MétaCan
Menu
Back to cohort
Record W4220993955 · doi:10.1111/geb.13487

Global soil microbiomes: A new frontline of biome‐ecology research

2022· article· en· W4220993955 on OpenAlexaff
Martti Vasar, John Davison, Siim‐Kaarel Sepp, Ladislav Mucina, Jane Oja, Saleh Al‐Quraishy, Sten Anslan, Mohammad Bahram, C. Guillermo Bueno, Juan José Cantero, Guillaume Decocq, Lauchlan H. Fraser, Inga Hiiesalu, Wael N. Hozzein, Kadri Koorem, Yiming Meng, Mari Moora, V. G. Onipchenko, Maarja Öpik, Meelis Pärtel, Tanel Vahter, Leho Tedersoo, Martin Zobel

Bibliographic record

VenueGlobal Ecology and Biogeography · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental DNA in Biodiversity Studies
Canadian institutionsThompson Rivers University
FundersEuropean Regional Development FundTartu ÜlikoolMinistry of Science and Higher Education of the Russian FederationVetenskapsrådetEesti Teadusagentuur
KeywordsBiomeMicrobiomeBiodiversityEcologyBiologyGrasslandTaxonCommunity structureEcosystem

Abstract

fetched live from OpenAlex

Abstract Aim Organisms on our planet form spatially congruent and functionally distinct communities, which at large geographical scales are called “biomes”. Understanding their pattern and function is vital for sustainable use and protection of biodiversity. Current global terrestrial biome classifications are based primarily on climate characteristics and functional aspects of plant community assembly. These and other existing biome schemes do not take account of soil organisms, including highly diverse and functionally important microbial groups. We aimed to define large‐scale structure in the diversity of soil microbes (soil microbiomes), pinpoint the environmental drivers shaping it and identify resemblance and mismatch with existing terrestrial biome schemes. Location Global. Time period Current. Major taxa studied Soil eukaryotes and prokaryotes. Methods We collected soil samples from natural environments world‐wide, incorporating most known terrestrial biomes. We used high‐throughput sequencing to characterize soil biotic communities and k ‐means clustering to define soil microbiomes describing the diversity of microbial eukaryotic and prokaryotic groups. We used climatic data and soil variables measured in the field to identify the environmental variables shaping soil microbiome structure. Results We recorded strong correlations among fungal, bacterial, archaeal, plant and animal communities, defined a system of global soil microbiomes (producing seven biome types for microbial eukaryotes and six biome types for prokaryotes) and showed that these are typically structured by pH alongside temperature. None of the soil microbiomes are directly paralleled by any current terrestrial biome scheme, with mismatch most substantial for prokaryotes and for microbial eukaryotes in cold climates; nor do they consistently distinguish grassland and forest ecosystems. Main conclusions Existing terrestrial biome classifications represent a limited surrogate for the large‐scale diversity patterns of microbial soil organisms. We show that empirically defined soil microbiomes are attainable using metabarcoding and statistical clustering approaches and suggest that they can have wide application in theoretical and applied biodiversity research.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0000.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.252
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations51
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueGlobal Ecology and BiogeographySame topicEnvironmental DNA in Biodiversity StudiesFrench-language works237,207