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Record W2740950097 · doi:10.1098/rsbl.2017.0271

Feeding the enemy: loss of nectar and nectaries to herbivores reduces tepal damage and increases pollinator attraction in <i>Iris bulleyana</i>

2017· article· en· W2740950097 on OpenAlexaff
Ya-Ru Zhu, Min Yang, Jana C. Vamosi, W. Scott Armbruster, Tao Wan, Yanbing Gong

Bibliographic record

VenueBiology Letters · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Calgary
FundersNational Natural Science Foundation of China
KeywordsNectarBiologyPollinatorHerbivorePollinationTepalBotanyPollenZoophilyEcologyStamen

Abstract

fetched live from OpenAlex

Floral nectar usually functions as a pollinator reward, yet it may also attract herbivores. However, the effects of herbivore consumption of nectar or nectaries on pollination have rarely been tested. We investigated Iris bulleyana , an alpine plant that has showy tepals and abundant nectar, in the Hengduan Mountains of SW China. In this region, flowers are visited mainly by pollen-collecting pollinators and nectarivorous herbivores. We tested the hypothesis that, in I. bulleyana , sacrificing nectar and nectaries to herbivores protects tepals and thus enhances pollinator attraction. We compared rates of pollination and herbivory on different floral tissues in plants with flowers protected from nectar and nectary consumption with rates in unprotected control plants. We found that nectar and nectaries suffered more herbivore damage than did tepals in natural conditions. However, the amount of tepal damage was significantly greater in the flowers with protected nectaries than in the controls; this resulted in significant differences in pollinator visitation rates. These results provide the first evidence that floral nectar and nectaries may be ‘sacrificed’ to herbivores, leading to reduced damage to other floral tissues that are more important for reproduction.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.559
Threshold uncertainty score0.996

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 head0.029
GPT teacher head0.233
Teacher spread0.205 · 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

Citations9
Published2017
Admission routes1
Has abstractyes

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