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Record W4220676301 · doi:10.3389/fevo.2022.803996

Continuous Variation in an Aposematic Pattern Affects Background Contrast, but Is Not Associated With Differences in Microhabitat Use

2022· article· en· W4220676301 on OpenAlexaff
Justin Yeager, James B. Barnett

Bibliographic record

VenueFrontiers in Ecology and Evolution · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicAmphibian and Reptile Biology
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAposematismBiologyEvolutionary biologyPopulationCrypsisPredationVariation (astronomy)Contrast (vision)Selection (genetic algorithm)Divergence (linguistics)EcologyPredator

Abstract

fetched live from OpenAlex

Variation in aposematic signals was once predicted to be rare, yet in recent years it has become increasingly well documented. Despite increases in the frequency with which polytypism and polymorphism have been suggested to occur, population-wide variance is rarely quantified. We comprehensively sampled a subpopulation of the poison frog Oophaga sylvatica , a species which is polytypic across its distribution and also shows considerable within-population polymorphism. On one hand, color pattern polymorphism could be the result of multifarious selection acting to balance different signaling functions and leading to the evolution of discrete sub-morphs which occupy different fitness peaks. Alternatively, variance could simply be due to relaxed selection, where variation would be predicted to be continuous. We used visual modeling of conspecific and heterospecific observers to quantify the extent of within population phenotypic variation and assess whether this variation produced distinct signals. We found that, despite considerable color pattern variation, variance could not be partitioned into distinct groups, but rather all viewers would be likely to perceive variation as continuous. Similarly, we found no evidence that frog color pattern contrast was either enhanced or diminished in the frogs’ chosen microhabitats compared to alternative patches in which conspecifics were observed. Within population phenotypic variance therefore does not seem to be indicative of strong selection toward multiple signaling strategies, but rather pattern divergence has likely arisen due to weak purifying selection, or neutral processes, on a signal that is highly salient to both conspecifics and predators.

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 score0.013
Threshold uncertainty score0.777

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 head0.192
Teacher spread0.183 · 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
Published2022
Admission routes1
Has abstractyes

Explore more

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