiddink, Jan G.

PY - 2018/12

Y1 - 2018/12

N2 - 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 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.

AB - 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 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.

U2 - 10.1111/2041-210x.13097

DO - 10.1111/2041-210x.13097

M3 - Article

SN - 2041-210X

VL - 9

SP - 2362

EP - 2371

JO - Methods in Ecology and Evolution

JF - Methods in Ecology and Evolution

IS - 12

ER -

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

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