Estimating low-density snowshoe hare populations using fecal pellet counts
Bibliographic record
Abstract
Snowshoe hare (Lepus americanus) populations found at high densities can be estimated using fecal pellet densities on rectangular plots, but this method has yet to be evaluated for low-density populations. We further tested the use of fecal pellet plots for estimating hare populations by correlating pellet densities with estimated hare numbers on 12 intensive study areas in Idaho; pellet counts from extensive transects (n = 615) across northern Idaho enabled rectangular plots (0.155 m 2 ) to be compared with paired small (0.155 m 2 ) and large (1 m 2 ) circular plots (metre-circle plots). Metre-circle plots had higher pellet prevalence, lower sample variance, and lower estimates of pellet density than the other plot types. Transects comprising circular plots required less establishment time, and observer training reduced the pellet-count bias attributable to plot shape. The number of hares occupying intensive study sites was correlated with pellet density on all plot types, but rectangular plots provided a slightly closer linear fit to hare numbers than did metre-circle plots. The relationship between pellet density and hare number may have been curvilinear rather than linear, but linear and nonlinear models provided similar numerical estimates over much of the range of pellet densities. These results indicate that pellet counts are a robust estimator of hare numbers in low-density populations, and that metre-circle plots represent an improvement over standard rectangular plots in terms of unbiased pellet counts, sacrificing little predictive power. We recommend using pellet counts in metre-circle plots for estimating populations of snowshoe hares in their southern distribution.
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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 | 0.000 | 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) | 0.011 | 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".