Amphibian reproductive technologies: approaches and welfare considerations
Bibliographic record
Abstract
Abstract Captive breeding and reintroduction programs have been established for several threatened amphibian species globally, but with varied success. This reflects our relatively poor understanding of the hormonal control of amphibian reproduction and the stimuli required to initiate and complete reproductive events. While the amphibian hypothalamo–pituitary–gonadal (HPG) axis shares fundamental similarities with both teleosts and tetrapods, there are more species differences than previously assumed. As a result, many amphibian captive breeding programs fail to reliably initiate breeding behaviour, achieve high rates of fertilization or generate large numbers of healthy, genetically diverse offspring. Reproductive technologies have the potential to overcome these challenges but should be used in concert with traditional methods that manipulate environmental conditions (including temperature, nutrition and social environment). Species-dependent methods for handling, restraint and hormone administration (including route and frequency) are discussed to ensure optimal welfare of captive breeding stock. We summarize advances in hormone therapies and discuss two case studies that illustrate some of the challenges and successes with amphibian reproductive technologies: the mountain yellow-legged frog (Rana muscosa; USA) and the northern corroboree frog (Pseudophryne pengilleyi; Australia). Further research is required to develop hormone therapies for a greater number of species to boost global conservation efforts.
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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 | 0.005 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".