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Record W1874274327 · doi:10.1111/jzo.12074

A direct comparison of the effectiveness of two anti‐predator strategies under field conditions

2013· article· en· W1874274327 on OpenAlexafffund
JUSTIN CARROLL, Thomas N. Sherratt

Bibliographic record

VenueJournal of Zoology · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPredationAposematismCrypsisBiologyPredatorEcologyPlasticineCamouflageMimicryZoology

Abstract

fetched live from OpenAlex

Abstract Aposematism and crypsis are two widespread defensive strategies that have evolved in organisms to reduce attacks by predators. However, although both have been studied extensively, predation rates on unpalatable conspicuous prey have seldom been directly compared to those on palatable cryptic prey, and never in the field. In this study, we use established methods to compare the effectiveness of both defensive traits, by presenting artificial prey targets on trees where they were subject to attack by wild avian predators in a natural field setting. When partially consumed prey and those that had been completely removed were both treated as attacked by predators, there were no differences in attack rates between targets with the two defensive strategies. However, aposematic prey were completely consumed less often than cryptic prey, and partially consumed more often. This suggests that predators engage in taste rejection of unpalatable prey and/or feed on conspicuous prey more cautiously (‘go‐slow’ predation). We also observed significant differences in predation among experimental sites, in spite of their similarity and relatively close proximity, and among trials, which suggests that prey may experience highly variable predation in the wild. If aposematic prey are capable of surviving attacks by predators, then this represents a potential defensive benefit of aposematism over crypsis.

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 score0.678
Threshold uncertainty score0.132

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 head0.282
Teacher spread0.243 · 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

Citations34
Published2013
Admission routes2
Has abstractyes

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