Position Descriptions

Understanding the physiology of an organism can give insight to the organism’s growth, resource use, and impact on its environment. In the Great Lakes, two invasive mussel species, zebra and quagga mussels, have had significant impacts on their invaded ecosystem; however, the majority of previous mussel physiology research has focused on zebra mussels and much less is known about quagga mussel physiology. To address this knowledge gap, experiments are needed to investigate quagga mussel growth and filtration under controlled environmental conditions. These data are required for a bioenergetic quagga mussel model that is being developed by GLERL and CIGLR to predict mussel growth, biomass, and potential ecosystem impacts in different environments throughout the Great Lakes.

Research Questions:

This research project will be part of a series of lab experiments investigating quagga mussel growth and/or filtration in controlled environmental conditions. These experiments will aim to answer the following:

• How do different food concentrations impact mussel filtration and growth rates with controlled temperature and food type?

Project Activities:

The summer fellow will assist with a series of lab experiments to determine filtration and growth rates of quagga mussels. The summer fellow will assist with:

• In-lab culture of juvenile and adult quagga mussels
• Food and culture media preparation
• Experimental set-up and design
• Data collection/ record keeping
• Statistical analysis and visualization of collected experimental data

Required Skills:

The candidate should have an interest in invasive species, physiology, and/or lab experimentation and aquaculture. Experience working in a lab or with live specimens would be preferred but is not required.

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

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

Extratropical cyclones (ETCs) play an important role in the weather and climate in the Great Lakes Region (GLR), particularly during the cold season (October – March). ETCs are associated with high winds, large temperature gradients, and significant precipitation, all of which have an impact on the GLR’s hydroclimate. To better understand how our changing climate will affect the water budget within the GLR, it is important to understand how climate change is manifesting within Great Lakes ETCs. After compiling a dataset of historical Great Lakes ETCs, we show that storm trajectories are shifting northward, and the ETCs themselves are getting warmer and wetter, but the inter-annual variability of the ETC characteristics has not yet been explored. Global teleconnections may be contributing to this inter-annual variability, and it is possible that the effects of the teleconnections are trending in time, as well.

Research Questions:

• Is Great Lakes ETC structure (e.g., temperature, moisture, precipitation, vertical structure) correlated with teleconnection phase
• Keeping teleconnection phase constant, do Great Lakes ETCs show a trend with time (e.g., do El Niño cyclones from the 1960s look the same as El Niño cyclones from the 2010s)?

Project Activities: 

This research will involve analyzing a gridded dataset of historical ETC activity within the GLR and finding statistical relationships between ETC characteristics and different teleconnection indices (El Niño Southern Oscillation, Pacific Decadal Oscillation, North Atlantic Oscillation, etc.). Analysis will be both Lagrangian (identifying any correlations in the structure of the ETCs themselves) and Eulerian (determining if specific regions of the GLR have experienced significant changes in winter weather due to climate trends and teleconnection indices).

Required Skills:

Minimum qualifications include some familiarity with programming and/or analyzing gridded spatial datasets in Python, Matlab, C, R, Fortran, or a similar program. The mentors will provide some training for these skills if necessary. A successful candidate would also benefit from prior knowledge of atmospheric science or meteorology, but it is not required.

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

The Great Lakes receive nutrient inputs from various drainage catchments, which can contribute to eutrophication and harmful algal blooms (HABs) at different temporal and geographic scales. Few studies have investigated the impacts of spatial-temporal variability of nutrient loading from different catchments on lake ecological responses, including HABs. Due to the spatial heterogeneity among catchments’ features (e.g., precipitation, land use, etc.) and in-lake characteristics (e.g., wind, bathymetry, temperature), complex interactions exist between terrestrial nutrient transport pathways and lake hydro-ecological processes. In recent years, significant progress has been made in estimating nutrient loads and water flows from various tributaries. Together with high-frequency water quality data collected by GLERL and CIGLR in Saginaw Bay from 2012 to 2019, this offers a significant opportunity to explore the spatial-temporal correlations between nutrient loading from different catchments and lake ecological responses in this ecosystem. This study could provide preliminary results and guidance for developing a process-based catchment-lake modeling chain to connect catchment drivers and lake responses in other areas of the Great Lakes impacted by HABs.

