Invasive plant species can change an ecosystem’s food web by destroying or replacing native food sources, providing little to no food value for wildlife. They are also able to alter the abundance of diversity of species that are important habitats for native wildlife. Some aggressive species are even capable of changing the ecosystem conditions, from soil chemistry to the intensity of wildfires. This project uses several algorithms to understand the factors that contribute to the spread of invasive species. Our model uses three different algorithms: One-Way Analysis of Variance (ANOVA), Linear Regression (LR) as well as Nonlinear Regression (NR) to examine the characteristics of the ecological profile of current invasive species, specifically its spread and length in time it has had to spread. Based on statistical profiling of 77,075 records of 75 invasive species, over 10% of the species have matched the profile of known invasive plants, which are likely to become the next global invaders. More importantly, in a 12-month time period, invasive species, on average, spread over 102,000 square meters from where they grew. These results present an opportunity to implement timely and proactive management strategies against biological invasions.

Keywords:
Machine Learning, Invasive Species, Linear Regression, ANOVA, Nonlinear Regression.