One of the primary challenges facing applied ecology in the 21st century is the proliferation of invasive plants into native ecosystems. Once introduced, invasive plants can erode biodiversity by outcompeting native plants, altering plant-pollinator interactions, and creating toxic soil conditions for other plants and animals. Consequentially, unchecked plant invasions can severely handicap ecosystem functioning in ways that are impossible to restore. Thus, as global change progresses, it is crucial to understand the consequences of plant invasions and predict how and when they are most likely to occur. In this research, we present two cases of how data-driven individual-based models can be used to explore the causes and consequences of plant invasions. First, we show how garlic mustard (Alliaria petiolata)—a widespread North American plant invader—can displace native plant competitors based on garlic mustard’s ability to erode mycorrhizal fungal mutualisms. We show that the invasion of garlic mustard is strongly linked to the native community’s dependence on mycorrhizal fungi; we reveal that strong and weak mutualisms between native plant communities and mycorrhizal fungi were more likely to lead to the dominance of garlic mustard. In contrast, intermediate levels of mycorrhizal dependence resulted in higher resistance to garlic mustard invasion. In a second example, we show how woody plant invasions into grassland ecosystems could influence the prevalence of lone star ticks (Amblyomma americanum) and associated disease risk. Preliminary findings suggest that high invasion rates of Ashe juniper (Juniperus ashei) shrubs in Texas grasslands can encourage higher abundance of their host (e.g., white-footed mouse [Peromyscus leucopus]), thereby increasing the number of ticks and their pathogens. Together, these findings illustrate the utility of data-informed individual-based models in advancing applied ecology and how plant invasions can disrupt ecological processes.
