ReviewThe ISME journal2019
How sample heterogeneity can obscure the signal of microbial interactions.
Review in The ISME journal, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Coexistence Theory for Microbial Ecology, and Vice Versa.Environmental microbiology · 2025Pooled it
- Article
- Navigating complexity: key considerations for studying fungal-bacterial interactions.mSystems · 2026Article
- Microscopic-scale gut microbiota dataset of Kunming mice revealed by 16S rRNA gene high-throughput sequencing.Data in brief · 2026Article
- Linking complex microbial interactions and dysbiosis through a disordered Lotka-Volterra model.eLife · 2026Article
- Achromobacter xylosoxidans modulates Pseudomonas aeruginosa virulence through a complex multi-target competition.Scientific reports · 2025Article
- Response of cbbL Carbon-Sequestering Microorganisms to Simulated Warming in the River Source Wetland of the Wayan Mountains.Biology · 2025Article
- A novel robust network construction and analysis workflow for mining infant microbiota relationships.mSystems · 2025Article
- Millimeter-scale niche differentiation of N-cycling microorganisms across the soil-water interface has implications for N2O emissions from wetlands.The ISME journal · 2025Article
- Impact of micro-habitat fragmentation on microbial population growth dynamics.The ISME journal · 2025Article
- Biogeography and assembly processes of abundant and rare soil microbial taxa in the southern part of the Qilian Mountain National Park, China.Ecology and evolution · 2024Article
- Aqueous habitats and carbon inputs shape the microscale geography and interaction ranges of soil bacteria.Communications biology · 2023Article
- Biogeographic Patterns and Community Assembly Processes of Bacterioplankton and Potential Pathogens in Subtropical Estuaries in China.Microbiology spectrum · 2023Article
- Monitoring bacterial composition and assemblage in the Gulf of Corcovado, southern Chile: Bacteria associated with harmful algae.Current research in microbial sciences · 2023Article
- Insights into the composition and assembly mechanism of microbial communities on intertidal microsand grains.Frontiers in microbiology · 2023Article
- Toward FAIR Representations of Microbial Interactions.mSystems · 2022Article
- Functionally-explicit sampling can answer key questions about the specificity of plant-microbe interactions.Environmental microbiome · 2022Article
- Historical contingencies and phage induction diversify bacterioplankton communities at the microscale.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Microbial community structure is stratified at the millimeter-scale across the soil-water interface.ISME communications · 2022Article
- From diversity to complexity: Microbial networks in soils.Soil biology & biochemistry · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Microbial community data are commonly subjected to computational tools such as correlation networks, null models, and dynamic models, with the goal of identifying the ecological processes structuring microbial communities. A major assumption of these methods is that the signs and magnitudes of species interactions and vital rates can be reliably parsed from observational data on species' (relative) abundances. However, we contend that this assumption is violated when sample units contain any underlying spatial structure. Here, we show how three phenomena-Simpson's paradox, context-dependence, and nonlinear averaging-can lead to erroneous conclusions about population parameters and species interactions when samples contain heterogeneous mixtures of populations or communities. At the root of this issue is the fundamental mismatch between the spatial scales of species interactions (micrometers) and those of typical microbial community samples (millimeters to centimetres). These issues can be overcome by measuring and accounting for spatial heterogeneity at very small scales, which will lead to more reliable inference of the ecological mechanisms structuring natural microbial communities.
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Registered trials
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.