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

Ye Joo Mcnamara-Oh,Tiago A Marques,Stacy L Deruiter,Patricia Arranz,John Calambokidis,David A Sweeney

doi:10.1186/s40317-019-0169-3

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.

Highlights

Studying animal behavior while minimizing levels of invasiveness is a challenge many biologists face [1,2,3]
We demonstrate the overall performance of a newly designed automated detection method, operational in many versions of three widely used software programs (R [32], MATLAB [33], and Octave [34]), at detecting the times of Risso’s dolphin (Grampus griseus) and blue whale (Balaenoptera musculus) foraging events from the norm-jerk signal
We have developed an automated behavioral event detection method, which is successful at identifying the times of blue whale and Risso’s dolphin feeding attempts using the norm-jerk time series

Summary

Introduction

Studying animal behavior while minimizing levels of invasiveness is a challenge many biologists face [1,2,3]. Difficulty arises while attempting to observe animals in environments and during time periods that are relatively inaccessible to humans [4]. The scientific field of biologging arose, in part, to address these two main obstacles [5]. Since the first use of tagging devices on animals in 1963, the field of biologging has evolved into a discipline that allows for the detailed behavioral study of animals ranging from chipmunks to blue whales [5,6,7]. Using data obtained by these tags, scientists can determine the exact time

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Discussion

Conclusion