The effect of social group size on feather corticosterone in the co-operatively breeding Smooth-billed Ani (Crotophaga ani): An assay validation and analysis of extreme social living
Bibliographic record
Abstract
Living closely with others can provide a myriad of fitness benefits, from shared territory defense to co-operative resource acquisition. Costs of social aggregation are not absent, however, and likely influence optimal and observed groups' sizes in a social species. Here, we explored optimal group size in a joint-nesting cuckoo species (the Smooth-billed Ani, Crotophaga ani) using endocrine markers of stress physiology (corticosterone, or CORT). Smooth-billed Anis exhibit intense reproductive competition that is exacerbated in atypically large groups. We therefore hypothesized that intra-group competition (measured by social group size) mediates the desirability and physiological cost of social group membership in this species. To test this hypothesis, we captured 47 adult Smooth-billed Anis (31 males, 16 females) during the breeding seasons of 2012-2014 in south-western Puerto Rico, and documented social group sizes. Tail feathers were sampled and used to quantify CORT (pg/mg) in enzyme-linked immunosorbent assays (ELISAs) (n = 50). Our analyses show significant differences in feather-CORT of adults between categorical group sizes, with individuals from atypically large social groups (≥ x + 1SD) having highest mean concentrations (33.319 pg/mg), and individuals from atypically small social groups (≤ x - 1SD) having lowest mean concentrations (8.969 pg/mg). Whether reproductive competition or effort is responsible for elevated CORT in atypically large social groups, however, remains unclear. Our results suggest that living in atypically large groups is physiologically expensive and may represent an evolutionarily unstable strategy. To our knowledge, this is the first study to explore a correlation between stress physiology and group size in a joint-nesting species.
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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) | 0.001 | 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".