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Size‐Assortative Shoaling in the Guppy (<i>Poecilia reticulata</i>): The Role of Active Choice

2010· article· en· W2043536055 on OpenAlexaff
Darren P. Croft, Indar W. Ramnarine, Jean‐Guy J. Godin

Bibliographic record

VenueEthology · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsCarleton University
FundersNatural Environment Research CouncilSight Research UK
KeywordsShoaling and schoolingGuppyPoeciliaShoalPredationAssortative matingBiologyEcologyPopulationPoeciliidaeZoologyFish <Actinopterygii>FisheryDemographyMatingOceanography

Abstract

fetched live from OpenAlex

Abstract Many fish species exhibit size‐assortative shoaling, which is often thought to be driven by predation risk. Recent fieldwork has revealed that guppies ( Poecilia reticulata ) are more size assorted in high‐predation populations than in low‐predation ones. However, size assortment does nonetheless occur in some low‐predation populations, suggesting that predation is unlikely the sole driving force behind size‐assortment. Here, we investigated in the laboratory the potential role of active choice in size‐assortative shoaling in wild‐caught female guppies originating from two populations of the same river system in Trinidad. Small or large focal females from each population were offered a binary choice of shoaling with either four small female conspecifics or four large ones. Observed shoaling preferences depended on the body size of the focal fish, suggesting phenotype‐mediated conflict over group composition. Large focal fish preferred to shoal with the size‐matched stimulus shoal of large fish. In contrast, small focal fish did not shoal assortatively but also preferred to shoal with larger females. Our results suggest that size‐assortative shoaling in female guppies is likely to be due to factors other than active choice, such as habitat segregation and sexual harassment.

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)0.001
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 score0.064
Threshold uncertainty score0.907

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.250
Teacher spread0.244 · 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

Citations23
Published2010
Admission routes1
Has abstractyes

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