ArticleMicrobiologyOpen2020
Molecular ecological network analysis of the response of soil microbial communities to depth gradients in farmland soils.
Article in MicrobiologyOpen, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 12 citations in OpenAlex.
- Integrating host-microbiome multi-omics with machine learning: methods, benchmarks, and translational applications.Science China. Life sciences · 2026Review
- Soil humus-reducing bacteria in the Xisha islands, South China sea: unveiling diversity and predicting functions.BMC microbiology · 2025Article
- The effects of cultivation patterns and nitrogen levels on fertility and bacterial community characteristics of surface and subsurface soil.Frontiers in microbiology · 2023Article
- Distribution of soil microorganisms in different complex soil layers in Mu Us sandy land.PloS one · 2023Article
- Effects of soft rock on soil properties and bacterial community in Mu Us Sandy Land, China.PeerJ · 2022Article
- Computational Biology and Machine Learning Approaches to Understand Mechanistic Microbiome-Host Interactions.Frontiers in microbiology · 2021Review
- Molecular ecological network analysis of the response of soil microbial communities to depth gradients in farmland soils.MicrobiologyOpen · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Soil microorganisms are considered to be important indicators of soil fertility and soil quality. Most previous studies have focused solely on surface soil, but there were numerous active cells in deeper soil layers. However, studies regarding microbial communities in deeper soil layers were not comprehensive and sufficient. In this study, phylogenetic molecular ecological networks (pMENs) based on the 16S rRNA Miseq sequencing technique were applied to study the response of soil microbial communities to depth gradients and the changes of key genera along 3 meter depth gradients (0-0.2 m, 0.2-0.4 m 0.4-0.6 m, 0.6-0.8 m, 0.8-1.0 m, 1.0-1.3 m, 1.3-1.6 m, 1.6-2.0 m, 2.0-2.5 m, and 2.5-3.0 m). The results showed that the modularity of microbial communities was consistently high in all soil layers and each layer was similar, which indicated that microbial communities were more resistant to depth changes. The pMENs further demonstrated that microbial community interactions were stable as the depth increased and they cooperated well to adapt to changes in different soil gradients. This was evidenced by similar positive links, average degree, and average clustering coefficient. In addition, key genera were obtained by analyzing module hubs in the pMENs. There may be at least one dominant genus in each layer that adapted to and resisted changes in the soil environment. It seems microbial communities demonstrate a stable and strong adaptability to depth gradients in farmland soils.
Indexed as
Identifiers
What OpenQuestion holds
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.