Relative influence of human harvest, carnivores, and weather on adult female elk survival across western<scp>N</scp>orth<scp>A</scp>merica
Bibliographic record
Abstract
Summary Well‐informed management of harvested species requires understanding how changing ecological conditions affect demography and population dynamics, information that is lacking for many species. We have limited understanding of the relative influence of carnivores, harvest, weather and forage availability on elk Cervus elaphus demography, despite the ecological and economic importance of this species. We assessed adult female survival, a key vital rate for population dynamics, from 2746 radio‐collared elk in 45 populations across western North America that experience wide variation in carnivore assemblage, harvest, weather and habitat conditions. Proportional hazard analysis revealed that ‘baseline’ (i.e. not related to human factors) mortality was higher with very high winter precipitation, particularly in populations sympatric with wolves Canis lupus . Mortality may increase via nutritional stress and heightened vulnerability to predation in snowy winters. Baseline mortality was unrelated to puma Puma concolor presence, forest cover or summer forage productivity. Cause‐specific mortality analyses showed that wolves and all carnivore species combined had additive effects on baseline elk mortality, but only reduced survival by <2%. When human factors were included, ‘total’ adult mortality was solely related to harvest; the influence of native carnivores was compensatory. Annual total mortality rates were lowest in populations sympatric with both pumas and wolves because managers reduced female harvest in areas with abundant or diverse carnivores. Mortality from native carnivores peaked in late winter and early spring, while harvest‐induced mortality peaked in autumn. The strong peak in harvest‐induced mortality during the autumn hunting season decreased as the number of native carnivore species increased. Synthesis and applications . Elevated baseline adult female elk mortality from wolves in years with high winter precipitation could affect elk abundance as winters across the western US become drier and wolves recolonize portions of the region. In the absence of human harvest, wolves had additive, although limited, effects on mortality. However, human harvest, and its apparent use by managers to offset predation, primarily controls overall variation in adult female mortality. Altering harvest quotas is thus a strong tool for offsetting impacts of carnivore recolonization and shifting weather patterns on elk across western North America.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".