Know Thy Enemy: Experience Affects Elk Translocation Success in Risky Landscapes
Bibliographic record
Abstract
ABSTRACT To maximize success, reintroduction programs generally select predator‐free release areas having high habitat quality. Past studies provide little insight into recovery efforts where multiple, potentially novel, mortality hazards occur. The ability of translocated animals to cope with novel environments can be affected by both pre‐ and postrelease experiences with habitat and mortality risks. We experimentally released elk (Cervus elaphus) having different background experiences into an area where predators and hunters were prevalent and habitat quality varied. Using a competing risks approach, we predicted the postrelease survival of individuals and their fidelity to release areas as a function of animal source and postrelease encounters with forage resources and areas used by wolves (Canis lupus) or humans. Mortality patterns were consistent with prerelease exposure to mortality risks but not habitat differences among source areas. Wolf predation, poaching, and legal Native hunting were equivalent in magnitude and accounted for the majority of elk mortalities. Familiarity with either wolves or hunters prior to release yielded first‐year survival rates 1.9‐2.2 times greater than observed for animals naive to both risks. These 2 primary sources of mortality traded off temporally as well as spatially given the proximity of roads, which wolves avoided. The prevalence of forage resources in release areas increased fidelity to release sites but coincided with higher mortality risk during the critical first year, potentially setting an ecological trap for animals naïve to local risks. Translocated individuals largely mediated their respective vulnerabilities over time, showing second‐year survival rates equivalent to resident elk. In addition to using source populations that are able to adjust to mortality risks in release areas, spatial and temporal variation in mortality risks might be exploited when planning releases to increase the success of translocations into risky landscapes. (JOURNAL OF WILDLIFE MANAGEMENT 71(2):541–554; 2007)
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".