Interplay of structured and random interactions in complex ecosystems dynamics.

Area of Science:

• Ecology
• Theoretical Ecology
• Network Science

Background:

• Minimal ecosystem models often assume random species interactions.
• Ecological networks frequently exhibit structures like hierarchies and functional groups.
• Existing models may not fully capture real-world ecosystem complexity.

Purpose of the Study:

• To investigate how community-level network structures alter predictions from random interaction models.
• To understand the interplay between randomness and structure in ecological networks.
• To assess the impact of network structure on macroscopic community observables.

Main Methods:

• Utilized a Lotka-Volterra model incorporating both structured and random pairwise species interactions.
• Studied macroscopic community-level observables, including abundance distributions and dynamical regimes.
• Analyzed the combined effects of randomness and network structure on ecosystem patterns.

Main Results:

• Contributions from randomness and structure to community patterns are largely independent.
• The interplay between structure and randomness has non-trivial consequences, particularly in out-of-equilibrium conditions.
• Static patterns (species presence, abundance) are less robust to network structure than dynamical regimes.

Conclusions:

• The importance of interaction structure depends on the specific ecological pattern being studied.
• Random interaction models may provide less robust predictions for static community properties compared to dynamical behaviors.
• Incorporating network structure is essential for accurate ecological modeling, especially for understanding ecosystem stability and dynamics.