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Record W3198354309 · doi:10.1093/beheco/arab070

Context-dependent group size: effects of population density, habitat, and season

2021· article· en· W3198354309 on OpenAlexafffundabout
Quinn M. R. Webber, Eric Vander Wal

Bibliographic record

VenueBehavioral Ecology · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsForagingBiologyEcologyHabitatPopulationPopulation sizePopulation densityCompetition (biology)Context (archaeology)ForageDemography

Abstract

fetched live from OpenAlex

Abstract Group size can vary in relation to population density, habitat, and season. Habitat and season may also interact with population density and affect group size through varying foraging benefits of social aggregation in different ecological contexts. We tested the hypothesis that group size varies across ecological contexts, including population density, habitat type, and season, for woodland caribou (Rangifer tarandus) in ten herds over 25 years in Newfoundland, Canada. We predicted that group size would increase as a function of population density. Based on the foraging benefits of social aggregation, we predicted larger groups as habitat openness increased because open areas tend to have higher quality foraging resources. We predicted larger groups during winter when foraging resources are covered in snow because caribou and other social animals exploit social information about the location of foraging resources. In contrast to our prediction, group size decreased as a function of population density. In support of our prediction, group size was larger in winter than calving and summer, and we found that group size increased with habitat openness in some, but not all, cases. Patterns of animal grouping are context-dependent and the additive effect of different ecological contexts on variation in group size informs our understanding of the implicit trade-offs between competition, predation risk, and profitability of forage.

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 score0.014
Threshold uncertainty score0.810

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 head0.010
GPT teacher head0.241
Teacher spread0.232 · 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

Citations28
Published2021
Admission routes3
Has abstractyes

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