Tree-growth response to climatic variability in two climatically contrasting treeline ecotone areas, central Himalaya, Nepal
Bibliographic record
Abstract
Tree growth at the treeline ecotone is known to be sensitive to climate variability and is thus considered to be a worldwide biomonitor of climate change. However, our understanding of within-region variation in growth responses through space and time is limited. A dry south-facing slope dominated by Pinus wallichiana A.B. Jacks. and a wet north-facing slope dominated by Abies spectabilis (D. Don) Spach in Nepal, central Himalaya, were used to analyze the intersite (i.e., dry vs. wet sites) and intrasite (i.e., treeline vs. forest line elevations) tree-growth relationships, as well as response to monthly and seasonal temperature and precipitation at annual and bidecadal time scales. At both study sites and at two elevations within each site, growth can be strongly affected by growing-season and nongrowing-season factors; however, there are inconsistencies in terms of the climate–growth relationship across space and over time. At the dry site, only a weak positive growth response to summer temperature is observed. At both sites, there is a negative growth response to winter precipitation at both high and low elevations, and this response is markedly independent of the summer and winter temperature trends of the respective site. At the wet site, growth at the higher elevation is negatively correlated to the early summer temperature, whereas a positive growth response to spring precipitation is observed at the lower elevation, indicating a possible drought effect. The results illustrate how different climatic drivers may govern tree-growth responses both between sites with contrasting climates within a region and along elevational gradients within the treeline ecotone. This underlines the need for multiscale studies and a focus on multiple climate variables when analyzing treeline ecotone responses to climate change.
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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 | 0.001 |
| 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) | 0.000 | 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".