Position Descriptions

2022 Great Lakes Summer Fellows Program

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  1. Great Lakes Bay Watershed Education and Training (B-WET)
    Mentors: Sarah Waters (NOAA ONMS, sarah.a.waters@noaa.gov), Ellen Brody (NOAA ONMS)

Project 1 Information:

The NOAA Great Lakes Bay Watershed Education and Training (B-WET) program supported by NOAA’s Office of National Marine Sanctuaries is a place-based education grant program that focuses on training teachers and engaging K-12 students in Meaningful Watershed Educational Experiences (MWEEs). The program, currently headquartered at Thunder Bay National Marine Sanctuary’s offices in Alpena, Michigan, is seeking a summer research fellow to assist with program analysis and evaluation, content creation for various audiences, and training support for grantees located across the Great Lakes basin. The fellow will become familiar with NOAA’s national B-WET program, connect with staff in the seven B-WET regions, and engage with B-WET grant recipients leading place-based education projects throughout the Great Lakes.

Research Question: The Meaningful Watershed Educational Experience (MWEE) is a learner-centered framework that focuses on investigations into local environmental issues and leads to informed action. What are the most effective uses of the MWEE framework to achieve K-12 stewardship action projects, as demonstrated in the Great Lakes region’s B-WET funded projects? 

Project Activities: The summer fellow will assist the Great Lakes B-WET Program Manager in the following activities, as well as attending national and regional B-WET networking opportunities:

  • Analyze recipient projects by reviewing reports and comparing to MWEE framework
  • Evaluate effective uses of MWEE framework that achieved stewardship action projects 
  • Make recommendations of successful project examples to form case studies
  • Create content for general audience based on successful project examples / case study templates
  • Participate in Great Lakes regional grantee events and trainings as representative of B-WET team
  • Support trainings and meetings for grantees hosted by Great Lakes B-WET region 

Required & Desired Skills: Desired skills include experience in using Google Suite, and good communication skills, including strong writing ability, and general organizational skills.

Location: If conditions permit in-person work, this position will be housed at the NOAA Thunder Bay National Marine Sanctuary in Alpena, Michigan. Remote work is possible for this position even if an in-person fellowship is offered.

  1. Pigment-specific identification of phytoplankton in Lake Erie
    Mentors: Reagan Errera (NOAA GLERL, reagan.errera@noaa.gov), Hank Vanderploeg (NOAA GLERL), Jim Hood (Ohio State University)

Project 2 Information:

Identifying shifts in phytoplankton assemblage could improve cyanobacterial harmful algal bloom (cHAB) forecasting and diagnose bloom and post-bloom drivers. Microscope identification is time consuming and requires individuals trained in algal taxonomy.  As a result, researchers often use proxies, such as photopigments, to identify shifts in the phytoplankton assemblage. In 2020 and 2021, the GLERL/CIGLR monitoring program collected bulk phytoplankton composition data via FluoroProbe (bbe moldaenke©) and photopigment analysis, providing the opportunity to examine bloom dynamics using multiple methods.   

Research Questions:

  • What is the seasonal progression of phytoplankton in the western basin of Lake Erie from April – Oct, as determined based on FluoroProbe (bbe moldaenke©) readings and  photopigment analysis; and are the methods comparable?
  • Is there a relationship between FluoroProbe or photopigments to microcystin concentrations?

Project Activities:

  1. Assess phytoplankton group dynamics from 2015 – 2021 using FluoroProbe readings.
  2. Determine bulk community composition from photopigements analysis using CHEMTAX.  
  3. Identify periods when phytoplankton assemblage composition varies based on the method used.
  4. Determine if a relationship is present between cyanobacteria concentrations and microcystin toxin concentrations.
  5. Complete dilution experiments using Lake Erie-based cultures to determine threshold conditions for FluoroProbe*.      

*Requires onsite laboratory access in Ann Arbor, MI, which is to be determined based on local pandemic conditions and restrictions during the fellowship period. In the event of a virtual fellowship, this activity will not be included in the project.

Required & Desired Skills:  An understanding of aquatic ecology, with a basic understanding of phytoplankton groups and factors influencing growth characteristics and population dynamics. Use of Excel or other spreadsheets to manipulate data sets.  General understanding of statistics and experience with statistical programs (such as R, or Python).

