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Trends Ecol Evol. 2014 Jul;29(7):417-28. doi: 10.1016/j.tree.2014.05.004. Epub 2014 Jun 5.

Automated image-based tracking and its application in ecology.

Author information

1
Systemic Conservation Biology, Department of Biology, Georg-August University Göttingen, Göttingen, Germany. Electronic address: adell@gwdg.de.
2
HasOffers Inc., 2220 Western Ave, Seattle, WA, USA.
3
Howard Hughes Medical Institute, Janelia Farm Research Campus, Ashburn, VA, USA.
4
Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.
5
Instituto Cajal, CSIC, Av. Doctor Arce, 37, Madrid, Spain.
6
Noldus Information Technology BV, Nieuwe Kanaal 5, 6709 PA Wageningen, The Netherlands.
7
Computation and Neural Systems Program, California Institute of Technology, Pasadena, CA, USA.
8
Research Institute of Molecular Pathology (IMP), Vienna, Austria.
9
Max Planck Institute for Ornithology, Radolfzell, Germany; Biology Department, University of Konstanz, Konstanz, Germany.
10
Systemic Conservation Biology, Department of Biology, Georg-August University Göttingen, Göttingen, Germany.

Abstract

The behavior of individuals determines the strength and outcome of ecological interactions, which drive population, community, and ecosystem organization. Bio-logging, such as telemetry and animal-borne imaging, provides essential individual viewpoints, tracks, and life histories, but requires capture of individuals and is often impractical to scale. Recent developments in automated image-based tracking offers opportunities to remotely quantify and understand individual behavior at scales and resolutions not previously possible, providing an essential supplement to other tracking methodologies in ecology. Automated image-based tracking should continue to advance the field of ecology by enabling better understanding of the linkages between individual and higher-level ecological processes, via high-throughput quantitative analysis of complex ecological patterns and processes across scales, including analysis of environmental drivers.

KEYWORDS:

automated image-based tracking; behavior; bio-logging; ecological interactions; tracking

PMID:
24908439
DOI:
10.1016/j.tree.2014.05.004
[Indexed for MEDLINE]

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