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Record W4303474889 · doi:10.1139/cjb-2021-0206

Are epicuticular waxes a surface defense comparable to trichomes? A test using two <i>Solanum</i> species and a specialist herbivore

2022· article· en· W4303474889 on OpenAlexvenueno aff
Sakshi Watts, Rupesh Kariyat

Bibliographic record

VenueBotany · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect-Plant Interactions and Control
Canadian institutionsnot available
Fundersnot available
KeywordsTrichomeEpicuticular waxBiologySolanumHerbivoreBotanyWaxLepidoptera genitaliaPlant defense against herbivoryManduca sextaSolanaceaeInsect

Abstract

fetched live from OpenAlex

Although plants possess a suite of structural defenses, most studies have focused on trichomes. Trichomes can have both pre- and post-ingestive effects and have been consistently found to reduce herbivory. Along with trichomes, a few studies have focused on epicuticular waxes as an important defense; however, manipulated comparisons examining herbivore growth and development are limited. In this study, using two Solanum species ( Solanum glaucescens Zuccarini and Solanum macrocarpon Linnaeus) that vary in both defenses, we tested the hypothesis that variation in defenses will affect herbivore feeding, primarily by restricting feeding commencement. We used electron microscopy together with a series of plant- and diet-based manipulative experiments, using tobacco hornworm ( Manduca sexta Linnaeus; Lepidoptera: Sphingidae) as the herbivore. We found that Solanum glaucescens leaves had significantly fewer trichomes and significantly higher wax content when compared to Solanum macrocarpon. We also found that Solanum glaucescens waxes acted as a strong physical barrier resulting in lower mass gain and higher mortality of caterpillars compared to Solanum macrocarpon. Artificial diet manipulation experiments also suggested the possible toxicity of waxes. Collectively, we show that epicuticular waxes can play a significant role as a strong surface barrier and should be examined further.

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 score0.933
Threshold uncertainty score0.911

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.236
Teacher spread0.206 · 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

Citations10
Published2022
Admission routes1
Has abstractyes

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