Seasonal thaw and landscape position determine foliar functional traits and whole‐plant water use in tall shrubs on the low arctic tundra
Bibliographic record
Abstract
Climate warming is driving tundra shrub expansion with implications for ecosystem function and regional climate. Understanding associations between shrub ecophysiological function, distribution and environment is necessary for predicting consequences of expansion. We evaluated the role of topographic gradients on upland shrub productivity to understand potential constraints on shrub expansion. At a low arctic tundra site near Inuvik, Northwest Territories, Canada, we measured sap flow, stem water potential and productivity-related functional traits in green alder, and environmental predictors (water and nutrient availability and seasonal thaw depth) across a toposequence in alder patches. Seasonal thaw reduced stem sap flow whereas topographic position predicted stem water potential and productivity-related functional traits. Upslope shrubs were more water-limited than those downslope. Shrubs in drainage channels had traits associated with greater productivity than those on the tops of slopes. The effect of thaw depth on sap flow has implications for seasonal water-use patterns and warming impacts on tundra ecohydrology. Topographic variation in functional traits corresponds with observed spatial patterns of tundra shrub expansion along floodplains and concave hillslopes rather than in upland areas. Green alder is expanding rapidly across the low arctic tundra in northwestern North America; thus, anticipating the implications of its expansion is essential for predicting tundra function.
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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 | 0.000 | 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".