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Variance in ecological consumer-resource interactions.

L Benedetti-Cecchi1

  • 1Dipartimento di Scienze dell'Uomo e dell'Ambiente, Pisa, Italy. bencecc@discat.unipi.it

Nature
|October 3, 2000
PubMed
Summary

Food-web models can better predict consumer-resource dynamics by incorporating variance in trophic interactions. This approach explains why both strong and weak consumer effects can increase resource variability in ecosystems.

Area of Science:

  • Ecology
  • Theoretical Ecology
  • Ecosystem Dynamics

Background:

  • Traditional food-web models predict consumer effects on species abundance variability.
  • Existing models suggest strong consumer effects increase variability, while weak effects dampen it.
  • However, empirical data show opposite patterns in natural ecosystems.

Purpose of the Study:

  • To investigate how variance in trophic interactions influences spatial resource distribution.
  • To develop a more robust food-web model that accounts for interaction variance.
  • To reconcile model predictions with empirical observations of consumer effects.

Main Methods:

  • Developed a food-web model incorporating the mean and variance of trophic interaction effect sizes.
  • Conducted simulations to assess the impact of interaction variance on resource population spatial variance.

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  • Validated model predictions against field experiments and published consumer-resource data.

    • Main Results:

    • Spatial variance in resource populations is sensitive to both the mean and variance of trophic interactions.
    • Both strong and weak direct consumer effects can increase resource spatial variability.
    • Only trophic interactions with a large mean effect size can reduce resource variation.

    Conclusions:

    • Food-web models need to incorporate variance in trophic interactions for accurate predictions.
    • This enhanced modeling approach explains diverse empirical findings on consumer impacts.
    • The model demonstrates robustness across various environmental conditions and life histories.