The Interactive Role of Climatic Transfer Distance and Overstory Retention on Douglas‐Fir Seedling Survival and Height Growth in Interior British Columbia
Bibliographic record
Abstract
The future climatic niche of interior Douglas-fir (Pseudotsuga menziesii var. glauca [Mirb.] Franco) is expected to have little spatial overlap with its current range due to climate change. The resulting misalignment of the climatic niche and species distribution is expected to result in many forests becoming maladapted in their current location, thus increasing vulnerability to disturbance and reducing productivity. This novel study examined the individual and interactive effects of climatic transfer distance and silviculture systems on planted 3-year-old Douglas-fir seedlings across the natural range of interior Douglas-fir in British Columbia. Several climatic transfer distance variables were considered, and the silviculture systems tested comprised the following gradients of tree retention: 0% retention (clearcut), 10% dispersed retention (seed-tree), 30% aggregate retention, and 60% aggregate retention with thinning from below. Using linear mixed effect models, we found that survival and height were positively correlated with movements of seedlings to warmer, wetter, and more humid climates. Moisture availability had a stronger influence than temperature, indicating that seedlings transferred to warmer but more arid climates would experience decreased survival and height. Where seedlings were transferred to climates with greater frost frequency or decreased humidity, greater retention of overstory trees improved survival and height. Conversely, movements to more favorable climatic conditions (warmer and wetter) resulted in improved survival and height where overstory retention was low. Our findings suggest that genetic reshuffling of populations through assisted migration could benefit from overstory retention where stressful climatic conditions due to aridity or increased frost frequency occur.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".