Social flocking increases in harsh and challenging environments
Bibliographic record
Abstract
Abstract Grouping with others can provide enhanced information about resources and threats. A key hypothesis in social evolution proposes that individuals can benefit from social information in environments where it is challenging to meet energetic needs. Here, we test this hypothesis by examining the environmental drivers of conspecific flocking behaviour in a large archive of citizen science observations of two common North American birds, the dark-eyed junco ( Junco hyemalis ) and black-capped chickadee ( Poecile atricapillus ). To quantify flocking behaviour, we apply the index of dispersion, D , as a metric of clumpiness in each species’ spatiotemporal distribution. We show that juncos in winter are nearly always more clustered than a random expectation, whereas chickadees span a range from uniform to socially clustered distributions. In both species, the degree of social clustering strongly increases with abundance. We identify several key environmental variables that explain the extent of conspecific flocking in both species. Flocks are more socially clustered at higher latitudes, higher elevations, closer to midwinter, and at temperatures that are colder than average given the location and time of year. Together, these findings support the hypothesis that sociality is a key strategy for coping with harsh environments. HIGHLIGHTS Grouping with others can be an important source of information about resources We analyzed how flocking behaviour changes throughout winter in two bird species We used the index of dispersion to quantify social clustering at a broad scale In both species, social clustering increases in response to climate challenges
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".