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Record W2886114272 · doi:10.1093/beheco/ary103

Urbanization and individual differences in exploration and plasticity

2018· article· en· W2886114272 on OpenAlexafffund
M. J. Thompson, Julian Evans, Sheena Parsons, Julie Morand‐Ferron

Bibliographic record

VenueBehavioral Ecology · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaNature Conservancy of CanadaNature Conservancy
KeywordsUrbanizationVariation (astronomy)BiologyHabitatWildlifeEcology

Abstract

fetched live from OpenAlex

Urban environments impose novel challenges on animals and, as a result, the behaviors of urban wildlife are changing. In particular, high exploratory tendencies and an ability to gather more information from the environment may facilitate adoption of novel ecological opportunities. As of yet, very few studies have examined if urbanization predicts the way in which animals explore novel environments, or the extent of among-individual variation within these habitats. Here, we assess exploration and its temporal plasticity in black-capped chickadees (Poecile atricapillus; N = 169 individuals, 14 sites) caught along an urban gradient to examine individual differences in exploration and changes in exploration over time and assays under a reaction-norm framework. As predicted, urban birds were significantly faster explorers in a novel environment (contacted more features and moved more), however urbanization did not predict individual differences in the change in exploration over time. Exploration score was moderately repeatable; interestingly, urban chickadees were more repeatable in their initial exploration behaviors, but seemed less repeatable in how they explored over time between assays in comparison to forest birds. Our results support the importance of high exploratory tendencies for urban animals, and suggest, for the first time, that individuals from urban and non-urban habitats differ in the amount of among-individual variation in exploration, and thus urban individuals may benefit from diverging more from one another in their behavior. Future work should examine the extent to which this variation in exploration and plasticity of exploration behaviors represent differences in how individuals gather information from their environment.

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 score0.070
Threshold uncertainty score0.237

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 head0.090
GPT teacher head0.266
Teacher spread0.177 · 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

Citations49
Published2018
Admission routes2
Has abstractyes

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