Ocean and Coastal Research Experiences for Undergraduates

REU: OCEANUS is an interdisciplinary research program that advances scientific understandings of coastal system sustainability. Funded by the National Science Foundation (Award Number: 1950910), OCEANUS invites talented students from diverse backgrounds to participate in a 10-week immersive research experience.

Program Description

May 31st - August 5th, 2022

Email questions to Dr. Jenna A. Lamphere at jlamphere@tamug.edu

Undergraduate Research

Program Description:

In order to address the increasing vulnerability of coastal systems, greater scientific understanding of social, economic, and ecological processes is needed. OCEANUS annually pairs 10 undergraduate students from across the country with TAMUG faculty whose research advances coastal system sustainability. REU students have access to state-of-the-art facilities and receive research and professional development training. Overall, our program advances scientific understanding of coastal system sustainability, while also training the next generation of scientists and engineers who are essential for making the scientific discoveries and technologies of the future.

OCEANUS features:

  • Independent student research, supervised by TAMUG faculty and staff
  • Individualized student mentorship by TAMUG faculty and staff
  • Research and professional development workshops on research design and analysis, scientific communication, ethics in research, and more
  • Student research presentations at the annual OCEANUS research symposium
  • Post-graduation preparation for graduate school and careers in STEM


  • Enhance scientific research and communication skills through high-impact learning and hands-on training
  • Gain tools to navigate the academic pipeline
  • Build social capital through the Aggie Network
  • Receive a $6,000 stipend, travel allowance, campus meal plan, and room and board

Program Requirements


Open to all STEM majors.



Previous research experience not necessary.


Applicants must be U.S. citizens, U.S. nationals, or permanent residents. Students currently enrolled in an undergraduate program and expected to graduate in December of 2022 or later are eligible to apply. Students from underrepresented groups, affiliated with the Louis Stokes Alliance for Minority Participation (LSAMP) program, enrolled in minority-serving institutions, or attending colleges or universities with limited STEM research are especially encouraged to apply.

How to apply

  1. Complete Application via  https://tamu.qualtrics.com/jfe/form/SV_cU4hcZhtvaRkG34
  2. Submit:
    Unofficial academic transcripts
    Personal statement
    Diversity statement
    Contact information for two references
    Curriculum vitae or resume
  3. Applications will be accepted until positions are filled. Review of applications will begin February 15, 2022.
  4. Notification of acceptance will begin on March 15, 2022.

Research Areas

Marine Biology

Response of Mangrove Seedlings to Disturbance Events

with Anna Armitage

Climate change may make disturbance events such as hurricanes and cold snaps more common in the Gulf of Mexico. These events can cause rapid leaf loss from black mangroves (Avicennia germinans). After these leaves are dropped, them may decompose and supply nutrients that will boost plant recovery, or they may inhibit seedling recruitment and establishment. This project will examine how mangrove seedlings will respond to fluxes of litter due to disturbance events like freezes.

The REU intern will assist a graduate student in field work and laboratory studies to cultivate mangrove seedlings and conduct greenhouse growth experiments.

Salinity and Nutrient Effects on Plant Species of Management Concern in Coastal Wetlands

with Anna Armitage

Common reed (Phragmites australis) is a plant species of management concern in coastal wetlands in the Gulf of Mexico. Phragmites growth and reproduction may be reduced by high salinity and enhanced by nutrient input. As human activities alter water quality in our estuaries, these changes may have consequences for the proliferation of Phragmites and other nuisance plant species.

The REU intern will assist a graduate student with field and laboratory studies to examine Phragmites fitness under different salinity and nutrient enrichment conditions. 

