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Record W3123637262 · doi:10.1093/beheco/araa127

Behavioral variation in natural contests: integrating plasticity and personality

2020· article· en· W3123637262 on OpenAlexafffund
Charline Couchoux, Dany Garant, Maxime Aubert, Jeanne Clermont, Denis Réale

Bibliographic record

VenueBehavioral Ecology · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversité de SherbrookeUniversité du Québec à RimouskiUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsAnimal Behavior SocietyUniversité du Québec à MontréalNational Geographic Society
KeywordsCONTESTBiologyAgonistic behaviourVariation (astronomy)TerritorialityPersonalityPhenotypic plasticityBehavioral syndromeEcologySocial psychologyPsychologyAggression

Abstract

fetched live from OpenAlex

Abstract Animals often interact aggressively when competing over limited resources. Aggressive decisions can be complex and may result from multiple sources of behavioral variation. The outcome of contests may be explained through contest theory and personality by considering conjointly plasticity and individual consistency. This integrative approach also allows investigating individual differences in responsiveness to environmental changes. Here, we observed multiple agonistic interactions occurring among eastern chipmunks (Tamias striatus) competing for food resources supplied at different distances from their burrows. Using an individual reaction norm approach, we found that the probability of winning a contest depended on an individual’s intrinsic characteristics (mass and age but not sex) but was also adjusted to characteristics of its opponents. Winning a contest also depended on extrinsic environmental characteristics, such as distance to the contestants’ burrows, but not the order of arrival at the feeding patch. We found consistent individual differences in the probability of winning, potentially related to differences in aggressiveness and territoriality. We also found that individuals differed in their plasticity level in response to changes in different characteristics of their social and physical environments. Plasticity, personality, and individual differences in responsiveness may thus all play a role in predicting contest outcome and in the evolution of animal contests.

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 score0.478
Threshold uncertainty score0.349

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 head0.051
GPT teacher head0.281
Teacher spread0.230 · 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

Citations27
Published2020
Admission routes2
Has abstractyes

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