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Record W4411374309 · doi:10.1111/ele.70138

The Human Shield Hypothesis: Does Predator Avoidance of Humans Create Refuges for Prey?

2025· review· en· W4411374309 on OpenAlexafffund
Kaitlyn M. Gaynor, Eamonn I. F. Wooster, April Robin Martinig, Jennifer R. Green, Aimee Chhen, Sandra Cuadros, Ryan Gill, Gopal Khanal, Nicola Love, Rekha Marcus, C. Lauren Mills, Kwasi Wrensford, Nicholas S. Wright, Stefano Mezzini, Jessa Marley, Michael Noonan

Bibliographic record

VenueEcology Letters · 2025
Typereview
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British Columbia
KeywordsPredationContext (archaeology)EcologyDisturbance (geology)WildlifePredator avoidancePredatorEnvironmental resource managementBiologyEnvironmental science

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.544
Threshold uncertainty score0.708

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.273
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations31
Published2025
Admission routes2
Has abstractyes

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