Climate-Sensitive Growth and Yield Models and Their Application to Assisted Migration
Bibliographic record
Abstract
Abstract Growth and yield (G&Y) of forest plantations can be significantly impacted by maladaptation resulting from climate change, and assisted migration has been proposed to mitigate these impacts by restoring populations to their historic climates. However, currently used genecology models for guiding assisted migration lack accounting for impacts of climate change on cumulative growth and requires assumption that responses of forest population to climate do not change with age. Using provenance trial data for interior lodgepole pine (Pinus contorta subsp. latifolia Douglas) and white spruce (Picea glauca (Moench) Voss) in western Canada, we integrated Universal Response Functions (URFs), representing the relationship of population performance with their provenance and site climates, into a G&Y model (Growth and Yield Projection System, GYPSY), to develop a climate-sensitive G&Y model for both species, and therefore to estimate climate change’s impacts on G&Y of local and moving populations and guiding assisted migration. Our findings reveal that climate change is expected to have varying effects on forest productivity across the landscape, with partial areas projected to experience a slight increase in productivity by the 2050s, while rest areas projected to face a significant decline in productivity for both species. Adoption of assisted migration with optimal populations selected was projected to maintain and even improve its productivity at the provincial scale. The findings of this study highlight the importance of accounting for climate change in forest management practices and underscores the relevance and benefits of incorporating assisted migration approaches to mitigate the negative impacts of climate 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".