Open-top warming chambers reduce animal pollination of two subalpine herbs
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".