The Economics of Spruce Budworm Monitoring and Management in Eastern Canada
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».