Anthropogenic Influence on Blackfin Sucker (Thoburnia atripinnis) Distribution, in the Upper Barren River System, Kentucky and Tennessee

• Main Author: Hurak, Christa Rose
• Format: Text
• Language: unknown
• Published: Eastern Kentucky University 2016
• Subjects: blackfin sucker; endemic; GIS; land use; remote sensing; Upper Barren River; Natural Resources and Conservation; Terrestrial and Aquatic Ecology; Beaver Creek
• Online Access: https://encompass.eku.edu/etd/380; https://encompass.eku.edu/cgi/viewcontent.cgi?article=1378&context=etd

We evaluated the effects of land use and cover on endemic blackfin sucker (Thoburnia atripinnis) catch per unit effort and abundance within the Upper Barren River (UBR) system, a priority conservation area, in south-central Kentucky. Anthropogenic impacts have rendered T. atripinnis a “species of greatest conservation need” by the Kentucky Department of Fish and Wildlife Resources. This study focused on determining if land use surrounding blackfin sucker sampling sites and certain physicochemical parameters could be impacting their inhabitance at these sites. Data collection and ground truthing occurred between September 2015 and June 2016. ArcGIS was used to extract land use proportions within 100m and 390m buffers around 41 sites and ERDAS imagine was used to create a supervised and unsupervised classification of the study area. Based on the error matrices land use/cover was classified with higher accuracy values for supervised classification over unsupervised classification. Within the study area, Barren River and Long Creek watersheds were found to be made up of primarily forest while Beaver Creek, Skaggs Creek, and Peter Creek watersheds were mainly hay pasture. Principal component analysis (PCA) was utilized using 11 variables to investigate the impact of land use/cover and physicochemical parameters on blackfin sucker catch per unit effort (CPUE). No significant correlations between principal components and blackfin sucker CPUE occurred. Stepwise regression models revealed that temperature was the best explanatory variable for blackfin sucker CPUE. Although no statistically significant results were found, this study showed how ArcGIS and remote sensing techniques can be applied to a pre-existing biological dataset. However, with these results, further conclusions can be drawn about the blackfin sucker and their ideal habitat.