Position Descriptions

2023 Great Lakes Summer Fellows Program

Microcystin production by benthic communities in western Lake Erie
Mentors: Casey Godwin (CIGLR, [email protected]), Reagan Errera (NOAA GLERL)

Project 1 Information:

Previous studies have shown that surface sediments of shallow lakes can serve as seed stocks for planktonic Microcystis blooms. Further, monitoring and preliminary experiments by GLERL and CIGLR suggest that Microcystis populations in Lake Erie surface sediments are producing dissolved microcystin toxin that diffuses into the water column prior to the bloom. However, we lack direct measurements of toxin flux from the sediments in the months immediately prior to bloom initiation (May, June, July). In 2023 we will perform experiments under the Decision Support Tools project aimed at 1) measuring this rate with intact sediment cores and 2) assessing the influence of key environmental conditions (e.g., light) on toxin release.

Research Questions:

What is the range in rates of benthic microcystin production in the months preceding the water column bloom?
Is benthic microcystin production influenced by light intensity?

Project Activities:

Participate in cruises on western Lake Erie to collect sediment cores and water samples
Maintain laboratory incubations of sediment cores
Collect and analyze experiment samples for phytoplankton composition, carbon, pigments, and microcystin toxin
Analyze data from the experiment to estimate toxin flux rates and quantify variability

Required & Desired Skills: A successful applicant for this position would have some prior lab experience with phytoplankton (marine or freshwater, natural or cultured) and/or water quality analyses. Some experience with inferential statistics is desirable, but the ability to make descriptive plots and summaries is essential.

Location: NOAA Great Lakes Environmental Research Laboratory, Ann Arbor, MI

*This position is not eligible for a fully remote fellowship due to required lab and field work*

Microcystis interactions with associated bacteria and controls on bloom characteristics
Mentors: Greg Dick (CIGLR & University of Michigan, [email protected]), Anders Kiledal (University of Michigan), Sara Rivera (University of Michigan), Lauren Hart (University of Michigan)

Project 2 Information:

Cyanobacteria responsible for harmful algal blooms (cHABs) interact with a variety of other bacteria. While these associated bacteria benefit from cyanobacterial primary production, a number of growth-enhancing benefits have been observed for the cyanobacteria. Associated bacteria have the potential to further control bloom proliferation or modify bloom characteristics via mechanisms like cyanobacterial toxin degradation, resource competition, or inhibition. We have collected Microcystis and heterotrophic bacterial isolates from Lake Erie, presenting an opportunity to perform laboratory experiments on interactions relevant to Lake Erie Microcystis-dominated cHABs.

Research Questions:

Which associated bacterial isolates have strong growth-enhancing or growth-inhibiting effects on Lake Erie Microcystis cultures?
What processes and/or metabolites are involved in growth enhancement or inhibition?
Is there agreement between laboratory inferred interactions and interactions that are computationally inferred from amplicon sequencing of field samples?

Project Activities:

Field
- Assist with sample collection from Lake Erie for microbial culturing and omics (amplicon sequencing, metagenomics, metatranscriptomics, metabolomics, etc. )
Lab
- Microbial isolation and identification from field samples
- Microbial growth experiments: co-culturing, addition of extracted metabolites, etc.
Computer analysis of omics data
- Microbial interaction network analysis of Lake Erie amplicon time series
- Analysis of xenic Microcystis culture composition over time

Required & Desired Skills:

Familiarity with basic data analysis (excel, R, python, or other statistical packages)
Coursework in molecular- and/or microbiology
A desire to participate in field sampling of Lake Erie

Location: University of Michigan and NOAA Great Lakes Environmental Research Laboratory, Ann Arbor, MI

*This position is not eligible for a fully remote fellowship due to required lab and field work.*

Hydrologic modeling over the Great Lakes domain for sub-seasonal to annual forecast of the net basin supply
Mentors: Yi Hong (CIGLR, [email protected]), Lauren Fry (NOAA GLERL)

Project 3 Information:

Recent extreme fluctuations in water levels on the Great Lakes have resulted in increased demand for resilient design and adaptive water management. The sub-seasonal to annual forecast (S2A) of the Net Basin Supply (NBS) provides opportunities for enhanced application-focused capabilities to complement existing water level prediction products. Most existing models over the Great Lakes domain focus on historical period and long-term future projections, and very few studies have been performed at the S2A time scale. With the availability of geospatial datasets and modeling configurations, there is a huge potential for the application of hydrologic models for the Great Lakes region at the S2A time scale.

