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Emergent copepod communities in an adaptive trait-structured model

Author

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  • Record, N.R.
  • Pershing, A.J.
  • Maps, F.

Abstract

Population dynamics for the most dominant copepod species have to some degree a mechanistic underpinning grounded in theory. However, important ecosystem shifts involve whole communities of species. Algorithms adopted from evolutionary computation provide one avenue for understanding community-level properties. We developed a pelagic copepod community model based on ecological tradeoffs in trait space, with a focus on development and growth rates, which determine fundamental properties such as size and generation length. The model is generalized to represent a broad range of possible copepod taxa. We used this framework in an adaptive-computing context to examine the different communities that assemble under different temperature and food regimes across a latitudinal gradient. Emergent communities resembled observed communities in structure and biodiversity, and showed life history strategies with clear analogs to real species.

Suggested Citation

  • Record, N.R. & Pershing, A.J. & Maps, F., 2013. "Emergent copepod communities in an adaptive trait-structured model," Ecological Modelling, Elsevier, vol. 260(C), pages 11-24.
    • Handle: RePEc:eee:ecomod:v:260:y:2013:i:c:p:11-24
    DOI: 10.1016/j.ecolmodel.2013.03.018
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    References listed on IDEAS

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    1. Kishi, Michio J. & Kashiwai, Makoto & Ware, Daniel M. & Megrey, Bernard A. & Eslinger, David L. & Werner, Francisco E. & Noguchi-Aita, Maki & Azumaya, Tomonori & Fujii, Masahiko & Hashimoto, Shinji & , 2007. "NEMURO—a lower trophic level model for the North Pacific marine ecosystem," Ecological Modelling, Elsevier, vol. 202(1), pages 12-25.
    2. M. D. Ohman & H.-J. Hirche, 2001. "Density-dependent mortality in an oceanic copepod population," Nature, Nature, vol. 412(6847), pages 638-641, August.
    3. Banas, Neil S., 2011. "Adding complex trophic interactions to a size-spectral plankton model: Emergent diversity patterns and limits on predictability," Ecological Modelling, Elsevier, vol. 222(15), pages 2663-2675.
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