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The Allometry of Prey Preferences

Author

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  • Gregor Kalinkat
  • Björn Christian Rall
  • Olivera Vucic-Pestic
  • Ulrich Brose

Abstract

The distribution of weak and strong non-linear feeding interactions (i.e., functional responses) across the links of complex food webs is critically important for their stability. While empirical advances have unravelled constraints on single-prey functional responses, their validity in the context of complex food webs where most predators have multiple prey remain uncertain. In this study, we present conceptual evidence for the invalidity of strictly density-dependent consumption as the null model in multi-prey experiments. Instead, we employ two-prey functional responses parameterised with allometric scaling relationships of the functional response parameters that were derived from a previous single-prey functional response study as novel null models. Our experiments included predators of different sizes from two taxonomical groups (wolf spiders and ground beetles) simultaneously preying on one small and one large prey species. We define compliance with the null model predictions (based on two independent single-prey functional responses) as passive preferences or passive switching, and deviations from the null model as active preferences or active switching. Our results indicate active and passive preferences for the larger prey by predators that are at least twice the size of the larger prey. Moreover, our approach revealed that active preferences increased significantly with the predator-prey body-mass ratio. Together with prior allometric scaling relationships of functional response parameters, this preference allometry may allow estimating the distribution of functional response parameters across the myriads of interactions in natural ecosystems.

Suggested Citation

  • Gregor Kalinkat & Björn Christian Rall & Olivera Vucic-Pestic & Ulrich Brose, 2011. "The Allometry of Prey Preferences," PLOS ONE, Public Library of Science, vol. 6(10), pages 1-11, October.
  • Handle: RePEc:plo:pone00:0025937
    DOI: 10.1371/journal.pone.0025937
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    1. Soetaert, Karline & Petzoldt, Thomas & Setzer, R. Woodrow, 2010. "Solving Differential Equations in R: Package deSolve," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i09).
    2. Chris Carbone & Georgina M. Mace & S. Craig Roberts & David W. Macdonald, 1999. "Energetic constraints on the diet of terrestrial carnivores," Nature, Nature, vol. 402(6759), pages 286-288, November.
    3. José M. Montoya & Stuart L. Pimm & Ricard V. Solé, 2006. "Ecological networks and their fragility," Nature, Nature, vol. 442(7100), pages 259-264, July.
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