Position Descriptions

2021 Great Lakes Summer Fellows Program

  1. Fine-scale analysis of Lake Michigan glider data
    Mentors: Michael Fraker (CIGLR, mfraker@umich.edu), Russ Miller (CIGLR), Lauren Marshall (NOAA GLERL Affiliate)

Project 1 Information:

Autonomous underwater gliders provide limnological observations over broad horizontal and vertical ranges that can complement ship-, mooring-, and satellite-based observational data. CIGLR and NOAA GLERL have operated an underwater glider since 2012 in Lake Michigan, but its observations have not been fully analyzed yet. During initial analyses, we noted substantial fine-scale spatiotemporal variation in surface mixing, light attenuation and chlorophyll fluorescence within glider deployments. Quantifying this fine-scale variation and helping to identify its biophysical drivers will provide an improved understanding of key drivers of ecosystem structure and function in Lake Michigan.

Research Questions:

  • What are the spatiotemporal scales of variation in the limnological observations made by the glider, and what environmental drivers (e.g., meteorological, hydrodynamic) best explain this variation?
  • What can we learn by pairing glider observations with ship- and mooring-based observations?

Project Activities: We will follow up initial analysis of the glider data by analyzing variation in limnological observations at a finer scale (kms instead of basin). The specific project activities will be decided during initial meetings with the Summer Fellow and be based on the Fellow’s interests and background.

  • Become familiar with the types of data available and how they may be used
  • Use various spatial statistics to identify patterns and drivers

Required & Desired Skills: Ideally, the Fellow will have an interest in analyzing large datasets and experience using R. Knowledge of specific statistical approaches is useful, but not required. A familiarity with aquatic ecology and limnology also would be useful, but is not required.

    1. GLANSIS impact assessment and profile development
      Mentors: Rochelle Sturtevant (Michigan State University-Michigan Sea Grant, rochelle.sturtevant@noaa.gov), Ashley Elgin (NOAA GLERL), Lindsay Chadderton (The Nature Conservancy), Alisha Davidson (independent consultant)

    Project 2 Information:

    The Great Lakes house at least 188 nonindigenous aquatic species, and this count does not include amphibian and reptile species. The Nature Conservancy is leading a subcommittee of the Great Lakes Panel on Aquatic Nuisance Species (GLPANS) in 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 expressed a strong interest in expanding GLANSIS to include a number of species not currently included – amphibians, reptiles, and a number of additional species not currently included in GLANSIS (mostly organisms in trade – including potentially established species, range expanders and watchlist species) that were separately assessed by Alisha Davidson and TNC for a regional surveillance process. This forms a strong partnership between GLANSIS, CIGLR, GLERL, Sea Grant, TNC, and the GLPANS. The selected fellow will closely interact with the GLANSIS team, TNC representative, and Alisha Davidson.

    Research Questions: The Fellow’s research will contribute to answering larger research questions such as “Which nonindigenous species established in the Great Lakes basin have or are predicted to have the greatest impact to the Great Lakes’ ecology and ecosystem services?”, “Which species have known benefits that may create a conflict of interest for management?”, and “Can data on impacts, benefits, species traits and pathways be used to objectively rank and prioritize management needs for established invasive species in the Great Lakes.” The Fellow’s research will directly answer the research question “What are the known impacts and benefits of particular invasive species present in the Great Lakes basin?”

    Project Activities: The fellow will focus on completing assessments and profiles needed to add new species to the GLANSIS database.

    • Review literature (which is already compiled in PDFs) and complete any partial assessments (most species still need to have potential benefits assessed).
    • Organize data into spreadsheet format compatible with the GLANSIS Risk Clearinghouse, including vetting data with Alisha Davidson.
    • For species meeting criteria for inclusion in the GLANSIS nonindigenous, range expansion, or watch lists:
      • Enter all reference and impact data into the USGS Non-indigenous Aquatic Species (NAS) database (GLANSIS backbone database).
      • Develop full species profiles.  Fellow will be listed as a co-author for any new GLANSIS profiles.
      • Profiles will be subject to an external review process prior to publication in the database. As time allows, the Fellow will initiate the review process (find appropriate reviewers, incorporate reviewer feedback into drafts, etc).

