Soil moisture controls Engelmann spruce (<i>Picea engelmannii</i>) seedling carbon balance and survivorship at timberline in Utah, USA
Bibliographic record
Abstract
Most hypotheses about controls over high-altitude forests, including treeline, the elevation for upright woody plants, or timberline, the upper elevation for aggregated forest, suggest that low temperature drives forest dynamics, either through effects on cell division and tree growth or indirectly through frost damage or nutrient availability. However, abiotic factors other than temperature, including water availability, may serve as other important controls at high elevations, particularly for seedlings. To test the hypothesis that the timing and amount of precipitation exerts a strong control over the high-elevation forest boundary on the Wasatch Plateau in central Utah, USA, we conducted a field experiment that manipulated water availability and monitored photosynthesis, growth, and survivorship in Picea engelmannii Parry ex Engelm. seedlings. Survivorship increased from the driest to the wettest conditions, whereas the timing of precipitation did not explain differences in survival. However, we found that large, infrequent rain events increased maximum photosynthetic flux density compared with small, frequent rain events. Our results highlight the potential role of growing season water availability in limiting timberline expansion below the low-temperature thermal limits of P. engelmannii. As a consequence, the infilling of trees below the treeline in this region in response to climate change is likely to be episodic and driven by multiyear periods of high water availability and frequency that overcome drought limitations.
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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".