Differential declines in Alaskan boreal forest vitality related to climate and competition
Bibliographic record
Abstract
Rapid warming and changes in water availability at high latitudes alter resource abundance, tree competition, and disturbance regimes. While these changes are expected to disrupt the functioning of boreal forests, their ultimate implications for forest composition are uncertain. In particular, recent site-level studies of the Alaskan boreal forest have reported both increases and decreases in productivity over the past few decades. Here, we test the idea that variations in Alaskan forest growth and mortality rates are contingent on species composition. Using forest inventory measurements and climate data from plots located throughout interior and south-central Alaska, we show significant growth and mortality responses associated with competition, midsummer vapor pressure deficit, and increased growing season length. The governing climate and competition processes differed substantially across species. Surprisingly, the most dramatic climate response occurred in the drought tolerant angiosperm species, trembling aspen, and linked high midsummer vapor pressure deficits to decreased growth and increased insect-related mortality. Given that species composition in the Alaskan and western Canadian boreal forests is projected to shift toward early-successional angiosperm species due to fire regime, these results underscore the potential for a reduction in boreal productivity stemming from increases in midsummer evaporative demand.
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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) | 0.001 | 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".