Drivers of resource allocation for breeding under variable environments in a bet hedger
Bibliographic record
Abstract
Abstract The evolutionary theory of life histories predicts that there is a trade‐off between survival and reproduction: since adult survival in long‐lived organisms is high, then breeding investment is more variable and more dependent on conditions (e.g. food availability and individual experience). Clutch features influence fitness prospects, but how a bet hedger builds its clutch in temporally varying environments is quite unknown. Using 27‐year data on 2847 clutches of known‐age breeders, we analyse how Audouin's gulls (Larus audouinii), a species showing a combination of conservative and adaptive bet‐hedging breeding strategies, can allocate energy by laying clutches and eggs of different sizes. Results show that both food availability and age influenced clutch size and total egg volume in a clutch. Interestingly, we found an interaction between food and age on egg parameters: total volume in two‐egg clutches, laid mostly by younger breeders, did not significantly change with food availability and the quadratic pattern in clutch size over the range of ages was less marked as long as food conditions became harsher. With increased food, females invested more by building larger first eggs, whereas they were more conservative on second and third eggs. Furthermore, asymmetries in egg volume within three‐egg clutches increased with food availability for old females. Egg size profiles of two‐egg clutches suggest that gulls should exhibit progressive reduction of the size of the third egg before shifting to a two‐egg clutch size. Food availability influenced all parameters studied, whereas age affected the amount of energy allocated for producing eggs (their size and number) but not the way of allocating those energies (i.e. asymmetries within the clutch). Despite the range of factors affecting the clutch, results suggest that females can allocate the amount of resources in a clutch optimally to increase their fitness under variable environments via bet‐hedging.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 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".