Influence of habitat features and hunter behavior on white‐tailed deer harvest
Bibliographic record
Abstract
Abstract Sport hunting may help in controlling cervid populations over large areas. As with natural predators, several environmental factors can influence sport harvest. A better understanding of the environmental variables that limit the efficiency of sport hunting could provide guidelines for more efficient wildlife management using hunting. We studied white‐tailed deer ( Odocoileus virginianus ) hunting on a high deer density island where hunting was the sole form of predation. Our objective was to study the behavior of sport hunters and determine the habitat characteristics (e.g., abundance of deer forage, visibility of the deer from the hunter's point of view, and accessibility of the territory to hunters) that are associated with a successful harvest. We collected movements and harvest site location data from 477 hunters equipped with handheld Global Positioning System (GPS) units. Harvest sites were visited and characterized, along with a paired random site, to determine the environmental conditions associated with a successful hunt. We also developed a model to predict the daily number of deer seen by hunters considering weather conditions, hunter characteristics (e.g., age, experience), and date of hunting. We used the mean number of deer seen per hunter per day as a relative index of local density in each hunted territory. At both the site and landscape scales, the combination of visibility and access had a positive effect on the distribution of harvested deer. Habitat types with less visual obstruction from vegetation enabled hunters to see more deer in a given day. At the site scale, harvested deer were located in areas with a lower density of access routes compared to areas where hunters travelled throughout the day. Using an innovative approach of studying hunter behavior with GPS technology, digital maps, and questionnaires, we highlighted the factors associated with hunter success. Our study suggests that habitat characteristics could be modified to increase harvest by improving accessibility and visibility near roads. Creating openings in mature and regenerating forest near access roads could make sport hunting a more efficient management tool, but the potential impact of increased forage availability in forest openings should not be overlooked. © 2012 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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".