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Marine Biology

, Volume 159, Issue 2, pp 373–387 | Cite as

GPS and time-depth loggers reveal underwater foraging plasticity in a flying diver, the Cape Cormorant

  • Timothée R. Cook
  • Maike Hamann
  • Lorien Pichegru
  • Francesco Bonadonna
  • David Grémillet
  • Peter G. Ryan
Original Paper

Abstract

Knowledge on how divers exploit the water column vertically in relation to water depth is crucial to our understanding of their ecology and to their subsequent conservation. However, information is still lacking for the smaller-bodied species, due mostly to size constraints of data-loggers. Here, we report the diving behaviour of a flying diving seabird, the Cape Cormorant Phalacrocorax capensis, weighing 1.0–1.4 kg. Results were obtained by simultaneously deploying small, high resolution and high sampling frequency GPS and time-depth loggers on birds breeding on islands off Western South Africa (34°S, 18°E) in 2008. In all, dive category was assigned to all dives performed by 29 birds. Pelagic dives occurred almost as frequently as benthic dives. Pelagic dives were shallow (mean: 5 m) and took place over seafloors 5–100 m deep. Benthic dives were deeper, occurring on seafloors mainly 10–30 m deep. Dive shape was linked to dive category in only 60% of dives, while the descent rate, ascent rate and bottom duration/dive duration ratio of a dive best explained its dive category. This shows that only the concomitant use of tracking and depth tags can adequately classify diving strategies in a diver like the Cape Cormorant. Diet was mainly Cape Anchovy Engraulis encrasicolis, suggesting that birds probably displayed two contrasted strategies for capturing the same prey. Flexible foraging techniques represent an important key to survival inside the highly productive but heterogeneous Benguela upwelling ecosystem.

Keywords

Diving Behaviour Dive Depth Dive Duration Small Pelagic Fish Body Angle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

We thank South African National Parks and CapeNature for permission to work on Cape Cormorants and for logistical support on the islands. Marie Cochet provided invaluable help in data-capture. Colin Attwood kindly lent BlueChart Atlantic 7.0 and MapSource 6.5 and Coleen Moloney, Rita Covas and Tim Reid gave valuable advice on discriminant function analyses and generalised linear and additive mixed-effects models. Janet Coetzee and Carl Van der Lingen kindly shared their experience of the behaviour of pelagic fish in the Benguela upwelling ecosystem. We would like to thank several anonymous reviewers and Alasdair Houston for their constructive comments on earlier versions of the article. This study was supported by a NRF/DST Centre of Excellence fellowship.

Supplementary material

227_2011_1815_MOESM1_ESM.doc (821 kb)
Supplementary material 1 (DOC 798 kb)

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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Timothée R. Cook
    • 1
  • Maike Hamann
    • 1
  • Lorien Pichegru
    • 1
  • Francesco Bonadonna
    • 2
  • David Grémillet
    • 2
  • Peter G. Ryan
    • 1
  1. 1.Percy FitzPatrick Institute of African Ornithology, DST/NRF Centre of ExcellenceUniversity of Cape TownCape TownSouth Africa
  2. 2.Centre d’Écologie Fonctionnelle et Évolutive, UMR 5175MontpellierFrance

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