Causal links indicating ecosystem functioning in food webs.

Area of Science:

• Ecology
• Network Analysis
• Ecosystem Dynamics

Background:

• Food web complexity obscures regulatory effects.
• Identifying top-down or bottom-up control is challenging.
• Topological importance (TI) metrics can incorporate indirect interactions.

Purpose of the Study:

• To apply interaction asymmetry analysis for examining causality in food webs.
• To identify critical interactions using topological importance (TI) metrics.
• To evaluate the method's performance against existing network metrics.

Main Methods:

• Applied interaction asymmetry analysis to 34 food web models.
• Constructed asymmetry graphs to create directed networks.
• Compared asymmetry metrics with widely used network metrics.

Main Results:

• Ecosystems with higher total biomass showed stronger bottom-up causal links.
• Greater consumer diversity was linked to stronger bottom-up effects.
• Asymmetry-based metrics effectively identified causal interactions.

Conclusions:

• Interaction asymmetry analysis provides robust quantitative indicators of causality.
• This method offers a powerful tool for assessing ecosystem functioning and health.
• The approach simplifies the analysis of complex food web dynamics.