Behavioral variation changes across an urbanization gradient in a population of great tits
Bibliographic record
Abstract
Urbanization is occurring globally at an unprecedented rate and, despite the eco-evolutionary importance of individual variation in adaptive traits, we still have very limited insight on how phenotypic variation is modified by anthropogenic environmental change. Urbanization can increase individual differences in some contexts, but whether this is generalizable to behavioral traits, which directly affect how organisms interact with and respond to environmental variation, is not known. Here we examine variation across three behavioral traits (breath rate, handling aggression and exploration behaviour) in great tits Parus major along an urbanization gradient (n > 1000 phenotyped individuals accross nine years) to determine whether among-individual variance in behavior increases with the degree of urbanization and spatial heterogeneity. Urban birds were more aggressive and faster explorers than forest birds. They also displayed higher among-individual variation for breath rate and aggression (1.5 and 1.8 times increase, respectively), but lower among-individual variation for exploration (3.3 times decrease). Only individual variation in exploration clearly changed along the continuous urbanization gradient; individual differences in exploration declined with increasing impervious surface area. Collectively our results suggest that individuals in the city may have more diverse behavioral stress responses, yet display stronger similarity in their behavioral responses to novelty. Our results suggest that generalizations about urbanization’s impacts on behavioral variation are not appropriate. Instead our results suggest that urbanization can shape individual variation differently across behavioral functions and we may expect decreased individual diversity in urban birds for traits related to behavioral response to novelty.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".