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Bayesian estimation of animal movement from archival and satellite tags
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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.
|Journal or Publication Title:||PLOS One|
|Identification Number - DOI:||10.1371/journal.pone.0007324|
© 2009 Sumner et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
|Date Deposited:||19 Oct 2009 04:10|
|Last Modified:||18 Nov 2014 04:06|
|Item Statistics:||View statistics for this item|
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