    Required & Desired Skills: 

    • Excellent technical writing and organizational skills
    • Familiarity with Excel, Word, Google sheets, etc.
    • Interest in invasion biology
    1. Detroit citizen science and water quality modeling
      Mentors: Casey Godwin (CIGLR, cgodwin@umich.edu), Tim Maguire (CIGLR), Sally Petrella (Friends of the Rouge)

    Project 3 Information:

    Community action groups (CAGs) engage with a diverse range of stakeholders in order to improve social and environmental conditions locally, regionally, and nationally. Watershed association CAGs leverage the interest of citizen scientists to gauge the health of rivers and lakes, then use these data to lobby for and coordinate stream restoration projects and remediation efforts in response to historic pollution. The Friends of the Rouge (FOTR) have been collecting citizen science data since 2001 on the invertebrate populations of the River Rouge in urban Detroit, a tributary to Western Lake Erie and a watershed with a legacy of degradation. The objective of this proposal is to conduct a spatial-temporal analysis on the accumulated FOTR data and use the results to understand the effects of development and water quality deterioration within Great Lakes tributaries.

    Research Question: Can we use modeling to help watershed associations and other citizen science data collectors assimilate geospatial data and increase the utility of their monitoring data for addressing environmental questions?

    Project Activities:

    Successful completion of the project will require frequent meetings and mentorship from CIGLR and FOTR staff, intensive computer programming, and presentation of data to both scientific and general audiences.

    • Data assimilation from disparate online repositories
    • Data curation and quality control
    • Address missing and censored data
    • Model building and model comparison using spatial stream network techniques.1 Comparisons will include controlling observer bias with random effects or hierarchical variance, coefficient selection based on a variety of probabilistic information criteria, and model selection by comparing outputs to a naïve null model.
    • Interpretation of results in the context of community action
    • Generate presentations of results for scientific and public audiences
      1. https://www.fs.fed.us/rm/boise/AWAE/projects/SpatialStreamNetworks.shtml

    Required & Desired Skills: Required skills are competency in both R and ArcGIS software. Desired skills are experience with sparse data, citizen science data, analysis with spatially correlated data, time-series analysis, and experience curating metadata.

      1. Development of an integrated data visualization and user interface for digital charting table onboard a vessel
        Mentor: Philip Chu (NOAA GLERL, philip.chu@noaa.gov)

      Project 4 Information:

      NOAA recently signed a cooperative research and development agreement (CRADA) with Vikings Cruise to collect environmental data onboard vessels in order to improve NOAA’s modeling and forecasting capability in the Great Lakes region. One of the projects is to develop, integrate and display NOAA GLERL’s forecast maps and animation products into the digital charting table onboard Viking vessels.

      Research Question: What existing and potential technologies for scientific data visualization, user interface design, touch screen, and programming tools will best display Great Lakes Operational Forecast System (GLOFS) data and forecast products for visualization onboard Viking vessels?

      Project Activities: The goal of this project is to research and explore new technology and methods to visualize and display the data and forecast products generated by the Great Lakes Operational Forecast System (GLOFS). GLOFS data and products have been used by a wide group of stakeholders. New methods to visualize the data and model outputs become available as computer and web technology advance. The outcome also has the potential to be applied to the NOAA-Viking CRADA data visualization project.

      The selected summer fellow is expected to work on the following tasks:

      • Literature review of NOAA forecasts service, products and digital format
      • Evaluate existing digital display technology such as touch screen tools, NOAA Science on Sphere (SOS) and digital chart table
      • Evaluate the feasibility, requirement, cost and graphic user interface (GUI) for a proposed system integration
      • Design and build a concept framework or prototype to demonstrate feasibility

      Required & Desired Skills: We are seeking graduate or undergraduate student majoring in computer sciences, electrical engineering or mechanical engineering with programming skills in C++, JAVA, Python, R or data analysis/visualization application tools.  Hardware and firmware design will be a plus.

