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Methodology article
Inferring an animal's environment through biologging: quantifying the environmental influence on animal movement
Jasper A.J. Eikelboom
Wageningen University and Research
Corresponding Author
ORCiD: https://orcid.org/0000-0002-5107-4427
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at Movement Ecology.
Background: Animals respond to environmental variation by changing their movement in a multifaceted way. Recent advancements in biologging increasingly allow for detailed measurements of the multifaceted nature of movement, from descriptors of animal movement trajectories (e.g., using GPS) to descriptors of body part movements (e.g., using tri-axial accelerometers). Because this multivariate richness of movement data complicates inference on the environmental influence on animal movement, studies generally use simplified movement descriptors in statistical analyses. However, doing so limits the inference on the environmental influence on movement, as this requires that the multivariate richness of movement data can be fully considered in an analysis.
Methods: We propose a data-driven analytic framework, based on existing methods, to quantify the environmental influence on animal movement that can accommodate the multifaceted nature of animal movement. Instead of fitting a simplified movement descriptor to a suite of environmental variables, our proposed framework centres on predicting an environmental variable from the full set of multivariate movement data. The measure of fit of this prediction is taken to be the metric that quantifies how much of the environmental variation relates to the multivariate variation in animal movement. We demonstrate the usefulness of this framework through a case study about the influence of grass availability and time since milking on cow movements using machine learning algorithms.
Results: We show that on a one-hour timescale 37% of the variation in grass availability and 33% of time since milking influenced cow movements. Grass availability mostly influenced the cows’ neck movement during grazing, while time since milking mostly influenced the movement through the landscape and the shared variation of accelerometer and GPS data (e.g., activity patterns). Furthermore, this framework proved to be insensitive to spurious correlations between environmental variables in quantifying the influence on animal movement.
Conclusions: Not only is our proposed framework well-suited to study the environmental influence on animal movement; we argue that it can also be applied in any field that uses multivariate biologging data, e.g., animal physiology, to study the relationships between animals and their environment.
Keywords
behaviour classification; collective movement; cows; foraging; group dynamics; lactation; machine learning; random forest regression; resource availability; support vector machine
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CURRENT STATUS: Published
Version 4
Posted 18 Sep, 2020
Published
On 19 Oct, 2020
No community comments so far
Editorial decision: Accept
On 08 Oct, 2020
Review #1 received
Received 05 Oct, 2020
Reviewers invited
Invitations sent on 21 Sep, 2020
Reviewer #1 agreed
On 21 Sep, 2020
Editor assigned
On 17 Sep, 2020
Submission checks complete
On 16 Sep, 2020
Editor invited
On 16 Sep, 2020
No comments provided
Editorial decision: Major Revision
On 27 Aug, 2020
Review #3 received
Received 21 Aug, 2020
Reviewer #2 agreed
On 05 Aug, 2020
Review #2 received
Received 05 Aug, 2020
Reviewer #3 agreed
On 03 Aug, 2020
Reviewers invited
Invitations sent on 01 Aug, 2020
First submitted
On 20 Jul, 2020
Submission checks complete
On 20 Jul, 2020
No comments provided
Editorial decision: Major Revision
On 26 Jun, 2020
Review #2 received
Received 25 Jun, 2020
Review #1 received
Received 15 Jun, 2020
Reviewer #2 agreed
On 04 Jun, 2020
Reviewers invited
Invitations sent on 02 Jun, 2020
Reviewer #1 agreed
On 02 Jun, 2020
Editor assigned
On 28 May, 2020
Submission checks complete
On 27 May, 2020
Editor invited
On 27 May, 2020
First submitted
On 26 May, 2020
No comments provided
Editorial decision: Reject with resubmission (open reject)
On 03 Feb, 2020
Review #2 received
Received 31 Jan, 2020
Review #1 received
Received 31 Jan, 2020
Reviewers invited
Invitations sent on 13 Jan, 2020
Reviewer #1 agreed
On 13 Jan, 2020
Reviewer #2 agreed
On 13 Jan, 2020
Editor invited
On 09 Jan, 2020
Editor assigned
On 09 Jan, 2020
First submitted
On 08 Jan, 2020
Submission checks complete
