Spatial patterns of population regulation in sage grouse (<i>Centrocercus</i>spp.) population viability analysis
Bibliographic record
Abstract
Summary Population viability analyses (PVAs) are commonly used to identify species of concern. Many PVA techniques assume that all populations are regulated by a single mechanism. We compared population viability predictions for three subspecies of sage grouse (Centrocercusspp.) based on the assumptions that: (i) population regulation was density‐independent vs. dependent on more complex feedback mechanisms; (ii) the mechanism of population regulation was homogeneous within a region vs. heterogeneous among leks; (iii) environmental variation was spatially correlated within regions vs. uncorrelated among leks. We used sage grouse as a model species for this analysis because counts of lekking male grouse are available in some areas since the 1950s, these counts are known to fluctuate widely, and sage grouse appear to be declining throughout their range. We fit population regulation models to data including density‐independence, density‐dependence, delayed density‐dependence and a simplified version of Turchin & Taylor's (1992) response surface model. We show that the best‐fit models typically include spatial heterogeneity in mechanisms of population regulation. Inclusion of spatial heterogeneity increased expected time for population persistence, and changed the rank order of risk of extinction for different regions. We suggest that it is important to consider multiple models of population regulation when applying population viability analysis techniques because viability projections are influenced strongly by model structure.
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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 | 0.002 |
| 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".