Variation in Volume Production Through Clonal Deployment: Results From a Simulation Model to Minimize Risk for Both a Currently Known and Unknown Future Pest
Bibliographic record
Abstract
Abstract A simulation model was developed to examine optimum patterns of deploying selected clones in the hypothetical situations of both a currently known pest and an unknown future pest. We modelled the interactions between Sitka spruce (Picea sitchensis (Bong.) Carr.), an economically important forest tree in British Columbia and the northwestern U.S., and the spruce terminal weevil (Pissodes strobi (Peck)), a major pest in western spruces. The model is combined with the Province of British Columbia’s Tree and Stand Simulator (TASS) model to drive individual tree growth and stand establishment and development. Two clonal-sampling strategies are examined: a randomly drawn set of genotypes or clones, to depict the potential consequences of a new (e.g., exotic) or a previously unimportant natural pest attacking a ‘random’ set of genotypes, and a ‘fixed’ set of clones, emulating a ‘commercial’ or known set of clones for growth and resistance mechanisms. Simulations use a range of numbers of genotypes or clones (2, 6, 18 and 30), and three deployment patterns (a random mixture of ramets, single- clone blocks, and a mosaic of smaller clonal blocks), in one and five hectare (Ha) stands. Total merchantable timber volume on a per Ha basis at harvest age 80 is used to compare the various combinations and schemes. With both random and fixed chosen sets of clones, the random planting pattern (i.e., random mixture of ramets from the clonal set) produced the most volume. Eighteen randomly chosen clones generally produced more volume, than 2, 6 and 30 clones, but differences among 6, 18 and 30 clones were small in most cases, irrespective of planting pattern. For fixed clones, the use of more resistant clones with higher growth potential produced more volume; however, pure clonal blocks of the best clone were not better than a mixture of that clone and an inferior one. Reducing the effects of insect activity and attack on trees, by lowering the average annual temperature in the model, or turning off all insect ‘activity’, increased merchantable volume but did not change the optimum number of clones (~18) or deployment pattern (random mixture). Forestry agencies can weigh these findings against economic advantages of block plantings of similar genotypes, in the choice of an appropriate number of clones and a deployment strategy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".