Plant–pollinator interactions and phenological change: what can we learn about climate impacts from experiments and observations?
Bibliographic record
Abstract
Climate change can affect plant–pollinator interactions in a variety of ways, but much of the research attention has focused on whether independent shifts in phenology will alter temporal overlap between plants and pollinators. Here I review the research on plant–pollinator mismatch, assessing the potential for observational and experimental approaches to address particular aspects of the problem. Recent, primarily observational studies suggest that phenologies of co‐occurring plants and pollinators tend to respond similarly to environmental cues, but that nevertheless, certain pairs of interacting species are showing independent shifts in phenology. Only in a few cases, however, have these independent shifts been shown to affect population vital rates (specifically, seed production by plants) but this largely reflects a lack of research. Compared to the few long‐term studies of pollination in natural plant populations, experimental manipulations of phenology have yielded relatively optimistic conclusions about effects of phenological shifts on plant reproduction, and I discuss how issues of scale and frequency‐dependence in pollinator behaviour affect the interpretation of these ‘temporal transplant’ experiments. Comparable research on the impacts of mismatch on pollinator populations is so far lacking, but both observational studies and focused experiments have the potential to improve our forecasts of pollinator responses to changing phenologies. Finally, while there is now evidence that plant–pollinator mismatch can affect seed production by plants, it is still unclear whether this phenological impact will be the primary way in which climate change affects plant–pollinator interactions. It would be useful to test the direct effects of changing climate on pollinator population persistence, and to compare the importance of phenological mismatch with other threats to pollination.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".