Creating a distribution model of three crayfish species of the genus Faxonius (Decapoda, Cambaridae) in Michigan streams using publicly accessible data - Homan, Robert C; Baker, Kathleen M; Roth, Brian M; Smith, Kelley R (Crustaceana, 2023)
Abstract This research seeks to create a predictive model of habitat suitability for use in determining waterbodies vulnerable to introduced species within the state of Michigan. Three members of the genus Faxonius (Decapoda, Cambaridae) were selected as test taxa for the model due to several species’ propensity for significantly altering the ecosystems they inhabit in Michigan and elsewhere. Michigan State University and the Michigan Department of Natural Resources (MDNR) conducted extensive field surveys of crayfish species assemblages across 461 stream sites from 2014-2016. This project compares these field data to data from publicly available national datasets with the purpose of revealing ecosystems that are vulnerable to population expansion. We identify patterns in Faxonius habitat at local (100 acres) and landscape (1000 acres) scales by associating crayfish occurrences throughout Michigan with variables characterizing landscape conditions thought to be important factors affecting their spread, growth, and survival. An Artificial Neural Network (ANN) model using variables from Soil Survey Geographic Database (SSURGO) and National Land Cover Database (NLCD) successfully identified stream sites and watersheds in Michigan vulnerable to range expansion by Faxonius rusticus (Girard, 1852), Faxonius propinquus (Girard, 1852), and/or Faxonius virilis (Hagen, 1870). We found several habitat variables that influence our predictions. The most important variable describing F. rusticus presence was local (100-acre) scale Open Water land cover class, whereas for F. propinquus, the high-intensity developed land cover class at the local scale was the most important, while it was the shrubland land cover class at the local scale for F. virilis. This research demonstrates a powerful method to identify locations using remote sensing data that can be prioritized for conservation efforts that are threatened by invasive crayfish species.