PELLET COUNT INDICES COMPARED TO MARK–RECAPTURE ESTIMATES FOR EVALUATING SNOWSHOE HARE DENSITY
Bibliographic record
Abstract
Snowshoe hares (Lepus americanus) undergo remarkable cycles and are the primary prey base of Canada lynx (Lynx canadensis), a carnivore recently listed as threatened in the contiguous United States. Efforts to evaluate hare densities using pellets have traditionally been based on regression equations developed in the Yukon, Canada. In western Montana, we evaluated whether or not local regression equations performed better than the most recent Yukon equation and assessed whether there was concordance between pellet-based predictions and mark–recapture density estimates of hares. We developed local Montana regression equations based on 224 data points consisting of mark-recapture estimates and pellet counts, derived from 38 sites in 2 different areas sampled for 1 to 5 years using 2 different pellet plot shapes. We evaluated concordance between estimated density and predicted density based on pellet counts coupled with regression equations at 436 site-area-season combinations different from those used to develop the regression equations. At densities below 0.3 hares/ha, predicted density based on pellets tended to be greater than for mark–recapture; the difference was usually <1 hare per ha on an absolute scale, but at low densities this translated to proportional differences of 1,000% or greater. At densities above 0.7 hares/ha, pellet regressions tended to predict lower density than mark–recapture. Because local regression equations did not outperform the Yukon equation, we see little merit in further development of local regression equations unless a study is to be conducted in a formal double-sampling framework. We recommend that widespread pellet sampling be used to identify areas with very low hare densities; subsequent surveys using mark–recapture methodology can then focus on higher density areas where density inferences are more reliable.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".