Estimating where and how animals travel: an optimal framework for path reconstruction from autocorrelated tracking data. Issue 3 (March 2016)

• Record Type: Journal Article
• Authors: Fleming, C. H.; Fagan, W. F.; Mueller, T.; Olson, K. A.; Leimgruber, P.; Calabrese, J. M.
• Is Part Of: Ecology. Volume 97:Issue 3(2016)
• Page Start: 576
• Page End: 582
• Publication Date: 2016-03
• Subjects: autocorrelation -- Brownian bridge -- CRAWL -- Krige -- Mongolian gazelle -- Procapra gutturosa -- telemetry error -- tracking data -- tracking data gaps -- utilization distribution; Ecology -- Periodicals; Ecology -- Periodicals; Écologie -- Périodiques; Ecologie; Écologie; Écologie animale; Écologie végétale; Ecology; Periodicals; 577.05
• DOI: 10.1890/15-1607.1
• Languages: English
• ISSNs: 0012-9658

Abstract

Abstract: An animal's trajectory is a fundamental object of interest in movement ecology, as it directly informs a range of topics from resource selection to energy expenditure and behavioral states. Optimally inferring the mostly unobserved movement path and its dynamics from a limited sample of telemetry observations is a key unsolved problem, however. The field of geostatistics has focused significant attention on a mathematically analogous problem that has a statistically optimal solution coined after its inventor, Krige . Kriging revolutionized geostatistics and is now the gold standard for interpolating between a limited number of autocorrelated spatial point observations. Here we translate Kriging for use with animal movement data. Our Kriging formalism encompasses previous methods to estimate animal's trajectories—the Brownian bridge and continuous‐time correlated random walk library—as special cases, informs users as to when these previous methods are appropriate, and provides a more general method when they are not. We demonstrate the capabilities of Kriging on a case study with Mongolian gazelles where, compared to the Brownian bridge, Kriging with a more optimal model was 10% more precise in interpolating locations and 500% more precise in estimating occurrence areas.