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Record W2913409255 · doi:10.1002/ecs2.2598

Thermal disruption of soil bacterial assemblages decreases diversity and assemblage similarity

2019· article· en· W2913409255 on OpenAlexaff
Michael D. Weiser, Daliang Ning, Vanessa Buzzard, Sean T. Michaletz, Zhili He, Brian J. Enquist, Robert B. Waide, Jizhong Zhou, Michael Kaspari

Bibliographic record

VenueEcosphere · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Community Ecology and Physiology
Canadian institutionsUniversity of British Columbia
FundersOregon State UniversityU.S. Forest ServiceNational Science Foundation
KeywordsMesocosmSpecies richnessEcologyTaxonAssemblage (archaeology)BiologySimilarity (geometry)Extinction (optical mineralogy)Null modelEcosystemPaleontology

Abstract

fetched live from OpenAlex

Abstract The metabolic theory of ecology assumes that rates of selection and adaptation for organisms are functions of temperature. Niche theory predicts that strong selection pressure should simplify assemblages as species are extirpated and taxa pre‐adapted for the new environment thrive. Here, we use closed mesocosms to test the prediction that higher temperatures decrease species richness and increase assemblage similarity more and faster than lower temperatures. We incubated two temperate forest soil types at constant temperatures from 10° to 35°, destructively sampling mesocosms at 30, 180, and 440 d. We quantified taxonomic richness and assemblage similarity of soil bacteria using 16S rRNA gene amplicons. As predicted, mesocosms at higher temperatures lost more taxa than those at lower temperature. Contrary to predictions, the simplified assemblages at higher temperatures became less similar to each other over time. After 440 d of incubation, the number of taxa lost was a linear function of the difference between treatment temperature and site mean annual temperature, while assemblage similarity decreased as an accelerating function of this temperature difference.

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 categoriesInsufficient 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 score0.456
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0300.000

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 head0.009
GPT teacher head0.207
Teacher spread0.198 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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