Mimicry in hoverflies (Diptera: Syrphidae): a field test of the competitive mimicry hypothesis
Bibliographic record
Abstract
Although most studies on the evolution of mimicry and warning coloration in insects have considered predators as the major selective force, it is possible that competition for food resources could also facilitate selection for these conspicuous signals. For example, when warningly colored social wasps visit flowers, then they frequently behave aggressively toward heterospecifics, and they also attack and feed on other flying insects. Under these conditions, a resemblance to a wasp might provide a mimetic hoverfly with improved access to floral resources by reducing the frequency with which it is disturbed by other pollinators. We experimentally evaluated whether wasp-like colors and patterns were important in preventing other flower visitors from sharing the same flower resource, using pairwise presentations of both natural and artificial prey in the field. Flower visitors were more likely to visit unoccupied flowers compared with the flowers pinned with either natural or artificial specimens in 2 plant species with different inflorescences. However, flower visitors did not show a significantly reduced rate of visitation to flowers pinned with specimens bearing wasp-like colors and patterns compared with the flowers occupied by similar-sized specimens that were nonmimetic. Overall, we found no compelling evidence in this study to support the contention that wasp-like warning signals of hoverflies prevent other flower visitors from sharing flower resources, although insects showed a greater tendency to avoid visiting flowers pinned with a wasp compared with flowers pinned with a nonmimetic fly.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".