Our Students and Their Research

The Ce anomaly has been used for years as a paleoredox probe when analyzing ocean sediments, but the specifics of Ce’s redox chemistry have not been well characterized. Since Ce’s redox pathways are not well defined, its use as a proxy is questionable. The goal of this research is to gain a clearer understanding of Ce(III)’s oxidation while in complex with the organic ligand, desferrioxamine B (DFOB). This was achieved using spectrophotometry to gain kinetic data of Ce(IV)DFOB’s formation. Spectrophotometry was also used to identify one or more reductants that can slow the oxidation enough to define the Ce(III)DFOB complex through titration. Ascorbic acid was identified as a reductant that could not only slow, but also reverse the oxidation, and when used for titrations of Ce and DFOB, stability constants (logβ values) of 5.23, 8.44 and 11.31 were measured for the Ce(III)DFOB complex. These measurements are critical for describing the kinetics and mechanisms for Ce(III)DFOB’s oxidation to Ce(IV)DFOB. Curve fitting to kinetic data of Ce(IV)DFOB’s growth also showed a pH dependency in the reaction. Understanding more about Ce’s redox chemistry in this complex should affect how its presence in sediment is interpreted since this research indicates that Ce’s oxidation is more complicated than what it is currently assumed to be. Experimentation on tin was also performed as a comparison to Ce’s redox chemistry since Sn has some similarities in that it can also be oxidized to a valence of IV.

Calvert County, Maryland is a peninsula of land bounded by the Patuxent River on one side and by the mainstem of the Chesapeake Bay on the other. Its shores are punctuated by shallow, tidal estuaries referred to locally as tidal creeks. Over the past decade, a monitoring program has studied eleven of these creeks and found symptoms of eutrophication affecting water quality. In the Chesapeake Bay, eutrophication is a complex phenomenon but is associated with increased nitrogen loading caused by land use change and septic system leakage. Our study set out to determine the land use makeup of these eleven watersheds, and to calculate the land use and septic inputs of nitrogen to these watersheds. We used land use raster data from 2013-14 to determine the land use makeup by sector, and downscaled from the CAST model to determine nitrogen inputs. Overall, natural land occupies 65% of the combined area of the watersheds and is responsible for 15% of nitrogen load; the respective figures for developed land are 22% and 35%; for agricultural land, 10% and 27%; and for open water, 3% and 4%. Septics contribute 19% of nitrogen load. Average nitrogen yields are 13.15 lbs/ac/yr from agricultural land, 7.65 lbs/acre/year from developed land, 1.12 lbs/acre/year from natural land, and 6.71 lbs/acre/year from open water. We conclude that nitrogen loads are driven by anthropogenic activities. Reducing atmospheric nitrogen emissions, protecting and restoring natural land, altering dominant patterns of developed land use, reducing yields from agriculture, and upgrading septic systems are all possible steps to decrease nitrogen load and potentially ameliorate eutrophication in these creeks.

Recent reports suggest that several green turtle (Chelonia mydas) subpopulations may be recovering after decades of decline. However, these reports are difficult to verify. The current practice of identifying individuals, determining their genetic lineage, and assigning them to a subpopulation requires a large team of scientists capturing individuals to obtain DNA samples, and analyzing specific genetic markers. While DNA sampling is a proven technique, it is time-consuming and expensive, therefore the sample size is typically small and may not be representative of the whole subpopulation. Recent studies have found a relationship between the genetic markers and the shape of an individual’s carapace, suggesting that individuals can be assigned to subpopulations by careful analysis of carapace images. In this study, this technique was further improved by using an image segmentation model and computer vision to automatically isolate the carapace from the rest of an image and prepare it for further carapace analysis. This significantly decreased the time needed to analyze a large collection of images, making the technique a practical alternative to DNA sampling. This technique can be used to distinguish among green turtle subpopulations and verify the recovery of populations.

