Maryland Sea Grant Research Experiences for Undergraduates Final Student Papers Summer 2013

Edited by Mike Allen and Jenna Clark

Sponsored by Maryland Sea Grant Maryland Sea Grant College Publication number UM-SG-TS-2014-01

Copies of this publication are available from:

Maryland Sea Grant College Program 4321 Hartwick Road, Suite 300 College Park, MD 20740

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For more information, visit the Maryland Sea Grant web site: http://www.mdsg.umd.edu/

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This publication, produced by the Maryland Sea Grant College Program, is a compilation of the final REU student fellow papers produced for summer 2013.

This report was prepared under award NA10OAR4170072 from Maryland Sea Grant, National Oceanic and Atmospheric Administration, U.S. Department of Commerce. The statements, findings, conclusions and recommendations are those of the author(s) and do not necessarily reflect the views of Maryland Sea Grant, the National Oceanic and Atmospheric Administration or U.S. Department of Commerce. Contents

Chesapeake Biological Laboratory 4

Measured Responses of Gray Tree Frog (Hyla versicolor) Tadpoles to Selenomethionine and Selenium Dioxide ...... 5 Chloe Anderson, REU Fellow Mentor: Dr. Christopher Rowe, Associate Professor

The Effects of Nutrients on the Biomechanics of Spartina alterniflora on Poplar Island...... 17 Jade Bowins, REU Fellow Mentor: Dr. Lora Harris, Assistant Professor

The Microbial Effects of the Addition of Oil to the Anoxic Layers of Benthic Sediments from the Chesapeake Bay ...... 30 Gene Patrick Geronimo, REU Fellow Mentor: Dr. Laura Lapham, Assistant Professor

Bioaccumulation of Synthetic Musk Fragrances in Northern Diamondback Terrapins (Malaclemys terrapin terrapin) of Jamaica Bay, New York, USA ...... 55 Lisa McBride, REU Fellow Mentor: Dr. Andrew Heyes, Associate Professor

Modeling of Blue Crab Catchability in Chesapeake Bay Using Winter Dredge Surveys ...... 71 Andrew Mealor, REU Fellow Mentor: Dr. Michael Wilberg, Associate Professor

Calibration of Modern Coral Climate Signals to Ensure Accuracy of Paleoclimate Determinations in Anegada, British Virgin Islands ...... 88 Sean Pearson, REU Fellow Mentor: Dr. K. Halimeda Kilbourne, Research Assistant Professor

Marine Chromophoric Dissolved Organic Matter Distribution in the Atlantic Ocean ...... 99 Sandra Pittelli, REU Fellow Mentor: Dr. Michael Gonsior, Assistant Professor

Levels of PAHs in Marine Biofouling Organisms Attached to Oil Rigs in the Gulf of Mexico 113 Zach Watkins, REU Fellow Mentor: Dr. Carys Mitchelmore, Associate Professor

Quantifying Growth Variation of Juvenile Blue Crab (Callinectes sapidus) using RNA:DNA in Response to Elevated Water Temperature and Nutritional Rations ...... 127 Arthur Williams, REU Fellow Mentor: Dr. Thomas Miller, Director and Professor

1 Horn Point Laboratory 143

Factors Affecting Cyanobacterial Ecology in a Restoration Wetland on Poplar Island, Chesapeake Bay ...... 144 Austin Boardman, REU Fellow Mentor: Dr. Judy O’Neil, Research Assistant Professor

Characterizing Flow at the Susquehanna Flats ...... 164 Angela Cole, REU Fellow Mentor: Dr. Lawrence Sanford, Professor

Wind-induced Lateral Circulation and Mixing in the Chesapeake Bay...... 176 Jenessa Duncombe, REU Fellow Mentor: Dr. William Boicourt, Professor

Effects of N:P Ratio Variation on Feeding of Dinoflagellate K. veneficum on Cryptophyte Rhodomonas sp...... 188 Chrissie Schalkoff, REU Fellow Mentor: Dr. Patricia Glibert, Professor

The Evaluation of Basic Calculations Concerning the Effects of Non-storm Waves on Offshore Sediment at Jefferson-Patterson Park, MD ...... 200 Nick Taylor, REU Fellow Mentor: Dr. Cindy Palinkas, Associate Professor

Hypoxic Impacts on Egg Respiration Rates of the Copepod Acartia tonsa ...... 210 Cristina Villalobos, REU Fellow Mentor: Dr. Jamie Pierson, Research Assistant Professor

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UNIVERSITY OF MARYLAND CENTER FOR ENVIRONMENTAL SCIENCE Chesapeake Biological Laboratory

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Measured Responses of Gray Tree Frog (Hyla versicolor) Tadpoles to Selenomethionine and Selenium Dioxide

Chloe Anderson, REU Fellow Maryland Sea Grant

Dr. Christopher Rowe, Associate Professor Chesapeake Biological Laboratory, University of Maryland Center for Environmental Science

Dr. Andrew Heyes, Research Associate Professor Chesapeake Biological Laboratory, University of Maryland Center for Environmental Science

Abstract

Selenium (Se) is a contaminant of concern in areas affected by coal ash disposal and runoff from seleniferous soils, and may occur in inorganic or organic forms. To quantify the different effects of organic and inorganic selenium, we dosed the food of gray tree frog (Hyla versicolor) tadpoles with selenium dioxide at concentrations of 16.27 and 34.2 ug g-1 Se dw and selenomethionine (SeMet) concentrations of 14.23 and 32.8 ug g-1 Se dw. As tadpoles accumulated Se throughout metamorphosis, we measured biological endpoints including metabolic rate, survival, and growth. A behavioral test was also administered. Chemical analyses on total accumulated Se and mercury (Hg) have yet to be completed. Analysis of food was done via chemical digestion and mass spectrometry. Midpoint respirometry analysis -1 -1 showed that SeMet high tadpoles had significantly higher metabolic rates in µl O2 g min , with a mean of 3.26, than control (mean=2.20, p=0.0024), SeO2 low (mean=2.52, p=0.0337), and SeMet low (mean=2.66, p=0.0176) tadpoles. No further significance was found regarding respirometry, growth, survival, or behavior. However, survival to the end of the study was lowest for SeMet high and SeMet low tadpoles, suggesting organic Se had more toxic effects. SeO2 did not appear to be toxic, but because of the possibility of conversion to an organic form in a natural ecosystem, efforts to restrict release of Se rich wastes are warranted.

Keywords

Selenium, amphibian, coal ash, metabolic rate

Introduction

Aquatic pollution by contaminants such as pesticides, industrial chemicals, heavy metals, and air pollutants has historically received greater attention than pollution by trace elements such as selenium (Se) (Lemly 2004). However, Se has the potential to affect aquatic environments on a much broader scale than many other contaminants, and has been singled out as the trace element of primary concern in some contaminated sites (Hamilton 2004). This is principally because Se is known to bioaccumulate in food chains, and small increases in Se

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concentrations have the potential to be toxic and lethal (Lemly 2004; Thomas et al. 2013). The margin between the essentiality and toxicity of Se is very narrow, with an additional 2.0 µg g-1 in food sources potentially causing detrimental effects to certain fish species (Thomas et al. 2013). Additionally, because Se is a trace element, it does not break down into less toxic counterparts, retaining its toxicity for decades (Lemly 2004). This toxicity has had negative impacts on many forms of wildlife, but much investigation is still needed, especially in regards to amphibians.

2- 2- Selenium enters an ecosystem as either inorganic selenate (SeO4 ) or selenite (SeO3 ) from various sources, including but not limited to agricultural drain water, sewage sludge, fly ash from coal-fired power plants, oil refineries, and mining of phosphates and metal ores (Chapman et al. 2010). Because Se introduction is associated with such varied activities, it has the propensity to be an environmental concern worldwide (Lemly 2004). After its initial introduction, Se most likely follows a biogeochemical cycle similar to that of sulfur (Lockard et al. 2013). Although inorganic Se may not pose health threats at low concentrations, it can be converted to organic forms such as selenomethionine (SeMet) by primary producers as it passes through the food chain, which allows a greater bioaccumulative potential and possibility for trophic transfer (Thomas et al. 2013). Studies as early as 1985 reported SeMet to be more toxic to fish than selenite (Hamilton 2004), but this still has not been thoroughly investigated in amphibians. Figure 1 demonstrates how Se may enter an ecosystem in the inorganic form and later convert to a more hazardous organic form as it moves up the food web during trophic transfer (Chapman et al. 2010).

Although excessive SeMet may not be toxic to primary producers and invertebrates, extensive documentation of its side effects has been reported for mammals, birds, fish, reptiles, and amphibians. For example, SeMet is known to cause problems during fatty acid synthesis in mammals (Mueller et al. 2008). Mortality and reproductive problems were observed in aquatic birds by Ohlendorf et al. (2010) and Hothem and Ohlendorf (1989) in the Kesterson Reservoir, where Se concentrations in aquatic biota were up to 64 times that of the minimum concentration reported to reduce bird reproductive success (Schuler et al. 1990). Fish kills in North Carolina and Texas have been directly attributed to Se release (Hamilton 2004). In a laboratory experiment with adult zebrafish (Danio rerio), mortality was significantly greater for fish fed food with high Se concentrations. Swimming performance was decreased and differences were also seen among metabolic rates, triglycerides, and glycogen (Thomas et al. 2013; Thomas and Janz 2011). In high doses, Se has caused rear limb deformities, decreased survival, and inhibited metamorphosis in frogs (Lockard et al. 2013). Additionally, Rowe et al. (2011) concluded that gray tree frogs (Hyla versicolor) may serve as a vector of Se to terrestrial organisms during trophic transfer. However, specific consequences for amphibians at lower Se concentrations have not been considered in detail.

In addition to investigating the effects of Se on organisms, much research has looked into the interactions between Se and other elements. Interactions between Se and other trace elements are varying, from additive or synergistic to antagonistic (Hamilton 2004). Interactions with mercury (Hg) have been researched more than others, with the consensus that Se has a tendency to ameliorate the effects of Hg in an antagonistic relationship thought to occur through the “formation of metabolically inert mercury selenides” (Chapman et al. 2010). However, this observation often relies on the species tested and involves a threshold Hg concentration, above which the relationship may not hold true (Hamilton 2004). There is also a lack of information in regards to this relationship in amphibians.

Although amphibians seem to be the least investigated class of organisms with respect to Se accumulation and effects, they may be particularly at risk because it is common for them

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to lay their eggs in pools affected by Se contamination (Rowe et al. 1996; Lockard et al. 2013). Larvae are at an even higher risk when they are confined to these pools, with their only food resources containing high concentrations of toxic elements (Rowe et al. 2001). Because of previously mentioned bioaccumulative properties, Se may pass from amphibian tissues to the terrestrial food chain (Chapman et al. 2010).

Two sites polluted by Se in the past and used for various previous studies are the Savannah River Site in South Carolina and the Kesterson Reservoir in California. The Savannah River Site is polluted by coal ash, a by-product of coal and petroleum combustion. As fossil fuel use increases worldwide, a common waste disposal practice continues to be sluicing the ash with water and dumping it into open basins (Raimondo et al. 1998). These basins are used in an attempt to allow the particulate matter and toxic elements to settle and separate from the water. However, elements such as Se can be oxidized, which allows them to remain in the water and acquire the potential to leach as well as accumulate in aquatic organisms (Raimondo et al. 1998). The Kesterson Reservoir was polluted by saline subsurface drain water from surrounding agricultural fields located on seleniferous soils in the western San Joaquin Valley in California (Schuler et al. 1990). In the early 1980s, concentrations in the reservoir were averaging 300 µg l-1, while nearby concentrations were only 2 µg l-1 (Hothem and Ohlendorf 1989).

Lockard et al. (2013) demonstrated that exposure of H. versicolor larvae to SeMet at concentrations of 50.1 and 489.9 µg Se g-1 dw (“dw”= dry weight) can cause rear limb malformations, edema, reduced survival, and failed metamorphosis. However these concentrations exceed those observed in all but the most heavily contaminated sites. By using the gray tree frog (H. versicolor) as a test subject and applying concentrations of Se in the range between those recorded at the Savannah River Site (6-12 µg g-1 dw) (Rowe et al. 2001) and the Kesterson Reservoir (20-332 ug g-1 dw) (Schuler et al. 1990), we were able to further investigate the impacts of Se on the understudied amphibians, including organic vs. inorganic effects, as well as the relationship between Hg and Se, since Hg exists as a background contaminant in food.

Materials and Methods

Food Preparation and Analysis

Tadpole food was prepared by combining 40 g of commercial rabbit chow (Hartz) with 60 g of aquaculture fish based gelatin (Aquatic Ecosystems Inc.), both ground in a commercial blender and dried in a drying oven overnight. The low and high inorganic Se doses were 1 prepared by adding 3.35 and 0.67 mL of SeO2 stock solution (1500 µg mL- ) each to 180 mL of well water, for final measured concentrations of 162.7, later diluted to 16.27, and 34.2 ug g-1 Se dw. The SeMet stock solution was made by adding 0.075 g of solid Se to 20 mL H2O. The low and high organic Se doses were prepared by adding 3.35 and 0.67 mL of SeMet stock solution -1 each to 180 mL H2O, for final concentrations of 142.3, later diluted to 14.23, and 32.8 ug g Se dw (Table 1). Once five 100 g batches of food were made with their respective doses of Se, subsamples were removed and freeze dried overnight for chemical analysis. The control food received only 180 mL of H2O, and no additional Se in either an organic or inorganic form, for a final (background) concentration of 3.6 ug g-1 Se dw.

For chemical analysis, approximately 0.2 g of each food dose were weighed, mixed with 4 mL of 45% nitric acid, and digested in vials using an EZ Microwave Digestion System. Weights were taken before and after the dilution. Samples were then run using an ICP Mass

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Spectrometer for Se concentrations. Concentrations from the ICPMS were converted from ug l-1 to ug g-1. Food samples were also analyzed for Hg content with a Tekran Model 2600 CVAFS Mercury Analysis System. The initial concentration of Hg in the food samples was 42 ± 3 ng g1dw. Replicates were run for both Se and Hg analyses.

Tadpole Collection and Feeding Regimens

Over 300 gray tree frog tadpoles were collected from a small private pond with no known history of contamination in Saint Mary’s County, MD. Tadpoles were transported to the Chesapeake Biological Laboratory in Solomons, MD and their water was aerated before transfer into tubs. Three hundred tadpoles of similar size were divided into twenty 6.6 L tubs (15 tadpoles per tub), filled with approximately 5 L of well water. Tubs were divided into groups of 5 for each dosage, with 4 replicates for each dose as well as the control. Food dosages were assigned to certain tubs randomly. Food of specific doses (Control, SeO2 low, SeO2 high, SeMet low, SeMet high) was added every 2 days ad libitum to corresponding tubs. Tubs were cleaned and water replaced weekly. The experiment will continue until all animals have achieved metamorphosis, although results from only the first two months are reported here.

Measured Biological Endpoints

Whole body bioaccumulation of Se and Hg at two time points will be measured in two randomly selected tadpoles from each tub. These tadpoles have been collected and preserved and currently await chemical analysis. Analysis for Se and Hg will be completed through ICP Mass Spectrometry. Tadpoles that complete metamorphosis will also be digested and analyzed for Se and Hg as frogs. Time points were halfway through the experiment and at the end. Similarly, metabolic rate was measured at two time points, days 23 and 42 of exposure, following the methods outlined by Rowe et al. (1998). A respirometer (Mirco-Oxymax, Columbus Instruments, Columbus, Ohio) was used to determine O2 consumed (Rowe et al. 1998). Growth was measured by calculating the average tadpole weight before feeding began and again near the end of the experiment. Survival was also determined as a proportion of living tadpoles at the end of the experiment. To determine if Se affects tadpole neurological systems, a behavioral endpoint was included, based off modified methods from Raimondo et al. (1998). Swimming observations and response tests were conducted in a plastic trough with dimensions of 96.52 cm long x 2.54 cm wide x 1.905 cm deep. It was filled approximately 1 cm deep with well water. Swimming observations were made by calculating the total distance swum over one minute after initial placement into the trough. Tadpoles were given an additional minute to acclimate and then were prodded with a small metal prod three times. The distances and time it took to swim these distances were recorded after each prod. Each assay was filmed and later reviewed to ensure more accurate data collection. Three tadpoles from each tub were tested for a total of 12 tadpoles per treatment, with the exception of SeMet high (in which high mortality was observed) for which only 7 tadpoles were tested.

Statistical Analyses ANOVA will be used to analyze whole body accumulation of Se at two different time points for the five different treatments. ANOVA will also be used to analyze the relationship between Se and Hg accumulation at two different time points. ANCOVA was used to analyze standard metabolic rate and behavior, with tadpole wet mass being the covariate. Data for tadpole survival were transformed by arcsine square roots prior to analysis by ANOVA. Mass data were also analyzed with ANOVA. All statistics were completed with Minitab Statistical Software.

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Results

Although the analyses for whole body accumulation of Se and Hg have not yet been completed, midpoint respirometry analysis revealed that tadpoles part of the SeMet high treatment had significantly higher metabolic rates. On average, SeMet high tadpoles were found -1 -1 to have a standard metabolic rate (SMR) of 3.264 µl O2 g min , higher than the control (mean=2.2, p=0.0024), SeO2 low (mean=2.52, p=0.0337), and SeMet low (mean=2.657,p=0.0176) tadpoles (Figure 2). The second respirometry test, which excluded SeMet High due to mortality in this treatment, revealed no significant differences, but mass still appeared to influence metabolic rate (Figure 2). Although analysis for survival until the end of the study did not reveal significant differences, tadpoles receiving SeMet high treatment had the lowest survival at the end of the study (Figure 3). Our behavioral test also did not yield significant differences. Growth, measured by mass, did not vary significantly between treatments over time.

Discussion

We expected similar results to those represented in Figure 1 as previous studies have shown increased SMRs as a result of contaminant exposure (Rowe et al. 1998). Other studies have shown varied metabolic responses to contaminant exposure, from no change in O2 consumption to decreased O2 consumption, leading one to believe physiological responses may differ for each species (Rowe et al. 1998). Hyla versicolor tadpoles initially seemed to follow a pattern where higher doses of Se would result in higher metabolic rates (Figure 2). At day 23 of exposure, SeMet high tadpoles had significantly higher SMRs than all other tadpoles, with the exception of the SeMet low treatment. This likely represents elevated maintenance costs as their bodies overcompensate for excess Se. This elevated cost of maintenance must require the tadpoles to reallocate their energy from other activities such as growth, storage, and reproduction (Rowe et al. 1998), limiting the potential for long term survival. Although differences were not significant, it is important to note that tadpoles in treatments SeO2 low, -1 -1 SeO2 high, and SeMet low had slightly elevated O2 consumption g min compared to control tadpoles, further demonstrating the possibility of energy being used to maintain body functions with additional Se present. This result aligns with what is already known about the total mixture of contaminants characteristic of coal ash, and shows that Se, too, is an additional factor when considering the effects of coal ash pollution on the metabolic rates of the organisms present in an ecosystem (Chapman et al. 2010).

A second test of respirometry near the end of the experiment on day 42 revealed no significant results. This may be partially attributed to our removal of SeMet high from statistical tests due to mortality of all tadpoles in two replicates. For control, SeO2 low, SeO2 high, and -1 -1 SeMet low treatments, mean µg O2 g min remained relatively similar. However, the remaining -1 -1 two SeMet high replicates had a mean SMR 1.13 µg O2 g min lower than the mean for SeMet high tadpoles from the first respirometry test three weeks previous. It is possible that the two SeMet high replicates that had complete mortality before the second respirometry test had elevated SMRs before death that increased the mean of the whole treatment. The two SeMet high replicates that survived to the second respirometry test may have initially exhibited lower SMRs than those that died early, more similar to the other four treatments. Indeed, the mean SMR of the two replicates that eventually died out was 3.38 ± 0.234 while the mean SMR of the -1 -1 two replicates that survived to the end of the study was 3.14 ± 0.085 µg O2 g min . However, immediately prior to death, organisms often exhibit substantial reductions in SMRs, not elevations.

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This variability in SeMet high survival also affected statistics performed for survival to the end of the study. The high amount variability (calculated as standard error) in SeMet high data is most likely the reason statistical analyses did not result in significance. While SeMet high mean survival was the lowest, at 0.341± 0.198, no other treatment produced such variability, with the next most variable treatment, SeMet low, having a SE of only ± 0.087. Again, this variability was caused by 100% of the tadpoles from two SeMet high replicates dying while 72.7 and 63.6% survived to the end of the study in the remaining two replicates. The cause of this variation in survival is unknown, but because two replicates experienced 100% mortality, and two did not, we cannot discount either result.

A small increase in survival over the control in both SeO2 treatments may represent the essentiality of Se in organisms, showing that slight increases may not present an imminent threat. However, mortality for both SeMet low and SeMet high indicate organic Se is most definitely a cause for worry in aquatic environments, and that organic SeMet is likely more toxic than inorganic Se, SeO2, in this case. Higher toxicity levels of organic Se vs. inorganic Se have been reported as early as 1985 (Hamilton 2004). In our study, it appears that at even low concentrations (14.23 and 32.8 µg g-1 dw) of SeMet, higher mortality is experienced than in control tadpoles. If an entire aquatic system were to experience these levels of SeMet, even converted from an inorganic form, a huge percentage of this population would likely experience mortality, affecting the entire ecosystem by limiting food sources, contributing to bioaccumulation, and transferring Se into the terrestrial environment after successful metamorphosis. One additional point to note about survival to the end of study results is that no data from one control replicate were included in statistics due to high mortality most likely as a result of contamination from a ceiling leak.

Our behavioral test may not have had the potential to correctly represent the neurological functioning of the test subjects. Although similar behavioral tests have yielded significant results (Raimondo et al. 1998), our experimental design may have been insufficient and not able to capture the true effects of Se on gray tree frogs neurologically. It is possible that with a better developed behavioral test more suited to small tadpoles, results could reveal that Se slows reaction time, swimming speed, or have other unknown effects. In a study with adult zebrafish, Thomas and Janz (2011) used a seemingly effective method to quantify swimming performance, a water velocity-controlled swim tunnel. Perhaps a method with less room for variability such as this would better serve as a behavioral test. Despite the possibility of a flawed design, our test results suggest that at the time of testing, which was mid-experiment, different doses of Se in the food did not cause tadpoles to react significantly different to placement into a trough or prodding.

Mass did not significantly vary between treatments and remained fairly constant from day 23 to 42 (Table 2). This was expected, as sampled tadpoles were at the same relative stage of metamorphosis and therefore had not gained substantial additional weight. Tadpoles will additionally be weighed when front legs develop and when the tail completely adsorbs. These weights have the potential to differ among treatments, but most likely will not, as no differences were previously observed. The only observable difference to note is that control tadpoles had the lowest (but not statistically significant) mean mass at both 23 and 42 days, suggesting tadpoles may have slightly higher masses as a result of excess Se (Table 2).

Results of Se and Hg content from chemical analyses should improve our ability to draw bigger conclusions. These data will allow for comparisons between Se and Hg content as well as provide possible relationships between Se content and measured endpoints throughout metamorphosis. We expect Hg concentrations to decrease as Se concentrations increase, but

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do not yet know how this relationship will be maintained in inorganic Se treatments. The control treatment should have the highest concentrations of Hg, as it has the lowest concentrations of Se. Additionally, examination of the differences in total accumulation of organic and inorganic Se may provide further insight into how these two forms may act in natural environments. Because the concentrations of various forms of Se are unknown in coal ash and other sources of Se pollution worldwide, we have to assume that both are present. Even if less toxic inorganic forms compose most of coal ash, they can be converted biologically into more toxic organic forms and have toxic effects in organisms, as shown with gray tree frogs here.

Conclusion

Concentrations of selenomethionine in gray tree frog tadpoles as low as 32.8 µg g-1 dw can cause significantly higher metabolic rates than control tadpoles, which then limit growth, reproduction, or survival in the wild. Tadpoles dosed with this concentration as well as a lower SeMet concentration of 14.23 µg g-1 experienced higher levels of mortality, on average, than the control and tadpoles treated with SeO2. Concentrations similar to these, or higher, have been recorded in Se polluted sites (Rowe et al. 2001; Schuler et al. 1990). Limitations should be established and enforced to control Se pollution into aquatic environments to prevent further degradation of these ecosystems and the organisms that live in them.

Acknowledgements

I appreciate the guidance and assistance of Dr. Chris Rowe and Dr. Andrew Heyes, who both made this project possible. This research was supported by the National Science Foundation and Maryland Sea Grant.

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References

Chapman, P.M., W.J. Adams, M.L. Brooks, C.G. Delos, S.N. Luoma, W.A. Maher, H.M. Ohlendorf, T.S. Presser, and D. P. Shaw. 2010. Ecological assessment of selenium in the aquatic environment. CRC Press.

Hamilton, S.J. 2004. Review of selenium toxicity in the aquatic food chain. Science of the Total Environment 326: 1-31.

Hothem, R.L. and H.M. Ohlendorf. 1989. Contaminants in foods of aquatic birds at Kesterson Reservoir, California, 1985. Archives of Environmental Contamination and Toxicology 18: 773-786.

Lemly, A.D. 2004. Aquatic selenium pollution is a global safety issue. Ecotoxicology and Environmental Safety 59: 44-56.

Lockard, L., C.L. Rowe, and A. Heyes. 2013. Dietary selenomethionine exposure induces physical malformations and decreases growth and survival to metamorphosis in an amphibian (Hyla chrysoscelis). Archives of Environmental Contamination and Toxicology 64: 504-513.

Muller, A.S., S.D. Klomann, N.M. Wolf, S. Schneider, R. Schmidt, J. Spielmann, G. Stangl, K. Eder, and J. Pallauf. 2008. Redox regulation of protection tyrosine phosphatase 1B by manipulation of dietary selenium affects the triglyceride concentration in rat liver. Journal of Nutrition 138: 1328-2336.

Ohlendorf, H.M., S.M. Covington, E.R. Byron, and C.A. Arenal. 2010. Conducting site-specific assessments of selenium bioaccumulation in aquatic systems. Integrated Environmental Assessment and Management 7: 314-324.

Raimondo, S.M., C. L. Rowe, and J.D. Congdon. 1998. Exposure to coal ash impacts swimming performance and predator avoidance in larval bullfrogs (Rana catesbeiana). Journal of Herpetology 32: 289-292.

Rowe, C. L., O. M. Kinney, A.P. Fiori, and J.D. Congdon. 1996. Oral deformities in tadpoles (Rana catesbeiana) associated with coal ash deposition: Effects on grazing ability and growth. Freshwater Biology 3: 723-730.

Rowe, C. L., W. A. Hopkins, and J.D. Congdon. 2001. Integrating individual-based indices of contaminant effects. How multiple sublethal effects may ultimately reduce amphibian recruitment from a contaminated breeding site. The Scientific World Journal 1: 703-712.

Rowe, C.L., A. Heyes, and J. Hilton. 2011. Differential patterns of accumulation and depuration of dietary selenium and vanadium during metamorphosis in the Gray Tree Frog (Hyla versicolor). Archives of Environmental Contamination and Toxicology 60: 336-342.

Rowe, C.L., A. Heyes, and W. Hopkins. 2009. Effects of dietary vanadium on growth and lipid storage in a larval anuran: Results from studies employing ad libitum and rationed feeding. Aquatic Toxicology 91: 179-186.

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Rowe, C.L., O.M. Kinney, R.D. Nagle, and J.D. Congdon. 1998. Elevated maintenance costs in an anuran (Rana catesbeiana) exposed to a mixture of trace elements during the embryonic and early larval periods. Physiological Zoology 71: 27-35.

Schuler, C.A., R.G. Anthony, and H.M. Ohlendorf. 1990. Selenium in wetlands and waterfowl foods at Kesterson Reservoir, California, 1984. Archives of Environmental Toxicology and Contamination 19: 845-853.

Thomas, J.K. and D.M. Janz. 2011. Dietary selenomethionine exposure in adult zebrafish alters swimming performance, energetics and the physiological stress response. Aquatic Toxicology 102: 79-86.

Thomas, J.K., S. Wiseman, J.P. Giesy, and D.M. Janz. 2013. Effects of chronic dietary selenomethionine exposure on repeat swimming performance, aerobic metabolism and methionine catabolism in adult zebrafish (Danio rerio). Aquatic Toxicology 130-131: 112- 122.

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Tables and Figures

Table 1. Concentrations of Se in five treatments fed to gray tree frog tadpoles over 51 days, measured through ICP mass spectrometry after digestion.

Treatment [Se] (µg g-1 dw) Control 3.6 SeO2-L 16.27* SeO2-H 34.2 SeMet-L 14.23* SeMet-H 32.8 * represents a nominal dose that has not yet been measured.

Table 2. Average masses of tadpoles receiving different treatments of Se at different days of exposure. Day 0 means calculated by randomly sampling 15 tadpoles. Day 23 and 42 means calculated by sampling two tadpoles per replicate that also were sampled for respirometry analysis.

Treatment Day 0 (g) Day 23 (g) Day 42 (g) Control .0612 .1852 .1958 SeO2-L .0612 .2276 .2236 SeO2-H .0612 .2319 .2039 SeMet-L .0612 .2314 .1941 SeMet-H .0612 .2203 .2388

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Anthropogenic and natural inputs

Solid Se: rock, ore, waste, soil, sediment, dust (elemental Se, selenite, selenate) ↕

Aqueous Se: drainage, effluent, runoff, pore water (selenate, selenite, organo -Se) ↕ Enrichment Function

Particulate Se: biofilm, phytoplankton, plants, detritus, sediment (organo-Se, adsorbed selenite, selenate, elemental Se)

↓ Trophic transfer

*Primary consumers: invertebrates, fish, other vertebrates (Organo-Se)

↓ Trophic transfer

*Secondary consumers: fish, birds, herps, mammals (Organo-Se)

↓ Trophic transfer

*Higher-order consumers: birds, herps, mammals, humans (Organo-Se)

*=Selenium hazard

Figure 1. Various ways in which selenium may enter an ecosystem, change forms, and pass through the food web during trophic transfer, creating potential hazards along the way (Chapman et al. 2010).

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3.5 A AB 3 B B 2.5 B

/ g min 2 2 Day 23 1.5 µg O Day 42 1

0.5

0 Control SeO -L SeO -H SeMet-L SeMet-H Treatment ₂ ₂

Figure 2. Metabolic rates of gray tree frog tadpoles after 23 and 42 days of exposure to different selenium (Se) treatments. SeMet high data from day 42 represent only two replicates due to 100% mortality in remaining two. These data were therefore not included in statistical analysis. A is significantly different from B (p <0.005). Data were measured with Mirco-Oxymax Respirometer (Colombus Instruments), analyzed with ANCOVA through MiniTab, and graphed in MS Excel.

1 0.9

0.8 0.7 0.6 0.5 0.4 0.3

Surival to End0.2 of Study

Survival to End of Study of End to Survival 0.1 0 Control SeO2-L SeO2-H SeMet-L SeMet-H Treatment

Figure 3. Tadpole survival to the end of the study at day 51 of exposure as proportions of living tadpoles. Control data exclude one replicate due to ceiling leak. Data were analyzed by ANOVA with MiniTab and graphed in MS Excel.

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The Effects of Nutrients on the Biomechanics of Spartina alterniflora on Poplar Island

Jade Bowins, REU Fellow Maryland Sea Grant

Dr. Lora Harris, Assistant Professor Chesapeake Biological Laboratory, University of Maryland Center for Environmental Science

Abstract

Due to high nutrient content in dredge material encompassing Poplar Island significant amounts of lodging has resulted. Additions of silicon are used in order to determine their effect on Spartina alterniflora on Poplar Island. Biomechanical tests were performed in order to determine the elastic modulus and measurements were taken to determine the second moment of area. ANOVA statistical analysis was performed and determined there was no statistical significance in any mean values depicted in the bar graphs in average stem length.

Keywords: Spartina, Elastic Modulus, Nutrients, Poplar Island

Introduction

An increase in eutrophication is causing a significant change in Spartina alterniflora with regard to its production of biomass. This is noted by an increase in its production of above ground biomass, in addition to, a decrease in its production of below ground biomass normally consisting of stabilizing roots. The increased production of above ground biomass leads to growth of taller and heavier shoots than S. alterniflora is accustomed to managing. Spartina alterniflora is typically found in nutrient limited salt marshes. As the human population expands, common habitats of S. alterniflora experience an abundance of N and P derived from agriculture, sewage wastewater, and fossil fuel consumption. Whole-scale ecosystem experiments suggest this nutrient enrichment is leading to an increasing loss of salt marshes. Although salt marshes are normally hot spots for denitrification and carbon storage, the current nutrient levels in many habitats are overwhelming the salt marsh’s ability to remove N and P (Deegan 2012). This nutrient over enrichment can lead to eutrophication, algal blooms, and dead zones, which cause extreme economic and ecological losses (Paerl 2009).

Most commonly, the loss of salt marsh plant life is caused by uprooting due to a lack of root stability (Puijalon 2008). This decrease in stability then leads to bank collapse, creating wider creeks and larger mudflats. The change in distribution of biomass among S. alterniflora and the morphological instability it leads to are main factors contributing to the loss of salt marshes. Salt marshes are an important barrier between land and water because of their ability to remove nutrients, offer storm protection, hold carbon, and offer habitats for many fish, birds, and invertebrates. Many factors such as sea level rise and sediment loss, along with rising

17 nutrient levels contribute to great amounts of salt marsh loss. Inevitably, in conjunction with sea level rise, a decrease in bank stability is leading to accelerated rates of erosion. Sea level rise is synergistically connected to the increase in nutrient loading as well (Deegan 2012). Difficulties arise because the nutrient enrichment in salt marshes is creating a positive feedback mechanism.

When there are slight changes in a land plant’s environment, it is usually able to reconfigure its morphology in order to prevent mechanical failure (Ennos 1999). In conjunction with this theory, plants exposed to mechanical stress should respond by modifying their performance through enhancing their morphology. This form of plastic response is very beneficial for plants undergoing mechanical stress induced by both aerodynamics and hydrodynamics. Morphological variations induced by mechanical stress can, in turn, enhance a plant’s performance by either allowing the plant to minimize the mechanical stress or by increasing the plant's tolerance for the stress. Phenotypic plasticity is positively selected for in plants because a change in morphology that increases its performance will offer the plant many benefits (Puijalon 2008). Aerodynamics are a driving force when it comes to survival of S. alterniflora because of the synonymous effects in regards to nutrient enrichment. The importance of aerodynamics normally coincides with light competition, leading to the evolution of taller plants over time. Taller plants are able to shade competitors and increase their supply of carbon dioxide. The negatives that correlate with taller plants include exposure to greater amounts of wind drag and an increased difficulty of supplying leaves with water due to a greater gravitational pull (Ennos 1999). As plants grow taller with respect to their neighbors, they are able to protect each other from mechanical stress caused by wind (Anten 2005).

Spartina. alterniflora is exposed to drag from both wind and water depending on its location. When calculating drag, it is important to note its proportionality to both velocity and the length of the plant being studied. There is both a linear relationship between drag and velocity as well as between drag and length. In order to cope with aerodynamics, plants have been noted to have more dense bases, as they grow taller. On the other hand, it has also been seen as beneficial for smaller plants to be flexible so they are less likely to be uprooted by strong winds. Previous experiments have tested the maximum drag plants can withstand by investigating their shape, anatomy, and shoot system, and by putting live plants in a customized water tunnel (Ennos 1999). Drag is positively correlated with plant size; therefore, size reduction is the most common response to mechanical stress. In addition to size reduction, increased density in the root system and tougher tissues give a plant greater stability and an increased resistance when battling mechanical stress (Puijalon 2008). Anten (2005) performed an experiment in order to compare the effects of plant density on mechanical stability, growth and reproductive success. In this study plants were not exposed to actual wind, but were instead manually “flexed” in order to prevent skewed results based on the change in microenvironment wind causes. The plants exposed to mechanical stress produced both shorter and thicker shoots than control plants, ones not exposed to any mechanical stress. The effects caused by mechanical stress were independent of the effects caused by population density. Plants are designed to support their own weight while also resisting mechanical stress. In order to be successful, plants must invest part of their resources in their shoot system and part of their resources in their roots. Taller plants suffer from greater amounts of mechanical stress because wind speed increases as height above ground increases (Anten 2005). This experiment also noted the occurrence of thigmomorphogenesis, when a plant increases its resistance in order to counteract the effects of mechanical stress. For example, when plants are exposed to wind, tides or rubbing, they produce shorter and thicker shoots and allocate more mass into their roots for stabilization (Anten 2005). When plants undergo alterations to their root system this can lead to an increased resistance against uprooting due to mechanical stress (Puijalon 2008).

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Both wind shielding and competition for light play a key role in the growth of shoots within dense groups. One hypothesis suggests when grown in crowded groups, it is more beneficial for plants to grow tall in order to reach more sunlight and shade their competitors. In light of this, many plant species respond to closeness of neighbors by producing taller and thinner stems while also reducing the size of any lateral branches. This environmental acclimation plants portray when grown in dense groups is known as the shade avoidance syndrome (Anten 2005).

On Poplar Island, where the S. alterniflora for the following experiment were collected, the experimental plots had been treated with silicon for one year. The silicon treatment is expected to last up to three years before the granular silicon must be replaced. The S. alterniflora for this experiment were collected from Cell 1B which was created in 2012 and consists of 35 acres. Unlike many other nutrients, silicon does not diffuse through the soil rapidly; therefore, the plots were relatively close in location. The treatment and control plots were grown in substrate with very minimal pore size, which allows the substrate to hold nutrients well. The reference plot on Poplar Island consisted of a sandy substrate which limits the amount of nutrients the soil could hold to nourish the S. alterniflora. The S. alterniflora collected from the reference plot on Knapp’s Narrow were grown intermittently among Spartina patens. It should also be noted the S. alterniflora collected for this experiment also suffered from fungal infections and insect infestation, which could affect the results of the experiment in comparison to healthy plants.

Materials and Methods

The aim of this experiment was to determine the “modulus of elasticity” (E) and “second moment of area” (I) of salt marsh plant stems. These measurements can determine the properties (E) and morphology (I) of the plant stems, which can help us understand how vulnerable they are to mechanical stress (Niklas and Spatz 2012). All specimens were acquired from three different nutrient and control plots on Poplar Island, one reference plot on Poplar Island and one reference plot on Knapp’s Narrow. Twelve specimens from each plot were removed, resulting in 96 total stems, to complete the protocol. All collected specimens were placed in large PVC pipes to aid in safe transport. Once returned to the lab, the specimens were put into a container of ambient seawater in order to maintain turgor pressure. Parameters measured in the field include plot density, plant height, and basal diameter. For all measurements, stems were cut with clippers at the substrate before being brought back to the lab.

In order to compute E, we must describe the following equation using explicit parameters, which is explained in the numbered text (Niklas 2012):

= (3 ) 2 (1) 𝑃𝑃𝑏𝑏 𝑚𝑚𝑚𝑚𝑚𝑚 1. The experimental framework explaining𝛿𝛿 (1)6𝐸𝐸𝐸𝐸 can𝑙𝑙 be −𝑏𝑏 determined by referring to Figure 1. Consider a stem cantilevered at a 90° angle attached to a stand by clamps adjusted and aided with floral foam to gently hold the substrate-cut portion of the stem. In Figure 1, the load is applied at a point (b) to measure the vertical deflections (δx). In the case of (1), the appropriate deflection would be the maximum recorded distance (δmax) the stem is altered prior to plastic deformation. The center of mass is recorded for calculations and is defined as the point where basal portions of the plant (measured in meters) are balanced by apical portions of the plant (a, measured in meters) (Niklas 2012).

