A machine-learning approach to predict success of a biocontrol for invasive Eurasian watermilfoil reduction

Abstract

Myriophyllum spicatum, more commonly known as Eurasian watermilfoil (EWM), is one of the most invasive aquatic plants in North America, causing negative ecological and economic impacts in ecosystems where it proliferates. Many control strategies have been developed and implemented to mitigate EWM growth and spread, although the results are mixed and there is no consensus on lake-specific strategies. Here, we describe the development of a predictive model using a support vector technique, that predicts the success of biological pest control using Euhrychiopsis lecontei (the milfoil weevil), a milfoil specialist, to reduce EWM in lakes. Such a model is informed by lake characteristics (limnological and landscape) and augmentation strategies. To develop our predictive model, we performed a metadata analysis from 133 published peer-reviewed literature and professional reports of milfoil weevil augmentation field experiments that contained information on lake characteristics. The predictive model's algorithm uses a support vector machine (SMV) to learn patterns among lake characteristics, along with the recorded augmentation strategy and the reported success of each study, where success is a measure of EWM change over a season and is recorded in a variety of ways (e.g., EWM biomass change, EWM percent change, EWM visual change, etc.,). Overall, the model results suggests that shallower lakes, more frequent weevil augmentations, and larger weevil overwintering habitat are the most important predictors for EWM reduction success by weevil augmentation. Although watermilfoil weevil augmentation is a promising mitigation strategy, it may not work for all lakes. However, in terms of suggesting weevil augmentation, our model is a valuable tool for lake stakeholders and resource managers, who can use it to determine whether milfoil weevil augmentation, which can be very costly due to the difficulties in finding and raising milfoil weevils, will be a useful and sustainable approach to control EWM in their lake community.

Keywords: Eurasian watermilfoil; biocontrol; machine learning; milfoil weevils; predictive modeling; support vector machine.