Oyster Stressors in Galveston Bay

with Laura Jurgens

Maintaining long-term viability of eastern oysters (Crassostrea virginica) in Galveston Bay, Texas’s largest estuary, is fundamental to the ecological health and economic success of the region. Galveston Bay oysters support an important wild fishery, but historical overfishing and hurricane impacts have necessitated extensive restoration efforts. A nascent oyster aquaculture industry has the potential to increase sustainability and profits for Texans. However, there are important risks, such as from dermo disease due to the pathogen Perkinsus marinus. Dermo has caused significant mortality of wild and cultured oysters in the Gulf and elsewhere. It can also reduce market value in live but infected oysters through tissue emaciation resulting from disease. While past data reveal its presence, no dermo monitoring has been done in recent years in Galveston Bay. The emerging aquaculture industry will need data to assess spatial aspects of disease risk, and managers will need to understand how wild and farm-based populations interact to enhance or reduce disease prevalence. The project currently underway in our lab will determine the current distribution of P. marinus infections (including prevalence and intensity) in Galveston Bay, and quantify the disease pressure coming from wild reefs that could impact farmed oysters. 

The REU intern will have an opportunity to assist with the project by helping with collections, microscopy, and analyzing historical data. Alternative projects are also possible, including short-term experiments to determine the effects of dermo on hurricane resilience in oyster populations.

Marine Sciences

Reconstructing Droughts, Rainfall, and Hurricanes from Blue Hole Sediment

with Pete van Hengstum

Changing patterns of rainfall and hurricane activity in the North Atlantic Ocean represent significant challenges that coastal populations currently face on our current warming planet. Problematically, there is considerable uncertainty in the models that forecast the how these changes will be geographically expressed in the coming decades. In the tropical North Atlantic Ocean, The Bahamas is a key region to evaluate how the North Atlantic Subtropical High impacted rainfall and hurricane activity over thousands of years, and where clues are preserved in the sediment of Bahamas blue holes.

In the laboratory, the REU student will learn and apply basic methods in sedimentology, micropaleontology, and geochemistry by using sediment cores that were previously collected from blue holes in The Bahamas to evaluate long term changes in rainfall and climate.

Measuring, Mapping, and Managing Flood Risk

with Sam Brody and Wesley Highfield

Objectives involve developing novel ways to measure, map, and communicate flood impacts as a guide for local communities seeking to better prepare for and reduce the adverse impacts of future storm events. The project will produce maps and other visuals that paint a more complete picture of flood risk by integrating multiple data sources and models. These data include advanced hydraulic models, insurance- and aid-based flood payouts, crowd sourced data, socioeconomic characteristics, and survey responses. Maps will be shared with local stakeholders to obtain feedback on how to refine our products and make them most effective in helping localities prepare, mitigate, and recover from flood events. This project integrates advanced risk modeling and community engagement to create new mapping tools that expand stakeholders' capacity to mitigate flood risk. We will integrate two different types of modeling methods to estimate flood risk: physics-based models and machine learning algorithms. Physics-based models are widely used by scientists and engineers to simulate where flood waters pool or move and how long it takes for water to recede. In this study, we will utilize advanced hydraulic models to better capture flooding in areas in and outside of the conventional floodplains. The output from the models will provide estimates of the likelihood that a given location will flood based on the design storms. An emerging approach for flood risk prediction are machine learning (ML) algorithms which quantify the interaction of factors that influence flood risk.

The REU student will learn about and participate in multiple aspects of the flood project, but focus on measuring aspects of development, the loss of natural functions like wetlands, and quantitatively understand how these features affect the degree and spatial concentration of flood loss on the upper coast of Texas. Activities include working with Geographic Information Systems (GIS), learning about machine learning and AI modeling techniques, producing maps and visuals of outputs, and participating in online meetings with experts, state officials, and community members. The student will contribute to multiple deliverables, but also craft their own related project and present their results to the project team and funding organizations.