        1. Connection between Great Lakes and arctic ice cover in response to teleconnection forcing
          Mentors: Jia Wang (NOAA GLERL, jia.wang@noaa.gov), Yu-Chun Lin (CIGLR), Philip Chu (NOAA GLERL), Ayumi Fujisaki-Manome (CIGLR)

        Project 5 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 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. Tools in R-program for data analyses include time series analysis, correlation, and/or empirical orthogonal function analysis and regression analysis.

        Required & desired skills: Minimum qualifications include programming in Fortran, Python or C. And the intern is willing to learn R-software, Fortran, Python, and/or other programming skills to analyze data if s/he did not know before.

          1. The role of increased water clarity on fine-scale vertical distribution and density of macrozooplankton and fish larvae in Lakes Michigan and Huron
            Mentors: Henry Vanderploeg (NOAA GLERL, henry.vanderploeg@noaa.gov), Ed Rutherford (NOAA GLERL), Doran Mason (NOAA GLERL), Craig Stow (NOAA GLERL), Lacey Mason (NOAA GLERL)

          Project 6 Information:

          Bythotrephes longimanus (spiny water flea) and larval fish are visual predators of zooplankton and, along with Mysis relicta, are important prey for pelagic fish. Results of recent surveys using a laser optical plankton counter, a variety of nets including the Multiple Opening/Closing Net Environmental Sensing System (MOCNESS) and fishery acoustics show diel vertical migrations of these plankton and larval fishes are highly responsive to changing environmental conditions in Lakes Michigan and Huron. Specifically, water clarity and light in both visible and ultraviolet (UV) spectra have increased dramatically and food availability (chlorophyll) has dropped owing to dreissenid mussel grazing. In contrast to pre-dreissenid years, we are beginning to see strong vertical migrators occupy deeper depths during daytime, and surface avoidance by taxa not expected to avoid the surface. Our recent observations utilize relatively fine-scale (10-m) vertical sampling of the water column with nets during day and night to better match zooplankton prey densities with their predators (fish, Mysis, Bythotrephes, fish larvae). We are combining data from the net tows with data from a laser optical counter and other tools that give fine-scale results of zooplankton sized particles and chlorophyll at < 1-m scale. Results will inform ongoing studies of lower food web disruption by invasive dreissenid mussels and declining phosphorus concentrations.  

          Research Questions:

          • Has the near surface abundance of zooplankton and larval fishes  decreased owing to (1) increased UV radiation at the surface or (2) decreased chlorophyll at the surface relative to pre-dreissenid years?
          • Is the increased daytime depth distribution of deep migrators such as Daphnia driven by increased foraging depth of Bythotrephes, which because of increased light, can migrate deeper in the water column to prey on zooplankton?

          Project Activities: The fellow will organize the data and quantify how light, temperature, and prey and predator density affect fine scale vertical distributions of zooplankton, Mysis, Bythotrephes and larval fish using archived samples from recent years.

          Required & Desired Skills: 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.

            1. Upper-level atmospheric circulation patterns associated with Great Lakes ice cover
              Mentors: Brent Lofgren (NOAA GLERL, brent.lofgren@noaa.gov), Jia Wang (NOAA GLERL)

            Project 7 Information:

            Teleconnection patterns such as the well-known El Niño-Southern Oscillation and the North Atlantic Oscillation have been extensively studied by atmospheric scientists, and there is some skill in predicting their development over multiple months. While they show some correlation with such things as Great Lakes’ annual maximum ice cover, and this is presumed to mean causation, there are atmospheric circulation patterns that seem to be more tightly connected with Great Lakes ice cover, but are not necessarily strongly connected to the familiar teleconnection patterns. We wish to investigate whether we can use these patterns to improve the skill of seasonal prediction of ice cover.  This project will use data from the North American Regional Reanalysis project, the ERA-Interim global atmospheric reanalysis product, ice cover history developed at GLERL, and teleconnection index time series from NOAA’s Climate Prediction Center.

