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Record W2803271609 · doi:10.7717/peerj.4807

Geographical variation in colour of female threespine stickleback (<i>Gasterosteus aculeatus</i>)

2018· article· en· W2803271609 on OpenAlexaboutno aff
Travis Ingram, Daniel I. Bolnick

Bibliographic record

VenuePeerJ · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsnot available
Fundersnot available
KeywordsSticklebackGasterosteusEcologyBiologyThree-spined sticklebackAdaptation (eye)Fish <Actinopterygii>Fishery

Abstract

fetched live from OpenAlex

The ecological multifunctionality of colour often results in multiple selective pressures operating on a single trait. Most research on colour evolution focuses on males because they are the most conspicuous sex in most species. This bias can limit inferences about the ecological drivers of colour evolution. For example, little is known about population divergence in colour of female threespine stickleback ( Gasterosteus aculeatus ), which is among the most intensively-studied model vertebrates in evolution, ecology, and behaviour. In contrast, the evolution and ecology of colour in male stickleback has received considerable attention. One aspect of female colouration that is lacking previous research is non-ornamental body colour. Non-ornamental colour can play defensive and social roles, and indicate other aspects of female stickleback ecology. To remedy this knowledge gap, we measured the colour and brightness of one dorsal and one ventral lateral area on female stickleback from nine lake populations on Vancouver Island. We found that lake populations varied in overall colour brightness and dorso-ventral contrast. In addition, we found that female brightness increased with lake size, indicating potential ecological drivers of these colour differences. Our results demonstrate that there is substantial scope for future research on female colour diversification, which has been overlooked because past researchers focused on dramatic male nuptial colours.

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 score0.727
Threshold uncertainty score0.353

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 head0.021
GPT teacher head0.242
Teacher spread0.221 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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Same venuePeerJSame topicAnimal Behavior and ReproductionFrench-language works237,207