Management assessment of mountain pine beetle infestation in Cypress Hills, SK

• Author(s) / Creator(s)

  • Mélodie Kunegel-Lion
  • Rory L. McIntosh
  • Mark A. Lewis

• 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 trees infested in previous years. Given the risk posed by MPB, there
  is a need to evaluate how well such a control strategy performs. Therefore, the goal of this study is to assess the current detection
  strategy compared with competing strategies (random search and search based on model predictions via machine learning),
  while taking management costs into account. Our model predictions via machine learning used a generalized boosted classification tree to predict locations of new infestations from ecological and environmental variables. We then ran virtual experiments to determine control efficiency under the three detection strategies. The classification tree predicts new infested locations
  with great accuracy (AUC = 0.93). Using model predictions for survey locations gives the highest control efficiency for larger
  survey areas. Overall, the current detection strategy performs well but control could be more efficient and cost-effective by
  increasing the survey area, as well as adding locations given by model predictions.

• Date created

  2019-02-01

• Subjects / Keywords

  • control efficiency
  • detection
  • management cost
  • insect epidemics
  • beetle pressure

• Type of Item

  Article (Published)

• DOI

  https://doi.org/10.7939/r3-7vqh-ky11

• License

  Attribution-NonCommercial 4.0 International