Treeline ecotones shape the distribution of avian species richness and functional diversity in south temperate mountains
Bibliographic record
Abstract
Mountains produce distinct environmental gradients that may constrain or facilitate both the presence of avian species and/or specific combinations of functional traits. We addressed species richness and functional diversity to understand the relative importance of habitat structure and elevation in shaping avian diversity patterns in the south temperate Andes, Chile. During 2010-2018, we conducted 2202 point-counts in four mountain habitats (successional montane forest, old-growth montane forest, subalpine, and alpine) from 211 to 1,768 m in elevation and assembled trait data associated with resource use for each species to estimate species richness and functional diversity and turnover. We detected 74 species. Alpine specialists included 16 species (22%) occurring only above treeline with a mean elevational range of 298 m, while bird communities below treeline (78%) occupied a mean elevational range of 1,081 m. Treeline was an inflection line, above which species composition changed by 91% and there was a greater turnover in functional traits (2-3 times greater than communities below treeline). Alpine birds were almost exclusively migratory, inhabiting a restricted elevational range, and breeding in rock cavities. We conclude that elevation and habitat heterogeneity structure avian trait distributions and community composition, with a diverse ecotonal sub-alpine and a distinct alpine community.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".