A functional model for characterizing long-distance movement behaviour

March 1, 2016

Advancements in wildlife telemetry techniques have made it possible to collect large data sets of highly accurate animal locations at a fine temporal resolution. These data sets have prompted the development of a number of statistical methodologies for modelling animal movement.
Telemetry data sets are often collected for purposes other than fine-scale movement analysis. These data sets may differ substantially from those that are collected with technologies suitable for fine-scale movement modelling and may consist of locations that are irregular in time, are temporally coarse or have large measurement error. These data sets are time-consuming and costly to collect but may still provide valuable information about movement behaviour.
We developed a Bayesian movement model that accounts for error from multiple data sources as well as movement behaviour at different temporal scales. The Bayesian framework allows us to calculate derived quantities that describe temporally varying movement behaviour, such as residence time, speed and persistence in direction. The model is flexible, easy to implement and computationally efficient.
We apply this model to data from Colorado Canada lynx (Lynx canadensis) and use derived quantities to identify changes in movement behaviour.

Publication Year	2016
Title	A functional model for characterizing long-distance movement behaviour
DOI	10.1111/2041-210X.12465
Authors	Frances E. Buderman, Mevin Hooten, Jacob S. Ivan, Tanya M. Shenk
Publication Type	Article
Publication Subtype	Journal Article
Series Title	Methods in Ecology and Evolution
Index ID	70192614
Record Source	USGS Publications Warehouse
USGS Organization	Coop Res Unit Seattle

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