Modelling animal movement as Brownian bridges with covariates

Abstract Background 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 a...

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Main Author: Bart Kranstauber
Format: Article
Language:English
Published: BMC 2019-06-01
Series:Movement Ecology
Subjects:
Online Access:http://link.springer.com/article/10.1186/s40462-019-0167-3
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spelling doaj-010444c382674b18b2a0986aac1d21a02020-11-25T03:52:40ZengBMCMovement Ecology2051-39332019-06-017111010.1186/s40462-019-0167-3Modelling animal movement as Brownian bridges with covariatesBart Kranstauber0Department of Evolutionary Biology and Environmental Studies, University of ZurichAbstract Background 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.http://link.springer.com/article/10.1186/s40462-019-0167-3Animal trackingBrownian bridge covariates modelBrownian bridge movement modelMeerkatsMovement ecologySuricata suricatta
collection DOAJ
language English
format Article
sources DOAJ
author Bart Kranstauber
spellingShingle Bart Kranstauber
Modelling animal movement as Brownian bridges with covariates
Movement Ecology
Animal tracking
Brownian bridge covariates model
Brownian bridge movement model
Meerkats
Movement ecology
Suricata suricatta
author_facet Bart Kranstauber
author_sort Bart Kranstauber
title Modelling animal movement as Brownian bridges with covariates
title_short Modelling animal movement as Brownian bridges with covariates
title_full Modelling animal movement as Brownian bridges with covariates
title_fullStr Modelling animal movement as Brownian bridges with covariates
title_full_unstemmed Modelling animal movement as Brownian bridges with covariates
title_sort modelling animal movement as brownian bridges with covariates
publisher BMC
series Movement Ecology
issn 2051-3933
publishDate 2019-06-01
description Abstract Background 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.
topic Animal tracking
Brownian bridge covariates model
Brownian bridge movement model
Meerkats
Movement ecology
Suricata suricatta
url http://link.springer.com/article/10.1186/s40462-019-0167-3
work_keys_str_mv AT bartkranstauber modellinganimalmovementasbrownianbridgeswithcovariates
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