2018 Summer Fellows Position Descriptions

Muskegon Lake is a freshwater estuary along the eastern shore of Lake Michigan and is designated an Area of Concern by EPA and a Habitat Blueprint by NOAA. It is an ideal site to test hypotheses about how hydrodynamic drivers such as material loading, winds, water circulation, and hydrological input shape lake ecology, thus aiding ongoing restoration efforts led by NOAA, EPA, and state agencies. This project aims to shed light on how hydrodynamics affect the ecology of Muskegon Lake and the recurring hypoxia in bottom waters. A systematic biophysical model is needed to understand the nutrient cycles and plankton dynamics in Muskegon Lake, as well as their responses to climate change.

The fellow will work closely with the research team to study how hydrodynamic drivers and anthropogenic stressors affect nutrient and plankton dynamics in Muskegon Lake by:

• Applying and calibrating a coupled bio-physical model with observations,
• Analyzing results and engaging in discussion, and
• Carrying out process-oriented numerical experiments.

Candidates should have a strong interest or background in basic physical oceanography and limnology. Experience with data processing/analysis, computer programming, and ecosystem modeling are preferred.

The environmental problems we face today are complex, with many social and ecological causes and impacts. The potential solutions to these problems are often costly and hinge on conflicting interests and priorities of social groups. Harmful algal blooms (HABs) in western Lake Erie are one such “wicked problem”, which has proliferated in the past twenty years with increasing bloom size, frequency, and occurrence of toxicity. Before society can compare the costs and benefits of possible solutions, a full picture of who is affected and how they are impacted is needed. Although there are studies starting to describe the economic and public health impacts associated with HABs in western Lake Erie, a great deal remains unknown about how HABs impact the behaviors and quality of life of Lake Erie shoreline residents and tourists. This project will focus on the impacts of HABs on tourism, water-based recreation, and public perception.

The fellow will be trained in social science methods, and participate in data collection and management. The fellow will also be supported to conduct analysis matching his or her research and professional interests. Tasks will include, but not be limited to:

• Participate in survey and/or interview instruments development
• Travel to study sites to conduct stakeholder surveys and/or interviews
• Enter survey data, if needed
• Conduct preliminary data analysis through statistical analyses of survey data and/or coding of qualitative data
• Practice professional and science writing through reporting of results

The candidate should feel comfortable to travel by themselves to study sites, manage field work logistics with attention to details, and represent a positive image of CIGLR and UM to stakeholders. Experience with social science research methods is a plus.

Over the past decade there has been a resurgence in the Great Lakes in the development of toxic cyanobacteria blooms (HABs), which pose significant impairment for both recreational use and to source water for public drinking water supply. Microcystis is a dominant cyanobacterial species composing these blooms and has several physiological attributes that may be responsible for its ability to outcompete other phytoplankton taxa and form such dense blooms.

This project will involve a series of weekly experiments to evaluate the importance of extra-cellular enzymatic activity, which is used by phytoplankton and bacteria to acquire limiting dissolved nutrients, and evaluate how the pattern of this activity changes throughout seasonal bloom development. These experiments will be designed to answer the following questions:

• Does the ability of Microcystis to acquire additional nutrient supplies via extra-cellular enzymatic breakdown of bound organic phosphorus play a key role in its seasonal dominance and bloom formation in western Lake Erie?
• How do rates of enzymatic activity differ among various phytoplankton communities?
• Are rates of enzymatic activity directly related to cellular demand as expressed by stoichiometric relationships between carbon, nitrogen, and phosphorus?

The fellow will support field and laboratory research designed to monitor the timing, extent, distribution, and toxicity of cyanobacterial HABs in western Lake Erie and Saginaw Bay, Lake Huron. The fellow will participate in weekly experimental measurements of extra-cellular enzyme activity in conjunction with samples collected during our HABs monitoring program.  Results from experiments will be analyzed against observed changes in community composition, algal biomass, nutrient availability and cellular nutrient stoichiometry.

Candidates should have a strong interest or background in aquatic ecology and have some previous laboratory experience related to biological or chemical water quality assays.  The work may also involve participating in monitoring cruises so experience working on small research vessels is highly valuable.

Ice cover has been observed and recorded in the Great Lakes for more than 50 years.  2018 is projected to be a relatively low ice year.  If this happens, it will be the third consecutive low ice year after two very high ice years (2014: 2nd highest, 2015: 6th highest).  Investigating whether the extreme ice cover in recent years is due to random chance or if this is a trend that is expected to continue is a highly valuable task. The outcomes will help to gain insight for understanding our regional climate and benefit the skill of future ice cover projections. This project seeks to identify what we can learn about long-term trends in Great Lakes ice cover from the extreme highs and lows of recent years.

In particular, the fellow will:

• Process the most recent 2018 observational data and append it to the current ice record
• Perform data analysis of long-term and short term trends in Great Lakes ice cover (spectral analysis, EOF, and other statistical methods will be used)
• Compare short-term observational data to physical model data and validate remote observations with in situ observations

Candidates should possess strong quantitative skills and have experience with data analysis or computer programming (R, C++, Matlab). Background in statistics, geophysical sciences, oceanography or meteorology is a plus.

