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Record W1974115626 · doi:10.1139/z10-018

Ambient light alters temporal-updating behaviour during mate choice in a Neotropical frog

2010· article· en· W1974115626 on OpenAlexvenueno aff
Alexander T. Baugh, Michael J. Ryan

Bibliographic record

VenueCanadian Journal of Zoology · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsnot available
FundersSmithsonian Tropical Research InstituteNational Science Foundation
KeywordsBiologyMate choiceNocturnalPredationAttractivenessContext (archaeology)EcologyPredatorZoologyMatingPsychology

Abstract

fetched live from OpenAlex

It is well known that animal decision-making can be influenced by environmental variables, such as the risk of predation. During the breeding season, nocturnal amphibians encounter a range of environmental conditions at breeding aggregations, including variable ambient light conditions. For nocturnal frogs, illumination is expected to minimize conspicuous movement that might increase predator detection. Previous work has shown that female Physalaemus pustulosus (Cope, 1864) (= Engystomops pustulosus (Cope, 1864)) are sensitive to variation in light levels during mate choice. Here we use an acoustic playback design in which stimuli are adjusted for intensity and complexity during female phonotaxis to show that choosiness is influenced by light level. Frogs were more likely to commit to an initial mate choice despite a dynamic reduction in mate attractiveness under dim light conditions compared with darkness. These results suggest that females are trading off the attractiveness of potential mates with the perceived costs of executing mate choice by committing to an initial decision and thereby reducing assessment time and movement. The dynamic playback design used here provides an approach that could be applied in other systems in which context-dependent decision-making is thought to be important.

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 score0.362
Threshold uncertainty score0.986

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 head0.012
GPT teacher head0.218
Teacher spread0.206 · 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

Citations33
Published2010
Admission routes1
Has abstractyes

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