Impacts of Climatic Variation on the Growth of Black Spruce Across the Forest-Tundra Ecotone: Positive Effects of Warm Growing Seasons and Heat Waves Are Offset by Late Spring Frosts
Bibliographic record
Abstract
Climate strongly limits the physiological processes of trees near their range limits, leading to increased growth sensitivity. Northeastern North America is experiencing considerable warming, so the growth of trees near the northern treeline represents a key indicator of forest responses to climate change. However, tree-ring series and corresponding climatic data are scarce across the forest-tundra ecotone when compared to southern boreal regions, resulting in fewer studies on growth-climate relationships focused on this ecotone. Using daily climatic data, we identified trends in growing season heat accumulation and the intensity of acute climatic events over the last several decades in the southern and the northern parts of the forest-tundra ecotone in northeastern North America, and investigated their influence on black spruce radial growth. We found that black spruce trees responded positively to the increase in growing season temperatures and heat wave intensity, suggesting that growth is currently limited by suboptimal temperatures. While tree growth in the southern region generally benefited from warm spring temperatures, vulnerability to late spring frosts reduced tree growth in the northern region and increased probability of abrupt growth decline. In this region, late spring frosts offset approximately half of the additional growth that would otherwise occur over the course of a warm growing season. This vulnerability of northern trees may result from local adaptations to short growing seasons, which initiate biological activities at colder temperatures in the spring. Overall, our results highlight the need to explicitly incorporate acute climatic events into modeling efforts in order to refine our understanding of the impact of climate change on forest dynamics.
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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 | 0.000 | 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) | 0.000 | 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".