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

David A. Sweeney; Stacy L. Deruiter; Ye Joo McNamara-Oh; Tiago A. Marques; Patricia Arranz; John Calambokidis

doi:10.1186/s40317-019-0169-3

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

David A. Sweeney^*, Stacy L. Deruiter, Ye Joo McNamara-Oh, Tiago A. Marques, Patricia Arranz, John Calambokidis

^*Corresponding author for this work

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

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.

Original language	English
Article number	7
Number of pages	10
Journal	Animal Biotelemetry
Volume	7
DOIs	https://doi.org/10.1186/s40317-019-0169-3
Publication status	Published - 4 Apr 2019

Keywords

Blue whale
Detection
Lunge
Norm-jerk
Prey capture
Risso's dolphin

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10.1186/s40317-019-0169-3Licence: CC BY

Sweeney_2019_AB_Automatedpeak_CC
Copyright © The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Final published version, 2.25 MBLicence: CC BY

Tools and training to enhance capacity: Tools and training to enhance capacity for analyzing high-resolution tag data
Marques, T. A. (PI)
1/09/16 → 31/12/17
Project: Standard

Data from: Automated peak detection method for behavioral event identification: detecting Balaenoptera musculus and Grampus griseus feeding attempts
Sweeney, D. A. (Creator), DeRuiter, S. L. (Creator), McNamara-Oh, Y. J. (Creator), Marques, T. A. (Creator), Arranz Alonso, P. (Creator) & Calambokidis, J. (Creator), Dryad, 9 Apr 2019
DOI: 10.5061/dryad.bd8j403
Dataset

Cite this

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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{\textquoteright}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{\textquoteright}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{\c c}{\~a}o para a Ci{\^e}ncia e a Tecnologia, Portugal, through the project UID/MAT/00006/2013).",

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doi = "10.1186/s40317-019-0169-3",

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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

SN - 2050-3385

VL - 7

JO - Animal Biotelemetry

JF - Animal Biotelemetry

M1 - 7

ER -

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

Abstract

Keywords

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Tools and training to enhance capacity: Tools and training to enhance capacity for analyzing high-resolution tag data

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

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