Header

UZH-Logo

Maintenance Infos

Modelling animal movement as Brownian bridges with covariates


Kranstauber, Bart (2019). Modelling animal movement as Brownian bridges with covariates. Movement Ecology, 7:22.

Abstract

The ability to observe animal movement and possible correlates has increased strongly over the past decades. Methods to analyze trajectories have developed in parallel, but many tools fail to make an immediate connection between a movement model, covariates of the movement, and animal space use.

Methods
Here I develop a novel method based on the Brownian Bridge Movement Model that facilitates investigating and testing covariates of movement. The model makes it possible to flexibly investigate different covariates including, for example, periodic movement patterns.

Results
I applied the Brownian Bridge Covariates Model (BBCM) to simulated trajectories demonstrating its ability to reproduce the parameters used for the simulation. I also applied the model to a GPS trajectory of a meerkat, showing its application to empirical data. The value of the model was shown by testing the interaction between maximal daily temperature and the daily movement pattern.

Conclusion
This model produces accurate parameter estimates for covariates of the movements and location error in simulated trajectories. Application to the meerkat trajectory also produced plausible parameter estimates. This new method opens the possibility to directly test hypotheses about the influence of covariates on animal movement while linking these to space-use estimates.

Abstract

The ability to observe animal movement and possible correlates has increased strongly over the past decades. Methods to analyze trajectories have developed in parallel, but many tools fail to make an immediate connection between a movement model, covariates of the movement, and animal space use.

Methods
Here I develop a novel method based on the Brownian Bridge Movement Model that facilitates investigating and testing covariates of movement. The model makes it possible to flexibly investigate different covariates including, for example, periodic movement patterns.

Results
I applied the Brownian Bridge Covariates Model (BBCM) to simulated trajectories demonstrating its ability to reproduce the parameters used for the simulation. I also applied the model to a GPS trajectory of a meerkat, showing its application to empirical data. The value of the model was shown by testing the interaction between maximal daily temperature and the daily movement pattern.

Conclusion
This model produces accurate parameter estimates for covariates of the movements and location error in simulated trajectories. Application to the meerkat trajectory also produced plausible parameter estimates. This new method opens the possibility to directly test hypotheses about the influence of covariates on animal movement while linking these to space-use estimates.

Statistics

Citations

Dimensions.ai Metrics

Altmetrics

Downloads

6 downloads since deposited on 06 Apr 2020
6 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Evolutionary Biology and Environmental Studies
Dewey Decimal Classification:570 Life sciences; biology
590 Animals (Zoology)
Scopus Subject Areas:Life Sciences > Ecology, Evolution, Behavior and Systematics
Language:English
Date:25 June 2019
Deposited On:06 Apr 2020 06:05
Last Modified:22 Apr 2020 23:22
Publisher:BioMed Central
ISSN:2051-3933
OA Status:Gold
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1186/s40462-019-0167-3

Download

Gold Open Access

Download PDF  'Modelling animal movement as Brownian bridges with covariates'.
Preview
Content: Published Version
Language: English
Filetype: PDF
Size: 2MB
View at publisher
Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)