Predicting insect outbreaks using machine learning: a mountain pine beetle case study

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dc.contributor.authorRamazi, Pouria
dc.contributor.authorKunegel-Lion, Mélodie
dc.contributor.authorGreiner, Russell
dc.contributor.authorLewis, Mark A.
dc.date.accessioned2025-04-15T19:22:07Z
dc.date.available2025-04-15T19:22:07Z
dc.date.issued2021
dc.description.abstractPlanning 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 tasks. However, there are many subtle challenges in applying them: identifying the best learning models and the best subset of available covariates (including time lags) and properly evaluating the models to avoid misleading performance-measures. We systematically address these issues in predicting the chance of a mountain pine beetle outbreak in the Cypress Hills area and seek models with the best performance at predicting future 1-, 3-, 5- and 7-year infestations. We train nine machine-learning models, including two generalized boosted regression trees (GBM) that predict future 1- and 3-year infestations with 92% and 88% AUC, and two novel mixed models that predict future 5- and 7-year infestations with 86% and 84% AUC, respectively. We also consider forming the train and test datasets by splitting the original dataset randomly rather than using the appropriate year-based approach and show that this may obtain models that score high on the test dataset but low in practice, resulting in inaccurate performance evaluations. For example, a k-nearest neighbor model with the actual performance of 68% AUC, scores the misleadingly high 78% on a test dataset obtained from a random split, but the more accurate 66% on a year-based split. We then investigate how the prediction accuracy varies with respect to the provided history length of the covariates and find that neural network and naive Bayes, predict more accurately as history-length increases, particularly for future 1- and 3-year predictions, and roughly the same holds with GBM. Our approach is applicable to other invasive species. The resulting predictors can be used in planning forest and pest management and planning sampling locations in field studies.
dc.description.reviewstatusReviewed
dc.description.scholarlevelFaculty
dc.description.sponsorshipThe research was partly funded by Alberta Environment & Parks (AEP). This research was also supported by a grant to M.A.L. from the Natural Sciences and Engineering Research Council of Canada (grant no. NET GP 434810-12) to the TRIA Network, with contributions from Alberta Agriculture and Forestry, Foothills Research Institute, Manitoba Conservation and Water Stewardship, Natural Resources Canada-Canadian Forest Service, Northwest Territories Environment and Natural Resources, Ontario Ministry of Natural Resources and Forestry, Saskatchewan Ministry of Environment, West Fraser and Weyerhaeuser. M.A.L. is also grateful for the support through the NSERC Discovery and the Canada Research Chair programs. R.G. is grateful for funding from NSERC Discovery, and Alberta Machine Intelligence Institute.
dc.identifier.citationRamazi, P., Kunegel-Lion, M., Greiner, R., & Lewis, M. A. (2021). Predicting insect outbreaks using machine learning: a mountain pine beetle case study. Ecology and Evolution, 11(19), 13014-13028. https://doi.org/10.1002/ece3.7921
dc.identifier.urihttps://doi.org/10.1002/ece3.7921
dc.identifier.urihttps://hdl.handle.net/1828/21929
dc.language.isoen
dc.publisherEcology and Evolution
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectfuture infestations
dc.subjectinsect spread
dc.subjectmachine learning
dc.subjectmountain pine beetle
dc.subjectpredictive ecology
dc.subjecttemporal prediction
dc.titlePredicting insect outbreaks using machine learning: a mountain pine beetle case study
dc.typeArticle

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