SysEB
Student Competition Poster
Category: Student Competition Poster
Grad Poster: SysEB, Taxonomy, Biogeography, and Biodiversity
Morgan L. Malone
Blacksburg, VA, USA
Sally Taylor
Associate Professor
Cotton Incorporated
Cary, North Carolina, United States
Kaloyan Ivanov
Associate Curator
Virginia Museum National History
Martinsville, Virginia, United States
Roger Schürch
Department of Entomology, Virginia Tech
Blacksburg, Virginia, United States
The Red Imported Fire Ant, Solenopsis invicta, is an invasive species with a broad array of detrimental impacts to agricultural systems, native ecosystems, and human health. Once established, S. invicta rapidly spreads through the area and is virtually impossible to manage. Following its original detection in the United States in the 1930s in Mobile, Alabama; this species has become pervasive throughout the southeast, recently including parts of Virginia. As the effects of climate change and globalization increase, the habitable area of S. invicta is predicted to further expand. Early detection is a key factor in managing invasive species and limiting their damaging impacts. The full extent of spread of S. invicta in Virginia, situated along the northernmost edge of its introduced range in North America, is currently unknown. In this study, we systematically examined the current distribution of S. invicta using multiple data sources. During the summer of 2020, we conducted a series of visual surveys along public roadways. We also used multi-year infestation reports from the Virginia Department of Agriculture and Consumer Services (VDACS) collected from 2016 to 2020. Lastly, we surveyed local naturalists, county extension agents, landscape and nursery businesses, and land managers. We compared these findings with previously published models quantifying the potential spread of S. invicta and investigate deviations from these models. Our results show that both westward and northward expansion are greater than predicted by one widely sourced model. Validating models is important for model accuracy and early detection.