Kin grouping is insufficient to explain the inclusive fitness gains of conspecific brood parasitism in the common eider
Bibliographic record
Abstract
Conspecific brood parasitism allows females to exploit other females' nests and enhance their reproductive output. Here, we test a recent theoretical model of how host females gain inclusive fitness from brood parasitism. High levels of relatedness between host and parasitizer can be maintained either by: (a) kin recognizing and parasitizing each other as a form of cooperative breeding or (b) natal philopatry and nest site fidelity facilitating the formation of kin groups, thereby increasing the probability of parasitism between relatives nesting in close proximity. To address these two hypotheses we genotyped feathers and hatch membranes of common eiders (Somateria mollissima) from western Hudson Bay, Canada, using a noninvasive sampling methodology. We found that most instances of brood parasitism do result in inclusive fitness gains. Furthermore, females with failed nests moved an average of 492 m from their previous year's nest site, while successful females only moved an average of 13 m. Therefore, we observed host-parasite relatedness can occur at levels higher than would be expected by chance even in the absence of kin grouping, suggesting that closely related females nesting near one another is not essential to maintain high host-parasitizer relatedness. In addition, kin grouping is only a transient phenomenon that cannot occur every year due to the propensity for females of failed nests to nest farther away from their nest site in subsequent years than females with successful nests, which provides support for kin recognition as a more likely mechanism to maintain high host-parasitizer relatedness over time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".