            Research Questions:

            • What are the atmospheric patterns of geopotential height that are best correlated to ice cover in each of the Great Lakes?
            • Is there significant skill in predicting these patterns over a 2-4 month period?
            • Begin to explore methods of estimating the uncertainty of these projections.

            Project Activities: The datasets mentioned above are publicly available. The spatially distributed reanalysis data will need to be analyzed in terms of regression and correlation with ice cover on each of the Great Lakes, as well as with teleconnection indices established by prior research. 

            • Determine regressions and correlation coefficients between 700, 500, and 300 hPa geopotential heights at many points in the Northern Hemisphere over the Atlantic, Pacific, and Arctic Oceans as well as North America vs. Great Lakes ice cover.
            • Compare the resulting patterns to those produced by standard teleconnection indices, starting with El Niño-Southern Oscillation and North Atlantic Oscillation, and then extending to selected others.
            • Find optimal fitting methods to get skillful prediction while avoiding over-fitting. Begin to explore quantification of uncertainty in these methods.

            Required & Desired Skills: Candidates should have strong scientific and computing skills, and experience with data analysis (e.g., using Python or R). Knowledge of the UNIX/LINUX operating environment and background in meteorology or oceanography is desired.

              1. Plankton assemblage variations and identifying shifts in the Kane’s Index of plankton integrity in western Lake Erie
                Mentors: Jim Hood (OSU, hood.211@osu.edu), Reagan Errera (NOAA GLERL)

              Project 8 Information:

              The Laurentian Great Lakes system is one of the most rapidly changing ecosystems through threats from land use, invasive species, harmful algal blooms, hypoxia, and loss of native populations.  Shifts in basal community dynamics is notably apparent in Lake Erie due to its human population, land use, and basin dynamics.  Several tools have been developed to connect and quantify biological changes to water quality measurements through ecosystem indices.  Kane’s Planktonic Index of biotic Integrity (P-IBI) was developed to assess Lake Erie ecosystem health. However, the last published assessment used data through 2002 which is prior to the onset of recent harmful algal blooms and more recent changes in plankton monitoring programs. A comparison of these data sets is needed and a new calculation of Lake Erie ecosystem health would benefit on-going management efforts.

              Research Questions:

              • Does the P-IBI change as predicted based on identified stressors, such as phosphorus loading, harmful algal blooms, or hypoxia?

              • Are the individual components of the P-IBI still effective predictors of ecosystem state since the development of re-eutrophication conditions within Lake Erie?

              • Are current monitoring efforts sufficient to calculate the P-IBI and inform on-going management efforts? 

              Project Activities:

              Activity 1: Compare plankton assemblage demographics across multiple data sets.

              • Evaluate and compare spatial and temporal patterns in multiple plankton data sets (LEPAS, NOAA, GLENPO, etc.) collected over the last 15 years.

              Activity 2: Combine data from multiple monitoring programs to estimate P-IBI from 1995 to 2020.

              • Evaluate intra-annual patterns in P-IBI and the individual components of this metric.

              Activity 3: Explore environmental mechanisms related to plankton community shifts and the P-IBI.

              • Correlate environmental factors (spatially and temporally) to plankton community changes.

              Required & Desired Skills: An understanding of aquatic ecology, with a basic understanding of plankton groups and environmental factors influencing population dynamics. General understanding of statistics and experience with statistical programs (such as R, Python, or Matlab).

              Work Location: If in-person work is possible, the fellow may choose to work at NOAA GLERL in Ann Arbor, Michigan, or The Ohio State  University in Columbus, Ohio.