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Record W3026942742 · doi:10.1002/lol2.10160

Warming advances virus population dynamics in a temperate freshwater plankton community

2020· article· en· W3026942742 on OpenAlexaff
Thijs Frenken, Corina P. D. Brussaard, Mandy Velthuis, Ralf Aben, Garabet Kazanjian, Sabine Hilt, Sarian Kosten, E.T.H.M. Peeters, Lisette N. de Senerpont Domis, Susanne Stephan, Ellen van Donk, Dedmer B. Van de Waal

Bibliographic record

VenueLimnology and Oceanography Letters · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsUniversity of Windsor
FundersLeibniz-GemeinschaftGIESKES-STRIJBIS FONDSLeibniz-Institut für Gewässerökologie und BinnenfischereiNederlandse Organisatie voor Wetenschappelijk OnderzoekUniversiteit UtrechtKoninklijke Nederlandse Akademie van Wetenschappen
KeywordsMesocosmBiologyEcologyPopulationEcosystemTemperate climateObligateGlobal warmingPlanktonMicrobial population biologyNutrient cycleClimate changeBacteria

Abstract

fetched live from OpenAlex

Viruses are important drivers in the cycling of carbon and nutrients in aquatic ecosystems. Since viruses are obligate parasites, their production completely depends on growth and metabolism of hosts and therefore can be affected by climate change. Here, we investigated if warming (+4°C) can change the outcome of viral infections in a natural freshwater virus community over a 5‐month period in a mesocosm experiment. We monitored dynamics of viruses and potential hosts. Results show that warming significantly advanced the early summer peak of the virus community by 24 d, but neither affected viral peak abundances nor time‐integrated number of viruses present. Our results demonstrate that warming advances the timing of viruses in a natural community. Although warming may not necessarily result in a stronger viral control of bacterial and phytoplankton communities, our results suggest it can alter host population dynamics through advanced timing of infections, and thus timing of carbon and nutrient recycling.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.174
Threshold uncertainty score0.537

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 head0.009
GPT teacher head0.215
Teacher spread0.207 · 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 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

Citations21
Published2020
Admission routes1
Has abstractyes

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