Changes in growth of pristine boreal North American forests from 1950 to 2005 driven by landscape demographics and species traits
Bibliographic record
Abstract
Abstract. In spite of the many factors that are occurring and known for positively affecting the growth of forests, some boreal forests across North America have recently felt the adverse impacts of environmental changes. Knowledge of causes for productivity declines in North American boreal forests remains limited, and this is owed to the large spatial and temporal scales involved, and the many plant processes affected. Here, the response of pristine eastern boreal North American (PEBNA) forests to ongoing climatic changes is examined using in situ data, community ecology statistics, and species-specific model simulations of carbon exchanges forced by contemporary climatic data. To examine trends in forest growth, we used a recently acquired collection of tree-ring width data from 252 sample plots distributed in PEBNA forests dominated by black spruce (Picea mariana [Mill.] B.S.P.) and jack pine (Pinus banksiana Lamb.). Results of linear trend analysis on the tree growth data highlight a dominating forest growth decline in overmature forests (age > 120 years) from 1950 to 2005. In contrast, improving growth conditions are seen in jack pine and mature (70–120 years) black spruce stands. Multivariate analysis of climate and growth relationships suggests that responses of PEBNA forests to climate are dependent on demographic and species traits via their mediation of temperature and water stress constraints. In support of this hypothesis, the simulation experiment suggests that in old-growth black spruce stands the benefit to growth brought on by a longer growing season may have been low in comparison with the increasing moisture stress and respiration losses caused by warmer summer temperatures. Predicted increases in wildfire frequency in PEBNA forests will likely enhance the positive response of landscape-level forest growth to climate change by shifting the forest distribution to younger age classes while also enhancing the jack pine component.
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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".