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Record W2883048981 · doi:10.1093/beheco/ary099

The shortfall of sociality: group-living affects hunting performance of individual social spiders

2018· article· en· W2883048981 on OpenAlexafffund
Gyan Harwood, Leticia Avilés

Bibliographic record

VenueBehavioral Ecology · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSocialityBiologyForagingPredationSocial groupGroup livingTraitPopulationEcologyCooperative breedingZoologyDemographySocial psychologyPsychology

Abstract

fetched live from OpenAlex

Ineffective hunters in cooperative foraging groups may be shielded from natural selection by their more effective group mates, whereas those living solitarily would starve and thus be removed from the population. The problem may be exacerbated in large groups where it may be easier for individuals to withhold participation. Group foragers may thus be ineffective individual hunters or exhibit greater inter-individual variation in hunting abilities, in particular, when living in large groups. We test these hypotheses in spider species of the genus Anelosimus that differ in their level of sociality and, among social species, in colonies of different sizes. We found that individuals from the more social species, and those from larger groups, reacted more slowly to prey than those from solitary species or small groups. Individuals from these categories also had greater inter-individual variation in reaction times. Individuals from large social groups also had lower prey-capture success than those from small ones. These differences may have been driven by the size of the group from which the social individuals were taken, as those from small colonies behaved similarly to individuals of the 2 less social species. This finding suggests that hunting ability may develop as a phenotypically plastic trait.

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 score0.303
Threshold uncertainty score0.431

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 studies0.0010.001
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 head0.046
GPT teacher head0.285
Teacher spread0.239 · 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

Citations10
Published2018
Admission routes2
Has abstractyes

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