Research Question: What existing models and datasets are ideal for setting up appropriate configurations of input data and parameters for S2A forecast of the NBS?

Project Activities:

This project aims to use a hydrologic model to evaluate the NBS to the Great Lakes at S2A time scale. The summer fellow will use existing modeling tools and datasets to generate appropriate input data and parameters for simulations at the S2A time scale. Project tasks may include:

Collecting and analyzing data for model configuration.
Implementation of the model with appropriate meteorological forcing and parameter sets.
Extracting and analyzing the model outputs.

Required & Desired Skills: We are looking for a highly motivated student with interest in hydrology, data analysis, and numerical modeling. The ideal candidate will have or be working toward a degree in an appropriate discipline (such as Hydrology, Engineering, etc.) with strong skills in data analysis and computer science.

Location: NOAA Great Lakes Environmental Research Laboratory in Ann Arbor, MI, or remote

Development of genetic assays for identification of larval fish
Mentors: S. Rao Chaganti (CIGLR, [email protected]), Ed Rutherford (NOAA GLERL)

Project 4 Information:

Variability in abundance of fish populations is likely set during early life by factors causing episodic or subtle changes in density, growth, or survival of eggs or larvae. The ability to predict potential fish recruitment (numbers of juvenile or adult fish entering a fishery) through estimates of larval density, growth, and survival can help inform future fisheries management decisions, but only if the species can be correctly identified during the larval stage. Some fish species are notoriously difficult to identify as larvae using traditional methods (pigmentation pattern, body morphology, meristic counts), especially species in the whitefish family (Coregonidae: lake whitefish Coregonus clupeaformis; lake herring or cisco – Coregonus artedi; bloater Coregonus hoyi) and minnow family (Cyprinidae). Genetic identification of fish species in the larval stage is a proven and accurate technique, and would enhance estimates of potential fish production. We have developed a database of primers for Great Lakes fishes from current literature that requires further development of species-specific primers. These primers, along with PCR-based methods, could permit accurate identification of larval fish and increase confidence in the predictions of recruitment potential based on larval surveys.

Research Question: What genes and gene regions would be most suitable to distinguish closely-related larval fish species such as members of the minnow family?

Project Activities:

Assist with collecting and preserving larval fish during a field survey for further processing in the laboratory.
Identify larval fish using microscopy and validate them with sequencing and PCR-based methods
Isolate DNA from larval fish
Develop species-specific primers for larval fish for qPCR assays
Perform qPCR assays for identification of larval fish
Sequence larval fish DNA
Analyze larval fish sequences
Prepare SOPs and reports

Required & Desired Skills: Students need to have basic knowledge of ecology and molecular biology, and have experience in a laboratory. Experience in DNA extractions and primer designing is desired, but not required.

Location: NOAA Great Lakes Environmental Research Laboratory in Ann Arbor, MI.

