Climate-induced variation in vital rates of an unharvested large-herbivore population
Bibliographic record
Abstract
Variation in vital rates of an unharvested elk (Cervus elaphus) population was studied using telemetry for 7 consecutive years, 19911998. We found pronounced senescence in survival rates, but no evidence for reproductive senescence. Prime-age females (<10 years old) experienced very high annual survival rates (mean = 0.97, SE = 0.02), with lower survival rates for senescent animals ([Formula: see text]10 years old; mean = 0.79, SE = 0.06). There was evidence that the severity of snowpack conditions had little effect on survival of prime-age animals except during the most extreme winter, while survival of senescent animals was progressively depressed as the severity of snowpack conditions increased. Reproductive rates remained essentially constant, near their biological maxima (mean = 0.91, SE = 0.02). Annual re cruitment was highly variable. Snowpack had a pronounced effect on recruitment (r2 = 0.91), the most severe snowpack conditions resulting in the virtual elimination of a juvenile cohort. Population estimates and recruitment rates obtained during this investigation and historic data collected from 1965 to 1980 support the premise that the population has been maintained in a dynamic equilibrium for at least three decades despite the stochastic effects of climate variation on vital rates. We conclude that the population is resource-limited, with variation about the equilibrium caused primarily by variable recruitment driven by stochastic annual snowpack.
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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 | 0.000 | 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 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".