Building Flood Resilience in the Rio Grande Valley, Texas

with Ashley Ross

Located along the US-Mexico border in the southeastern corner of the state of Texas, the Rio Grande Valley (RGV), has long experienced major flooding due to its low-lying lands and proximity to the Gulf of Mexico. The flooding problem is rooted in a number of issues across the built, natural, and social environments. Despite multiple mitigation efforts by local authorities, the flooding problem persists. Because of future climate variability, flooding events like these are more likely and will continue to present challenges. A lack of a thorough resilience plan and an integrative decision support system to cope with natural and anthropogenic hazards, coupled with insufficient resources, have made the area more vulnerable, particularly to consecutive disasters. This study holistically approaches the flooding problem through convergence research that brings together community stakeholders and an interdisciplinary research team with the objective to develop a resilience roadmap focused on viable adaptation strategies. To hone research gaps and questions, the study will focus on one critical hot spot of flooding that represents the cumulation of all the aforementioned complex issues - the City of La Feria and the surrounding rural area in Cameron County.

Microplastics in Coastal Ecosystems

with Karl Kaiser

Plastics underpin modern human life, and its accumulation in natural environments poses a significant threat to biological organisms and foodwebs. Microplastics (<5 mm) represent the majority (>70%) of plastic emissions, and they can also act as important carriers of pollutants. Rivers and estuaries ultimately control the delivery of microplastics to the ocean, acting both as “traps” or conduits. Their function and efficiency as microplastic delivery systems to the oceans is linked to geomorphological and hydrodynamic characteristics and varies with flow regimes. By 2100, more than 50% of the world’s population will live within close proximity of the coast accelerating microplastic pollution and pressures on water quality and ecosystem services. These regions also experience sea-level rise and storm activity, which is predicted to increase in future climate scenarios. As a result, there is a critical need to better define mass fluxes and transport pathways of microplastics in coastal estuaries and bays. Project activities provide more information on microplastics flow in a coastal estuary during normal conditions and storm disturbance.

Work includes the collection of samples across two estuaries in Texas, sample work-up in a clean lab environment, and the analysis of plastics through pyrolysis gas chromatography with mass detection. Analysis data will be integrated in existing hydrodynamic models to study the movement of microplastics in Galveston and Matagorda Bay.

Determining Storm-Driven Salt Marsh Resilience along the Texas Coast

with Timothy Dellapenna

Tidal marshes are simultaneously among the most valuable and vulnerable ecosystems on Earth. Marshes support estuarine and marine food webs, provide critical habitat for endangered species, improve water quality, protect coasts from storms, and sequester carbon. Yet, more than half of the world’s coastal marshes have been lost due to direct land use change, wave erosion, and sea-level rise. Although there is an emerging body of literature on the devastating effects of tropical storms, it remains unclear how resilient salt marshes are to tropical storms and whether they can survive multiple tropical storms without collapsing. Therefore, understanding and mitigating the effects of tropical storms on marshes represents an important issue for marine conservation. To address this topic, a pilot study of a typical upper Texas marsh, along Follets Island will be selected. Follets Island, located just to the southwest of Galveston Island was devastated by Hurricane Ike (2008) and with 14 years of recovery, the timing is ideal to assess both the impact and recovery of the system using novel approaches only recently available (e.g., drone-based elevations and 239+240Pu geochronology).

The goal of this pilot project is to use novel remote sensing techniques and sediment coring to determine how tropical storms influence marsh erosion and evaluate their effectiveness at a test site on a Follet’s Island salt marsh. Follet’s Island is an ideal study site as it was dramatically impacted by Hurricane Ike (2008) and LiDAR data (aerial swath elevation) is available from the Texas General Land Office from both before and after Ike. This work will begin with an assessment of the existing data as well as a detailed examination of the historical salt marsh erosion processes by using historical maps, other archived LiDAR surveys (swath elevation) and aerial images to determine the position of the historical marsh-water boundary. We will also collect sediment cores to measure sediment physical properties as well as vertical marsh accretion rates (using 239+240Pu geochronology). Additionally, we will survey the elevation of the marsh surface and the marsh-water boundary by using RTK GPS and conducting UAV flights (drones) to determine how the marsh has since the last LiDAR surveys. Finally, trends in marsh erosion will be compared to local wind and wave data to understand the effects of storms on salt marshes.