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2. Additional variables in (1) include the length of the stem (l) and the second moment of area (I), which describe the morphology of the structure. For a hollow stem, the equation for the second moment of area is provided in (2) with variables R and r defined as inner and outer radii as pictured in Figure 2 (Niklas 2012).

= ( ) (2) 𝜋𝜋 4 4 𝐼𝐼 4 𝑅𝑅 − 𝑟𝑟 = 4 (3) 𝜋𝜋𝑅𝑅 3. There are other forms of (1) that can be𝐼𝐼 helpful4 when deflections are measured at a point that differs from the center of gravity. In these instances, b defines the range from the base of the stem to the point where the load is applied. Equation (4) is provided for conditions when x is equal to a (Niklas 1992). = 3 (4) 𝑃𝑃𝑏𝑏 𝑎𝑎 4. To perform the calculations, a small beaker𝛿𝛿 3𝐸𝐸𝐸𝐸 is attached by a thin string to the center of mass of the stem. A second stand holding a level adjusted ruler is made parallel with the apical end of the stem. The load (P) at this point is then applied by filling the beaker with DI water, after which the deflection is measured by the change in height of the apical end of the stem along the ruler. To calculate the load, the string, beaker, and water must be weighed (Niklas 2012).

5. The initial height of the specimen without the force of gravity should be noted, along with the height of the specimen after gravity and following loads are applied.

6. Other characteristics of importance for parameterizing the equations are stem length, diameter at the basal end and center of gravity, R and r for hollow stems, distances a and b, and if possible the mass of a and b (Niklas 1992).

7. A variety of weights and deflections should be measured on each stem so (E) is measured five times for each individual stem examined.

8. After collection of the data and computation of (E), we should examine the distribution frequency patterns in (E). If the distribution is normal, then we can compute a mean and know the experiment was performed correctly. However, if the distribution of (E) is non parametric, it is an impartial way of determining the experiments were not performed properly (Niklas 2012).

Results

The average stem length values for the high nitrogen plot, low nitrogen reference plot, and silicon addition plot are the only significant values portrayed by Figure 4. For average stem length, Figure 4 shows plants from the low nitrogen reference plot were significantly shorter than the plants from the high nitrogen and silicon addition plots. For average stem diameters (Figure 5) and average second moment of area (Figure 6), the means from all three plots were not significantly different. Figure 7 portraying average stem deflection shows plants removed from the silicon addition plots had slightly greater amounts of deflection, although values are not significantly different as well. The average elastic modulus showed slight variation with the plants from the low nitrogen reference plot having a greater average elastic modulus, but again,

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the values did not produce enough variation to show any significance (Figure 8). Average flexural stiffness showed slight variations with the plants from the silicon addition plots having a lesser average, but the variation was not great enough to show any significant differences (Figure 9). Statistical testing was performed by ANOVA.

Discussion

The objective of this experiment was to determine if the plots containing the silicon additions benefited the S. alterniflora in a significant manner. This was performed by documenting the plant morphology among different nutrient environments followed by measuring the biomechanical properties of each stem collected. The elastic modulus was used in order to calculate the biomechanical properties of each stem. The mass of each applied load, deflections after each load was applied, second moment of area, and the point of applied load, which was determined based on tidal inundations, were calculated for each stem. The point at which the load was applied was kept constant for all stems in the experiment. The second moment of area was calculated in order to determine the morphological characteristics of each stem. The second moment of area was calculated both for hollow and solid stems. Using the equation for solid stems on the S. alterniflora is an assumption due to the aerenchyma cells within the seemingly solid stems. Further analysis will include image J analysis of the photos taken of the cross sectional area of stems.

Data collection took place both in the field at Poplar Island and in in the lab at Chesapeake Biological Laboratory. In the field, plot density, plant height and basal stem diameter were measured for ten different stems in each individual plot prior to their removal. The plants removed were all of the same height in order to perform the biomechanical testing on plants of the same age. Poplar Island is a reconstruction site that received its dredge material from the Baltimore Harbor to replenish and restore the marshes. The sediment from the dredges was especially high in nutrients, and it has been noted lodging occurs in high nitrogen environments during the late summer months (August and September). The measurements taken for this experiment on July 24, 2013 were initial measurements and were followed up by subsequent samplings in early September. The collection site on Poplar Island consisted of three high nitrogen plots, three silicon addition plots and a low nitrogen reference plot. Twelve stems were collected from each plot totaling 84 stems from Poplar Island. Measurements taken in the lab after transport from Poplar Island included total stem length, stem diameter at the point of applied load, inner radius at the point of applied load, length from base of plant to the applied load, initial height without gravity, the mass of each load applied, and the deflections after each load was applied. Initial height without gravity was needed in order to determine the extent of each deflection. The initial load applied was gravitation acceleration (9.8 ms-2). Each additional load was applied by adding deionized water in 20 mL increments to a small beaker attached to the indicated area by a thin string and a small hook. In addition to the initial load of gravitational acceleration, four subsequent loads were applied and their deflection was measured.

In order to successfully determine the statistical significance of the data, bar graphs showing means of stem length, stem diameter at the applied load, second moment of area at the applied load, stem deflection, elastic modulus at the applied load, and flexural stiffness were created and analyzed by ANOVA. As stated previously, only Figure 4 showed any statistical significance depicted average stem length. In Figure 4, the low nitrogen reference plots had significantly shorter stems than the plants from the silicon addition plots and the plants from the high nitrogen plots. This is a reasonable result since nitrogen is a significant nutrient when it comes to plant growth. For average stem diameter, the plants collected from the high nitrogen

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plots had slightly thicker stems than the plants removed from the other plots (Figure 5). However, the distance between stem diameters was not significantly different from one another. The data from Figure 5 depicting the average stem diameter directly correlates to Figure 6 of the average second moment of area. Unfortunately, again this data was not significantly different. For average stem deflection, the plants from the silicon addition plots produced results of greater stem deflection than the plants from the high nitrogen plots and the low nitrogen reference plots which were unexpected (Figure 7). The values were variable within treatments though and significant differences were not found among the treatments. Average elastic modulus is the measure of elasticity and plants from the low nitrogen reference plot had slightly greater elastic modulus values (Figure 8). The plants from the high nitrogen plots and silicon addition plots were not variable enough in comparison to the results from the low nitrogen reference plot to produce any statistical significance. Average flexural stiffness is a calculation that multiplies the elastic modulus by the second moment of area. It differs from the elastic modulus because it determines how rigid a stem is instead of a stem’s elasticity. Again, there were no significant differences among these averages (Figure 9).

Plants were also collected from a reference plot on a different location not on Poplar Island within the Chesapeake Bay, Knapp’s Narrow. The data collected from these stems showed significant difference in elastic modulus (Figure 11) and second moment of area (Figure 10). The plants removed from Knapp’s Narrow resulted in a greater average elastic modulus and a lower average second moment of area.

Some uncontrollable variables that may have affected the results include fungal infections and insect herbivory. The S. alterniflora suffered from a fungus that caused black spotting on the basal stems along with a separate fungus that caused yellow spotting along the leaves. It should be noted the plants collected from the reference sites showed no visible signs of herbivory or infection. Further sampling may need higher sampling number in order to determine any significant differences among plots.

Conclusion

Statistical significance was assessed by ANOVA and was only found for average stem length. The silicon additions did not aid in the biomechanics of the stems as expected. The sampling time in July may have had a significant impact on the data with regard to an increase in lodging that occurs in August and September. Better results may be found after the plants have been exposed to the silicon additions for a longer amount of time or when plant sampling is taken in early September. Since Poplar Island has been reconstructed from dredge material the entire island has been exposed to high volumes of nutrients. Therefore, the low nitrogen reference site on Poplar Island is only low in comparison to the high nitrogen plot on Poplar Island. Further studies may show in relation to other areas, such as Knapp’s Narrow, the low nitrogen reference plot on Poplar Island may still contain greater than average amounts of nitrogen compared to reference sites away from Poplar Island.

Salt marshes are important because they offer unanticipated benefits such as storm protection, carbon storage, nutrient removal, as well as perform as a habitat (Deegan 2012). When nutrient levels are only slightly increased, in comparison to normal rates, salt marshes can protect the Chesapeake Bay by removing land derived nutrients (Deegan 2012). When the amounts of nutrients in the bay are insurmountable, salt marshes are unable to successfully remove an adequate amount of land derived nutrients. Due to an increase in nutrients loading, simultaneous negatives such as sea level rise, decrease in bank stability and accelerated erosion have been quick to follow (Deegan 2012).

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Acknowledgements

I acknowledge Lorie Staver, Court Stevenson, and Jessica Foley.

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References

Anten, N.P.R, R. Casado-Garcia, and H. Nagashima. 2005. Effects of mechanical stress and plant density on mechanical characteristics, growth and lifetime reproduction of tobacco plants. The American Naturalist 166: 650-660.

Deegan, L.A., D.S. Johnson, R.S. Warren, B.J. Peterson, J.W. Fleeger, S. Fagherazzi, and W.M. Wollheim. 2012. Coastal eutrophication as a driver of salt marsh loss. Nature 490: 388-392.

Duarte, C.M., D.J. Conley, J. Carsten and M. Sánchez-Camacho. 2008. Return to neverland: shifting baselines affect eutrophication restoration targets. Estuaries and Coasts 32:29-36

Ennos, A.R. 1999. The aerodynamics and hydrodynamics of plants. The Journal of Experimental Biology 202: 3281-3284.

Mann, K.H. 2000. Salt marshes, p. 31-34. In Ecology of coastal waters: with implications for management. Blackwell Science.

Niklas, K.J. 1992. Plant biomechanics: An engineering approach to plant form and function. The University of Chicago Press.

Niklas, K.J., H-C. Spatz. 2012. The effects of geometry, shape and size, p. 159-194. In Plant physics. The University of Chicago Press.

Paerl, H.W. 2009. Controlling eutrophication along the freshwater-marine continuum: dual nutrient (N and P) reductions are essential. Estuaries and Coasts 32: 593-601.

Puijalon, S., J-P. Léna, N. Rivière, J-Y Champagne and J-C. Rostan. 2008. Phenotypic plasticity in response to mechanical stress: hydrodynamic performance and fitness of four aquatic plant species. New Phytologist 177: 907-917.

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Figures and Tables

Figure 1. Method for determining deflections at applied loads.

Figure 2. Cross-sectional image for calculating second moment of area for hollow stem.

Figure 3. Cross-sectional image for calculating second moment of area for solid stem.

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Average Stem Length 2 1.75 1.5 1.25 1 0.75 0.5 Stem Length (m) Length Stem 0.25 0 N+ Reference Silicon Sediment Treatments

Figure 4. Bar graph of average stem length for high nitrogen plots, reference on Poplar Island plots and silicon plots.

Average Stem Diameter 0.012

0.01

0.008

0.006

0.004

Stem Diameter (m) 0.002

0 N+ Reference Silicon Sediment Treatments

Figure 5. Bar graph of average stem diameter for high nitrogen plots, reference on Poplar Island plots and silicon plots.

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Average Second Moment of Area (I) 1.2E-09

1E-09

8E-10

6E-10

4E-10

2E-10

0

Second Moment of Area (m) Area of Moment Second N+ Reference Silicon Sediment Treatments

Figure 6. Bar graph of average second moment of area for high nitrogen plots, reference on Poplar Island plots and silicon plots.

Average Stem Deflection 0.5

0.4

0.3

0.2

0.1 Stem Deflection (m) Deflection Stem 0 N+ Reference Silicon Sediment Treatments

Figure 7. Bar graph of average stem deflection for high nitrogen plots, reference on Poplar Island plots and silicon plots.

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Elastic Modulus (E) 7.E+06 2) - 6.E+06 5.E+06 4.E+06 3.E+06 2.E+06 1.E+06 0.E+00 Elastic (kgm/s^ Modulus Elastic N+ Reference Silicon Sediment Treatments

Figure 8. Bar graph of average elastic modulus for high nitrogen plots, reference on Poplar Island plots and silicon plots.

Average Flexual Stiffness 2.0E-03 2) - 1.6E-03

1.2E-03

8.0E-04

4.0E-04

0.0E+00 N+ Reference Silicon

Flexural Stiffness (kgm^2s^ Sediment Treatments

Figure 9. Bar graph of average flexural stiffness for high nitrogen plots, reference on Poplar Island plots and silicon plots.

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Average Second Moment of Area (I) 6E-10

5E-10

4E-10

3E-10

2E-10

1E-10

0

Second Moment of Area (m) Area of Moment Second N+ Reference Silicon Knapp's Narrow Sediment Treatments

Figure 10. Bar graph of average second moment of area for high nitrogen plots, reference on Poplar Island plots. Reference on Knapp’s Narrow plot and silicon plots.

Average Elastic Modulus 1.4E+08 2) - 1.2E+08 1.0E+08 8.0E+07 6.0E+07 4.0E+07 2.0E+07 0.0E+00 N+ Reference Silicon Knapp's Elastic (kgms^ Modulus Elastic Narrow Sediment Treatments

Figure 11. Bar graph of average elastic modulus of area for high nitrogen plots, reference on Poplar Island plots. Reference on Knapp’s Narrow plot and silicon plots.

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The Microbial Effects of the Addition of Oil to the Anoxic Layers of Benthic Sediments from the Chesapeake Bay

Gene Patrick Geronimo, REU Fellow Maryland Sea Grant

Dr. Laura Lapham, Assistant Professor Chesapeake Biological Laboratory, University of Maryland Center for Environmental Science

Abstract

The Macando oil spill three years ago released an unprecedented amount of oil into the Gulf of Mexico. The environmental impacts of this massive influx oil are still being studied today. This project focuses on the effects oil has on microbes in deep-sea anoxic sediments. In the anoxic sediments, three major microbial zones exist due to competitive-substrates. The primary microbes studied within each of the three zones respectively are the sulfate reducing bacteria, the methanotrophs, and the methanogens. In this experiment, incubations were done to produce a time series for each zone tracking the changes in concentration of sulfate and methane. It was hypothesized that the addition of oil will cause sulfate to be depleted faster in the sulfate reduction zone and methane to be produced sooner in the methane production zone. The results showed that sulfate remained constant throughout the time series despite oil being added, and that methane was produced sooner for the oil groups than the control groups in two of the three zones. These results contradicted the idea of competitive substrates. In conclusion, a new hypothesis was formed stating that the addition of oil has a greater effect on methane production via non-competitive substrates.

Keywords: methanogenesis, competitive-substrates, anaerobic, Macando oil spill

Introduction

On April 20, 2010, the Deepwater Horizon (DH) drilling platform exploded resulting in eleven deaths and the release of oil from the Macando well at a water depth of 1.5 km. The well was eventually sealed on July 15, 2010, but approximately 4.4 million barrels (700 million L ± 20%) of oil was released into the Gulf of Mexico (Crone et al. 2010). In addition, about 2 million oil barrel equivalents of natural gas were leaked (Joye et al. 2011). The release of oil in the deep waters resulted in deep hydrocarbon plumes. However, Kessler et al. (2011) showed that bacterial communities were able to oxidize methane, one of the types of hydrocarbons released, to carbon dioxide within a couple of months.

Despite the presence of oil-degrading bacteria in deep (Camilli et al. 2010; Hazen et al. 2010; Valentine et al. 2010; Kessler et al. 2011) and surface waters (Edwards et al. 2011), and the human efforts to mechanically remove oil, at least 26% of the released oil remains in coastal ecosystems, especially within the benthic sediments (Lubchenco et al. 2010). Although these

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numerous studies examine the response of indigenous bacteria in deep and surface waters to large inputs of a carbon source (i.e. an oil spill), there are few studies regarding the response of indigenous microbes in the sediments (Kostka et al. 2011; Mortazavi et al. 2013). Even less has been studied regarding the effects of oil on benthic sediments, especially deep-sea systems (Boehm et al. 1998).

Organic matter is a biological carbon source that is decaying, such as dead plants, animals, and bacteria. Oil can be considered as organic matter due to its high carbon content. When organic matter settles on the seafloor, it becomes incorporated into the sediments. Over time, the incoming layers of organic matter will bury this initial layer. Once on the seafloor, this organic matter undergoes both aerobic and anaerobic decomposition by the indigenous microbes (Schulz and Zabel 2000).

Thus to understand how the addition of oil, which is essentially an increase in organic matter, may affect the indigenous microbes, it is important to first understand the basic processes of sediment microbes.

To get energy, microbes respire, which is a process in which a series of chemical reactions result in adenosine triphosphate (ATP). This process is a reduction-oxidation (REDOX) reaction: for the organic matter to be OXidized, a substrate must be REDuced. Organic matter is the electron donor. The substrate is the electron acceptor. In aerobic respiration, oxygen is the electron acceptor. In the sediments, aerobic respiration occurs only as deep as oxygen can penetrate – thus the aerobic layer is the upper few millimeters to centimeters of the sediments (Schulz and Zabel 2000).

Anaerobic respiration occurs deeper within the sediments when the oxygen is exhausted. After oxygen, the general sequence of electron acceptors in relation to increasing depth is nitrate, manganese (IV), iron (III), sulfate, carbon dioxide (Schulz and Zabel 2000). This sequence is related to the decrease of redox potential, which leads to a decrease of free energy by respiration with these different electron acceptors. Thus as depth increases, the energy of respiration decreases as a result of the changing of electron acceptors. Nitrate plays a minor role in oxidizing organic matter. Manganese (IV) and iron (III) are both in the solid phase and are dependent on bioturbation, which is the mixing of sediments by organisms. When compared to the other electron acceptors, sulfate has the greatest number of oxidation steps, with the 2- sulfur being reduced from +6 in SO4 to -2 in H2S. When compared to one mole of iron oxide (an iron (III) compound), one mole of sulfate has an 8-fold higher oxidation capacity. Thus in the anaerobic layers of the sediments, sulfate is the predominant oxidant, oxidizing about 44% of the organic carbon in the sediment (Jørgensen 1982).

In the sulfate reduction zone, sulfate is the substrate that is reduced by sulfate reducing bacteria (SRB) (1) (Barnes and Goldberg 1976). Below the sulfate reduction zone is the methanogenic zone, where carbon dioxide is reduced by methanogens and methane is produced (2) (Whiticar 1999).

CH2O + SO4  H2S + CO2 (1)

CO2 + 4H2  CH4 + 2H2O (2)

In the transition layer between these two zones is a third, smaller zone: the sulfate reduction + methane oxidation zone. This zone is referred to as the sulfate-methane transition zone (SMTZ). SRB exist in this zone, but the methanotrophs (i.e. methane oxidizing) are active

31 as well. The methanotrophs are able to oxidize methane as their source of carbon through the reduction of sulfate (3) (Barnes and Goldberg 1976).

CH4 + SO4  H2S + CO2 (3)

It is important to note that the reason these zones exist like this is because of competitive substrates. SRB and methanogens compete for the same substrates (Oremland and Polcin 1982). However, the SRB outcompetes the methanogens for hydrogen, acetate, and other carbon substrates (Oremland and Polcin 1982; Whiticar 1999), which is why they are more active in the shallower depths of the sediment. Sulfate becomes depleted at the deeper levels of the column. Without sulfate, the SRB cannot be as active – thus the methanogens take over deeper in the sediment. The methane that these methanogens produce will diffuse up the sediments. When this methane reaches the boundary of the sulfate reduction zone and methanogenic zone, it is used up by the methanotrophs. Methanotrophs become more active than SRB due to this small flux of methane, which explains the small sulfate-methane transition zone that exists between the two larger zones (Figure 1).

In summary, the three major anaerobic zones of sediments are sulfate reduction zone, the sulfate-methane transition zone, and the methanogenic zone (Figure 1). The organisms present in these zones respectively are the SRB, SRB + methanotrophs, and the methanogens. There are other electron acceptors present and other microbes that utilize these acceptors, but these three zones are the dominant anaerobic zones that will be studied.

The goal of this project is to obtain a better understanding as to how oil and organic matter, in general, are degraded by these anaerobic microbial processes. Though the driving force of this experiment is the DH oil spill in the Gulf of Mexico, sediments from the Chesapeake Bay in Maryland will be used as a proxy for Gulf of Mexico sediments. Chesapeake Bay sediments are similar in microbial zone structure to Gulf of Mexico sediments, and by using Chesapeake Bay sediments, the amount of oil added to each sample can be controlled. It is unclear how much oil has already seeped into Gulf of Mexico sediments, and the extent of leakage to the different zones is unknown. Thus by using sediments not affected by the DH oil spill, it can be ensured that the control sediment samples of the three zones did not experience a huge flux of oil in recent history.

I hypothesized that the addition of oil will increase the activity of the microbes in each of the three zones. For the sulfate reduction zone, I hypothesized that sulfate in the sediment will be depleted more quickly when oil is added. For the methanogenic zone, I hypothesized that methane will be produced sooner when oil is added. For the sulfate-methane transition zone, I hypothesized that the results will be similar to those of the sulfate reduction zone. When this zone is isolated, there is no influx of methane from the methanogenesis zone, thus the SRB becomes the most active. These hypotheses were tested using a two-phase approach. Phase 1 was to obtain sediment cores in order to determine the zonation. Phase 2 was to run incubation experiments to track the changes in concentrations of sulfate and methane within each zone over time.

Methods

Phase 1 – Defining the Sulfate and Methane Profiles in Chesapeake Bay Sediments

Field Work

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Two sediment gravity core samples were collected from just outside the mouth of the Patuxent River in the Chesapeake Bay (Figure 2) on the morning of May 23, 2013 aboard the R/V Rachel Carson. One core was used to obtain a sediment profile (phase 1). The other core was used for the incubations (phase 2). After the research cruise, the two cores were stored in a cold room kept at 9.4°C.

Sectioning the Core

One core was used to obtain a sulfate-methane profile. The core was sectioned on June 7, 2013, 15 days after the core was collected. Samples were taken every 2 cm for the first 10 cm, and then every 5 cm from the 10 cm mark to the bottom of the core. To collect a pore water sample for sulfate analysis, a small hole (4 mm) was drilled into the plastic liner. A 10 mL syringe attached to a rhizon filter of 0.2 µm (Pohlman et al. 2008) was inserted into the hole, and the pore water sample was then withdrawn by pulling the syringe and wedging in a wooden stick (Figure 3). 1.5 mL of pore water sample from each depth was stored in a 1.5 mL microcentrifuge tubes with an o-ring cap. These samples were not preserved in acid, thus it was possible that sulfide reoxidzed back to sulfate. Also, the samples were stored in room temperature.

To collect a sediment sample for methane analysis, a larger hole (10.7 mm) was drilled. A push core was taken at each depth using 3 mL cut-off syringes (Figure 4). Approximately 3 mL of sediment was taken for each depth. Each 3 mL sample was stored in a 10 mL vial that was capped with a butyl rubber stopper and then crimped with an aluminum seal. For each sediment sample, 3 mL of 1 M KOH was added in order to preserve the sample.

With the analysis of one core, the zones for sulfate reduction, sulfate-methane transition, and methanogenesis will be defined. The second core, also called a sister core since the two are from the same site, will be used to conduct the incubations in phase 2.

Analytical Procedure for Sulfate and Methane Determinations

Ion Chromatograph (IC) - To determine the sulfate concentrations in the pore water samples, a Dionex ICS-1000 ion chromatograph was used. Chloride concentrations were also determined. 40 μL of each pore water sample was diluted with 5.4 mL of Milli-Q water. Samples were run alongside standards of various dilutions of standard seawater (blank, 1/1000, 1/500, 1/100, 1/10, 1/5, 1/4, 1/2, 3/4, 1/1).

Gas Chromatograph (GC) - To determine methane concentrations, a SRI 8610C gas chromatograph equipped with a flame ionization detector (FID) was used. The samples were extracted from the headspace of the vials containing the sediment samples. The samples were over-pressurized with 10 mL of helium. After thoroughly mixing the sample, 10 mL of headspace were withdrawn. After the standard has been established using tank gas, the headspace samples were injected into the gas chromatograph.

Phase 2 – Incubations Experiment

Experimental Set-Up of Incubations

Each zone was taken out separately from the sister core. The methane production zone was released from the bottom of the core into a Ziploc bag. After the methane production zone was taken out, a new Ziploc bag was immediately exchanged at the bottom of the core and the

33

sulfate-methane transition zone was then collected. Once the transition zone was taken out, a new Ziploc bag was exchanged and the same process was done for the sulfate reduction zone. Each zone was then homogenized to produce a slurry. The control mud was homogenized by hand in a Ziploc bag. The oil-amended mud was homogenized using a standard kitchen blender. The oil-amended slurry contained a 5:1 sediment to oil ratio. The oil was provided by BP and was categorized as sweet petroleum crude oil-MC 252 (ID number: SO-20111116- MPDF-003).

The experimental set up used by Lapham et al. (1999) was modified to fit the parameters of this experiment. A 10 mL push core cut-off syringe (Figure 4) was used to draw 10 mL of sediment or sediment/oil sample from a specific zone. The syringe was then capped using a rubber stopper (#17; McMaster Carr Product Number 6448K93). The stopper was secured using electrical tape.

Each zone has a control group and an oil-amended group. The experiment ran for ten weeks. The first five time points were taken in 2-5 day intervals (twice a week). All subsequent time points were taken in 6-7 day intervals (once a week). All push core cut-off syringes were prepared at the same time on July 1, 2013. This enabled a new syringe to be taken at each respective time point (i.e. sacrificed). The original design of a 6-week long run with sampling twice a week resulted in 12 syringes per group. Factoring in the control group and the oil- amended group, a total of 24 syringes were needed per zone. This number was doubled so as to have duplicates. Thus, 48 syringes were prepared for each zone, leading to 144 syringes in total for all three zones.

The syringes were stored in a glove bag containing a N2-H2 gas mixture (containing 5% H2). This simulated the anoxic environment of the sediments. Before the experiment, the empty syringes were degassed in the glove bag for three days. To put the syringes into the glove bag, they were placed in the vacuum chamber. After the chamber door was closed, the chamber was vacuumed down. After being vacuumed down, the chamber was filled with N2 gas. The chamber was then vacuumed a second time, and afterwards the chamber was filled with N2 gas again. The chamber was then vacuumed a third time, but this time, after being vacuumed down, the chamber was filled with the N2-H2 gas mixture. While filling the chamber with the gas mixture, the chamber door lever inside of the glove bag was lifted up. The inside door was then popped opened by the pressure of the gas mixture filling the chamber. After this process, the inside door was safe to open and work inside of the glove can then be done. When taking syringes out of the glove bag, this same process was done before the inside door was opened.

The samples were stored at a room temperature of 22°C inside of the glove bag. Though the actual temperature of the sediments is colder, having the experiment run at room temperature sped up the metabolic processes of the microbes.

Experimental Time Points

At each time point, two syringes were taken from each bag (a total of 12 syringes; the control and oil-amended groups from each zone plus duplicates). To sub-sample the syringes, the electrical tape was taken off the end and the stopper removed. Then, similar to the core profiling, a 3 mL sub-sample of sediment from the end of each syringe was taken and stored in a 10 mL vial. Each vial was immediately capped with a butyl rubber stopper and then crimped with an aluminum seal. 3 mL of 1M KOH was then added to preserve the sample. These sediment samples were analyzed for methane using the gas chromatograph, according to procedures stated below in section Analytical Procedure for Incubation Samples.

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The remaining 7 mL of sediment was expelled into a 50 mL centrifuge tube and was centrifuged using a Sorvall RT 6000D centrifuge to separate the sediment and pore water. The settings for the centrifuge run were: 3000 rpm; +20°C; 10 minutes. Once done, 0.5 mL of the supernatant pore water was drawn into a 3 mL syringe with a needle. If possible, pore water closest to the water-sediment interface was withdrawn to avoid oxygenated water. With the water still in the syringe, the needle was removed and a 0.45 μm polypropylene syringe filter was attached. The pore water was pushed through the filter into a 1.5 mL microcentrifuge tube with an o-ring cap. 0.5 mL of the filtered pore water was then pipetted into another 1.5 mL tube containing 50 μm of 0.1 M H3PO4 to preserve the sample. These acidified pore water samples were analyzed for sulfate using the ion chromatograph, according to procedures stated below in section Analytical Procedure for Incubation Samples.

The centrifuge tubes containing the excess mud and pore water were stored in the freezer.

Experimental Checks for Oxygen Contamination

Several experimental checks were done to verify that anaerobic conditions were met. At time point 6 (day 21), three empty 10 mL syringes were placed in the glove bag, and glove bag air was withdrawn. These three samples were run on the GC to check for the presence oxygen in the glove bag. The procedure for entering and exiting the glove bag was described above in section Experimental Set-Up of Incubations. The analytical procedure using the GC is described below in section Analytical Procedure for Incubation Samples.

To determine if the syringes degassed oxygen during the incubation time, six 10mL plastic syringes were filled with helium, and, with the stopcock closed, were placed in the glove bag. The purpose of this helium syringe test was to determine whether or not oxygen was bleeding into the samples from the plastic syringes themselves. Three syringes were analyzed one day later. The other three syringes were analyzed six days later. The analysis was done using the GC according to the procedure stated below in section Analytical Procedure for Incubation Samples.

To determine oxygen on the GC, we had to modify the method for methane by turning on the Thermal Conductivity Detector (TCD). The TCD was used for time points 6 and 7 to check for the presence of oxygen within the samples themselves.

Analytical Procedure for Incubation Samples

Ion Chromatograph (IC) – procedure is the same as phase 1.

Gas Chromatograph (GC) – Our original method for measuring methane on the GC had to be modified because of all the higher hydrocarbon components in the oil-amended sediments. In the end, unique methods were derived for the control group and for the oil- amended group. In the control group, a method was created to monitor for oxygen using the TCD. For the oil-amended group, a new temperature and component method needed to be created to account for the higher hydrocarbons present in these samples. For the oxygen glove bag check and the helium syringe check, the control group method was used. The specific parameters and progression of methods is located in Table 1.

35

Results

Phase 1 – Defining the Sulfate and Methane Profiles in Chesapeake Bay Sediments

The sulfate-methane concentration profile can be seen in Figure 5. The sulfate concentration was 14.7 mM at 1 cm below the seafloor. It gradually decreased to 6.5 mM at a depth of 22.5 cm. It then began to increase until the max of 8.0 mM at 37.5 cm. From that depth on, sulfate concentration decreased and approached zero.

The methane concentration was 0.0 M at 1 cm below the seafloor. This concentration stayed relatively constant until a depth of 32.5 cm. A large increase of methane concentration was seen from depths 42.5 cm to 82.5 cm. At 82.5 cm the methane concentration reached 3.1 mM. From about 80 cm onwards, the methane concentration fluctuated, with the maximum concentration of 3.8 mM at a depth of 97.5 mM.

From this profile, the sulfate reduction zone (SR) was determined to be from 0 cm to 40 cm. The average sulfate concentration for this zone was 9.8 ± 3 mM. The average methane concentration was minimal.

The sulfate-methane transition zone (SMTZ) was determined to be from 40 cm to 80 cm. The average sulfate concentration was 3.7 ± 3 mM. The average methane concentration was 1.0 ± 0.8 mM.

The methane production zone (MP) was determined to be from 80 cm to 100c m. The average sulfate concentration was minimal. The average methane concentration was 2.7 ± 0.7 mM.

The chloride concentrations at each depth were also determined, and it was found that there was no trend of chloride concentration related to depth. The average chloride concentration was 285 ± 4 mM. Thus, chloride concentration remained constant throughout the sediment column.

Phase 2 – Incubations Experiment

Experimental Checks for Oxygen Contamination

When the glove back was checked for oxygen at time point 6 (day 21), 3-4% oxygen was discovered to be present. For the helium syringes in the glove bag, no oxygen was present on day 1. On day 6, however, the helium syringes yielded approximately 4-5% oxygen. The TCD consistently detected about 4-5% oxygen accompanying the methane readings in the control samples of time points 6 and 7.

Sulfate Time Series

Sulfate concentrations were measured for each time point for each zone separately (Figure 7). For the sulfate reduction zone, the initial sulfate concentration for the control group was 12.5 mM and for the oil-amended group it was 12.8 mM. The concentration decreased slightly to 11.8 mM and 11.7 mM for the control group and oil-amended group respectively for time point 2. For the rest of the time points, the concentration increased slightly and stayed relatively constant. Averaging the sulfate concentration across all time points, the average concentration for the control group was 12.8 ± 0.6 mM, and the average concentration for the

36 oil-amended group was 12.3 ± 0.5 mM. Statistically, there is no change of concentrations over time for both the control and oil-amended groups.

For the sulfate-methane transition zone, the initial sulfate concentration for the control group was 6.9 mM and for the oil-amended group it was 7.6 mM. For time point 2, the concentrations for both decreased about 0.5 mM, and then stayed relatively constant. The average concentration for the control group was 6.7 ± 0.3 mM, and the average concentration for the oil-amended group was 7.3 ± 0.3 mM. Statistically, there is no change of concentrations over time for both the control and oil-amended groups.

For the methane production zone, the initial sulfate concentration for the control group was 8.0 mM and for the oil-amended group it was 10.3 mM. For the control group, sulfate concentration increased to 9.5 mM and stayed constant for time points 3 and 4. For time point 5, the concentration decreased to 6.7 mM. In time points 6 and 7 there was a gradual increase to 9.7 mM at time point 7 (day 28). However, there is an experimental error for time points 2 and 4 that were ± 1.7 mM and ± 2.2 mM respectively. For the oil-amended group, the concentration dipped slightly at time point 2, and then stayed relatively constant. The average concentration for the control group was 8.3 ± 1.2 mM, and the average concentration for the oil-amended group was 10.5 ± 0.4 mM. Statistically, there is no change of concentrations over time for both the control and oil-amended groups.

Figure 8 shows the control group zones together and the oil-amended group zones together. Though the initial concentrations for each zones were different, the rate of change between each time points were generally similar.

Methane Time Series

Sulfate concentrations were measured for each time point for each zone separately (Figure 9). For the sulfate reduction zone, the initial methane concentration for the control group was 5.0 μM and for the oil-amended group it was 3.5 μM. The control group stayed relatively constant until time point 5 (day 15). The methane concentration for the control group then increased from 6.5 μM at time point 5 to 22.6 μM at time point 6 (day 21). The final concentration at time point 7 (day 28) was 17.5 μM. For the oil-amended group, the methane concentration increased gradually to a maximum of 16.5 μM at time point 5. It then decreased gradually to a concentration of 12.3 μM at time point 7.

For the sulfate-methane transition zone, the initial methane concentration for the control group was 28.8 μM and for the oil-amended group it was 15.5 μM. For the control group, at time point 2 (day 5) the concentration decreased from its initial concentration to 11.9 μM. This concentration stayed relatively constant until time point 6 (day 21) where it reached a concentration of 32.1 μM. At the last time point (day 28), the concentration was 34.8 μM. For the oil-amended group, the methane concentration increased to a maximum of 52.9 μM at time point 3 (day 8). It then decreased gradually to a final concentration of 23.6 μM at time point 7.

For the methane production zone, the initial methane concentration for the control group was 30.4 μM and for the oil-amended group it was 38.9 μM. The control group dipped slightly down to a low of 12.8 μM at time point 3 (day 8). It then increased to 43.8 μM at time point 6 (day 21), and the final time point had a concentration of 41.9 μM. For the oil-amended group, there was a large spike from time points 2 to 3 (days 5 to 8), jumping from a concentration of 42.7 μM to 103.5 μM. It then decreased sharply to 49.6 μM at time point 4 (day 13). The concentration gradually decreased to the final concentration of 32.0 μM at time point 7 (day 28).

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Figure 10 shows the methane time series of the control group zones together and the oil-amended group zones together. For the control group, the SR zone stayed constant from the initial concentration to time point 2 (day 5), whereas the concentrations for SMTZ and MP decreased significantly from their initial concentrations. Beyond time point 2, all three zones followed the same rates of change. For the oil-amended group, the SR zone stayed relatively constant. A methane spike was seen in both SMTZ and MP at time point 3 (day 8). The spike for MP was greater than that of SMTZ, but there was a considerably large experimental error for that MP spike (26% error) compared to the other points.

Observational Results

Physical anomalies were seen within the syringes themselves during each time point. In some syringes, there were air pockets present (Figure 11). Table 2 shows in which syringes at all the time points where air pockets were present.

Also, noticeable dark regions near the plunger and stopper of the control SR zone and the oil-amended SR zone began to appear starting in time point 4. A dark region is representative of sulfide that is bounded to iron. An example of this dark region can be seen in Figure 12, and pictures of the syringes at every time point are available from the Lapham Lab.

Discussion

An enormous amount of oil was deposited on the seafloor during the Macondo oil spill. Yet, very little is known about how deep-water sedimentary microbial communities deal with that oil, and, even less is known about how anaerobic processes do this. Here, I carried out laboratory incubations to determine the rates of sulfate reduction, anaerobic oxidation of methane, and methane production between control (background organic matter) and oil amended sediments. I hypothesized that sulfate in the sulfate reduction zone would be depleted more quickly when oil was added. I also hypothesized that methane would be produced sooner in the methane production zone when oil was added. In order to test these hypotheses, I collected sediment cores from the Chesapeake Bay to determine the zonation. From phase 1 of the project, the sulfate reduction zone (SR) was determined to be from 0 cm to 40 cm, the sulfate-methane transition zone (SMTZ) was from 40 cm to 80 cm, and the methane production zone (MP) was from 80 cm to 100 cm. The mud within each of these zones was then used for phase 2. In phase 2, incubations were done to determine the changes of sulfate and methane concentrations within each of the three zones over time and how oil affected these rates. From phase 2, sulfate concentrations remained relatively constant for all three zones for both the control and oil-amended groups. In terms of methane, the initial and final methane concentrations for each respective control and oil-amended group for all zones were similar. It was seen that the increase of methane occurred sooner in the oil-amend groups of the SMTZ and MP zones than the control groups.

From the results in phase 2, it was shown that the sulfate concentrations remained relatively constant for all zones in both the control and oil-amended groups throughout the time series. This contradicts the hypothesized decrease of sulfate, especially within the oil-amended groups. One explanation that can account for this constant sulfate concentration is oxygen contamination. Oxygen contamination would greatly disrupt this experiment, since the goal of this project is to study anaerobic processes, or processes in which oxygen is absent. If oxygen were present, then sulfate reduction, which is strictly an anaerobic process, would not proceed. Another way in which oxygen can disrupt this experiment is that oxygen oxidizes the sulfide

38

present in the sediments into sulfate, thus “erasing” any decrease in sulfate due to sulfate reduction. The expected starting sulfate concentrations based on the profile from phase 1 for SR, SMTZ, and MP, respectively, were 9.8 ± 3 mM, 3.7 ± 3 mM, and near zero. The initial sulfate concentrations from the phase 2 incubations for SR, SMTZ, and MP, respectively, were 12.5 mM (control) and 12.8 mM (oil), 6.9 mM (control) and 7.6 mM (oil), and 8.0 mM (control) and 10.3 mM(oil). Oxygen contamination and the resulting oxidation of sulfide into sulfate could explain why the experimental sulfate concentrations were higher than the expected concentrations for each zone. The experimental check results showed that oxygen contamination is plausible.

However, for the purposes of this discussion, I will assume that oxygen contamination did not occur. Thus, we can continue to evaluate the data. From time point 2 (day 5) to time point 3 (day 8), there was a noticeable increase in methane concentrations in the SMTZ and MP zones of the oil-amended group. For all three zones in the control group, there was a large increase in methane from time point 5 (day 15) to time point 6 (day 21). Methanogenesis is generally considered to be an anaerobic process. The fact that there was a spike in methane suggests that the syringes at one point or another did achieve anaerobic conditions.

