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Record W2144478018 · doi:10.1155/2012/510920

Visual and Chemical Prey Cues as Complementary Predator Attractants in a Tropical Stream Fish Assemblage

2012· article· en· W2144478018 on OpenAlexafffund
Chris K. Elvidge, G. E. Brown

Bibliographic record

VenueInternational Journal of Zoology · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les TechnologiesConcordia University
KeywordsPredationForagingPredatorAttractionSensory cueBiologyEcologyAttractivenessPsychology

Abstract

fetched live from OpenAlex

To date, little attention has been devoted to possible complementary effects of multiple forms of public information similar information on the foraging behaviour of predators. In order to examine how predators may incorporate multiple information sources, we conducted a series of predator attraction trials in the Lower Aripo River, Trinidad. Four combinations of visual (present or absent) and chemical cues (present or absent) from each of two prey species were presented. The occurrences of three locally abundant predatory species present within a 1 m radius of cue introduction sites were recorded. The relative attractiveness of cue type to each predator was directly related to their primary foraging modes, with visual ambush predators demonstrating an attraction to visual cues, benthivores to chemical cues, and active social foragers demonstrating complementary responses to paired cues. Predator species-pair counts were greatest in response to cues from the more abundant prey species, indicating that individuals may adopt riskier foraging strategies when presented with more familiar prey cues. These differences in predator attraction patterns demonstrate complementary effects of multiple sensory cues on the short-term habitat use and foraging behaviour of predators under fully natural conditions.

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 score0.220
Threshold uncertainty score0.414

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 head0.028
GPT teacher head0.311
Teacher spread0.283 · 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

Citations23
Published2012
Admission routes2
Has abstractyes

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