Unpredictable perturbation reduces breeding propensity regardless of pre‐laying reproductive readiness in a partial capital breeder
Bibliographic record
Abstract
Theoretically, individuals of migratory species should optimize reproductive investment based on a combination of timing of and body condition at arrival on the breeding grounds. A minimum threshold body mass is required to initiate reproduction, and the timing of reaching this threshold is critical because of the trade‐off between delaying breeding to gain in condition against the declining value of offspring with later reproductive timing. Long‐lived species have the flexibility within their life history to skip reproduction in a given year if they are unable to achieve this theoretical mass threshold. Although the decision to breed or not is an important parameter influencing population dynamics, the mechanisms underlying this decision are poorly understood. Here, we mimicked an unpredictable environmental perturbation that induced a reduction in body mass of Arctic pre‐breeding (before the laying period) female common eiders Somateria mollissima ; a long‐lived migratory seaduck, while controlling for individual variation in the pre‐laying physiological reproductive readiness via vitellogenin (VTG) – a yolk‐targeted lipoprotein. Our aim was to causally determine the interaction between body condition and pre‐laying reproductive readiness (VTG) on breeding propensity by experimentally reducing body mass in treatment females. We first demonstrated that arrival body condition was a key driver of breeding propensity. Secondly, we found that treatment and VTG levels interacted to influence breeding propensity, indicating that our experimental manipulation, mimicking an unpredictable food shortage, reduced breeding propensity, regardless of the degree of pre‐laying physiological reproductive readiness (i.e. timing of ovarian follicles recruitment). Our experiment demonstrates that momentary environmental perturbations during the pre‐breeding period can strongly affect the decision to breed, a key parameter driving population dynamics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".