Research Questions: 

• What are the spatial-temporal correlations between nutrient loading from different river tributaries (discharge volume, P, N) and ecological indicators (Chlorophyll, phycocyanin, microcystins) at routine sampling stations in Saginaw Bay?
• Could modeling water flows and nutrient loading from multiple river tributaries improve water quality management in Saginaw Bay beyond using gauged river flow data alone?
• What are the key catchments for nutrient-based management prioritization in Saginaw Bay?

Project Activities:

The main responsibility of a summer fellow student will be to collect and analyze hydro-ecological data for Saginaw Bay and the surrounding river estuaries and perform spatial-temporal analysis. Project tasks may include:

• Collecting and analyzing water flow data from the National Water Model (NWM), and nutrient loading estimates from a Spatially Referenced Regression On Watershed attributes (SPARROW) model, for different catchments around Saginaw Bay.
• Collecting and analyzing in-situ measurements, remote sensing, and simulation data of various ecological factors at multiple stations in Saginaw Bay. This can include the fellow assisting in one of the routine sampling cruises in Saginaw Bay to better understand the ecosystem and data at the center of this project (optional depending on the fellow’s availability).
• Performing spatial-temporal analysis on the gathered datasets.

Required Skills:

We seek a highly motivated graduate/undergraduate student interested in environmental statistics, data analysis, and ecological science. The ideal candidate will have or be working toward a degree in an appropriate discipline (such as Environmental Science, Statistics, etc.) with some experience in data analysis in a scripting environment such as R, Python, or Matlab.

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

While the complexity of Michigan’s coastal dynamics has increased due to climate change, most notably because of more rapid variability in Great Lakes water levels, the ability of local resource managers to respond has not kept pace. With this lack of capacity, decisions that have long-term impacts on shorelines are often made under duress, with incomplete information and/or without the ability to think long-term about impacts and sustainability. Currently, the unprecedented opportunity of federal investment and the lower water levels represent a critical window for action to build capacity and knowledge while undertaking innovative projects. The Fellow will be involved in Year 1 of a multi-year effort to leverage this opportunity by providing useable data and resources to Michigan’s coastal resiliency managers, while helping to connect and link institutions and agencies working on coastal resiliency in Michigan. There will be an opportunity to work specifically in Benton Harbor on projects in the Ox Creek watershed.

Research Questions: 

• What are the key information needs of Michigan’s coastal resilience managers?
• What are the gaps in filling those needs currently?
• How can a new resource guide and other materials help fill these gaps?
• How can best practices in resilience be applied to Ox Creek?

Project Activities:

• Groundtruthing (via interviews) and specifying the needs of Michigan’s coastal resilience managers
• Vetting and finalizing creation of an online resource guide/information guide housed at Michigan Sea Grant
• Assisting with training to use this guide
• Assisting with application of guide and other resources utilizing Benton Harbor’s Ox Creek as a model/test case

Required Skills:  

• Strong communication (including writing) and interpersonal skills
• Interest in and knowledge of coastal resilience in the Great Lakes
• Basic knowledge of ecology and resource policy
• Interviewing and qualitative data analysis research skills (preferred but not required)

Location: University of Michigan campus and/or NOAA Great Lakes Environmental Research Laboratory in Ann Arbor, MI, hybrid, or remote

