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Record W2911998962 · doi:10.1093/beheco/arz006

How size and conspicuousness affect the efficacy of flash coloration

2019· article· en· W2911998962 on OpenAlexaff
Sangryong Bae, Doyeon Kim, Thomas N. Sherratt, Tim Caro, Changku Kang

Bibliographic record

VenueBehavioral Ecology · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsCarleton University
FundersNational Research Foundation of Korea
KeywordsPredationBiologyConfusionFlash (photography)PredatorColoredAffect (linguistics)EcologyZoologyCommunicationPsychologyVisual arts

Abstract

fetched live from OpenAlex

Some prey are cryptic at rest but expose conspicuous colors when in motion. Previous findings suggest that these “flash displays” deceive would-be predators by providing false information about the color of prey, tricking them into continuing to look for prey with the conspicuous color when the prey have actually returned to their cryptic resting state. These results raise questions about the properties of flash coloration that make it effective. Here, using humans as visual foragers searching for artificial prey models on a computer screen, we tested whether the effectiveness of flash coloration depends on the size of artificial prey. In addition, we tested whether flashing a different, but inconspicuous, color other than the resting color of prey is sufficient to deceive predators, or whether the flash coloration actually needs to be conspicuous to elicit predator confusion. Results indicate that 1) flash coloration tends to be more effective in large prey and 2) only conspicuous flash displays substantially reduce predation. Our findings help to explain why hidden color patches are more likely to be found in large insect species and why flash coloration is so often conspicuous. This study provides direct experimental evidence that the effectiveness of flash coloration is conditional, in that not all forms of flash display increase survivorship.

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 score0.351
Threshold uncertainty score0.169

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 head0.039
GPT teacher head0.243
Teacher spread0.204 · 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

Citations25
Published2019
Admission routes1
Has abstractyes

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