Intrinsic and extrinsic sources of variation in the dynamics of large herbivore populations
Bibliographic record
Abstract
We evaluated the relative importance of intrinsic (density-dependent) and extrinsic (density-independent) sources of resource variability in the dynamics of bighorn sheep ( Ovis canadensis Shaw, 1804) populations in southwestern USA. We tested the hypothesis that populations experiencing greater variation in forage availability are less likely to be at equilibrium density with forage supplies, and thus, would demonstrate weaker evidence of intraspecific competition. We used regression to relate population growth rate to rainfall (forage conditions) and to abundance (intraspecific competition) for 20 sheep populations, and we estimated the strength of the effects of density and rainfall for each population. Then we compared among populations to look for relationships between the strengths of rainfall and density effects and the variability in rainfall experienced by each population. Populations demonstrating a density effect were associated with environments having lower variability than those that did not. Populations showing a rainfall effect were associated with environments having a variability higher than those that lacked a rainfall effect. These findings are consistent with the hypothesis that populations experiencing higher variation in forage resources are less likely to achieve an equilibrium density with forage supplies where intraspecific competition would be a large determinant to the dynamics of that population.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".