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Exploring the role of vision in social foraging: What happens to group size, vigilance, spacing, aggression and habitat use in birds and mammals that forage at night?

2007· review· en· W2024147599 on OpenAlexaff
Guy Beauchamp

Bibliographic record

VenueBiological reviews/Biological reviews of the Cambridge Philosophical Society · 2007
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversité de MontréalCegep de Saint Hyacinthe
Fundersnot available
KeywordsForagingVigilance (psychology)PredationAggressionEcologyBiologyHabitatNocturnalSocial animalPsychologySocial psychology

Abstract

fetched live from OpenAlex

I examined the role of vision in social foraging by contrasting group size, vigilance, spacing, aggression and habitat use between day and night in many species of birds and mammals. The literature review revealed that the rate of predation/disturbance was often reduced at night while food was considered more available. Social foraging at night was prevalent in many species suggesting that low light levels at night are not sufficient to prevent the formation and cohesion of animal groups. Group sizes were similar or larger at night than during the day in more than half the bird populations and in the majority of mammal populations. Factors such as calls, feeding noises or smells may contribute to the formation and cohesion of groups at night. Larger numbers of foragers at night may also facilitate the aggregation of more foragers. Vigilance levels were usually lower at night perhaps as a response to the lower predation risk or to the decreased value of scanning for predators that are difficult to locate. Low light levels may also make visual cues that promote aggression less conspicuous, which may be a factor in the lower levels of aggression documented at night. Spacing varied as a function of time of day in response to changes in foraging mode or food availability. Habitats that are avoided during the day were often used at night. Foraging at night presents birds and mammals with a new set of constraints that influence group size, time budgeting and habitat use.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0010.001
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 head0.283
GPT teacher head0.348
Teacher spread0.065 · 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.

Study designOther design
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

Citations69
Published2007
Admission routes1
Has abstractyes

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