Master of Science (MS), Ohio University, 2014, Environmental Studies (Voinovich)

Great Lakes beaches have a high number of beach action days (advisory or closure of beach) each swimming season, many of which are inaccurate. Sampling design and data analysis methods therefore need to be optimized to improve pollutant source identification and accelerate remediation efforts. The effect of sampling frequency (once-weekly versus four times-weekly), sampling duration (one to three years), and spatial representation (individual transects versus an average of all transects) of routine beach sanitary survey data was investigated at three beaches in Racine, Wisconsin to determine differences in microbial pollutant source identification. Virtual Beach 3.0, primarily utilized for predictive modeling, was found to have limitations for routine beach sanitary survey data analysis, but is the best current option. This study determined that more frequent data collection, for longer duration, at the maximal spatial representation provided more consistent and increased microbial pollutant source identification.

Committee: Natalie Kruse Daniels (Advisor); Holly Raffle (Committee Member); Willem Roosenburg (Committee Member); Julie Kinzelman (Committee Member) Subjects: Environmental Health; Environmental Management; Environmental Science; Environmental Studies; Water Resource Management

Master of Science, The Ohio State University, 2021, Environment and Natural Resources

Artificial light at night (ALAN) is one of the most pervasive and rapidly expanding sources of anthropogenic pollution. A growing body of research suggests that ALAN poses a significant threat to ecological communities and global biodiversity. As important sources of biodiversity, aquatic ecosystems may be especially vulnerable to the effects of ALAN, particularly in urban and suburban areas. However, research on the environmental effects of light pollution is scattered and tends to focus on individual taxa and terrestrial systems. Thus, as the human population grows and the influence of ALAN expands, there is an urgent need to improve our understanding of the impacts of light pollution on aquatic ecosystems. Here, my two main objectives were to 1) experimentally determine the extent to which nighttime exposure to different intensities of artificial lighting alters foraging efficiency in Bluegill (Lepomis macrochirus), a common North American freshwater fish, and, 2) identify differences in freshwater fish assemblages and Bluegill diet composition along the lighting gradients created by artificially lit bridges. I predicted that Bluegill foraging efficiency would increase to a point under low to moderate ALAN intensities but decrease at higher intensities. I also predicted that fish assemblages would vary with light intensity because forage fish will take advantage of increased feeding opportunities in brightly lit areas, while predators hunt those forage fish at the edges of the light. Additionally, I expected that the diet of wild-caught Bluegill diet would vary with artificial light intensity as the abundance and type of prey available changed. I used a combination of mesocosm experiments and field sampling to test these predictions. Using an array of outdoor mesocosm tanks, I separated juvenile hatchery-reared Bluegill into five treatment groups, each of which was exposed to a different intensity of ALAN over the course of six weeks. I conducted weekly ni (open full item for complete abstract)

Committee: Suzanne Gray (Advisor); Lauren Pintor (Committee Member); Mažeika Sullivan (Committee Member) Subjects: Ecology