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Application of information theory methods to food web reconstruction

Author

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  • Moniz, L.J.
  • Cooch, E.G.
  • Ellner, S.P.
  • Nichols, J.D.
  • Nichols, J.M.

Abstract

In this paper we use information theory techniques on time series of abundances to determine the topology of a food web. At the outset, the food web participants (two consumers, two resources) are known; in addition we know that each consumer prefers one of the resources over the other. However, we do not know which consumer prefers which resource, and if this preference is absolute (i.e., whether or not the consumer will consume the non-preferred resource). Although the consumers and resources are identified at the beginning of the experiment, we also provide evidence that the consumers are not resources for each other, and the resources do not consume each other. We do show that there is significant mutual information between resources; the model is seasonally forced and some shared information between resources is expected. Similarly, because the model is seasonally forced, we expect shared information between consumers as they respond to the forcing of the resources. The model that we consider does include noise, and in an effort to demonstrate that these methods may be of some use in other than model data, we show the efficacy of our methods with decreasing time series size; in this particular case we obtain reasonably clear results with a time series length of 400 points. This approaches ecological time series lengths from real systems.

Suggested Citation

  • Moniz, L.J. & Cooch, E.G. & Ellner, S.P. & Nichols, J.D. & Nichols, J.M., 2007. "Application of information theory methods to food web reconstruction," Ecological Modelling, Elsevier, vol. 208(2), pages 145-158.
    • Handle: RePEc:eee:ecomod:v:208:y:2007:i:2:p:145-158
    DOI: 10.1016/j.ecolmodel.2007.05.016
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    References listed on IDEAS

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    2. Simon N. Wood, 2004. "Stable and Efficient Multiple Smoothing Parameter Estimation for Generalized Additive Models," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 673-686, January.
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