Abstract

The pine bark beetle is a devastating forest pest, causing significant forest losses worldwide, including 25% of pine forests in Honduras. This study focuses on Dendroctonus frontalis and Ips spp., which have affected four of the seven native pine species in Honduras: Pinus oocarpa, P. caribaea, P. maximinoi, and P. tecunumanii. Artificial intelligence (AI) is an essential tool for developing susceptibility models. However, gaps remain in the evaluation and comparison of these algorithms when modeling susceptibility to bark beetle outbreaks in tropical conifer forests using Google Earth Engine (GEE). The objective of this study was to compare the effectiveness of three algorithms-random forest (RF), gradient boosting (GB), and maximum entropy (ME)-in constructing susceptibility models for pine bark beetles. Data from 5601 pest occurrence sites (2019-2023), 4000 absence samples, and a set of environmental covariates were used, with 70% for training and 30% for validation. Accuracies above 92% were obtained for RF and GB, and 85% for ME, along with robustness in the area under the curve (AUC) of up to 0.98. The models revealed seasonal variations in pest susceptibility. Overall, RF and GB outperformed ME, highlighting their effectiveness for implementation as adaptive approaches in a more effective forest monitoring system.