Marine heatwaves (MHWs) are extreme ocean temperatures lasting for an extended period of time, and they can be detected from ocean temperatures. MHWs negatively impact the survival and adaptation of many aquatic species and ecosystems. MHW research has mainly been conducted on a global scale, and they were found to be increasing in intensity, frequency, and duration over time as a result of climate change. However, the effects of MHWs on coastal waters are largely unknown. This research focuses on the Chesapeake Bay because it is located next to the Northwest Atlantic Ocean and contains many marine species that are sensitive to extreme temperatures. Historical ocean surface temperature data was collected from buoys and used to detect MHWs and quantify their intensities, frequencies, and durations in four representative regions from 1984 to 2020 at the 95th percentile threshold. The regions share many similarities and differences among the detected MHWs and their properties. The maximum intensity and duration do not appear to be increasing over time and instead fluctuate. The frequency of MHWs and days above the threshold in the Upper Bay and the Atlantic Ocean are increasing over time. These results shed lights on understanding the effects of historical MHWs on marine ecosystems and predicting future MHW events. Additionally, having this information on past and potential future events also provides insight for fishery harvesting and bay restoration.

The earth’s natural climate variability fluctuates on a large range of time scales. The impact of anthropogenic greenhouse gas emissions on specific patterns of climate variability is a subject of current research. The North Atlantic Oscillation (NAO) is one such pattern of climate variability. The NAO is the dominant wintertime atmospheric pressure pattern in the Atlantic region and is hypothesized to be connected to other climate system features such as Atlantic Multidecadal Variability (AMV) or Atlantic Meridional Overturning Circulation (AMOC) which describe sea surface temperature changes, and deep ocean circulation changes respectively. Measurements of the NAO extend into the 1800s, and many reconstructions using paleoclimate proxy data have been created to extend that instrumental record back in time. To assess the fidelity of these reconstructions, this study analyzed existing reconstructions using a spearman's correlation, coefficient of efficiency, and a root mean squared error. The results indicated that the Cook et al. (2019), Luterbacher et al. (2001), and Michel et al. (2020) performed better than other NAO reconstructions when compared to three different indices of historical NAO patterns. The exact ranking of each reconstruction depended on the exact metric and comparison dataset, reflecting the different calibration targets of individual studies as well as the strengths and weaknesses of different approaches.

Living shorelines are native marsh plantings that control coastal erosion and provide coastal resilience to sea level rise (SLR) by migrating upland during SLR. Living shorelines require breakwaters to dissipate wave energy to prevent marsh boundary erosion and facilitate sedimentation. Currently, breakwaters are built with riprap which cannot adapt to SLR. Alternatively, oyster castles are modular cinder blocks for building breakwaters that initiate the development of oyster reefs which can adapt to SLR by growing equivalently with rising sea level. This research used Delft3D to model the effects of SLR on coastal morphology with 3 domain configurations: 1) only marsh, 2) traditional living shorelines with riprap and 3) living shorelines with oyster castles. The model objectives included comparing marsh deposition between oyster castle and riprap breakwaters and determining the important parameters affecting living shoreline evolution. All domains were 2 km wide by 1 km in length to mimic coastal bay conditions. Simulations were set for a temporal scale of 150 days and increased by a morphological factor of 150 to project for 60 years. Experimental parameters included vegetation density, nearshore slope, SLR, and suspended sediment concentration (SSC). Oyster castles facilitated greater marsh deposition than riprap at +8.9 mm under current sea level, +3.5 mm with SLR of 0.4 m, and +3.3 mm with SLR of 0.8 m. Increased nearshore slope and higher SSC both increased sediment deposition in the marsh. Increased sea level and higher marsh density decreased maximum bed shear stress. Therefore, coastal restoration efforts should strive to integrate oyster castles into living shorelines and increase marsh density to enhance sediment deposition and coastal resilience. These modelling efforts focused on quantifying the impacts of coastal processes on marsh dynamics and building the framework of a model for use in future research.

Emergent harmful algal bloom (HAB) species and phytoplankton communities are understudied in nearshore/offshore water, which is problematic given that climate change is causing major ecological and physical shifts along the Mid-Atlantic Bight. Climate change can facilitate better conditions for algae to thrive therefore leading to frequent blooms. This project aimed to see if higher temperatures led to higher HAB abundance because warmer climates promote algal growth. By exploring data from research cruises conducted in 2018 and 2019 off the Delmarva peninsula, it was found that temperature did have a significant impact on phytoplankton community distribution, but a longer-term data set is necessary to investigate whether increased temperatures lead to greater HAB abundance. Forms of nutrients (nitrogen and phosphorus), dissolved oxygen levels, and salinity were also major drivers behind community composition. These variables are known to play a role in bloom formation and should be explored more in future research as their role in terms of ecosystem importance could be heightened with changing climate. As environmental conditions change drastically, this could potentially exacerbate the harmful phytoplankton species that were identified within the coastal samples. There were shifts seen in community diversity during a period of sampling when hurricane activity was present. These types of weather patterns and their impacts on biodiversity warrant further investigation and analysis. This project helped to address the composition of phytoplankton communities in an area where there is a lack of data. It can help direct future management practices. Temperature, nutrients, dissolved oxygen, and salinity were all significant indicators of population distribution and should continue to be a major focus behind conservation and restoration efforts.

