- Fine-scale analysis of Lake Michigan glider data
Mentors: Michael Fraker (CIGLR, firstname.lastname@example.org), Russ Miller (CIGLR), Lauren Marshall (NOAA GLERL Affiliate)
Project 1 Information:
- 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.
- GLANSIS impact assessment and profile development
Mentors: Rochelle Sturtevant (Michigan State University-Michigan Sea Grant, email@example.com), Ashley Elgin (NOAA GLERL), Lindsay Chadderton (The Nature Conservancy), Alisha Davidson (independent consultant)
Project 2 Information:
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
Detroit citizen science and water quality modeling
Mentors: Casey Godwin (CIGLR, firstname.lastname@example.org), Tim Maguire (CIGLR), Sally Petrella (Friends of the Rouge)
Project 3 Information:
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?
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
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.
- Development of an integrated data visualization and user interface for digital charting table onboard a vessel
Mentor: Philip Chu (NOAA GLERL, email@example.com)
Project 4 Information:
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.
Connection between Great Lakes and arctic ice cover in response to teleconnection forcing
Mentors: Jia Wang (NOAA GLERL, firstname.lastname@example.org), Yu-Chun Lin (CIGLR), Philip Chu (NOAA GLERL), Ayumi Fujisaki-Manome (CIGLR)
Project 5 Information:
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?
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.
- 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, email@example.com), Ed Rutherford (NOAA GLERL), Doran Mason (NOAA GLERL), Craig Stow (NOAA GLERL), Lacey Mason (NOAA GLERL)
Project 6 Information:
- 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.
Upper-level atmospheric circulation patterns associated with Great Lakes ice cover
Mentors: Brent Lofgren (NOAA GLERL, firstname.lastname@example.org), Jia Wang (NOAA GLERL)
Project 7 Information:
- 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.
Plankton assemblage variations and identifying shifts in the Kane’s Index of plankton integrity in western Lake Erie
Mentors: Jim Hood (OSU, email@example.com), Reagan Errera (NOAA GLERL)
Project 8 Information:
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?
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.