Do human–wildlife interactions predict offspring hiding strategies in peri-urban fallow deer?
Bibliographic record
Abstract
Human activities can induce significant behavioural changes in wildlife. Often explored through extractive interactions (e.g. hunting) that can favour certain behavioural traits, the implications of non-extractive ones, such as wildlife feeding, remain understudied. Research shows that people tend to favour bolder individuals within populations despite their dynamics and consequences being unclear. Using fallow deer in a peri-urban environment, we studied whether mothers that show reduced fear of humans and consistently approach them for food adopt weaker anti-predator strategies by selecting less concealed fawning bedsites closer to human hotspots. This would provide the advantage of additional feeding opportunities in comparison with shyer mothers while keeping their fawns close. Our dataset encompassed 281 capture events of 172 fawns from 110 mothers across 4 years. Surprisingly, mothers that regularly accepted food from humans selected more concealed bedsites farther from human hotspots, giving their offspring better protection while also benefitting from additional food during lactation. Our results show behavioural adaptations by a subset of females and, for the first time, link the tendency to approach humans and strategies to protect offspring. Given previous findings that these begging females also deliver heavier fawns at birth, our research further investigates human-wildlife feeding interactions and their behavioural implications.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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 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".