Polar Biology

, Volume 37, Issue 4, pp 507–517 | Cite as

A method for estimating colony sizes of Adélie penguins using remote sensing imagery

  • M. A. LaRueEmail author
  • H. J. Lynch
  • P. O. B. Lyver
  • K. Barton
  • D. G. Ainley
  • A. Pollard
  • W. R. Fraser
  • G. Ballard
Original Paper


Adélie penguins (Pygoscelis adeliae) are important predators of krill (Euphausia spp.) and Antarctic silverfish (Pleuragramma antarctica) during summer, are a key indicator of the status of the Southern Ocean ecosystem, and are therefore a focal species for the Committee for the Conservation of Antarctic Marine Living Resources (CCAMLR) Ecosystem Monitoring Program. The ability to monitor the population size of species potentially affected by Southern Ocean fisheries, i.e., the Adélie penguin, is critical for effective management of those resources. However, for several reasons, direct estimates of population size are not possible in many locations around Antarctica. In this study, we combine high-resolution (0.6 m) satellite imagery with spectral analysis in a supervised classification to estimate the sizes of Adélie penguin breeding colonies along Victoria Land in the Ross Sea and on the Antarctic Peninsula. Using satellite images paired with concurrent ground counts, we fit a generalized linear mixed model with Poisson errors to predict the abundance of breeding pairs as a function of the area of current-year guano staining identified in the satellite imagery. Guano-covered area proved to be an effective proxy for the number of penguins residing within. Our model provides a robust, quantitative mechanism for estimating the breeding population size of colonies captured in imagery and identifies terrain slope as a significant component influencing apparent nesting density. While our high-resolution satellite imagery technique was developed for the Adélie penguin, these principles are directly transferrable to other colonially nesting seabirds and other species that aggregate in fixed localities.


Adélie penguin Antarctica Generalized linear mixed models GIS High-resolution imagery Population estimation Supervised classification 



This research was funded by National Science Foundation (OPP-0217282, OPP-0823101, ANT-0739515, ANT-0944411, OPP-1109962, PLR-1255058) and New Zealand’s Ministry for Business, Innovation and Employment (C09X0510, C01X0505, C01X1001, CONT-21216-BKBN). Field logistics were provided by the US Antarctic Program and Antarctica New Zealand, and helicopter support was provided by PHI, Inc., and Helicopters NZ. We thank the team of counters over the years, but in particular Peter Wilson, Bruce Thomas, Brian Karl, Keven Drew, Caroline Thomson, Morgan Coleman, Quoyah Barr-Glintborg, and Mario Fichtner. The Polar Geospatial Center facilitated use of imagery for analysis, and we thank Claire Porter for assistance with image processing. We would like to thank the editor and 3 anonymous reviewers for their feedback and insight in previous drafts of this manuscript. Point Blue Conservation Science contribution #1945.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • M. A. LaRue
    • 1
    Email author
  • H. J. Lynch
    • 2
  • P. O. B. Lyver
    • 3
  • K. Barton
    • 4
  • D. G. Ainley
    • 5
  • A. Pollard
    • 6
  • W. R. Fraser
    • 7
  • G. Ballard
    • 6
  1. 1.Conservation Biology Graduate ProgramUniversity of MinnesotaSt. PaulUSA
  2. 2.Department of Ecology and EvolutionStony Brook UniversityStony BrookUSA
  3. 3.Landcare ResearchLincolnNew Zealand
  4. 4.BartonK SolutionsNelsonNew Zealand
  5. 5.HT Harvey and AssociatesLos GatosUSA
  6. 6.Point Blue Conservation SciencePetalumaUSA
  7. 7.Polar Oceans Research GroupSheridanUSA

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