Brain size, innovative propensity and migratory behaviour in temperate Palaearctic birds
Bibliographic record
Abstract
The evolution of migration in birds remains an outstanding, unresolved question in evolutionary ecology. A particularly intriguing question is why individuals in some species have been selected to migrate, whereas in other species they have been selected to be sedentary. In this paper, we suggest that this diverging selection might partially result from differences among species in the behavioural flexibility of their responses to seasonal changes in the environment. This hypothesis is supported in a comparative analysis of Palaearctic passerines. First, resident species tend to rely more on innovative feeding behaviours in winter, when food is harder to find, than in other seasons. Second, species with larger brains, relative to their body size, and a higher propensity for innovative behaviours tend to be resident, while less flexible species tend to be migratory. Residence also appears to be less likely in species that occur in more northerly regions, exploit temporally available food sources, inhabit non-buffered habitats and have smaller bodies. Yet, the role of behavioural flexibility as a response to seasonal environments is largely independent of these other factors. Therefore, species with greater foraging flexibility seem to be able to cope with seasonal environments better, while less flexible species are forced to become migratory.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".