*This position is not eligible for a fully remote fellowship due to required field and lab work.*

Exploring Great Lakes ecosystem services valuation
Mentors: Kate Quigley (NOAA Office for Coastal Management, [email protected] ), Lara O’Brien (NOAA Office for Coastal Management, [email protected]), Jennifer Day (NOAA GLERL), Chiara Zuccarino-Crowe, Michigan Sea Grant)

Project 5 Information:

The Great Lakes management, policy, leadership, and advocacy communities agree there is a need to improve the collection, analysis, and dissemination of economic data on Great Lakes-dependent industries, also known as the Blue Economy. Blue Economy valuation and development is also a priority at a national scale for NOAA. Over the last couple years, NOAA staff and partners have been collaborating closely with regional leaders and economists to further define aspects of the Blue Economy specific to the Great Lakes, collect associated data, and integrate this information with existing national programs such as NOAA’s Economics National Ocean Watch (ENOW) database using a regional economic accounting methodological framework. This project will involve further exploration of an accounting of existing ecosystems, their ecosystem services, and their associated value, contributing to the broader efforts focused on Great Lakes ecosystem services valuation across the region among NOAA partners. The resulting products will ultimately support a better understanding of the Great Lakes Blue Economy among regional and national stakeholders.

Research Questions:

What is the economic value of “blue carbon” stored in wetlands in the Great Lakes?
Do the ecological and land cover data exist to make blue carbon estimates that are considered reliable and comprehensive?
How does the economic value of Great Lakes blue carbon compare to economic contributions from key industries in the region?

Project Activities: The fellow will research the current state of knowledge on accounting of blue carbon stored in wetlands as a key ecosystem service of the Great Lakes and conduct an initial calculation of the value of blue carbon. This exploration of the potential incorporation of a new environmental sector to those tracked by NOAA will help evaluate the feasibility of coupling environmental and economic accounting within the national and regional framework used by NOAA and the Bureau of Economic Analysis for valuation of the marine economy. Specific project tasks would include:

Conducting a literature review of blue carbon assessment and associated valuation in the Great Lakes
Conducting a geographic scoping and compiling methodological options for a comprehensive blue carbon assessment
Identifying existing useful data sources such as NOAA LIDAR land cover data and identifying data gaps
Conducting an initial, simplified calculation of the estimated value of Great Lakes blue carbon
Describing potential uses of a blue carbon assessment and valuation
Compilation of a summary report with links to ecological and economic data sets; a proposed plan of operations; and recommendations for future data collection, analysis, and research

Required & desired skills: Successful candidates must have an understanding of basic macroeconomic theory and ecosystem service valuation, or a soil science background with a focus on carbon storage. They should also have knowledge of or interest in Great Lakes socioeconomics and ecosystem services, and be comfortable reaching out to potential data sources in a professional manner.

Location: NOAA Great Lakes Environmental Research Laboratory in Ann Arbor, MI, or remote.

Connection between Great Lakes and Arctic Ice Cover in response to teleconnection forcing
Mentors: David Cannon (CIGLR, [email protected]), Jia Wang (NOAA GLERL)

Project 6 Information:

Great Lakes ice cover and thickness are not only controlled by local weather conditions, including temperature and wind, but are also impacted by large-scale patterns in atmospheric pressure and circulation, known as teleconnection. Variability in both Great Lakes and Arctic sea ice 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. 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.

Research Questions:

Is ice cover in the Great Lakes correlated with teleconnection patterns or multidecadal oscillations, as has been observed with Arctic sea ice cover? 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 the percentage of ice cover variability associated with teleconnection patterns, multidecadal variability, and/or global warming?

Project Activities: The research includes the development of regression models for predicting ice cover using climate teleconnection indices and physical forcings. For example, atmospheric teleconnection pattern indices could be used to formulate statistical regression models to hindcast lake/sea ice cover, which could in turn be used to explain the variability of ice cover associated with multidecadal variability and global warming. Analysis tools in statistical programs include time series analysis, correlation, and/or empirical orthogonal function analysis and regression analysis.

Required & Desired Skills: Minimum qualifications include experience programming and analyzing data in Fortran, Python, C, R, or Matlab. The intern should also be willing to learn and improve programming skills in relevant software if previous experience is minimal.