Alewife (Alosa pseudoharengus) and yellow perch (Perca flavescens) are prey fishes that support economically-important Great Lakes fisheries and play key roles in the ecosystem. The numbers of young fish surviving to adulthood (e.g., recruitment success) largely depends on the retention of larvae in, or advection away from, productive nearshore habitat. Predicting larval fish distribution and potential recruitment remains a challenging and critical goal of fisheries scientists. While traditional net catch surveys provide information on larval fish distributions, these estimates are biased by extrusion of small larvae through the mesh and avoidance by larger larvae that can swim away from the gear. In addition, species identification of larvae is hampered by several factors, including condition of the larvae after capture, variability in the presence and location of larval pigmentation, and variability in number of muscle elements (myomeres). In contrast, environmental DNA (eDNA) methods permit accurate identification of species, are non-invasive, and can easily detect presence of the species of interest or of the whole larval community in the aquatic environment. In 2024, we will evaluate the application of eDNA for tracking the dispersal and relative abundance of alewife and yellow perch larvae in comparison with results from traditional repetitive net samples in nearshore and offshore environments. From May-July, biweekly sampling will be conducted at nearshore locations using traditional net sampling and water collected for eDNA analysis. In late July, intensive sampling at multiple sites in nearshore, mid-depth and offshore habitats will be conducted using nets and water samples of eDNA to compare larvae distributions and relative abundance. This study will help define the distributions and relative abundances of alewife and yellow perch larvae and their coincidence with favorable environmental conditions, inform analysis of fish recruitment dynamics that is critical for fisheries and ecosystem management, and aid future restoration efforts of other threatened or endangered species. 

Research Questions: 

• Can eDNA be used to map distribution and relative abundance of larval fish?  
• Can eDNA be used for estimate bias in net samples of larval fish distribution and relative abundance of larval fish? 
• Do environmental characteristics differ between sites positive for eDNA and sites where larval fish were caught by traditional plankton nets?

Project Activities: 

• Assist with collecting and preserving larval fish and eDNA samples during biweekly sampling events for further processing in the laboratory. 
• Isolate DNA from larval fish and water samples  
• Perform qPCR assays for detection of eDNA specific to targeted fish 
• Analyze eDNA sample data and compare with the traditional methods and modeling. 
• Prepare standard operating procedures (SOPs) and reports

Required skills: 

Students need to have basic knowledge of ecology and molecular biology, and have some experience in molecular laboratory techniques such as DNA/RNA extractions and optimize PCR conditions. 

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*

Although ice cover in the Laurentian Great Lakes has declined significantly over the last several decades, the observational record is relatively short, with records of lake-wide ice cover maxima starting in 1960. Recent modeling efforts suggest that climate variability in the Great Lakes may be strongly correlated with multidecadal climate patterns, like the Atlantic Multidecadal Oscillation (AMO; period 60 – 80 years), but observational records are too short to confirm model results. This project is designed to extend the observational record back to 1897 using reconstructed ice cover time series. A student researcher will be responsible for developing a “freezing-degree day” model for each lake, using daily observed air temperatures to estimate annual ice cover averages and maxima for the period 1897 – 2023. Models will be calibrated and validated using observations from more recent decades (1979 – 2023), and the full reconstructed historical time series will be used to investigate the role of multidecadal climate patterns in driving ice cover variability.

Research Questions:

• Can average and maximum ice cover in the Great Lakes be reliably predicted using simple freezing degree day models, where ice growth and decay is modeled as a function of observed air temperature?
• Are recent ice cover losses in the Great Lakes a consequence of anthropogenic climate warming, or are they (at least in part) related to multidecadal climate variability (i.e., AMO, PDO, etc.) in the region?

Project Activities: 

The intern will work independently to develop freezing degree day models for each lake (Lakes Superior, Michigan, Huron, Erie, Ontario) under the guidance of project mentors. Model development will require regression and time series analysis of observational data. The intern will be responsible for writing code (using their preferred coding language), writing a technical memo to describe their results, participating in weekly group meetings, and presenting research findings at the end of the summer.

Required 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, remote, or hybrid.

