MSB-FRA Modeling Invasion Dynamics Across Scales (MIDAS)

Purdue University, West Lafayette IN

Investigators

Abstract

Invasions by exotic species pose a major threat to nearly all ecosystems. In fact, about 40 percent of U.S. forests have been invaded by exotic plants, resulting in significant economic loss (over $100 billion annually) and ecological damages. Unfortunately, current understanding of large scale invasion patterns and distribution processes is still limited. This project, Modeling Invasion Dynamics Across Scales (MIDAS), seeks to understand how the underlying biological, geophysical and socioeconomic factors lead to the emergence of large scale invasion patterns. Findings of the project will help to inform forest managers, policy makers, and other stakeholders on how to better prevent and mitigate the economic and ecological damages of invasive species through multiple venues such as workshops and webinars, and online planning tools. The project will have high impact on the education of a diverse group of students and researchers through research opportunities, mentoring, and workshops. It will facilitate broad, long-lasting interdisciplinary and cross-institutional collaborations. In addition, the project will contribute to greater understanding of the general phenomenon of how "things" (e.g., disease, technology) spread, thus helping to sustain the spread of benefits while limiting unintended harms to ecosystems and society that emerge from social connectivity. The goal of this project is to determine the underlying mechanisms contributing to macroscale invasion patterns for two important taxa of invaders (plants and insect pests) in forests across the continental United States. The project will utilize a novel cross-modeling approach by combining: (1) machine learning-based statistical models aimed at capturing key within- and cross-scale interactions and tipping points among data challenged by different spatial and temporal resolutions, and (2) Bayesian models designed to capture model and parameter uncertainties. This project will result in improved mechanistic understanding and more robust predictions of macroscale invasion. The proposed conceptual framework can advance the field of invasion ecology by formulating new invasion theories and consolidating various existing ones. The project will also help advance the field of MacroSystems Biology by synthesizing and developing multidisciplinary datasets and by developing and refining a highly useful analytical tool for investigations involving many variables in which manipulative experiments would be challenging.