The evolution of egg shape in birds: selection during the incubation period
Bibliographic record
Abstract
A recent broad comparative study suggested that factors during egg formation – in particular ‘flight efficiency’, which explained only 4% of the interspecific variation – are the main forces of selection on the evolution of egg shape in birds. As an alternative, we tested whether selection during the incubation period might also influence egg shape in two taxa with a wide range of egg shapes, the alcids (Alcidae) and the penguins (Spheniscidae). To do this, we analysed data from 30 species of these two distantly related but ecologically similar bird families with egg shapes ranging from nearly spherical to the most pyriform eggs found in birds. The shape of pyriform eggs, in particular, has previously proven difficult to quantify. Using three egg‐shape indices – pointedness, polar‐asymmetry and elongation – that accurately describe the shapes of all birds’ eggs, we examined the effects of egg size, chick developmental mode, clutch size and incubation site on egg shape. Linear models that include only these factors explained 70–85% of the variation in these egg‐shape indices, with incubation site consistently explaining > 60% of the variation in shape. The five species of alcids and penguins that produce the most pyriform eggs all incubate in an upright posture on flat or sloping substrates, whereas species that incubate in a cup nest have more spherical eggs. We suggest that breeding sites and incubation posture influence the ability of parents to manipulate egg position, and thus selection acting during incubation may influence egg‐shape variation across birds as a whole.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".