Role of predation and hunting on eastern cottontail mortality at Cape Cod National Seashore, Massachusetts
Bibliographic record
Abstract
The degree that hunting may influence game populations depends on whether hunting mortality is additional to (additive) or replaces (compensatory) natural-caused mortalities. In response to limited information on the effects of exploitation on eastern cottontail ( Sylvilagus floridanus (J.A. Allen, 1890)) populations within Cape Cod National Seashore (CCNS), we initiated an investigation of cause-specific mortality using transmitter-equipped cottontails in hunted and nonhunted areas as a way to examine the additive versus compensatory aspect of hunting. Predation caused >70% of all deaths, whereas hunting caused 10% of deaths in the areas sampled. Survival rate was substantially lower among hunted sites (0.05) than at nonhunted sites (0.19) during the winter–spring of year 1, but there was no difference between hunted (0.33) and nonhunted (0.40) sites during year 2. Lower survival in year 1 was likely due to deep and persistent snow that increased vulnerability to predation and probably reduced the prospect for hunting mortalities to be compensated by reductions among other mortality factors. However, at least partial compensation apparently occurred during year 2 when winter weather was less severe. Cottontails at CCNS are near the northern edge of their geographic range and therefore may be ultimately limited by severe weather conditions. Compared with predation, we do not believe that the current levels and distributions of hunting influence cottontail populations at CCNS.
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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 | 0.000 | 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) | 0.002 | 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".