Variation in Reproductive Success Across Captive Populations: Methodological Differences, Potential Biases and Opportunities
Bibliographic record
Abstract
Abstract Our understanding of fundamental organismal biology has been disproportionately influenced by studies of a relatively small number of ‘model’ species extensively studied in captivity. Laboratory populations of model species are commonly subject to a number of forms of past and current selection that may affect experimental outcomes. Here, we examine these processes and their outcomes in one of the most widely used vertebrate species in the laboratory – the zebra finch (Taeniopygia guttata). This important model species is used for research across a broad range of fields, partly due to the ease with which it can be bred in captivity. However despite this perceived amenability, we demonstrate extensive variation in the success with which different laboratories and studies bred their subjects, and overall only 64% of all females that were given the opportunity, bred successfully in the laboratory. We identify and review several environmental, husbandry, life‐history and behavioural factors that potentially contribute to this variation. The variation in reproductive success across individuals could lead to biases in experimental outcomes and drive some of the heterogeneity in research outcomes across studies. The zebra finch remains an excellent captive animal system and our aim is to sharpen the insight that future studies of this species can provide, both to our understanding of this species and also with respect to the reproduction of captive animals more widely. We hope to improve systematic reporting methods and that further investigation of the issues we raise will lead both to advances in our fundamental understanding of avian reproduction as well as to improvements in future welfare and experimental efficiency.
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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 | 0.097 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".