Climate sensitive growth models for predicting diameter growth of western Canadian boreal tree species
Bibliographic record
Abstract
Abstract In the face of anticipated climate change, growth models require modifications to effectively model likely future growth and survival of trees and stands. To support incorporation of climate effects in the Mixedwood Growth Model (MGM), we developed climate-sensitive mixed effects models for lodgepole pine (Pinus contorta Douglas ex Loudon), jack pine (Pinus banksiana Lamb.), trembling aspen (Populus tremuloides Michx.), balsam poplar (Populus balsamifera L.) and white spruce (Picea glauca (Moench) Voss), and examined how tree size, intra- and interspecific competition and climate will influence individual-tree diameter growth. Remeasurement data from permanent growth and yield sample plots from across the western Canadian boreal forest were used for this study. Based on the model that we developed, tree size, competition and climate had varying effects on growth, among tree species. Although intraspecific competition had a stronger negative effect on growth for deciduous and white spruce trees, interspecific competition had a stronger negative effect on the growth of pine. Deciduous trees growing in stands having longer frost-free period experienced an increase in growth but a negative effect of frost-free period was observed for conifers. In addition, higher moisture was found to increase tree growth, but the influence of available moisture on growth was dependent on competition. Overall, these results suggest that, under climate warming, between-species differences in growth responses will contribute to our understanding of the stand dynamics in the western boreal region of Canada. Climate sensitive growth models developed in this study will be useful in the recalibration of MGM and other individual-tree models, and predictions will contribute to better-informed decisions about silvicultural treatments for these economically important boreal tree species.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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".