Disentangling the Contributions to Phenotypic Change in Conservation Breeding: A Case Study in a Breeding Program of an Endangered Migratory Songbird
Bibliographic record
Abstract
ABSTRACT Conservation breeding programs play a crucial role in managing endangered wildlife, yet successful reintroduction remains challenging due to the low fitness of released individuals and complexities in conservation breeding. This study addresses these ongoing challenges by investigating morphological changes in the endangered Eastern loggerhead shrike ( Lanius ludovicianus migrans ) breeding program across multiple facilities in southern Canada and the United States, which was started in 1997. Using mixed‐effect animal models and estimated breeding values, we explore potential signals of evolutionary change in the program and assess variables contributing to morphological variation and change. Our findings reveal dynamic variation in shrike morphology across years and generations, with some evidence of evolutionary change in shrike mass. However, mass changes appear to be unrelated to selection in captivity. Additionally, recent gene flow from the wild correlates with mass and tarsus width, suggesting gene flow from the wild might drive some of the morphological change. This study provides insights into the evolutionary processes in shaping morphological traits in a conservation breeding program. The identified genetic drivers offer considerations for refining breeding practices and insights into evolutionary change in a conservation breeding program, presenting significant implications for wildlife managers and conservation practitioners. Importantly, observed temporal heterogeneities and trends are likely influenced not only by genetic changes but also by plasticity, and plasticity could mask simultaneously occurring genetic change. More research is needed to characterize and determine how and if plastic responses are directionally shaping phenotypic distributions in captivity.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".