Large-scale climate events such as Climate change or ENSO (El Nino Southern Oscillation) has led to a shift in terrestrial plant life (Körner and Basler, 2010). These same large-scale events will have a resulting impact on the phytoplankton phenology of the global ocean. Phytoplankton serve as the primary energy source for virtually all pelagic marine ecosystems. Therefore, any changes in Phytoplankton Bloom events will be felt upwards through higher trophic levels and may result in critical changes to marine fisheries. Here we have defined methods to observe changes in phytoplankton phenology in the North Atlantic region. We have expanded upon the work of Racault et al. (2012) and added about a decade worth of daily chlorophyll data to our analyses. In the North Atlantic, we were able to capture an earlier bloom initiation of about six days per decade and a longer bloom duration of about six days. This is the first step in summarizing all significant trends over the years 1998-2020.

In the Chesapeake Bay, microorganisms including bacteria and single celled eukaryotes live both free in the water column, and on various-sized particles. Through their metabolic processes, these organisms shape the chemistry of the Chesapeake Bay and surrounding natural environments. We explore the relationships between microbial cells and the particles they live on, hypothesizing that the microbial abundance is proportional to the mass of the particles on which the microorganisms live. We compare two different methods for quantifying the number of microbial cells associated with different locations and particle sizes. In the first method, microbial abundances are quantified microscopically, and the second they are calculated from DNA sequencing data. This project aims to gain a better understanding of the relationship between q microbial abundance and particle characteristics. This project can potentially be used alongside other research to see how the Bay will react in conjunction with nutrients and toxins based on how many and what sizes of microbes are present in the Bay.

Neomysis americana is an abundant, omnivorous mysid in Chesapeake Bay and its tributaries that serves as an important forage taxon for many adult and juvenile fisheries species. Neomysis americana displays diel vertical migration, aggregating near the bottom during the day and expanding their distribution to include near-surface waters at night; however, it is unclear what effect summertime hypoxia has on their ability to access deep water habitats. The objective of this study was to quantify and compare the relative contributions of benthic and pelagic trophic pathways to the assimilated diet of N. americana between two Chesapeake Bay tributaries (the Patuxent and Choptank rivers) that typically differ in summertime dissolved oxygen conditions. Specimens of N. americana and putative trophic endmembers were collected at 3 locations in each river during the summer months of 2018 and 2019 for stable isotope analysis (δ13C, δ15N). Mixing models were used to estimate the contribution of benthic and pelagic prey sources and isotope niche area (proxy for trophic niche) for N. americana in each tributary. The contribution of benthic pathways to diet and the niche area of mysids were compared to local water quality to determine if hypoxia has an identifiable effect on benthic-pelagic coupling or realized niche area. Results showed no significant difference in the isotopic niche areas of N. americana or trophic endmembers between tributaries, though pelagic connectivity was greater in the Patuxent River. Further, no relationship was found between bottom water dissolved oxygen or river flow and the isotope-based trophic metrics. These findings suggest a greater isotopic variability of mysid diet sources in the Patuxent River and a difference in the structural food web components supporting mysids between rivers.

Methane exists in estuarine sediment in high concentrations due to microbial activity, lack of oxygen, and organic matter availability, but is understudied. Estuarine methane is not a significant source of emissions to the atmosphere, as most of the methane remains in the aquatic environment. Here it can be transferred to the water column, depending on sulfate availability, and oxidized aerobically into carbon dioxide. An influx of CO₂ can lower the pH, causing acidification, which can be harmful to aquatic ecosystems. The primary goals of this study were to quantify the sedimentary methane flux across the salinity gradient of the Patuxent River (PAX) and to determine the impact of the methane on the water column pH. Modeling programs were used to determine flux and subsequent pH changes. Methane flux to the water column of the PAX does not appear to be correlated with the salinity gradient. Further study into the many factors that impact flux and the specific makeup of each station is needed to solidify this conclusion. Current methane concentrations are not impacting the pH in the PAX, but there is potential for heightened methane flux to occur as anthropogenic climate change worsens, leading to the lowering of pH in the water column. When greater methane concentrations were modeled, the pH was lowered by up to 1 unit. Further study of the climate dependent factors that impact methane flux is necessary to make a definite conclusion, however, it’s clear that methane has the potential to reduce pH in marine systems if concentrations increase.

