Loss of Predator Discrimination by Critically Endangered Vancouver Island Marmots Within Five Generations of Breeding for Release
Bibliographic record
Abstract
Conservation translocations, which involve the intentional movement and release of organisms for conservation benefit, are increasingly required to recover species of conservation concern. In order to maximize post-release survival, and to accomplish conservation translocation objectives, animals must exhibit behaviors that facilitate survival in the wild. The Vancouver Island marmot (Marmota vancouverensis) is a critically endangered endemic in Canada which has been captive-bred for 24 years for reintroductions and reinforcements that have increased the wild population from ~30 to more than 200 individuals. Despite this success many marmots are killed by predators after release and predation represents a major hurdle to full marmot recovery. To better understand if captive-bred marmots are prepared for the novel environment into which they will be released, and to determine whether such suitability changes over time, we presented taxidermy mounts of mammalian predators and non-predators to marmots that were wild-caught, and captive born for between one and five generations. We also examined mortality of offspring from marmots we tested that had been released to the wild. A minimum of 43% of offspring were killed by predators in the wild over 17 years, most by cougars. Marmots in captivity generally responded to taxidermy mounts by decreasing foraging and increasing vigilance, and overall responded more strongly to predators than non-predators, especially wolves. However, marmots in captivity for more than two generations lacked discrimination between cougars, non-predators, and controls, suggesting a rapid loss of predator recognition. This study was only possible because predator-recognition trials were initiated early in the conservation translocation program, and could then be repeated after a number of generations. The finding that changes occurred relatively rapidly (within five generations during which changes in genetic diversity were negligible) suggests that behavioral suitability may deteriorate more rapidly than genetics would suggest. Strategies addressing potential behavior loss should be considered, including sourcing additional wild individuals or pre-release training of captive-born individuals. Subsequently, post-release survival should be monitored to determine the efficacy of behavior-optimization strategies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".