For future studies, the presence of oxygen must be monitored closely so as to verify anaerobic conditions. The atmosphere in the glove bag should be checked for the presence of oxygen before the experiment and at each time point. We also need to determine a threshold for oxygen contamination, i.e. when is it ok to have a little bit of oxygen and when is there too much? Also, using glass syringes instead of plastic syringes and using saran wrap to cap the syringe instead of a rubber stopper can prevent possible oxygen contamination from the plastic and rubber materials.

In summary, oxygen contamination is likely, but the increase of methane in both the control and oil-amended groups suggests that the syringes were anaerobic. For the sake of this discussion, it is assumed that the syringes did achieve anaerobic conditions. Note that with this assumption, the following discussion points are tentative.

The sulfate concentration for all zones in both the control and oil-amended groups remained relatively constant. But despite this, there was a noticeable increase in methane concentrations observed for the control SR, SMTZ, MP and oil-amended SMTZ and MP. The sulfate-reducing bacteria (SRB) and methanogens compete for the same substrates, but the SRB outcompetes the methanogens until sulfate runs out. As sulfate decreases, the SRB become less active, and then the methanogens kick in, as seen with an increase in methane. So it is only when sulfate decreases should we see an increase in methane. In phase 2, it was shown that the sulfate concentration remained constant, yet there still was an increase in methane. Furthermore, it was seen that the increase in methane generally occurred sooner for the oil-amended groups than the control groups. These two observations suggest that competitive substrates do not play a major factor with the addition of oil.

Another anaerobic process that occurs is methane production through non-competitive substrates. Previous studies have shown that methane production can occur in the presence of sulfate because it does not use the same substrates (like H2) as do sulfate reducers. Non- competitive substrates include methanol, methyl amines, methionine, and organic sulfur compounds (Oremland and Polcin 1982; Whiticar 1999). A typical fermentation reaction using these non-competitive substrates (represented by “A”) is shown below (Whiticar 1999):

CH3-A + H2O  CH4 + CO2 + A-H (4)

39

Oremland and Polcin (1982) stated that methanogenesis can occur in “anoxic, sulfate- rich sediments provided there is an adequate supply of noncompetitive substrates.” The fact that an increase in methane was seen in both the control and oil-amended groups with constant sulfate concentrations suggest that these non-competitive substrates are present in both the control and oil-amended muds. It was also shown that the methane increase occurred sooner for the oil-amended groups than the control groups. Thus, it could be that the addition of oil somehow affected the abundance of non-competitive substrates. One possible explanation is that other bacterial processes breakdown the oil, and the resulting by-products are non- competitive substrates for methanogenesis. For example, there have been previous studies that have shown that the bacterially mediated breakdown of other substrates results in non- competitive substrates that can be utilized by methanogens (Reed et al. 1984; King 1984; Ollivier et al. 1994). A similar type of process could be happening with the breakdown of the hydrocarbons present in oil.

Thus, in summary, the addition of oil may have a greater effect on non-competitive methane production as opposed to the competitive substrate competition between the SRB and methanogens. Future studies should focus on this idea, with methods to monitor non- competitive substrates in addition to sulfate and methane at each time point. One non- competitive substrate that has high potential in being a primary substrate is trimethylamine (King et al. 1983). This experiment should be repeated, with special attention to the presence of oxygen and additional analysis to monitor non-competitive substrates.

Conclusion

Though the DH oil spill occurred three years ago, its effects can still be seen in the present day ecosystems. Oil that has not been mechanically removed or biodegraded has been trapped in the sediments. Kostka et al. (2011) have studied the bacterial response to oil in beach sands. Mortazavi et al. (2013) have studied the bacterial response to oil in intertidal sandy sediments. But presently, there are no studies regarding the bacterial response to oil in the benthic sediments.

The goal of this project was to determine how the addition of oil would affect microbial processes present in the anoxic sediments. Through incubation experiments, we found that the addition of oil might have a greater impact on methane production via non-competitive substrates as opposed to the production of methane through competitive substrates. Future work should be focused on determining whether or not this is the case.

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References

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Figures and Tables

Concentration

Figure 1. Sediment core and sulfate-methane concentration vs. depth profile with the zones labeled.

43

Figure 2. Location of the gravity core site. Image credit: http://ian.umces.edu/imagelibrary/displayimage-topn--127-5815.html

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Figure 3. Extraction of pore water from a sediment core.

Figure 4. A push core cut-off syringe (10 mL) used for sediment sampling.

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2- Real Data of [SO4 ] - [CH4] profile

2- SO4 Concentration (mM) 0 2 4 6 8 10 12 14 16 0

SR 20

40 SMTZ CH4 60 SO4 80 Depth (cm) Depth MP 100

120 0 0.5 1 1.5 2 2.5 3 3.5 4

CH4 Concentration (mM)

Figure 5. Sulfate–methane concentration profile from phase 1 with zones labeled. The blue diamonds represent methane concentrations and the red squares represent sulfate concentrations. SR is the sulfate reduction zone, SMTZ is the sulfate-methane transition zone, and MP is the methane production zone.

[Chloride] Profile from Phase 1

Chloride Concentration (mM) 250 260 270 280 290 300 0

20

40

60

Depth (cm) Depth 80

100

120

Figure 6. Chloride concentration profile from phase 1. The chloride concentration remained constant throughout the sediment column with an average of 285 ± 4mM.

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Sulfate: SR

13

11

9 Control mM SO4 7 Oil

5 0 5 10 15 20 25 30 Day

Sulfate: SMTZ

13

11

9 Control mM SO4 7 Oil

5 0 5 10 15 20 25 30 Day

Sulfate: MP

13

11

9 Control mM SO4 7 Oil

5 0 5 10 15 20 25 30 Day

Figure 7. Sulfate time series of each zone. The control group are the open diamonds, and the oil-amended group are the closed, black squares. The standard errors of each point are shown as error bars, but are usually within the time point marker.

47

Control Sulfate 14

12

10

8 SR 6 mM SO4 SMTZ 4 MP

2

0 0 5 10 15 20 25 30 Day

Oil-Amended Sulfate 14

12

10

8 SR+ 6 mM SO4 SMTZ+ 4 MP+ 2

0 0 5 10 15 20 25 30 Day

Figure 8. Sulfate time series for control and oil-amended groups. In the control group, the green diamonds are the sulfate reduction zone (SR), the orange squares are the sulfate-methane transition zone (SMTZ), and the gray triangles are the methane production zone (MP). In the oil- amended group, the blue diamonds are the sulfate reduction zone plus oil (SR+), the red squares are the sulfate-methane transition zone plus oil (SMTZ+), and the yellow triangles are the methane production zone plus oil (MP+). The standard errors of each point are shown as error bars, but are usually within the time point marker.

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Methane: SR 140 120 100 80 60 Control μM CH4 40 Oil 20 0 0 5 10 15 20 25 30 Day

Methane: SMTZ 140 120 100 80 60 Control μM CH4 40 Oil 20 0 0 5 10 15 20 25 30 Day

Methane: MP 140 120 100 80 60 Control μM CH4 40 Oil 20 0 0 5 10 15 20 25 30 Day

Figure 9. Methane time series of each zone. The control group are the open diamonds, and the oil-amended group are the closed, black squares. The standard errors of each point are shown as error bars, but are usually within the time point marker.

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Control Methane 140

120

100

80 SR

M CH4 60 μ SMTZ 40 MP

20

0 0 5 10 15 20 25 30 Day

Oil-Amended Methane 140

120

100

80 SR+ 60 μM CH4 SMTZ+ 40 MP+

20

0 0 5 10 15 20 25 30 Day

Figure 10. Methane time series for control and oil-amended groups. In the control group, the green diamonds are the sulfate reduction zone (SR), the orange squares are the sulfate- methane transition zone (SMTZ), and the gray triangles are the methane production zone (MP). In the oil-amended group, the blue diamonds are the sulfate reduction zone plus oil (SR+), the red squares are the sulfate-methane transition zone plus oil (SMTZ+), and the yellow triangles are the methane production zone plus oil (MP+). The standard errors of each point are shown as error bars, but are usually within the time point marker.

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Figure 11. Presence of air pockets in syringes at time point 4 (day 13).

Figure 12. Top picture shows a syringe without the presence of dark regions in the oil-amended MP at time point 6 (day 21). The bottom picture shows a syringe with the presence of dark regions near stopper and plunger in the control SR at time point 6 (day 21).

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Table 1a. GC Method for Control Samples.

Control FID and TCD Temperature > MeasureO2CH4CO2_LOOP.tem Initial Temp 35°C Hold 6.050 min Ramp 60°C/min Final Temp 84.98°C Run Time 10 min

Events > MeasureO2CH4CO2_LOOP.evt Time Event 0.050 G ON (Valve Rotate) 2.550 A ON (Stop Flow) 6.050 G OFF (Valve Rotate) 6.100 A OFF (Stop Flow)

Components > MeasureO2CH4CO2_direct.cpt Peak Name Start End 0 O2 1.943 2.380 1 N2 2.660 3.160 2 CH4 4.070 5.170 3 CO2 6.626 7.126 4 unknown CH4 7.223 7.723

Previous Control Methods Method What was wrong Temperature > TestCedricSensitivity.tem lower temp; no ramp Events > LoopInject_in_Col1_bypassCol2-on2.evt not for TCD Components > Measure CH4_3ccLoop_intoCol1_noCol2.cpt only air, CH4, CO2

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Table 1b. GC Method for Oil-Amended Samples.

Oil- Amended FID Temperature > oil_sediments_ramp_2ramps.tem Initial Temp 26°C Initial Temp 150°C Hold 5 min Hold 12.5 min Ramp 60°C/min Ramp 60°C/min Final Temp 150°C Final 200°C Run time 50 min

Events > LoopInject_in_Col1_bypassCol2-on2.evt Time Event 0.000 ZERO 0.050 G ON (Valve Rotate) 0.600 G OFF (Valve Rotate)

Components > oil_sediments_ramp_Loop_intoCol1_noCol2.cpt Peak Name Start End 1 air 0.716 1.216 2 methane 1.677 2.277 3 CO2 4.270 4.770 4 ethane 7.530 8.130 5 propane 10.590 11.090 6 1st butane 15.128 16.054 7 2nd butane 16.451 17.716 0 - 24.123 25.383

Previous Oil-Amended Methods Method What was wrong Temperature > TestCedricSensitivity.tem lower temp; no ramp > oil_sediments_ramp.tem not high enough temp Events > None Components > Measure CH4_3ccLoop_intoCol1_noCol2.cpt only air, CH4, CO2

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Table 2. Presence of air pockets in syringes at each time point. x = air pocket present. T1, t2, t3, and so forth represents the respective time points in which these observations were made. SR is the sulfate reduction zone, SMTZ is the sulfate-methane transition zone, MP is the methane production zone, SR+ is the sulfate reduction zone plus oil, SMTZ+ is the sulfate-methane transition zone plus oil, and MP+ is the methane production zone plus oil. The numbers 1 and 2 beside each zone represent the two separate syringes, as duplicates of each zone were taken at each time point.

t1 t2 t3 t4 t5 t6 t7 SR 1 x SR 2 x x SMTZ 1 x SMTZ 2 x MP 1 x MP 2 SR+ 1 x SR+ 2 SMTZ+ 1 x x x x SMTZ+ 2 x x MP+ 1 x x x MP+ 2 x x

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Bioaccumulation of Synthetic Musk Fragrances in Northern Diamondback Terrapins (Malaclemys terrapin terrapin) of Jamaica Bay, New York, USA

Lisa McBride, REU Fellow Maryland Sea Grant

Dr. Andrew Heyes, Research Associate Professor Chesapeake Biological Laboratory, University of Maryland Center for Environmental Sciences

Dr. Christopher L. Rowe, Associate Professor Chesapeake Biological Laboratory, University of Maryland Center for Environmental Sciences

Abstract

Synthetic musk fragrances are found in almost all scented personal care products. They have been measured in water, air, sewage sludge, and various biota all over the world. However, no studies have measured the maternal transfer of musk fragrances. The northern diamondback terrapin was selected as a bioindicator because of its long life span, high trophic level, high nest fidelity, and its ability to live across a wide salinity gradient. In this preliminary study we targeted one nitro-musk (musk xylene [MX]) and one polycyclic musk (galaxolide [HHCB]). We assessed their presence in eggs of terrapins inhabiting a contaminated site (Jamaica Bay, NY, USA) and compared those results to a less impacted site (United States Patuxent Naval Air Station along the Patuxent River in Maryland). We developed a method for extraction and used gas chromatography-mass spectrometry to analyze these parent compounds in terrapin eggs. We found that MX maternally transferred to the offspring. Work is still being done to measure the accumulation of HHCB and quantify the concentration of the musk fragrances in individuals. This field-based study is the first step in assessing the degree of exposure of terrapin embryos to synthetic musk fragrances accumulated by their parents.

Keywords: Bioaccumulation, Diamondback Terrapin, Environmental Toxicology, Synthetic Musk Fragrances

Introduction

Personal care products (PCPs) include a wide range of compounds found in soaps, lotions, toothpastes, deodorants, and sunscreens. Unlike pharmaceuticals, PCPs are intended for external rather than internal use (Brausch and Rand 2011). Large quantities of PCPs enter the environment unaltered, because they are never subjected to changes by metabolism. These compounds join the long list of other organic and inorganic chemicals such as polychlorinated biphenyls (PCBs); mercury, lead and other heavy metals; pesticides such as dichlorodiphenyltrichloroethane (DDT); and polycyclic aromatic hydrocarbons (PAHs) that enter the environment (EPA, 2000). It is known that PCPs have the potential to bioaccumulate in

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organisms (Peck and Hornbuckle 2006). Several studies have examined the potential harm to organisms that PCPs (Bester 2009; Brausch and Rand 2011; Dietrich and Hitzfield 2004; Hájková et al. 2007; Kelly et al. 2008) released into aquatic environments pose but the number of these studies pale in comparison to the number investigating pharmaceuticals. PCPs enter the environment from industrial discharges, surface runoff, leaks and spills, landfills, sewage effluent, inputs from combined sewer overflows, and atmospheric depositions (Romany 2010).

Within the last century, humans have expelled thousands of organic pollutants into the Earth’s waters such as PCBs, heavy metals, PAHs, and other contaminants. At the beginning of the 20th century, thousands of kilograms of PCBs were released into the Hudson River, NY by General Election Corporation (Kelly et al. 2008). As a consequence PCBs in snapping turtles were 47-fold higher in concentration compared to their reference site (Kelly et al. 2008). It has not been until the last several decades that studies have assessed the effects that such pollutants have on the biota. Some contaminants such as methylmercury (Stefansson et al. 2013) and PCBs (Kelly et al. 2008) bioaccumulate, while others such as PAHs are metabolized at energetic cost so they do not generally accumulate in organisms. In all cases such exposure causes risk for both parent and future offspring through genetic damage or the maternal transfer of the contaminants themselves.

Studies are often targeted to assess acute and chronic exposure. In testing for chronic exposures, some have focused on the long term effects on organisms, while others have examined the impact on offspring (Kelly et al. 2008; Rowe et al. 2008). Others take into account the life stage. For example, Rowe et al. (2008) found that in dietary exposure of larval leopard frogs (Rana sphenocephala) to the metal vanadium (V) presents risks during their metamorphic period. In another study by Rowe et al. (2011), dietary exposure of V and selenium (Se) to the larval tree frog (Hyla versicolor) showed no effects. These studies show that similar species may not respond in the same way to similar exposures and emphasize the need to assess numerous species.

The focus of this study is the bioaccumulation by maternal transfer of synthetic musk fragrances. Fragrances are another form of PCPs that are known to be released into aquatic environments (Chen and Bester 2009). Synthetic musks are fragrances that are found in many personal and household care products. There are two common types: polycyclic and nitro- musks. The groups of nitro-musks were the first ones used due to the affordability during the end of the 19th century (Hájková et al. 2007). Nitro and polycyclic musks are water soluble, but it is the high octanol-water coefficients (log Kow's) that signal the high potential for bioaccumulation in aquatic organisms (Geyer et al. 1994; Brausch and Rand, 2011).

Most sewage treatment plant processes fail to remove or break down musk fragrances and thus polycyclic and nitro-musks appear in aquatic environments. In the United States and Europe, only about 14.6% to 50.6% of fragrance materials are removed by primary wastewater treatment (Osemwengie and Gersetenberg 2004). Just like any chemical, its ability to bioaccumulte in organisms depends on their bioavailability, lipophilicity, and the metabolizing ability of the aquatic species. Experiments have been done on the environmental persistence of synthetic musk fragrances but not enough data has been collected to complete a toxicological risk assessment (Hájková et al. 2007).

Of all the types of nitro-musks, musk xylene (MX) and musk ketone (MK) were the ones mainly used since being introduced in 1888 (Mottaleb et al. 2004). In Japan, MX and MK were first detected in fish from coastal waters in 1980 (Peck and Hornbuckle 2006). The study found MX in 100% and MK in 80% of all the freshwater fish samples they collected from various

56 waters in Japan: Tama River, a dam, and Tokyo Bay from 1980 to 1981 (Yamagishi et al. 1983). These results suggested that these compounds existed as bioaccumulating pollutants. Nitro- musks have also been detected in the North Sea, rivers, and freshwater (Gatermann et al. 1995;1998); domestic and industrial sewage sludge and sediments (Berset et al. 2000; Bopp et al. 1993; Sapozhnikova et al. 2010); human adipose tissue and breast milk (Rimkuss et al. 1994; Müller et al. 1996); sewage treatment effluent (Osemwengie and Steinberg 2001); municipal wastewaters (Osemwengie and Gersetenberg 2004); developing and adult rats (Suter-Eichenberge et al. 1998); and fish, mussels, and shrimp (Gatermann et al. 2002; Hájková et al. 2007; Mottaleb et al. 2004; Rimkus and Wolf 1995; Sapozhnikova et al. 2010). The use of nitro-musks is slowly being phased out because of their persistence in the environment and toxicity to aquatic organisms (Brausch and Rand 2011; Daughton and Terns 1999).

Today, polycyclic musks are being used in higher quantities than nitro-musks. The two most commonly used are galaxolide (HHCB) and toxalide (AHTN). These two polycyclic musks are even being classified as high-volume chemicals with an average use per capita of about 15.5 mg/day in 1995 (van de Plassche and Balk 1997). Recently, polycyclic musks have been detected in human adipose tissue (Müller et al. 1996). These findings are leading scientists to research more about the potential dermal reabsorption and chronic toxicity that these musks possess. The cause of concern is heightened and further studies are being done to screen for the presence of synthetic musks in aquatic environments.

Diamondback terrapins (Malaclemys terrapin) are one of the only turtles that are endemic to the waters of the salt marsh ecosystem. In fact, the term “terrapin” is derived from the Native American language which describes a turtle that can live in fresh or brackish waters (Brennessel 2006). The range over which diamondback terrapins can be found in Figure 1A (Tihansky 2009). Osmoregulation is a huge challenge for diamondbacks because the salinity of the diamondback terrapin habitat is incredibly variable. The diamondback terrapin is able to survive in fresh water, brackish water, and water with marine salinity levels. Freshwater emydid turtles risk the chance of becoming salt loaded and dehydrated when kept in 100 percent seawater (Bressennal 2006). The diamondback terrapin is the only emydid turtle that has the ability to do this for weeks at a time. Although snapping turtles are known to venture into brackish waters, they are unable to survive there for very long.

Several physiological adaptations to salinity have allowed diamondback terrapins to survive in such a variable environment. Diamondback terrapins have a pair of orbital glands similar to sea turtles (Bressennel 2006). When diamondback terrapins are in high salt concentrations, the lacrymal glands allow them to produce tearlike secretions. The glands work like an accessory kidney. Several studies have been done to test the activation of the glands under different salt concentrations. Dunson and Dunson (1975) showed that in 100 percent seawater, the gland isn’t active enough to prevent salt concentrations in blood and body fluids from increasing. Thus the gland is not sufficient by itself to combat the increasing salt concentrations that a diamondback terrapin will experience in seawater. The lacrymal glands along with other adaptations are what help the diamondback terrapin to live in water of high salt concentration. The diamondback terrapin is uniquely adapted to survive a very variable habitat.

Diamondback terrapins have continued to face population declines throughout history. They have also been hunted for food, more so during the late 19th century (Ismail 2010). Currently, they are facing threats to their habitat with direct correlation to urbanization of coastal areas (Hauswaldt and Glenn 2005). Due to this change, toxic contaminants have been expelled into the waters. Researchers have studied levels of PCBs concentrations in snapping turtles (Kelly et al. 2008). Recently a study was done looking at PCBs in diamondback terrapins

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(Ismail 2010) but no studies have been done to measure bioaccumulation of synthetic musk fragrances in these animals. Salt marshes are one of the most productive ecosystems in the world because of their many interactions and trophic levels (Silliman and Bertness 2002). Measuring the bioaccumulation of chemicals in salt marshes can help provide critical information about the overall health of the ecosystem.

The diamondback terrapin is an excellent candidate for a bioindicator for several reasons: long life span, high trophic level, and high nest fidelity (Brennessel 2006). Although diamondback terrapins have a large species distribution, individuals within populations have a narrow home range (Ismail 2010). Those individuals do not venture off very far through their life span so the chemical burdens reflect the contaminants that are present in a certain area. Over time, diamondback terrapins have the potential to accumulate levels of synthetic musk fragrances from consumption as well as skin absorption through water and sediment (Gatermann et al. 2001; Pagano et al. 1999) Northern diamondback terrapins (Malaclemys terrapin terrapin) from the Jamaica Bay in New York, USA and the Patuxent River in Maryland, USA are the focus of this study.

This study will quantify synthetic musk exposure in Northern diamondback terrapin populations in two geographic regions: Jamaica Bay, NY, a portion of the New York Harbor area that receives large volumes of waste water and near the mouth of the Patuxent River in Maryland, a region where no waste water inputs are known to occur. It is not known whether diamondback terrapins have the ability to accumulate synthetic musk fragrances so this will be tested during this study. Also the ability for these compounds to transfer from parent to offspring will also be tested. Concentrations of synthetic musk fragrances in eggs from Jamaica Bay, New York will be compared to the control site, Patuxent River, Maryland.

Materials and Methods

Study Sites and Egg Collection

Eggs were collected from two geographical areas. One was designated as “experimental” and the other as “control” as shown in Figure 1B. The experimental site was a section along the coast of Jamaica Bay, which is New York’s largest wetland (Figure 2). Some of the industrial pollution to Jamaica Bay has been reduced; however, it still receives nearly 300 million gallons of sewage effluent discharge per day (Orton 2011). The control site is a protected intertidal zone located on the United States Patuxent Naval Air Station, which is along the mouth of the Patuxent River in southern Maryland. Eight clutches of five to seven eggs were collected from nests at both sites.

Initially, sample collection in Maryland was done using large minnow-like traps placed in the tidal marsh, which proved to be a difficult task. Arrangements were then made to have a field biologist (who was doing a population survey of diamondback terrapins in the area) collect the remaining clutches for this study. One pregnant female was caught and brought back to the Bernie Fowler Laboratory in Solomons, Maryland. The eggs were collected from the female by chemically induced ovipostion by intraperitoneal injection of oxytocin. After the eggs were collected, she was tagged and released back to the Patuxent River. All clutches were numbered and frozen at -20°C for synthetic musk analysis.

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Chemicals

All solvents that were used for the procedures in the laboratory were of ultra-high purity grade and purchased from J.T. Baker (Phillipsburg, NJ, USA). The musk xylene was purchased from 02Si Smart Solutions, galaxolide from EQ Laboratories Inc., and internal standards from Cambridge Isotopes Laboratories.

Extraction

In preparation for analyses, three eggs (replicates) from each clutch were randomly chosen, thawed to room temperature (27°C), and rinsed with distilled water. The eggs were cut open with a scalpel that was rinsed with 1:1 v/v mixture of acetone hexane. Its contents (yolk and albumin) were weighed to the nearest 0.01 g. The egg samples in this study weighed between 6-9 g. The samples were homogenized prior to mixing with approximately 40 g of anhydrous sodium sulfate (Na2SO4) (EMD Chemicals, Gibbstown, NJ, USA) to remove water. A unique ID was assigned to each sample before extraction to ease identification of individuals. Samples were then refrozen at -20°C until extraction.

The first extraction step was accelerated solvent extraction (ASE) using an ASE 300 extractor (Dionex, UT, USA). Alumina was activated at 550°C for 4 hours and partially deactivated with the addition of 6.0% of its mass of deionized H2O and thoroughly mixed for 15 minutes. Afterwards, about 18 g of deactivated alumina was weighed and poured into each 66 mL ASE cell. The homogenate (samples and Na2SO4) was packed on top of the alumina and then approximately 15 g of anhydrous Na2SO4 was poured to top it off. To each cell, 100 µl of surrogate (d-10 Naphthalene, d-10 Fluorene, d-10 Fluoranthene, d-12 Perylene) was added. Every set of 12 ASE cells included a blank and a randomly selected sample that was to be the spike. To assess recovery, the one egg designated as a spike was injected with 500 µl of MX (1000 ng/ml) and HHCB (200 ng/ml) standard. Samples and standards were extracted with dichloromethane in an ASE 300. The ASE method parameters were set to: temperature, 100°C; static time, 10 min; flush volume, 60 mL; purge time, 100 seconds; static cycles, 2; cell size, 66 m.

The extract was collected in a glass container and then about 10 g of Na2SO4 was added to rid samples of excess H2O. The extract was transferred to a 500 mL round bottom flask and exchanged to hexane using a rotary-evaporator (Buchi, Switzerland) with a 50°C water bath. The samples were transferred to a 4 mL vial, rinsing the round bottom flask twice with approximately 2 mL of hexane.

A glass chromatographic column was assembled with a clean glass wool plug at the bottom and then pre-rinsed with petroleum ether. Alumina was activated at 550°C for 4 hours and partially deactivated with the addition of 6.0% of its mass of deionized H2O and thoroughly mixed for 15 minutes. Afterwards, 4 g of alumina was poured into the column, covered by about 1 g of Na2SO4 and then pre-eluted once with 25 ml of petroleum ether. Samples were added to the column then eluted with 25 mL of petroleum ether. The eluent was then exchanged to hexane using a rotary-evaporator at 50°C. The samples were transferred to a 4 mL vial, rinsing the round bottom flask twice with approximately 2 mL of hexane.

The remaining 4 mL of sample was reduced in volume to 0.5 mL by bubbling ultra-high purity nitrogen gas (UHP N2) over the sample. The samples were then transferred to 2 mL autosampler vials and 100 µl of internal standard (d-10 Acenaphthene, d-10 Phenanthrene, d-

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12 Benz[a]anthracene, d-12 Benzo[a]pyrene, d-12 Benzo[ghi]perylene) was added. Laboratory blanks were processed with and in the same manner as all egg samples by extracting 40 g of Na2SO4 in each ASE cell spiked with the same internal standards.

Gas Chromatography/GC-MS detection

An Agilent 7890 gas chromatograph with a 5975 MS detector (GC-MS) was used for analysis. A DB5-MS 30m x 0.25 mm x 0.25 µm capillary column was utilized to quantify congener specific synthetic musk fragrances. The gas chromatograph was operated in spiltless mode using the ultra-pure helium carrier gas with a flow rate of 0.6 mL/min. The oven temperature of the program was as follows: 45°C for 1 minute, 10°C/min to 280°C for 0 minutes, 5°C/min to 310°C for 16.5 minutes. The injector temperature was set at 250°C and the detector temperature at 150°C.

Agilent Technologies Enhanced Chemstation E.02 was used to monitor and view results for each chromatogram. Peaks were indentified based on chromatographic retention times and confirmed using quantification ions of 282.0 for MX and 243.2, 213.2, and 258.1 for galaxolide in comparison of the internal standards that were added and those of the calibration standard. Quantification of MX and HHCB will eventually be performed using the relative response factors generated from the calibration curve of the calibration standard.

Statistical Analysis

Mean concentrations of synthetic musk fragrances are to be compared between the sampling sites by ANOVA, where each clutch represents a replicate observation. Normality of the model will be tested and the data will be transformed (for example, by logarithms) as necessary.

Results

A test run consisting of a control sample and spike sample was performed to determine if any musk fragrances could be seen by the GC-MS in the samples. The MX was present in the control sample as shown in Figure 3. Having determined that we could detect the target musks we moved on to measuring MX and HHCB in a batch of samples.

Five clutches were extracted from the United States Patuxent Naval Air Base in Maryland and a set from Jamaica Bay, NY. Each clutch contained three replicates all labeled first with the clutch number followed by the replicate. The Jamaica Bay clutches were analyzed in succession as has been done for numerous other compounds. However, it became apparent that the performance of the GC-MS quickly deteriorated with successive samples. Upon examination of the instrument it was clear that the drop in performance resulted from instrument contamination as illustrated in Figure 4. Lipid was still present in the sample and further sample clean-up was needed as loss of chromatographic sensitivity through a run is undesirable for the quantification of MX and HHCB. Figure 5 shows the loss of sensitivity by the GC-MS when trying to analyze samples from Jamaica Bay, NY.

Discussion

A test run showed that the target musks (MX, HHCB) could be detected by the GC-MS. Having not run a calibration curve, the concentrations were unable to be calculated. After receiving clutches from Jamaica Bay, NY and the United States Naval Air Base along the

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Patuxent River, MD, they were set up to be analyzed for MX and HHCB. The GC-MS lost sensitivity due to lipid interference and quantification of MX and HHCB was unable to be performed.

The GC-MS needed to be cleaned after the initial batch run and unfortunately we could not perform any additional analysis within our time frame. What is clear is that further clean-up will be needed, and steps to rid the samples of the excess lipid material will be performed. There are several options that can be undertaken in order to limit the amount of lipid in the samples. Kelly et al. (2008), DeGrady and Halbrook (2006), and Ikonomopoulou et al. (2012) used gel permeation chromatography (GPC) to remove lipid from eggs in the study of PCBs. GPC is a common cleanup practice but is not currently available in our lab. However, we have not tested the impact of using GPC on synthetic musk fragrances. While suitable for PCBs, we may lose these compounds during the cleanup step, thus recovery testing is required.

There a couple of other clean-up options. The sample extracts can be passed over silica gel to remove lipid from samples. This option uses sulfuric and water to rinse the samples and then runs them through 3.6 g 2% deactivated silica gel which then produces an eluent that can be run through the GC-MS successfully. However, the impact of this clean-up step on the synthetic musks must also be examined using spike recovery approaches as we have done for earlier steps and shown in Figure 3.

All of these options pose possible ways for us to remove excess lipid content from our samples. With the lipid removed, we would be able to measure the concentrations of MX and HHCB in the clutches and make a comparison from our two sites.

Given the results of our test runs, we showed that GC-MS is a viable option to measure synthetic musk fragrances in organisms. Although we couldn’t analyze the samples from our experimental site, we were able to determine that the musk compounds (MX and HHCB) accumulated in the diamondback terrapin, suggesting that they are also maternally transferred.

Conclusions

Even though our samples from the experimental and reference sites were unable to be measured for MX and HHCB concentrations, we were able to validate two out of three of our hypotheses. The test run was able to determine that the northern diamondback terrapin does accumulate the synthetic musk fragrances. Also the fact that the embryo had MX accumulation suggests that the compounds are indeed maternally transferred. The test run proved to be a major success. In the next month, lipid extraction and separation will be performed. Once we are able to clean-up the samples, we will be able to determine the degree of exposure that terrapin embryos have to synthetic musk fragrances accumulated by the parents.

Acknowledgements

Thank you to my mentor Dr. Andrew Heyes for his guidance, advice, and patience throughout this project. Also I want to thank Dr. Christopher L. Rowe for his support and giving me opportunities to help with the field work. I want to give a big thanks to Mrs. Cheryl Clark, without her I wouldn’t have learned everything I did in the Organic-Analytical Laboratory. Also, I want to give additional thanks to Dr. Russell Burke of Hofstra University and Sarah Funck (a Student Conservation Association intern working with the Patuxent River Natural Resources Division at the United States Patuxent Naval Air Base) for providing us with diamondback terrapin eggs for this study. Thank you to the National Science Foundation, Maryland Sea

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Grant, and the University of Maryland Center for Environmental Sciences at Chesapeake Biological Laboratory for funding and providing this amazing Research Experience for Undergraduates (REU). This research experience has been invaluable and it is a summer that I will never forget.

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Figures and Tables

Figure 1. Map of the eastern United States showcasing the range of diamondback terrapins and collection sites of this study. (A) Diamondback terrapins live along coastal regions from Massachusetts down south to Texas, which is represented in a red outline. (B) Reference of the two sites being studied during this experiment. Jamaica Bay, NY is indicated by the green star and the United States Naval Air Base along the Patuxent River in Maryland is represented by a yellow star.

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Brooklyn JFK

Queens

Figure 2. Jamaica Bay in New York, USA. The boroughs of Brooklyn and Queens surround the bay. To the northeast of the bay is the John F. Kennedy (JFK) International Airport. The key provides representation of several sewer outfalls, water pollution control plants, and landfills.

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Spike

Sample

Time (min)

Figure 3. GC-MS chromatogram of the test run that we performed on a sample and spike. The retention time of MX is around 18.50 minutes and is shown in both white and green in the chromatograph above via two peaks. The white is a sample from the United States Naval Air Base along the Patuxent River (control). The green is a sample from the control site that was spiked with 500 μl of MX (1000 ng/ml) in order to assess recovery. This test run proved that the GC-MS could detect the target compounds.

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Figure 4. Auto tune print out that showcases the loss of sensitivity in the GC-MS on July 24, 2013. After running the Jamaica Bay samples on July 23, 2013, the GC-MS stopped working. The number of peaks underlined in red show that the detector is too noisy. The range should be around 150-155 peaks. The many peaks in the graph indicate the interferences with hydrocarbons, lipids, and oils that shouldn’t be in the samples. The nitrogen gas percentage circled in red shows that there is a leak in the machine. A desired range should show nitrogen gas equal to or less than 5%.

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July 24

July 23

Time (min)

Figure 5. GC-MS chromatogram of MX and HHCB standards ran on two separate dates and then overlaying them. The green chromatogram clearly shows MX and HHCB standards at correct retention times on July 23, 2013. These standards were analyzed prior to the analysis of the Jamaica Bay samples. The white chromatograph shows the loss of sensitivity of the GC-MS on July 24, 2013 after the Jamaica Bay samples were passed through the machine. The high lipid material caused the loss in sensitivity and presence of oils resulted in interferences making it impossible to clearly resolve MX and HHCB.

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Modeling of Blue Crab Catchability in Chesapeake Bay Using Winter Dredge Surveys

Andrew Mealor, REU Fellow Maryland Sea Grant

Dr. Michael Wilberg, Associate Professor Chesapeake Biological Laboratory, University of Maryland Center for Environmental Science

Introduction

The Chesapeake Bay has historically supported the most productive blue crab (Callinectes sapidus) fishery in the world, but the fishery has experienced a decline in yield during the 1900s to the mid-2000s (Kennedy et al. 2007; Miller et al. 2011). This decline has been largely attributed to overfishing, and an intensive monitoring program is conducted to inform better management of the fishery (Bunnell et al. 2010). However, the blue crab population remains below the historical average abundance despite reductions in fishing effort (Maryland Sea Grant College. et al. 2001; Miller et al. 2011). It has been hard to attribute how much of the recent decrease is due to declining abundance or an artifact of the methods used to estimate abundance. For this reason, it is important to examine potential sources of bias in population estimates and to devise methods that improve estimates used for management.

Blue Crab

In the Chesapeake Bay, the blue crab is both an ecologically and economically important species. Therefore a management plan that allows for both the continued harvest of blue crabs while maintaining appropriate abundances is very important in order for the blue crab to serve its ecological role in the Bay. In 2011, the total blue crab catch in the Chesapeake Bay was worth over 80 million dollars, making it one of the most valuable commercial species in the region and the most valuable commercial fishery in the Bay (National Marine Fisheries Service 2011). While the commercial harvest has varied considerably since 1950, the blue crab remains a relatively abundant and intensely exploited species (Kennedy et al. 2007; Figure 1). Blue crab is a crucial component of the food web as a predator, detrivore, and as prey (Laughlin 1982). The most recent management plan set by the Bi-State Blue Crab Technical Advisory Committee attempts to keep the population size above a minimum threshold set at 86 million mature crabs, and an exploitation rate below 53% (Miller et al. 2011).

Winter Dredge Survey and Catchability

Maryland and Virginia have conducted a winter dredge survey of blue crabs in the Chesapeake Bay yearly since the winter of 1989/1990 in order to determine the amount of crabs available for harvesting and the rate of exploitation in previous years (Sharov et al. 2003). During the winter, colder waters (<10°C) cause the blue crabs to enter a state of dormancy and bury themselves in the sediment (Vølstad et al. 2000). This allows for a survey to be conducted

71 under the reasonable assumption of a closed system, because blue crabs will not move into or out of the surveyed area. Each sample consists of a 100 m tow of a 1.8 m wide Virginia crab dredge in stratified random locations throughout the Bay. Blue crabs caught in the dredge are counted, measured, and sexed in addition to having their maturity status determined. The survey covers nearly all of Chesapeake Bay in order to accurately depict the size and distribution of the blue crab population (Miller et al. 2004; Figure 2).

A single tow of a dredge likely does not catch all of the crabs in an area, which means some estimate of the efficiency of the gear, or catchability, is required to extrapolate the number of crabs caught to the total number of crabs initially present (Vølstad et al. 2000). Currently, the estimates of catchability for the winter dredge survey are very low, typically ranging between 0.1-0.2 for Virginia and 0.2-0.4 for Maryland, meaning that only 10 to 40% of crabs in an area are thought to be caught by the dredge. These estimates were obtained from depletion experiments, where a specific area is repeatedly dredged until no further crabs are obtained (Vølstad et al. 2000). When used to determine population size, correcting with these catchability values will result in a population size estimate that is 2-10 times larger than would be obtained if sampling with perfectly efficient gear. However, if these estimates are negatively biased, then the true population size could be substantially smaller than is currently calculated.

The winter dredge survey depletion experiments can be precise, but they are likely to be biased. The gear used to conduct the depletion experiment creates a survey area that is relatively very narrow compared to the amount of location error associated with positioning the vessel (Wilberg et al. In Press). A horizontal location error of only a meter would result in the sample area being mislocated by over half its width. The vessel and location errors that occur while resampling an area could result in catching crabs that were outside of the intended sampling zone, thus causing a negative bias in the catchability estimate. Indeed, methods used to estimate catchability in the winter dredge survey can be substantially biased under realistic scenarios of sampling (Gould et al. 1997; Wilberg et al. In Press).

The goal of this study was to develop and test a new method for estimating catchability of the winter dredge survey. This new approach (i.e., checkerboard method) utilizes perpendicular tows in a checkerboard pattern as opposed to parallel tows, because the parallel tow approach has been shown to cause inaccuracy in estimating catchability (Hennen et al. 2012). The objectives of this study were to determine the accuracy and precision of both the current and checkerboard depletion experiment method of estimating gear catchability at varying levels of sampling effort and actual catchability.

Materials and Methods

I compared the accuracy and precision of two depletion experiment methods by conducting a simulation study using the program R (R Development Core Team. 2008). The first (current) method consists of repeatedly conducting three adjacent parallel tows until catch reaches zero (Vølstad et al. 2000), while the new (checkerboard) method consists of conducting two perpendicular sets of disjoint parallel tows. Catchability was estimated using each method for several levels of true catchability and amounts of sampling effort.

Current Method

The current method followed the methodology modified from the one currently used to determine catchability for the winter dredge survey (Vølstad et al. 2000). This entails repeated sampling in the same location until no further blue crabs are caught, or seven sets of tows (21

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tows total) are conducted (Figure 3). A sample in the current removal experiment consists of three parallel, non-overlapping tows of 100 m (Vølstad et al. 2000). Catchability can be determined from the slope of the regression of catches in each removal experiment using the Leslie model (Leslie and Davis 1939).

