Mixing tree species and density management to reduce drought susceptibility in coastal plantation forests of British Columbia
Bibliographic record
Abstract
The coastal forests of British Columbia have been experiencing longer and more intense droughts in recent years. To evaluate the response of species composition and planting density (500, 1000, and 1500 stems ha −1 ) to drought, a study was conducted in a plantation consisting of 1:0, 1:1, 1:3 and 0:1 Douglas-fir ( Pseudotsuga menziesii var. menziesii (Mirb.) Franco): western redcedar ( Thuja plicata Donn ex D. Don in Lamb.) mixtures, located along the east side of Vancouver Island, Canada. Measurements were taken to evaluate soil moisture, drought tolerance, and water use efficiency of these stands. Soil moisture was significantly lower in the highest compared to the lowest density stands ( p = 0.016). Drought indices calculated from tree cores showed that drought resistance, resilience and recovery increased with decreasing stand basal area. Water use efficiency (WUE), inferred from wood δ¹ ³C, was significantly affected by density × mixture × species interaction ( p = 0.020) but not seasonal variation ( p = 0.155). In the 1:3 mixture, western redcedar at the lowest density exhibited higher WUE than at 1000 and 1500 stems ha⁻¹ ( p = 0.038 and 0.005, respectively) but pure western redcedar at moderate density (1000 stems ha⁻¹) appeared to have the highest overall WUE, exceeding several other treatment combinations by 1.32–1.92 ‰ ( p < 0.001–0.047). The results indicate that reducing stand basal area, which can be achieved by mixing species with different growth rates and controlling stand density, can help reduce the drought susceptibility of these forests. • Stand basal area, but not tree height, influenced drought tolerance. • Drought tolerance improved with lower stand basal area. • Water use efficiency varied by species, mixture, and planting density interaction. • Silvicultural design can reduce drought susceptibility in coastal forests.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".