Testing time-geographic density estimation for home range analysis using an agent-based model of animal movement
Authored by Joni Downs, Mark Horner, David Lamb, Rebecca W Loraamm, James Anderson, Brittany Wood
Date Published: 2018
DOI: 10.1080/13658816.2017.1421764
Sponsors:
United States National Science Foundation (NSF)
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Abstract
Time-geographic density estimation (TGDE) is a method of movement
pattern analysis that generates a continuous intensity surface from a
set of tracking data. TGDE has recently been proposed as a method of
animal home range estimation, where the goal is to delineate the spatial
extents that an animal occupies. This paper tests TGDE's effectiveness
as a home range estimator using simulated movement data. First, an
agent-based model is used to simulate tracking data under 16 movement
scenarios representing a variety of animal life history traits (habitat
preferences, homing behaviour, mobility) and habitat configurations
(levels of habitat fragmentation). Second, the accuracy of TGDE is
evaluated for four temporal sampling frequencies using three adaptive
velocity parameters for 30 sample data sets from each scenario. Third,
TGDE accuracy is compared to two other common home range estimation
methods, kernel density estimation (KDE) and characteristic hull
polygons (CHP). The results demonstrate that TGDE is the most effective
at estimating core areas, home ranges and total areas at high sampling
frequencies, while CHP performs better at low sampling frequencies. KDE
was ineffective across all scenarios explored.
Tags
Simulation
behavior
selection
patterns
Wildlife
Size
Tracking
Home range
Autocorrelation
Dynamic interaction
Time geography
Movement pattern analysis
Computational movement analysis
Squares cross-validation