To accomplish our objectives, we follow Schofield, 2009 and Schofield, 2010 and Betancur-R et al. (2011)and examine lionfish capture and sighting records obtained from the USGS Non-indigenous Aquatic Species (NAS) database. This database is a compilation of records received from literature, governmental, and private sources for the United States and Caribbean nations. Records in the USGS-NAS database include capture date and location, providing a means to temporally sequence first occurrences of lionfish in the introduced area. We use computational GIS to combine lionfish records with ocean current, salinity, depth, and temperature data at the observation and capture sights to (1) determine their possible compounding effects and (2) establish a predictive Regional Scale Model (RSM – defined in this study as a model which uses a discrete geographical unit area of approximately 100 km × 100 km) of the establishment based on these parameters which could be an important tool in forecasting invasions of lionfish in other areas. The performance of this RSM, which covers the eastern seaboard of the United States, Bahamas, Gulf of Mexico, and Caribbean basin, will be validated through correlation testing as well as the construction of a high resolution Local Scale Model (LSM – defined in this study as a model which uses a discrete geographical unit area of approximately 10 km × 10 km) in an area where appropriately rich ancillary data can be compiled; the eastern seaboard of the United States and the Bahamas. The resulting models (RSM and LSM) are not species specific and can be applied to other marine invasives. Additionally, we provide a stage-map illustrating a series of current-driven and proximity-based recruitment periods. As a secondary objective of this study, we also present a detailed statistical analysis of physical parameters present at capture locations, analysis of juvenile versus adult lionfish records, as well as a forecast of captures for 10 years into the future.