Development of optimized methods for unbiased dusky grouse population monitoring using real and simulated data
Bibliographic record
Abstract
Rigorous state-wide monitoring programs are lacking for dusky grouse (Dendragapus obscurus), a North American species of forest grouse with relatively low detectability that is found in coniferous and mountainous areas in the western United States and Canada. Hierarchical models for estimating abundance show promise for overcoming issues associated cryptic forest grouse behavior and low population densities. Our objectives were to evaluate protocols (i.e., number of sites, visits, and route type) and analytical methods for producing annual unbiased and precise indices of abundance (CV < 15%) to inform a statewide monitoring program. During 2019–2022, we designed and implemented multiple survey protocols throughout western Montana, USA, including spring point-counts (> 2200 unique sites) and transect-level (> 390 unique transects) distance sampling. We used an iterative process of field data collection and simulation analyses to evaluate the performance of four different statistical estimators for abundance (N-mixture model, hierarchical time-removal model with distance sampling, detection-naïve model, and hierarchical distance sampling model) for point-counts and transects to produce unbiased and precise estimates of dusky grouse abundance. Simulations demonstrated that increasing the number of sites visited or probability of detection decreased the requisite amount of survey effort for obtaining precise abundance estimates for all estimators. Unbiased and precise estimates of abundance were unachievable under most realistic point-based distance sampling protocols. N-mixture protocols where point-counts conducted during periods of high probability of detection at 80 sites visited four times per area of inference (e.g. study area or region) resulted in unbiased estimates of population size with the highest precision. Our study provides baseline information necessary for the development of state-wide monitoring programs of dusky grouse and more broadly illustrates an approach for developing rigorous and achievable monitoring programs for other species of forest grouse.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".