Negative impacts of high temperatures on growth of black spruce forests intensify with the anticipated climate warming
Bibliographic record
Abstract
An increasing number of studies conclude that water limitations and heat stress may hinder the capacity of black spruce (Picea mariana (Mill.) B.S.P.) trees, a dominant species of Canada's boreal forests, to grow and assimilate atmospheric carbon. However, there is currently no scientific consensus on the future of these forests over the next century in the context of widespread climate warming. The large spatial extent of black spruce forests across the Canadian boreal forest and associated variability in climate, demography, and site conditions pose challenges for projecting future climate change responses. Here we provide an evaluation of the impacts of climate warming and drying, as well as increasing [CO2 ], on the aboveground productivity of black spruce forests across Canada south of 60°N for the period 1971 to 2100. We use a new extensive network of tree-ring data obtained from Canada's National Forest Inventory, spatially explicit simulations of net primary productivity (NPP) and its drivers, and multivariate statistical modeling. We found that soil water availability is a significant driver of black spruce interannual variability in productivity across broad areas of the western to eastern Canadian boreal forest. Interannual variability in productivity was also found to be driven by autotrophic respiration in the warmest regions. In most regions, the impacts of soil water availability and respiration on interannual variability in productivity occurred during the phase of carbohydrate accumulation the year preceding tree-ring formation. Results from projections suggest an increase in the importance of soil water availability and respiration as limiting factors on NPP over the next century due to warming, but this response may vary to the extent that other factors such as carbon dioxide fertilization, and respiration acclimation to high temperature, contribute to dampening these limitations.
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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".