Behavioural synchronization of large‐scale animal movements – disperse alone, but migrate together?
Bibliographic record
Abstract
Dispersal and migration are superficially similar large-scale movements, but which appear to differ in terms of inter-individual behavioural synchronization. Seasonal migration is a striking example of coordinated behaviour, enabling animal populations to track spatio-temporal variation in ecological conditions. By contrast, for dispersal, while social context may influence an individual's emigration and settlement decisions, transience is believed to be mostly a solitary behaviour. Here, we review differences in drivers that may explain why migration appears to be more synchronized than dispersal. We derive the prediction that the contrast in the importance of behavioural synchronization between dispersal and migration is linked to differences in the selection pressures that drive their respective evolution. Although documented examples of collective dispersal are rare, this behaviour may be more common than currently believed, with important consequences for eco-evolutionary dynamics. Crucially, to date, there is little available theory for predicting when we should expect collective dispersal to evolve, and we also lack empirical data to test predictions across species. By reviewing the state of the art in research on migration and collective movements, we identify how we can harness these advances, both in terms of theory and data collection, to broaden our understanding of synchronized dispersal and its importance in the context of global change.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".