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Record W2768328591 · doi:10.1098/rsbl.2017.0536

Interactions between cleaner-birds and ungulates are personality dependent

2017· article· en· W2768328591 on OpenAlexafffund
Rob Found

Bibliographic record

VenueBiology Letters · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsParks Canada
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Conservation Association
KeywordsInterspecific competitionBiologyEcologyPersonalityPica (typography)Cervus elaphusPsychologySocial psychology

Abstract

fetched live from OpenAlex

While a growing body of literature explores the ecological implications of consistent individual variation in the behaviour of wildlife, few studies have looked at the reciprocal influences of personality within interspecific interactions, despite the potentially significant impacts on biodiversity. Here I used two species involved in cleaner-bird behaviour—black-billed magpies ( Pica pica ) and Rocky mountain elk ( Cervus canadensis )—to show that the exhibition of mutualistic behaviour can depend on the personality of the individual involved. I recorded suites of correlated behaviours in both elk and magpies to derive personality gradients from ‘shy’ to ‘bold’, which I compared with observations of interspecific interactions. I measured each half of this mutualistic relationship separately. I found that bold elk were more likely to aggressively reject magpie landings, while shy elk allowed magpies to land and groom them. Contrastingly, I found it was bold magpies that were willing to risk landings, while shy magpies rarely attempted landings. These results show that the exhibition of interspecific behaviour is predicated on the personality of the individuals, and thus likely contributes to the selection and maintenance of personality variation within populations.

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 score0.371
Threshold uncertainty score0.437

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 studies0.0010.000
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 head0.052
GPT teacher head0.289
Teacher spread0.237 · 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

Citations6
Published2017
Admission routes2
Has abstractyes

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