Influence of environmental change, harvest exposure, and human disturbance on population trends of greater sage-grouse
Bibliographic record
Abstract
Hunter harvest of greater sage-grouse (Centrocercus urophasianus; hereafter "sage-grouse") has been regulated by wildlife agencies during most of the past century. Hunting season regulations were maintained with the intention of providing sustainable hunting opportunities. Sage-grouse populations oscillate over time, and population growth can be influenced by seasonal weather and habitat disturbance. From 1995-2013, we compared sage-grouse lek trends from 22 relatively distinct sage-grouse population segments in 9 western U.S. states and 2 Canadian provinces. We stratified these populations into 3 broad categories (non-hunted [n = 8], continuously hunted [n = 10], and hunting season discontinued between 1996-2003 [n = 4]) with 8 different regulation histories to evaluate the potential impact of harvest on sage-grouse populations. Concomitantly, we assessed the effects of proportion burned, forested and cropland habitat; winter, spring, and summer precipitation; and human population, road, and oil and gas well densities on initial and time-varying lek counts. Density-dependent models fit lek trend data best for all regulation histories. In general, higher proportions of burnt, forested, and cropland habitat; and greater human population and oil and gas well densities were associated with lower equilibrium abundance (K). We found mixed results regarding the effect of hunting regulations on instantaneous growth rate (r). The cessation of harvest from 1996-2001 in approximately half of the largest sage-grouse population in our analysis was associated with higher r. Continuously harvested sage-grouse populations with permit hunting seasons had higher r during years with higher proportion of area exposed to permitted hunting rather than general upland game seasons. However, more liberal hunting regulations were positively associated with higher r in populations continuously harvested under general upland game hunts. Our results suggest that discontinuing harvest in the largest population resulted in greater population growth rates; however, this was not consistently the case for smaller populations. To no surprise, not all sage-grouse populations were influenced by the same environmental change or human disturbance factors. Our results will assist managers to understand factors associated with K, which provides the best targets for conservation efforts.
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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 | 0.001 |
| 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".