Data for: Should I breed or should I go? Manipulating individual state during migration influences breeding decisions in a long-lived bird species
Bibliographic record
Abstract
Documentation of Carry-Over Effects (COEs), defined as effects resulting from events that occurred in a previous time period, has largely been observational and understanding of specific mechanisms underlying COEs is still lacking. To investigate this, we simulated an environmental perturbation during the spring migration of a long-lived bird species and looked at the subsequent effects on various breeding parameters. We captured female greater snow geese (<em>Anser caerulescens atlanticus</em>) on their spring staging sites and maintained individuals in captivity for up to four days before releasing them. We re-observed females 3000 km North, on their Arctic breeding grounds, to estimate their breeding propensity (i.e., probability of initiating a reproductive event for a given year), and measure their arrival date, laying date, clutch size, and nesting success. Only proxies of breeding propensity were affected by our manipulation, which decreased as the time spent in captivity increased. However, females were able to overcome the effects of captivity in two out of the three years of experimentation with normal or good environmental conditions at the breeding site. When facing the additional challenge of poor environmental conditions, many individuals manipulated during migration apparently curtailed their reproductive effort by skipping breeding. This experiment is the first to show that breeding propensity is an important parameter affected by COEs resulting from stressful events prior to reproduction in long-lived species.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 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 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".