Growth dynamics of black spruce (<i>Picea mariana</i>) in a rapidly thawing discontinuous permafrost peatland
Bibliographic record
Abstract
Abstract High‐latitude warming has led to radical changes in abiotic conditions influencing forest growth. In the North American boreal forest, widespread declines in forest productivity (particularly in western regions) and changing climate‐growth relationships have been documented. Previous studies have proposed that this decline can be attributed to drought stress as increasing temperatures may cause evapotranspirative demand to exceed available moisture. We used tree ring studies to document growth dynamics of black spruce, one of the most dominant boreal tree species, in a boreal peatland experiencing rapid permafrost thaw. We specifically look at how changing permafrost conditions influence growth. Growth of black spruce at this site has declined steadily since the mid‐1900s and exhibited a shift from positive responses to temperature pre‐1970 to predominantly negative responses in recent decades, despite precipitation increasing over time at this site. Our results show that there is no apparent effect of landscape position or rate of lateral permafrost thaw on growth trends of black spruce, despite gradients in soil moisture and active layer thickness across the mosaic of wetlands and drier permafrost plateaus at this site. However, this does not imply no effect of permafrost thaw on growth; our results support growing evidence that vertical permafrost thaw (i.e., active layer thickening) is causing drought stress in these slow‐growing, shallow‐rooted trees. To our knowledge, this study is the first to investigate permafrost as a driver of within‐site variability in growth‐climate responses, and we provide insight into the widespread growth declines and divergence of climate‐growth relationships in high‐latitude forests.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".