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Objectively identifying landmark use and predicting flight trajectories of the homing pigeon using Gaussian processes

Overview of attention for article published in Journal of The Royal Society Interface, July 2010
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Title
Objectively identifying landmark use and predicting flight trajectories of the homing pigeon using Gaussian processes
Published in
Journal of The Royal Society Interface, July 2010
DOI 10.1098/rsif.2010.0301
Pubmed ID
Authors

Richard Mann, Robin Freeman, Michael Osborne, Roman Garnett, Chris Armstrong, Jessica Meade, Dora Biro, Tim Guilford, Stephen Roberts

Abstract

Pigeons home along idiosyncratic habitual routes from familiar locations. It has been suggested that memorized visual landmarks underpin this route learning. However, the inability to experimentally alter the landscape on large scales has hindered the discovery of the particular features to which birds attend. Here, we present a method for objectively classifying the most informative regions of animal paths. We apply this method to flight trajectories from homing pigeons to identify probable locations of salient visual landmarks. We construct and apply a Gaussian process model of flight trajectory generation for pigeons trained to home from specific release sites. The model shows increasing predictive power as the birds become familiar with the sites, mirroring the animal's learning process. We subsequently find that the most informative elements of the flight trajectories coincide with landscape features that have previously been suggested as important components of the homing task.

Mendeley readers

The data shown below were compiled from readership statistics for 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 6 10%
Spain 3 5%
Israel 1 2%
Italy 1 2%
India 1 2%
United States 1 2%
Unknown 48 79%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 23%
Researcher 14 23%
Student > Bachelor 9 15%
Unspecified 6 10%
Student > Master 4 7%
Other 11 18%
Unknown 3 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 34 56%
Unspecified 6 10%
Physics and Astronomy 4 7%
Engineering 3 5%
Computer Science 3 5%
Other 8 13%
Unknown 3 5%