The Economics of Spruce Budworm Monitoring and Management in Eastern Canada
Bibliographic record
Abstract
This dissertation uses techniques that were developed for renewable natural resource and invasive pest management to describe the two principal challenges of eastern spruce budworm (SBW) monitoring and management in Eastern Canada, with a specific focus on the province of Quebec. The primary empirical components of this dissertation can be found in Chapters 2 and 3. Chapter 1 provides the necessary historic, entomological, ecological and policy context to understand Chapters 2 and 3. Chapter 4 provides a conclusion to this dissertation by proposing extensions that would make the models presented in Chapters 2 and 3 more readily applicable to real-world spruce budworm management. These extensions involve making the models spatially explicit, as the models presented in Chapters 2 and 3 are spatially crude for the sake of tractability. Chapter 2 describes the management of an endemic irruptive forest pest and, using the spruce budworm-balsam fir forest interaction, proposes an optimization model that determines optimal pest treatment and forest harvest levels for a single, dimensionless forest stand that is currently undergoing an active budworm outbreak. Budworms cause both growth reductions and mortality on the forest biomass stock, and increasing forest biomass will provide budworms with more prey, causing their growth rate to increase. Treatment decisions are limited to three discrete possibilities (0, 1 or 2 on the landscape) that impose mortality on budworms, while harvests remove a proportion of the forest biomass. Using a numerical solution algorithm, we find that the optimal policy is generally to apply treatments over budworms and to harvest the forest at a sustainable rate, which confirms that the current management programs used in Eastern Canada are welfare-improving relative to letting an outbreak run its course. The time path for our baseline scenario indicates that budworms can be treated down to endemic levels quickly. Sensitivity analysis describes scenarios where budworm levels will rebound every year as well as scenarios where the optimal policy is to clearcut the forest as quickly as possible. Chapter 3 considers the pre-outbreak monitoring phase for spruce budworm management. This context is informed by the Early Intervention Strategy, a management practice that is currently being successfully employed in New Brunswick and other Maritime provinces. EIS requires extensive monitoring and proactive treatment. As such, we adapt a model known as CESAT to determine the locations for which EIS would yield positive net benefits in eastern Quebec. Under our baseline scenario, we find that EIS is optimal in some zones bordering New Brunswick, which indicates that EIS is unlikely to be welfare-improving over current management practices used in Quebec. Under different assumptions, however, we find that EIS is optimal across a much larger landscape, yielding millions of CAD net benefits over a thirty year horizon.
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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".