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Record W1986911399 · doi:10.1093/beheco/arm114

Increasing vulnerability to predation increases preference for the scrounger foraging tactic

2007· article· en· W1986911399 on OpenAlexaff
Kimberley J. Mathot, Luc‐Alain Giraldeau

Bibliographic record

VenueBehavioral Ecology · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsBiologyPredationForagingVulnerability (computing)Ecology

Abstract

fetched live from OpenAlex

When animals forage in groups, individuals can search for food themselves (producer tactic) or they can search for and join other individuals that have located food (scrounger tactic). The scrounger tactic may provide greater antipredator benefits than the producer tactic because “scroungers” hop with their heads up and tend to occupy central positions in a group, whereas “producers” hop with their heads down and tend to occupy edge positions. We tested whether increasing an individual's vulnerability to predation (using wing-loading manipulations) causes an increased preference for the scrounger tactic in zebra finches (Taeniopygia guttata). Wing-loading manipulations were effective at increasing focal individuals' perception of vulnerability to predation; treatment individuals increased their total time allocated to vigilance, whereas control individuals did not. Treatment individuals also increased their use of the scrounger tactic (proportion of hops with head up) and scrounged a greater proportion of patches, whereas control individuals exhibited no changes. Our results are consistent with the hypothesis that the scrounger tactic confers greater antipredator benefits than the producer tactic, although whether antipredator benefits are achieved through differences in head orientation, spatial position, or both, remains unclear. Our finding that individuals adjust their use of the scrounger tactic according to changes in their phenotype provides evidence for phenotype-limited allocation strategies in producer–scrounger games.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.484
Threshold uncertainty score0.401

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.092
GPT teacher head0.326
Teacher spread0.234 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations35
Published2007
Admission routes1
Has abstractyes

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