Silvicultural management of white pines in western North America
Bibliographic record
Abstract
Summary Since the introduction prior to 1915 of white pine blister rust (Cronartium ribicola) into the forests of western North America, many populations of native white pine species have seriously declined. Because western white pine (Pinus monticola) and sugar pine (P. lambertiana) are highly valued timber species, their silviculture under intensive management is well‐documented. The silviculture of other white pine species has received less attention but is no less important. For all western species, silvicultural management is a key component for sustaining and restoring viable white pine populations. This review examines approaches for assessing and reducing blister rust hazard, regenerating white pine stands, and tending established stands to reduce damage and impact from blister rust. Hazard and risk ratings provide means for assessing the potential severity of blister rust infestation and its probable impacts on management. An epidemiological simulation model is available for describing complex pathosystem interactions, their consequences on white pine growth and survival, and likely outcomes of silvicultural activities. Until the 1960s,Ribeseradication was the principal method for blister rust control; it is now rarely used except for high‐value trees. The choices of harvest and site preparation methods are critical for successful white pine regeneration. As host responses to blister rust infection are inherited, regeneration is an opportunity to increase seedling survival and disease resistance. For artificial regeneration, the western genetics programmes provide improved planting stock. For natural regeneration, the selection and retention of well‐adapted white pines as seed sources can enhance stand genetics. Thinning and pruning are common silvicultural activities for tending stands and are readily modified for blister rust‐infested stands. Although biological and chemical agents have been used, their performance has been less than satisfactory. Likewise, genetics and other silvicultural practices have also demonstrated limited success in blister rust control. An alternative, adaptive approach could use both silvicultural and genetic techniques to mitigate impacts and maintain white pines.
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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 | 0.001 | 0.001 |
| 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".