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A dynamic Brownian bridge movement model to estimate utilization distributions for heterogeneous animal movement
Author(s) -
Kranstauber Bart,
Kays Roland,
LaPoint Scott D.,
Wikelski Martin,
Safi Kamran
Publication year - 2012
Publication title -
journal of animal ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.134
H-Index - 157
eISSN - 1365-2656
pISSN - 0021-8790
DOI - 10.1111/j.1365-2656.2012.01955.x
Subject(s) - brownian bridge , autocorrelation , movement (music) , computer science , sampling (signal processing) , path (computing) , distribution (mathematics) , measure (data warehouse) , brownian motion , econometrics , statistics , data mining , mathematics , computer vision , mathematical analysis , philosophy , filter (signal processing) , programming language , aesthetics
Summary 1.  The recently developed Brownian bridge movement model (BBMM) has advantages over traditional methods because it quantifies the utilization distribution of an animal based on its movement path rather than individual points and accounts for temporal autocorrelation and high data volumes. However, the BBMM assumes unrealistic homogeneous movement behaviour across all data. 2.  Accurate quantification of the utilization distribution is important for identifying the way animals use the landscape. 3.  We improve the BBMM by allowing for changes in behaviour, using likelihood statistics to determine change points along the animal's movement path. 4.  This novel extension, outperforms the current BBMM as indicated by simulations and examples of a territorial mammal and a migratory bird. The unique ability of our model to work with tracks that are not sampled regularly is especially important for GPS tags that have frequent failed fixes or dynamic sampling schedules. Moreover, our model extension provides a useful one‐dimensional measure of behavioural change along animal tracks. 5.  This new method provides a more accurate utilization distribution that better describes the space use of realistic, behaviourally heterogeneous tracks.

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