The Human Shield Hypothesis: Does Predator Avoidance of Humans Create Refuges for Prey?
Bibliographic record
Abstract
As anthropogenic disturbance restructures ecological communities worldwide, ecologists have developed and tested hypotheses about which species "win" and "lose" in the face of human impacts. One heavily invoked paradigm is that of the human shield, which posits that predators avoid areas of human disturbance due to perceived risk from humans, and prey therefore seek refuge in these areas of perceived safety. Since its introduction in 2007, the human shield hypothesis (HSH) has gained popularity in the ecological literature, although there are more passing mentions of human shields than there are robust tests of the HSH. Here, we systematically review evidence for the HSH and evaluate how it is commonly discussed and tested. While there are several clear-cut cases of human shields, the emergence of human shields is highly context-dependent. By formally outlining the assumptions of the HSH, we derive predictions about what ecological and anthropogenic contexts are most likely to be conducive to human shields. Further robust studies that compete the HSH against alternative hypotheses and account for confounding factors can shed light on the role of human shields in human-modified ecosystems and inform the conservation and management of wildlife in a changing world.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".