Population viability analysis to identify management priorities for reintroduced Elk in the Cumberland Mountains, Tennessee
Bibliographic record
Abstract
Abstract We used an individual‐based population model to perform a viability analysis to simulate population growth ( λ ) of 167 elk ( Cervus elaphus manitobensis ; 71 male and 96 female) released in the Cumberland Mountains, Tennessee, to estimate sustainability (i.e., λ > 1.0) and identify the most appropriate options for managing elk restoration. We transported elk from Elk Island National Park, Alberta, Canada, and from Land Between the Lakes, Kentucky, and reintroduced them beginning in December 2000 and ending in February 2003. We estimated annual survival rates for 156 radio‐collared elk from December 2000 until November 2004. We used data from a nearby elk herd in Great Smoky Mountains National Park to simulate pessimistic and optimistic recruitment and performed population viability analyses to evaluate sustainability over a 25‐year period. Annual survival averaged 0.799 (Total SE = 0.023). The primary identifiable sources of mortality were poaching, disease from meningeal worm ( Parelaphostrongylus tenuis ), and accidents (environmental causes and unintentional harvest). Population growth given pessimistic recruitment rates averaged 0.895 over 25 years (0.955 in year 1 to 0.880 in year 25); population growth was not sustainable in 100% of the runs. With the most optimistic estimates of recruitment, mean λ increased to 0.967 (1.038 in year 1 to 0.956 in year 25) with 99.6% of the runs failing to be sustainable. We suggest that further translocation efforts to increase herd size will be ineffective unless survival rates are increased in the Cumberland Mountains. © 2011 The Wildlife Society.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".