= [ ], (1)

𝑖𝑖 0 𝑖𝑖−1 where is the catch from the ith removal𝑦𝑦 𝑞𝑞 ∗(set𝑃𝑃 of− 𝐾𝐾three parallel adjacent tows), is the catchability coefficient, is the number of crabs initially present in the survey area before the 𝑖𝑖 experiment,𝑦𝑦 and is the aggregate catch of crabs before the ith removal. 𝑞𝑞 0 𝑃𝑃 In practice,𝐾𝐾𝑖𝑖−1 catchability for the winter dredge survey has been estimated using the Ricker method as opposed to the Leslie method (Sharov et al. 2003). However, Wilberg et al. (In press) suggested that the Leslie method was less biased and more accurate than the Ricker method, and was therefore used in this study.

Checkerboard method

The checkerboard method attempts to mitigate bias by reducing the likelihood of unintentional overlap among tows. In this method, n parallel tows of 50 m in length are evenly spaced along a width of 50 m. Then, a second set of n tows, perpendicular to, and overlapping with, the first set are conducted (Figure 3). Because this approach does not rely on perfectly adjacent tows, horizontal displacement error of sufficiently small size will not result in a change in the area that is sampled twice, and therefore this method should not require as high of location accuracy as the current method.

I calculated the expected area of intersection of the two sets of tows in order to determine the area that was sampled twice. The catches from the first and second experiments were then used to calculate catchability,

( ) (2) = 1 , 𝑦𝑦2 𝑆𝑆1+𝑆𝑆2 −𝑦𝑦1∗𝑆𝑆1 𝑞𝑞 − 𝑦𝑦1∗𝑆𝑆2 where is the catch from the first set of n parallel tows, is the catch from the second set of n tows, perpendicular to , is the expected area in m2 in the plot that was sampled exactly 1 2 once, and𝑦𝑦 is the expected area in m2 in the plot that was𝑦𝑦 sampled exactly twice. Because 1 1 each dredge has a width𝑦𝑦 of𝑆𝑆 1.8 m, the area of the intersection of two perpendicular tows is 2 3.24m2, 𝑆𝑆was calculated as

𝑆𝑆1 = 2 1.8( 1.8), (3)

and was calculated as 𝑆𝑆1 𝑛𝑛 ∗ 𝑙𝑙 − 𝑛𝑛 ∗

𝑆𝑆2 = 3.24 , (4) 2 where n was the number of tows per set. 𝑆𝑆2 ∗ 𝑛𝑛

A maximum likelihood approach was used to estimate catchability for the checkerboard method. For this approach, upper and lower bounds were set for density ( ) and catchability (q) by restricting their values to a physically possible domain. To begin the numerical search for the 𝐷𝐷 73

parameter estimates, and q were given initial values ( and ) close to the maximum likelihood value. The initial catchability value is calculated using the arctangent function in order restrict the input to the𝐷𝐷 domain of the negative log likelihood𝐷𝐷0 function,𝑞𝑞0

( ) = + 0.5, (5) arctan 4∗𝑞𝑞−2 𝑞𝑞0 𝜋𝜋 where is the catchability estimate given by (2). The initial value for density was determined by dividing the maximum mean catch per tow by the product of the tow area and the arctangent transformed𝑞𝑞 catchability value,

( ( ), ( )) = . (6) max mean 𝑦𝑦1 mean 𝑦𝑦2 0 𝐷𝐷 𝑤𝑤∗𝑙𝑙∗𝑞𝑞0 Minimum density and catchability were set at * , and maximum density was set at the arbitrarily high value of one crab m-2. Maximum catchability was also set at one, which is the highest possible value. 𝐷𝐷0 𝑞𝑞0

The maximum likelihood approach was developed by minimizing the negative log likelihood of catching the observed number of blue crabs in each using a negative binomial distribution, = ( + , + ( ) + 1 + , log( ) 2 𝑛𝑛 𝑘𝑘 (5) −𝐿𝐿𝐿𝐿 ∑𝑗𝑗=1 ∑𝑖𝑖=1 − 𝛤𝛤�𝑘𝑘 𝑦𝑦𝑗𝑗 𝑖𝑖� log𝛤𝛤 ( 𝑘𝑘 )),𝛤𝛤 � 𝑦𝑦 𝑗𝑗 𝑖𝑖� − 𝑘𝑘 ∗ 𝐸𝐸𝑗𝑗+𝑘𝑘 − 𝐸𝐸𝑗𝑗 ∗ 𝐸𝐸𝑗𝑗 where is the gamma function, is the dispersion𝐸𝐸𝑗𝑗+𝑘𝑘 parameter of the negative binomial th th distribution, , is the catch from the i tow in the j set of parallel tows, and is the expected catch from𝛤𝛤 a single tow in the jth 𝑘𝑘set of parallel tows. Expected catch for each of the first set of 𝑗𝑗 𝑖𝑖 𝑗𝑗 parallel tows𝑦𝑦 was calculated as the product of the area swept by the tow, density𝐸𝐸 of crabs, and the catchability of the gear,

= . (7)

Expected catch for the second set of parallel𝐸𝐸1 𝑙𝑙 ∗tows𝑤𝑤 ∗ 𝐷𝐷was∗ 𝑞𝑞 calculated as minus the product of the mean catch of the first set of tows, catchability, the number of tows per set, and dredge width divided by dredge length, 𝐸𝐸1

= mean( ) . (8) 𝑛𝑛 Note that this is a special case𝐸𝐸2 of the𝑤𝑤 ∗ Rago𝑞𝑞 ∗ �𝐷𝐷 et∗ 𝑙𝑙al.− (2006) 𝑦𝑦estimator.1 ∗ 𝑙𝑙 �

Simulation Study

I used the program R to conduct a simulation study based on the design of Wilberg et al. (In Press). The simulations generated data sets using a range of true values of catchability, number of samples per experiment, and number of experiments per estimate (Table 1). The number of tows per experiment were chosen to keep sampling effort close to that currently used in depletion experiments on the Chesapeake Bay. True catchability covered almost the full range of possible values. A grid was created with each cell having a width and length of 0.18 m. 2 Crabs were randomly allocated to cells with an average density of 0.2 crabs/m , Only one crab could be assigned per cell. Crabs were removed from the grid from cells that were included

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within the tow area with a probability equal to the true catchability. The current method was simulated following the protocol in Wilberg et al. (In press) that utilized the Leslie method. A location error from a normal distribution with a standard deviation of 0.55 m was used based on the error associated with a high accuracy Geographical Positioning System (GPS) unit (Witte and Wilson 2005). An additional 500 simulations per scenario were conducted using the same treatment levels (Table 1) but without location error.

Performance

Accuracy of each method was accessed by comparing the estimated catchability to the true catchability for each scenario. I calculated the relative error (RE) of estimated catchability as the difference between true and estimated catchability relative to the true catchability,

= (9) 𝑞𝑞� − 𝑞𝑞 𝑅𝑅𝑅𝑅 where was the estimated catchability and was𝑞𝑞 the true catchability for each trial. Root mean square relative error (RMSRE) was also calculated to provide a summary of the accuracy of each method,𝑞𝑞� 𝑞𝑞

= (10) N 2, ∑i=1 𝑅𝑅𝑅𝑅𝑖𝑖 where N was the number of simulations𝑅𝑅𝑅𝑅 𝑅𝑅per𝑅𝑅𝑅𝑅 scenario.� 𝑁𝑁

The previous methods detail the methodology for estimating catchability using a single experiment, but in practice multiple experiments are conducted to estimate catchability. I employed resampling techniques in order to simulate the effect of repeating the experiment multiple times. For each method, 1-15 experiments were selected from the 500 simulated experiments for each scenario without replacement, and the mean and median catchability values were calculated. The RE of estimated catchability from the set of experiments was then calculated. An additional, more sophisticated, resampling technique was used in conjunction with the maximum likelihood estimation method. Instead of simply averaging the estimates, the actual data sets of catches obtained on each tow for all the experiments were combined in a joint maximum likelihood approach. For each number of experiments scenario, 500 sets of experiments were resampled.

Results

Neither method performed better across all scenarios, but in the majority of scenarios the checkerboard method resulted in less biased estimates of catchability. The performance of both methods was affected by true catchability, with current method performing better at low catchability and the checkerboard method better at medium to high catchability. RE of catchability estimation for the checkerboard method ranged at low levels of catchability and sampling effort from -2 to 2, and at high levels of catchability and sampling effort from less than -0.1 to 0.1.

Calculation Methods

In general, the maximum likelihood (ML) estimator performed better than the method of moments estimator for the checkerboard method. The ML estimator resulted in lower or equivalent RMSRE values at every catchability level for a given number of tows per experiment

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(Figure 4). The ML estimator also resulted in less negative bias for low catchability levels at every level of effort (Figure 5).

Effects of Location Error

The checkerboard method was much less sensitive to location error than the current method. At every level of catchability, the difference in RMSRE values for simulations done with and without incorporating location error was much smaller using the checkerboard method than the current method (Figure 4). The current method produced a negative median RE at every catchability level when location error was included, but with perfect accuracy produced unbiased estimates (Figure 6). At high levels of catchability and high numbers of tows per sample, the checkerboard method’s estimates of catchability became slightly negatively biased when location error was included (Figure 5).

Effects of True Catchability

For a given number of tows per sample, the estimated catchability from the checkerboard method had a greater RMSRE than the current method at low catchability levels, but had lower RMSRE at medium to high catchability levels (Figure 4). In general, the checkerboard method was more sensitive to catchability than the current method, with much lower RMSRE values resulting from high catchability levels than very low catchability levels. For both methods, increasing the true catchability level increased the precision of catchability estimates (Figures 5 and 6).

Effects of Number of Experiments

Increasing the number of experiments improved both the accuracy and precision of the checkerboard method, but did not improve the accuracy of the current method. Increasing the number of experiments sampled reduced the range of RE in estimated catchability for both methods. Increasing the number of experiments produced more accurate catchability estimates in the checkerboard method, but the current method remained negatively biased for all numbers of experiments (Figure 7). The most accurate and precise estimates of catchability were obtained by taking the mean value of the catchability estimates across the set of experiments. In scenarios without location error, the current method performed better both in terms of accuracy and precision than the checkerboard method (Figure 8).

Discussion

The checkerboard depletion experiment for estimating catchability performed substantially better under almost all scenarios with location error than the current method. Importantly, location error only caused a negative bias at high sample sizes with substantial overlap among parallel tows. The checkerboard method was robust to small amounts of location error that could be obtained using high accuracy GPS, while the current method has a consistent negative bias in the presence of this level of location error. The checkerboard method, therefore, should produce more accurate catchability estimates and result in improved estimates of abundance in the winter dredge survey.

Of the two catchability estimators for the checkerboard method, the ML estimator performed better. This method incorporated data from each tow individually, therefore utilizing information on a finer scale than the method of moments estimator, which combined catches across tows. The ML estimator also can be constructed such that the parameter estimates are

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constrained to realistic values. My implementation constrained catchability to values between 0- 1. The ML estimator could be further modified to restrict the estimate of crab density if additional information on density is available. However, the ML estimator for multiple experiments did not result in more accurate or precise catchability estimates than simply taking the mean of the values produced from each experiment separately. If the checkerboard method was to be used in practice, I would recommend estimating catchability of each experiment separately using the MLE, and then taking the mean of these values.

For the level of location error used in this study, an experiment consisting of 19 tows in each direction per experiment produced the most reliable estimates of catchability. Experiments with fewer tows resulted in greater variability of estimates and therefore had a lower level of accuracy. Experiments with more than 19 tows resulted in biased estimates because the parallel tows were so close that they overlapped one another. This reduced the amount of area that was sampled exactly one time proportionally more than the area that is sampled exactly twice, artificially reducing the effectiveness of the tow and creating a negative bias.

Another benefit of the checkerboard method is that its accuracy can be increased by conducting multiple experiments during a season. Increasing the number of experiments conducted using the current method does not improve the accuracy if location error is present, because the estimates are negatively biased. A desired level of confidence can be achieved with the checkerboard approach by increasing the number of experiments.

The results from this study are very similar to those produced by Wilberg et al. (In press). Both studies examined the accuracy of the current method using the Leslie estimator, high accuracy GPS, and high crab density with and without location error. In Wilberg et al. (In press), RE of the estimated catchability was also calculated, and was found to become progressively more biased as actual catchability increased in situations with location error. Furthermore, variation in the RE of estimated catchability was found to significantly decrease as catchability increased. A very similar pattern was found in this study as well. This was an expected result, as the methodology was modified very little between these two studies.

My study implemented location errors in the depletion experiment, but did not account for other forms of error or stochasticity. I assumed that all tows in a set could be conducted perfectly parallel to one another. If a boat were to travel diagonally across the sample area, this would change the area of each intersection and change the proportion of areas that are sampled exactly once to those that are sampled twice. Further simulation could be conducted to determine the significance of this source of error. However, if buoys were deployed to mark the boundaries of the survey area, it is reasonable to expect that the boat could be piloted in a relatively straight line. Furthermore, I did not incorporate error that could result from the heterogeneity of the bay-floor sediment, which could cause heterogeneous catchabilities among sites, or that crabs might have different inherit catchability levels. Finally, I assumed that all experiments were conducted in locations with the same density of blue crabs.

The number experiments done per year may to be increased to provide precise catchability estimates when all real world uncertainties are included. These complications could also introduce more spatial error than what is modeled in this this study, necessitating greater spacing between tows in order to avoid negative bias associated with accidental overlap. However, it would be necessary to conduct further studies to access whether or not these factors would introduce bias into the checkerboard model. Wilberg et al. (In press) found that varying crab population density did not introduce bias into the catchability estimation for the

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current method, and it reasonable to expect that a similar result for the checkerboard method, although the length of each dredge tow may need to be increased.

The checkerboard method would require an increase in sampling effort from what is used to conduct the current method. At the recommended level of 38 tows per sample, the checkerboard method significantly exceeds the current method which uses a maximum of 21 tows per sample. Furthermore, it would be reasonable to expect an average higher catch per tow from the checkerboard method than the current method as it does not sample until depletion. This would require more time spent counting crabs aboard the vessel, further increasing the resources needed to conduct the checkerboard method. Much of the time would probably be spent maneuvering the vessel into location for each tow. However, the increase in resources needed to conduct the checkerboard method is relatively small when compared to the total resources needed to conduct the winter dredge survey, which consists of over 1,000 tows per year (Sharov et al. 2003).

The checkerboard approach should provide better estimates of catchability than the approaches that have been used. The winter dredge survey is an important information source for blue crab management in Chesapeake Bay (Sharov et al. 2003). Over 1,000 tows are conducted each year for the blue crab winter dredge survey, making it a very resource intensive effort (Sharov et al. 2003). In order to maximize the usefulness of the data collected from this survey, it is necessary to have accurate estimates of catchability. Even if the checkerboard method is more resource intensive, it is still justifiable in that would only represent a small fraction of the resources used for the entire winter dredge survey and is crucial to the overall usefulness of the project.

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National Marine Fisheries Service. 2011. Fisheries of the United States. National Marine Fisheries Service.

R Development Core Team. 2008. R: A language and environment for statistical computing. R Foundation for Statistical Computing.

Rago P. J., J. R. Weinberg, C. Weidman. 2006. A spatial model to estimate gear efficiency and animal density from depletion experiments. Canadian Journal of Fisheries and Aquatic Sciences 63: 2377-2388.

Sharov, A. F., J. H. Volstad, G. R. Davis, B. K. Davis, R. N. Lipcius, and M. M. Montane. 2003. Abundance and exploitation rate of the blue crab (Callinectes sapidus) in Chesapeake Bay. Bulletin of Marine Science 72: 543-565.

Vølstad, J. H., A. F. Sharov, G. Davis, and B. Davis. 2000. A method for estimating dredge catching efficiency for blue crabs, Callinectes sapidus, in Chesapeake Bay. Fish B 98: 410-420.

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Wilberg, M. J., J. M. Robinson, S. A. M. Rains, J. L. Humphrey, and R. N. Lipcius. In Press. Effects of location erros on estimates of dredge catchability from depletion based methods. 1-17.

Witte, T. E., and A. M. Wilson. 2005. Accuracy of WAAS-enabled GPS for the determination of position and speed over ground. Journal of Biomechanics 38: 1717-1722.

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Figures and Tables

Table 1. Summary of parameter values used for each simulation experiment.

Parameter Standard New Method Method Values Values Crab Density 0.2 0.2 True Catchability 0.1,0.2,…,0.9 0.1,0.2,…,0.9 Dredge Length 100m 50m Number of tows 3 10,12,13,14, per sample 16,19,22,27 Max Number of 7 2 Samples

55 3 50 45 40 35 30 25 20 Landings, metric tonnes *10 tonnes metric Landings, 15 1950 1960 1970 1980 1990 2000 2010 Year Figure 1. Commercial landings of blue crab in the Chesapeake Bay, 1950 to 2010. Source: National Marine Fisheries Service

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Figure 2. Spatial distribution of all winter dredge survey locations for one year in the Chesapeake Bay. Source: Sharov et al. (2003)

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A) B) 1.8m 1.8m

100m

𝑙𝑙

Figure 3. Methods for conducti ng depletion experiments. The current winter dredge survey methods (A) use sets of three parallel, adjacent tows to repeatedly sample an area until zero crabs are caught. In the checkerboard method (B), one set of disjoint, parallel tows is conducted followed by a second, perpendicular set of tows. The darker regions indicate areas sampled twice. The length of tows (l) in the checkerboard method was 50 m.

Checkerboard Method Current Method New Method Standard

4

3

RMSRE 2

1

0 0.2 0.4 0.6 0.8 0.2 0.4 0.6 0.8 Catchability Figure 4. Root mean square relative error (RMSRE) for the checkerboard and current experiment methods at each simulated catchability level. The checkerboard method was conducted using 19 tows per experiment. Circles depict simulations incorporating location error, triangles do not incorporate location error. For the checkerboard method, black points were from the method of moments estimator and the gray points were from the maximum likelihood estimator.

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Figure 5. Relative error (RE) for the checkerboard experiment method at several simulated catchability levels and number of tows per experiment. Numbers in the top right corner of each box indicates catchability level of all simulations in the box. Non-shaded boxes depict the method of moments estimator, and shaded boxes indicate the maximum likelihood estimator. The top row of boxes incorporate location error, and the bottom row does not. Solid lines indicate the median, boxes depict interquartile range, and whiskers show the 90th and 10th percentiles.

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Figure 6. Relative error (RE) for the current experiment method at each simulated catchability level. Non-shaded boxes depict simulations with location error, shaded boxes do not incorporate location error. Solid lines indicate the median, boxes depict interquartile range, and whiskers show the 90th and 10th percentiles.

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Checkerboard Method Current Method New Standard

1 0.2 0.2 0.5 0 -0.5 -1

1 0.4 0.4 0.5 0 -0.5 -1 Relative Error 1 0.6 0.6 0.5 0 -0.5 -1

1 0.8 0.8 0.5 0 -0.5 -1

1 3 5 7 9 11 13 15 1 3 5 7 9 11 13 15 Number of Experiments

Figure 7. Relative error (RE) for the checkerboard and current experiments incorporating location error at several simulated catchability and number of replicate experiments. Catchability was calculated using the maximum likelihood (ML) estimator for the checkerboard method and the Leslie estimator for the current method. Experiment number indicates how many experiments were resampled to produce RE. Numbers in the top right corner of each box indicate catchability level. White boxes indicate use the mean RE of the catchability across experiments, light gray use the median catchability, and dark gray, checkerboard method only, use a joint ML approach using data from all experiments combined. Solid lines indicate the median, boxes depict interquartile range, and whiskers show the 90th and 10th percentiles.

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CheckerboardNew Method StandardCurrent Method 1 0.2 0.2 0 -1 1 0.4 0.4 0 -1 Relative Error 1 0.6 0.6 0 -1 1 0.8 0.8 0 -1 1 3 5 7 9 11 13 15 1 3 5 7 9 11 13 15 Number of Experiments

Figure 8. Same as Figure 7 except that the scenarios include no location error. Solid lines indicate the median, boxes depict interquartile range, and whiskers show the 90th and 10th percentiles.

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Calibration of Modern Coral Climate Signals to Ensure Accuracy of Paleoclimate Determinations in Anegada, British Virgin Islands

Sean Pearson, REU Fellow Maryland Sea Grant

Dr. K. Halimeda Kilbourne, Research Assistant Professor Chesapeake Biological Laboratory, University of Maryland Center for Environmental Science

Abstract

Three modern Diploria strigosa corals from Anegada, British Virgin Islands were analyzed for Sr/Ca ratios over their lifetime to create a modern calibration between Sr/Ca and sea surface temperature (SST) for use in climate reconstructions of subfossil coral specimens from the same site. Results were compared to those of Hetzinger et al. 2006 and Giry et al 2010. Average mean Sr/Ca between the three corals was 9.03 mmol/mol, yielding an expected intercoral mean Sr/Ca range in the Caribbean for this species of approximately 9.00±0.15 mmol/mol when the other studies are incorporated. The average Sr/Ca-SST relationship for the three corals using ordinary least squares regression was -0.043 mmol/mol/°C using the monthly Sr/Ca from corals and gridded temperature from the ERSST 3b data set (Smith et al. 2008), which agrees with previous findings. The average Sr/Ca-SST relationship for the three corals using reduced major axis regression was -0.0564 mmol/mol/°C, whereas the average Sr/Ca- SST relationship was -0.122 mmol/mol/°C based on the monthly anomalies of both data sets. This calibration informs further work reconstructing Medieval climate data from subfossil corals at this site.

Keywords: palaeoclimatology, Diploria strigosa, Sr/Ca, coral

Introduction

Policy makers and governments must use the best and most up to date scientific data on global and regional climate in order to make informed decisions on subjects ranging from energy policy to disaster management. Predicting the nature of ongoing and future climate change requires understanding the processes involved in a changing climate. A thorough and extensive record of Earth’s past climate changes can provide the data necessary to understand these processes. Consistent instrumental climate data only go back as far as 1850 at best and more often only back to the mid-1950s, which is not long enough to study decadal to centennial climate fluctuations (Jones et al. 2001; Kennedy et al. 2011a, b). Researchers use natural climate-sensitive archives to extend the climate record back in time, and corals are often used for this purpose, especially in the tropics (Jones et al. 2001). Coral heads can grow for multiple centuries, and they leave geochemical evidence in their skeletons of sea surface temperature (SST) and sea surface salinity (SSS) at a monthly resolution (Corrège 2006). Additionally, their skeletons contain annual density bands (Knutson et al. 1972) that can be absolutely dated using

88 radiometric techniques. With data from seasonal to centennial time scales, corals provide higher resolution data than other proxies, such as most sediment cores, ice cores, or speleothems, while also providing longer-term data than our instruments provide (Jones et al. 2001). The range of resolvable timescales in corals is ideal for the analysis of changes in seasonal to interannual variability over decades to centuries, in a single sample (Jones et al. 2009). Climate information from corals can provide useful information to put the modern climate and its fluctuations in historic context, and help fill in current gaps in our understanding of the nature and causes of climate variability at decadal to centennial time scales.

Surface ocean temperatures can be determined from a coral by measuring the ratio of strontium to calcium in their aragonite (CaCO3) skeleton. As corals grow, they incorporate some strontium in place of calcium in their skeleton, at a rate dependent on temperature and the oceanic Sr/Ca ratio. Assuming a constant ocean Sr/Ca ratio in the last hundred thousand years, the Sr/Ca ratio in corals is used to measure sea surface temperature at the time and place of coral growth, leading to broader conclusions about the climate at the time (Beck et al. 1992).

Bands of alternating density in coral cross-sections show differential growth based on seasonality. The work of Knutson et al. (1972) established that each pair of dense and less dense bands represents a year of growth, so density bands can be counted to estimate the longevity of the coral specimen as well as the age of samples taken from a live coral. The most common method for determining the absolute date on samples not taken from a live coral is uranium series dating. Uranium series dating methods in corals are based on the fact that dissolved seawater uranium decays to thorium, which precipitates out of the water as soon as it is formed. The lack of thorium in surface waters ensures that thorium found in coral skeletons originated as uranium, and the amount of each isotope shows the progress of the radioactive decay series, which allows calculation of the age of the coral (Edwards et al. 1987). Using annual density bands and radiometric dating, we can determine absolute ages for each geochemical measurement in a coral. These ages give dates to such highly-resolved and long- term climate data, sometimes up to hundreds of thousands of years in the past (Edwards et al. 1987, Kilbourne et al. 2004).

The larger project, of which my summer research is a part, focuses on corals from the island of Anegada in the British Virgin Islands that were washed ashore in an overwash event, likely by one or a series of tsunamis several centuries ago (Atwater et al. 2012; pers. comm. 2013). Current research is actively determining whether these deposits are tsunami or hurricane in origin, as well as the number of events and their dates. Initial dates on the corals from Anegada indicate the coral records will provide climate information about the years spanning approximately 1200-1400 CE. This is a time period when the Earth was transitioning from the Medieval Climate Anomaly to the Little Ice Age. This time period is of particular interest to climate scientists who are trying to understand the causes of global climate fluctuations over the last millennium so that they can ensure similar processes are included in the climate models used for predicting future climate change. Anegada is a low-lying carbonate island, isolated northeast of the rest of the British Virgin Islands. It lies on the southern edge of the Puerto Rico Trench, part of the subduction zone between the North American and Caribbean plates. This tectonic boundary has generated tsunami-forming earthquakes, such as in 1690 and 1867, and leaves Anegada vulnerable to similar events (Atwater et al. 2012).

The larger project will reconstruct the climate in the northern Caribbean in the centuries before the overwash event(s), as well as date the coral boulders washed ashore to help determine the timing and frequency of past tsunamis in the area. My summer research project assists the larger study by performing Sr/Ca analysis on modern Diploria strigosa corals from

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Anegada and comparing the results to known sea surface temperature values from instrumental data. This establishes a relationship between Sr/Ca and SST in D. strigosa at this site that will be used as calibration for temperature reconstruction from the island’s subfossil corals.

Materials and Methods

Sample cores were taken in March 2013 along the maximum growth axis from coral heads washed ashore onto the island of Anegada in the British Virgin Islands. These include the coral species D. strigosa and Montastraea faveolata. Subfossil corals were found inland as part of the overwash deposits, while modern samples were found piled up on the beach, washed ashore by more recent storms. The modern corals used for the project’s calibration are all D. strigosa and were collected from the beach at Soldier Point, approximately 18.749°N, 64.337°W (Figure 1). They include sample numbers 13AN17, 13AN18, and 13AN19, measuring 12 cm, 17 cm, and 4 cm and representing approximately 18 years, 30 years, and 6 years of growth, respectively. These specific samples were chosen because they did not show signs of weathering, an indication the coral heads were washed up on the beach recently. They also had pristine-looking surfaces without bioerosion or evidence of encrusting organisms, indicating that they were alive when they washed up on the beach and that they died from exposure to air.

The cores were cut into 0.5-1 cm thick longitudinal slabs with a tile saw. The slabs were ultrasonically cleaned for a minimum of 30 minutes in deionized water and air dried. Once dry, the slabs were micro-drilled using a computer-controlled Taig MicroMill from Super Tech and Associates with a 0.5 mm dental drill bit along the thecal walls to collect approximately 12 sequential coral powder sub-samples per year. 13AN17, 13AN18, and 13AN19 were drilled at 0.625 mm, 0.483 mm, and 0.549mm intervals, respectively, reflecting their average monthly growth rates over six to ten years. 100-200 µg aliquots from each sub-sample were weighed using a Sartorius microbalance and dissolved in 2-4 ml 2% nitric acid to produce an optimal Ca concentration of 20±5 ppm. Samples were analyzed for strontium and calcium using a Perkin Elmer 8300 inductively coupled plasma optical emission spectrometer (ICP-OES). Data were corrected for instrumental drift by running internal gravimetric standards between every sample, following the methods of Schrag (1999). These corrections allow use of a less expensive and more efficient sampling instrument while optimizing precision to a level comparable to that of thermal ionization mass spectrometry techniques (Schrag 1999). The precision of individual Sr/Ca determinations was determined by replicate analysis of a laboratory coral standard made from the species M. faveolata.

Approximately 400-mg subsections of the coral slabs were removed for uranium-series dating following the method of Shen et al. (2012). Samples were analyzed for the abundance of 236U, 235U, 234U, 233U, 232Th, 230Th, and 229Th using a multicollector inductively coupled plasma mass spectrometer (MC-ICP-MS), with their signals amplified by a secondary electron multiplier (Shen et al. 2012). The rare isotopes 236U, 233U, and 229Th are used as an isotope dilution spike to improve the precision of the other environmentally relevant isotopes (Shen et al. 2012). The formulas given for age determination in corals using U-series data relate amounts of 238U, 234U, and 230Th as follows: 230 234 Th − λ 230T δ U (0) λ230 (λ 234− λ 230)T 1− = e − (1− e ) 238 1000 λ − λ (1) [ U ]act ( )( 230 234 )

234 234 λ T δ U (T )= δ U (0)e 234 (2)

90 where the subscript “act” indicates the activity ratio, δ234U(0) indicates the initial δ234U value of the sample, λ’s are decay constants, and T is the age of the sample in years (Edwards et al. 1987). δ234U is in delta notation, defined as the 234U/238U ratio in the sample expressed as a proportion of the 234U/238U ratio expected at secular equilibrium and multiplied by 1,000. Uranium series dating was used to verify the modernity of the corals.

Sr/Ca values for each sample were averaged with their rerun as well as with samples that overlapped in time due to sampling of a separate thecal wall when applicable. This series was age modeled, by matching minima in SST with maxima in Sr/Ca and vice versa, using the software program Analyseries, so that the seasonal cycle inherent in Sr/Ca data would correspond with the cycle in SST data from NOAA’s Extended Reconstructed Sea Surface Temperature (ERSST) v3b dataset (Smith et al 2008). The age modeled series was then linearly interpolated using Analyseries so that there would be exactly one Sr/Ca data point per month. Annual averages of the Sr/Ca and SST data were also calculated using an April-March climatological year. Both the monthly and the annual data were shifted relative to the SST data year by year and the correlation between the Sr/Ca and SST calculated at each lag to determine the span of growth with the highest correlation between the two sets. The age model in each core was shifted to correspond to the time span with the highest correlation between the two sets. The U-series dates to be determined in the last week of August 2013 will provide a means to confirm or reject this initial age model.

Once the age model was optimized, reduced major axis (RMA) and ordinary least squares (OLS) regression calculations were made on the paired monthly Sr/Ca and SST data to calculate the slope and intercept in the Sr/Ca-SST relationship in these corals. Monthly anomalies, the difference between a month’s data point and the average value for that month over the coral’s life, were put through the same regression analyses to determine how Sr/Ca reflects monthly variability. SST was then reconstructed from Sr/Ca anomaly data and the determined slope to test the precision of this model against the instrumental SST anomalies. Mean Sr/Ca for each coral as well as their slope in relation to SST were compared to the two other Sr/Ca-SST calibrations of D. strigosa (Hetzinger et al. 2006, Giry et al. 2010).

Results

The lagged correlations between Sr/Ca and temperature for each coral are shown in Figure 2. AN17 and AN19 have maximal correlations of -0.68 and -0.87 respectively when correlated to temperature, with a most recent date in winter 2011-2012. This indicates that these two corals died sometime during the winter of 2011-2012. Maximal correlation for AN18 was - 0.37 with a most recent date in winter 2005-2006 (Figure 2). This indicates a death six years earlier than the other two corals in the winter of 2005-2006.

Linearly interpolated age modeled Sr/Ca data for each coral are shown in Figure 3, while monthly anomalies of these data are shown in Figure 4. Mean Sr/Ca ratios over the lengths of AN17, AN18, and AN19 were 8.99 mmol/mol, 9.13 mmol/mol, and 8.96 mmol/mol, respectively. This range encompasses Giry’s mean of 9.08 mmol/mol (2010), and is not significantly different from Hetzinger’s mean of approximately 8.85 mmol/mol (2006) (Table 1).

The Sr/Ca versus SST slopes using OLS regression for these corals were -0.039±0.002 mmol/mol/°C, -0.051±0.002 mmol/mol/°C, and -0.039±0.003 mmol/mol/°C, respectively. These values are comparable to the other papers' slope of -0.042±0.002 mmol/mol/°C, which was reported using OLS. RMA Sr/Ca-SST relationships for these corals were -0.052±0.005 mmol/mol/°C, -0.063±0.004 mmol/mol/°C, and -0.054±0.009 mmol/mol/°C, a steeper

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relationship than OLS but well within the expected range for most coral species, and a consistent result between cores. Regression of anomaly data was more variable, with OLS slopes ranging from -0.028±0.002 mmol/mol/°C to -0.084±0.009 mmol/mol/°C. RMA regression of anomaly data consistently yields slopes less than -0.1, about twice as steep as the monthly Sr/Ca-SST relationships calculated in the same way. These results are summarized in Table 2.

The standard deviations of reconstructed SST from Sr/Ca anomalies are similar to instrumental SST anomalies in the same time period as each coral’s growth (AN17: 0.336°C versus 0.335°C; AN18: 0.305°C versus 0.325°C; AN19 0.393°C versus 0.294°C), showing a precise calibration.

Discussion

These modern corals were washed up on the beach, thrown ashore by large storms. These storms were originally assumed to be hurricanes, possibly Hurricane Earl which swept through the northern Caribbean in August 2010, but the Sr/Ca data show that these corals all died in the winter. Large winter storms in 2005-2006 (for AN18) and 2011-2012 (for AN17 and AN19) were likely the cause of the coral heads’ placement on the beach. Further research into the weather history of the area could identify the exact storm events that may have been responsible. Uranium-series dating results will also help verify the death date of these corals.

The three coral cores analyzed in this study, as well as the two in previous studies, were all from the same species, time, and warm Caribbean environment, so they should have very similar signals. Sr/Ca averages throughout each coral were fairly consistent, all falling within 9.00±0.15 mmol/mol. This range establishes an expected intercoral variability in Sr/Ca means within Caribbean D. strigosa.

Sr/Ca-SST slopes calculated using OLS regression are similar to the slope provided by Hetzinger and confirmed by Giry. However, OLS regression ignores any error in the x variable, and we know that the temperature readings, although fairly precise, also contain some error. RMA regression was used for this reason, as it accounts for errors in both variables, albeit assuming equal error in both. We report steeper slopes using RMA regression. This is likely to be a more accurate evaluation of the relationship between Sr/Ca and SST. Another, unbiased estimation, such as total least squares regression, may be even more suitable, as it accounts for the proper amount of error in each variable. Future studies in this field should shift to using more accurate regression methods.

Anomaly slopes (mean = -0.122 mmol/mol/°C) were about twice as steep as Sr/Ca-SST slopes (mean = -0.0564 mmol/mol/°C). Taken at face value, these results would indicate that the Sr/Ca-SST relationship is dependent on timescale, meaning a monthly calibration could not be applied to yearly or decadal Sr/Ca variations. This timescale dependence is contrary to the main premise of coral paleoclimatology that the Sr/Ca-temperature dependence is fundamentally controlled by thermodynamics, with a mean offset caused by the biological control of calcification. Before concluding a result so diametrically opposed to previous work in the field, the data must be checked for more likely scenarios, such as errors in the anomaly calculations and data analysis, or a math artifact of the difference in signal to noise ratios of the two datasets. Additionally, several samples that were outliers from the seasonal cycle will be rerun to correct for possible instrumental error. These reruns may improve the signal to noise ratio and reduce problems in the anomaly results.

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Conclusions

A calibration has been established between the Sr/Ca ratio in D. strigosa corals from Anegada and SST. Sr/Ca-SST correlation for the three modern corals indicate death dates in the winters of 2005-2006 and 2011-2012. Mean Sr/Ca values for these corals and those in the two previous studies yield an expected intercoral variability in this species of approximately 9.00±0.15 mmol/mol. The Sr/Ca-SST monthly relationship found using ordinary least squares regression is similar to the slope of -0.042±0.002 mmol/mol/°C established by previous studies. Reduced major axis regression yields slightly steeper slopes and may be a better indicator of the Sr/Ca-SST relationship because it takes into account errors in both variables. This work indicates that corals from this site are valid indicators of SST and will inform further work on temperature estimates from the project’s subfossil corals. These estimates will provide temperature data for the Medieval northern Caribbean and will be compared to coeval coral- based climate reconstructions in the Pacific to determine the extent of influence that the tropical Pacific has on interannual variability in the tropical Atlantic.

Acknowledgments

I would like to thank Dr. Hali Kilbourne and Yuanyuan Xu for all of their help and guidance throughout this project. I would also like to thank the National Science Foundation for funding my role in this project through Maryland Sea Grant and for funding the larger project.

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References

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Atwater, B.F. Personal communication to K.H. Kilbourne, 2013.

Beck, J.W., R.L. Edwards, E. Ito, F.W. Taylor, J. Recy, F. Rougerie, P. Joannot, and C. Henin. 1992. Sea-surface temperature from coral skeletal strontium/calcium ratios. Science 257:644-647.

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Corrège, T. 2006. Sea surface temperature and salinity reconstruction from coral geochemical tracers. Palaeogeography, Palaeoclimatology, Palaeoecology 232:408-428.

Edwards R.L., J.H. Chen, and G.J. Wasserburg. 1987. 238U-234U-230Th-232Th systematics and the precise measurement of time over the past 500,000 years. Earth and Planetary Science Letters 81:175-192.

Giry, C., T. Felis, M. Kölling, and S. Scheffers. 2010. Geochemistry and skeletal structure of Diploria strigosa, implications for coral-based climate reconstruction. Palaeogeography. doi: 10.1016/j.palaeo.2010.10.022

Hetzinger, S., M. Pfeiffer, W.-C. Dullo, E. Ruprecht, and D. Garbe-Schönberg. 2006. Sr/Ca and δ18O in a fast-growing Diploria strigosa coral: Evaluation of a new climate archive for the tropical Atlantic. Geochemisty Geophysics Geosystems. doi: 10.1029/2006GC001347

Jones, P.D., T.J. Osborn, and K.R. Briffa. 2001. The evolution of climate over the past millenium. Science 292:662-667.

Jones P.D., K.R. Briffa, T.J. Osborn, J.M. Lough, T.D. van Ommen, B.M. Vinther, J. Luterbacher, E.R. Wahl, F.W. Zwiers, M.E. Mann, G.A. Schmidt, C.M. Ammann, B.M. Buckley, K.M. Cobb, J. Esper, H. Goosse, N. Graham, E.Jansen, T. Kiefer, C. Kull, M. Küttel, E. Mosley-Thompson, J.T. Overpeck, N. Riedwyl, M. Schulz, A.W. Tudhope, R. Villalba, H. Wanner, E. Wolff, and E. Xoplaki. 2009. High-resolution palaeoclimatology of the last millennium: a review of current status and future prospects. The Holocene 19:3–49.

Kennedy, J.J., N.A. Rayner, R.O. Smith, M. Saunby, and D.E. Parker. 2011a. Reassessing biases and other uncertainties in sea-surface temperature observations since 1850 part 1: measurement and sampling errors. J. Geophys. Res.. doi:10.1029/2010JD015218

Kennedy J.J., N.A. Rayner, R.O. Smith, M. Saunby, and D.E. Parker. 2011b. Reassessing biases and other uncertainties in sea-surface temperature observations since 1850 part 2: biases and homogenisation. J. Geophys. Res. doi:10.1029/2010JD015220

Kilbourne, K.H., T.M. Quinn, and F.W. Taylor. 2004. A fossil coral perspective on western tropical Pacific climate ~350 ka. Paleoceanography. doi:10.1029/2003PA000944

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Figures and Tables

Figure 1. The island of Anegada, British Virgin Islands. The green arrow indicates Soldier’s Point, the beach where the three modern corals in this study were found. Image: Google Maps

Figure 2. Sr/Ca-SST correlations based on annual averages to determine the life span of modern corals from Anegada.

