Enhancing microbial predator–prey detection with network and trait-based analyses
Abstract Background Network analyses are often applied to microbial communities using sequencing survey datasets. However, associations in such networks do not necessarily indicate actual biotic interactions, and even if they do, the nature of the interactions commonly remains unclear. While network...
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BMC
2025-02-01
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| Series: | Microbiome |
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| Online Access: | https://doi.org/10.1186/s40168-025-02035-8 |
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| author | Cristina Martínez Rendón Christina Braun Maria Kappelsberger Jens Boy Angélica Casanova-Katny Karin Glaser Kenneth Dumack |
| author_facet | Cristina Martínez Rendón Christina Braun Maria Kappelsberger Jens Boy Angélica Casanova-Katny Karin Glaser Kenneth Dumack |
| author_sort | Cristina Martínez Rendón |
| collection | DOAJ |
| description | Abstract Background Network analyses are often applied to microbial communities using sequencing survey datasets. However, associations in such networks do not necessarily indicate actual biotic interactions, and even if they do, the nature of the interactions commonly remains unclear. While network analyses are valuable for generating hypotheses, the inferred hypotheses are rarely experimentally confirmed. Results We employed cross-kingdom network analyses, applied trait-based functions to the microorganisms, and subsequently experimentally investigated the found putative predator–prey interactions to evaluate whether, and to what extent, correlations indicate actual predator–prey relationships. For this, we investigated algae and their protistan predators in biocrusts of three distinct polar regions, i.e., Svalbard, the Antarctic Peninsula, and Continental Antarctica. Network analyses using FlashWeave indicated that 89, 138, and 51 correlations occurred between predatory protists and algae, respectively. However, trait assignment revealed that only 4.7–9.3% of said correlations link predators to actually suitable prey. We further confirmed these results with HMSC modeling, which resulted in similar numbers of 7.5% and 4.8% linking predators to suitable prey for full co-occurrence and abundance models, respectively. The combination of network analyses and trait assignment increased confidence in the prediction of predator–prey interactions, as we show that 82% of all experimentally investigated correlations could be verified. Furthermore, we found that more vicious predators, i.e., predators with the highest growth rate in co-culture with their prey, exhibit higher stress and betweenness centrality — giving rise to the future possibility of determining important predators from their network statistics. Conclusions Our results support the idea of using network analyses for inferring predator–prey interactions, but at the same time call for cautionary consideration of the results, by combining them with trait-based approaches to increase confidence in the prediction of biological interactions. Video Abstract |
| format | Article |
| id | doaj-art-8e6d1946cc994ff49848bc90f1fbddbd |
| institution | Kabale University |
| issn | 2049-2618 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Microbiome |
| spelling | doaj-art-8e6d1946cc994ff49848bc90f1fbddbd2025-02-09T12:46:47ZengBMCMicrobiome2049-26182025-02-0113111810.1186/s40168-025-02035-8Enhancing microbial predator–prey detection with network and trait-based analysesCristina Martínez Rendón0Christina Braun1Maria Kappelsberger2Jens Boy3Angélica Casanova-Katny4Karin Glaser5Kenneth Dumack6Terrestrial Ecology, Institute of Zoology, University of CologneInstitute of Ecology and Evolution, Friedrich Schiller University JenaInstitute of Planetary Geodesy, Technical University of DresdenInstitute of Earth System Sciences, Leibniz Universität HannoverDepartment of Environmental Sciences, Faculty of Natural Resources, Catholic University of TemucoInstitute for Biosciences, TU Bergakademie FreibergTerrestrial Ecology, Institute of Zoology, University of CologneAbstract Background Network analyses are often applied to microbial communities using sequencing survey datasets. However, associations in such networks do not necessarily indicate actual biotic interactions, and even if they do, the nature of the interactions commonly remains unclear. While network analyses are valuable for generating hypotheses, the inferred hypotheses are rarely experimentally confirmed. Results We employed cross-kingdom network analyses, applied trait-based functions to the microorganisms, and subsequently experimentally investigated the found putative predator–prey interactions to evaluate whether, and to what extent, correlations indicate actual predator–prey relationships. For this, we investigated algae and their protistan predators in biocrusts of three distinct polar regions, i.e., Svalbard, the Antarctic Peninsula, and Continental Antarctica. Network analyses using FlashWeave indicated that 89, 138, and 51 correlations occurred between predatory protists and algae, respectively. However, trait assignment revealed that only 4.7–9.3% of said correlations link predators to actually suitable prey. We further confirmed these results with HMSC modeling, which resulted in similar numbers of 7.5% and 4.8% linking predators to suitable prey for full co-occurrence and abundance models, respectively. The combination of network analyses and trait assignment increased confidence in the prediction of predator–prey interactions, as we show that 82% of all experimentally investigated correlations could be verified. Furthermore, we found that more vicious predators, i.e., predators with the highest growth rate in co-culture with their prey, exhibit higher stress and betweenness centrality — giving rise to the future possibility of determining important predators from their network statistics. Conclusions Our results support the idea of using network analyses for inferring predator–prey interactions, but at the same time call for cautionary consideration of the results, by combining them with trait-based approaches to increase confidence in the prediction of biological interactions. Video Abstracthttps://doi.org/10.1186/s40168-025-02035-8Cross-kingdom network analysesTrait-based ecologyPredator–prey interactionsBiocrustsMicrobial ecologyMicrobial communities |
| spellingShingle | Cristina Martínez Rendón Christina Braun Maria Kappelsberger Jens Boy Angélica Casanova-Katny Karin Glaser Kenneth Dumack Enhancing microbial predator–prey detection with network and trait-based analyses Microbiome Cross-kingdom network analyses Trait-based ecology Predator–prey interactions Biocrusts Microbial ecology Microbial communities |
| title | Enhancing microbial predator–prey detection with network and trait-based analyses |
| title_full | Enhancing microbial predator–prey detection with network and trait-based analyses |
| title_fullStr | Enhancing microbial predator–prey detection with network and trait-based analyses |
| title_full_unstemmed | Enhancing microbial predator–prey detection with network and trait-based analyses |
| title_short | Enhancing microbial predator–prey detection with network and trait-based analyses |
| title_sort | enhancing microbial predator prey detection with network and trait based analyses |
| topic | Cross-kingdom network analyses Trait-based ecology Predator–prey interactions Biocrusts Microbial ecology Microbial communities |
| url | https://doi.org/10.1186/s40168-025-02035-8 |
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