Site index as a predictor of the effect of climate warming on boreal tree growth
Bibliographic record
Abstract
The boreal forest represents the terrestrial biome most heavily affected by climate change. However, no consensus exists regarding the impacts of these changes on the growth of tree species therein. Moreover, assessments of young tree responses in metrics transposable to forest management remain scarce. Here, we assessed the impacts of climate change on black spruce (Picea mariana [Miller] BSP) and jack pine (Pinus banksiana Lambert) growth, two dominant tree species in boreal forests of North America. Starting with a retrospective analysis including data from 2591 black spruces and 890 jack pines, we forecasted trends in 30-year height growth at the transitions from closed to open boreal coniferous forests in Québec, Canada. We considered three variables: (1) height growth, rarely used, but better-reflecting site potential than other growth proxies, (2) climate normals corresponding to the growth period of each stem, and (3) site type (as a function of texture, stoniness, and drainage), which can modify the effects of climate on tree growth. We found a positive effect of vapor pressure deficit on the growth of both species, although the effect on black spruce leveled off. For black spruce, temperatures had a positive effect on the height at 30 years, which was attenuated when and where climatic conditions became drier. Conversely, drought had a positive effect on height under cold conditions and a negative effect under warm conditions. Spruce growth was also better on mesic than on rocky and sub-hydric sites. For portions of the study areas with projected future climate within the calibration range, median height-change varied from 10 to 31% for black spruce and from 5 to 31% for jack pine, depending on the period and climate scenario. As projected increases are relatively small, they may not be sufficient to compensate for potential increases in future disturbances like forest fires.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".