Evidence map›Paper›PMID 41045078›Full record

ArticleMolecular ecology2025

Soil Microbial Blueprint: Predicting Soil Dominant Bacterial Genera Distribution Across Australia.

Mingming Du, Peipei Xue, Budiman Minasny, Alex McBratney, Mario Fajardo Pedraza, Vanessa Pino, Patrice de Caritat, JiZheng He, Qinglin Chen, Andrew Bissett

Abstract read
In one paragraph

Article in Molecular ecology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Mingming DuSchool of Life and Environmental Sciences, The University of Sydney, Camperdown, New South Wales, Australia.ORCID 0009-0003-9185-8386
Peipei XueSchool of Life and Environmental Sciences, The University of Sydney, Camperdown, New South Wales, Australia.
Budiman MinasnySchool of Life and Environmental Sciences, The University of Sydney, Camperdown, New South Wales, Australia.
Alex McBratneySchool of Life and Environmental Sciences, The University of Sydney, Camperdown, New South Wales, Australia.
Mario Fajardo PedrazaSchool of Life and Environmental Sciences, The University of Sydney, Camperdown, New South Wales, Australia.
Vanessa PinoSchool of Life and Environmental Sciences, The University of Sydney, Camperdown, New South Wales, Australia.ORCID 0000-0001-7590-8361
Patrice de CaritatJohn de Laeter Centre, Curtin University, Bentley, Western Australia, Australia.
JiZheng HeSchool of Agriculture, Food and Ecosystem Sciences, Faculty of Science, The University of Melbourne, Parkville, Victoria, Australia.
Qinglin ChenSchool of Agriculture, Food and Ecosystem Sciences, Faculty of Science, The University of Melbourne, Parkville, Victoria, Australia.
Andrew BissettCSIRO, Hobart, Tasmania, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Soil bacteria play a crucial role in soil processes, such as carbon sequestration and nutrient cycling. While soil bacterial communities and their interactions with pedo-climatic factors have been well documented, most studies typically focus on broad taxonomic levels, leaving distribution and responses at the genus level unexplored. This study optimized machine learning models to predict the distribution of dominant bacterial genera across Australia on a comprehensive dataset of 1971 topsoil samples. Our high-resolution digital maps (~1 km resolution) reveal four distinct distribution patterns for the dominant bacterial genera: coastal or inland enriched patterns and latitude-related patterns. Each genus exhibited unique responses to critical factors, including temperature, precipitation, soil organic carbon (SOC), and pH. Notably, our findings highlight the importance of genus-level analysis, as bacterial genera within the same phylum can respond markedly differently to pedo-climatic conditions. Intensive land use significantly homogenized bacteria composition and increased the relative abundance of Rubrobacter, RB41, Microvirga, and Sphingomonas. Overall, this study enhances our understanding of bacterial macroecological trends and offers insights for more precise interventions to improve soil health and resilience against environmental changes.

Indexed as

BacteriaSoil MicrobiologyAustraliaMachine LearningRNA, Ribosomal, 16SSoilTemperatureRNA, Ribosomal, 16SSoilclimatedigital mappingdominant bacterial generaland use

Identifiers

PMID41045078
PMCPMC12573719

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.