Location: If conditions permit in-person work, this position will be housed at the NOAA Great Lakes Environmental Research Laboratory in Ann Arbor, Michigan. Remote work is possible for this position even if an in-person fellowship is offered.

  1. Exploring the buoyancy potential of Microcystis using metagenomic data
    Mentors: Reagan Errera (NOAA GLERL, reagan.errera@noaa.gov), Hank Vanderploeg (NOAA GLERL), Cody Sheik (University of Minnesota-Duluth)

Project 3 Information:

Microcystis aeruginosa, the major toxic cyanobacteria in the Great Lakes, is able to produce gas vacuoles which allow the cyanobacteria to become buoyant and move vertically in the water column. The potential to produce gas vacuoles may change during a cyanobacterial harmful algal bloom (cHAB) based on the strains of Microcystis present. In 2018 and 2019, NOAA GLERL deployed a long-range autonomous underwater vehicle equipped with a 3rd generation Environmental Sample Processor (LRAUV- 3G ESP). The instrument has the capacity to collect and store ‘omics-based data while moving throughout the bloom. In addition, the instrument collects environmental information such as chlorophyll a, temperature, conductivity and dissolved oxygen.   

Research Questions:

  • Do buoyancy-related genes (gvp gene cluster) or other gene clusters vary during the peak of a cHAB? 
  • Does the presence of the gvp gene cluster vary between a small cHAB bloom (2018) and a large bloom (2019)?
  • Is there a relationship between the chosen gene cluster and other environmental parameters?   

Project Activities:

  • Explore metagenomic data collected during the 2018 and 2019 Lake Erie cHAB from the 3G ESP and annotate the chosen gene clusters.  
  • Explore associated environmental data and correlate environmental factors (spatially) to gene cluster presence.   
  • Annotate gene cluster data from other Great Lakes systems (such as Lake Huron).  

Required & Desired Skills:

  • Strong interest in Microcystis aeruginosa blooms research. 
  • General understanding of statistics and experience with programs (such as R or Python).
  • General understanding of genetic pipelines (such as QIIME2, mothur, etc.).

Location: If conditions permit in-person work, this position will be housed at the NOAA Great Lakes Environmental Research Laboratory in Ann Arbor, Michigan. Remote work is possible for this position even if an in-person fellowship is offered.

    1. Status and trends for Saginaw Bay time-series observations
      Mentors: Craig Stow (NOAA GLERL, craig.stow@noaa.gov), Steve Ruberg (NOAA GLERL)

    Project 4 Information:

    A real-time, high resolution time-series buoy has been deployed in Saginaw Bay near Au Gres MI at a depth of 13 meters since 2010. Buoy observations of meteorology, physics, chemistry, and biology are being used to understand Saginaw Bay ecosystem ecology during ice-free periods. These observations along with routine laboratory analysis of water samples collected from research vessels and satellite remote sensing products will be used to assess status and trends that will inform Saginaw Bay management scenarios.

    Research Questions:

    • Are the frequency of ecologically important events such as episodic hypoxia and harmful algal blooms changing over time in Saginaw Bay?
    • What are the long-term trends in the physical, chemical, and biological properties of Saginaw Bay?
    • The fellow will be encouraged to articulate and address additional questions that arise as the project proceeds.

    Project Activities: The fellow will assemble the data into a common database, plot and analyze the data using common statistical techniques and write a short report describing the main findings and conclusions. The project requires the student to become familiar with Saginaw Bay buoy, benthic, and lab processed data.

    • Analysis of buoy time-series data over the period 2010-2021
    • Assess relationship of buoy time-series data to satellite remote sensing, benthic, and lab data for the period

    Required & Desired Skills: Proficiency using R software, familiarity with basic graphing and statistical techniques and technical writing capability is needed. Candidates should have a strong background in freshwater or marine science and have taken an introductory statistics course. However, a fellow with a strong background in statistical analysis using the R analysis package (e.g.: formal model-selection and multiple linear regression; partial least squares regression; ordinary least squares regression; generalized additive models and other statistical tools) to analyze the fine-scale data will be a preferred candidate.

    Location: If conditions permit in-person work, this position will be housed at the NOAA Great Lakes Environmental Research Laboratory in Ann Arbor, Michigan. Remote work is possible for this position even if an in-person fellowship is offered.

