Bivariate Gaussian bridges: directional factorization of diffusion in Brownian bridge models

Movement Ecology

Frederic Bartumeus¹  Kamran Safi²  Bart Kranstauber² 

[1] , Centre for Ecological Research and Forestry Applications (CREAF), Barcelona, Spain;Department of Biology, University of Konstanz, Konstanz, Germany

关键词: Home range and space use modelling;    GPS;    Animal tracking;    Utilisation distribution;    Dynamic Brownian bridge movement model;    Dynamic Bivariate Gaussian bridge;   

Others  :  802447
DOI  :  10.1186/2051-3933-2-5

 received in 2013-11-18, accepted in 2014-02-05,  发布年份 2014

【 摘 要 】

Background

In recent years high resolution animal tracking data has become the standard in movement ecology. The Brownian Bridge Movement Model (BBMM) is a widely adopted approach to describe animal space use from such high resolution tracks. One of the underlying assumptions of the BBMM is isotropic diffusive motion between consecutive locations, i.e. invariant with respect to the direction.

Here we propose to relax this often unrealistic assumption by separating the Brownian motion variance into two directional components, one parallel and one orthogonal to the direction of the motion.

Results

Our new model, the Bivariate Gaussian bridge (BGB), tracks movement heterogeneity across time. Using the BGB and identifying directed and non-directed movement within a trajectory resulted in more accurate utilisation distributions compared to dynamic Brownian bridges, especially for trajectories with a non-isotropic diffusion, such as directed movement or Lévy like movements. We evaluated our model with simulated trajectories and observed tracks, demonstrating that the improvement of our model scales with the directional correlation of a correlated random walk.

Conclusion

We find that many of the animal trajectories do not adhere to the assumptions of the BBMM. The proposed model improves accuracy when describing the space use both in simulated correlated random walks as well as observed animal tracks. Our novel approach is implemented and available within the “move” package for R.

【 授权许可】

   
2014 Kranstauber et al.; licensee BioMed Central Ltd.

【 图 表 】

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