Community assembly, functional traits, and phylogeny in Himalayan river birds
Bibliographic record
Abstract
Heterogeneity in riverine habitats acts as a template for species evolution that influences river communities at different spatio-temporal scales. Although birds are conspicuous elements of these communities, the roles of phylogeny, functional traits, and habitat character in their niche use or species' assembly have seldom been investigated. We explored these themes by surveying multiple headwaters over 3000 m of elevation in the Himalayan Mountains of India where the specialist birds of montane rivers reach their greatest diversity on Earth. After ordinating community composition, species traits, and habitat character, we investigated whether river bird traits varied with elevation in ways that were constrained or independent of phylogeny, hypothesizing that trait patterns reflect environmental filtering. Community composition and trait representation varied strongly with increasing elevation and river naturalness as species that foraged in the river/riparian ecotone gave way to small insectivores with direct trophic dependence on the river or its immediate channel. These trends were influenced strongly by phylogeny as communities became more clustered by functional traits at a higher elevation. Phylogenetic signals varied among traits, however, and were reflected in body mass, bill size, and tarsus length more than in body size, tail length, and breeding strategy. These variations imply that community assembly in high-altitude river birds reflects a blend of phylogenetic constraint and habitat filtering coupled with some proximate niche-based moulding of trait character. We suggest that the regional co-existence of river birds in the Himalaya is facilitated by this same array of factors that together reflect the highly heterogeneous template of river habitats provided by these mountain headwaters.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".