Dual‐frame lek surveys for estimating greater sage‐grouse populations
Bibliographic record
Abstract
ABSTRACT Effective monitoring programs are important for ensuring proper management of wildlife populations, but they require substantial resources, effort, and funding. For this reason, managers often use indices of animal abundance. However, indices typically rely on untested assumptions and may be unreliable for estimating population size, status, and trend. Therefore, it is important to test the assumptions underlying indices and to evaluate more rigorous methods of population estimation. Counts of males at leks are used as an index to monitor populations of greater sage‐grouse ( Centrocercus urophasianus ), a species of conservation concern, throughout its range in the western United States and Canada. However, not all leks are known, and wildlife managers typically have no rigorous, quantitative estimates of the number of leks and males in any given population. Therefore, it remains unclear what proportion of leks and males are included in, or excluded from, lek‐based population status assessments, trend analyses, and management decisions. We used dual‐frame surveys, in combination with occupancy analysis to adjust for imperfect detection, to estimate the number of active leks, the number of lekking males, and the proportion of active leks and lekking males counted on annual lek surveys over 3 consecutive breeding seasons in a small, low‐density greater sage‐grouse population in northwestern Colorado, USA. We estimated that annual lek surveys captured an average of 45–74% of active leks and 43–78% of lekking males each year. Our results suggest that many active leks remain unknown and annual counts fail to account for a substantial, but variable, proportion of the number of active leks and lekking males in the population in any given year. Managers need to recognize this potential source of bias in lek‐count data and, if possible, account for it in trend analyses and management efforts. Increased cost likely precludes annual use of dual‐frame lek surveys. However, we recommend using analytical methods that account for imperfect detection, mapping potential lek habitat to improve survey efficiency, and conducting dual‐frame surveys over multiple years to better understand temporal variation in the proportion of active greater sage‐grouse leks and lekking males being counted in specific populations of interest. © 2018 The Wildlife Society.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".