Stable carbon isotope analysis reveals widespread drought stress in boreal black spruce forests
Bibliographic record
Abstract
Unprecedented rates of climate warming over the past century have resulted in increased forest stress and mortality worldwide. Decreased tree growth in association with increasing temperatures is generally accepted as a signal of temperature-induced drought stress. However, variations in tree growth alone do not reveal the physiological mechanisms behind recent changes in tree growth. Examining stable carbon isotope composition of tree rings in addition to tree growth can provide a secondary line of evidence for physiological drought stress. In this study, we examined patterns of black spruce growth and carbon isotopic composition in tree rings in response to climate warming and drying in the boreal forest of interior Alaska. We examined trees at three nested scales: landscape, toposequence, and a subsample of trees within the toposequence. At each scale, we studied the potential effects of differences in microclimate and moisture availability by sampling on northern and southern aspects. We found that black spruce radial growth responded negatively to monthly metrics of temperature at all examined scales, and we examined ∆(13)C responses on a subsample of trees as representative of the wider region. The negative ∆(13)C responses to temperature reveal that black spruce trees are experiencing moisture stress on both northern and southern aspects. Contrary to our expectations, ∆(13)C from trees on the northern aspect exhibited the strongest drought signal. Our results highlight the prominence of drought stress in the boreal forest of interior Alaska. We conclude that if temperatures continue to warm, we can expect drought-induced productivity declines across large regions of the boreal forest, even for trees located in cool and moist landscape positions.
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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 | 0.000 |
| 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".