Estimating Fine-Scale Movement Patterns of Black Bear using GPS Telemetry
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Authors
Callahan, Catherine
Date
2010-05-27
Type
Thesis
Language
en
Keywords
Alternative Title
Abstract
Animal behavior is guided by needs and constraints in response to surroundings
and is measured by the individual’s location. Our goal was to explore the use of fine-
scale movement data collected with GPS telemetry. The objectives were to evaluate low-
cost Global Positioning System (GPS) telemetry collars and programming required to
obtain fine-scale movement data of American black bear (Ursus americanus). The data
provided insight to landscape factors influencing access by black bears to human-related
food sources. Technology costs have been reduced substantially in the past few years,
making short-term deployments of GPS telemetry collars a cost effective method of
collecting fine-scale spatial and temporal data. The study was conducted during spring
and summer 2007-2009 with planned collar deployments at four weeks and GPS
collection at 10-minute intervals to distinguish bear activity within the community. A
Brownian Bridge Movement Model (Horne et al. 2007) was used to determine
probability of occurrence distributions based on GPS locations with measured error.
Fixed kernel density functions show spatial clustering and define areas of animal activity.
Brownian Bridge probability densities show spatial pattern of animal movement. The
Brownian Bridge probability surfaces were used to select values of co-occurring
landscape features and analyzed using a classification tree method to determine which
landscape features bears selected when moving through human communities. Fine-scale
movement data could also be used to relate continuous probability of occurrence to
landscape features, using the Marzluff et al. (2004) Resource Utilization Function
approach to remove spatial autocorrelation. Identifying fine-scale habitat selection or use
of travel corridors will require short GPS sampling intervals to determine which
landscape factors influence movement.