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Record W3010532419 · doi:10.1038/s41467-020-14953-2

Enhanced heat tolerance of viral-infected aphids leads to niche expansion and reduced interspecific competition

2020· article· en· W3010532419 on OpenAlexaff
Mitzy F. Porras, Carlos A. Navas, James H. Marden, Mark C. Mescher, Consuelo Μ. De Moraes, Sylvain Pincebourde, Andrés Felipe Sandoval-Mojica, Juan A. Raygoza Garay, Germán Andrés Holguín Londoño, Edwin G. Rajotte, Tomás A. Carlo

Bibliographic record

VenueNature Communications · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect and Pesticide Research
Canadian institutionsUniversity of Toronto
FundersFundação de Amparo à Pesquisa do Estado de São PauloDepartamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS)National Institute of Food and AgricultureNational Science Foundation
KeywordsAphidBiologyHost (biology)Vector (molecular biology)Competition (biology)Interspecific competitionNichePlant virusHeat shock proteinVirusVirologyEcologyGeneBotanyGenetics

Abstract

fetched live from OpenAlex

Vector-borne pathogens are known to alter the phenotypes of their primary hosts and vectors, with implications for disease transmission as well as ecology. Here we show that a plant virus, barley yellow dwarf virus, increases the surface temperature of infected host plants (by an average of 2 °C), while also significantly enhancing the thermal tolerance of its aphid vector Rhopalosiphum padi (by 8 °C). This enhanced thermal tolerance, which was associated with differential upregulation of three heat-shock protein genes, allowed aphids to occupy higher and warmer regions of infected host plants when displaced from cooler regions by competition with a larger aphid species, R. maidis. Infection thereby led to an expansion of the fundamental niche of the vector. These findings show that virus effects on the thermal biology of hosts and vectors can influence their interactions with one another and with other, non-vector organisms.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.472
Threshold uncertainty score0.190

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 head0.039
GPT teacher head0.304
Teacher spread0.265 · 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

Citations68
Published2020
Admission routes1
Has abstractyes

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