Effects of population reduction on home ranges of female white-tailed deer at high densities
Bibliographic record
Abstract
The relationship between deer density and home range size is important in assessing the effectiveness of deer reduction programs and predicting the effects of deer on habitat. We quantified annual home range and core area size and spatial configuration of adult female white-tailed deer (Odocoileus virginianus) exposed to a population reduction program and a control group exposed to no population reduction program over a 4-year period (19941997). Deer were removed from Bluff Point during a 9-day shotgun hunt in 1996 and a 4-day removal program in 1997. Annual home range size during high deer densities (8891 deer/km2) were larger than during periods of moderate (20 deer/km2) and low deer densities (11 deer/km2). We found a positive relationship between deer density and home range size. Annual home range size for the control group of deer did not differ among years. There were no significant shifts in the spatial arrangement of deer home ranges as deer densities were reduced. Significant improvements in deer herd health and reductions in deer browsing were documented during the 2-year deer reduction program. Population reduction programs at our study area did not cause the resident deer population to expand home range size or shift into adjacent habitat. We believe that localized deer reduction programs can be effective tools to manage problem deer herds. Deer removal efforts initiated to reduce deer damage to vegetation, particularly in urban areas, may have an added effect of reducing foraging range of the remaining resident deer.
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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 | 0.001 |
| 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 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".