Post Doctoral Researchers
|Cole Easson was postdoctoral researcher at the Halmos College for Natural Sciences and Oceanography and a research scientist for the DEEPEND consortium. He earned his BS in Biology and PhD in Environmental Toxicology from the University of Mississippi. His PhD research investigated stressors to coral reef organisms, particularly anthropogenic inputs of nutrients, marine diseases, and how these stressors affected interactions among reef organisms. He was a postdoctoral scholar at the University of Alabama at Birmingham focusing on microbial ecology and symbioses, with an emphasis on tropical marine sponges, as well as the development of online bioinformatics tools and workflows for processing large comparative datasets. For the DEEPEND project, Cole is in charge of characterizing the microbial community dynamics of the northwest Gulf of Mexico over space and time, and how these dynamics relate to broader oceanographic features. Microbial communities will be assessed using an lllumina MiSeq next-generation sequencer to capture the environmental microbiome of the Northwest Gulf of Mexico.|
Donnelly thesis – https://nsuworks.nova.edu/occ_stuetd/485/
Freed thesis – https://nsuworks.nova.edu/occ_stuetd/480/
Skutas thesis – https://nsuworks.nova.edu/occ_stuetd/461/
Roebuck thesis – https://nsuworks.nova.edu/occ_stuetd/462/
Karns thesis – https://nsuworks.nova.edu/occ_stuetd/453/
O’Connell thesis – https://nsuworks.nova.edu/occ_stuetd/391/
Diana Aranda – J. of Water and Health. 14 (1) 81-89; http://jwh.iwaponline.com/content/14/1/81.full; doi: 10.2166/wh.2015.030.
Using probabilities of enterococci exceedance and logistic regression to evaluate long term weekly beach monitoring data
Recreational water quality surveillance involves comparing bacterial levels to set threshold values to determine beach closure. Bacterial levels can be predicted through models which are traditionallybased upon multiple linear regression. The objective of this study was to evaluate exceedance probabilities, as opposed to bacterial levels, as an alternate method to express beach risk. Data were incorporated into a logistic regression for the purpose of identifying environmental parameters most closely correlated with exceedance probabilities. The analysis was based on 7,422 historical sample data points from the years 2000–2010 for 15 South Florida beach sample sites. Probability analysesshowed which beaches in the dataset were most susceptible to exceedances. No yearly trends wereobserved nor were any relationships apparent with monthly rainfall or hurricanes. Results from logistic regression analyses found that among the environmental parameters evaluated, tide was most closely associated with exceedances, with exceedances 2.475 times more likely to occur at high tide compared to low tide. The logistic regression methodology proved useful for predicting future exceedances at a beach location in terms of probability and modeling water quality environmental parameters with dependence on a binary response. This methodology can be used by beach managers for allocating resources when sampling more than one beach.
Microbial Communities Associated with Sponge Orange Band Disease in the Giant Barrel Sponge, Xestospongia muta
by Rebecca Mulheron, MS
On stressed coral reefs, lethal Sponge Orange Band (SOB) disease periodically afflicts the giant barrel sponge, Xestospongia muta. We hypothesized bacterial pathogenecity after a 2012 outbreak in South Florida, and thus tracked the disease progression through microbiome characterization. Near the outbreak peak, 14 X. muta individuals were collected from two outbreak sites to profile the microbiomes of diseased and healthy sponge mesohyl. From each diseased individual, three different SOB disease states were collected and compared: healthy mesohyl from diseased sponges (HoD); boundary layer (BL); and diseased mesohyl (D). Six healthy controls (HC) from adjacent non-diseased sponges, and the environment were also collected and compared. Fifty-one 16S rRNA V4 libraries were pyrosequenced, and beta-diversity analyses demonstrated that healthy microbiomes showed less variability than BL or diseased mesohyl. Specific OTUs significantly associated with SOB disease were Flavobacteria,, Verrucomicrobia, Planctomycetes, Armatimonadetes, Fibrobacteres, Fusobacteria, WPS-2 and ZB3. The INSIGHT algorithm resolved Blastopirellula cremea/marina, Loktanella atrilutea, Rubritalea sp. and Ruegeria atlantica/lacuscaerulensi species among others in BL and D tissues only and thus potential candidate pathogens. This study provides extensive microbiome profiles within Xestospongia muta, implying a possible polymicrobial origin for SOB.