Lake Erie provides valuable ecosystem services to regional communities, including provision of drinking water and a productive fishery. Hypoxia in the central basin of Lake Erie has detracted from these ecosystem services, resulting in costs to the regional economy. CIGLR and NOAA GLERL are developing forecast models that predict when and where hypoxia events will occur, enabling people to avoid or prepare for these events and thereby mitigate their impacts. Hypoxia is caused by a combination of physical factors (stratification, meteorology) and biological factors (nutrient loads, algal growth), although the relative importance of these factors in explaining inter-annual variation in hypoxia is not fully known.

The summer fellow will develop statistical models to predict the response of Lake Erie (hypoxia, algal biomass, nutrient concentrations) to nutrient loads and physical variables using a large database of in-lake observations from federal and state agencies and universities. The fellow will conduct data analysis in a scripting environment, working with several empirical data sets, to develop visualizations, summary statistics, and predictive or explanatory statistical models relating the response of Lake Erie to nutrient loads.

Ideal candidates would have training in quantitative aspects of freshwater ecology, limnology, environmental engineering, or a similar field. Experience or interest in statistical modeling and data analysis in a scripting environment (e.g., R, Python, IDL, or Matlab) is also desirable.

For nearly a decade, NOAA GLERL has produced and archived real-time data from a series of buoys and stations in and around the Great Lakes. These data come in various formats and are not consistent from year to year. There is a great need to identify how real-time data from Great Lakes stations and buoys can be better managed, and what is the best vehicle for disseminating data on the NOAA GLERL website.

The fellow will analyze archived real-time data, their formats, and documentation, developing (and possibly begin executing) plans to re-format and store the data in a more accessible format or data management system. Such a system would be an ideal platform for data dissemination in-house and on the GLERL webpage. In particular, the fellow will:

• Engage with technicians and engineers in the GLERL Marine Instrumentation Laboratory to get a briefing on available real-time data and their locations
• Analyze real-time data, their formats, and documentation
• Develop plans to re-format and store the data in a more accessible format or data management system
• Develop plans to disseminate data on NOAA-GLERL website
• Begin execution of plans

Ideal candidates would be concentrating in Data, Computer, Information Science and/or a related field and have previous coursework and/or professional experience involving problems surrounding Big Data and general digital information challenges.

Mysis diluviana (opossum shrimp), Bythotrephes longimanus (spiny water flea), and larval fish are visual predators of zooplankton and are important prey for pelagic fish. Density estimates of these groups are highly variable but critical for quantifying predator-prey interactions in food web models that inform salmonid stocking decisions. Current monitoring of zooplanktivore densities is conducted using replicate vertical tows of metered nets whose relative catch efficiencies are unknown.  In 2016, we evaluated catch efficiency of Mysis in a Multiple Opening/Closing Net and Environmental Sensing System (MOCNESS) with LED strobe lighting that has been used to efficiently sample Euphausid shrimp (krill), plankton, and fish larvae in marine waters. Preliminary results indicate that density estimates of Bythotrephes and fish larvae, but not Mysis, are higher in the MOCNESS net having an opening of 1 m²  with strobe lighting compared to traditional sampling nets of different openings (0.5, 1, and 2 m²).

This project will test the hypothesis that relative efficiency of traditional plankton nets is low for estimating density of native and invasive zooplankton predators. Results will be used to answer whether the use of a strobe flash unit on MOCNESS sampling gear that temporarily stuns zooplankton predators improves efficiency for sampling zooplankton predators compared to using MOCNESS with no flash, or to traditional zooplankton nets. The fellow will quantify how mesh size, net volume, net type, and light affect density estimates of Mysis, Bythotrephes, and larval fish.  The fellow will supplement data analysis of new samples collected during diel surveys in Lake Michigan with analysis of prior plankton collections in 2016 and 2017.

Candidates should have a strong background in Great Lakes or marine science, have taken an introductory statistics course, and be experienced working on boats in variable weather conditions.

After the expansion of dreissenid mussels in Lakes Michigan and Huron, there has been a significant increase in water clarity, a decrease in phytoplankton abundance, and altered food web function. We are using multiple technologies to map out the fine-scale vertical and nearshore-offshore distributions of physical variables (temperature, light, UV radiation) and biotic variables (chlorophyll, native zooplankton species, non-indigenous predatory Bythotrephes, fish) to document the impacts of these changes on diel vertical migration (DVM) of zooplankton and offshore-nearshore patterns.  Preliminary analysis of the data points to a radically different depth distributions between day and night with zooplankton showing concentrations within very narrow bands in the water column post Dreissena expansion. This project will examine the biotic and abiotic drivers of DVM and these observed extreme fine-scale patterns.

The fellow will use a variety of statistical approaches in the R programing environment to visualize data and to determine abiotic and biotic factors driving diel vertical migration of zooplankton and larval fishes, as well as participate in at least one 5-day spatial cruise to collect data.

Candidates are expected to have a strong background in statistical analysis and data visualization using the R analysis package to analyze the fine-scale data.