Elk population dynamics when carrying capacities vary within and among herds
Bibliographic record
Abstract
Population and land management relies on understanding population regulation and growth, which may be impacted by variation in population growth parameters within and among populations. We explored the interactions between variation in carrying capacity (K), intrinsic population growth rate (r), and strength of density dependence (β) within and among elk (Cervus elaphus) herds in a small part of the geographic range of the species. We also estimated stochastic fluctuations in abundance around K for each herd. We fit linear Ricker growth models using Bayesian statistics to seven time series of elk population survey data. Our results indicate that K and β varied among herds, and that r and β varied temporally within herds. We also found that herds with smaller K had less stochastic fluctuation in abundances around K, but higher temporal variation in β within herds. Population regulation and the rate of return to the equilibrium abundance is often understood in terms of β, but ecological populations are dynamic systems, and temporal variation in population growth parameters may also influence regulation. Population models which accommodate variation both within and among herds in population growth parameters are necessary, even in mild climates, to fully understand population dynamics and manage populations.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".