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Record W2313482166 · doi:10.1093/femsec/fiw071

Influence of dormancy on microbial competition under intermittent substrate supply: insights from model simulations

2016· article· en· W2313482166 on OpenAlexaff
Konstantin Stolpovsky, Ingo Fetzer, Philippe Van Cappellen, Martin Thullner

Bibliographic record

VenueFEMS Microbiology Ecology · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsBiologyAbiotic componentEcologyCompetition (biology)DormancyAbundance (ecology)CommunitySubstrate (aquarium)Biological systemBotanyEcosystemGermination

Abstract

fetched live from OpenAlex

Most natural environments are characterized by frequent changes of their abiotic conditions. Microorganisms can respond to such changes by switching their physiological state between activity and dormancy allowing them to endure periods of unfavorable abiotic conditions. As a consequence, the competitiveness of microbial species is not simply determined by their growth performance under favorable conditions but also by their ability and readiness to respond to periods of unfavorable environmental conditions. The present study investigates the relevance of factors controlling the abundance and activity of individual bacterial species competing for an intermittently supplied substrate. For this purpose, numerical experiments were performed addressing the response of microbial systems to regularly applied feeding pulses. Simulation results show that community dynamics may exhibit a non-trivial link to the frequency of the external constraints and that for a certain combination of these environmental conditions coexistence of species is possible. The ecological implication of our results is that even non-dominant, neglected species can have a strong influence on realized species composition of dominant key species, due to their invisible presence enable the coexistence between important key species and by this affecting provided function of the system.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.921
Threshold uncertainty score0.479

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.025
GPT teacher head0.205
Teacher spread0.180 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations15
Published2016
Admission routes1
Has abstractyes

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