Abstract
Emerald ash borer (EAB), a wood-boring insect native to Asia, was discovered near Detroit in 2002 and has spread and killed millions of ash trees throughout the eastern United States and Canada. EAB causes severe damage in urban areas where it kills high-value ash trees that shade streets, homes, and parks and costs homeowners and local governments millions of dollars for treatment, removal, and replacement of infested trees. We present a multistage, stochastic, mixed-integer programming model to help decision-makers maximize the public benefits of preserving healthy ash trees in an urban environment. The model allocates resources to surveillance of the ash population and subsequent treatment and removal of infested trees over time. We explore the multistage dynamics of an EAB outbreak with a dispersal mechanism and apply the optimization model to explore surveillance, treatment, and removal options to manage an EAB outbreak in Winnipeg, a city of Manitoba, Canada. Recommendation to Resource Managers Our approach demonstrates that timely detection and early response are critical factors for maximizing the number of healthy trees in urban areas affected by the pest outbreak. Treatment of the infested trees is most effective when done at the earliest stage of infestation. Treating asymptomatic trees at the earliest stages of infestation provides higher net benefits than tree removal or no-treatment options. Our analysis suggests the use of branch sampling as a more accurate method than the use of sticky traps to detect the infested asymptomatic trees, which enables treating and removing more infested trees at the early stages of infestation. Our results also emphasize the importance of allocating a sufficient budget for tree removal to manage emerald ash borer infestations in urban environments. Tree removal becomes a less useful option in small-budget solutions where the optimal policy is to spend most of the budget on treatments.
Original language | English (US) |
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Article number | e12267 |
Journal | Natural Resource Modeling |
Volume | 34 |
Issue number | 1 |
DOIs | |
State | Published - Feb 2021 |
All Science Journal Classification (ASJC) codes
- Modeling and Simulation
- Environmental Science (miscellaneous)
Keywords
- Canada
- emerald ash borer
- mixed integer programming
- stochastic optimization
- surveillance
- uncertainty