Food load manipulation ability shapes flight morphology in females of central-place foraging Hymenoptera
Bibliographic record
Abstract
BACKGROUND: Ecological constraints related to foraging are expected to affect the evolution of morphological traits relevant to food capture, manipulation and transport. Females of central-place foraging Hymenoptera vary in their food load manipulation ability. Bees and social wasps modulate the amount of food taken per foraging trip (in terms of e.g. number of pollen grains or parts of prey), while solitary wasps carry exclusively entire prey items. We hypothesized that the foraging constraints acting on females of the latter species, imposed by the upper limit to the load size they are able to transport in flight, should promote the evolution of a greater load-lifting capacity and manoeuvrability, specifically in terms of greater flight muscle to body mass ratio and lower wing loading. RESULTS: Our comparative study of 28 species confirms that, accounting for shared ancestry, female flight muscle ratio was significantly higher and wing loading lower in species taking entire prey compared to those that are able to modulate load size. Body mass had no effect on flight muscle ratio, though it strongly and negatively co-varied with wing loading. Across species, flight muscle ratio and wing loading were negatively correlated, suggesting coevolution of these traits. CONCLUSIONS: Natural selection has led to the coevolution of resource load manipulation ability and morphological traits affecting flying ability with additional loads in females of central-place foraging Hymenoptera. Release from load-carrying constraints related to foraging, which took place with the evolution of food load manipulation ability, has selected against the maintenance of a powerful flight apparatus. This could be the case since investment in flight muscles may have to be traded against other life-history traits, such as reproductive investment.
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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) | 0.002 | 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".