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- 2Machine learning
- 2mountain pine beetle
- 1Automatic learning
- 1Bayesian network
- 1Direct control
- 1Future infestations
- 6Biological Sciences, Department of
- 6Biological Sciences, Department of/Journal Articles (Biological Sciences)
- 4The NSERC TRIA Network (TRIA-Net)
- 4The NSERC TRIA Network (TRIA-Net)/Journal Articles (TRIA-Net)
- 3Mathematical and Statistical Sciences, Department of
- 3Mathematical and Statistical Sciences, Department of/Research Publications (Mathematical and Statistical Sciences)
Although ecological models used to make predictions from underlying covariates have a record of success, they also suffer from limitations. They are typically unable to make predictions when the value of one or more covariates is missing during the testing. Missing values can be estimated but...
Mountain pine beetle (MPB) outbreaks have caused major economic losses and ecological consequences in North American pine forests. Ecological and environmental factors impacting MPB life-history and stands susceptibility can help with the detection of MPB infested trees and thereby, improve...
Insect epidemics such as the mountain pine beetle (MPB) outbreak have a major impact on forest dynamics. In Cypress Hills, Canada, the Forest Service Branch of the Saskatchewan Ministry of Environment aims to control as many new infested trees as possible by conducting ground-based surveys around...
The efficacy of direct control methods in bark beetle outbreaks is a disputed topic. While some studies report that control reduces tree mortality, others see little effect. Existing models, linking control rate to beetle population dynamics and tree infestations, give insights, but there is a...
Planning forest management relies on predicting insect outbreaks such as mountain pine beetle, particularly in the intermediate‐term future, e.g., 5‐year. Machine‐learning algorithms are potential solutions to this challenging problem due to their many successes across a variety of prediction...