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Record W2531797713 · doi:10.1111/eth.12564

A Potential Cost of Long Genitalia in Male Guppies: the Effects of Current Speed on Reproductive Behaviour

2016· article· en· W2531797713 on OpenAlexafffund
Lucia Kwan, Adam N. Dobkin, F. Helen Rodd, Locke Rowe

Bibliographic record

VenueEthology · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsSexual selectionGuppyPoeciliaBiologyPoeciliidaeMatingSex organReproductive successTrade-offSexual conflictZoologySelection (genetic algorithm)EcologyFish <Actinopterygii>FisheryDemographyPopulationGenetics

Abstract

fetched live from OpenAlex

Abstract In the fish family Poeciliidae, male genitalia, the gonopodia, are remarkably diverse across species; however, we still do not have a good understanding of the evolutionary processes promoting this diversity. For one trait, gonopodium length, several studies support a role for sexual conflict in selection for longer gonopodia. However, genital elongation may come at a cost of reduced locomotor abilities (e.g. resulting from greater drag and resistance). In this study, we were interested in the potential role of natural selection on the evolution of gonopodium length in poeciliids. Specifically, we asked whether a greater genital length impedes male reproductive behaviours at higher flow rates in the Trinidadian guppy, Poecilia reticulata . Using a flow chamber, males were placed with females in low‐ and high‐flow regimes and reproductive behaviours were measured. We did not find evidence for a cost of bearing a longer gonopodium at high flow. However, males did alter their mating tactics in response to current flow. We discuss the implications of our findings, in the light of habitat selection, on the forms of selection operating on gonopodium length and the mating interactions between the sexes in poeciliids.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.715
Threshold uncertainty score0.128

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 head0.026
GPT teacher head0.285
Teacher spread0.259 · 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

Citations3
Published2016
Admission routes2
Has abstractyes

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