Doctor of Philosophy, The Ohio State University, 2018, Evolution, Ecology and Organismal Biology

Most broadly, this study aimed to develop a better understanding of how organisms evolve novel functions and traits, and examine how seemingly complex adaptive trait syndromes can convergently evolve. As an ideal example of this, the carnivorous plants were chosen. This polyphyletic grouping contains taxa derived from multiple independent evolutionary origins, in at least five plant orders, and has resulted in striking convergence of niche and morphology. First, a database study was performed, with the goal of understanding the evolutionary trends that impact carnivorous plants as a whole. Using carnivorous and non-carnivorous plant genomes available from GenBank. An a priori list of Gene Ontology-coded functions implicated in plant carnivory by earlier studies was constructed via literature review. Experimental and control samples were tested for statistical overrepresentation of these functions. It was found that, while some functions were significant in some taxa, there was no overall shared signal of plant carnivory, with each taxon presumably having selected for a different subset of these functions. Next, analyses were performed that targeted Sarracenia alata specifically. A reference genome for S. alata was assembled using PacBio, Illumina, and BioNano data and annotated using MAKER-P with additional preliminary database filtration. From these, it was found that Sarracenia alata possesses significant and substantial overrepresentation of genes with functions associated with plant carnivory, at odds with the hypothesis that the plant primarily relies on symbioses. Finally, pitcher fluid was collected from S. alata in the field. RNA was extracted from the fluid, sequenced via Illumina, and assembled with Trinity. Sequences were sorted into host plant and microbiome based on BLAST match to the S. alata reference genome. It was found that, while S. alata contributes two-thirds of the transcripts, these encode no digestive enzymes and a very limited set o (open full item for complete abstract)

Committee: Bryan Carstens Ph.D. (Advisor); Marymegan Daly Ph.D. (Committee Member); Zakee Sabree Ph.D. (Committee Member); Andrea Wolfe Ph.D. (Committee Member) Subjects: Bioinformatics; Biology; Botany

Master of Science, The Ohio State University, 2015, Atmospheric Sciences

Flight operations on the Ross Ice Shelf rely on accurate forecast aided by the AMPS-Polar WRF. To improve the performance of the model planetary boundary layer (PBL), this study examines two datasets containing vertical meteorological data on the western Ross Ice Shelf and compares them against the AMPS-Polar WRF. The first dataset contains nearly two years of meteorological data provided by the Alexander Tall Tower! (ATT) automated weather station from March 2011 – July 2012. Maintained by a joint team at the Space Science and Engineering Center (SSEC) and the Cooperative Institute for Research in Environmental Sciences (CIRES), the ATT provides instantaneous observations at 10-minute intervals at various levels up to 30 m. The second dataset comes from the same researchers from CIRES who conducted a field campaign from January 13th–26th, 2014 using aerial Small Unmanned Meteorological Observer (SUMO) vehicles to measure PBL conditions near the ATT site. The ATT provides temperature, moisture, wind speed, wind direction, pressure, longwave radiation, and shortwave radiation measurements while the SUMO flight data contains observed temperature and relative humidity, and calculated wind speed and wind direction. For the analysis of the ATT data, the 5-km AMPS-Polar WRF data is run daily at 00 UTC and 12 UTC. Each model run is given a 12-hour spin-up and the subsequent 12-23 forecast hours are concatenated to create a continuous hourly forecast record. For the SUMO analysis, flight data and ATT data are compared against vertically and horizontally interpolated 3-km AMPS-Polar WRF data up to 800 m above the surface. On a synoptic scale, European Centre for Medium-Range Forecasting ERA-Interim and AMPS-Polar WRF are utilized to identify possible sources of error in the AMPS-Polar WRF PBL. From the combined analysis from the ATT – AMPS climatology and the SUMO case studies, the most common errors are: a systematic dry bias and high wind speed bias in AMPS-Polar WR (open full item for complete abstract)

Committee: David Bromwich Professor (Advisor); Jay Hobgood Professor (Committee Member); Jialin Lin Professor (Committee Member) Subjects: Atmosphere; Atmospheric Sciences; Meteorology