Bayesian estimation of animal movement from archival and satellite tags

• Published in: PloS one Vol. 4; no. 10; p. e7324
• Main Authors: Sumner, Michael D, Wotherspoon, Simon J, Hindell, Mark A
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 13.10.2009; Public Library of Science (PLoS)
• Subjects: Algorithms; Animal ecology; Animal Migration; Animals; Arctocephalus australis; Bayes Theorem; Bayesian analysis; Behavior, Animal; Biogeography; Decision making; Ecology; Ecology/Behavioral Ecology; Ecology/Marine and Freshwater Ecology; Ecology/Spatial and Landscape Ecology; Environmental Monitoring; Estimates; Foraging behavior; Geographic Information Systems; Leptonychotes; Light levels; Makaira indica; Marine ecology; Mathematical models; Microsatellite Repeats; Models, Statistical; Monte Carlo Method; Normal distribution; Oceanography; Remote sensing; Satellite Communications; Satellite tracking; Satellites; Species Specificity; Statistical mechanics; Studies; Tagging; Tags; Telemetry - instrumentation; Uncertainty; Australia; Tasmania Australia
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0007324

The reliable estimation of animal location, and its associated error is fundamental to animal ecology. There are many existing techniques for handling location error, but these are often ad hoc or are used in isolation from each other. In this study we present a Bayesian framework for determining location that uses all the data available, is flexible to all tagging techniques, and provides location estimates with built-in measures of uncertainty. Bayesian methods allow the contributions of multiple data sources to be decomposed into manageable components. We illustrate with two examples for two different location methods: satellite tracking and light level geo-location. We show that many of the problems with uncertainty involved are reduced and quantified by our approach. This approach can use any available information, such as existing knowledge of the animal's potential range, light levels or direct location estimates, auxiliary data, and movement models. The approach provides a substantial contribution to the handling uncertainty in archival tag and satellite tracking data using readily available tools.