Title |
Bayesian Estimation of Animal Movement from Archival and Satellite Tags
|
---|---|
Published in |
PLOS ONE, October 2009
|
DOI | 10.1371/journal.pone.0007324 |
Pubmed ID | |
Authors |
Michael D. Sumner, Simon J. Wotherspoon, Mark A. Hindell |
Abstract |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 1 | 100% |
Demographic breakdown
Type | Count | As % |
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Scientists | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 4 | 1% |
Australia | 3 | 1% |
Italy | 2 | <1% |
Spain | 2 | <1% |
France | 1 | <1% |
Netherlands | 1 | <1% |
Brazil | 1 | <1% |
Portugal | 1 | <1% |
Germany | 1 | <1% |
Other | 1 | <1% |
Unknown | 268 | 94% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 79 | 28% |
Student > Ph. D. Student | 76 | 27% |
Student > Master | 55 | 19% |
Student > Bachelor | 17 | 6% |
Other | 10 | 4% |
Other | 27 | 9% |
Unknown | 21 | 7% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 167 | 59% |
Environmental Science | 48 | 17% |
Earth and Planetary Sciences | 11 | 4% |
Mathematics | 6 | 2% |
Computer Science | 6 | 2% |
Other | 20 | 7% |
Unknown | 27 | 9% |