Ecological drivers of global gradients in avian dispersal inferred from wing morphology
Bibliographic record
Abstract
An organism's ability to disperse influences many fundamental processes, from speciation and geographical range expansion to community assembly. However, the patterns and underlying drivers of variation in dispersal across species remain unclear, partly because standardised estimates of dispersal ability are rarely available. Here we present a global dataset of avian hand-wing index (HWI), an estimate of wing shape widely adopted as a proxy for dispersal ability in birds. We show that HWI is correlated with geography and ecology across 10,338 (>99%) species, increasing at higher latitudes and with migration, and decreasing with territoriality. After controlling for these effects, the strongest predictor of HWI is temperature variability (seasonality), with secondary effects of diet and habitat type. Finally, we also show that HWI is a strong predictor of geographical range size. Our analyses reveal a prominent latitudinal gradient in HWI shaped by a combination of environmental and behavioural factors, and also provide a global index of avian dispersal ability for use in community ecology, macroecology, and macroevolution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.001 |
| 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 teacher head, 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".