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Identification of animal movement patterns using tri-axial magnetometry

Overview of attention for article published in Movement Ecology, March 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (91st percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

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47 X users
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244 Mendeley
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Title
Identification of animal movement patterns using tri-axial magnetometry
Published in
Movement Ecology, March 2017
DOI 10.1186/s40462-017-0097-x
Pubmed ID
Authors

Hannah J. Williams, Mark D. Holton, Emily L. C. Shepard, Nicola Largey, Brad Norman, Peter G. Ryan, Olivier Duriez, Michael Scantlebury, Flavio Quintana, Elizabeth A. Magowan, Nikki J. Marks, Abdulaziz N. Alagaili, Nigel C. Bennett, Rory P. Wilson

Abstract

Accelerometers are powerful sensors in many bio-logging devices, and are increasingly allowing researchers to investigate the performance, behaviour, energy expenditure and even state, of free-living animals. Another sensor commonly used in animal-attached loggers is the magnetometer, which has been primarily used in dead-reckoning or inertial measurement tags, but little outside that. We examine the potential of magnetometers for helping elucidate the behaviour of animals in a manner analogous to, but very different from, accelerometers. The particular responses of magnetometers to movement means that there are instances when they can resolve behaviours that are not easily perceived using accelerometers. We calibrated the tri-axial magnetometer to rotations in each axis of movement and constructed 3-dimensional plots to inspect these stylised movements. Using the tri-axial data of Daily Diary tags, attached to individuals of number of animal species as they perform different behaviours, we used these 3-d plots to develop a framework with which tri-axial magnetometry data can be examined and introduce metrics that should help quantify movement and behaviour.. Tri-axial magnetometry data reveal patterns in movement at various scales of rotation that are not always evident in acceleration data. Some of these patterns may be obscure until visualised in 3D space as tri-axial spherical plots (m-spheres). A tag-fitted animal that rotates in heading while adopting a constant body attitude produces a ring of data around the pole of the m-sphere that we define as its Normal Operational Plane (NOP). Data that do not lie on this ring are created by postural rotations of the animal as it pitches and/or rolls. Consequently, stereotyped behaviours appear as specific trajectories on the sphere (m-prints), reflecting conserved sequences of postural changes (and/or angular velocities), which result from the precise relationship between body attitude and heading. This novel approach shows promise for helping researchers to identify and quantify behaviours in terms of animal body posture, including heading. Magnetometer-based techniques and metrics can enhance our capacity to identify and examine animal behaviour, either as a technique used alone, or one that is complementary to tri-axial accelerometry.

X Demographics

X Demographics

The data shown below were collected from the profiles of 47 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 244 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 244 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 54 22%
Student > Master 42 17%
Researcher 35 14%
Student > Bachelor 34 14%
Student > Doctoral Student 10 4%
Other 23 9%
Unknown 46 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 111 45%
Environmental Science 40 16%
Earth and Planetary Sciences 7 3%
Engineering 5 2%
Computer Science 4 2%
Other 20 8%
Unknown 57 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 28. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 October 2019.
All research outputs
#1,291,356
of 24,294,745 outputs
Outputs from Movement Ecology
#55
of 352 outputs
Outputs of similar age
#26,235
of 312,648 outputs
Outputs of similar age from Movement Ecology
#3
of 10 outputs
Altmetric has tracked 24,294,745 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 352 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 22.5. This one has done well, scoring higher than 84% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 312,648 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 7 of them.