On 08 Jan, 2020
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This preprint is under consideration at Movement Ecology. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Methodology article
Inferring an animal's environment through biologging: quantifying the environmental influence on animal movement
Jasper A.J. Eikelboom
Wageningen University and Research
Corresponding Author
ORCiD: https://orcid.org/0000-0002-5107-4427
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
Version 4
Posted 18 Sep, 2020
Published
On 19 Oct, 2020
No community comments so far
Editorial decision: Accept
On 08 Oct, 2020
Review #1 received
Received 05 Oct, 2020
Reviewers invited
Invitations sent on 21 Sep, 2020
Reviewer #1 agreed
On 21 Sep, 2020
Editor assigned
On 17 Sep, 2020
Submission checks complete
On 16 Sep, 2020
Editor invited
On 16 Sep, 2020
No comments provided
Editorial decision: Major Revision
On 27 Aug, 2020
Review #3 received
Received 21 Aug, 2020
Reviewer #2 agreed
On 05 Aug, 2020
Review #2 received
Received 05 Aug, 2020
Reviewer #3 agreed
On 03 Aug, 2020
Reviewers invited
Invitations sent on 01 Aug, 2020
First submitted
On 20 Jul, 2020
Submission checks complete
On 20 Jul, 2020
No comments provided
Editorial decision: Major Revision
On 26 Jun, 2020
Review #2 received
Received 25 Jun, 2020
Review #1 received
Received 15 Jun, 2020
Reviewer #2 agreed
On 04 Jun, 2020
Reviewers invited
Invitations sent on 02 Jun, 2020
Reviewer #1 agreed
On 02 Jun, 2020
Editor assigned
On 28 May, 2020
Submission checks complete
On 27 May, 2020
Editor invited
On 27 May, 2020
First submitted
On 26 May, 2020
No comments provided
Editorial decision: Reject with resubmission (open reject)
On 03 Feb, 2020
Review #2 received
Received 31 Jan, 2020
Review #1 received
Received 31 Jan, 2020
Reviewers invited
Invitations sent on 13 Jan, 2020
Reviewer #1 agreed
On 13 Jan, 2020
Reviewer #2 agreed
On 13 Jan, 2020
Editor invited
On 09 Jan, 2020
Editor assigned
On 09 Jan, 2020
First submitted
On 08 Jan, 2020
Submission checks complete
On 08 Jan, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at Movement Ecology.
Background: Animals respond to environmental variation by changing their movement in a multifaceted way. Recent advancements in biologging increasingly allow for detailed measurements of the multifaceted nature of movement, from descriptors of animal movement trajectories (e.g., using GPS) to descriptors of body part movements (e.g., using tri-axial accelerometers). Because this multivariate richness of movement data complicates inference on the environmental influence on animal movement, studies generally use simplified movement descriptors in statistical analyses. However, doing so limits the inference on the environmental influence on movement, as this requires that the multivariate richness of movement data can be fully considered in an analysis.
Methods: We propose a data-driven analytic framework, based on existing methods, to quantify the environmental influence on animal movement that can accommodate the multifaceted nature of animal movement. Instead of fitting a simplified movement descriptor to a suite of environmental variables, our proposed framework centres on predicting an environmental variable from the full set of multivariate movement data. The measure of fit of this prediction is taken to be the metric that quantifies how much of the environmental variation relates to the multivariate variation in animal movement. We demonstrate the usefulness of this framework through a case study about the influence of grass availability and time since milking on cow movements using machine learning algorithms.
Results: We show that on a one-hour timescale 37% of the variation in grass availability and 33% of time since milking influenced cow movements. Grass availability mostly influenced the cows’ neck movement during grazing, while time since milking mostly influenced the movement through the landscape and the shared variation of accelerometer and GPS data (e.g., activity patterns). Furthermore, this framework proved to be insensitive to spurious correlations between environmental variables in quantifying the influence on animal movement.
Conclusions: Not only is our proposed framework well-suited to study the environmental influence on animal movement; we argue that it can also be applied in any field that uses multivariate biologging data, e.g., animal physiology, to study the relationships between animals and their environment.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6