2018 Summer Fellows Position Descriptions
- Ecological modeling of a freshwater estuary
Mentors: Qianqian Liu (UM CIGLR; email@example.com), Eric J. Anderson (NOAA GLERL)
Project 1 Information:
The fellow will work closely with the research team to study how hydrodynamic drivers and anthropogenic stressors affect nutrient and plankton dynamics in Muskegon Lake by:
- Applying and calibrating a coupled bio-physical model with observations,
- Analyzing results and engaging in discussion, and
- Carrying out process-oriented numerical experiments.
Candidates should have a strong interest or background in basic physical oceanography and limnology. Experience with data processing/analysis, computer programming, and ecosystem modeling are preferred.
- Impacts of HABs on Lake Erie coastal communities
Mentors: Tian Guo (UM CIGLR; firstname.lastname@example.org), Devin Gill (UM CIGLR), Mark Rowe (UM CIGLR), Victoria Campbell (UM SEAS)
Project 2 Information:
The fellow will be trained in social science methods, and participate in data collection and management. The fellow will also be supported to conduct analysis matching his or her research and professional interests. Tasks will include, but not be limited to:
- Participate in survey and/or interview instruments development
- Travel to study sites to conduct stakeholder surveys and/or interviews
- Enter survey data, if needed
- Conduct preliminary data analysis through statistical analyses of survey data and/or coding of qualitative data
- Practice professional and science writing through reporting of results
The candidate should feel comfortable to travel by themselves to study sites, manage field work logistics with attention to details, and represent a positive image of CIGLR and UM to stakeholders. Experience with social science research methods is a plus.
- Understanding the role of extra-cellular enzyme activity in promoting toxic HABs
Mentors: Tom Johengen (UM CIGLR; email@example.com), Steve Ruberg (NOAA GLERL)
Project 3 Information:
This project will involve a series of weekly experiments to evaluate the importance of extra-cellular enzymatic activity, which is used by phytoplankton and bacteria to acquire limiting dissolved nutrients, and evaluate how the pattern of this activity changes throughout seasonal bloom development. These experiments will be designed to answer the following questions:
- Does the ability of Microcystis to acquire additional nutrient supplies via extra-cellular enzymatic breakdown of bound organic phosphorus play a key role in its seasonal dominance and bloom formation in western Lake Erie?
- How do rates of enzymatic activity differ among various phytoplankton communities?
- Are rates of enzymatic activity directly related to cellular demand as expressed by stoichiometric relationships between carbon, nitrogen, and phosphorus?
The fellow will support field and laboratory research designed to monitor the timing, extent, distribution, and toxicity of cyanobacterial HABs in western Lake Erie and Saginaw Bay, Lake Huron. The fellow will participate in weekly experimental measurements of extra-cellular enzyme activity in conjunction with samples collected during our HABs monitoring program. Results from experiments will be analyzed against observed changes in community composition, algal biomass, nutrient availability and cellular nutrient stoichiometry.
Candidates should have a strong interest or background in aquatic ecology and have some previous laboratory experience related to biological or chemical water quality assays. The work may also involve participating in monitoring cruises so experience working on small research vessels is highly valuable.
- Great Lakes ice climate study
Mentors: James Kessler (UM CIGLR; firstname.lastname@example.org), Jia Wang (NOAA GLERL), Haoguo Hu (UM CIGLR)
Project 4 Information:
In particular, the fellow will:
- Process the most recent 2018 observational data and append it to the current ice record
- Perform data analysis of long-term and short term trends in Great Lakes ice cover (spectral analysis, EOF, and other statistical methods will be used)
- Compare short-term observational data to physical model data and validate remote observations with in situ observations
Candidates should possess strong quantitative skills and have experience with data analysis or computer programming (R, C++, Matlab). Background in statistics, geophysical sciences, oceanography or meteorology is a plus.
- Water quality statistical modeling
Mentors: Mark Rowe (UM CIGLR; email@example.com), Craig Stow (NOAA GLERL)
Project 5 Information:
The summer fellow will develop statistical models to predict the response of Lake Erie (hypoxia, algal biomass, nutrient concentrations) to nutrient loads and physical variables using a large database of in-lake observations from federal and state agencies and universities. The fellow will conduct data analysis in a scripting environment, working with several empirical data sets, to develop visualizations, summary statistics, and predictive or explanatory statistical models relating the response of Lake Erie to nutrient loads.
Ideal candidates would have training in quantitative aspects of freshwater ecology, limnology, environmental engineering, or a similar field. Experience or interest in statistical modeling and data analysis in a scripting environment (e.g., R, Python, IDL, or Matlab) is also desirable.
- Improving the management of real-time data in the Great Lakes
Mentors: Joeseph Smith (UM CIGLR; firstname.lastname@example.org), Steve Ruberg (NOAA GLERL), Philip Chu (NOAA GLERL)
Project 6 Information:
The fellow will analyze archived real-time data, their formats, and documentation, developing (and possibly begin executing) plans to re-format and store the data in a more accessible format or data management system. Such a system would be an ideal platform for data dissemination in-house and on the GLERL webpage. In particular, the fellow will:
- Engage with technicians and engineers in the GLERL Marine Instrumentation Laboratory to get a briefing on available real-time data and their locations
- Analyze real-time data, their formats, and documentation
- Develop plans to re-format and store the data in a more accessible format or data management system
- Develop plans to disseminate data on NOAA-GLERL website
- Begin execution of plans
Ideal candidates would be concentrating in Data, Computer, Information Science and/or a related field and have previous coursework and/or professional experience involving problems surrounding Big Data and general digital information challenges.
- Estimating gear bias and relative efficiency for zooplankton predators in Lake Michigan
Mentors: Ed Rutherford (NOAA GLERL; Ed.Rutherford@noaa.gov), Henry Vanderploeg (NOAA GLERL)
Project 7 Information:
This project will test the hypothesis that relative efficiency of traditional plankton nets is low for estimating density of native and invasive zooplankton predators. Results will be used to answer whether the use of a strobe flash unit on MOCNESS sampling gear that temporarily stuns zooplankton predators improves efficiency for sampling zooplankton predators compared to using MOCNESS with no flash, or to traditional zooplankton nets. The fellow will quantify how mesh size, net volume, net type, and light affect density estimates of Mysis, Bythotrephes, and larval fish. The fellow will supplement data analysis of new samples collected during diel surveys in Lake Michigan with analysis of prior plankton collections in 2016 and 2017.
Candidates should have a strong background in Great Lakes or marine science, have taken an introductory statistics course, and be experienced working on boats in variable weather conditions.
- Biotic and abiotic drivers of diel vertical migration in post-dreissenid Lakes Michigan and Huron
Mentors: Hank Vanderploeg (NOAA GLERL; email@example.com), Craig Stow (NOAA GLERL), Ed Rutherford (NOAA GLERL), Doran Mason (NOAA GLERL)
Project 8 Information:
The fellow will use a variety of statistical approaches in the R programing environment to visualize data and to determine abiotic and biotic factors driving diel vertical migration of zooplankton and larval fishes, as well as participate in at least one 5-day spatial cruise to collect data.
Candidates are expected to have a strong background in statistical analysis and data visualization using the R analysis package to analyze the fine-scale data.