A new time tree of birds reveals the interplay between dispersal, geographic range size, and diversification
Bibliographic record
Abstract
The spatial and temporal dynamics of biodiversity are shaped by complex interactions among species characteristics and geographic processes. A key example is the effect of dispersal on geographical range expansion and gene flow, both of which may determine speciation rates. In this study, we constructed a time-calibrated phylogeny of over 9,000 bird species and leveraged extensive data on avian traits and spatial occurrence to explore the connections between dispersal, biogeography, and speciation. Phylogenetic path analyses and trait-dependent diversification models reveal that geographic range size is strongly associated with the hand-wing index, a proxy for wing aspect ratio related to flight efficiency and dispersal ability. By contrast, we found mixed evidence for the effect of dispersal on diversification rates: dispersive lineages show either slightly higher speciation rates or higher extinction rates. Our results therefore suggest that high dispersal ability increases range expansion and turnover, perhaps because dispersive lineages expand into islands or other geographically restricted environments and have lower population sizes. Our results highlight the nuanced and interconnected roles of dispersal and range size in shaping global patterns of avian diversification and biogeography and provide a richly sampled phylogenetic template for exploring a wide array of research questions in macroecology and macroevolution.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".