Analyzing animal movements using Brownian bridges

By studying animal movements, researchers can gain insight into many of the ecological characteristics and processes important for understanding population-level dynamics. We developed a Brownian bridge movement model (BBMM) for estimating the expected movement path of an animal, using discrete loca...

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Bibliographic Details
Published in:Ecology (Durham) 2007-09, Vol.88 (9), p.2354-2363
Main Authors: Horne, J.S, Garton, E.O, Krone, S.M, Lewis, J.S
Format: Article
Language:English
Subjects:
Analysis
Animal and plant ecology
Animal behavior
animal ecology
Animal Migration
Animal migration behavior
Animal, plant and microbial ecology
Animals
Animals, Wild
Biological and medical sciences
Brownian bridge
Brownian motion
dispersal behavior
Ecological modeling
Ecosystem
equations
Fundamental and applied biological sciences. Psychology
General aspects
home range
Likelihood Functions
Maximum likelihood estimation
migration
migration behavior
Models, Biological
Musical intervals
Perceptual localization
Population Density
population distribution
Population Dynamics
probability analysis
random walk
resource selection
road crossings
Spatial Behavior
Statistical variance
Stochastic models
stochastic process
Stochastic Processes
utilization distribution
Wildlife ecology
Zoology
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Summary:By studying animal movements, researchers can gain insight into many of the ecological characteristics and processes important for understanding population-level dynamics. We developed a Brownian bridge movement model (BBMM) for estimating the expected movement path of an animal, using discrete location data obtained at relatively short time intervals. The BBMM is based on the properties of a conditional random walk between successive pairs of locations, dependent on the time between locations, the distance between locations, and the Brownian motion variance that is related to the animal's mobility. We describe two critical developments that enable widespread use of the BBMM, including a derivation of the model when location data are measured with error and a maximum likelihood approach for estimating the Brownian motion variance. After the BBMM is fitted to location data, an estimate of the animal's probability of occurrence can be generated for an area during the time of observation. To illustrate potential applications, we provide three examples: estimating animal home ranges, estimating animal migration routes, and evaluating the influence of fine-scale resource selection on animal movement patterns.
ISSN:0012-9658
1939-9170
DOI:10.1890/06-0957.1