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Vigilance in Przewalski's gazelle: effects of sex, predation risk and group size

2008· article· en· W2037852086 on OpenAlexaff
Zekun Li, Zhigang Jiang, Guy Beauchamp

Bibliographic record

VenueJournal of Zoology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversité de Montréal
FundersNanjing UniversityChinese Academy of Sciences
KeywordsVigilance (psychology)PredationBiologyUngulateDemographyZoologyEcologyHabitat

Abstract

fetched live from OpenAlex

Abstract Vigilance in social animals is often aimed at detecting predators. Many social and environmental factors influence vigilance, including sex, predation risk and group size. During the summer of 2007, we studied Przewalski's gazelle Procapra przewalskii , an endemic ungulate to the Qinghai‐Tibet Plateau, to test whether and how these three factors affect vigilance. We distinguished groups consisting of males, mothers with lambs and females without lambs making observations on groups in the presence or absence of nearby predators. We assessed the group‐size effect on vigilance and how this varied with levels of predation risk and sex. Males and mothers scanned longer and with a higher frequency than females without lambs. Individuals were more vigilant under direct predation threat. Although vigilance generally decreased with group size, the extent of the decrease was independent of predation risk and was not significant in males. The results suggest that mothers are more vigilant suggesting greater vulnerability and that males may have increased their vigilance to compete for higher social ranks. The positive correlation between vigilance and predation risk and the negative correlation between vigilance and group size are consistent with earlier findings, but we failed to find an interaction between group size and predation risk on vigilance perhaps because vigilance levels are low even in small groups, thus making similar vigilant upward adjustments in both small and large groups.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.173

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 head0.004
GPT teacher head0.191
Teacher spread0.187 · 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

Citations58
Published2008
Admission routes1
Has abstractyes

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