With climate change increasing the intensity and frequency of major precipitation events in and around the Chesapeake Bay, resulting changes to freshwater flow have the capability to cause large-scale alterations to natural salinity patterns in the Bay which could negatively impact important shellfish aquaculture operations. However, with most modeling studies focused on the long-term effects of environmental issues like climate change, there are relatively few studies that have attempted to provide accurate information regarding the short-term impacts that might result from these issues. Therefore, the goal of this project was to develop a short, 1–7-day salinity forecast for the Chesapeake Bay. The following three methods were used to develop forecasts: STL (seasonal trend decomposition using LOESS), non-seasonal autoregressive integrated moving average (ARIMA) model with Fourier series for seasonal regression, and LSTM (long short-term memory) neural network. Comparing the predicted salinity with the observed values in the testing set allowed us to quantify the error and therefore, the efficacy of each model. Our results showed that the LSTM model produced the most accurate 1-day forecast, with only 0.137 to 0.185 ppt of error. For 2-7-day forecasts, on average, the ARIMA-Fourier model was most accurate, although the STL model was fairly close in accuracy. Future studies attempting to improve this salinity forecast, or create a forecast for another water quality parameter, should focus on optimizing a machine learning method such as LSTM, as it shows the most promise of the 3 methods.

Mixotrophic phytoplankton, capable of both photoautotrophy and heterotrophy, have recently been gaining scientific interest. The type of heterotrophy mixotrophs perform, either phagotrophy (in which species eat other species) or osmotrophy (uptake of nutrients from water), depend on the species type. Mixotrophs complicate the way scientists have historically understood marine trophic structure, as they increase the trophic transfer to higher trophic levels than what would be expected based on rigid autotrophic and heterotrophic feeding modes. Therefore, simplification and miscategorization of mixotrophs as strict autotrophs and heterotrophs neglects the significance these species have throughout the entire food web, which is relevant to policy makers and coastal communities, as well as for fisheries management. Despite being widespread, especially in estuarine ecosystems like the Chesapeake Bay, mixotrophs remain understudied and overlooked. We utilized the R package phyloseq and phytoplankton and water quality data from the Chesapeake Bay Monitoring program to analyze the spatial distribution of phytoplankton in the Chesapeake Bay and the spatiotemporal patterns associated with phytoplankton assemblages. We determined there was a significant difference between the sampling done at the MSU/PEARL lab in Maryland and the ODU/PEL lab in Virginia, which both contributed to the Chesapeake Bay phytoplankton dataset. We identified 121 mixotrophic taxa based on a literature review; this will ultimately also be incorporated into assessment of phytoplankton community composition. We observed that there is a clear salinity gradient across surface water samples, which implies it is a major driver of phytoplankton assemblages. Future work will build on this by incorporating other parameters, namely riverine flow rates and season. Understanding the major drivers of assemblages in the Chesapeake Bay in both surface waters and near pycnocline depths will inform a more comprehensive assessment and investigation of what environmental variables impact mixotroph abundance. This project also demonstrates the capacity for utilizing phyloseq as an efficient way to process and analyze phytoplankton data.

The eastern oyster (Crassostrea virginica) is an important shellfish species in the Chesapeake Bay. Restoration projects have the goal of reviving the population to revive the keystone organism’s ability to filter the water column, create habitat, and provide commercial harvest. Oyster aquaculture is also expanding into diverse regions of the estuary. Numerical models provide a tool to predict how oyster growth is affected by changes in environmental conditions (e.g., temperature, food availability) and identify ideal locations for restoration and aquaculture. I adapted an existing oyster growth model, ShellSIM, to analyze the effect of environmental factors such as salinity and chlorophyll-a concentrations (proxy for food availability) on oyster growth to determine the regions better suited for restoration projects and aquaculture operations. Applications of the model for three-year simulations at 179 locations in Chesapeake Bay showed greatest growth in shell length and tissue weight in the Maryland Coastal Bays, northern mesohaline Bay and in regions of the James and York Rivers. These locations varied in ranges of salinity and had higher chlorophyll-a concentrations compared to the rest of the Bay. While these regions experienced the greatest simulated growth in C. virginica, further analysis will need to be done to quantify the effect of calcium carbonate variability and food quality (not simply quantity) on oyster growth in the Chesapeake Bay.

