Date of Award
Master of Science
Dr. Kathleen Baker
Dr. Brian Roth
Dr. Gregory Veeck
GIS, invasive species, artificial neural network, geography, crayfish
Masters Thesis-Open Access
The purpose of this thesis is to create a predictive model of habitat suitability for the invasive rusty crayfish (Faxonius rusticus) throughout the state of Michigan. F. rusticus often outcompete and extirpate native crayfish species, so understanding their habitats of success is instrumental in monitoring vulnerable ecosystems. Michigan State University and the Michigan DNR conducted extensive field surveys across 461 streams sites from 2014-2016. This project compares this field data set to data from publicly available national datasets with the purpose of revealing the ecosystems most vulnerable to the introduction of F. rusticus. The pattern of F. rusticus habitat at a local (100 acres) scale and landscape (1000 acres) scale are determined by comparing the current locations of the species in Michigan against a number of variables quantifying the physical geography of the locations that may affect the spread, growth and survivability of these crayfish. The presence of F. rusticus is also compared to the presence/absence of other species at each surveyed site. An Artificial Neural Network (ANN) model using variables from Soil Survey Geographic Database (SSURGO) and National Land Cover Database (NLCD) datasets found 45 stream locations vulnerable to F. rusticus invasion. This model also determines the variables that have the greatest influence on the model at the center of this research.
Homan, Robert C., "Creating a Distribution Model of Invasive Rusty Crayfish (Faxonius Rusticus) in Michigan Streams Using Publically Accessible Data" (2020). Master's Theses. 5136.