      1. Long-term trend of the winter storm frequency/intensity over the Great Lakes 
        Mentors: Ayumi Fujisaki-Manome (CIGLR, ayumif@umich.edu), Abby Hutson (CIGLR)

      Project 5 Information:

      Surface air over the Great Lakes region has warmed overall in the recent decades; however, wavy behavior of the tropospheric jet stream associated with the Arctic Amplification (faster warming of the Arctic than in other parts of the world) brings cold air outbreaks down to the Midwest and results in extreme cold events in the region. Mesoscale storms, such as lake-effect snowstorms, often accompany such extreme cold events. Despite the overall warming, are mesoscale winter storms in the recent years as frequent and/or intense as before? This project aims to quantify frequency and intensity of winter mesoscale storms over the Great Lakes region and evaluate how they have changed over the long period. 

      Research Question: How has the frequency and intensity of winter storms over the Great Lakes region changed during the past decades?

      Project Activities: The fellow will conduct meteorological data analysis using existing storm detection algorithms to examine the winter mesoscale storm frequency/intensity over the Great Lakes region.

      • Conduct literature review on existing storm detection algorithms, as well as on relevant research
      • Download atmospheric re-analysis dataset with select variables (e.g., air pressure, wind speed) over the target period
      • Apply an existing storm detection algorithm(s) to the obtained atmospheric re-analysis dataset for select storm cases and verify the results by comparing with the storm reports from National Weather Service.
      • If time allows, expand the analysis to the longer period, calculate the statistics (e.g., frequency, maximum wind speed), and evaluate the trend over the past decades

      Required & desired skills: Required qualifications are strong interest in pursuing a career in earth science, such as atmospheric science and physical oceanography, experience with gridded data analysis, and familiarity with one or more programming languages such as Python, R, and Fortran.  

      Location: If conditions permit in-person work, this position will be housed at the NOAA Great Lakes Environmental Research Laboratory in Ann Arbor, Michigan. Remote work is possible for this position even if an in-person fellowship is offered.

        1. GLANSIS – Assessment of aquatic plants in trade
          Mentors: Ashley Elgin (NOAA GLERL, ashley.elgin@noaa.gov), Rochelle Sturtevant (Michigan Sea Grant, Michigan State University)

        Project 6 Information:

        The Great Lakes house at least 189 nonindigenous aquatic species.  A subcommittee of the Great Lakes Panel on Aquatic Nuisance Species (GLPANS) is developing a prioritized list of invasive species for control in the Great Lakes basin.  One component of this prioritization relies on impact data compiled by the Great Lakes Aquatic Nonindigenous Species Information System (GLANSIS).  The subcommittee has recommended several additions to the GLANSIS species lists (established species, range expanders and watchlist species), mostly aquatic plants in trade.  Some of these were separately assessed for a regional surveillance process, but need to be assessed to the GLANSIS standard protocols for consistency.  The selected fellow will closely interact with the entire GLANSIS team in addition to the co-mentors.

        Research Questions: For each species, the selected fellow will answer the research questions:

        • Is this species already established (overwintering and reproducing) in the Great Lakes?
        • If so, what are its distribution as well as realized and potential impacts? 
        • If not, does this species pose a significant risk to the Great Lakes for introduction, establishment, and impact? 

        Project Activities: The summer fellow will conduct a mini-review of the available literature for 5-10 species (candidate species include: Akebia quinata, Oenanthe javanica, Barbarea stricta, Barbarea verna, Filipendula ulmaria, Impatiens balfourii, Petasites hybridus, Robinia pseudoacacia, Salix atrocinerea, Silphium perfoliatum, Valeriana officinalis, Egeria najas, Hydrocotyle ranunculoides, Lythrum virgatum, and Sagittaria subulata).  For each species this includes organizing all literature into the database (including abstracts, pdfs, keyword tags, etc); extracting and organizing the distribution, impact, and ecological information into database formats; conducting a standardized risk assessment; and writing a species profile if the species is determined to meet listing criteria. To the extent time allows, the fellow will help to shepherd assessments and profiles through the internal, external, and NOAA review processes.  All assessments are published as part of the NOAA Tech Memo Series and the fellow will have the opportunity to be listed as a contributing author.  Profiles are also considered to be citable NOAA publications.

