Enhancing microbial predator-prey detection with network and trait-based analyses.

Area of Science:

• Ecology
• Microbiology
• Bioinformatics

Background:

• Network analyses are widely used for microbial community studies but often infer correlations, not confirmed biotic interactions.
• The nature of interactions suggested by network analyses remains unclear and rarely experimentally verified.
• Hypothesis generation through network analysis requires robust validation methods.

Purpose of the Study:

• To evaluate the accuracy of network analysis in predicting predator-prey interactions.
• To combine network analysis with trait-based functions for improved interaction prediction.
• To experimentally validate inferred predator-prey relationships in polar microbial communities.

Main Methods:

• Cross-kingdom network analyses were performed using FlashWeave on microbial community data.
• Trait-based functions were applied to microorganisms to assess predator-prey suitability.
• Putative predator-prey interactions were experimentally investigated in polar biocrusts (Svalbard, Antarctic Peninsula, Continental Antarctica).

Main Results:

• Network analysis identified numerous correlations, but trait assignment revealed only 4.7-9.3% linked suitable predators and prey.
• Hierarchical Modeling of Species Communities (HMSC) modeling corroborated these findings, with 4.8-7.5% suitable predator-prey links.
• Experimental validation confirmed 82% of predicted predator-prey interactions when combining network and trait analyses; highly predatory species showed higher network centrality.

Conclusions:

• Network analysis can infer predator-prey interactions but requires cautious interpretation.
• Integrating trait-based approaches significantly increases confidence in predicting biological interactions.
• Network statistics may help identify key predators within ecological networks.