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Automated peak detection method for behavioral event identification: detecting Balaenoptera musculus and Grampus griseus feeding attempts

Research output: Contribution to journalArticle

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Automated peak detection method for behavioral event identification : detecting Balaenoptera musculus and Grampus griseus feeding attempts. / Sweeney, David A.; Deruiter, Stacy L.; McNamara-Oh, Ye Joo; Marques, Tiago A.; Arranz, Patricia; Calambokidis, John.

In: Animal Biotelemetry, Vol. 7, 7, 04.04.2019.

Research output: Contribution to journalArticle

Harvard

Sweeney, DA, Deruiter, SL, McNamara-Oh, YJ, Marques, TA, Arranz, P & Calambokidis, J 2019, 'Automated peak detection method for behavioral event identification: detecting Balaenoptera musculus and Grampus griseus feeding attempts', Animal Biotelemetry, vol. 7, 7. https://doi.org/10.1186/s40317-019-0169-3

APA

Sweeney, D. A., Deruiter, S. L., McNamara-Oh, Y. J., Marques, T. A., Arranz, P., & Calambokidis, J. (2019). Automated peak detection method for behavioral event identification: detecting Balaenoptera musculus and Grampus griseus feeding attempts. Animal Biotelemetry, 7, [7]. https://doi.org/10.1186/s40317-019-0169-3

Vancouver

Sweeney DA, Deruiter SL, McNamara-Oh YJ, Marques TA, Arranz P, Calambokidis J. Automated peak detection method for behavioral event identification: detecting Balaenoptera musculus and Grampus griseus feeding attempts. Animal Biotelemetry. 2019 Apr 4;7. 7. https://doi.org/10.1186/s40317-019-0169-3

Author

Sweeney, David A. ; Deruiter, Stacy L. ; McNamara-Oh, Ye Joo ; Marques, Tiago A. ; Arranz, Patricia ; Calambokidis, John. / Automated peak detection method for behavioral event identification : detecting Balaenoptera musculus and Grampus griseus feeding attempts. In: Animal Biotelemetry. 2019 ; Vol. 7.

Bibtex - Download

@article{2264f32ec48444e3a85222de8858bd84,
title = "Automated peak detection method for behavioral event identification: detecting Balaenoptera musculus and Grampus griseus feeding attempts",
abstract = "The desire of animal behaviorists for more flexible methods of conducting inter-study and inter-specific comparisons and meta-analysis of various animal behaviors compelled us to design an automated, animal behavior peak detection method that is potentially generalizable to a wide variety of data types, animals, and behaviors. We detected the times of feeding attempts by 12 Risso’s dolphins (Grampus griseus) and 36 blue whales (Balaenoptera musculus) using the norm-jerk (rate of change of acceleration) time series. The automated peak detection algorithm identified median true-positive rates of 0.881 for blue whale lunges and 0.410 for Risso’s dolphin prey capture attempts, with median false-positive rates of 0.096 and 0.007 and median miss rates of 0.113 and 0.314, respectively. Our study demonstrates that our peak detection method is efficient at automatically detecting animal behaviors from multisensor tag data with high accuracy for behaviors that are appropriately characterized by the data time series.",
keywords = "Blue whale, Detection, Lunge, Norm-jerk, Prey capture, Risso's dolphin",
author = "Sweeney, {David A.} and Deruiter, {Stacy L.} and McNamara-Oh, {Ye Joo} and Marques, {Tiago A.} and Patricia Arranz and John Calambokidis",
note = "This project was supported by the U.S. Office of Naval Research (Grant No. N00014-16-1-3089). Tag deployments used here from the SOCAL-Behavioral Response Study were funded by the U.S. Office of Naval Research and Navy Living Marine Resources under multiple grants and contracts. Further funding was provided by the Kuipers Research Fellowship and the Calvin Alumni Association. TAM thanks partial support by CEAUL (funded by FCT—Funda{\cc}{\~a}o para a Ci{\^e}ncia e a Tecnologia, Portugal, through the project UID/MAT/00006/2013).",
year = "2019",
month = "4",
day = "4",
doi = "10.1186/s40317-019-0169-3",
language = "English",
volume = "7",
journal = "Animal Biotelemetry",
issn = "2050-3385",
publisher = "BioMed Central",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Automated peak detection method for behavioral event identification

T2 - detecting Balaenoptera musculus and Grampus griseus feeding attempts

AU - Sweeney, David A.

AU - Deruiter, Stacy L.

AU - McNamara-Oh, Ye Joo

AU - Marques, Tiago A.

AU - Arranz, Patricia

AU - Calambokidis, John

N1 - This project was supported by the U.S. Office of Naval Research (Grant No. N00014-16-1-3089). Tag deployments used here from the SOCAL-Behavioral Response Study were funded by the U.S. Office of Naval Research and Navy Living Marine Resources under multiple grants and contracts. Further funding was provided by the Kuipers Research Fellowship and the Calvin Alumni Association. TAM thanks partial support by CEAUL (funded by FCT—Fundação para a Ciência e a Tecnologia, Portugal, through the project UID/MAT/00006/2013).

PY - 2019/4/4

Y1 - 2019/4/4

N2 - The desire of animal behaviorists for more flexible methods of conducting inter-study and inter-specific comparisons and meta-analysis of various animal behaviors compelled us to design an automated, animal behavior peak detection method that is potentially generalizable to a wide variety of data types, animals, and behaviors. We detected the times of feeding attempts by 12 Risso’s dolphins (Grampus griseus) and 36 blue whales (Balaenoptera musculus) using the norm-jerk (rate of change of acceleration) time series. The automated peak detection algorithm identified median true-positive rates of 0.881 for blue whale lunges and 0.410 for Risso’s dolphin prey capture attempts, with median false-positive rates of 0.096 and 0.007 and median miss rates of 0.113 and 0.314, respectively. Our study demonstrates that our peak detection method is efficient at automatically detecting animal behaviors from multisensor tag data with high accuracy for behaviors that are appropriately characterized by the data time series.

AB - The desire of animal behaviorists for more flexible methods of conducting inter-study and inter-specific comparisons and meta-analysis of various animal behaviors compelled us to design an automated, animal behavior peak detection method that is potentially generalizable to a wide variety of data types, animals, and behaviors. We detected the times of feeding attempts by 12 Risso’s dolphins (Grampus griseus) and 36 blue whales (Balaenoptera musculus) using the norm-jerk (rate of change of acceleration) time series. The automated peak detection algorithm identified median true-positive rates of 0.881 for blue whale lunges and 0.410 for Risso’s dolphin prey capture attempts, with median false-positive rates of 0.096 and 0.007 and median miss rates of 0.113 and 0.314, respectively. Our study demonstrates that our peak detection method is efficient at automatically detecting animal behaviors from multisensor tag data with high accuracy for behaviors that are appropriately characterized by the data time series.

KW - Blue whale

KW - Detection

KW - Lunge

KW - Norm-jerk

KW - Prey capture

KW - Risso's dolphin

U2 - 10.1186/s40317-019-0169-3

DO - 10.1186/s40317-019-0169-3

M3 - Article

VL - 7

JO - Animal Biotelemetry

JF - Animal Biotelemetry

SN - 2050-3385

M1 - 7

ER -

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