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Record W2055945312 · doi:10.1371/journal.pone.0026447

Coordination and Synchronisation of Anti-Predation Vigilance in Two Crane Species

2011· article· en· W2055945312 on OpenAlexaff
Ge Chen, Guy Beauchamp, Zhongqiu Li

Bibliographic record

VenuePLoS ONE · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversité de Montréal
FundersNanjing University
KeywordsVigilance (psychology)ForagingPredationFlockOddsEcologyBiologyPoison controlPsychologyCognitive psychologyMedicineEnvironmental healthStatisticsMathematics

Abstract

fetched live from OpenAlex

Much of the previous research on anti-predation vigilance in groups has assumed independent scanning for threats among group members. Alternative patterns that are based on monitoring the vigilance levels of companions can also be adaptive. Coordination of vigilance, in which foragers avoid scanning at the same time as others, should decrease the odds that no group member is alert. Synchronisation of vigilance implies that individuals are more likely to be vigilant when companions are already vigilant. While synchronisation will increase the odds that no one is vigilant, it may allow a better assessment of potential threats. We investigated temporal sequences of vigilance in family flocks consisting of two parents and at most two juveniles in two species of cranes in coastal China. We established whether the observed probability that at least one parent is alert was greater (coordination) or lower (synchronisation) than that predicted under the null hypothesis of independent vigilance. We documented coordination of vigilance in common cranes (Grus grus) foraging in an area with high potential for disturbance by people. We documented synchronisation of vigilance in red-crowned cranes (Grus japonensis) in the less but not in the more disturbed area. Coordination in small flocks leads to high collective vigilance but low foraging rates that may not be suitable in areas with low disturbance. We also argue that synchronisation should break down in areas with high disturbance because periods with low vigilance are riskier. Results highlight the view that temporal patterns of vigilance can take many forms depending on ecological factors.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.499
Threshold uncertainty score0.115

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.093
GPT teacher head0.231
Teacher spread0.138 · 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

Citations40
Published2011
Admission routes1
Has abstractyes

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