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Record W3204985696 · doi:10.26786/1920-7603(2021)638

Open-top warming chambers reduce animal pollination of two subalpine herbs

2021· article· en· W3204985696 on OpenAlexvenueno aff
C Adamson, Amy M. Iler

Bibliographic record

VenueJournal of Pollination Ecology · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
Fundersnot available
KeywordsPollinatorBiologyPollinationAbiotic componentPollenEcologyReproductive successPlant reproductionPollen sourcePopulation

Abstract

fetched live from OpenAlex

Open top chambers (OTCs) are a popular method for studying the biological effects of climate change through passive heating, but their effects on biotic interactions are poorly understood, especially for pollination. Here we use the subalpine plants Delphinium nuttallianum and Potentilla pulcherrima to examine the possibility that the effects of OTCs on plant reproduction are not the result of warming but rather OTCs acting as barriers to pollinator movement. Pollinator observations were conducted and stigmas collected from plants inside and outside of OTCs in a meadow in the Rocky Mountains of Colorado, USA. Very few visitors were observed inside of OTCs, which led to severe reductions in visitation rates, by 92% in Delphinium and 85% in Potentilla. The number of conspecific pollen grains on stigmas was 73% lower in OTCs for Delphinium but not Potentilla, likely because it is capable of autogamous self-pollination. This study clearly shows that OTCs can reduce animal pollination, which is also likely to reduce plant reproductive output of outcrossing plants via decreases in the quantity or quality of pollen. OTCs may therefore confound effects of warming on plant reproduction with pollination effects. Although the unintended effects of OTCs on abiotic conditions are well-studied, this study highlights that their effects on biotic interactions require further investigation.

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 score0.888
Threshold uncertainty score0.373

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 head0.052
GPT teacher head0.293
Teacher spread0.241 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

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