When less is more: Visitation by generalist pollinators can have neutral or negative effects on plant reproduction
Bibliographic record
Abstract
Selection for specialized coevolutionary relationships can arise if generalized opportunistic pollinators, while still delivering some pollen, operate as less effective pollen delivery agents. Nevertheless, generalization could buffer high-latitude communities from loss of specialist pollinator species by providing some pollination service. Currently, there is limited understanding of the ecosystem services provided by generalized pollinators and whether they increase the fitness of the plants they visit. Network data and thorough observations of floral visitors, paired with estimates of seed set, offer some insight into the role of generalists, which in turn can inform us about how plants are likely to respond to ecosystem disturbances, such as losses of some pollinators, or changes in land cover. Here, we report on plant-pollinator visitation networks in Canada with high levels of generalization and examine the effects of generalization on seed set under different disturbance histories. We also then take a case study of one crop wild relative, Rubus arcticus or Arctic raspberry, and report on a near-complete characterization of pollinator interactions in different environmental conditions. Our findings indicate that generalized pollinators, though frequent and robust to variable temperatures and moisture conditions, do not appear to play a strong role in increasing the reproductive output of many plant species, and may provide only a weak buffer against the stronger effects of disturbance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".