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Figure 3. Sr/Ca ratios vs depth of the coral core over the proposed life span of modern D. strigosa corals from Anegada, BVI.

Figure 4. Monthly Sr/Ca anomalies based on linearly interpolated age modeled Sr/Ca data its difference to the monthly climatology during each coral's life span.

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Table 1. Mean Sr/Ca ratios for modern Caribbean Diploria strigosa corals. Mean Sr/Ca (mmol/mol) AN17 8.99±0.008 AN18 9.13±0.007 AN19 8.96±0.015 Hetzinger's Gua1 8.85 Giry's BON-9-A 9.08

Table 2. Sr/Ca-SST relationships of modern Diploria strigosa corals in the Caribbean.

Sr/Ca-SST RMA Sr/Ca-SST OLS Sr/Ca-SST RMA Sr/Ca-SST OLS seasonal seasonal anomaly anomaly relationship relationship relationship relationship (mmol/mol/°C) (mmol/mol/°C) (mmol/mol/°C) (mmol/mol/°C) AN17 Slope: - Slope: - Slope: - Slope: - 0.052±0.005 0.039±0.002 0.112±0.014 0.039±0.003 Intercept: Intercept: 10.08 Intercept: -1.39*10- Intercept: -1.16*10- 10.43±0.006 r: -0.76 3 3 r: -0.76 p<0.0001 r: -0.35 r: -0.35 p<0.0001 p<0.0001 p<0.0001 AN18 Slope: - Slope: - Slope: - Slope: - 0.063±0.004 0.051±0.002 0.131±0.011 0.028±0.002 Intercept: Intercept: 10.54 Intercept: -8.79*10- Intercept: -5.96*10- 10.87±0.003 r: -0.81 11 3 r: -0.81 p<0.0001 r: -0.10 r: -0.10 p<0.0001 p=0.028 p=0.028 AN19 Slope: - Slope: - Slope: - Slope: - 0.054±0.009 0.039±0.003 0.149±0.031 0.084±0.009 Intercept: Intercept: 10.06 Intercept: -1.83*10- Intercept: 1.87*10- 10.48±0.022 r: -0.72 16 16 r: -0.72 p<0.0001 r: -0.56 r: -0.56 p<0.0001 p<0.0001 p<0.0001

Slope: -0.042±0.002 Intercept: 10.01±0.063 r: -0.65 Hetzinger and Giry p<0.0001

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Marine Chromophoric Dissolved Organic Matter Distribution in the Atlantic Ocean

Sandra Pittelli, REU Fellow Maryland Sea Grant

Dr. Michael Gonsior, Assistant Professor Chesapeake Biological Laboratory, University of Maryland Center for Environmental Science

Abstract

Seawater samples (200) containing dissolved organic matter (DOM), collected in the Atlantic Ocean, have been solid-phase extracted, analyzed by EEM Fluorescence Spectroscopy and used to create a robust Parallel Factor Analysis (PARAFAC) model. Using a 24 bottle rosette and CTD profiler aboard the R/V Atlantic Explorer, a total of 24 samples (10 liter volume), were collected at the Bermuda Atlantic Time Series Station (BATS) at every 200 m down to 4,530 m. Each sample was then solid-phase extracted. The quinine sulfate normalized fluorescent peaks that represent humic-like and fulvic-like peaks showed a continuous increase until constant levels were reached at about 1,400 m depth. The protein-like fluorescent peak showed exactly the opposite trend, indicating that this fluorescent component is correlated with the abundance of marine biota (autotrophic and heterotrophic organisms) as there is more marine life in the surface waters of the ocean. The source of the humic and fulvic-like fluorescence at greater depths is not yet known, but it is still plausible that it is related to heterotrophic microorganisms. Additionally, sunlight would severely photobleach these fluorescent dissolved organic matter (FDOM) components at the surface ocean and deplete them. The PARAFAC model was then applied to determine the relative contribution of the six PARAFAC components found for Atlantic solid-phase extracted marine FDOM. The components established for humic-like, fulvic-like and protein-like fluorescence followed the same trends as described above, indicating that the PARAFAC model nicely resembles the results obtained through the quinine-sulfate normalized method. These trends have also been confirmed in previous studies using direct water samples and hence the solid-phase extraction method, combined with PARAFAC analysis used in this study is suitable to obtain representative fluorescent data with the advantage of using much more concentrated samples and therefore a greatly improved signal.

Keywords: EEM Fluorescence Spectroscopy, Dissolved Organic Matter (DOM), PARAFAC, Solid-phase Extraction

Introduction

Dissolved organic matter (DOM) is present in every naturally occurring aquatic environment on Earth and is a major constituent in the global carbon cycle. It is estimated that the amount of carbon that is stored as DOM in the ocean is approximately equivalent to the total mass of carbon in the form of CO2 in the atmosphere (Hansell and Carlson 1998). Despite the

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major role that DOM plays in the carbon cycle, the specific composition remains largely unknown (Benner 2002). The lack of characterization is due to the fact that DOM consists of a wide variety of compounds with a diverse range of structures and molecular weights. It also has a multitude of sources of origin, some of which include: soil and sediments, extracellular release from algae, grazing and excretion of zooplankton, and viral lysis of bacteria and algae cells (Nagata et al. 2000). DOM is complex and its reactivity is far from understood. It is for this reason that further analysis of DOM is necessary to help understand some of its diverse interactions in aquatic ecosystems, with a particular focus on the global ocean system. The apparent radiocarbon (δ14C) age of the deep ocean DOM was calculated to be between 3,700 and 6,000 years old (Bauer et al. 1992) and would suggest that deep sea DOM is refractory. Previous studies have proposed the idea that the microbial carbon pump (MCP) produces refractory DOM (Jiao et al. 2010), which can remain intact in the ocean for thousands of years, and thus playing a role in the storage of CO2 in the deep ocean (Bauer 2002).

What constitutes as DOM is so diverse that its molecular composition still remains largely uncharacterized. However, the light-absorbing component of DOM, or chromophoric DOM (CDOM), can be analyzed based on its optical properties. CDOM is important because it has been suggested that a significant component of refractory DOM is part of this pool. Along with the production from microbial communities, CDOM largely originates from terrestrial sources with lignin and tannins being the major classes of known compounds that strongly absorb light (Hansell and Carlson 2002). Lignins are only produced by woody plants and therefore originate from terrestrial sources whereas tannins have been found both on land and in some marine macro algae.

CDOM absorbs ultraviolet and visible light (Hansell and Carlson 2002) and plays an important role in the ocean not only as an integral part of the carbon cycle, but as also a protective layer for organisms by absorbing harmful UV-A and UV-B radiation (Caporaso et al. 2010). Additionally, while CDOM acts as a protective barrier, it also undergoes photolysis and degrades with the absorption of UV-A and UV-B radiation. This process known as photobleaching, is dependent on the intensity of sunlight and hence photochemical processes are much more pronounced in surface water and do not affect CDOM in the deeper waters outside the reach of sunlight (Yamashita and Tanoue 2008). This means that marine deep-sea CDOM is presumably bio-refractory, but it is likely to remain photochemically more reactive when compared to surface CDOM.

Beside the optically-active CDOM, DOM can also be characterized on its biological reactivity or bioavailability. Previously, three categories were used to classify DOM reactivity: labile DOM (LDOM), semi-labile DOM (SLDOM) and refractory DOM (RDOM) (Caporaso et al. 2010). LDOM and SLDOM are degraded on relatively short time scales, whereas RDOM is believed to remain in marine systems for thousands of years. Due to its long half-life, the formation of RDOM is of the most interest when studying aquatic systems because it defines the sequestered portion of dissolved carbon in the deep ocean. Much of the CDOM faction of DOM is considered to be within the RDOM classification and may be a good proxy for this bio- refractory RDOM. It should be noted that deep-water RDOM has a longer half-life when compared to surface water RDOM indicating that RDOM further accumulates in the abyssal sea (Caporaso et al. 2012). This may be explained by its photochemical activity and photobleaching at the surface. It remains unresolved why deep-sea DOM has such an old apparent 14C age, because the meridian overturning circulation (MOC) would expose this deep-sea DOM after an estimated 1,000 years to sunlight at the surface, but the apparent 14C age was calculated to be between 3,700 and 6,000 years. We know that this deep-sea DOM is photochemically active and must undergo photochemical transformation at the surface. A possible explanation for this

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controversy is that the deep-sea is supplied by 14C-free DOM so that the apparent age of deep- sea DOM appears to be very old, but is in fact less than 10% of DOM that is necessary to be derived from 14C-free sources to explain the apparent age. Sources may include oil seepages, the oxidation of methane, and/or DOM produced by hydrothermal vent activity.

CDOM not only contains absorbing organic molecules but also fluorescent compounds. This fluorescent pool of CDOM is known as FDOM. When taking these characteristics into consideration it is possible to use FDOM as a tracer using fluorescence spectroscopy, as fluorescence measurements are in general, 1,000-fold more sensitive when compared to UV- Vis absorbance. Using fluorescence spectroscopy it is possible to determine the relative amount of FDOM in a seawater sample when using quinine sulfate normalized fluorescence units (Yamashita and Tanoue 2008). While the relative amounts of FDOM in certain parts of the ocean can be compared, it is still unknown exactly which compounds make up the FDOM, CDOM, and DOM classifications. The fluorescence method determines only a very specific optical parameter of the bulk DOM pool and is limited in supplying structural information of compounds. However, fluorescence is a useful technique in determining the presence of compound classes that fluoresce and their relative levels.

Fluorescence spectroscopy has been a helpful tool in characterizing FDOM for a number of years. However more recently, fluorescence excitation emission matrix (EEM) spectroscopy has been utilized to characterize terrestrial and marine components of DOM (Coble 1996). The peak regions in Figure 1 are marked and have been described in previous work (Coble 1996; Coble et al. 1998). These regions refer to the specific compound families that are thought to contribute to the overall fluorescence of the water sample. “Protein-like” fluorescence is due to the fluorescence of aromatic rings in amino acids (Coble 1996) and “humic-like” fluorescence arises from other compounds that are not well known. These components vary with depth (Figure 2). This is to be expected and our study should demonstrate the same concept. Due to photo-bleaching, there should not be as much FDOM in surface water when compared to deep water samples. The sample referred in Figure 2 as “surface water” was collected at 99 m down the water column and is well within the mixed layer of the Sargasso Sea. At this depth, the fluorescence of chlorophyll was most pronounced, which indicates the maximum concentration of phytoplankton and further suggests that algae do not significantly contribute to humic and/or fulvic-like FDOM in this region.

When pairing EEM with statistical techniques such as parallel factor analysis (PARAFAC), additional information about components of FDOM can be determined. PARAFAC is a suitable tool to statistically define different fluorescent areas that are potentially indicative for dynamic changes within specific fluorescent classes. Additionally, it has been shown in previous work that distinctive fluorophores (e.g. fluorescent amino acids) can be determined with this method (Stedmon et al. 2003). PARAFAC components were also assigned to “humic- like” and “fulvic-like” components similar to the peaks given in Figure 1. These classifications were based on commonly found components in freshwater systems. With the identification of these FDOM components, it was possible to gain a better insight in the dynamic changes of these FDOM components in aquatic systems. In other words, PARAFAC allows investigators to gain a “second-level” of information from the fluorescence data.

This “second-level” data is better known as different components of the fluorescence spectrum. PARAFAC allows investigators to explain variation within the dataset. This variation can be explained by each statistical component. Figure 2 was adapted from (Murphy et al. 2008) and further demonstrates the idea of marine FDOM components when using the PARAFAC model. This figure illustrates signatures of nine components of FDOM fluorescence

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that were statistically determined from a large marine data set. When looking at the figure, each of the components that are characterized as “protein-like” occur around the same location in the spectrum. This is also true for the components that are characterized as “humic-like”. This indicates that components of the same family have similar properties in terms of fluorescence, which is to be expected. These individual components change in the fluorescence spectrum for each water sample and are indicative of processes influencing FDOM in the water. Once it is known what kind of FDOM is present in a certain region, it is easier to make predictions of its dynamic behavior. Because of the requirement of large datasets, joint efforts to collect samples and create robust PARAFAC models are indispensable.

EEM and PARAFAC have been used to analyze CDOM in seawater in previous studies (Stedmon and Markager 2005). These studies used direct analysis of collected seawater which allowed the investigators to gain important information, however, some CDOM components that may exist in marine environments have concentrations that are too low to be detected with direct EEM measurements and escaped previous analysis. In this study we extracted DOM using a solid phase extraction method to isolate the DOM and to yield much more intense fluorescent signals when compared to direct measurements of FDOM in marine water samples.

Materials and Methods

Approximately 200 samples (5 liter volume) of seawater were previously collected along a transect from 50.2o N to 31.4o S in the eastern Atlantic Ocean during the cruise of the R/V Polarstern (2008). These samples were then solid phase extracted using solid phase extraction as described in a previous study (Dittmar et al. 2007). This process involved acidification of filtered samples with pro analysi (p.a.) grade hydrochloric acid to a pH of 2. The samples were then passed through solid-phase extraction cartridges (Agilent Bond Elut, PPL). The process of extracting each sample involved first rinsing cartridges with methanol followed by acidified ultra- pure water, and finally 5 L of seawater were gravity-fed through each cartridge. After extraction, these samples were eluted with methanol. For fluorescence measurements, 500 µL of the methanol extracts were dried under nitrogen and re-dissolved in water. The fluorescence spectrum of each sample was taken using the Aqualog EEM Fluorometer and a PARAFAC model was created.

A PARAFAC model was created using only the fluorescence data from the solid-phase extracted samples. In previous work, PARAFAC models have been created using MATLAB software (Stedmon et al. 2003), however in this study newly available software was used. The program Solo from Eigenvector Research, Inc. was used to create the PARAFAC model for the data in this study. Solo is able to directly import data from the Aqualog Fluorometer and analyze the statistical data to recognize trends in order to create fluorescence components. These components define which “types” of CDOM are present in the water samples.

In the Sargasso Sea and during the cruise of the R/V Atlantic Explorer, in July 2013, twenty-four samples were collected at the Bermuda Atlantic Time Series Station (BATS) at every 200 m between the surface and 4,530 m. A 40 mL volume of these samples was directly filtered through a 0.2 µm syringe filter and analyzed using the Aqualog EEM Fluorometer. A second set of samples, where 10 L of seawater from all individual depths mentioned above were extracted using the solid phase extraction method. Back in the laboratory, 100 µL aliquots were dried under nitrogen and re-dissolved in LCMS grade water. These samples were then analyzed using the same Aqualog Fluorometer. Finally, the previously established solid-phase DOM PARAFAC model was implemented to calculate the relative contribution of the PARAFAC components in each of the 24 samples collected along the depth profile.

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Results

PARAFAC Model

In this study, six independent components were established using PARAFAC analysis (Figure 3). These components vary independently from one another and were derived from the 200 marine water samples from the 2008 R/V Polarstern cruise. When comparing these components with the components described in a previous study (Murphy et al. 2008), distinct differences were present. However, it can be assumed that component 1 corresponds to humic- like and component 5 to a fulvic-like component, because these components are in general, in agreement with a more recent study (Jorgensen et al. 2011). It can be concluded that these components are a close match as the excitation and emission pairings for the humic and fulvic- like components are very similar. Furthermore, the components 2, 3, and 4 are likely to be related to protein-like fluorescence because these components occupy the same general area of protein-like fluorescence, as they very closely match components 2, 3, and 5 of the PARAFAC model found in the Jorgensen (2011) study. Again, it can be concluded that the components in our study are protein-like as they have very similar excitation and emission pairings to those of Jorgensen.

Component Variation with Depth

Each PARAFAC component varied with depth at BATS (Figure 4). Components 1 and 5 increase with depth until about 1,400 m and remain relatively constant at greater depths. Components 2, 3, and 4 show the opposite trend and decrease with depth, until a constant behavior, again, below 1,400 m. Both of these trends are very consistent with the trends found in the literature (Mopper and Schultz, 1993; Chen and Bada, 1992). Component 6 was discarded for this portion of analysis as it only explains very little of the data. Since this component did not describe a very large percentage of the data, any resulting trends would be of little significance. It is for this reason that component 6 was discarded. Perhaps if there were more samples run to create the PARAFAC model, component 6 would be more robust and useful for analysis. Unfortunately, this step takes a great amount of time and could not be factored in to the timeline for this project.

Fluorescence Intensity Variation with Depth

The quinine sulfate normalized fluorescence intensities calculated for the humic-like, fulvic-like and protein-like fluorescent areas varied with depth in a similar manner to the trends described above using the PARAFAC components. The protein-like (ex 279 nm), humic-like (ex 240 nm) and fulvic-like (ex 342 nm) fluorescence intensity changes are therefore supporting the PARAFAC component trends. This data was obtained by first averaging a number of resulting emission wavelength intensities from each excitation wavelength at each depth and then averaging these data points from similar depth regions. For excitation wavelengths of 240 nm and 342 nm, an emission wavelength range of 430 nm – 450 nm was used and for the excitation wavelength of 279 nm, an emission wavelength of 339 – 348 nm was used. These excitation and emission wavelengths were selected based on the common knowledge of where fulvic-like, humic-like, and protein-like fluorescence transitions typically occur.

Discussion

One of the primary objectives of this study was to build a PARAFAC model with solid- phase extracted marine DOM samples that were collected from various parts of the Atlantic

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Ocean. This model was successfully created and used to analyze samples that were collected in the Sargasso Sea in July 2013. This model consisted of 6 independent components (Figure 3). While these components did have similarities with the components found in the study performed by Murphy et al. (2008), they did not match exactly. Both this study and the previous study involved humic-like, fulvic-like, and protein-like components, but when comparing the components of the two models they were not identical. While this study did not exactly match the components of Murphy et al. (2008), it is quite similar to the components found in a more recent study (Jorgensen et al. 2011). When comparing with these components the results looked similar, supporting the components calculated in this study. Hence, it can be concluded that solid-phase extraction of sea-water samples did not significantly alter the FDOM composition, but simply concentrated it and increased the fluorescence signal.

The degree to which each PARAFAC component described the dataset can be seen in Figure 4. Components 4 and 5 described the majority of the dataset, while the remaining components described significantly less of the variation. It should be noted that component 6, which is given in Figure 3, has been discarded, because it explained very little of the dataset. Figure 4 also showed that when comparing component percentage with depth, components 1 and 5 appeared to increase with depth, while components 2, 3, and 4 decrease with depth. All of the components remain relatively constant after 1,400 m. These trends matched exactly with the trends found in Figure 5, reassuring that the statistically-derived PARAFAC components were in fact, describing the same fluorescent areas. It should also be reiterated that components 1 and 5 were predicted to be humic and fulvic-like components while components 2, 3, and 4 are presumably protein-like.

The opposite trends of protein-like versus humic and fulvic-like fluorescence is interesting and indicated by the decoupling of DOM derived directly from cell lyses (protein) and the production of polyphenolic compounds that presumably produce the humic and fulvic-like fluorescence. These trends elaborate that different components of FDOM have distinctly different sources and that the sources are dependent on ocean depth. The humic and fulvic-like fluorescence trends cannot solely be explained with the depletion at the surface caused by photobleaching because these FDOM components would have reached a dynamic equilibrium much higher in the water column and would not be expected to keep increasing until 1,400 m. One may expect that FDOM should continue to increase with depth along the water column, but apparently there is some sort of threshold depth at which FDOM remains constant. Furthermore, the protein-like and fulvic-like FDOM levels are relatively close to one another after 1400 m, while the humic-like FDOM is at much higher levels (Figure 5).

These results supported the previously established trend that FDOM increased with depth. This is a clear indicator that fulvic and humic-like FDOM is associated with the presumably refractory deep-sea DOM. The fact that fulvic and humic-like FDOM increased with depth and only the protein-like FDOM decreased with depth indicates that the source of the protein-like DOM is presumably derived from hetero and autotrophic organisms in the ocean (Determann 1998). There is a much higher abundance of organisms in the shallower parts of the ocean, which would create the observed higher levels of protein-like DOM at the surface. This trend was consistent with the results found in other studies as well (Mopper 1993). In this previous study it was found that protein-like DOM decreased down the water column until a constant low level in the bathypelagic layer (>1000 m) was reached. Hence, protein-like DOM is presumably directly dependent on the activity and abundance of both phytoplankton and bacteria (Determann 1998). The origin of humic and fulvic-like FDOM remains unclear, but sources in the interior of the ocean must exist.

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Conclusion

Both the quantitative and the qualitative results of this study indicated that fulvic and humic-like FDOM increased with depth, while protein-like FDOM decreased with depth. These trends could be due to a number of different factors associated with the physico-chemical and community changes of microbes and bacteria within the water column. Some of these factors could range from a decrease in protein-like DOM sources down the water column to more photobleaching at the surface where fulvic and humic-like DOM are more susceptible to photodegradation. Further studies involving the sources of various types of FDOM and the relative effects of photobleaching could be very helpful in explaining the trends shown in this study. Solid-phase extraction is a suitable and affective method to analyze FDOM in marine environments and to significantly increase the fluorescent signal that is usually very low when fluorescence is measured with direct marine samples. To summarize, the solid-phase extraction method used in this study was affective in concentrating FDOM, did not significantly alter the composition of dissolved organic matter in the samples and should therefore be considered a useful method to assess FDOM in marine environments.

Acknowledgements

I would like to thank Dr. Michael Gonsior, who came up with the idea for this project and guided me throughout the whole process. Thanks are also extended to Dr. Laura Lapham who answered my questions when Dr. Gonsior was not available. To Dr. Maureen Conte who made the research cruise in Bermuda possible. And to the National Science Foundation, Maryland Sea Grant, and University of Maryland all of which provided funding for this project and summer experience. I would also like to thank all who made it possible for me to have this experience, it was certainly an unforgettable summer and I have gained an indescribable amount of knowledge about the scientific world.

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References

Bauer, J. E. 2002. Carbon isotopic composition of DOM, p. 405-453. Academic Press.

Benner, R. 2002. Chemical composition and reactivity, p. 59-90. In D. A. Hansell and C. A. Carlson [eds.], Biogeochemistry of Marine Dissolved Organic Matter. Academic Press.

Coble, P. G. 1996. Characterization of marine and terrestrial DOM in seawater using excitation- emission matrix spectroscopy. Marine Chemistry 51: 325-346.

Coble, P. G., C. E. Del Castillo, and B. Avril. 1998. Distribution and optical properties of CDOM in the Arabian Sea during the 1995 Southwest Monsoon. Deep Sea Res. Part II 45: 2195-2223.

Determann, S., J.M. Lobbes, R. Reuter, and J. Rullkotter. 1998. Ultraviolet fluorescence excitation and emission spectroscopy of marine algae and bacteria. Marine Chemistry 62: 137-156.

Dittmar, T., K. Whitehead, E. C. Minor, and B. P. Koch. 2007. Tracing terrigenous dissolved organic matter and its photochemical decay in the ocean by using liquid chromatography/mass spectrometry. Marine Chemistry 107: 378-387.

Hansell, D. A., and C. A. Carlson. 1998. Deep-ocean gradients in the concentration of dissolved organic carbon. Nature 395: 263-266.

Hansell, D. A., and C. A. Carlson. 2002. Biogeochemistry of Marine Dissolved Organic Matter, p. 547-602.

Jiao, N. and others 2010. Microbial production of recalcitrant dissolved organic matter: long- term carbon storage in the global ocean. Nature Reviews Microbiology 8: 593-599.

Jorgensen, L., C. A. Stedmon, T. Kragh, S. Markager, M. Middelboe, and M. Sondergaard. 2011. Global trends in the fluorescence characteristics and distribution of marine dissolved organic matter. Marine Chemistry 126: 139-148.

Mopper, K., and C. A. Schultz. 1993. Fluorescence as a possible tool for studying the nature and water column distribution of DOC components. Marine Chemistry 41: 229-238.

Murphy, K. R., C. A. Stedmon, T. D. Waite, and G. M. Ruiz. 2008. Distinguishing between terrestrial and autochthonous organic matter sources in marine environments using fluorescence spectroscopy. Marine Chemistry 108: 40-58.

Nagata, T., H. Fukuda, R. Fukuda, and I. Koike. 2000. Bacterioplankton distribution and production in deep Pacific waters: large-scale geographic variations and possible coupling with sinking particle fluxes. Limnology Oceanography 45: 426-435.

Stedmon, C. A., and S. Markager. 2005. Resolving the variability in dissolved organic matter fluorescence in a temperate estuary and its catchment using PARAFAC analysis. Limnology and Oceanography 50: 686-697.

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Stedmon, C. A., S. Markager, and R. Bro. 2003. Tracing dissolved organic matter (DOM) in estuaries: Aggregation and bioavailability. Marine Chemistry 82: 239-254.

Yamashita, Y., and E. Tanoue. 2008. Production of bio-refractory fluorescent dissolved organic matter in the ocean interior. Nature Geoscience 1: 579-582.

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Figures and Tables

Figure 1. Exitation emission matrix (EEM) fluorescence spectra from two depths of the same water column in the Sargasso Sea, note Fmax on the 99 m depth image, refers to the region of the water column where the algae production is the greatest, otherwise known as the fluorescence maximum of chlorophyll a. A refers to “humic-like”, T refers to “protein-like”, M refers to “marine fulvic-like” and C refers to “fulvic-like” fluorescent components. “Humic-like” and “protein-like” fluorescence occur at different regions of the electromagnetic spectrum (while both being within the UV-Visible portion) (Coble 1996) .

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Figure 2. EEM PARAFAC components based on a large set of direct measurements of marine samples. Figure adopted from (Murphy et al. 2008). In this figure, components 1, 6, and 7 are “protein-like”, components 2 and 8 are “humic-like”, component 5 is thought to be a dye, component 9 is a photochemical product of terrestrial organic matter and component 4 is unknown.

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Figure 3. EEM PARAFAC components based on extracted water samples from Bermuda research cruise July 2013.

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Figure 4. Component percentage versus depth for the Bermuda Atlantic Time Series Station.

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Figure 5. Fluorescence Intensity versus depth for the Bermuda Atlantic Times Series Station

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Levels of PAHs in Marine Biofouling Organisms Attached to Oil Rigs in the Gulf of Mexico

Zach Watkins, REU Fellow Maryland Sea Grant

Dr. Carys Mitchelmore, Associate Professor Chesapeake Biological Laboratory, University of Maryland Center for Environmental Science

Abstract

The Deepwater Horizon oil spill was a tragedy resulting in multiple people losing their lives and millions of gallons of oil being leaked into the Gulf of Mexico. This spilled oil contained compounds known as polycyclic aromatic hydrocarbons that are known for their toxicity, specifically, their carcinogenic properties. In this study, Orange Cup Coral and Crested Oysters were collected from oil rigs located around the oil spill on two different sampling dates after the oil well was capped. These samples were analyzed for their concentrations of polycyclic aromatic hydrocarbons. Results were then compared through temporal and spatial analyses to determine where the polycyclic aromatic hydrocarbons were originating.

Keywords: Polycyclic Aromatic Hydrocarbons, PAHs, oil spill, petrogenic

Introduction

One of the best ways to assess the extent of pollution in marine environments is to use immobile organisms that inhabit the area of interest. These sessile organisms can be used to measure the concentrations of pollutants bioaccumulated in their tissues and/or biological endpoints that may respond to the pollution. These sessile creatures are good to use as environmental biomonitors as they simply have to deal with their surroundings, no matter what they may be. In the Gulf of Mexico, oysters are assessed along the shoreline all the time to look for pollution levels (e.g. by the EPA or FDA), especially as these are commercial species that are used for human consumption. However, a benefit with these organisms is that they grow on any hard substrate so oil rigs act as huge substrates for them to attach and spend their lives on. Therefore, not only are they easily accessible to be collected, they also are residing in the Gulf of Mexico and could be used to identify and quantify any possible source(s) of pollution.

Polycyclic aromatic hydrocarbons (PAHs) are major pollutants that have drawn much attention because of their toxicity, specifically, their carcinogenic properties (Sabourin et al. 2012). These compounds generally consist of two or more benzene rings and have hydrophobic properties (NRC 2003). PAHs can be found naturally, however they are largely produced by: 1) organisms - biogenic, 2) combustion - pyrogenic, 3) fossil fuels - petrogenic, and 4) transformations in soils and sediments - diagenic (Hylland 2006). Each method of production creates structurally different PAHs (Hylland 2006). For example, petrogenic PAHs are generally alkylated and are of lower molecular weight (LMW) compared to pyrogenic PAHs which usually

113 are of parent PAH structure and tend to be of higher molecular weight (HMW) (NRC 2003). In other words, by analyzing the concentrations of different PAHs, we will be able to determine where the majority of the pollutant is originating.

Ever since the industrial revolution, anthropogenic PAH levels have noticeably increased (Lima 2005). This increase stems from automobiles, industry, electricity generation, marginally greater oil usage, etc. These anthropogenic activities lead to pyrogenic, and to a lesser extent, petrogenic, processes as the primary producers of PAHs (Hylland 2006). A common misconception when considering PAHs is that oil spills would be the leading candidate of PAH release. While oil spills do release large quantities of PAHs into the environment, they do not last nearly as long as other sources such as continual runoff from anthropogenic means; however, they often provide a very concentrated short-term pulse of PAHs, known as an acute toxicity event (Qian et al. 2001). Low exposure to PAHs over long periods of time, chronic toxicity, causes major problems in marine ecosystems (Gomiero et al. 2011). These chronic events can derive from PAHs, often at low levels, continually reaching the aquatic environment or they could be manifested as delayed or long-term consequences after an acute event, such as an oil spill. Very limited data exist on chronic long-term effects of major spills, but the spills may be more detrimental to population and ecosystem functioning as a whole (NRC 2005).

As mentioned above, oil spills/leaks are still a major concern through their rapid exposure of PAHs to the environment because of their acute and chronic toxicity, especially to an ecosystem as productive as the Gulf of Mexico. On April 20, 2010 the Deepwater Horizon floating oil rig was drilling the Macondo well, MC252, when it suffered a blowout (Oldenburg et al. 2011). This disaster led to 11 people losing their lives and roughly 4.9 million barrels of oil seeping out into the Gulf of Mexico over a 152 day period (Oldenburg et al. 2011). With this oil, of course, came large concentrations of petrogenic PAHs. Furthermore, to aid in containing the spill to the water column, rather than it reaching the sensitive shorelines, a large quantity of chemical dispersant was applied to the surface of the spill and also at a depth directly at the wellhead. The oil plume covered a large area that not only had immediate impacts on the surrounding ecosystems, but there may be many effects from the spill that have yet to be seen.

This study was conducted in conjunction with a similar research project already completed by Hannah Pie. Ms. Pie, a graduate student working w Dr. Carys Mitchelmore, has already completed a PAH profile study on coral and oysters that were collected in May 2011. In this current study, samples of oysters and coral were collected in April 2011 from strategically selected oil rigs that potentially may have been exposed to oil (petrogenic PAHs) from the Deepwater Horizon (DWH) incident at different concentrations. Samples from rigs east of the wellhead would theoretically have the most exposure, followed by rigs south of the wellhead, while samples from the oil rigs to the west should serve as a control (Figure 1). However, it should be noted that these are mainly all active oil rigs. Therefore, these organisms may be constantly exposed to some level of pyrogenic PAHs as ships dock to load and unload, and possibly petrogenic PAHs via production water released. Furthermore, the Mississippi River exerts a huge influence on the systems, as brown water (i.e. river discharge) can reach 60-100 miles off the coastline depending upon the time of year, also bringing all types of PAHs (including diagenic PAHs like perylene). Note that samples used for Hannah Pie's analysis were collected immediately after the fresh water surge of the Mississippi flooding in 2010. There may be fluctuation of PAHs as a direct result of location, but there also may be a variation in PAH concentrations through each organisms’ mode of feeding. Oysters are filter feeders which bring in all of the surrounding environment to feed; whereas corals are organisms that selectively catch their prey.

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The goal of this project was to look at the levels of PAHs in sessile oyster and coral samples collected off a variety of oil rigs temporally and spatially. Once collected, the samples were extracted and analyzed for individual PAH concentrations to determine potential patterns and sources of exposure. In particular, this study focused on three main analyses: total PAH, parent to alkylated PAH ratios, and low molecular weight to high molecular weight ratios.

Hypotheses

• Organisms from oil rigs closer to the Macondo well (East and South rigs) will have elevated levels of total PAHs, particularly alkylated and low molecular weight PAHs, compared to those collected from rigs farther away (West). (Figure 1) • Oysters will have higher concentrations of PAHs because of the nature of their feeding compared to coral. • Oysters collected during these sampling dates will not have accumulated high levels of perylene compared to oysters collected during the May sampling.

Methods

Homogenization/Drying

Oyster and coral samples that were collected in April 2011 from specific oil rigs in the Gulf of Mexico were homogenized and dried. To homogenize the samples, a standard mini-food processor was used, and wet weights were noted. Samples were dried using clean, baked-out sodium sulfate (Na2SO4) at roughly a 6:1 (salt:sample) mass ratio. They were ground using a mortar pestle until completely dry.

Extraction

The extraction process began by deactivating clean alumina with 6% deionized water, by mass. This mixture was sealed and shaken for roughly 15 minutes to ensure complete deactivation. A cellulose filter was first placed into a solvent cleaned extraction cell followed by 18 g of deactivated alumina. Another cellulose filter was then placed on top of the alumina layer. About 1 inch of clean Na2SO4 was then added to the cell and tapped down. Roughly 30 g (exact weight was noted) of sample was added to the cell. The exact dry weight of sample added was measured to account for normalization of PAH measurements. Finally, the remaining space in the cell was filled with clean Na2SO4 and tapped down. Internal standards were also added to the cell to determine recovery of PAHs at each ring size over the entire extraction process. The PAHs were then extracted from each prepared cell using a DIONEX Accelerated Solvent Extractor 300 (ASE) and dichloromethane. During each round of extractions four controls were run including a blank cell, a duplicate sample cell, a PAH spike cell, and a cell containing a standard reference material (SRM) sample for mussels.

Transfer into new solvent

The PAHs were extracted into dichloromethane using the ASE, but transferred into hexane to be run through the gas chromatography/mass spectrometry (GC/MS) using a rotary evaporator (Rotavap). Clean Na2SO4 was added to the extracted samples to remove any remaining aqueous components. The resulting solution was transferred into a round bottom flask. Dichloromethane was added to extraction bottle and the solution was decanted three times to ensure all sample had been transferred. The Rotavap was then used to evaporate the dichloromethane using a 30°C water bath. The remaining sample was reconstituted with hexane

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and allowed to evaporate almost to completion. This process was repeated three times to ensure all the dichloromethane was evaporated and the sample was fully in hexane. The volume was then brought down to 1 mL using nitrogen gas. Additional perdeuterated standards for each ring size were added to each vial to determine the GC/MS method efficiency.

GC/MS

Each sample was then analyzed by gas chromatography (GC Agilent 7890A) coupled to an Agilent 5875C Mass Selective Detector operated in electron ionization mode by selected ion monitoring. Samples were injected in splitless mode at an initial oven temperature of 45°C and an injector temperature of 250°C with helium as the carrier gas. The oven temperature was ramped at 10°C/min to 280°C and then 5.0°C/min to 310°C before holding at 310°C for 16.5 minutes. Standards for all PAHs including internal controls were run between every ten samples to determine the response factors and percent recovery. Response factors were calculated for each individual PAH using standard lab protocols. The PAH concentrations were calculated using these RF values and normalized to the dry weight of each sample as well as any PAH concentrations measured in blank control runs.

Statistical analysis

Statistical analyses for all experiments were performed using R (ver. 2.12.2). Normality and homogeneity of variances for all endpoints were examined first with the Shapiro-Wilk and Fligner-Killeen tests, respectively. If these requirements were met, a one-way ANOVA and Tukey’s HSD comparison of means test were used to determine significant between PAH bioaccumulation levels and diagnostic ratios from each organism from the different oil rigs. If data failed either the normality or homogeneity of variance tests, a log transformation was used. If after a log transformation, the normality was still not met, the non-parametric Wilcoxon rank- sum test was used to compare means. The significance level chosen for all analyses was α=0.05.

Results

Disclaimer: The GC column that was used during this project turned out to not be functioning properly. It was unable to accurately analyze four or five ringed PAHs. This was detrimental to the project as I was only able to analyze 26 of the 62 standardized PAHs. Not only did this influence the analyses of the standards, but it also kept us from accurately comparing total PAHs, parent to alkylated PAH ratios, and low molecular weight to high molecular weight ratios. The latter of the three analyses was completely discarded as it requires four and five ringed PAHs to calculate.

Coral

Due to the number of samples examined, the analyses were averaged by rig location rather than individual rigs or individual samples. Figure 2 shows the total PAHs of each of the three rig locations. The total PAH profile shows that the South rig location had the greatest abundance of PAHs, while the East and West rigs were very similar in total PAH concentrations.

A parent to alkylated PAH ratio analysis is depicted in Figure 3. The rigs located to the east were the only ones to show a ratio below one. The West rigs had the greatest parent to alkylated PAH ratios.

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Oyster

Due to the number of samples examined, the analyses were averaged by rig location rather than individual rigs or individual samples. Figure 4 shows the total PAHs of each of the three rig locations. The letters (A and B) indicate significant differences, i.e. South rigs were significantly different from East rigs but not West rigs. The South rigs showed the greatest amounts of PAHs while the East and West rigs were both around the same concentration.

A parent to alkylated PAH ratio analysis is shown in Figure 5. Both the West and the South rig locations had ratios below one while the east rigs had ratios slightly greater than one. The ratios from rigs located to the south were marginally smaller than any other rig location.

Discussion

As previously mentioned, this study had difficulties with the GC column that was used. Figures 5 and 6 show how much of an impact the issue really was. The first of the two figures compares the parent to alkylated ratios of oysters from both the May 2011 and April 2011 samplings. This study (May 2011 samples) shows ratios ranging from 0.3-1.2 whereas Pie's analysis (April 2011 samples) shows ratios ranging from 1.0-15. Figure 6 depicts the total PAHs found in oysters between the two different analyses. This study had a maximum total PAH concentration of 10 ng g-1 by dry weight at the south rig location, whereas Pie's study found a south rig sample that had a total PAH concentration of 120 ng g-1.

However detrimental the malfunction was to this project, some results that were salvaged. It was predicted that organisms from oil rigs closer to the Macondo well will have elevated levels of petrogenic PAHs. Although a complete picture can't be drawn from these results, coral samples followed the prediction. Figure 3 shows that the closer the rig was to the Macondo well (Figure 1), the greater the level of petrogenic PAHs (parent to alkylated ratio <1). In contrast, the oysters did not follow the same trend (Figure 5).

It was also predicted that oysters, being filter feeders, will bioaccumulate greater amounts of PAHs compared to coral, organisms that selectively feed on their prey. Figure 2 shows the coral total PAH analysis while Figure 4 depicts the same for the oysters. Although corals and oysters followed a nearly identical trend, a conclusion cannot be drawn. This is evident in Figure 6 where Pie's analysis shows oysters bioaccumulated 12-fold more PAHs than found in this study. The reason for the great difference is likely that oysters bioaccumulated a large amount of high molecular weight PAHs, which this study was unable to detect (Figure 7).

Finally, this study planned to focus on the impact of the Mississippi River flood that occurred between the two sampling dates. The May 2011 oysters showed elevated concentrations of pyrene, a diagenic PAH, that was thought to have been washed down the Mississippi River with the flood into the samples collected from the Gulf of Mexico (Figure 7). However, this study was unable to detect pyrene due to the condition of the GC column. Of note, a newer column was installed days before the study came to an end and some oysters were reran. Of the samples that were reran, none showed elevated levels of pyrene indicating that the flood of the Mississippi River was the cause of the large amounts of pyrene.

