Does fear of humans predict anti-predator strategies in an ungulate hider species during fawning?
Bibliographic record
Abstract
Abstract Humans are a major evolutionary force on wildlife via artificial selection. While often explored through the lens of extractive interactions (e.g., hunting) able to favour certain behavioural traits over others, the implications of non-extractive ones, such as wildlife feeding, remain under-studied. Research has recently shown that people tend to feed (and sometimes favour) a limited subset of bolder individuals within natural populations, although its dynamics and consequences are not fully clear. Using fallow deer living in a peri-urban setting as a model population, we studied whether mother deer that display reduced fear of humans and consistently approach them for food adopt weaker anti-predator strategies by selecting for fawning bedsites that are less concealed and closer to human hotspots, allowing them to take advantage of additional artificial feeding opportunities in comparison to shier mothers in this population. Our dataset encompassed 171 fawns from 109 mothers across 4 years. Contrary to our expectations, we found that mothers that regularly accepted food from humans selected for more concealed bedsites farther away from them, giving their offspring better protection while also taking advantage of additional artificial food during lactating. Our results show marked behavioural adaptation by a subset of females, making this the first time that the link between tendency to approach humans and strategies to protect offspring is explored. Given previous findings that these begging females also deliver heavier fawns at birth, our research adds a piece to the complex puzzle describing human manipulation of behaviour in natural populations and its fitness consequences.
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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 | 0.002 |
| 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) | 0.002 | 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 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".