Objective risk assessment of bark and ambrosia beetles non-indigenous to North America.

Pest risk assessment informs regulatory decisions to facilitate safe trade while also protecting a country's agricultural and environmental resources. The first step in pest risk assessment is pest categorization which can help determine whether an in-depth examination is needed. We created a model to predict the potential impact of non-indigenous bark and ambrosia beetles (Curculionidae: Scolytinae). This model uses biological variables derived from extensive assessment of alien species and produces a five-point scale of impact prediction. We accommodate uncertainty and missing data using random decision tree forests with Monte Carlo simulations. Non-indigenous bark beetles include both invasive species with significant ecological impacts, such as widespread tree death, and others that pose little risk. We assembled a comprehensive list of 60 introduced non-native bark beetle species in the continental United States as the training set. Forty-two potentially predictive variables were chosen from reports on behaviors, pestilence, recorded damage/interpretations in literature, biological traits, and interactions with fungi including plant pathogens. The model builds upon strategies used by USDA-APHIS in existing risk assessments, specifically the Objective Prioritization of Exotic Pests (OPEP) model, with changes in the following: (1) a transparent dataset for building and training the model enabling future updates and use in other systems, (2) uncertainty simulations using values derived from an extensive natural history matrix rather than an assumed equal distribution, and (3) predictions made on the probability of multiple impact levels, allowing users to decide based on acceptable risk. The model is designed for pest risk analysis for Scolytinae in the continental United States but can be adapted to other pests or regions. We tested the model's performance by iteratively removing each species from the training set and retraining the model. The retrained models accurately predicted the removed species. To demonstrate the model's application, we predicted the impact of scolytine beetles not yet present in the continental United States, Xylosandrus morigerus and Hypoborus ficus, plus an additional hypothetical species with no known data. Our model predicts that these species are likely to have moderate impacts and unlikely to have high impacts if they were introduced.