Climate change may increase Quebec boreal forest productivity in high latitudes by shifting its current composition
Bibliographic record
Abstract
Several recent studies point out that climate change is expected to influence boreal forest succession, disturbances, productivity, and mortality. However, the effect of climate change on those processes and their interactions is poorly understood. We used an ecophysiological-based mechanistic landscape model to study those processes and their interactions and predict the future productivity and composition under climate change scenarios (RCP) for 300 years (2010–2310). The effects of climate change and wildfires on forest composition, biomass carbon sequestration and storage, and mortality were assessed in three management units of Quebec boreal forest, distributed along a longitudinal gradient from west to east: North-of-Quebec (MU1), Saguenay–Lac-Saint-Jean (MU2), and Côte-Nord region (MU3). Coniferous mortality variation was explained by competitive exclusion and wildfires, which are related to climate change. In the studied MU, we found a decrease in coniferous pure occupancy at the landscape scale and an increase in mixed deciduous forests in MU1 and MU2, and an increase in mixed coniferous, mainly black spruce and balsam fir in MU3. On the other hand, for extreme scenarios (RCP4.5 and RCP8.5), in the absence of broadleaves dispersal, the open woodland occupancy could increase to more than 8, 22, and 10% in MU1, MU2, and MU3 respectively. Also, climate change might increase overall biomass carbon stock two times for RCP2.6 and RCP4.5 scenarios compared to the baseline this may be explained by the extension of the growing season and the reduction of potential cold-temperature injuries. Generally, western regions were more sensitive to climate changes than the eastern regions (MU3), in fact under RCP8.5 biomass carbon stock will be decreasing in the long-term for MU1 compared to the current climate. This study provides a good starting point to support future research on the multiple factors affecting forest C budget under global change.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".