Population Dynamics and Harvest Potential of Mountain Goat Herds in Alberta
Bibliographic record
Abstract
The understanding of population dynamics is a central issue for managing large mammals. Modeling has allowed population ecologists to increase their knowledge about complex systems and better predict population responses to diverse perturbations. Mountain goats (Oreamnos americanus) appear sensitive to harvest, but the relative influence of survival and reproductive rates on their population dynamics are not well understood. Using longitudinal data on age- and sex-specific survival and reproduction from a marked mountain goat population in Alberta, Canada, we built a stage-class matrix model to predict short-term numerical changes for 11 other goat populations in Alberta for which the only data available were from annual aerial surveys. Overall, the model provided an acceptable fit to changes in population size for 8 of 12 populations. Temporal trends in population size were underestimated in 2 populations and overestimated in another 2, suggesting that these populations had different vital rates than those of the intensively studied population. Sensitivity analyses revealed that the survival of mature females (aged 5 yr and older) had the greatest elasticity for population growth. Modeled management scenarios indicated that nonselective yearly harvest rates above 1% of goats aged 2 years and older were not sustainable over the short-term for some populations. The simulations also revealed that small (n = 25) and medium-size (n = 50) populations, which correspond to most goat populations in Alberta, had high extinction risk (18 to 82% over 40 years), even in the absence of harvest. Our results confirm that mountain goat populations are very sensitive to harvest, indicate that wildlife managers should prevent female harvest, and suggest that although a high demand for goat hunting exists in Alberta, most populations in this province—and probably small populations elsewhere—cannot withstand exploitation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".