Article

Competition-induced starvation drives large-scale population cycles in Antarctic krill

  • Nature Ecology & Evolution 1, Article number: 0177 (2017)
  • doi:10.1038/s41559-017-0177
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Abstract

Antarctic krill (Euphausia superba)—one of the most abundant animal species on Earth—exhibits a five to six year population cycle, with oscillations in biomass exceeding one order of magnitude. Previous studies have postulated that the krill cycle is induced by periodic climatological factors, but these postulated drivers neither show consistent agreement, nor are they supported by quantitative models. Here, using data analysis complemented with modelling of krill ontogeny and population dynamics, we identify intraspecific competition for food as the main driver of the krill cycle, while external climatological factors possibly modulate its phase and synchronization over large scales. Our model indicates that the cycle amplitude increases with reduction of krill loss rates. Thus, a decline of apex predators is likely to increase the oscillation amplitude, potentially destabilizing the marine food web, with drastic consequences for the entire Antarctic ecosystem.

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Acknowledgements

We thank A. Atkinson, L. B. Quetin and R. M. Ross for useful comments on the manuscript; A.B.R. acknowledges support from the Helmholtz Virtual Institute PolarTime (VH-VI-500); A.M.d.R. is supported by funding from the European Research Council under the European Unions Seventh Framework Programme (FP/2007–2013)/ERC Grant Agreement No. 322814. This work contributes to the PACES (Polar Regions and Coasts in a changing Earth System) program (Topic 1, WP 5) of the Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research. Data on krill abundance and biomass were retrieved from the Palmer LTER data archive (http://pal.lternet.edu/data) and data on size distributions were provided by L. B. Quetin and R. M. Ross. The Palmer LTER research was supported by the National Science Foundation, Office of Polar Programs, under Award Nos. OPP-9011927, OPP-9632763, OPP-0217282, ANT-1010688 and PLR-1440435, the Regents of the University of California, the University of California at Santa Barbara, and the Marine Science Institute, UCSB.

Author information

Affiliations

  1. Institute for Chemistry and Biology of the Marine Environment, University of Oldenburg, 26111 Oldenburg, Germany.

    • Alexey B. Ryabov
    • , Bettina Meyer
    •  & Bernd Blasius
  2. Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, 1090 GE Amsterdam, the Netherlands.

    • André M. de Roos
  3. Alfred Wegener Institute for Polar and Marine Research, Scientific Division Polar Biological Oceanography, 27570 Bremerhaven, Germany.

    • Bettina Meyer
  4. Helmholtz Institute for Functional Marine Biodiversity at the University of Oldenburg, 26129 Oldenburg, Germany.

    • Bettina Meyer
    •  & Bernd Blasius
  5. Australian Antarctic Division, Kingston, Tasmania, 7050, Australia.

    • So Kawaguchi
  6. Antarctic Climate and Ecosystems Cooperative Research Centre, Hobart, Tasmania, 7000, Australia.

    • So Kawaguchi

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Contributions

All authors designed the research; A.B.R., A.M.d.R., B.B. developed the model; A.B.R., S.K., B.M. parametrized the model, A.B.R. performed computer experiments and data analysis; A.B.R. and B.B. with contributions from other authors wrote the paper.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Alexey B. Ryabov.

Supplementary information

PDF files

  1. 1.

    Supplementary Information

    Supplementary Figures 1–12, Supplementary Tables 1–5

Zip files

  1. 1.

    Supplementary Code

    C++ source code of the ontogenetic krill model. The archive includes computer model C++ code and additional files, which specify the initial conditions, model parameters, and settings tailoring the model output. All files should be copied into the same folder and compiled with gcc or a similar C++ compiler. The parameters of krill and resource dynamical model are specified in params.ini, the parameters of the simulation output, initial conditions, etc. are specified in the files named “krill.*”. To solve the differential equations, we use the Escalator Boxcar Train approach, see https://staff.fnwi.uva.nl/a.m.deroos/EBT/ for a detailed description of the solver setup. Any additional information can be requested from the corresponding author.