Extensive studies on nutrient dynamics in western Lake Erie (WLE) have supported initiatives that aim at reducing coastal cultural eutrophication and mitigating harmful algal blooms. Variation in phosphorus concentrations across different WLE nearshore areas suggests potential for spatial variation in the quantity of phytoplankton biomass generated per unit of phosphorus (i.e., phosphorus use efficiency). The co-variation between phosphorus use efficiency and the trophic states across major phytoplankton groups could provide insight into spatio-temporal differences in ecological adaptation, nutrient allocation priorities, and growth strategies among key phytoplankton groups. The fellow will use statistical modeling to examine site-specific trophic state index (with Secchi depth, chlorophyll-a, and phosphorus) and phosphorus use efficiency using a dataset of water quality parameters and phytoplankton biomass. The results will illustrate phosphorus use efficiency of phytoplankton and lake temporal trophic states in WLE which will help to inform the decision support tool under development by CIGLR and GLERL for targeted phosphorus management in WLE.

Research Questions:

• How does phosphorus use efficiency vary with trophic state index in western Lake Erie?
• How does biomass of major phytoplankton phyla vary in relation with phosphorus use efficiency and trophic state index in Western Lake Erie?

Project Activities:

• Use datasets of phytoplankton community composition, phytoplankton biomass, and water quality parameters in WLE produced by CIGLR and GLERL
• Estimate phosphorus use efficiency and trophic state index
• Evaluate spatial patterns of phosphorus use efficiency and trophic states in relation to phytoplankton major phyla.

Required Skills:

• Basic skills in aquatic ecology, experience using R is preferred but not required

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

*This position is not eligible for a fully remote fellowship*

Coastal zones of the Great Lakes are dynamic spaces where human activities and infrastructure are vulnerable to natural forces, climate change, and extreme weather events such as flooding from storm surges. An ongoing CIGLR/GLERL project funded by the Bipartisan Infrastructure Law aims to expand NOAA’s Great Lakes Operational Forecasting System (GLOFS) model grid for each lake, beginning with Lake Ontario, to include surrounding floodplains in an effort to develop a hydrodynamic model that can accurately predict coastal flooding by capturing all the physical mechanisms at play. However, due to high model complexity and large geographic domains, the hydrodynamic model’s spatial resolution will be relatively coarse (50 – 100 m) in floodplains, which may lead to the loss of local features in the coastal environment like roads and houses, thus impacting the accuracy and reliability of predictions. On the other hand, static Geophysical Information Science (GIS) based techniques can be used to efficiently estimate coastal flood extents at very high resolution (1 – 10 m), but the conventional static approaches like the Bathtub Model (BTM) ignore hydrologic connectivity and can wrongly indicate low-lying areas remote from the coast as flooded. A promising solution could be the combination of hydrodynamic models with enhanced static GIS-based techniques. This combined approach can result in more accurate and efficient dynamic flood mapping for the Great Lakes Coasts.

Research Questions: 

• Can conventional BTMs be enhanced by instilling hydrological connectivity?
• Does dynamically combining the enhanced BTM with hydrodynamic model outputs provide more accurate and efficient coastal flood predictions?

Project Activities:

The main responsibility of a summer fellow student will be to (I) enhance conventional BTMs by considering hydrological connectivity, and (II) dynamically combine the enhanced BTM with hydrodynamic model outputs. Project tasks may include:

• Collection and analysis of the very high-resolution LiDAR topographic data for a section of the Great Lakes coasts.
• Using GIS and computer science techniques to develop an enhanced BTM by considering hydrologic connectivity.
• Dynamically combining the enhanced BTM with hydrodynamic model outputs and testing modeling performance.

Required Skills: 

We seek a highly motivated graduate/undergraduate student interested in Geospatial Data Sciences, Environmental Informatics, and Oceanography. The ideal candidate will have or be working toward a degree in an appropriate discipline (such as GDS, Environmental Informatics, etc.) with some experience in GIS and computer science.

