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Movement-Based Estimation and Visualization of Space Use in 3D for Wildlife Ecology and Conservation

Overview of attention for article published in PLOS ONE, July 2014
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9 news outlets
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3 X users

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Title
Movement-Based Estimation and Visualization of Space Use in 3D for Wildlife Ecology and Conservation
Published in
PLOS ONE, July 2014
DOI 10.1371/journal.pone.0101205
Pubmed ID
Authors

Jeff A. Tracey, James Sheppard, Jun Zhu, Fuwen Wei, Ronald R. Swaisgood, Robert N. Fisher

Abstract

Advances in digital biotelemetry technologies are enabling the collection of bigger and more accurate data on the movements of free-ranging wildlife in space and time. Although many biotelemetry devices record 3D location data with x, y, and z coordinates from tracked animals, the third z coordinate is typically not integrated into studies of animal spatial use. Disregarding the vertical component may seriously limit understanding of animal habitat use and niche separation. We present novel movement-based kernel density estimators and computer visualization tools for generating and exploring 3D home ranges based on location data. We use case studies of three wildlife species--giant panda, dugong, and California condor--to demonstrate the ecological insights and conservation management benefits provided by 3D home range estimation and visualization for terrestrial, aquatic, and avian wildlife research.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 225 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 3%
Brazil 3 1%
Austria 1 <1%
Australia 1 <1%
Colombia 1 <1%
Belize 1 <1%
Portugal 1 <1%
Thailand 1 <1%
Canada 1 <1%
Other 0 0%
Unknown 209 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 48 21%
Student > Master 42 19%
Student > Ph. D. Student 41 18%
Student > Bachelor 19 8%
Student > Doctoral Student 14 6%
Other 25 11%
Unknown 36 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 109 48%
Environmental Science 48 21%
Earth and Planetary Sciences 9 4%
Social Sciences 5 2%
Veterinary Science and Veterinary Medicine 3 1%
Other 8 4%
Unknown 43 19%