A new approach to evaluate the risk of bark beetle outbreaks using multi-step machine learning methods

Bark beetles (Coleoptera: Curculionidae) alter forest ecosystem functioning through tree mortality, thus causing billions of dollars in economic and ecological damages worldwide. Dendroctonus frontalis Zimmermann is considered one of the most significant insect pest species of pine (Pinus spp.) trees in the United States (U.S.), Central America, and Mexico. To manage this threat, research has sought to predict and forecast outbreaks and identify high risk areas to focus on preventative management and reduce economic losses. Prior work has focused on using environmental predictors, but limitations have arisen regarding data availability and structure. Our research objective was to improve on current D. frontalis outbreak prediction models using contemporary modeling techniques. Beetle outbreak data were obtained from the United States Department of Agriculture Forest Service (USDA-FS) and were paired with three spatial–temporal beetle outbreak dynamics from the prior year, fifteen climate variables (DAYMET and WORLDCLIM) (temperature, radiation, wind, and water resources), two terrain attributes (NASA) (elevation and compound topographic index), and four vegetation indices [Moderate Resolution Imaging Spectroradiometer (MODIS)] [maximum and mean normalized difference vegetation index (NDVI)] as predictive features. Extreme gradient boosting was used to create two separate models that predicted the probability and magnitude of beetle outbreaks, which were used to create interpolated prediction maps for the southeastern U.S. The interpolated maps were combined to estimate outbreak risk (i.e., risk = probability of outbreak × magnitude of outbreak). Overall model accuracy was 87.7% when tested on an independent dataset. Distance to prior year outbreak and mean NDVI were the most important features when predicting the probability of outbreak, while summer maximum temperature, distance to prior year outbreak, and winter minimum temperature were the highest weighted features when predicting the magnitude. Results indicated that most of the southeastern U.S. was at low risk (<0.0001% damage per hectare) for the years 2008–2020. Risk was highest for 2012, 2016, and 2017. A few areas in Alabama, Georgia, northern Florida, and South Carolina contained stands at higher risk for damage (>0.01% per hectare) and some locations were at risk for >90% damage per hectare. Extreme gradient boosting paired with outbreak probability and magnitude performed well and is proposed as a solution for future bark beetle prediction and forecasting for more timely management strategies. The inclusion of climatic variables in outbreak models allows for forecasting the effects of future climate change on pine pest populations globally.