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We identified significant compartments in complex food webs using a social network analysis method. This rigorous approach reveals how compartmentalized interactions enhance ecosystem stability.

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Area of Science:

  • Ecology
  • Network Science
  • Theoretical Biology

Background:

  • Compartments, subgroups within food webs with strong internal and weak external interactions, are theorized to increase network stability.
  • Detecting compartments in empirical food webs has been challenging due to methodological limitations and incompatible approaches.

Purpose of the Study:

  • To apply a social networking science method to identify significant compartments in empirical food webs.
  • To assess the influence of food web resolution and interaction weighting on compartment detection.
  • To provide a rigorous and compatible method for compartment identification and analysis.

Main Methods:

  • Utilized a compartment detection method from social networking science applied to empirical food webs.
  • Maximized an explicit function to rigorously identify non-overlapping compartments, assign membership, and test statistical significance.
  • Analyzed the impact of food web resolution and interaction weights on compartment detection.

Main Results:

  • Successfully identified significant compartments in three out of five complex, empirical food webs.
  • Demonstrated that food web resolution, including weighted interactions, influences compartment detection.
  • The method aligns with the definition of compartments by identifying boundaries of concentrated interactions.

Conclusions:

  • The social network analysis method provides a rigorous and effective means to detect compartments in empirical food webs.
  • Compartmentalization plays a crucial role in structuring food webs and potentially enhancing ecosystem stability.
  • Further research can explore disturbance scenarios to test hypotheses on how compartmentalized interactions increase food web stability.