        Required & Desired Skills:

        • Excellent technical writing and organizational skills.
        • Familiarity with Excel, Word, Google sheets, etc.
        • Interest in invasion biology and/or botany

        Location: If conditions permit in-person work, this position will be housed at the NOAA Great Lakes Environmental Research Laboratory in Ann Arbor, Michigan. Remote work is possible for this position even if an in-person fellowship is offered.

          1. Statistical modeling of HABs vertical distribution in Lake Erie
            Mentors: Casey Godwin (CIGLR, cgodwin@umich.edu), Reagan Errera (NOAA GLERL), Tim Maguire (CIGLR)

          Project 7 Information:

          As part of the routine monitoring for Lake Erie harmful algal blooms (HABs) supported by GLRI, GLERL and CIGLR have been collecting discrete samples from the near-surface and bottom of the lake’s water column. This sampling strategy was initiated to understand how buoyancy, wind-driven mixing, and other known drivers impact distribution of the bloom and to validate the primary sampling near the surface. These data have not been systematically analyzed or published and the work outlined in this proposal would potentially help inform design of this monitoring program into the future. 

          Research Question: How do biomass and toxin concentration differ between the surface and bottom and do those differences, if any, correspond to known drivers of vertical distribution? 

          Project Activities: This fellow will use our ongoing monitoring dataset and available environmental data in a retrospective statistical analysis to address this research question. That analysis will involve:

          • Identifying and assimilating available data, with guidance from mentors
          • Performing initial data exploration and visualization tasks
          • Building multiple statistical models using different approaches or assumptions, then comparing these models in terms of performance and interpretation

          Required & Desired Skills:

          • Required: Experience building statistical models in R
          • Desired: Experience working with sparse environmental data, limnological profiles of water quality parameters, and familiarity with HABs

          Location: If conditions permit in-person work, this position will be housed at the NOAA Great Lakes Environmental Research Laboratory in Ann Arbor, Michigan. Remote work is not possible for this position if an in-person fellowship is offered.

            1. Connection between Great Lakes and Arctic ice cover in response to teleconnection forcing
              Mentors: Jia Wang (NOAA GLERL, jia.wang@noaa.gov), Brent Lofgren (NOAA GLERL)

            Project 8 Information:

            Great Lakes ice cover and thickness are not only controlled by local temperature, but also impacted by external, remote teleconnection forcing.  Both Great Lakes and Arctic sea ice variability is driven by a combination of these teleconnection patterns, such as Arctic Oscillation/North Atlantic Oscillation, El Niño-Southern Oscillation, Pacific Decadal Oscillation, and Atlantic Multidecadal Oscillation, whose thermodynamic impacts are difficult to separate. CIGLR and GLERL intend to conduct in-depth research linking climate teleconnection patterns to the Great Lakes and Arctic climate and ice cover/thickness, leading to development of hindcast models: multi-variable non-linear regression models. The project is part of the prediction of ice cover/thickness in the Great Lakes and the Arctic in response to a changing climate on seasonal, interannual, and decadal time scales, which enables us to provide information to broader users in search and rescue operations, navigation (commercial shipping), and recreational ice fishing during the winter season. These forecasts provide decision makers with tools to aid in protecting the Great Lakes and the Arctic community.

            Research Questions:

            • In addition to water temperature and wind forcing, is Great Lakes ice cover correlated to Arctic sea ice cover in response to the same teleconnection forcing?  If so, how strong are these correlations and are they linear or non-linear?
            • Can we hindcast and predict Great Lakes and Arctic ice cover using physical forcings? Furthermore, can we estimate/hindcast ice thickness using satellite measurements and thermodynamic equations?

            Project Activities: The research includes the development of regression models using the statistical program R for predicting ice cover using climate teleconnection indices and physical forcings. For example, atmospheric teleconnection pattern indices are used to formulate statistical regression models to hindcast lake/sea ice cover, which should be able to explain the connection of lake/sea ice with atmospheric teleconnection patterns. R data analysis tools that will be used include time series analysis, correlation, and/or empirical orthogonal function analysis and regression analysis. 

            Required & Desired Skills: Minimum qualifications include programming in Fortran, Python, C, or R, or  willingness to learn these programming skills.

            Location: If conditions permit in-person work, this position will be housed at the NOAA Great Lakes Environmental Research Laboratory in Ann Arbor, Michigan. Remote work may be possible for this position if an in-person fellowship is offered.