Habitat area and environmental filters determine avian richness along an elevation gradient in mountain peatlands
Bibliographic record
Abstract
Globally, relationships between avian richness and elevation in mountain ecosystems typically reflect one of four well‐documented patterns, but the mechanisms responsible for these patterns are poorly understood. We investigated which pattern best described bird species richness in peatlands of the Upper Bow Basin of the Canadian Rocky Mountains (1300–2000 m a.s.l.) and used a model competition framework to investigate possible mechanisms. Avian richness displayed a plateauing (cubic) relationship in response to increasing elevation (AICc weight = 0.48). Log richness was significantly positively related to log peatland area (R 2 = 0.42, p = 0.001); however, and once we accounted for the richness–area relationship (area was not related to elevation (R 2 = 0.13, p = 0.083)), the richness–elevation relationship was best described by a negative linear model rather than a cubic model (AICc weight = 0.69, R 2 = 0.39). Consequently, we reject the neutral model of the mid‐domain effect and conclude that peatland area and one or more environmental filters are simultaneously driving relationships between avian richness and elevation in Rocky Mountain peatlands. Multicausality likely explains why researchers in different geographies observe inconsistent patterns between richness and elevation: drivers and interactions among drivers may vary spatially. Importantly, Natural Subregion was a stronger predictor of avian species richness than elevation per se (AICc weight = 0.96), suggesting that the responsible environmental filter(s) is relatively homogenous within ecological land classes (e.g. primary productivity) rather than directly variable with elevation (e.g. temperature). The results also lend insight into priorities for future research on richness–elevation patterns in mountain birds.
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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 | 0.000 |
| 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".