Conclusions

Although there were some issues along the way, some pre-conclusions can be drawn from this study. South rigs seemed to have the highest total PAH levels in both coral and

117 oysters compared to the other two sites (Figure 2, Figure 4). Not only did this study show such results, so did the analysis carried out by Pie (Figure 6). It was also evident in coral that the closer the sample was located to the Macondo well, the more petrogenic the PAHs were as indicated by low parent to alkylated ratios in Figure 3.

However, complete conclusions cannot be drawn regarding the different feeding mechanisms of the two species, because we were unable to detect the four and five ring PAHs due to experimental issues. In particular, no conclusions can be drawn about pyrene exposure from the Mississippi River flood; however, sample later examined from April 2011 showed little to no pyrene indicating that the flood was the source of the majority of that particular PAH. Re- analysis of remaining samples is necessary to draw further conclusions.

Acknowledgements

I would like to thank Dr. Carys Mitchelmore for taking the time out of her busy schedule to mentor me in this interesting topic. I would also like to thank Hannah Pie for helping me whenever I needed anything. And last, but not least, I would like to give thanks to Maryland Sea Grant for giving me this wonderful opportunity.

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References

Gomiero, A., L. Da Ros, C. Nasci, F. Meneghetti, A. Spagnolo, and G. Fabi. 2011. Integrated use of biomarkers in the mussel Mytilus galloprovincialis for assessing off-shore gas platforms in the Adriatic Sea: Results of a two-year biomonitoring program. Marine Pollution Bulletin 62:2483-2495, doi: 10.1016/j.marpolbul.2011.08.015

Hylland, K. 2006. Polycyclic aromatic hydrocarbon (PAH) ecotoxicology in marine ecosystems. Journal of Toxicology and Environmental Health 69:109-123, doi: 10.1080/15287390500259327

Lima, A., J. Farrington, and C. Reddy. 2005. Combustion-derived polycyclic aromatic hydrocrabons in the environments: A review. Environmental Forensics 6:109-131, doi: 10.1080/1527920590952739

National Research Council. 2003. Oil in the Sea III: Inputs, Fates, and Effects. Washington, DC: The National Academies Press.

National Research Council. 2005. Oil Spill Dispersants: Efficacy and Effects. Washington, DC: The National Academies Press.

Oldenburg, C. M., B. M. Freifeld, K. Pruess, L. Pan, S. Finsterle, and G. J. Moridis. 2011. Numerical simulations of the Macondo well blowout reveal strong control of oil flow by reservoir permeability and exsolution of gas. Proceedings of the National Academy of Sciences of the United States of America 109:20254-20259.

Qian, Y., T. L. Wade, and J. L. Serigano. 2001. Sources and bioavailability of polynuclear aromatic hydrocarbons in Galveston Bay, Texas. Estuaries 24:817-827.

Sabourin, D. T., J. E. Silliman, and K. B. Strychar. 2012. Polycyclic aromatic hydrocarbon contents of coral and surface sediments off the south Texas coast of the Gulf of Mexico. International Journal of Biology 5:1-12, doi: 10.5539/ijb.v5n1p1

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Figures and Tables

EAST 1 EAST 2

SOUTH 2 SOUTH 1 MACONDO- WEST 3

WEST 1 WEST 2

Figure 1. A map showing all of the rigs from which samples were collected. The site of the Macondo rig is also listed.

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60 50 40 Total PAH ng/g dry weight dry ng/g PAH Total 30 20 10 0 East South West RIG LOCATION

Figure 2. Total PAH dry weight (ng g-1) by rig location of coral samples.

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2.0 1.5 Parent to Alkylated PAH Ratio PAH Alkylated to Parent 1.0 0.5 0.0 East South West RIG LOCATION

Figure 3. Parent to alkylated PAH ratios by rig location of coral samples.

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14 10 8 Total PAH ng/g dry weigh dry ng/g PAH Total 6

4 B

2 A A,B 0 East South West RIG LOCATION

Figure 4. Total PAH dry weight (ng g-1) by rig location of oyster samples. The letters (A,B) show significant differences.

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2.5 2.0 1.5 Parent to Alkylated PAH PAH Alkylated to Parent 1.0 0.5 0.0 East South West RIG LACATION

Figure 5. Oyster parent to alkylated ratios are compared between the two different analyses. The upper bar graph shows the ratios from this study (April 2011) which ran into technical difficulties whereas the bottom graph shows the results that were found from the May 2011 sampling by Hannah Pie.

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14 10 8 Total PAH ng/g dry weigh dry ng/g PAH Total 6

4 B

2 A A,B 0 East South West RIG LOCATION

Figure 6. The total PAHs of oyster from the two different analyses. The upper bar graph shows the total PAHs from this study (April 2011) which ran into technical difficulties whereas the bottom graph shows the results that were found from the May 2011 sampling by Hannah Pie.

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Figure 7. The PAH profile of oysters from a rig from the May 2011 sampling (Hannah Pie). Pe, BaP, and BeP (Perylene, Benzo[a]pyrene, and Benzo[e]pyrene respectively) are all 4 ringed, high molecular weight PAHs which were not detected in this study.

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Quantifying Growth Variation of Juvenile Blue Crab (Callinectes sapidus) using RNA:DNA in Response to Elevated Water Temperature and Nutritional Rations

Arthur Williams, REU Fellow Maryland Sea Grant

Dr. Thomas Miller, Director and Professor Chesapeake Biological Laboratory, University of Maryland Center for Environmental Science

Abstract

The blue crab (Callinectes sapidus) is both ecologically and economically important in the Chesapeake Bay. It supports the most valuable fishery in the Chesapeake Bay and plays a key role in coupling energy flow between the benthic and pelagic ecosystems. As a result, future management is needed to maintain a sustainable habitat quality for the blue crab. To estimate the value of individual habitats, we must be able to estimate the production of blue crab. Yet, currently we lack knowledge on the growth of blue crab. The goal of this project is to evaluate the utility of a biochemical assay of nucleic acid concentrations as an index of growth. Although the amount of RNA and DNA in tissue varies between species, the quantity of DNA is constant in cells, but the level of RNA fluctuates with metabolic activity. This study explored the potential for RNA:DNA ratios to measure crab growth in laboratory settings, based on elevated water temperature and nutritional ration. Data indicated that concentrations of RNA, DNA, and RNA:DNA could be measured with precision. The experimental results showed a growth response to temperature and ration treatment. However, results indicated that the relationship between RNA:DNA and growth was equivocal. Based on these results RNA:DNA does not appear to be a valid index of growth in blue crab.

Keywords: Blue crab, growth, nucleic acid, RNA:DNA, ration, temperature

Introduction

Located on the Eastern seaboard, the Chesapeake Bay is America’s largest estuarine ecosystem, and supports a wide diversity of flora and fauna, producing about 500 million pounds of seafood a year. Despite recent eutrophication from excessive nutrient runoff, i.e. nitrogen and phosphorus (Kemp et al. 2005), the Chesapeake Bay sustains one the largest fisheries in Maryland – the blue crab (Callinectes sapidus) (Seitz 2003). The blue crab fishery brings millions in revenue every year (NOAA 2011). Nevertheless, the blue crab is highly sought after by many fishermen, mostly commercial fishermen. Not only does the blue crab provide economic benefits to the fisheries community, but it provides a key ecological connection to the benthic and pelagic food web by scavenging and inducing predator prey dynamics (Hines 2007). Unfortunately, the blue crab population faces stresses from harvest and nursery habitat loss as a result of surplus nutrient runoff into the rivers, loss of seagrass

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and oyster beds, dredging, and shoreline hardening. Overexploitation and habitat degradation pose the greatest threats to crab abundance.

Sustainable management is needed to support both the ecological and economic services that blue crab provides. This requires a detailed knowledge of the blue crab life history. In the Chesapeake Bay, blue crabs demonstrate a complex life history that involves time in the marine and the estuarine environment (Miller 2003). Males tend to remain in low salinity most of the year; however, mature females migrate to high salinity waters in late spring to release larvae, termed zoea. Zoea are transported into the coastal ocean and return to the bay later in the summer in the last larval stage, termed megalopae (Bunnell and Miller 2005). Megalopae tend to be recruited to structured habitat (Etherington and Eggleston 2000). Upon settlement, megalopae undergo a final molt into the juvenile stage. Juvenile blue crabs utilize a variety of habitats throughout the Chesapeake Bay and its tributaries. For example, small juveniles prefer shallow water habitats that afford some protection from red-drum, spot, croaker, and cannibalism (Dittel et al. 1995). Therefore, the creation of crab sanctuaries has been instrumental in protecting crabs as they travel through their complex life cycle (Moksnes 1998). Structured habitat is particularly important during intermolt stages when individuals are vulnerable to predation, offering protection. Thus, juvenile and adult crabs are frequently associated with structured habitats such as submerged aquatic vegetation and oyster bars, but these habitats are not essential for completion of the life cycle. However, despite evidence that crabs use a variety of habitats, an assessment of the importance of different habitats to the overall health and sustainability of the population is currently lacking.

To understand the role of different habitats to the overall production of blue crab requires us to be able to estimate habitat-specific productivities. Habitat quality is often indexed by the production the habitat supports (Beck et al. 2001). These indices rely on the product of an estimate of abundance and of a measure of instantaneous growth in the habitat (Beck et al. 2001). Habitat specific abundances are relatively straightforward to measure. However, habitat specific growth rates have proven more difficult to estimate.

The difficulty in ageing blue crab and the discontinuous nature of their growth has meant that, to date, attempts of habitat-specific productivity of this species have not been explored thoroughly (Bunnell and Miller 2005). One approach to measuring growth production would be to use a cellular measure of growth such as the concentration of vital enzymes (Fielder et al. 1998) or the ratio of nucleic acids (Buckley and Lough 1987). Quantification of RNA:DNA ratios in larval fish has been found to be a reliable method to compare the growth of fish larvae under different conditions (Caldarone et al. 2005). This technique relies on the assumption that the quantity of DNA per cell is a fixed, species-specific trait, but that the amount of RNA per cell varies with the growth rate of the tissue or organism. In small, relatively undifferentiated life stages, such as fish larvae, during which most energy is invested in growth, this may be a reliable assumption. It is not clear that RNA:DNA ratios can serve the same function in older life stages during which the fate of surplus metabolic energy is more diverse. However, recently Peck et al. (2003) demonstrated the potential for the technique to be applied in juvenile fish. Wang and Stickle (1986) conducted preliminary studies on the possible utility of RNA:DNA ratios to measure growth in blue crab by analyzing changes in nucleic acid concentrations in juvenile crabs that were starved. No subsequent work has been reported. The goal of this study is to explore the potential of RNA:DNA ratios to measure short term growth of blue crab, thereby expanding the inference possible from the experiments conducted by Wang and Stickle (1986). A customized version of the flourometric assay developed by Caldarone and Buckley (1989) would be used to obtain accurate values of metabolic rates in crabs (Heyer et al. 2001).

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Using RNA:DNA analysis, the central underlying objective of study is to assess whether RNA:DNA ratios are a reliable and accurate indicator of crab growth rates. Such an assessment relies on generating growth variation among crabs experimentally. Temperature and ration are two factors known to generate variation in crab growth (Brylawski and Miller 2006). Using this approach, we can raise crabs with known and different growth trajectories to assess whether the ratio of concentrations of RNA:DNA can serve as a reliable index of nutritional condition and growth in juvenile blue crabs. If successful this index may serve to map variability in growth of juvenile blue crab in the field, a required precursor to ranking the inherent value of different habitats in supporting blue crab production. Profoundly, this study aims to benefit the fisheries community in the long term, as the data collected from this work will assist in management plans for sustainability of blue crab in the Bay and the Atlantic coast. Furthermore, the proposed research study’s intention is to provide information on future health of crab in the Patuxent River, which subsequently positively impacts watermen crab yield of the Chesapeake Bay. We proposed three hypotheses:

• Juvenile C. sapidus that are reared at warmer temperatures (24 and 28 degrees C) will exhibit higher RNA:DNA ratios due to increased enzyme activity and therefore exhibit more growth compared to the crabs at lower temperature.

• C. sapidus that receive a higher ration of food will have more nutrients available to expend on growth, and will thus demonstrate higher ratios of concentrations of RNA:DNA.

• C. sapidus with high RNA:DNA ratios grow (weight, carapace width) faster than crabs with low RNA:DNA.

Methods

A controlled laboratory experiment was conducted to quantify effects of temperature and ration on crab growth and biochemical indices of crab growth.

Juvenile blue crabs (approximately 1-100 mm carapace width) used in the experiment were collected using dip nets from the Chesapeake Biological Laboratory research pier, which is located in the tidal portion of the Patuxent River (Figure 1). Crabs were collected on weekly basis from mid May 2013 to July 2013. Prior to use in experiments, crabs were held in flow through tanks, provided with filtered Patuxent River water and maintained on a 16:8 light to dark cycle to acclimate to laboratory conditions. Crabs ranging from 36-45 mm carapace width were selected at random from holding tanks for use in experiments. Only crabs with the entire set of appendages qualified for experiment.

A randomized complete block design involving three levels of temperature (20, 24, and 28ºC) and three levels of ration (0%, 30%, and 100% per day) was used to quantify the effects of temperature and ration on Δcarapace width(CW), Δweight(W), RNA, DNA, and RNA:DNA ratio. The experiment was run for 14 days. The experiment tanks were 5 gallon (18.9 L) aquaria, housing 2 crabs each separated by vertical plexiglass partitions to create 2-2.5 gallon chambers. Tanks were arranged in 3 blocks according to location relative to the door and ventilation system of a constant environmental chamber. Accordingly, the experiment employed 27 separate aquaria and 54 individual crabs.

Each aquarium was randomly assigned to one of three different temperatures of 20.0, 24.0, or 28.0ºC. The 24.0 and the 28.0 aquarium treatments were maintained by individual aquarium heaters (±0.9ºC). The salinity was held constant throughout the two week experiment

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(12.7 ppt). Aquaria were also assigned one of three food treatments: starvation, 20% Cmax, and ad libitum. 20% Cmax was based on the following equation (Brylawski and Miller 2003):

CB Cmax=CA * W (1) where Cmax is the maximum consumption possible (grams of food per gram wet weight of the animal per day, g/g/d), W is the crab’s wet weight, and CA and CB are regression parameters (CB = -0.39 and CA = 0.35 g/g/d). Experimental crabs were fed fish pellets, and the ad libitum crabs were fed more food than they would eat. Food was given fresh each day. On Day 5, ad libitum crabs were fed double the initial quantity of food to ensure a surplus of food for desired effects of feeding.

Tanks were cleaned daily by siphoning, and water changes were made every other day. It is important to siphon off residual food and fecal matter. Standard aerators maintain the optimum oxygen levels in the tanks and dissolved oxygen levels were recorded every other day with an YSI multiprobe data sonde (Ron Geis and G.McClain; Xylem Inc). A 16:8hr light:dark cycle was maintained throughout the experiment to simulate current natural conditions.

To begin the experiment, carapace width (distance between the tips of the lateral spines) and wet weight were measured for each individual crab. Molted crabs were re-measured and the dates of the molt were recorded. Observations of food consumption, aggression, and activity level were recorded throughout the 14 days for use of quantifying growth as well. Individuals that died were recorded and removed from experimental without being replaced. At the end of the two week treatment, each crab were removed, weighed, carapace width measured, and molt stage assessed by condition of the second maxillia (Moriyasu and Mallet 1986). Crabs were placed in a -80ºC freezer to preserve tissue nucleic acid concentration prior to biochemical analysis.

Laboratory Procedures

This study used biochemical indices to determine short term juvenile condition and growth in lab (Caldaron et al. 2006). Juvenile crabs were placed on ice for tissue dissection. Use of the RNA:DNA fluorometric protocol from Caldarone et al. (2001) was adapted for the quantification of juvenile blue crab nucleic acid concentration. This method included use of RNA:DNA extraction from crab tissue of the left middle thoracic sterna adjacent to the second leg appendage coxa. Details of the assay are provided by Caldarone (2001) and are only summarized here. Specific differences between the Caldarone technique and that adapted for blue crab are noted.

Crabs were individually defrosted from the -80ºC freezer and their carapace opened. A portion of tissue (~1 g) was taken from the thoracic sterna adjacent to the second leg. The tissue was placed in a microcentrifuge vial with 150 μL 1% N-lauroylsarcosine and vortexed vigorously for 60-120 minutes to separate the proteins. The tissue suspension was diluted with 1,350 μL TRIS-EDTA buffer and centrifuged for 15 minutes at 14,000 g to separate the solids and liquids in the sample. Triplicate volumes (75 μL) of the supernatant were transferred to individual wells of a white 96-well microplate. Dilutions of RNA and DNA standards, blanks, and control homogenate were also added to the microplate to ensure measurement accuracy and consistency. 75 μL ethidium bromide, as a fluorescent tag, was added to all wells and the initial fluorescence measured on a SpectraMax Gemini XPS (Model 05255, California, USA). 7.5 μL of RNase was then added to each cell and the fluorescence measured again for a final fluorescence reading.

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Fluorescence levels of nucleic acids were compared to the standard calibrations conducted on each plate to determine the concentration of the nucleic acids in the samples. All calibration curves had r2 values greater than r2 = 0.99. The concentration of DNA was determined from the second reading, and the concentration of RNA as the difference between the first and second readings.

Examined crabs were placed back in the freezer for repeated use if necessary.

Data analysis

Data were initially verified for quality by comparing the percentage deviation of the individual replicate values of the concentration of RNA and DNA and the ratio of these two concentrations from the mean. Any replicate that differed by more than 10% was considered an outlier and removed from subsequent analysis. This led to the rejections of six of 162 determinations. Once these outliers had been removed, analysis proceeded using the mean concentration of RNA, DNA, and their ratio.

To test the first two hypotheses, analyses of variance (ANOVA) appropriate to the experimental design were implemented in the R package. Temperature, ration, and temperature*ration were used as experimental variables. Separate ANOVAs were conducted for growth (change in weight and change in carapace width) and biochemical indices (concentration of RNA, DNA and their ratio).

To test the third hypothesis regarding the relationship between RNA:DNA and growth, simple linear regression was used.

Results

Four of the experimental crabs died during the course of the 14-day experiment. Those crabs that died all came from the 100% ration treatment, but from different temperatures, treatments, and blocks.

Of the 49 remaining crabs that survived the experiment, 32 crabs molted. The average molt increment (ΔCW) was 8.4 ± 2.31 mm (mean ± standard deviation). The average change in weight for all crabs was 1.9 ± 1.73 g (mean ± standard deviation). Both measures of growth appeared randomly distributed (Figure 2).

RNA and DNA concentrations ([RNA] and [DNA]) varied from [RNA] = 0.92 – 4.95 (μg/L) and [DNA] = 0.29 – 4.29 (μg/L). The ratios of RNA:DNA varied from 1.36 – 6.43.

The average ΔW decreased as temperature increased, however as the level of ration increased, the ΔW decreased (Table 1). The same can be inferred for the [RNA]. The concentration of DNA increased with rising temperature, but decreased with higher ration. The ratio of RNA:DNA decreased with increased levels of temperature, although the ratio of RNA:DNA fluctuated randomly with higher levels of ration (Table 1). However, no significant correlation was observed between RNA:DNA and three levels of food ration and temperature. In relationship to ΔW and ΔCW as a function of temperature and ration, the crabs in 0% ration treatment experienced a negative slope as temperature increased (Figure 3), ultimately backing the idea that RNA:DNA has no correlation with change in growth (ΔW, ΔCW) as a function of temperature and ration.

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Results of the ANOVA of each of the five responsible variables (ΔW, ΔCW, [RNA], [DNA], and RNA:DNA) are shown in Table 2. Results indicated non-significant responses of ΔW, ΔCW, [RNA] and [DNA] to temperature, ration, and temperature*ration (Table 2). However, the ANOVA indicated that the RNA:DNA ratio varied significantly among temperature and ration treatments, but not with respect to their interaction (Table 2).

A linear regression analysis was performed on ΔW and ΔCW as a function of RNA:DNA, and only two treatments (28oC and 0% ration) yielded significant R2 values (Figure 4). The results of this regression to support RNA:DNA as a good predictor of growth show a non- significant conclusions for RNA:DNA to predict growth in juvenile blue crabs.

Discussion

The results from this study show that measured growth responses are strongly influenced by temperature and ration. Blue crabs in each of the three levels of food and ration exhibited a response in growth and this pattern correlated to the growth-altering conditions. Previous work using RNA:DNA as a growth index for larval fish have shown to be successful for quantifying growth in relation to different conditions (Caldarone et al. 2005). The potential for similar results are shown in this study, however our data show that among these growth changes, considerable variation is present. Additionally, there was a tendency for RNA:DNA ratio to be higher in growing larval fish that experienced higher than normal levels of food abundance and environmental temperatures; the same might be occurring in blue crabs (Caldarone et al. 2005). On the other hand, contrary to expectations, RNA:DNA was not strongly related to measured growth as a function of ΔCW or ΔW in my experiment. There was no correlation between RNA:DNA serving as a good predictor of growth.

By comparison, other research has shown a relationship between RNA:DNA as a good predictor of growth. In exploring my experimental results, analyses indicated that the treatments did not generate significant differences in growth. According to our data, the p values for ΔW, ΔCW, [RNA], and [DNA] were statistically insignificant and as a result no relationship between them and change in growth was observed. However, the RNA:DNA as a function of temperature and ration revealed a relationship of independent interaction on the measured responses. We did not find growth-related connection between RNA:DNA and ΔCW and ΔW: we did see clear and significant similarities between growth and temperature and ration affecting these independently. Possible future research could test temperature and ration separately on juvenile blue crabs and quantify its influences on [RNA] and [DNA].

The lack of relationships may be explained by the experimental design not including an adequate range of temperature to induce a bigger growth response. Examples of temperature thresholds reported for other larval fish RNA:DNA have proven successful, and it is possible that, in the temperature range sampled here, blue crab growth may have responded negatively, and resulted in non-significant responses on the RNA:DNA. Ideally, the temperature of the aquaria water would be consistent and exactly 20, 24, 28 degrees C; however, the probability of that precision in measurement is unrealistic. Additionally, accuracy and consistency in measurements is important, and perhaps the lack of precision in the measurement for carapace width led to the results being non-significant. There is evidence that the limited crabs molting caused the nucleic acid concentrations as a function of ΔW and ΔCW to read low R2 values. Subsequently, as an animal that feeds primarily on mollusks, the blue crab could respond to laboratory fish food unexpectedly, which could be reflected in results showing no correlation to the hypothesis (NOAA 2012). Furthermore, our data showed crabs with lost appendages weighing less than crabs with appendages, thus potentially affecting the correlation between

132 crabs with loss appendages and their [RNA] [DNA]. We cannot dismiss the possibility that appendage loss has caused our change in growth recordings to skew.

To improve this study, future experiments should solicit a more in-depth variation of experimental conditions to induce greater growth response differences. One plausible solution to the lack in measurement precision is to quantify the growth of length on a smaller scale. In our study we measured and compared the relationship of [RNA] and [DNA] to growth. These measurements of nucleic acid concentrations are a minute measurement (mg/mL) compared to the growth of carapace width (mm) which reflects a considerably larger scale of measurement. Why not find a form of measurement that will be relative to actual change in observed growth, to help bridge the correlation cap. Future experiments might look at measuring the cervical groove or anterolateral teeth for change in length instead of the carapace width, and ultimately may respond a better relationship for RNA:DNA to predict growth. Similarly, with a longer experimental time frame, greater differentiation in growth would be established.

In order to determine the relationship between growth and RNA:DNA, an adapted nucleic protocol could be devised to only measure crabs in the same molt stage. Secondly, allowing the experiment to run until all crabs molted may also eliminate the discrepancy within the nucleic acid concentrations. Even though we were not able to account for all variation in the data, such modifications for future study may yield stronger conclusions.

Conclusion

In the present study, we provide empirical measurements of RNA:DNA in its relationship to growth. The problem of discontinuous growth of blue crabs remains a challenge to scientists who wish to quantify its unique growth rates, but future research could be more promising.

In conclusion, the experiment was successful in measuring a recorded growth change for temperature and ration. Since the data for the Δ CW and the Δ W were evenly distributed and randomized, they do not support our hypotheses, H1 and H2. We were able to understand that even though temperature and ration reproduced a response change in growth, the measurement of this growth was difficult to quantify. There is evidence to suggest that temperature and ration did have an effect on RNA:DNA, but only independently of each other. Subsequently, there is no evidence to suggest that temperature*ration produced a response. Therefore, we conclude that RNA:DNA did not appear to be a good predictor of growth in experiments.

While blue crab growth is still difficult to quantify, our results show it may be more successful with longer experiments. Given the current relationship between RNA and DNA and measured growth in this experiment, future research should examine whether alternative levels of temperature and food ration can be used to induce a more distinguished growth response. Nonetheless, a better understanding of this relationship will help blue crab growth estimations and allow us to predict the best habitat locations for management as well as yearly catch size. Predictions from studies like these are important to resource managers as they attempt to sustain the crab population and enforce the crab fishing limits.

Acknowledgements

I would like to express thanks to Dr. Thomas Miller for supervision on this study, and I would also like to thank Danielle Zaveta for guidance in the experiment and reviewing an early

133 draft of this document. I want to give recognition to the Maryland Sea Grant and the Chesapeake Biological Laboratory for funding and overseeing this REU program.

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References

Caldarone, E.M. and L.J. Buckley. 1991. Quantitation of DNA and RNA in crude tissue extracts by flow injection analysis. Analytical Biochemistry 199: 137-141.

----- 2005. Estimating growth in haddock larvae Melanogrammus aeglefinus from RNA/DNA ratio and water temperature. Marine Ecology Progress Series 293: 241-252.

Caldarone, E.M., M. Wagner, J. St. Onge-Burns, and L.J. Buckley. 2001. Protocol and guide for estimating nucleic acids in larval fish using a fluorescence microplate reader. Northeast Fisheries Science Center Reference Document 01-11; 22 p. Available from: National Marine Fisheries Service, 166 Water Street, Woods Hole, MA 02543-1026. With permission from Elaine Caldarone.

Beck, M.W., K.L. Heck Jr., K.W. Able, D.L. Childers, D.B. Eggleston, B. M. Gillanders, B. Halpern, C.G. Hays, K. Hoshino, T.J. Minello, R.J. Orth, P.F. Sheridan, and M.P. Weinstein. 2011. The identification, conservation, and management of estuarine and marine nurseries for fish and invertebrates. BioScience 51: 633–641.

Bryce, J. Brylawski and T. Miller. 2003. Bioenergetic modeling of the blue crab Callinectes sapidus using the fish bioenergetics (3.0) computer program. Bulletin of Marine Science 7242: 491-504.

Buckley, L.J., E.M. Caldarone, and C. Clemmesen. 2008. Multi-species larval fish growth model based on temperature and fluorometrically derived RNA/DNA ratios: Results from a meta-analysis. Marine Ecology Progress Series 371: 221-232.

Buckley, L.J. and R.G. Lough. 1987. Recent growth, biochemical composition, and prey field of larval haddock Melanogramrnus aeglefinus and Atlantic cod Gadus morhua on Georges Bank. Canadian Journal of Fisheries and Aquatic Sciences 44: 14-25.

Bunnell, D.B. and T.J. Miller. 2005. An individual-based modeling approach to per-recruit models: Blue crabs Callinectes sapidus in the Chesapeake Bay. Canadian Journal of Fisheries and Aquatic Sciences 62: 1560-15720.

Dittel, A., A. Hines, G. Ruiz, and K.K. Ruffin. 1995. Effects of shallow water refuge on behavior and density-dependent mortality of blue crabs in Chesapeake Bay. Bulletin of Marine Science. 57: 902 – 916.

Etherington, L. and D. Eggleston. 2000. Large-scale blue crab recruitment: linking postlarval transport, post-settlement planktonic dispersal, and multiple nursery habitats. Marine Ecology Progress Series. 204: 179–198.

Heyer, C., T.J. Miller, F. Binkowski, E. Caldarone, and J.A. Rice. 2007. Maternal effects as a recruitment mechanism in Lake Michigan yellow perch Perca flavescens. Canadian Journal of Fisheries and Aquatic Sciences 58: 1477-1487.

Lipcius, R.N, Stockhausen W.T., Seitz R.D., and P. J. Geer. 2003Spatial dynamics and value of a marine protected area and corridor for the blue crab spawning stock in Chesapeake Bay. Bulletin of Marine Science. 72: 453-469.

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Miller R.E., D.W. Campbell, and P.J. Lunsford. 1980. Comparison of sampling devices for the juvenile blue crab, Callinectes sapidus. Fishery Bulletin 78: 196-198.

Miller, T.J. 2003. Incorparating space into models of the Chesapeake Bay blue crab population. Bulletin of Marine Science 72: 567-588.

Moriyasu, M. and P. Mallet. 1986. Molt stages of the snow crab, Chionoecetes opilio, by observation of morphogenesis of setae on the maxilla. Journal of Crustacean Biology 6:709-718.

Moksnes, P. and K. Heck Jr. 2006. Relative importance of habitat selection and predation for the distribution of blue crab megalopae and young juveniles. Marine Ecology Progress series 308: 165–181. NOAA Chesapeake Bay Office. 2012. Blue Crab. http://chesapeakebay.noaa.gov/fishfacts/blue- crab.

Peck, M., J. Lawrence, E. Calderone, and D. Bengtson. 2003. Effects of food consumption and temperature on growth rate and biochemical-based indicators of growth in early juvenile atlantic cod Gadus morhua and haddock Melanogrammus aeglefinus. Marine Ecology Progress Series. 251: 233-243.

Wang,S. and W. Sickle. 1986. Changes in nucleic acid concentration with starvation in the blue crab Callinectes sapidus Rathbun. Journal of Crustacean Biology 6: 49-56

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Figures and Tables

Figure 1. Map of Crab collection. Arrow represents location of crab collection on the Patuxent River at the Chesapeake Bay Laboratory. Inset map shows the location of the estuary relative to the Atlantic Coast and states in the watershed region (Courtesy of Kemp et al. 2005).

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Average ∆W 5

4

3 20 2 24 Weight (g) 1 28

0 0 30 100 -1 Ration

Average ∆CW 15 13 11 9 20 7 24 5 3 28 Carapace Width (mm) Width Carapace 1

-1 0 30 100 Ration

Figure 2. The relationship between (Δ CW) and (Δ W) scattered random through data. Results show randomization of growth by temperature and ration, but growth nonetheless. Error bars suggest numerous variation factors.

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Change in crab weight(Initial-Final) as a function of temperature and ration 5

4

3

0 % Ration 2 30 % Ration 100 % Ration Weight Change Weight 1

0

-1 20 24 28 Temperature (oC)

Change in crab carapace width as a function of temperature and ration 14

12

10

8

6 0 % Ration 30 % Ration 4 100 % Ration CW Change CW 2

0

-2

-4 20 24 28 Temperature (oC)

Figure 3. Variation of crab change in growth as a function of temperature and ration. 0% ration crabs experienced a decreased Δ CW and Δ W in relation to the other treatments as temperature increased. There is not enough information to make a conclusive decision.

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Figure 4. The relationship between RNA:DNA and Δ W in predicting growth.

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Table 1. The mean and standard deviation for each of the variable responses.

Temperature (oC) Ration (% of Cmax) Source 20 24 28 0 30 100 Δ W (g) 2.26 ±1.9 2.19 ±1.6 1.73 ±1.5 1.31 ±1.6 2.19 ±1.7 2.88 ±1.4 Δ CW (mm) 5.4 ±5.1 5.74 ±4.6 4.75 ±4.4 3.59 ±4.8 5.76 ±4.8 6.93 ±3.7 [RNA] (Δ g/L) 3.30 ±.80 3.28 ±1.1 2.77 ±1.2 2.34 ±.80 3.20 ±.93 4.02 ±.73 [DNA] (Δ 0.881 g/L) 0.856 ±.27 ±.29 0.910 ±.32 0.843 ±.32 0.788 ±.26 1.05 ±.21 RNA:DNA 4.09 ±1.0 3.84 ±1.0 3.10 ±1.0 2.97 ±1.0 4.23 ±1.1 3.91 ±.74

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Table 2. Results of analysis of variance of dependent variables for growth (DCW and DW) and biochemical growth indices ([RNA], [DNA] and RNA:DNA) as a function of temperature, ration and their interaction.

Source df SS MS F p Δ CW Block 2 4 1.995 0.084 0.919 Temperature 2 3.8 1.876 0.079 0.924 Ration 2 61 30.484 1.291 0.285 Temperature*Ration 4 98.4 24.603 1.042 0.397 Residual 43 1015.3 23.612 Δ W Block 2 0.1 0.051 0.016 0.984 Temperature 2 3.85 1.924 0.601 0.553 Ration 2 9.81 4.905 1.531 0.228 Temperature*Ration 4 8.82 2.204 0.688 0.604 Residual 43 137.77 3.204 Δ RNA Block 2 13.58 6.789 4.691 0.0144 Temperature 2 4.94 2.471 1.707 0.1934 Ration 2 8.22 4.108 2.839 0.0695 Temperature*Ration 4 2.6 0.651 0.45 0.7718 Residual 43 62.23 1.447 Δ DNA Block 2 0.668 0.3342 2.426 0.1 Temperature 2 0.006 0.0032 0.023 0.977 Ration 2 0.028 0.0138 0.1 0.905 Temperature*Ration 4 0.452 0.113 0.82 0.52 Residual 43 5.925 0.1378 Δ RNA:DNA Block 2 7.73 3.866 2.368 0.10575 Temperature 2 12.23 6.113 3.744 0.03169 Ration 2 18.09 9.044 5.539 0.00724 Temperature*Ration 4 4.28 1.07 0.655 0.62649 Residual 43 70.2 1.633

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UNIVERSITY OF MARYLAND CENTER FOR ENVIRONMENTAL SCIENCE Horn Point Laboratory

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Factors Affecting Cyanobacterial Ecology in a Restoration Wetland on Poplar Island, Chesapeake Bay

Austin Boardman, REU Fellow Maryland Sea Grant

Dr. Judy O’Neil, Research Assistant Professor Horn Point Laboratory, University of Maryland Center for Environmental Science

Dr. Kevin Sellner, Research Associate Smithsonian Environmental Research Center

Abstract

A wetland habitat at the Poplar Island ecosystem restoration project (Cell 6) has recently become home to a variety of birds, which use the pond for its nearby food and protection from predators. Additionally, a variety of cyanobacteria inhabit the site, including Anabaena, Oscillatoria, and Microcystis. Toxins produced by some of these cyanobacteria resulted in bird fatalities after a Microcystis bloom in summer 2012. Here, we examine the role that temperature, nutrients, and species composition plays in predicting how blooms might occur, whether toxins are produced, and how nitrogen fixation is performed. We found that cultures of cyanobacteria (primarily Anabaena) grow well in temperatures as high as 30°C, and that Anabaena from Cell 6 produces low levels of microcystin. Nitrogen fixation levels in cultures tended to spike periodically, possibly in response to fluctuations in limiting nutrients. Finally, nutrient concentrations played an important role in cyanobacterial growth and nitrogen fixation.

Keywords: Microcystis, Anabaena, microcystin, nitrogen fixation, eutrophication, cyanobacteria, Poplar Island

Introduction

Poplar Island

Poplar Island, near Talbot County, MD, has long served as a shelter for birds, turtles, and a host of other organisms. Unfortunately, sea level rise in the 1900s caused much of the island to erode, reducing its area from 440 ha to just 2 ha (Erwin et al. 2007). Recently, a solution to this problem was found with the need to dispose of sediment removed from shipping channels in the Chesapeake Bay. Beginning in 1998, the U.S. Army Corps of Engineers began transporting non-contaminated sediment from harbors near Baltimore to the former site of Poplar Island and used the dredge material to form wetland and upland habitats tailored specifically to certain species of wildlife (Burton 2005).

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One major wetland habitat on the island, Cell 6 (Figure 1), is currently the subject of an investigation led by the Maryland Environmental Service (MES) and the Smithsonian Environmental Research Center (SERC) after a bloom of the cyanobacterium Microcystis in the summer of 2012. Toxins (microcystins) produced by this cyanobacterium were associated with bird mortalities (Maryland Environmental Service 2013) – a major roadblock in the central mission of the restoration project. Further, Cell 6 is also highly nutrient-rich, especially in nitrogen and phosphorus (MES, unpublished data) and varies greatly in salinity, with areas ranging from 9.1-14.1 ppt. These conditions make it favorable for a variety of species of cyanobacteria, many of which can produce harmful toxins (Johnston and Jacoby 2003; Smith 1983).

Microcystis

Microcystis aeruginosa is one cyanobacterium that is known to produce harmful toxins, namely microcystins. These hepatotoxins are known to have detrimental effects on mammals, but also have been associated with deaths of birds, turtles, and other vertebrates that live in or near aquatic habitats (Chen et al. 2009). As such, researchers have been closely monitoring aquatic habitats at Poplar Island to control blooms that may arise.

Microcystis aeruginosa tends to form harmful blooms in the late summer and thrives at higher temperatures, typically above 25°C (O'Neil et al. 2012; Robarts and Zohary 1987; Thomas and Walsby 1986). For the remainder of the year, when conditions are not favorable, Microcystis will fall to the sediment and remain dormant until conditions improve (Welch and Barbiero 1992). In some cases, it will outcompete other cyanobacteria in brackish water (O'Neil et al. 2012). Previous research has estimated the salt tolerance of Microcystis to be between 7- 14 ppt, with the ability to withstand salt-shocks of up to 17 ppt (Kotut and Krienitz 2011; Tonk 2007).

Habitat nutrient composition also plays a major role in determining the onset of a Microcystis bloom. Blooms are often associated with high levels of nitrogen (Jacoby et al. 2000), as well as high total phosphorus and nitrogen to phosphorus ratios (Johnston and Jacoby 2003). Highly eutrophic habitats are also associated with increased toxin production (O'Neil et al. 2012). Considering all of these conditions, Cell 6 seems to be an optimal habitat for a toxic Microcystis bloom in the late summer when the water reaches a suitable temperature.

Cyanobacterial Ecology / Nitrogen Fixation

A variety of factors influence the algal composition of a body of water, including temperature, salinity, and nutrient availability. Macronutrients such as nitrogen and phosphorus are of particular interest to those studying algal ecology. Specifically, nitrogen fixing cyanobacteria (diazotrophs) are likely to outcompete non-nitrogen fixers when nitrogen levels are low (Horne and Commins 1987), phosphorus is high (Trimbee and Prepas 1987), and the ratio of nitrogen to phosphorus is low (Smith 1983). This is because diazotrophs are often poor competitors for phosphorus, meaning that if other cyanobacteria have an ample supply of dissolved inorganic nitrogen (DIN), they will outlive the nitrogen fixers that cannot survive in low- phosphorus environments. Likewise, nitrogen-fixing cyanobacteria will be successful in low-DIN environments because of their ability to convert atmospheric N2 into useable NH3 (Moisander et al. 2012; Whitton and Potts 2000)

Nitrogen fixation has been observed in many genera of cyanobacteria, including Anabaena (Figure 2) and Oscillatoria (Figure 3)—two groups that have recently been found in

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Cell 6 (Morgan State University Estuarine Research Center, unpublished data). The presence of these diazotrophs in Cell 6 is curious, considering the high levels of DIN and relatively high salinity.