Maritime Business Administration

A Social and Economic Impact Assessment of Potential Oil Spill in the Galveston Bay Area

with Mawuli Afenyo and Livingston Ceasar

Natural and man-made disasters often pose risks to shipping businesses. Shipping is susceptible to uncertainties and events such as typhoons, terror attacks, hurricanes, and oil spills. The possibility of marine oil/chemical spills occurring along key shipping lanes such as the Galveston Bay may be low probability but with high consequence. Despite advancement in technology, and improved regulations the consequences are still high. Whereas regulations are designed to forestall the occurrence of these oil spills, they do not stop them from happening. Also, technologies can only work to an extent but inadequate in the face of new and varying risks and changing climate. New, sustainable, and proactive approaches to addressing the oil/chemical spill problem is thus needed. Objectives of the study are therefore: i) Understand the factors that may contribute to potential spillage of oil/chemical cargo spill in the Galveston Bay; ii) Develop a probabilistic and consequence model, referred to as the Socio-Economic Impact Model (SIM) for oil/chemical spill in the Galveston Bay. iii) Present a model that helps quantify in dollar terms, the economic and social consequences when a spillage occurs in the Galveston Bay enclave and by extension the Gulf of Mexico. iv) Develop transferrable methodological tools, guidelines, frameworks to support decision-making.

The research would use both qualitative and quantitative approach. The Bayesian theory for conditional probabilities will be used. We will develop a survey for community leaders, researchers, scientist, ship captains, insurance brokers, ship operators and owners, oil spill response entities and the Coast Guard that includes constructed scales for factors that could be impacted in the event of an oil spill. We will also conduct expert interviews to elicit values and risks from oil spill management experts (e.g., the Coast Guard, EPA, marine insurance brokers, and shipping companies). The qualitative data collection will enable us to build the framework for the model, while the quantitative data will enable us parametrized it. Objective (i) will be achieved through a data collection exercise. The investigation team members will travel to relevant places in selected towns and communities in the Galveston Bay and contact other appropriate stakeholders (e.g., marine insurers, ship operators, suppliers to communities) or by other means like phone, zoom, email etc. -Objective (ii) will be achieved through developing (SIM). Through undertaking the activities under Objective (i), we will develop the model to ensure that its attributes fit perfectly into the Galveston Bay context. -Objective (iii) will be achieved by the following: 1) transform (SIM), into an applicable, interactive tool which could be developed into a software in the future. The tool would be validated through stakeholder workshops (In-person/ online, depending on what may be permissible at the time of validation).

Marine Engineering Technology

A Machine Learning Approach for Cyber Attack Detection in a Marine Shipboard Power System

with Irfan Khan

Modern shipboard power systems are heavily dependent on Information and Communication Technology, and this dependency makes them prone to cyber-attacks. The occurrence of a cyber-attack has increased in recent years resulting in substantial damage to marine power systems, leading to disruption of sustainable marine shipping. For a reliable and stable operation, cyber protection, control, and detection techniques are becoming essential. This research project will focus on the automatic identification of both known and unknown cyber-attacks in marine shipboard power systems using Machine Learning.

Automated detection of cyberattacks with high accuracy is a challenge. To address this, we plan to propose a two-layer hierarchical machine learning model having an accuracy of more than 95% to improve the detection of cyberattacks. The first layer of the model will be used to distinguish between the two modes of operation – normal state or cyberattack. The second layer will be used to classify the state into different types of cyberattacks. The layered approach provides an opportunity for the model to focus its training on the targeted task of the layer, resulting in improvement in model accuracy. To validate the effectiveness of the proposed model, we will compare its performance against other recent cyber attack detection models proposed in the literature.