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

Lake effect precipitation is one of the most interesting meteorological events in the Great Lakes region. When cold air passes over warm lake waters, it causes more moisture to evaporate and precipitation to fall downwind.  Intense and sustained precipitation over the Great Lakes can lead to increased river discharge, exacerbating the risk of flooding in coastal areas.The complex mesoscale atmospheric interactions that drive lake effect precipitation make it difficult to model and predict accurately. By investigating the mesoscale processes driving lake effect precipitation and their connection to coastal flooding, we can uncover critical insights for flood prediction and mitigation strategies. The WRF model is a powerful tool for simulating mesoscale atmospheric events, making it an excellent candidate for the study of lake effect precipitation dynamics. The importance of this project lies in the complexity of atmospheric stability, changes in lake surface temperature, and orographic features that affect lake effect precipitation. Not only does this improve our scientific understanding, but it also helps to refine numerical models and ultimately improve the accuracy of weather forecasts. In addition, lake effect precipitation events have significant local impacts, like heavy snowfall and changed temperature gradients, so a thorough study of mesoscale dynamics is necessary to mitigate potential risks and build resilience in affected communities.

Research Questions: 

• How do variations in lake surface temperature influence the intensity and spatial distribution of lake effect precipitation?
• To what extent do orographic features (e.g., hills, dunes) near the lakeshore impact the local dynamics of lake effect precipitation? 
• How does the intensity and spatial distribution of lake effect precipitation events over the Great Lakes region correlate with increased river discharge and the heightened risk of coastal flooding in adjacent areas?

Project Activities:

The summer fellow’s main responsibility will be to assist with the data analysis on WRF simulations and observational data. They will also assist with creating plots and images that could be used in future publications. 

Project tasks may include:

• Collection of observational data and weather forecast for the Great Lakes region.
• Using programs like Python, R, and MATLAB to perform data analysis on simulation results and observational data.

Required Skills: 

We are seeking a highly motivated graduate/undergraduate student interested in Atmospheric Science, Meteorology, and Environmental Informatics. The ideal candidate will have or be working toward a degree in an appropriate field and have some experience with python, MATLAB, R, and gridded datasets. 

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

The Great Lakes, as the planet’s largest freshwater reservoir, not only provide drinking water for over 38 million people, but also serve as a vital resource for irrigation, shipping, hydroelectric power generation, and recreation. Because of their importance, observing and monitoring the Great Lakes is a critical activity. Due to the limited resources available for this much-needed observing and monitoring, it is crucial to make the most efficient use of available observing platforms (e.g., buoys, research vessels). For this fellowship project, we aim to deploy a novel machine learning method that leverages convolutional Gaussian neural processes to the problem of Great Lakes observing system design (Andersson et al., 2023). Our overall objective is to develop a quantitative framework for strategic placement of the next generation of observing stations, in order to best capture the variability of the dynamic Great Lakes. 

Research Questions: 

We have selected surface temperature as an initial target variable for this case study. In this context, our research question is simply “where should the next generation of temperature measurement sensors be placed in order to most efficiently improve our quantitative understanding of Great Lakes surface temperature variability?”

Project Activities:

The summer fellow will use DeepSensor, an open source Python package for probabilistically modeling environmental data with neural processes, to characterize Great Lakes surface temperature and to make informed suggestions for future temperature sensor locations. Specifically, the student will: 

• Use existing observational and model data, which will be prepared for use by the mentoring team before the fellowship begins, to train DeepSensor on Great Lakes surface temperature
• Create a list of target observing sites that would most efficiently reduce uncertainties in our quantitative representation of Great Lakes surface temperature variability 
• Prepare a brief report to visualize, characterize, and document the results 

Required Skills: 

Prospective fellows should have some experience with Python, machine learning, working with large volumes of data, high-performance computing, and with visualization and communication of research results. The mentoring team will offer some training resources on one or more of these skills as needed.

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