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Record W3133569653 · doi:10.1111/ele.13713

A shift from phenol to silica‐based leaf defences during long‐term soil and ecosystem development

2021· article· en· W3133569653 on OpenAlexaff
Félix de Tombeur, Étienne Laliberté, Hans Lambers, Michel‐Pierre Faucon, Graham Zemunik, Benjamin L. Turner, Jean‐Thomas Cornelis, Grégory Mahy

Bibliographic record

VenueEcology Letters · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSilicon Effects in Agriculture
Canadian institutionsUniversity of British ColumbiaUniversité de Montréal
FundersFonds De La Recherche Scientifique - FNRSDepartment of Biodiversity, Conservation and Attractions
KeywordsChronosequenceHerbivoreEcosystemNutrientBiologyEcologySoil waterPhosphorusBayEcological successionAgronomyChemistryGeography

Abstract

fetched live from OpenAlex

The resource availability hypothesis predicts that plants adapted to infertile soils have high levels of anti-herbivore leaf defences. This hypothesis has been mostly explored for secondary metabolites such as phenolics, whereas it remains underexplored for silica-based defences. We determined leaf concentrations of total phenols and silicon (Si) in plants growing along the 2-million-year Jurien Bay chronosequence, exhibiting an extreme gradient of soil fertility. We found that nitrogen (N) limitation on young soils led to a greater expression of phenol-based defences, whereas old, phosphorus (P)-impoverished soils favoured silica-based defences. Both defence types were negatively correlated at the community and individual species level. Our results suggest a trade-off among these two leaf defence strategies based on the strength and type of nutrient limitation, thereby opening up new perspectives for the resource availability hypothesis and plant defence research. This study also highlights the importance of silica-based defences under low P supply.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.008
GPT teacher head0.190
Teacher spread0.182 · 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 source (direct Gemma or distilled Codex), 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

Citations58
Published2021
Admission routes1
Has abstractyes

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Same venueEcology LettersSame topicSilicon Effects in AgricultureFrench-language works237,207