Monday, August 6, 2007 - 4:20 PM

COS 12-9: Using spatially explicit population models and GIS analysis to predict population spread of an invasive plant

Eleanor A. Pardini, Washington University in St. Louis, John M. Drake, University of Georgia, and Tiffany Knight, Washington University.

An important question for understanding and managing plant invasions is, given limited resources, where in a spreading population should managers target their efforts when individuals are non-randomly distributed in space? Many plant invasions consist of areas of high density where most of the individuals are localized (the core) and areas of low density, where isolated populations contain a few individuals (satellites). Because satellite individuals may contribute disproportionately to overall population spread, management focused on satellite individuals may be more effective. However, because more individuals can be killed per hour in cores than in satellites, management focused in the core may be more effective. Here we use a spatially explicit demographic model to determine how the spatial application of management (in cores or satellites) affects the rate of population spread of the invasive weed Alliaria petiolata (garlic mustard) into a habitat. We used field-collected data on density dependent vital rates (survival and fecundity) and dispersal distances to parameterize a model for population growth and invasion speed for structured populations. Our results indicate this species exhibits dynamic population cycling that changes across space as the invasion progresses. We extended this model by adding a parameter for management-induced adult mortality, applied in either cores or satellites, and present predictions for the two management types. We are testing the model by conducting experimental management in six sites (three core management and three satellite management sites) over the course of three years. Here we present GIS analysis of the abundance and density of garlic mustard in each site for the first two years of experimental management.