Location: NOAA Great Lakes Environmental Research Laboratory in Ann Arbor, MI, or remote

Closing the gaps: Using stakeholder engagement to inform diversity, equity, inclusion and justice approaches in the CIGLR Regional Consortium
Mentors: Riley Ravary (CIGLR, [email protected]), Megan DiCocco (CIGLR)

Project 7 Information:

The ultimate goal of this project is to sketch out the landscape of Diversity, Equity, Inclusion, and Justice (DEIJ) engagements underway to inform DEIJ strategies for CIGLR and Regional Consortium partners, strengthen Consortium partnerships and communications, and broaden engagement reach to underserved Great Lakes stakeholders. The inclusion of DEIJ strategies in the Great Lakes is of high importance–engaging with underserved groups broadens the impact of Great Lakes science and can result in the more equitable distribution of information, support, and services.

Preliminary inquiries indicate that members of CIGLR’s Regional Consortium are interested and involved in DEIJ engagements, however they have little knowledge of other engagements in the Consortium. Further, CIGLR leadership has identified interest in developing DEIJ initiatives in their strategic planning (e.g., improving recruitment diversity, establishing partnerships with Minority Serving Institutions (MSIs) or tribal organizations, expanding engagements with underserved stakeholders, etc.). This project seeks to utilize social science methods to identify Consortium-wide DEIJ engagements, Consortium stakeholders (Consortium institutional and/or individual partners) implementing engagements, Great Lakes stakeholders being engaged with, and Great Lakes stakeholders that are underserved.

The project is designed in three stages with the fellow completing the first phase at minimum and contributing to the subsequent phases as time allows. The first phase will involve an extensive search and literature review to better understand existing Consortium DEIJ engagements. The second phase will focus on conducting a social network analysis with the goal of defining Consortium DEIJ engagement networks and identifying Great Lakes stakeholders that are underserved. The third phase of this project will involve the co-design of a decision support tool for Consortium stakeholders interested in DEIJ engagement, as well as the co-design of a stakeholder engagement strategy with underserved Great Lakes stakeholders interested in establishing mutual partnerships.

Research Questions:

Stage 1: What DEIJ engagements are happening throughout the Consortium? Who are the Consortium stakeholders involved in these engagements? What resources do Consortium stakeholders have to support continued/future DEIJ engagements?
Stage 2 (if time allows): What are the relationships between Consortium stakeholders involved in DEIJ work? Who are the Great Lakes stakeholders that are being engaged? Who are the Great Lakes stakeholders that are underserved?
Stage 3 (if time allows): What tools do Consortium stakeholders need to better engage with underserved Great Lakes stakeholders? What are the needs of underserved Great Lakes stakeholders?

Project Activities:

Stage 0: Stakeholder Engagement Training

Observe work of CIGLR’s Stakeholder Engagement Specialist (SES) team.
Take online courses about data analysis software.
Become acquainted with CIGLR/Consortium structure.

Stage 1: Collect Data to Understand Consortium DEIJ Engagements

Conduct literature review on Consortium engagements with DEIJ.
Draft list of Consortium stakeholders.
Host workshop (or conduct interviews/survey if more appropriate) to engage with Consortium stakeholders and obtain more information about Consortium DEIJ engagements.

Stage 2: Social Network Analysis (if time allows)

Conduct social network analysis utilizing literature review data.
Engage with Consortium stakeholders through surveys and/or workshops to collect social network data.
Use social network analysis to identify underserved Great Lakes stakeholders.
Produce a report with findings, highlighting what engagements are happening and who is involved.

Stage 3: Co-Design a Decision Support Tool (if time allows)

Conduct user needs assessments for Consortium stakeholders and underserved Great Lakes stakeholders.
Co-design DST to support Consortium stakeholder DEIJ engagement efforts.
Co-design stakeholder engagement plan with underserved Great Lakes stakeholders.

Required & Desired Skills:

Possesses strong critical thinking skills, a willingness to learn R and qualitative data analysis software, a willingness to speak to and engage with a wide variety of stakeholders, and a commitment to DEIJ and developing cultural competencies.
Can conduct a literature review, use a computer and core software/programs (i.e., Microsoft suite, Google suite, Zoom/Google Meet).

