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

Planning forest management relies on predicting insect outbreaks such as mountain pine beetle, particularly in the intermediate-term future, e.g., 5-year.

Factors governing outbreak dynamics in a forest intensively managed for mountain pine beetle.

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 control. Temperatures, water stress, host characteristics, and beetle pressure are among those ecological and environmental factors.

Dataset of mountain pine beetle outbreak dynamics and direct control in Cypress Hills, SK.

The data presented in this article are related to the research article entitled "Mountain pine beetle outbreak duration and pine mortality depend on direct control effort" [1]. This article provides presence of mountain pine beetle infested trees detected by the Saskatchewan Forest Service on a grid covering the spatial extent of the Saskatchewan portion of the Cypress Hills interprovincial park between 2006 and 2018. For each grid cell, associated ecological and environmental covariates, such as topography, weather and vegetation, are also provided.

Mountain pine beetle outbreak duration and pine mortality depend on direct control effort.

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 need to take into account the environment spatial variability and its impact on beetle life cycle.