The latitudinal gradient in hand-wing-index: global patterns and predictors of wing morphology in birds
Bibliographic record
Abstract
Abstract An organism’s ability to disperse influences many fundamental processes in ecology. However, standardised estimates of dispersal ability are rarely available, and thus the patterns and drivers of broad-scale variation in dispersal ability remain unclear. Here we present a global dataset of avian hand-wing index (HWI), an estimate of wingtip pointedness widely adopted as a proxy for flight efficiency and dispersal in birds. We show that HWI is correlated with geography and ecology across 10,391 (>99 %) bird species, increasing at higher latitudes and in migratory and/or non-territorial species. After controlling for these effects, the strongest predictor of HWI is temperature variability (i.e. seasonality), with secondary effects of diet and habitat type. Our analyses (1) reveal a prominent latitudinal gradient in HWI shaped by ecological and environmental factors, and (2) provide a global index of avian dispersal ability for wider use in community ecology, macroecology, and macroevolution. Statement of authorship The study was conceived by CS and JAT. Data collection was led by JAT, SC, and CS, with contributions from CS, MNC, NA, SEIJ, CV, HEAM, TPB, and SC. CS performed the analyses. CS and JAT wrote the manuscript and all authors revised the text.
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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 | 0.001 |
| 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) | 0.002 | 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".