Abstract
BackgroundIn 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.ResultsOur 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.ConclusionWe 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.Electronic supplementary materialThe online version of this article (doi:10.1186/2051-3933-2-5) contains supplementary material, which is available to authorized users.
Highlights
In recent years high resolution animal tracking data has become the standard in movement ecology
The availability of global positioning system (GPS) and affordable satellite telemetry has revolutionised the study of animal movement, allowing users to estimate the location of individuals at high spatial and temporal resolution
Brownian bridges connect two consecutive locations by conditional Brownian random walks that start at a given location and end at the following location with a duration equal to the observed time lag between the two locations
Summary
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. Brownian bridge movement models (BBMM) estimate space use intensity by stochastically modelling the movement of animals between any two consecutive locations [1,2]. The BBMM has been extended to account for changes in the movement behaviour of animals (dynamic Brownian Bridge movement models: dBBMM [4]) by allowing the diffusion parameter of the Brownian motion to change according to changes in the behaviour of the animal along its trajectory [5,6]
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