Finding common ground: how hikers influence white-tailed deer space-use patterns in a UNESCO biosphere reserve
Bibliographic record
Abstract
Across most of North America, white-tailed deer (Odocoileus virginianus) populations are increasingly overabundant and the widespread impacts of their herbivory are having important ecological and economic repercussions. The cascading consequences of white-tailed deer overabundance are concerning as they can threaten even our most vulnerable forest ecosystems. In protected areas, where recreational activities often take place, human presence has been shown to affect wildlife in various ways, which include disturbing the spatial distribution of individuals. If white-tailed deer change their space use in response to human presence, their browsing pressure could be displaced and concentrated in certain areas. By understanding the factors that affect the distribution of white-tailed deer, we can therefore better predict where the impacts of their herbivory will be strongest and thus inform management decisions. Here, I first provide a historical context for the observed overabundances of white-tailed deer and review the repercussions of such overabundances. I then discuss the challenges of white-tailed deer management and review methods used to monitor deer populations. Finally, I present a case study, in which I investigated the effects of hiker presence on the spatial distribution of white-tailed deer in a UNESCO Biosphere Reserve. In this study, I used unbaited motion-activated camera traps to compare the relative abundance of white-tailed deer in areas of the reserve with hiking to areas without hiking. I found that white-tailed deer did not avoid hikers neither spatially nor temporally. I discuss the factors that may contribute to this distribution and the possible interaction between deer and hiker presence, such as habituation to humans, a preference for trail-altered habitats, the use of humans as a refuge from predators, or a lack of food resources. I conclude by proposing future studies to further understand the role of recreation on wildlife and to determine whether altered distributions of individuals affect browsing pressure across a landscape
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".