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

Cristina Martínez Rendón1, Christina Braun2, Maria Kappelsberger3

  • 1Terrestrial Ecology, Institute of Zoology, University of Cologne, Zülpicher Str. 47B, 50674, Cologne, Germany.

Microbiome
|February 5, 2025
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Summary

Combining network analysis with trait-based methods improves the accuracy of predicting predator-prey interactions in microbial communities. This approach helps validate inferred relationships, enhancing ecological understanding.

Keywords:
BiocrustsCross-kingdom network analysesExperimental validationMicrobial communitiesMicrobial ecologyPredator–prey interactionsTrait-based ecology

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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.