Data from: Plasticity versus evolutionary divergence: what causes habitat partitioning in urban-adapted birds?
Bibliographic record
Abstract
Habitat partitioning can facilitate the coexistence of closely related species, and often results from competitive interference inducing plastic shifts of subordinate species in response to aggressive, dominant species (plasticity), or the evolution of ecological differences in subordinate species that reduce their ability to occupy habitats where the dominant species occurs (evolutionary divergence). Evidence consistent with both plasticity and evolutionary divergence exist, but the relative contributions of each to habitat partitioning have been difficult to discern. Here we use a global dataset on the breeding occurrence of birds in cities to test predictions of these alternative hypotheses to explain previously described habitat partitioning associated with competitive interference. Consistent with plasticity, the presence of behaviorally dominant congeners in a city was associated with a 65% reduction in occurrence of subordinate species, but only when the dominant was a widespread breeder in urban habitats. Consistent with evolutionary divergence, increased range-wide overlap with dominant congeners was associated with a 56% reduction in occurrence of subordinates in cities, even when the dominant was absent from the city. Overall, our results suggest that both plasticity and evolutionary divergence play important, concurrent roles in habitat partitioning among closely related species in urban environments.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".