Journal Article

Predicting hotspots for invasive species introduction in Europe

Makowski, David, Schneider, Kevin, van der Werf, Wopke

2021

Overview

Plant pest invasions cost billions of Euros each year in Europe. Prediction of likely places of pest introduction could greatly help focus efforts on prevention and control and thus reduce societal costs of pest invasions. Here, we test whether generic data-driven risk maps of pest introduction, valid for multiple species and produced by machine learning methods, could supplement the costly species-specific risk analyses currently conducted by governmental agencies. An elastic-net algorithm was trained on a dataset covering 243 invasive species to map risk of new introductions in Europe as a function of climate, soils, water, and anthropogenic factors. Results revealed that the BeNeLux states, Northern Italy, the Northern Balkans, and the United Kingdom, and areas around container ports such as Antwerp, London, Rijeka, and Saint Petersburg were at higher risk of introductions. Our analysis shows that machine learning can produce hotspot maps for pest introductions with a high predictive accuracy, but that systematically collected data on species’ presences and absences are required to further validate and improve these maps.

• Publisher: IOP Publishing
• Subject: Algorithms / Anthropogenic factors / big data / Biological invasions / Cost analysis / Data collection / Ecology, environment / Ecosystems / elastic-net / Introduced species / Invasive species / Learning algorithms / Life Sciences / Machine Learning / Nonnative species / pest introduction / Pests / Risk analysis / Soil water / Statistics