Red deer behavioural response to hiking activity: a study using camera traps
Bibliographic record
Abstract
Abstract With increasing levels of outdoor recreation activities, consequences for wildlife through interactions with recreationists are highly variable. Behavioural changes in wildlife are one potential consequence of interactions with outdoor recreationists. In ungulate populations, vigilance and flight responses are well‐known antipredator behaviours, and an increase in their level indicates a more intense stress level which, ultimately, can have consequences for animal fitness. In Scotland, the geographical distribution of red deer ( Cervus elaphus ) overlaps greatly with areas used for popular outdoor activities such as hill walking. In this piece of research, we studied red deer time allocation, vigilance, and flight behaviours near a popular hiking path using camera traps. We positioned the cameras in transects at different distances (25, 75, and 150 m) from the path and recorded hiking activity using an automated people counter. Red deer behaviour was categorized from photo analysis as (1) no response (e.g. feeding and resting), (2) vigilant (i.e. upright head position), and (3) flight response. We also investigated demographic variables (group size and sex) and the direction of red deer movement relative to the trail. We used generalised linear mixed models to analyse behaviour in relation to the distance from the hiking track, hiking activity, time of the day, demographic, and environmental variables. We did not find an increase in vigilance or flight behaviour in relation to the distance from the hiking path or the hiking activity. These results suggest that red deer, in our study area, are habituated to the presence of hikers and may spatially avoid areas close to the hiking path instead of displaying cost‐intensive behaviour (i.e. flight or vigilance behaviour).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.001 | 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 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".