Our research seeks to answer the many questions surrounding the cyanobacteria populations at Poplar Island. For example, the conditions (temperature, salinity, etc.) that may lead to additional Microcystis blooms in Cell 6 are unclear. It is also unknown how these factors can influence toxin production. Additionally, it is unknown what role Anabaena and Oscillatoria may be playing, especially with regard to toxin production and nitrogen fixation. This project seeks to investigate the reasons these diazotrophs are so abundant in a eutrophic environment and identify the factors that may affect rates of nitrogen fixation, namely availability of nutrients. For this project, we hypothesize that: • Microcystis will form blooms in late July/early August 2013. • Microcystis will grow best at temperatures 28-30°C. • Nitrogen fixation in Anabaena will increase as DIN decreases. • Anabaena, in addition to Microcystis, will produce toxins.

Materials and Methods

Phase I: Bloom Monitoring & Temperature Assay

Seven water samples were collected from the NWC, SEC, SW16, and SWP sites (aka sites 1, 3, 4, and 5) at Poplar Island’s Cell 6 (Figure 4) on May 29, 2013. At each site, measurements for temperature, salinity, conductivity, dissolved oxygen, and percent oxygen saturation were taken using an appropriately equipped YSI Pro 2030. pH was taken using a General Tools PH-501 pH meter. Water was also tested for phycocyanin and chlorophyll a (chl a) fluorescence using a Turner Designs Aquafluor fluorometer. Samples were then processed as follows: • Samples 1 and 2 from each site were filtered through 64 μm mesh to remove zooplankton and through a GF/F filter to remove phytoplankton. 100 mL of the filtered water from each site was placed in autoclaved flasks. BG-11, a media commonly used for culturing cyanobacteria (Anderson 2005), was prepared at the salinities measured in the field and 100 mL was added to each flask. These served as controls and were placed in the environmental chamber. • Samples 4, 5, and 6 from each site were filtered through 64 μm mesh and 100 mL distributed to flasks containing 100 mL BG-11, prepared as above, and stored in the environmental chamber. • 50 mL of Sample 7 from each site was decanted to a 50 mL disposable capped tube, fixed with Lugol’s iodine solution, stored on ice, and transferred to Morgan State University for species identification and enumeration. • Sample 8 from each site was collected in 250 mL amber bottles and stored at -20°C for microcystin assay.

Three surficial sediment samples were also collected at each site using 60 mL polypropylene syringes. Syringes were placed directly into sediment to a depth of at least 5 cm, and capped with a rubber stopper, then stored on ice. Upon returning to the lab, sediment samples deeper than 5 cm were extruded from syringes and discarded. The remaining sediment was deposited into 250 mL flasks. 100 mL of GF/F filtered water and 100 mL of BG-11 media at the proper salinity was added to each flask, and the contents swirled. Samples were placed in the environmental chamber.

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Light in the chamber was set at a level appropriate for cyanobacterial growth (~100 μE m-2 s-1), on a 14:10 L:D cycle. Initial incubation temperature was 22°C, and was increased by 2°C every 3 days, to a final temperature of 30°C, where they were held for over 10 days.

Chl a and phycocyanin fluorescence in each sample was measured every three days to monitor for blooming cyanobacteria (presumably Microcystis). Previous research suggests that a chl a/phycocyanin ratio of <10 can be used as an indicator of Microcystis dominance in field samples.

Phase II: Toxin Analysis

Materials for the phosphatase inhibition assay were obtained from New England Biolabs, Inc. (Ipswich, MA). For this assay, 80 μL NEB-BSA buffer was added to all wells in a 96-well plate. Samples for a standard curve were generated using a 5 ng/mL microcystin standard, diluted in NEB-BSA buffer to levels of 2.5, 1.25, 0.63, 0.31, and 0.16 ng/mL. 40 μL of each standard was added to the plate, except for a 0 ng/mL standard and a blank, to which 40 μL and 80 μL of additional substrate were added, respectively. Standards were made in triplicate.

Approximately 50 mL from each culture was subsampled and placed in glass vials at the beginning and end of the temperature assay for microcystin analysis. Samples were stored at - 20°C, and later thawed to release toxins. Samples were then centrifuged for 1 minute at 5000 rpm to separate any cell material from the supernatant. 40 μL of each sample was added to the plate. Samples at 25% dilution were also made and added to the plate to increase the sensitivity of the test in case microcystin levels were exceptionally high. All samples were made in triplicate.

Using a multichannel pipette, 40 μL PP1 enzyme was added to each well, followed immediately by 40 uL pNPP substrate. Samples were mixed by gently pipetting up and down several times. Any bubbles that formed were popped using a toothpick. The plate was left to rest for 60 minutes, and then placed in a spectrophotometer and absorbance was read at 405 nm. A standard curve was produced in Excel and sample values were compared against standards.

Phase III: Nitrogen Fixation Tests

Cyanobacterial cultures from the temperature assay were periodically subsampled and tested for nitrogenase activity through Acetylene Reduction Assay (ARA) (Hardy et al. 1973). For this process, 4 mL from each culture was transferred to 10 mL gas chromatography vials and sealed with rubber caps. 0.6 μL acetylene gas (generated from calcium carbide) was then injected into each vial. Nitrogenase activity was measured in the headspace of each vial by detecting the ethylene content of a 100 μL gas sample in a gas chromatograph using ARA (Capone 1993). This process takes advantage of nitrogenase’s ability to preferentially fix acetylene, rather than atmospheric nitrogen, and convert it to ethylene.

Readings were taken at time intervals of approximately four hours, for up to 24 hours. GC peaks for samples were compared against a 100 ppm standard and converted to nmol ethylene using the following equation:

× ( ) × ( ) × ( )

𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 � � 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑔𝑔𝑔𝑔𝑔𝑔 𝑝𝑝ℎ𝑎𝑎𝑎𝑎𝑎𝑎 𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 147

In order to standardize ethylene volumes per cell, all samples identified and enumerated with light microscopy using a Sedgwick-Rafter chamber at 200x. The number of filaments, cells, and heterocysts was counted for each species and squares were counted until either 50 units of the most common taxa were observed or 100 squares had been examined. In some cases, highly concentrated samples were diluted at a 1:10 ratio with distilled water. The number of cells per mL was calculated as follows:

( ) (1000) ( ) × × ( ) 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣 𝑖𝑖𝑖𝑖 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 𝑖𝑖𝑖𝑖 𝑆𝑆𝑆𝑆 𝑐𝑐ℎ𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 Once samples were𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 counted,𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣 the 𝑖𝑖𝑖𝑖nmol𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 ethylene𝑑𝑑𝑑𝑑𝑑𝑑 produced𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛 𝑜𝑜𝑜𝑜was𝑠𝑠𝑠𝑠 divided𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 by the𝑜𝑜𝑜𝑜𝑜𝑜 number of cells or by the number of heterocysts to find the average nitrogenase activity per cell. The instantaneous rate of nitrogen fixation was then calculated by dividing this value by the number of hours from the previous reading.

Phase IV: Nutrient Bioassays

To examine what effect nutrients such as nitrogen and phosphorus have on cyanobacterial growth, Oscillatoria was grown in three different nutrient treatments. First, 8.3 mL Oscillatoria culture from the temperature assay was placed in flasks containing 41.6 mL BG-11 media to achieve a 1:5 dilution. Three flasks were set-aside as controls. The remaining flasks had nutrients added as follows: • Flasks 4, 5, and 6: +1 mM NH4 • Flasks 7, 8, and 9: +50 μM PO4 • Flasks 10, 11, and 12: +1 mM NH4 and +50 μM PO4

These high concentrations of nutrients reflected the levels measured at Cell 6 in May 2013. (Though nutrient data for June and July were collected weekly by the SERC, data were not available at the time of this report’s publication). Samples were incubated at a temperature of 27°C for 7 days, under the same light conditions described for the temperature assay. Readings for pigment fluorescence were taken at 0, 4, and 7 days.

A second nutrient bioassay was also performed using water samples collected from the Sassafras River on August 1, 2013, when Anabaena spiroides was known to be present (Maryland DNR 2013). 100 mL of river water was dispensed into flasks and nutrients were added as follows: • Flask 1: Control. No nutrient addition. • Flask 2: +20 μM NH4 • Flask 3: +2 μM NH4 • Flask 4: +2 μM PO4 • Flask 5: +1 μM PO4 • Flask 6: +20 μM NH4 and +2 μM PO4 • Flask 7: +20 μM NH4 and +1 μM PO4

These nutrient levels reflected the historical range of nutrients that may be found in the Sassafras River. Cultures were incubated in the same conditions as above for 4 days. Pigment fluorescence and nitrogenase activity were measured for each.

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Results

Phase I: Bloom Monitoring & Temperature Assay

As shown in Table 1, the measurements taken May 29, 2013 for temperature, salinity, and dissolved oxygen varied greatly between sites, despite the fact that all were within the same body of water. Pigment fluorescence also varied greatly, with the lowest phycocyanin at site 1. Pigment fluorescence seemed to loosely correlate with the dissolved oxygen levels. Taxonomy results complement the fluorescence data, showing sites 3, 4, and 5 to be much higher in cyanobacteria than site 1.

In the temperature assay, cyanobacterial growth seemed to increase as temperature increased. As shown in Figures 5-6, chl a fluorescence tended to decrease in higher temperatures, while phycocyanin fluorescence increased. Thus, the chl a:phycocyanin ratio decreased over time, falling below the threshold ratio of 10 in some cases. Since a low chl a:phycocyanin ratio is often indicative of a cyanobacterial bloom, it can be concluded that generally cyanobacteria exhibited stronger growth at higher temperatures.

Phycocyanin fluorescence of sediment samples was consistently low throughout the temperature assay. Though a scum (possibly Oscillatoria) did develop in many of the cultures, no additional experiments were performed on these.

Monitoring done by MES officials found that Microcystis was not identified at Cell 6 during the course of this study.

Phase II: Toxin Analysis

Microcystin was found at comparable concentrations in the four cultures that were tested, with levels ranging from 0.366 to 0.375 ng/L (Figure 7). At sites 3, 4, and 5, microcystin concentration was less at the end of the temperature assay than at the beginning, while the opposite was true at site 1.

Phase III: Nitrogen Fixation Tests

Nitrogen fixation was measured in all cultures on June 17, 2013 and June 24, 2013 and normalized to the number of Anabaena heterocysts counted for each vial. (Though some other diazotrophic cyanobacteria were present in the samples, their concentrations were negligible compared to Anabaena, as seen in Figure 9). Nitrogen fixation rates were strikingly different between the two observation dates, with sites 3 and 4 having the highest levels of nitrogenase activity on June 17, 2013, and site 5 having the highest levels on June 24, 2013 (Figures 9-10).

As shown in Figure 11, nitrogenase activity in general seemed to decline as temperatures increased, though it is unclear whether this was due directly to temperature; the progression of time and shifts in the species assemblage of the cultures may have confounded the results. Additionally, regressions of nitrogenase activity versus salinity show that nitrogen fixation tended to be higher in lower-salinity cultures (Figure 12).

Phase IV: Nutrient Bioassays

In the Oscillatoria nutrient bioassays, there was no significant difference between the control and P treatments in terms of their chl a:phycocyanin ratio (Figure 13). The N and N+P

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treatments, however, were significantly different than both the control and P treatments (p=0.042), with higher ratios indicating less cyanobacterial growth. N and N+P were not significantly different from each other in terms of their chl a:phycocyanin ratios, but their chl a fluorescence levels were significantly different.

Sassafras nutrient bioassays did not exhibit such clear differences, with data obscured by the high variation in gas chromatograph readings (Figure 14). No significant difference was found between any of the treatments, except for the distilled water and filtered water controls. A diel cycle, however, was found in the rates of N-fixation, with noticeably higher rates when cultures were exposed to light (Figure 15).

Discussion

The results of this research show that cyanobacterial ecology at Poplar Island is complex, affected by numerous factors including temperature, salinity, nutrients, and competing organisms. This was apparent from the beginning of the project when high variation in water quality and species composition was discovered within the same ecosystem. This could be due to a number of factors. One possibility is that wind conditions pushed filamentous cyanobacteria to the southern end of the pond, leaving fewer species in the northern side near site 1. This could be coupled with variations in salinity and nutrients between different areas of the pond, which may have resulted from inconsistent dredging techniques and rainfall runoff affecting north and south differently. These nutrient and salinity conditions likely also affected the performance of algal competitors, which may have worked to suppress cyanobacteria in certain areas.

The temperature assay results were unsurprising, considering it is widely accepted in the literature that cyanobacteria tend to perform well at high temperatures (O'Neil et al. 2012; Robarts and Zohary 1987). The absence of blooms from the sediment cultures, however, was not expected. It was hypothesized that dormant cells would rise from the sediment at higher temperatures. Blooms were not observed, possibly because of dredging that was performed in the months prior to sampling. The new sediment may not have contained any cyanobacteria and may have completely covered any dormant cells from summer 2012.

The winter dredging may have also been a reason for the absence of any Microcystis blooms at Cell 6. Additionally, MES attempted to suppress Microcystis growth by placing bales of barley straw in the water before the summer bloom season began. Barley straw releases phenolic compounds as it decomposes, which are known to be toxic to Microcystis (Ball et al. 2001; Pillinger et al. 1994). This can be enhanced by fungal decomposition at higher temperatures (K. Sellner, personal communication).

Since Microcystis was never observed at Poplar Island or in cultures over the course of this experiment, it is likely that Anabaena was the source of the microcystin that was measured in the toxin analysis. It should be noted, however, that the level of microcystin observed in the lab cultures was several orders of magnitude lower than that observed at Cell 6 in the summer of 2012. The highest level measured from the cultures was 0.375 ng/L, which is well below the WHO provisional guideline value of 0.001 mg/L (World Health Organization 1998).

Anabaena is also assumed to be the source of the high nitrogen fixation levels that were observed throughout this experiment, though Oscillatoria and Pseudanabaena are likely to have also contributed very small amounts. One obvious peculiarity of the ARA experiments was the high levels of nitrogenase activity at sites 3 and 4 on June 17, 2013 and the high levels at site 5

150 on June 24, 2013. A possible reason for this is that each site had a different concentration of Anabaena cells initially, meaning that each culture used the media’s nutrients differently over time. Although all cultures were supplied the same amount of media at intervals throughout the experiment, their consumption of nitrogen, phosphorus, and other nutrients was likely quite varied. Since nitrogen fixation is a costly process in terms of energy, it should follow that diazotrophs would benefit from consuming as much DIN as possible, before activating nitrogenase. It might be assumed that sites 3 and 4 had reached that point on June 17, 2013, while site 5 had sufficient nutrients and did not need to begin fixing nitrogen at higher levels until June 24, 2013. Alternatively, these spikes could be due to unmeasured variables, such as bioavailable Fe, which plays an important role in nitrogen fixation.

The trend of nitrogenase activity declining as temperature increased could be interpreted a number of ways. While it might appear that temperature was directly correlated with nitrogenase activity, it is more likely that a confounding variable such as addition of media or changes in the community assemblage is to blame. For example, it is possible that the first acetylene reduction test was performed at a time when the cyanobacteria had consumed most of their nutrients, while later tests may have been done after nutrients had been replenished. This would explain differences in nitrogenase activity at different temperatures, simply because elapsed incubation time coincided with incubation temperature. Alternatively, shifts in the cyanobacterial species composition of the cultures could explain the decrease in nitrogenase activitiy. Unpublished taxonomy data from A. M. Hartsig shows that over the course of the temperature experiment, concentrations of diazotrophs such as Anabaena declined, while non- diazotrophic cyanobacteria including Spirulina and Planktolyngbya limnetica were among the most common at the end of the experiment. It is unclear, however, whether this shift was due to temperature or some other factor.

Nitrogenase activity also seemed somewhat diminished at higher salinities, independent of time or temperature. This trend reflects previous research showing that cyanobacteria often exhibit reduced nitrogen fixation in higher-salinity environments (Severin et al. 2012; Zhang and Feng 2008). This can either be due to increased stress on the organisms, which reduces their capacity to expend energy on nitrogenase production, or due to a shift in the community assemblage towards organisms that are more halotolerant non-diazotrophic organisms. In this case, the latter was unlikely, as the species composition between cultures of different salinities was seemingly random and extremely varied.

The strikingly high levels of nitrogen used in the Oscillatoria nutrient bioassay leave little doubt that 1 mM NH4 has the capacity to overwhelm cyanobacteria to the point of inhibiting growth. It is hypothesized that these high nutrient levels also caused a decrease in nitrogen fixation, though this was not tested due to equipment failure.

Finally, we hypothesized that nutrient concentrations would affect nitrogen fixation levels in Sassafras River samples. Results from this experiment were not entirely clear; it appears the control treatment may have had the highest levels of nitrogen fixation, especially toward the end of the experiment, though the high variability in the data make this difficult to say conclusively. If the experiment were repeated, results might be improved by the using a more sensitive method to detect N-fixation. Also, the incubation period may have been too short to have a significant impact on nitrogen fixation. Regardless, it is apparent that even changes in nutrients as great as ten times the ambient concentration will not produce changes in nitrogen fixation that are easily measurable.

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Diel variations in nitrogenase activity were consistent with those expected for heterocystous cyanobacteria, where nitrogenase activity was higher in the presence of light. Previous studies have suggested that this may be due to the fact that the products of nitrogen fixation are used in photosynthesis, which only occurs during daylight hours (Capone et al. 1990).

Conclusions

Though one goal of this research project was to gain a better understanding of the factors that contribute to Microcystis blooms, the organism was never observed, possibly due to the addition of barley straw to Cell 6 waters. While a major victory for the environment, this means that our two hypotheses concerning the timing and climate of a Microcystis bloom could not be confirmed nor rejected. It was found, however, that other cyanobacteria such as Anabaena do grow well in high temperatures. Additionally, Anabaena was found to produce toxins, confirming our hypothesis. However, the levels of microcystin produced were far below WHO guidelines.

Nitrogen fixation was observed in cultures from Cell 6, probably chiefly due to Anabaena. Nitrogen fixation in our cultures tended to spike suddenly at times, possibly in response to limited nutrients. However, nutrient bioassays were not able to clearly define what role nutrients play in nitrogenase activity. Thus, our hypothesis regarding nitrogen fixation in Anabaena remains unconfirmed.

Acknowledgements

I would like to express my gratitude to Dr. Judy O’Neil for guiding me through every step of this project, Anne Gustafson for providing assistance in the field and in the lab, Dr. Eric Schott and Kevin Meyer for assistance with toxicity analysis, and Dana Bunnell-Young for equipment troubleshooting. I also thank Mike Allen, Fredrika Moser, the staff of Maryland Sea Grant, and the National Science Foundation for making this research experience possible.

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References

Anderson, R. A. 2005. Algal Culturing Techniques. Elsevier Academic Press.

Ball, A. S., M. Williams, D. Vincent, and J. Robinson. 2001. Algal growth control by a barley straw extract. Bioresource Technology 77: 177-181.

Burton, K. 2005. The island that almost vanished is slowly reappearing. U.S. Fish & Wildlife Service Chesapeake Bay Field Office.

Capone, D. G. 1993. Determination of nitrogenase activity in aquatic samples using the acetylene reduction procedure, p. 621-631. Handbook of Methods in Aquatic Microbial Ecology. Lewis Publishers.

Capone, D. G., J. M. O'Neil, J. Zehr, and E. J. Carpenter. 1990. Basis for diel variation in nitrogenase activity in the marine planktonic cyanobacterium Trichodesmium thiebautii. Applied and Environmental Microbiology 56: 3532-3536.

Chen, J., D. Zhang, P. Xie, Q. Wang, and Z. Ma. 2009. Simultaneous determination of microcystin contaminations in various vertebrates (fish, turtle, duck and water bird) from a large eutrophic Chinese lake, Lake Taihu, with toxic Microcystis blooms. Science of the Total Environment 407: 3317-3322.

Erwin, R. M., J. Miller, and J. G. Reese. 2007. Poplar Island Environmental Restoration Project: Challenges in waterbird restoration on an island in Chesapeake Bay. Ecological Restoration 25: 256-262.

Hardy, R. W. F., R. C. Burns, and R. D. Holsten. 1973. Applications of the acetylene-ethylene assay for measurement of nitrogen fixation. Soil Biology and Biochemistry 5: 47-81.

Horne, A. J., and M. L. Commins. 1987. Macronutrient controls on nitrogen fixation in planktonic cyanobacterial populations, p. 413-423.

Jacoby, J. M., D. C. Collier, E. B. Welch, F. J. Hardy, and M. Crayton. 2000. Environmental factors associated with a toxic bloom of Microcystis aeruginosa. Canadian Journal of Fisheries and Aquatic Sciences 57: 231-240.

Johnston, B. R., and J. M. Jacoby. 2003. Cyanobacterial toxicity and migration in a mesotrophic lake in western Washington, USA. Hydrobiologia 495: 79-91.

Kotut, K., and L. Krienitz. 2011. Does the potentially toxic cyanobacterium Microcystis exist in the soda lakes of East Africa? Hydrobiologia 664: 219-225.

Maryland DNR. 2013. Continuous Monitoring: Sassafras River - Budd's Landing.

Maryland Environmental Service. 2013. Subcontractors Scope of Work: Environmental Restoration Project Task 50. Maryland Environmental Service.

Moisander, P. H. and others 2012. Facultative diazotrophy increases Cylindrospermopsis raciborskii competitiveness under fluctuating nitrogen availability. FEMS Microbiology Ecology 79: 800-811.

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O'Neil, J. M., T. W. Davis, M. A. Burford, and C. J. Gobler. 2012. The rise of harmful cyanobacteria blooms: The potential roles of eutrophication and climate change. Harmful Algae 14: 313-334.

Pillinger, J., J. Cooper, and I. Ridge. 1994. Role of phenolic compounds in the antialgal activity of barley straw. Journal of Chemical Ecology 20: 1557-1569.

Robarts, R. D., and T. Zohary. 1987. Temperature effects on photosynthetic capacity, respiration, and growth rates of bloom-forming cyanobacteria. New Zealand Journal of Marine and Freshwater Research 21: 391-399.

Severin, I., V. Confurius-Guns, and L. Stal. 2012. Effect of salinity on nitrogenase activity and composition of the active diazotrophic community in intertidal microbial mats. Archives of Microbiology 194: 483-491.

Smith, V. H. 1983. Low nitrogen to phosphorus ratios favor dominance by blue-green algae in lake phytoplankton. Science 221: 669-671.

Thomas, R. H., and A. E. Walsby. 1986. The effect of temperature on recovery of buoyancy by Microcystis. Journal of general microbiology 132: 1665-1672.

Tonk, L. 2007. Impact of environmental factors on toxic and bioactive peptide production by harmful cyanobacteria. University of Amsterdam.

Trimbee, A. M. and E. E. Prepas.1987. Evaluation of total phosphorus as a predictor of the relative biomass of blue-green algae with emphasis on Alberta lakes. Canadian Journal of Fisheries and Aquatic Sciences 44: 1337-1342.

Welch, E. B., and R. P. Barbiero. 1992. Contribution of benthic blue-green algal recruitment to lake populations and phosphorus translocation. Freshwater Biology 27: 249-260.

Whitton, B. A., and M. Potts. 2000. The Ecology of Cyanobacteria. Kluwer Academic Publishing.

World Health Organization. 1998. Cyanobacterial toxins: Microcystin-LR in drinking-water: Background document for development of WHO Guidelines for Drinking-water Quality. World Health Organization.

Zhang, W., and Y. Feng. 2008. Characterization of nitrogen-fixing moderate halophilic cyanobacteria isolated from saline soils of Songnen Plain in China. Progress in Natural Science 18: 769-773.

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Figure 1. Master Plan of the Poplar Island Restoration Project. Note the location of Cell 6 in the bottom right corner. Image credit: http://www.talbotcountymd.gov

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Figure 2. Anabaena spiroides. Image credit: Judy O’Neil

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Figure 3. Oscillatoria spp. Image credit: Judy O’Neil

Figure 4. Map of Poplar Island Cell 6 study sites. (Site 2 was not used due to inaccessibility.) NWC: Northwest Cove (Site 1). SEC: Southeast Corner (Site 3). SW16: Spillway 16 (Site 4). SWP: Southwest Peninsula (Site 5).

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Temperature Assay: Chl a Trends 3500 32

3000 30

2500 28 2000 Site 1 26 Site 3 1500 Site 4

24 Temperature (C) Site 5 Chl a Concentration 1000 Temp

500 22

0 20 5/26/13 6/5/13 6/15/13 6/25/13 7/5/13 Time

Figure 5. Temperature assay chl a fluorescence trends over time, by site.

Temperature Assay: Phycocyanin Trends 90 32

80 30 70

60 28 Site 1 50 26 Site 3 40 Site 4

30 24 Temperature (C) Site 5 Temp 20 Phycocyanin Concentration 22 10

0 20 5/26/13 6/5/13 6/15/13 6/25/13 7/5/13 Time

Figure 6. Temperature assay phycocyanin fluorescence trends over time, by site.

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Microcystin Concentration in Cultures 0.378

0.372

t0 0.366 tf

0.360 1 3 4 5 Microcystin Concentration (ng/L) Concentration Microcystin Site

Figure 7. Microcystin concentration in temperature assay cultures from each site.

Cyanobacteria Counts from N2-fixation Experiments 1,000,000

100,000

10,000

Anabaena 1,000 Oscillatoria

Filaments per mL 100 Pseudanabaena

10 Spirulina

1

Date 1 7 J u n 2 4 J u n

Figure 8. Cyanobacterial composition of each nitrogen fixation subsample in filaments per mL. Average of all 20 cultures for each date of testing is shown. Note log scale on y-axis.

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Nitrogenase Activity by Site (6/17/13) 1.20E-04

1.00E-04

8.00E-05

Site 1 6.00E-05 Site 3

4.00E-05 Site 4 Site 5 2.00E-05 Production (nmol/hour/heterocyst)

Average Average Instantaneous Rate Ethyleneof 0.00E+00 0.00 5.00 10.00 15.00 20.00 25.00 30.00 Time (hours)

Figure 9. Nitrogenase activity in subsamples from temperature assay cultures, sampled June 17, 2013.

Nitrogenase Activity by Site (6/24/13) 9.00E-05

8.00E-05 Site 1 7.00E-05 Site 3 6.00E-05

5.00E-05 Site 4

4.00E-05 Site 5 3.00E-05

2.00E-05

1.00E-05 Production (nmol/hour/heterocyst)

Average Average Instantaneous Rate Ethyleneof 0.00E+00 0.00 5.00 10.00 15.00 20.00 25.00 30.00 -1.00E-05 Time (hours)

Figure 10. Nitrogenase activity in subsamples from temperature assay cultures, sampled June 24, 2013.

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Nitrogenase Activity vs. Temperature 1.4E-02

1.2E-02

1.0E-02

8.0E-03

6.0E-03

4.0E-03

2.0E-03 (nmol/hour/heterocyst) 0.0E+00 26.5 27.0 27.5 28.0 28.5 29.0 29.5 30.0

Instantaneous Instantaneous Rate Ethyleneof Production Temperature (C)

Figure 11. Regression of instantaneous rates of ethylene production versus incubation temperature at time of acetylene reduction reading.

Nitrogenase Activity vs. Salinity 1.40E-02

1.20E-02

1.00E-02

8.00E-03

6.00E-03

4.00E-03

2.00E-03 (nmol/hour/heterocyst) 0.00E+00 8 9 10 11 12 13 14 15

Instantaneous Instantaneous Rate Ethyleneof Production Salinity

Figure 12. Regression of instantaneous rates of ethylene production versus salinity.

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Oscillatoria Nutrient Bioassay Chl a:PC Ratio 350.0 A B A B 300.0 250.0 200.0 150.0 100.0 50.0

Chl a:Phycocyanin Ratio 0.0 C N P NP Treatment

Figure 13. Chlorophyll a:Phycocyanin ratios of Oscillatoria cultures in 4 nutrient treatments: control, nitrogen, phosphorus, and nitrogen+phosphorus.

Sassafras Nutrient Bioassay: Nitrogenase Activity by Treatment 0.700

0.600 DI 0.500

0.400 Control (Filtered)

0.300 N (High) 0.200 N (Low)

0.100 P (High) P (Low) 0.000 N+P (High)

nmol Ethylene Producedper per hour mL 0.00 5.00 10.00 15.00 20.00 25.00 30.00 -0.100 N+P (Low) Time (hours)

Figure 14. Sassafras river samples’ nitrogenase activity by treatment after 4-day incubation period.

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Sassafras Nutrient Bioassay: Diel Variation in Nitrogenase Activity 0.600

0.500

0.400

0.300

0.200

0.100

0.000

-0.100 DI  C F  C  N H  N L  P H  P L  N P H  N P L Average Average nmol Ethylene Producedper per hour mL Treatment

Light Dark

Figure 15. Diel variation in nitrogenase activity, shown in Sassafras nutrient bioassay. Treatments shown are (left to right): distilled water, filtered control, unfiltered control, high nitrogen, low nitrogen, high phosphorus, low phosphorus, high nitrogen+phosphorus, low nitrogen+phosphorus.

Table 1. Water quality data by site.

Site 1 3 4 5 Temperature (°C) 30 24 26 30 Salinity 12 14 14 9 DO (mg/L) 7.2 10.3 17.1 19.4 Chl a 693 1100 1177 959 Phycocyanin 5.8 23.4 26.8 22.7

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Characterizing Flow at the Susquehanna Flats

Angela Cole, REU Fellow Maryland Sea Grant

Dr. Lawrence Sanford, Professor Horn Point Laboratory University of Maryland Center for Environmental Sciences

Keywords: Susquehanna Flats, Submerged Aquatic Vegetation, Attenuation, Water Quality

Abstract

The Chesapeake Bay (CB) is the largest estuary in the United States. Water quality models in the CB depend on a predictive understanding of the physical, chemical, and biological processes that influence the transport and fate of sediments and nutrients. This study characterized flow at the Susquehanna Flats (SF) in the upper CB to show how submerged aquatic vegetation (SAV) influences hydrodynamics. The SF lie at the mouth of the Susquehanna River, which is the primary source of fresh water for the CB. From July 7-12, 2013, we deployed tide, wave, current velocity, and pressure gauges at seven sites around the SF to gather time series data on flow and its attenuation in and around the SAV bed at the SF. The design shows how spatial features including bathymetry, delta geometry, and grass bed geometry affect patterns of flow and transport. Data were processed and analyzed using standard MATLAB time series analysis techniques. Our hypothesis was that the flow would predominately be diverted around the grass bed into adjacent deep, unvegetated channels. Seagrass was minimally present during the deployment. Analysis shows that tide, river flow, and wind all play key roles in the physical processes at the SF.

Introduction

Submerged aquatic vegetation (SAV) protects coastlines against erosion (Christianen et al. 2013) and acts as nurseries for fisheries (Nagelkerken et al. 2000). SAV also plays a role in sediment dynamics and nutrient circulation (Bos et al. 2007; Gruber and Kemp 2010). Consequently, SAV is critical to water quality and wetland restoration (van derHeide et al. 2007). Times series characterization of the physical processes surrounding SAV provides a framework for the chemical and biological processes in the grass beds, and contributes to the large-scale predictive understanding of how sediment and nutrient influxes interact with the SAV and the surrounding environment.

While SAV coverage is declining globally (Orth et al. 2006), the Susquehanna Flats (SF) in upper Chesapeake Bay (CB) recently experienced an unexpected resurgence. About 40 years ago, the SAV beds almost entirely disappeared after Tropical Storm Agnes (Orth et al. 2010). The beds were sparse for 30 years. Then, in the mid-2000s, the beds suddenly rebounded (Fincham 2012). Figure 1 visualizes the seagrass decline and resurgence. Understanding why the beds disappeared and then rebounded is important locally because the

164 SAV acts as a seasonal trap for particulate phosphate and sediments, which reduces nutrient release into the CB (Ward et al. 1984; Caffrey and Kemp, 1992). Quantifying nutrient release is important for developing better models for predicting ecosystem responses to nutrient input. Understanding nutrient release and ecosystem responses is important for determining the best way to restore the CB. On a broader scale, understanding the conditions for resurgence provides critical insight on SAV restoration efforts globally.

Sediment and nutrient loading from the Susquehanna River basin are important for resource management policy. Farmers have an expensive commitment to adhere to total maximum daily load (TMDL) allocations. The Conowingo Dam is the last dam on the Susquehanna River before the river opens into the CB. The Conowingo Dam forms a reservoir where nutrient-rich sediments accumulate; as a result, the reservoir’s storage capacity is diminishing (Hirsch 2012). Short term, high-flow events increase the flux of particulate phosphate and suspended sediment from the reservoir to the CB. For example, the Tropical Storm Lee flooding event contributed 22% of the phosphorous and 39% of the sediment flux during the time period of the event, even though the flooding only accounted for 1.8% of the total stream flow (Hirsch 2012). This nutrient and sediment flux temporarily overwhelms other nutrient management efforts such as the TMDL allocations. Consequently, several Maryland counties are considering a joint lawsuit to challenge their TMDL allocations. Understanding the physical processes within and below the Conowingo reservoir will allow for more effective nutrient mitigation efforts.

Tidal, wave, and current flow attenuation are of particular interest in this study. Flow attenuation is a dominant physical process in SAV. Currently, there is no information describing how the flow attenuates at the SF. It is unclear whether flow moves through the grass beds as it attenuates, or if it is diverted around the grass bed into adjacent unvegetated channels. Flow patterns and flow attenuation directly influence two critical parameters of SF dynamics: sediment resuspension and nutrient flux. Sediment resuspension is important because it affects the penetration of light to the SAV. Nutrient flux is important because nutrients are required for photosynthesis, but excess nutrients can have adverse effects on SAV. Understanding flow and its attenuation is vital for a comprehensive understanding of SF physical and chemical dynamics.

The goal of this investigation was to describe tidal, wave, and current flows and their attenuation in and around the SF by 1) deploying tide, wave, current velocity, and pressure gauges, 2) analyzing the relationship among surface elevation outside of the grass bed, surface elevation at two locations within the grass bed, and flow within the bed, and 3) determining how stream flow, bed geometry, SAV characteristics, tides, and wind influence flow in and around the SF SAV bed.

Methods

A field deployment for this project will took place from July 7 to July 12, 2013. Figure 2 shows the equipment deployment described in this section. Table 1 enumerates the equipment and parameters measured during the deployment. Two wave gauge and current meter packages were deployed inside of the grass bed to gather wave, current velocity, and surface elevation data. The MD DNR platform in the southern half of the bed provided surface elevation data. The tripod south of the bed measured waves, surface elevation, and 3D velocity data. The tripod contained a 5-Mhz Sontek ADV that was sampled in bursts at a rate of 10 Hz for 4,096 samples every 30 minutes. All four of the sites also recorded time series of basic water quality parameters.

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The NOAA Chesapeake Bay Interpretive Buoy System (CBIBS) Susquehanna buoy located at the river mouth provided wind and current data (buoybay.noaa.gov). Data gathered from the MD DNR Havre de Grace dock included surface elevation, salinity, temperature, turbidity, and nutrient data (http://mddnr.chesapeakebay.net/eyesonthebay).

In addition to gathering wave, surface elevation, and current data, water samples were collected from automated water samplers (ISCO bottles) at the same two locations as the wave platforms. A multi-parameter data sonde (YSI 6600) was used to measure pH, temperature, and turbidity. Two sediment cores were taken at the two locations depicted in Figure 2 to perform erosion experiments.

The data from the time series sensors were processed and analyzed using standard time series MATLAB routines used by the Sanford group. The wave burst data were processed using scripts written by Steve Suttles, the lab technician in the Sanford group. Wave burst analysis characterized sea surface elevation during the deployment. Comparisons of surface elevation changes, current velocities, and wave amplitude across the bed help to decipher patterns of flow and wave attenuation, and the potential for flow blockage by the bed.

Preparation Timeline

Jun 24, 2013 Went out to the Susquehanna flats to check the status of plant growth and to verify that water level and bed geometry were appropriate for aquadopp deployment at the platform deployment locations. Jun 1-3, 2013 Retrieved sediment cores and performed sediment flux experiments. The details of the sediment flux experiments are enumerated after the timeline. Jul 5, 2013 Deployed tripod; 3 ISCO automatic water samplers at SF02, SF03, SF07; 2 YSI data sondes; aquadopps and peepers at SF03, SF04, SF07; and 2 wave gauges. We initiated sampling. Cores were collected at SF03, SF04, and SF07.

Deployment Timeline:

Jul 7, 2013 Arrive at North Bay. Jul 8-11, 2013 ISCO bottles were exchanged and separated for filtering daily. ISCO deployment methods are enumerated after the sediment flux experiment details. July 12, 2013 Retrieved all deployed equipment and returned to Horn Point Laboratory (HPL).

Results

Minimal sea grass was present, less than 10% of the peak biomass for June of 2012. Tidal pressures were compared for three sites: Havre de Grace (HdG), site 4 (SF04), and the tripod (ADV). Figure 3 shows the tidal pressures for these sites after the data were detrended and the mean was removed. The graph shows that flood tide is quicker than ebb tide. Figure 4 shows a characteristic tidal cycle in greater detail. Figure 4 shows that HdG reaches high tide earlier than the other sites, and it has the highest high tide of the other values. Additionally, the tide rises and falls most quickly at the ADV and slowest at HdG. Figure 5 compares the tidal cycles with pulsed river discharge. The Conowingo Dam is periodically opened to produce hydroelectric power. The times of high discharge correspond approximately to the times of higher tidal levels at HdG than at the ADV, and times of lower discharge correspond

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approximately to the times of nearly equal tidal level at the two locations. Figure 6 investigates the relationship between wind and background trends in the tidal plot. The region of high wind intensity correlates to the slow increase and decrease in pressure.

Discussion

Figure 3 shows that the ebb flood lasts longer at the SF than the flood tide. This point is important because flood and ebb tide influences erosion and accretion. Understanding the asymmetry between rates of flood and ebb tide provides a foundation for predicting sediment transport in the SF. Sediment dynamics, in turn, influence morphology. Figure 3 also shows that the tide rises and falls first at the ADV, then a little later at SF04, and tide rises and falls slowest at HdG. This trend is remnant of a tidal progression.

We did not expect HdG to reach high tide first because the site is on the opposite side of the flats from the incoming tide. One possible explanation for this behavior is that the tide is being diverted around the grass bed through adjacent, unvegetated channels and reaches the HdG site before the water that is travelling through the grass bed can reach HdG. This explanation seems unlikely, however, because the seagrass biomass was much lower than the expected value during this month. Further consideration must be given to conclude what irregular force is causing the tide to peak first at HdG.

Increased river discharge from opening the Conowingo Dam and intense wind periods also contribute to irregularities in the tidal patterns. Periods of high discharge seem to influence the HdG site the most, resulting in a wider range of tidal levels at the three sites; when the dam is closed and the river discharge is low, the tidal levels at the three sites are almost equal as shown in Figure 5. However, this relationship is only approximate and further analysis will be done in order to quantify the relationship. Figure 6 shows that high intensity wind periods, even if they have a short duration, contribute to background patterns in the tidal trends. This relationship is important, because it shows that erratic wind behavior can noticeably alter the dynamics at the SF.

Conclusion

The data suggest that the tidal, river, and wind forcing all play a key role in the physical processes at the SF.

Understanding flow patterns and flow attenuation between the grass beds and the surrounding water will give insight into the physical processes that govern the fluxes of sediment and nutrients at the SF. A better understanding of these fluxes, in turn, will contribute to the next generation of water quality models. This knowledge will also inform social commitment to nutrient mitigation; because local farmers are obligated to adhere to their TMDL allocation, the scientific community has a social commitment to providing the best explanation for trends such as SAV death and resurgence in the Bay. The data collected in this study will contribute to long term studies on seasonal sediment and nutrient dynamics in the Bay. Understanding the physical regime appropriate for SAV resurgence will also assist in identifying future targets for grass bed restoration projects.

