Role of Mixed-Species Stands in Attenuating the Vulnerability of Boreal Forests to Climate Change and Insect Epidemics
Bibliographic record
Abstract
We investigated whether stand species mixture can attenuate the vulnerability of eastern Canada’s boreal forests to climate change and insect epidemics. For this, we focused on two dominant boreal species, black spruce [Picea mariana (Mill.) BSP] and trembling aspen (Populus tremuloides Michx.), in stands dominated by black spruce or trembling aspen (“pure stands”), and mixed stands (M) composed of both species within a 36 km2 study area in the Nord-du-Québec region. For each species in each stand composition type, we tested climate-growth relations and assessed the impacts on growth by recorded insect epidemics of a black spruce defoliator, the spruce budworm (SBW) [Choristoneura fumiferana (Clem.)], and a trembling aspen defoliator, the forest tent caterpillar (FTC; Malacosoma disstria Hübn.). We implemented linear models in a Bayesian framework to explain baseline and long-term trends in tree growth for each species according to stand composition type and to differentiate the influences of climate and insect epidemics on tree growth. Overall, we found climate vulnerability was lower for black spruce in mixed stands than in pure stands, while trembling aspen was less sensitive to climate than spruce, and aspen did not present differences in responses based on stand mixture. We did not find any reduction of vulnerability for mixed stands to insect epidemics in the host species, but the non-host species in mixed stands could respond positively to epidemics affecting the host species, thus contributing to stabilize ecosystem-scale growth over time. Our findings partially support boreal forest management strategies including stand species mixture to foster forests that are resilient to climate change and insect epidemics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".