Predicting Naturalization versus Invasion using Stochastic CA Models
Jane Molofsky & Margaret J. Eppstein
University of Vermont
The detrimental effects of invasive plant species on ecosystems are well documented. While much research has focused on discovering ecological influences associated with invasiveness, it remains unclear how to predict which introduced exotic species are likely to become invasive threats. Here we develop a stochastic cellular automata model that incorporates the influences of seed dispersal, frequency independent growth rates, feedback relationships, and the spatial scale of interactions. Our results show that these ecological influences interact in complex ways, resulting in expected outcomes ranging from inability to establish, to naturalization, to conditional invasion dependent on quantity and spatial distribution of seeds, to unconditional takeover. We propose a simple and practical way to predict the likelihood of these four possible outcomes, for a species introduced into a given target community. Such information would enable conservation biologists to more efficiently and effectively craft strategies for prevention and/or remediation in order to help maintain biodiversity in ecological communities.