Location: NOAA Great Lakes Environmental Research Laboratory in Ann Arbor, MI, or remote

Investigating recruitment bottlenecks in the larval stage for key prey fishes
Mentors: Ed Rutherford (NOAA GLERL, [email protected]), Doran Mason (NOAA GLERL), Mark Rowe (NOAA GLERL), Maddie Tomczak (CIGLR)

Project 8 Information:

Recruitment of fish populations (numbers of young adults) is often determined by events affecting distributions, growth, and survival of sensitive egg and larval stages. Statistical analysis of factors affecting first-year survival of alewife, a key prey fish for salmon and trout in Lakes Michigan and Ontario, suggests warm spring-summer temperatures and low salmon predation (Madenjian et al. 2005) are positively correlated with higher-than-average survival of young alewife, but specific mechanisms are poorly known. Past studies of alewife larvae in Lake Michigan suggest that a major bottleneck to survival is the influence of wind-generated upwellings of cold water that can advect alewife larvae from warm, productive nearshore habitats to colder and less productive offshore habitats (Heufelder et al 1982, Höök et al 2006). Few studies have tracked larvae or their zooplankton prey from nearshore to offshore environments to assess their fate, so little is known about the impact of advection on growth or mortality. In summer 2023, we plan to use current models, subsurface drifters, and hourly sampling of alewife larvae and their zooplankton prey over 3-5 day periods to improve understanding of factors affecting alewife larvae distribution, growth, and survival in Lake Michigan. In addition, we will relate strength and duration of coastal upwelling during the larval period (June-July) to annual measures of alewife recruitment provided by USGS Great Lakes Science Center’s trawl survey. recorded by NOAA GLERL’s nearshore in-situ thermistors to alewife

Research Questions: We hypothesize that in years of strong and frequent upwellings, larvae will experience lower growth, higher mortality, and contribute less to recruitment than in years of weak or infrequent upwelling.

Project Activities: The summer fellow will assist with field surveys conducted for 3-5 days in June and 3-5 days in July to track and sample larval fish and their zooplankton prey in nearshore waters of southeast Lake Michigan off Muskegon. Drifters will be released on the first day that larvae are found to track movement of the water mass and advection of larvae. The change in position of the water mass, plankton, and larvae will be measured by daily movement of drifters and also predicted from forecasts of a validated current model. On each survey day, fish larvae, zooplankton, and ambient physical conditions (temperature, light) will be sampled around the drifter at 3-4 hour intervals throughout a 12 hr period to estimate short-term larval fish growth and survival rates. The fellow will:

Assist with sampling larvae, zooplankton, and ambient environmental conditions on 3-5 day field surveys in southeastern Lake Michigan.
In the laboratory, help sort and identify fish larvae and zooplankton, and determine larval fish age from otolith increments (marks on fish ear bones).
Estimate larval growth rates from changes in fish size and age over each 3-5 day survey period, and from differences between larval age and length at capture compared to size at hatch.
Estimate short-term survival rates of larval fish based on changes in larval abundance at age.
Relate estimates of larval growth and survival to temperature, light, and zooplankton densities in nearshore waters as determined from sensors and net tows.
Compare distributions, growth, and survival of larval alewife hatched early in the season to rates of those hatched later in the season. Help assess ability of models to predict advection of larval alewife by lake currents over 3-5 day periods.
Analyze a time series of upwelling data from NOAA GLERL’s buoy in nearshore Lake Michigan off Muskegon, and relate the temperature data to a time series of alewife recruitment data provided by the USGS Great Lakes Science Center.

Required & Desired Skills: Candidates should have a strong background in freshwater or marine science, have taken an introductory statistics course, and have some laboratory experience.

Location: NOAA Great Lakes Environmental Research Laboratory in Ann Arbor, MI.

*This position is not eligible for a fully remote fellowship due to required lab and field work.*