Forest Management Impacts on Tree Species Diversity: Effectiveness and Costs in Light of a Beetle Epidemic
Bibliographic record
Abstract
In western North America, a recent epidemic of mountain pine beetle (Dendroctonus ponderosae) caused widespread forest mortality. This outbreak was in part due to the changing climate, and damage from pests and diseases is expected to increase in the future. To learn from this event, we used a historical retrospective approach to evaluate business-as-usual and alternative management strategies effects on tree species diversity. The insurance hypothesis proposed that ecosystems with greater species diversity will have greater productivity due to the buffering effect against natural disturbances. Therefore, we hypothesized that management strategies to increase diversity before the beetle outbreak could result in higher growing stocks, harvest rates, and net present value. The assessment was based on simulation modelling of a 1.1 million ha landscape in British Columbia, Canada for 1980–2060. We applied different strategies to affect diversity: harvest more of the most dominant tree species, planting more diverse species, and increase natural regeneration. The most aggressive strategy resulted in higher diversity and growing stocks, higher harvest rates, and higher, more consistent net revenue over time than the business-as-usual strategy. However, the strategy that only employed a diversity of planting negatively affected those indicators. Thus, we have identified limitations to what management strategies may be able to achieve. Sensitivity analyses of species productivity and log price indicated a high level of robustness in the results. Our study showed that reducing forest health risks may be economically viable.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".