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Record W2901332705 · doi:10.1139/cjb-2018-0160

Physical defenses and herbivory vary more within plants than among plants in the tropical understory shrub <i>Piper polytrichum</i>

2018· article· en· W2901332705 on OpenAlexvenueno aff
William C. Wetzel, Mariah H. Meek

Bibliographic record

VenueBotany · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyHerbivoreUnderstoryShrubPlant tolerance to herbivoryBotanyEcologyCanopy

Abstract

fetched live from OpenAlex

There is a growing appreciation that much of the trait variation within plant species is represented within individuals, for example, occurring among leaves within a plant. Subindividual variation is predicted to have key ecological consequences, but empirical understanding of how subindividual variation relates to species interactions, such as herbivory, is limited. We measured two physical defenses and herbivore damage on multiple leaves within individual plants of Piper polytrichum C.DC. (Piperaceae), a tropical understory shrub. We partitioned variance among- and within-plants and quantified patterns of trait and damage variation vis-à-vis leaf size and architectural position. We found that variance was considerably higher within plants than among plants for toughness (97%) and trichome density (57%), and that herbivore damage also varied most within plants (74%). Surprisingly, leaf position and size explained only small amounts of variance in traits (2.5%–16.5%) and herbivory (≤4%), indicating subindividual variability had low spatial predictability. The data suggest that individual P. polytrichum plants represent heterogeneous and spatially unpredictable landscapes of physical traits, and that interactions with herbivores are similarly variable. Our results are consistent with the hypothesis that subindividual variability defends plants against herbivores by increasing the difficulty of foraging for high-quality tissue.

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 score0.117
Threshold uncertainty score0.496

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 head0.035
GPT teacher head0.218
Teacher spread0.183 · 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

Citations14
Published2018
Admission routes1
Has abstractyes

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