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Permanence does not predict the commonly measured food web structural attributes.

Nadiah P Kristensen1

  • 1Department of Mathematics and Department of Zoology and Entomology, University of Queensland, St. Lucia, Queensland 4072, Australia. nadiah.kristensen@csiro.au

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Food web assembly algorithms are useful for ecological studies. However, the permanence constraint, a measure of stability, does not improve predictive accuracy for these models.

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Area of Science:

  • Ecology
  • Theoretical Ecology
  • Mathematical Biology

Background:

  • Food web assembly algorithms are valuable tools for studying ecological dynamics like succession and invasibility.
  • Permanence, a stability constraint ensuring positive and finite species densities, has been proposed for these algorithms.
  • The effectiveness of the permanence constraint in improving ecological model predictions requires empirical validation.

Purpose of the Study:

  • To evaluate the impact of the permanence constraint on the predictive power of food web assembly algorithms.
  • To compare the performance of algorithms with and without the permanence constraint against real and model food webs.
  • To investigate how dynamical constraints influence food web structure and properties.

Main Methods:

  • Comparison of three food web assembly algorithms: one with permanence and feasibility constraints, one with feasibility alone, and one unconstrained.
  • Analysis of real-world food webs and existing model webs from scientific literature.
  • Evaluation of web properties such as connectance, trophic levels, and species composition.

Main Results:

  • The addition of the permanence constraint did not enhance the predictive accuracy of the assembly algorithm.
  • The permanence constraint primarily increased the efficiency of species selection within the simulated food webs.
  • Dynamically constrained webs exhibited lower connectance and less distinct trophic levels compared to real webs, attributed to the omission of species' physiology.
  • Despite theoretical links between omnivory/cycling and reduced permanence, food web assembly processes can overcome this limitation.

Conclusions:

  • The permanence constraint offers limited benefits for improving the predictive capabilities of food web assembly algorithms.
  • Dynamical constraints in assembly models, particularly when omitting physiological details, can lead to unrealistic food web structures.
  • Future research should address the challenges in testing system-level ecological hypotheses and the integration of species' physiological traits into assembly models.