Trading off hatching success and cost in the captive breeding of Whooping Cranes
Bibliographic record
Abstract
Abstract Captive breeding is an increasingly used conservation strategy for species with a high risk of extinction in the wild, but managing a captive breeding programme can be challenging if there is a deficiency in knowledge about the species’ breeding biology. A knowledge gap can make it difficult to evaluate different management options. For avian species, egg hatching success is a key demographic parameter, and data‐logging egg technology can provide important information on optimal species‐specific incubation conditions, which can help inform captive breeding practises and identify efficient captive management options. In the context of a captive breeding programme for endangered Whooping Cranes Grus americana, we investigated associations between hatching success and incubation conditions, including environmental parameters (temperature, relative humidity and egg turning rate), and incubation type (artificial incubation; foster incubation by Sandhill Cranes, Grus canadensis; and Whooping Crane incubation). Finally, we considered both cost and breeding output in an analysis of incubation practises. We found that daily mean temperatures were negatively associated with hatching success, and that hatching success was highest with incubation under Sandhill Cranes. However, incubation by artificial incubators, rather than Sandhill Cranes, provided a trade‐off between cost and breeding output that is likely to be acceptable to many captive programme managers. We encourage other captive breeding programmes to use innovations that help to increase potential release numbers for conservation translocations by considering biological and financial constraints.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".