Northwest Atlantic leatherback turtles, (Dermochelys coriacea), have been in decline and are listed as critically endangered. They are particularly vulnerable to interactions with human activities such as vessel traffic during the internesting period (between nesting bouts), when females are concentrated in nearshore habitats. In our study we used spatial statistics, including a switching state-space model (SSM) and home-range estimates to determine the distribution of the internesting leatherback population off Limón Province, Costa Rica. To determine if there was any impact on the turtle behavior, we then analyzed vessel traffic exposure and speed and compared it to turtle movement characteristics; speed, turning angle, and absolute angle. We found that turtles were concentrated off the nesting beach and their turning angles were significantly associated with vessel exposure and speed. Our results can help to inform managers and future studies about the impact vessel traffic may have on this endangered species and encourage potential mitigation, such as regulations on vessel speed.

Passive acoustic monitoring (PAM) provides an opportunity to study the population ecology of bottlenose dolphins (Tursiops truncatus) on a large geographic scale, but as technology advances, the greatest challenge researchers face is developing efficient methods for processing large volumes of PAM data. The creation of an automatic identification algorithm using animal calls, such as signature whistles, is a solution to the costly and impractical manual processing of large data volumes. Signature whistles are a resource for the development of an auto-ID algorithm because they contain identifying information about the dolphins that are present. Bottlenose dolphins are protected under the Marine Mammal Protection Act, which makes developing effective methods of population monitoring a top priority for resource managers. We created a deep convolutional neural network model capable of recognizing the signature whistles of bottlenose dolphins within PAM data. We developed an algorithm to automate the processing of PAM data including loading, windowing, and Fourier transformation. The model was trained on a balanced data set of 170 mel-spectrograms with 55.7% prediction accuracy. When applied to field data, the model was 33.3% accurate in discriminating between dolphin signature whistles and other calls and noise. The prediction accuracy indicates a deep learning network as a promising method of signature whistle identification. Our future work will focus on improving the performance of the method and adapting it for monitoring dolphin presence and population size based solely on acoustic data. The auto-ID algorithm will enhance the understanding of dolphin spatio-temporal distribution, migration, and social structure, and help to inform effective management and conservation policies.

My name is Cristina Alexandra Araújo, and I am a biology major in Antillean Adventist University in Puerto Rico. I have had the opportunity to volunteer in a fisheries laboratory, was an intern studying triple-negative breast cancer, and am currently an intern studying coral reefs of the Pacific Ocean. When I graduate with my bachelor’s degree, I would like to continue my studies and complete a PhD in biomedical sciences. For my career, I would like to integrate marine biology and biomedical sciences to find novel cures for rare diseases using powerful molecular compounds found in marine organisms.

My name is Ninoshka M. Betancourt Gómez, and I am a rising senior pursuing a bachelor’s degree in biology at Universidad Ana G. Mendez, Cupey campus in Puerto Rico. My goal is to continue my studies in marine science or in veterinary science, focusing on wildlife. As for myself, I love being outdoors and surrounded by nature and the animals around it, as I am an animal lover. At the same time, I am very open minded and dedicated.I enjoy working in groups, because it gives me opportunities to get to know new people and learn more about their cultures.

My name is Nikki Jackson, I use they/them/theirs pronouns, and I'm a junior/senior at Agnes Scott College. I'm a biology major with academic interests in philosophy, Africana studies, and environmental and sustainability studies. Right now for possible careers I foresee myself doing advocacy for underrepresented groups in science, doing ecological studies as they relate to environmental and climate change, and teaching in an undergraduate and community setting.

My name is Jon Leathers, and I am majoring in environmental science and technology at University of Maryland, College Park. As a lifelong Maryland resident, I am passionate about learning about the ecological challenges facing our state and Chesapeake Bay as a whole. In working toward a better understanding of our regional challenges, I am currently an intern for the Arundel Rivers Federation—an organization that uses science and community outreach to restore the health of the South, West, and Rhode rivers. After graduation I plan to work in the restoration field before earning an advanced degree to teach at the college level.