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Call For Abstracts

Graduate Program in Marine Biology
University of Charleston, South Carolina/Grice Marine Laboratory
Student Marine Biology Research Colloquium
September 23, 2017
Call For Abstracts

The Colloquium Committee is pleased to announce the Research Colloquium of the Graduate Program in Marine Biology (GPMB), to be held Saturday, September 23, 2017, in the auditorium of the Marine Resources Research Institute at the Fort Johnson campus.

The goals of the Colloquium are to:

  • increase awareness of ongoing marine biological research by GPMB and MES students,
  • give students experience with formal scientific presentations,
  • introduce new graduate students to research opportunities in marine biology, and
  • promote interaction among faculty and students.

All GPMB students (excluding incoming students) are expected to present their research (or proposed research) in the Colloquium. MES students conducting research in marine biology are also invited to participate. All Marine Biology students are expected to give at least one oral presentation in the Colloquium before graduating. 

An award will be given for the best overall oral presentation and an award will be given for the best poster. 

  • Deadline for Abstracts: September 1, 2017

Application instructions are below, and are also available on the Graduate Program in Marine Biology web site (

INSTRUCTIONS FOR ABSTRACTS (Due September 1, 2017 at 5pm)
Email your abstract , formatted as described below, as an attached file (Word document or plain text file), using the filename ‘yourlastname_abstract’ to Elizabeth Underwood ( In the text of the email, please answer the following questions:

1) What year are you in the GPMB program?
2) Have you given a talk at the Colloquium previously?
3) Have you presented a poster at the Colloquium previously?

If your abstract includes any special characters, symbols, or other text that might be lost in electronic transfer, it is strongly recommended that you either include a PDF version as a separate attachment, or deliver a hard copy to Shelly Brew in Rm. 102 at the Grice Marine Lab; otherwise, hard copies are not necessary. 

The title (all words bold and capitalized) should be followed by the authors’ names and affiliations (i.e., GPMB or MES, and your institutional affinity). The abstract must be 250 words or less. An example is provided below:

Pante, E. (GPMB, The Graduate School at the University of Charleston, SC) and Dustan, P. (College of Charleston)
The widespread degradation of coral reefs throughout the world urges the need of monitoring techniques on a global scale. While in situ monitoring programs accumulate high quality information on discrete areas and the use of satellite imagery is limited to shallow areas, automated calculation of coral cover may provide a cost and time effective way to assess changes in benthic communities on a large spatial scale. It is hypothesized that texture analysis can contribute to the automated analysis of video transects. Texture is defined as the spatial relationship between the pixels of an image, and has been previously used in pattern recognition studies. Digital images from the USEPA Coral Reef Monitoring Project were used to run the texture analysis. TIFF images frame-grabbed from video transects were point-counted to provide a robust estimate of coral cover. Eleven measures of texture were then calculated using a 3 by 3 kernel, and a stepwise multiple regression model was used to determine the relationship between coral cover and texture measures. Coral cover and texture were positively correlated (whole image: n=20, F=4.177,adjusted R squared=0.54; green band: n=20, F=7.2, adjusted R squared=0.70; blue band: n=20, F=8.629, adjusted R squared=0.71; red band: n=20, F=10.75, adjusted R squared=0.72). Despite the enlightened relationship between coral cover and texture, it remains challenging to automate coral cover calculation, mainly because of the amount of variation within images and transects. Current research directions include the use of larger kernels (larger in situ measures of variation), and tools of pattern recognition.