Bird communities and vegetation associations across a treeline ecotone in the Mealy Mountains, Labrador, which is an understudied part of the boreal forest
Bibliographic record
Abstract
We examined the factors structuring bird communities across a complex subarctic treeline in the Mealy Mountains, Labrador, Canada. Using point counts of bird abundance in 2007 and 2008, we show that changes in vegetation driven by elevation are strongly correlated with avian community structure in this treeline ecotone system. Overall, avian diversity was higher in the forest compared with other habitat classes (krummholz, deciduous shrub, and alpine). There were strong correlations between avian diversity and vegetation richness, as well as structure, among and within habitat class in 2008. Numerous habitat types (subset of habitat class) were correlated with avian composition, although some species were clearly habitat generalists. Contrary to expectation, avian species composition was associated with physiognomy (vegetation structure) in alpine and deciduous shrub, and with either physiognomy or floristics (vegetation species composition) in krummholz and forest. Given the strong impact of elevation on vegetation and the demonstrated influence on bird communities, we note that for bird species whose near-southernmost populations are found in the Mealy Mountains, climate change is likely to have a strong negative effect if alpine tundra habitat is lost. Furthermore, forest bird species are likely to benefit from the increased tree cover as treeline moves poleward and upward.
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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 | 0.001 |
| 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) | 0.001 | 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".