Mapping the environmental limitations to growth of coastal Douglas-fir stands on Vancouver Island, British Columbia
Bibliographic record
Abstract
Coastal Douglas-fir (Pseudotsuga menziesii spp. menziesii (Mirb.) Franco) occurs over a wide range of environmental conditions on Vancouver Island, British Columbia. Although ecological zones have been drawn, no formal spatial analysis of environmental limitations on tree growth has been carried out. Such an exercise is desirable to identify areas that may warrant intensive management and to evaluate the impacts of predicted climate change this century. We applied a physiologically based forest growth model, 3-PG (Physiological Principles Predicting Growth), to interpret and map current limitations to Douglas-fir growth across Vancouver Island at 100-m cell resolution. We first calibrated the model to reproduce the regional productivity estimates reported in yield table growth curves. Further analyses indicated that slope exposure is important; southwest slopes of 30 degrees receive 40% more incident radiation than similarly inclined northeast slopes. When combined with other environmental differences associated with aspect, the model predicted 60% more growth on southwest exposures than on northeast exposures. The model simulations support field observations that drought is rare in the wetter zones, but common on the eastern side of Vancouver Island at lower elevations and on more exposed slopes. We illustrate the current limitations on growth caused by suboptimal temperature, high vapor pressure deficits and other factors. The modeling approach complements ecological classifications and offers the potential to identify the most favorable sites for management of other native tree species under current and future climatic conditions.
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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 | 0.000 |
| 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 | 0.001 | 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".