Testing time-geographic density estimation for home range analysis using an agent-based model of animal movement

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.

KEYWORDS:

• Time geography
• home range
• simulation
• movement pattern analysis
• computational movement analysis

Acknowledgements

Portions of this research were funded by grants made to the authors from the National Science Foundation (NSF) [grant number BCS-1062947]. The contents of this article are the responsibility of the authors and do not reflect the views of the NSF.

Disclosure statement

No potential conflict of interest was reported by the author.

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Additional information

Funding

This work was supported by the National Science Foundation [1062924].