Population growth rates of pronghorn: influence of temporally and spatially explicit conditions, density dependence, and scale
Bibliographic record
Abstract
Density-independent and density-dependent population regulation has long been a subject of investigation. We examined density-dependent and density-independent factors on growth rates of pronghorn ( Antilocapra americana (Ord, 1815)) using a retrospective analysis of population survey data. Across Idaho, we found that as the proportion of the subpopulation harvested the previous year increased, growth rates increased. Similarly, as fawn recruitment increased, growth rates increased. We also found when the growth rate in the previous year increased, the growth rate the subsequent year decreased. When subpopulations were examined independently, we found that in a low-elevation desert subpopulation, growth rates were influenced by growth the previous year. In an agricultural-dominated site, growth rates were influenced by fawn recruitment in the current year and maximum temperature the previous fall. Growth rates in a mid-elevation shrub–steppe site were influenced by drought severity prior to parturition and the growth rates from the previous year. Growth rates in two mountain valley subpopulations were influenced by measures of vegetative greenness. At the statewide scale, while managers may strive for increased numbers of pronghorn, density dependence will limit the ability for a region-wide numerical response. On the localized scale, drivers of growth were temporally and spatially explicit, and biologists must consider site-specific actions.
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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 | 0.001 | 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".