ArticlePLoS computational biology2022
Species abundance correlations carry limited information about microbial network interactions.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 29 citations in OpenAlex.
- Microbial contamination profile on esthetic elastomeric ligatures through the checkerboard DNA-DNA hybridization technique : A randomized split-mouth study.Journal of orofacial orthopedics = Fortschritte der Kieferorthopadie : Organ/official journal Deutsche Gesellschaft fur Kieferorthopadie · 2024Trial
- SubCom analysis: dissecting microbial community functions into taxon-specific contributions and interaction contexts.Microbiome · 2026Article
- High-resolution profiling of bacterial and fungal communities using pangenome-informed taxon-specific long-read amplicons.Microbiome · 2025Article
- Modulatory effects of Boletus edulis on the gut microbiota in Atlantic salmon (Salmo salar) utilizing an artificial teleost gut model.Animal microbiome · 2025Article
- Scarce data, noisy inferences and overfitting: the hidden flaws in ecological dynamics modelling.Journal of the Royal Society, Interface · 2025Article
- Co-Extinctions and Co-Compensatory Species Responses to Climate Change Moderate Ecosystem Futures.Global change biology · 2025Article
- Testing for Consistency in Co-occurrence Patterns Among Bacterial Taxa Across the Microbiomes of Four Different Trematode Parasites.Microbial ecology · 2025Article
- Maize associated bacterial and fungal microbiomes show contrasting conformation patterns dependent on plant compartment and water availability.BMC plant biology · 2025Article
- Decoding emergent properties of microbial community functions through subcommunity observations and interpretable machine learning.The ISME journal · 2025Article
- Analyzing bacterial networks and interactions in skin and gills of Sparus aurata with microalgae-based additive feeding.Scientific reports · 2024Article
- Genetic tracing of market wildlife and viruses at the epicenter of the COVID-19 pandemic.Cell · 2024Article
- A comparative analysis of mutual information methods for pairwise relationship detection in metagenomic data.BMC bioinformatics · 2024Article
- The Potential of Co-Evolution and Interactions of Gut Bacteria-Phages in Bamboo-Eating Pandas: Insights from Dietary Preference-Based Metagenomic Analysis.Microorganisms · 2024Article
- Sparse species interactions reproduce abundance correlation patterns in microbial communities.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- MicroNet-MIMRF: a microbial network inference approach based on mutual information and Markov random fields.Bioinformatics advances · 2024Article
- Vitamin interdependencies predicted by metagenomics-informed network analyses and validated in microbial community microcosms.Nature communications · 2023Article
- Reconstructing multi-strain pathogen interactions from cross-sectional survey data via statistical network inference.Journal of the Royal Society, Interface · 2023Article
- Top-down identification of keystone taxa in the microbiome.Nature communications · 2023Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Unraveling the network of interactions in ecological communities is a daunting task. Common methods to infer interspecific interactions from cross-sectional data are based on co-occurrence measures. For instance, interactions in the human microbiome are often inferred from correlations between the abundances of bacterial phylogenetic groups across subjects. We tested whether such correlation-based methods are indeed reliable for inferring interaction networks. For this purpose, we simulated bacterial communities by means of the generalized Lotka-Volterra model, with variation in model parameters representing variability among hosts. Our results show that correlations can be indicative for presence of bacterial interactions, but only when measurement noise is low relative to the variation in interaction strengths between hosts. Indication of interaction was affected by type of interaction network, process noise and sampling under non-equilibrium conditions. The sign of a correlation mostly coincided with the nature of the strongest pairwise interaction, but this is not necessarily the case. For instance, under rare conditions of identical interaction strength, we found that competitive and exploitative interactions can result in positive as well as negative correlations. Thus, cross-sectional abundance data carry limited information on specific interaction types. Correlations in abundance may hint at interactions but require independent validation.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.