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Caffrey, J. M., and W. M. Kemp. 1992. Influence of the submersed plant, Potamogeton perfoliatus L., on nitrogen cycling in estuarine sediments: Use of 15N techniques. Limnology and Oceanography 37: 1483-1495.

Christianen, M.J., J. van Belzen, P. Herman, M. van Katwijk, L. Lamers, P. van Leent, and T. Bouma. 2013. Low-canopy seagrass beds still provide important coastal protection services. PLoS ONE 8: e62413.

Fincham, M.W. 2012. The Bay-grass surprise. Chesapeake Quarterly 11.

Hirsch, R.M. 2012. Flux of nitrogen, phosphorus, and suspended sediment from the Susquehanna River Basin to the Chesapeake Bay during Tropical Storm Lee, September 2011, as an indicator of the effects of reservoir sedimentation on water quality. U.S. Geological Survey Scientific Investigations Report 2012-5185. U.S. Geological Survey, 17 pp.

Nagelkerken, I., G. van der Velde, M. Gorissen, G. Meijer, T. Van’t Hof, and C. den Hartog. 2000. Importance of mangroves, seagrass beds and the shallow coral reef as a nursery for important coral reef fishes, using a visual census technique. Estuarine, Coastal and Shelf Science. 51:31-44.

Orth, R.J., T. Carruthers, W. Dennison, C. Duarte, J. Fourqurean, K. Heck, A. Hughes, G. Kendrick, W.J. Kenworthy, S. Olyarnik, F. Short, M. Waycott, and S. Williams. 2006. A global crisis for seagrass ecosystems. Bioscience 56: 987-996.

Orth, R. J., M. R. Williams, S. R. Marion, D. J. Wilcox, T. J. B. Carruthers, K. A. Moore, W. M. Kemp, W. C. Dennison, N. Rybicki, P. Bergstrom, and R. A. Batiuk. 2010. Long-term trends in submersed aquatic vegetation (SAV) in Chesapeake Bay, USA, related to water quality. Estuaries and Coasts 33: 1144-1163. van derHeide, T., E. van Nes, G. Geerling, A. Smolders, T. Bouma, and M. van Katwijk. 2007.Positive feedbacks in seagrass ecosystems: Implications for success in conservation and restoration. Ecosystems 10: 1311-1322.

Ward, L. G., W. M. Kemp, and W. R. Boynton. 1984. The influence of waves and seagrass communities on suspended sediment dynamics in an estuarine embayment. Marine Geology 59: 85-103.

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Figures and Tables

Figure 1. Trends in SAV abundance in SF based off of aerial photography analysis (www.chesapeakebay.net). Dashed line indicates the calculate turn-around point for resurgence.

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Figure 2. Equipment deployment locations.

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Figure 3. Tidal pressure signals with linear trend and mean removed.

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Figure 4. Zoomed in plot of a characteristic tidal cycle. The location of this zoomed region is highlighted on the complete tidal cycle graph on Figure 3.

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Figure 5. Subplot of tidal pressures and filtered river discharge. Local maximums on the discharge plot align with areas of higher tidal level at the ADV while local minimums on the discharge plot align with areas of approximately equal pressures at all sites.

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Figure 6. Comparison of tidal pressures and wind vectors where the speed is greater than five knots. Periods of high wind intensity correlate to background trends in tidal pressures.

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Table 1. List of instruments used and parameters measured on deployment. Bolded parameters are discussed in this paper; analysis of the other parameters were still ongoing.

Quantity Instrument Parameters Measured 3 Aquadopp Wave height, wave period, wave direction, current vector data 1 Tripod Pressure (water depth and wave height), temperature, salinity, turbidity, current velocity 3 ISCO Samplers Water samples to be analyzed for TSS, chl-a, nutrients 4 YSI Temperature, salinity, turbidity, chl-a, DO, pH 2 Pressure/wave gauges Pressure (water depth), wave bursts 1 Weather and surface water Air temperature, nitrates, barometric pressure, characteristics (CBIBS) chl-a, current velocity, DO, turbidity, salinity, water temperature, wind

1 Streamflow Gauge (USGS) River discharge

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Wind-induced Lateral Circulation and Mixing in the Chesapeake Bay

Jenessa Duncombe, REU Fellow Maryland Sea Grant

Dr. William Boicourt, Professor Horn Point Laboratory, University of Maryland Center for Environmental Science

Carole Derry, Research Assistant Horn Point Laboratory, University of Maryland Center for Environmental Science

Abstract

The lateral salinity gradient responds to wind and tidal forcing in the Chesapeake Bay with spatial variation in salinity. The layers of isohalines tilt to the western shore during flood tide and slack before ebb tide, while the isohalines flatten during ebb and slack before flood tide, creating a semi-diurnal seesaw motion of the salinity gradient. The degree of the tilt correlates to the sensitivity of the tide to the Coriolis Effect, a phenomenon previously not well understood spatially. Sensitivity to wind forcing occurs on a more drastic scale and results in tilting to the Eastern shore. Up–welling and down–welling zones appear on the shallow shoals during wind events. The shallow shoals also experienced regularly changing salinity from 11–15 PSU within the interval of six hours. Both up-welling and down-welling zones and the seesaw motion of the salinity gradient may indicate lateral mixing.

Keywords: Lateral, Wind, Tidal Cycle, Mixing

Introduction

Bottom waters of the Chesapeake Bay have begun to experience hypoxic and anoxic conditions more frequently (Hagy et al. 2004). These low oxygen conditions are harmful to the environmental and economic well being of the Bay. Bottom-dwelling organisms such as oysters, clams, crabs and fish discontinue reproduction or die when dissolved oxygen content sags, especially in the range below 1 mg L-1 DO (Wu et al. 2003; Hagey et al. 2004).

Although biological mechanisms play a role in creating low oxygen environments, physical factors determine the transport of oxygen in the water column. Highly stratified, un- mixed water restricts the flow of oxygenated water downward and encourages hypoxic and an anoxic conditions (Goodrich et al 1987). Physical forces break down or disrupt pockets of anoxic and hypoxic water. The primary components responsible for introducing oxygen to low- oxygenate water are vertical mixing and lateral circulation (Scully 2010).

The physical dynamics of longitudinal and vertical directions within an estuary have been studied rigorously. Yet lateral dynamics is not well understood, even though mixing in the lateral

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direction transfers oxygen as well as reinforces or breaks down the stratification of the water column, contributes to the transfer of momentum and contributes to sediment transport (Li and Li 2012). Nunes and Simpson (1985) made one of the first field observations of lateral dynamics in a well-mixed estuary. The study determined that surficial debris accumulating in the longitudinal direction was evidence of surface convergence and lateral circulation.

Evidence of lateral mixing can be seen as variations in structure and circulation. Lacy et al. (2003) noted that lateral structure is significant in the northern San Francisco Bay because the significant vertical density gradient resulted in circulation. Reynolds-Fleming and Luettich (2004) characterized the motion in the estuary by focusing particularly on the lateral circulation.

Past and contemporary observational studies on the Chesapeake Bay show similar lateral variations in structure and circulation. These variations are partially due to the wide, shallow nature of the Bay. Bathymetric variations affect the flow of longitudinal water and create asymmetric density gradients (Valle-Levinson, Boicourt and Roman 2003). The width of the Bay causes it to be sensitive to gravitational forces. Non-uniform circulation due to the Coriolis Effect causes a stronger circulation on the right bank (Lerczak and Geyer 2004). The pycnocline, which is an area of rapid change in the density, tilts laterally as a result of the Coriolis Effect (Pritchard 1952). Asymmetries in stratification and circulation instigate pockets of up-welling and down-welling (Smith, Leffler and Mackiernan 1992).

Although gravity and bathymetry affect the structure in circulation, the main drivers of lateral variations in the Chesapeake Bay are tidal and wind-forcing. Tides play a significant role in shaping vertical structure because of the lateral shear created with longitudinal flow. Ebb tides create less lateral flow and more stratification, while flood tides cause much stronger lateral circulation (Lerczak and Geyer 2004; Reyes-Hernandez and Valle-Levinson 2012). Yet observational data suggest that while tidal mixing does occur, its contribution to mixing as a whole is relatively small. Severe weather events, bringing fresh rainwater and winds, prompt large-scale mixing which thoroughly mix highly stratified profiles. This high-intensity mixing dramatically alters the characteristics of the water column. Goodrich et al (1987) states that wind-forcing is not only a contributor to mixing, but the primary driving mechanism.

A Chesapeake Bay model created by Malcolm (2010) determined that lateral circulation from wind forcing were two of the dominant processes responsible for the transport of oxygen. Numerical models allow isolation of wind and tidal-forcing to determine their impacts and relative features. Models show that the direction of wind and the degree of stratification dramatically alters the effect that it has. Wind-forcing flattens or tilts isopycnals and strengthens or weakens pre-existing circulation (Li and Li 2012; Gao and Valle-Levinson 2008). Circulation reverses directions entirely between counter-clockwise and clockwise depending on the wind direction (Li and Li 2012; Guo and Valle-Levinson 2008). Wind-driven circulation under stratified conditions shows a higher resistance to wind-forcing than unstratified conditions (Chen 2009; Lerczak and Gyer 2009; Guo and Valle-Levinson 2008).

The lateral response of wind and tidal-forcing in the Chesapeake Bay were examined using smaller spatial realities. Two features in particular were examined, the structure of the salinity gradient and the presence of up-welling and down-welling zones. The tilt and range of isohalines were measured to characterize the structure. Vertical isohaline lines disrupting the more common horizontal structure determined zones of up-welling and down-welling. Each features' spatial and temporal variations were used to reveal their sensitivity to each force.

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Materials and Methods

Data Collection

Measurements of physical and biological characteristics were made in the Chesapeake Bay during March through May 2012. The measurements are part of the third deployment for a multi-year, multi-party study sponsored by the National Science Foundation aimed at examining the role of wind in estuarine circulation. Both moored and shipboard instruments collected data in three cross-sections of the Bay located in the Northern (N), Middle (M) and Southern (S) section of the Bay (Figure 1). Each section is unique due to its bathymetry, but all sections were selected as locations with low bathymetric complexity. Figure 2 shows the fixed instruments deployed at these three locations, the majority of which were in the M cross-sectional array.

Solemnity data were collected by an undulating towed vehicle, a Scanfish. As a boat along a desired cross-section tows the Scanfish, it oscillates from 10 m to 30 m in depth sinusoidally at a period of 50 m to 200 m depending the speed of the boat. The Scanfish was towed directly parallel to the fixed instruments over the course of two weeks. The sampling speed (16 measurements per second) provides sufficient data to resolve the position and thickness of salinity structure.

Lateral current was observed using Acoustic Current Doppler Profile (ACDP) instruments in the fixed mooring (Figure 2). Frequent readings were recorded along- and cross- channel flow regularly every 5 minutes over the course of 60 days. The M array configuration provided extensive spatial resolution with 6 deployed ADCP instruments. The spatial resolution for the N and S arrays was more limited with each containing one instrument. The sensors successfully recorded velocity with the exception of the S array ADCP information.

Meteorological packages fastened to the moored buoys provided the corresponding wind data. Each buoy records direction, magnitude of the wind as well as transient wind gusts. A buoy run by National Oceanic and Atmospheric Administration (NOAA) called Goose Reef complemented the wind data. Goose Reef is deployed in line with the N array.

Data Analysis

The analysis of the observed data is the primary concentration of this study. In-house developed algorithms in MATLAB® were used to synthesize and interpret the salinity, current and wind data. The interpolation scheme most effective in representing the Scanfish data was kriging. A MATLAB code created by Woods Hole Oceanographic Institution called EasyKrig v.3 was used. Linear, nearest neighbor and natural interpolation schemes were found ineffective for describing the spatial distribution of the Scanfish data.

The data were grouped to characterize quiescent periods with little or no wind and major wind-forcing events. Axial winds were only considered and cross-channel winds ignored due to the comparably negligible fetch. Scanfish profiles taken during quiescence periods were separated into their respective tidal cycle. The tidal cycle was broken into four main phases based on the along channel velocity measured at the M array: maximum ebb, maximum flood, slack before ebb and slack before flood.

Salinity profiles were used to determine the lateral structure of the water column. The tilt of the isohalines were quantified, and using coherent analysis, compared to the tidal cycle. Not enough data were available during the wind events to separately examine northern and

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southern wind events. Instead, the salinity structure during wind events was compared to the structure during quiescence.

Evidence of up-welling and down-welling is seen in the salinity profiles. Similar to Smith, Leffler and Mackiernan (1992), who identified asymmetries in circulation to point to up-welling and down-welling zones, tilting of the salinity gradient is used to pin point zones. The determining characteristic of a zone is isohalines with a vertical component in contrast with a horizontal structure. The isohaline structure tilting towards the surface signifies a well-mixed water column and low stability.

Results

There were 24 total salinity profiles by the Scanfish from March 17–24, 2012. Wind activity during the two-week period was low and intense wind events for the remainder of the data. Southward winds were dominant and only one short northward wind event occurred during the Scanfish collection period. Figure 3 demonstrates the time period at which the Scanfish runs were taken. Also demonstrated in Figure 3 is the down channel current velocity. The velocity oscillates between seaward and inland current at the regular intervals of the tidal cycle.

Each contour plot is a lateral cross-section at the M array. The orientation of the plot is facing north pointed up the Chesapeake Bay. The tilt of the salinity structure to the left and right is representative of a tilt towards the western and eastern shore. Assigning contour lines in increments of 1 unit PSU reveals the structure of the salinity gradient. The range and salinity from was between 7 to 17 PSU.

The transects taken during quiescence, which include 20 contour plots total, show flat or left tilting isohalines. The deep waters show variation in structure, while the shallow waters show less consistent variation (Figure 6). The salinity in the deep channel congregates towards either the left or right side of the channel. The shallow waters are concentrated on the left side. Following the isohaline 15 PSU, the location on the left side of the plot varies from 3 to 6.25 km. The net depth between 11 and 15 PSU differed from 3 to 8 m.

The remaining four contour plots that were measured during wind events have flat isohalines or are tilting toward the right the opposite of the quiescent period (Figure 4). Only one transect (M-LAT-10) had a flat salinity structure and the other three show significant tilting to a much higher degree than the slightly tilting quiescence structure. The higher salinity water is secluded to the deep channel or the shallow left hand corner. There was a difference in depth of 2 to 3 m between the isohalines 11 and 15 PSU. The structure of M–LAT–10 differs drastically from M–LAT–12, 13, 14 in the tilt and the depth of the isohalines. Additionally, water with salinity of 11 PSU makes contact with the surface on the left side.

Discussion

Wind-forcing Response

Wind dramatically changes the tilt of the isohalines. Only four Scanfish transects occurred during wind events and each was under the influence of southward winds (Figure 4), but their salinity structure is remarkably different compared to structure during quiescence. M– LAT–10 has the most horizontal structure and the remaining contours have a tilting structure to the right. The disparity in structures is due to the timeline of wind forcing. The first transect while the remaining contours were taken days after the onset of wind forcing. This would suggest that

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the phase response of salinity structure is longer than two hours.

The thickness of the layers under wind forcing raises the question of the stability of the water column. The 12 to 17 PSU isohalines occur in close succession in the lower channel. The upper 12 m of the water had nearly homogeneous solemnity values and the remaining 12 to 24 m showed a high rate of change of salinity. M–LAT–12, 13, 14 reveal a close temporal comparison. Each section differs by 5 to 6 hours, showing the seclusion of a salty anomaly on the left side join the salty deep channel. The surface waters trend to lower salinity as time progresses and the wind continues to blow.

The influence of wind on mixing the water column has been observed and predicted. These results validate those claims and raise the question of stability of the water column. Further investigation into the Brunt–V frequency will shed light on the ease at which a wind forced water column may become mixed.

Up-welling conditions were prominent during wind events and scarce under quiescence (Figure 5). Only one of the twenty Scanfish transects had evidence of up-welling, which is marked by higher salinity rising to the surface waters. Conversely, salinity as high as 11 PSU rose to the surface waters in half of wind–forced structures. This is a well-accepted phenomenon that is important when considering the preservation or disruption of dead zones.

Tidal-forcing Response

Time series analysis of top to bottom salinity measurements taken from the tower, a fixed lattice structure in the M array, hinted at a variation in the tilt due to the tide. However, no such spatial resolution had been provided until the Scanfish data were analyzed. The spatial and temporal hypotheses were merged once the Scanfish was towed for twelve consecutive cross-sections spanning a six-hour portion of the tidal cycle from slack before ebb, full ebb, and slack before flood.

Tidal cycles influence the tilt of the pycnocline. Without the disruption of wind forcing, there was a distinct seesawing motion during quiescence. The salinity structure during ebb tide and slack before flood shows flat isohalines while maximum flood tide and slack before ebb, the isohalines were tilted towards the left (Figure 6). The shallow bottom, from 2 to 5 km, experienced a variation between 11 to 15 PSU each tidal cycle.

The force responsible for the seesaw motion is the Coriolis force. As the flood tide introduces a salty plume heading into the Bay, gravitational forces restrict the flow of the heavier salt water to the right-hand side, causing a tilt in the isohalines. When the tidal current flows out of the Bay, the Coriolis Effect again restricts the salty plume to the right, which in this case is on the opposite side. The increase in flow flattens the tilt and a horizontal isohalines structure is the result. Although the tidal movements are slow and over a relatively short timescale, the Coriolis Effect has a notable effect.

The implications of regular isohaline tilting are relevant to potential mixing processes that are caused by the movement of water on a daily basis. The interaction between the ingoing and outgoing tidal water on the isohalines suggests that mixing may occurring. Lateral circulation caused by wind has been proven to have significant impact on disrupting dead zones. The Chesapeake Bay is a micro-tidal estuary and the impact of tidally induced mixing has been overshadowed by wind. The more likely case is that tidal mixing occurs regularly and on a smaller scale than wind, but may influence the stability of the water column. Further analysis is

180 necessary to determine its influence on the widely accepted conceptual 2-layer model of estuarine circulation.

Conclusions (Anticipated Benefits)

Creating a holistic 3-dimensional view of mixing is difficult due to the over-lapping nature of the forces, especially when the data available are restricted spatially or temporally. Achieving high-resolution results from observations requires rigorous instrumentation, and highly-refined spatial contributions from models are difficult to produce. Therefore an observational study aimed at resolving smaller spatial and temporal realities using high-resolution equipment helps clarify past findings and further evolve the understanding of circulation and mixing.

Lateral variations have been observed and modeled at varying spatial and temporal resolutions. Analysis techniques have evaluated the contributions of wind and tidal forcing to mixing, and concluded that wind forcing is dominant to tidal forcing in the Chesapeake Bay. Yet there is now evidence for routine shifting of the salinity gradient due to tidal forcing. Further investigation into the presence of mixing in the shallow shoals will determine if this phenomenon is significant to lateral mixing. A future application of this research is the contribution of lateral mixing to the holistic, three-dimensional picture. An evolving description helps further the study of hypoxia and anoxia formation, which is relevant and useful to environmental managers and environmental action groups.

Acknowledgements

I would like to thank all of the wonderful people who helped make my summer experience phenomenal. I had the pleasure of working with Dr. Bill Boicourt and I want to thank him for all of his words of wisdom, dedication, patience, and thoughtfulness. I immensely enjoyed working with Carole Derry and I am absolutely positive that I would not have been as successful without her help. Thank you to Mike Allen for his guidance and well-crafted program. Thank you to Dr. Fredrika Moser for believing in me and acting as a role model. Thank you to my friends at HPL and to all of the horseflies on Horn Point road who made me run faster. Lastly, thank you to the National Science Foundation for making this all possible.

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References

Boicourt, W.C. 1992. Influences of Circulation Processes on Dissolved Oxygen in the Chesapeake Bay, p. 7-59. In D. E Smith, M. Leffler and G. Mackiernan [eds.], Oxygen Dynamics in the Chesapeake Bay. Maryland Sea Grant Press.

Chen, S. N., L. P. Sanford, and D. K. Ralston. 2009. Lateral circulation and sediment transport driven by axial winds in an idealized, partially mixed estuary. Journal of Geophysical Research-Oceans 114.

Goodrich, D. M., W. C. Boicourt, P. Hamilton, and D. W. Pritchard. 1987. Wind-induced destratification in Chesapeake Bay. Journal of Physical Oceanography 17: 2232-2240.

Guo, X. Y., and A. Valle-Levinson. 2008. Wind effects on the lateral structure of density-driven circulation in Chesapeake Bay. Continental Shelf Research. 28: 2450-2471.

Hagy, J. D., W. R. Boynton, C. W. Keefe, and K. V. Wood. 2004. Hypoxia in Chesapeake Bay, 1950-2001: Long-term change in relation to nutrient loading and river flow. Estuaries 27: 634-658.

Lacy, J. R., M. T. Stacey, J. R. Burau, and S. G. Monismith. 2003. Interaction of lateral baroclinic forcing and turbulence in an estuary. Journal of Geophysical Research- Oceans 108.

Li, Y., and M. Li. 2012. Wind-driven lateral circulation in a stratified estuary and its effects on the along-channel flow. Journal of Geophysical Research-Oceans 117.

Nunes, R. A., and J. H. Simpson. 1985. Axial Convergence in a well-mixed Estuary. Estuarine Coastal Shelf Science 20: 637-649.

Pritchard, D. W. 1952. Salinity Distribution and Circulation in the Chesapeake Estuarine System. Journal of Marine Research 11: 106-123.

Reyes-Hernandez, C., and A. Valle-Levinson. 2013. Fortnightly variations of the lateral structure of flow and hydrography at the Chesapeake Bay entrance. Continental Shelf Research. 52: 46-61.

Reynolds-Fleming, J. V., and R. A. Luettich. 2004. Wind-driven lateral variability in a partially mixed estuary. Estuarine, Coastal and Shelf Science 60: 395-407.

Scully, M.E. 2010. Wind Modulation of Dissolved Oxygen in Chesapeake Bay. Estuaries and Coasts 33:1164-1175.

Valle-Levinson, A., W. C. Boicourt, and M. R. Roman. 2003. On the linkages among density, flow, and bathymetry gradients at the entrance to the Chesapeake Bay. Estuaries 26: 1437-1449.

Wu, R. S. S., B. S. Zhou, D. J. Randall, N. Y. S. Woo, and P. K. S. Lam. 2003. Aquatic hypoxia is an endocrine disruptor and impairs fish reproduction. Environmental Science Technology 37: 1137-1141.

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Figures and Tables

Figure 1. The N array is immediately south of the Susquehanna River, the M array in the deepest section of the Bay and the S array on the flats preceding the outlet of the estuary.

Figure 2. Fixed mooring array showing ADCP current instruments and Met Packages, as well as other instruments deployed as part of the long-term study.

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Figure 3. Wind, current velocity and salinity measurements from fixed instruments on the M array. Wind events begin March 25, 2012 and persist for the remainder of the observations. The salinity measurements taken at 11 m oscillate semi-regularly.

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M-LAT10 Salinity M-LAT12 Salinity 0 17 0 17

11 9 9 16 16 9 5 5 15 13 15 11 11 11 13 14 14 10 13 13 10 11 15 15 15 13 15 13 13 11 13 13 13 15 15 12 15 15 17 12 Depth (m) Depth (m) 17 11 11 20 20 10 10

9 9 25 25 8 8

30 7 30 7 2 3 4 5 6 7 8 9 10 2 3 4 5 6 7 8 9 10 km km M-LAT13 Salinity M-LAT14 Salinity 0 17 0 17

16 16 511 5 15 11 15 11 11 11 14 11 14 10 10 13 13 13 13 13 11 13 15 13 15 15 12 15 15 12 17 17 Depth (m) Depth (m) 11 11 20 20 10 10

9 9 25 25 8 8

30 7 30 7 2 3 4 5 6 7 8 9 10 2 3 4 5 6 7 8 9 10 km km

Figure 4. Contour profile of salinity during wind events. M–LAT–10 was measured 2 hours after the onset of wind. The remaining plots show significant tilt to the East.

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M-LAT3 Salinity M-LAT12 Salinity 0 17 0 17

11 9 16 16 5 11 5 15 13 15 11 11 14 14 10 13 10 11 15 15 13 13 13 11 13 13 13 15 15 12 15 15 17 12 Depth (m) Depth (m) 17 11 11 20 20 10 10

9 9 25 25 8 8

30 7 30 7 2 3 4 5 6 7 8 9 10 2 3 4 5 6 7 8 9 10 km km M-LAT14 Salinity 0 17

16 5 11 15

11 14 10 13 11 13 13 15 15 12 17 Depth (m) 11 20 10

9 25 8

30 7 2 3 4 5 6 7 8 9 10 km

Figure 5. Contour profiles of salinity during quiescence and wind events. Each plot shows water with higher salinity rising to the surface on the western shore.

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M-LAT1 Salinity M-LAT5 Salinity 0 17 0 17

16 16 9 11 5 5 9 15 15 11 11 1 11 14 3 13 14 10 11 13 10 13 15 13 15 15 13 13 15 15 12 15 12 Depth (m) Depth (m) 17 11 11 20 20 17 10 10

9 9 25 25 8 8

30 7 30 7 2 3 4 5 6 7 8 9 10 2 3 4 5 6 7 8 9 10 km km M-LAT8 Salinity M-LAT9 Salinity 0 17 0 17

9 9 16 16 5 5 11 11 11 15 15 11 11 13 13 11 13 13 14 14 10 10 13 13 15 15 15 15 15 15 13 13

15 12 15 12 Depth (m) Depth (m) 11 11 20 20 10 10

9 9 25 25 8 8

30 7 30 7 2 3 4 5 6 7 8 9 10 2 3 4 5 6 7 8 9 10 km km Figure 6. Contour profile of salinity during quiescence over half of the tidal cycle. The down channel current starts at slack leading toward ebb tide, maximum ebb tide and slack leading toward flood tide. The isohalines tilt to the West during ebb tide.

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Effects of N:P Ratio Variation on Feeding of Dinoflagellate K. veneficum on Cryptophyte Rhodomonas sp.

Chrissie Schalkoff, REU Fellow Maryland Sea Grant

Dr. Patricia Glibert, Professor Horn Point Laboratory, University of Maryland Center for Environmental Science

Abstract

Harmful Algal Blooms (HABs) are a global problem in marine and freshwater systems, leading to adverse environmental, economic, and health effects. HABs are increasing in frequency, due largely to the eutrophication of marine and freshwater ecosystems caused by increased runoff of nitrogen and phosphorus. Many HABs are formed by mixotrophic algal species, and may change their behavior based on their nutrient conditions, or the nutrient composition of their food. We experimented with one species of algae, Karlodinium veneficum that can form harmful blooms. Karlodinium veneficum and a cryptophyte food source, Rhodomonas sp., were grown separately under two different nitrogen to phosphorus ratios, and feeding crosses were done to see the effect of these different nutrient conditions on the growth and feeding of K. veneficum. The results of this experiment showed differences between K. veneficum grown at an N:P ratio of 4 and K. veneficum grown at an N:P ratio of 24 when both were fed Rhodomonas sp., as the K. veneficum grown at an N:P of 24 showed growth similar to that of the control, and K. veneficum grown at an N:P of 4 showed negative growth compared to the control. Although the results of this project were inconclusive, continued research in this area is important for collecting data on growth of harmful algal species under different nutrient conditions. These data can then be incorporated into models that attempt to successfully predict when and where HABs may occur.

Keywords: Harmful Algal Blooms (HABs), mixotrophy, eutrophication, modeling

Introduction

While planktonic algae play an important and necessary role in marine ecosystems by contributing to primary production, they can also be harmful. When there is an increase in the population of some species of algae, it is referred to as a Harmful Algal Bloom, or HAB. These HABs can have detrimental environmental, health, and economic effects. Some species of algae that form HABs can produce toxins, which means that they can be harmful even when they are not present at extremely high cell densities, for example, during a bloom. These toxins can lead to fish kills, and can also cause seafood to become contaminated – leading to poisoning of humans if contaminated seafood is ingested (GEOHAB 2001). However, other species of algae that do not produce toxins can still be damaging at high densities, like during a bloom. Shading by the algae can cause death of sea grasses and seaweed plants. Additionally,

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HABs can lead to oxygen depletion when they begin to decay, and can cause fish to suffocate. One other possible effect of a HAB is simply the loss of suitable habitat for other marine organisms (GEOHAB 2006). In terms of economic effects, HABs can result in a loss of seafood harvesting, as well as a decrease in tourism in many areas.

HABs are especially of concern currently, as they have been increasing in frequency in waters worldwide. Part of this trend could be due to a recent improvement in scientific technology, as researchers are now better able to monitor and document these HABs, which leads to more being reported – thus, higher observed frequencies. However, other factors also play a role in this recent increase in HABs (GEOHAB 2001). One important reason for an increase in HABs is eutrophication/nutrient enrichment of marine and estuarine ecosystems (Heisler et al. 2008). Eutrophication has many different causes, but is mostly tied to human population growth and human agricultural and industrial processes. As the human population has increased, so have both point and non-point sources of these nutrients, which contribute to eutrophication. Point sources include sewage treatment plants or mines. Nonpoint sources include agricultural runoff (from fertilizers that contain nitrogen), aquaculture waste, and atmospheric pollution from the combustion of fossil fuels (Anderson et al. 2002). This is especially a problem in European countries, Asian countries, and the United States, as they release the most nitrogen and phosphorus (Glibert and Burkholder 2006).

Runoff of nutrients and the eutrophication of marine systems can have varied effects on different species of algae. Because nitrogen and phosphorus are necessary for algal growth, runoff of these nutrients could lead to HABs (GEOHAB 2001). Because production and runoff of nitrogen and phosphorus does not occur in a consistent proportion, eutrophication can also change the normal ratio of nitrogen to phosphorus (N:P) of a system (Li et al. 2011). This can have different effects on different species and can potentially even result in a shift in the species that are dominant in the ecosystem. However, despite many studies on eutrophication and HABs, the effects of nutrient enrichment on a system are still not understood completely (Anderson et al. 2002). More research is needed on different species of algae that are capable of forming HABs and how they respond to eutrophic environments or to environments where the N:P ratio has been changed.

One type of algae that can form HABs is dinoflagellates. These algae are unicellular organisms that possess two flagella which enable them to swim. Many, if not most, dinoflagellates are classified as mixotrophic, which means that they are capable of both photosynthesis and phagotrophy. Additionally, some dinoflagellates can produce toxins (Graham et al., 2009). One such dinoflagellate species is Karlodinium veneficum, a small, mixotrophic species of dinoflagellate that produces toxins called karlotoxins (Place et al. 2012). Karlotoxins can be hemolytic, ichthyotoxic, and cytotoxic and can cause fish kills. They work by changing the normal membrane permeability of a cell, causing ions to move into the cell. Water then follows by osmosis, causing the cell to lyse (Place et al. 2012). It is thought that K. veneficum is becoming more prevalent in many areas, including the Chesapeake Bay, where it can create blooms during the spring and summer months (Li et al. 2012). Because it is mixotrophic, K. veneficum is capable of grazing on other species of algae, including cryptophytes such as Rhodomonas sp. Several studies have suggested that the ability to graze on cryptophytes can provide dinoflagellates like K. veneficum with a valuable source of nutrients, especially when inorganic nutrients may be limiting in the environment (Glibert et al. 2009). Therefore, the presence of cryptophytes, like Rhodomonas sp., may increase the formation of toxic blooms of K. veneficum (Adolf et al. 2008).

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It is unclear exactly what effect eutrophication and changing nutrient ratios have on K. veneficum and its ability to graze, grow, and form blooms. A study by Li et al. (2011) looked at the effect of varying N:P ratios on growth and nitrogen uptake/assimilation of a different dinoflagellate species, Prorocentrum minimum. The results of the study indicated that changing the N:P ratio affected growth rates and nitrogen assimilation of P. minimum, with the highest growth rate occurring at an N:P ratio closest to the Redfield ratio of 16:1. Additionally, the study showed that the assimilation efficiency of P. minimum for taking up nitrogen was greater when phosphorus was present in large concentrations, because phosphorus is necessary for nitrogen to be assimilated (Li et al. 2011). However, most of the studies that have been conducted have looked at the effect of a nutrient limitation on the growth rate or grazing of K. veneficum.

In a eutrophic system, the N:P ratio may change even if both nitrogen and phosphorus are present in large enough quantities so as not to be limiting. In this case, more research is needed to find out what the effect of changing this ratio is on the growth of a dinoflagellate species like K. veneficum and to also see what the effect is on the grazing rate of K. veneficum on a cryptophyte species, such as Rhodomonas sp.

The objective of this study was to determine whether the N:P ratio of the dinoflagellate K. veneficum and its food, Rhodomonas sp., affected the rate of feeding by the dinoflagellate. We grew both K. veneficum and Rhodomonas sp. at two different N:P ratios, and measured feeding at all N:P ratio combinations. Our hypothesis was that the K. veneficum grown in high nitrogen conditions would show the highest growth when allowed to feed on the Rhodomonas sp. that was grown in high phosphorus conditions, and that the K. veneficum grown in high phosphorus would show the highest growth when feeding on the Rhodomonas sp. that was grown in high nitrogen. We expected this because the food with the reciprocal N:P ratio would be expected to provide the nutrient that was deficient in K. veneficum.

Materials and Methods

Culture Growth

Cultures of both K. veneficum (strain ccmp1975) and Rhodomonas sp. were obtained from the Horn Point Laboratory oyster hatchery culture collection. The K. veneficum culture was previously growing in Choptank River water, while the Rhodomonas sp. was grown in artificial sea water. Two turbidostat chambers were set up and kept in a culture room at 22°C, on a 14:10 hour light dark cycle, as described by Li et al. (2011). Each chamber was hooked up to a source of fresh media and to a waste container, to allow new media to be added and waste to be removed. Additionally, turbidity sensors were connected to the chambers and to a computer that read the level of turbidity and adjusted the addition of media, in order to keep the turbidity at a pre-set level. These turbidostat chambers contained K. veneficum. One turbidostat chamber – 3- contained media that was artificial sea water (ASW) with a salinity of 12, with NO3 and PO4 added to give an N:P ratio of 24. This was the “high nitrogen” condition. The second turbidostat chamber contained ASW media with an N:P ratio of 4. This was the “high phosphorus” condition. Additionally, two other batch chambers were set up, each containing Rhodomonas sp. Again, one contained ASW with an N:P ratio of 24, and the other had an N:P ratio of 4. In each case (high nitrogen and high phosphorus), there was enough of each nutrient so that neither was actually limiting.

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Experiment 1: 24 hour combinations in a dark chamber

After the samples showed steady growth, samples of K. veneficum and Rhodomonas sp. were combined in flasks in all possible combinations according to N:P level:

• K. veneficum grown at N:P of 24 with Rhodomonas sp. grown at N:P of 24 • K. veneficum grown at N:P of 24 with Rhodomonas sp. grown at N:P of 4 • K. veneficum grown at N:P of 4 with Rhodomonas sp. grown at N:P of 24 • K. veneficum grown at N:P of 4 with Rhodomonas sp. grown at N:P of 4

Initially, cell counts were done for each of the samples, separately, to determine cell density. Calculations were then made so that in each cross, K. veneficum and Rhodomonas sp. were combined in a 1:4 ratio in each flask. Before combining the K. veneficum and Rhodomonas sp., the samples of Rhodomonas sp. were gravity filtered to remove as much of the original media as possible. However, only approximately 25% of the media was able to be removed. There were also two control flasks of K. veneficum at N:P of 24 and 4 but without any Rhodomonas sp. added, for a total of six flasks in one treatment. Two replicates were done, so that there were 12 flasks in total. The samples were placed in a 22°C dark chamber for 24 hours after mixing, to ensure that any growth recorded was only from feeding and that no photosynthesis occurred.

Cell counts of K. veneficum and Rhodomonas sp. in each flask were taken at 0 hours and 24 hours. This was done by staining cells with glutaraldehyde solution and counting them on a Sedgewick Rafter cell counting chamber. Additionally, at the 0 hour and 24 hour time points, samples were filtered and we planned to analyze the samples for total dissolved nitrogen (TDN), total dissolved phosphorus (TDP), particulate nitrogen (PN), particulate phosphorus + (PP), particulate carbon (PC), and NH4 in each sample. However, this analysis has not yet been completed.

Experiment 2: 48 hour combinations in standard diel cycle conditions

A repeat of the first experiment was carried out several weeks after the first, but with slightly different conditions based upon preliminary results from the first experiment. The samples of K. veneficum and Rhodomonas sp. were combined in the same way, with initial cell counts done to ensure a 1:4 ratio of K. veneficum to Rhodomonas sp. However, this time the Rhodomonas sp. samples were not gravity filtered before the addition. Flasks with the combined samples were then placed back in the original growth chamber and kept on the same 14:10 hour light-dark cycle. Additionally, the experiment was allowed to run for 48 hours instead of only 24. Cell counts and nutrient filtering were also done at the 0, 24, and 48 hour timepoints. This time, cell counts were done by staining with Lugol’s iodine solution, as it allowed the cells to be stored for a longer period of time, so that they could be counted at any time after the experiment.

Results

Experiment 1: Dark Chamber

The concentration of K. veneficum decreased in both of the controls and all of the Rhodomonas sp. treatments during the 24 hour feeding experiment, with the exception of the KV 24/R 4 treatment (Figure 1). The final concentration of both controls was approximately half

191 of the original concentration (Figures 1 and 2). All but the KV 24/R 4 treatment also showed negative growth; however, all of the samples showing negative growth did not decrease as much in concentration as the controls, with the greatest negative percent change of -44.1% in the KV 24/R 24 treatment. The only treatment to show a positive percent change in cell concentration was the KV 24/R 4 treatment, with a percent change of 7.18%.

Experiment 2: Diel Cycle Chamber

During the second round of feeding experiments, the K. veneficum concentration increased in both controls and all of the treatments during the first 24 hours, and then continued to increase slightly during the next 24 hours in all of the treatments except for the two controls and the KV 24/R 24 treatment (Figure 3). Rhodomonas sp. also showed an increase in concentration during the first 24 hours in all of the treatments, and a slight increase in concentration during the next 24 hours in every treatment except for KV 4/R 4. The K. veneficum control at N:P 24 and the control at N:P of 4 showed a 173% change and a 104% change during the first 24 hours, respectively, followed by a slight negative percent change during the next 24 hours (Figure 4). In both of the treatments involving K. veneficum at N:P of 4, the K. veneficum experienced a very small positive percent change compared to the control (14.9% and 25.5%) during the first 24 hours. However, in these treatments, the Rhodomonas sp. showed a very large positive percent change (73.2% and 78.5%). Conversely, in the two treatments involving K. veneficum at N:P of 24, the K. veneficum showed a larger positive percent change (145% and 167%), while the Rhodomonas sp. showed a smaller percent change (60.9% and 4.62%) during the first 24 hours.

Discussion

The results of both of these experiments did not indicate clear trends that could be used to accept or reject the hypothesis of this project; however, both experiments still provided interesting results that can add to the current knowledge of how K. veneficum behaves in different conditions. In the first experiment, all of the treatments except one experienced negative growth. However, the treatments with Rhodomonas sp. added did experience less of a negative percent change in concentration when compared with the controls, and one treatment even showed a small positive percent change. These results could be due to the fact that overall, the K. veneficum was sensitive to the sudden change in its environment during this experiment (especially the fact that it was taken from its normal diel cycle and was shut in a dark chamber for 24 hours), and did not grow well as a result of this. Additionally, K. veneficum is usually classified as a Type II mixotroph, which means that it carries out photosynthesis more than it feeds on other organisms (Li et al. 2011). Therefore, the fact that there was no light available in this experiment could explain why the K. veneficum did not grow well, as it prefers to obtain energy through photosynthesis. Additionally, the absence of light may explain why there was at least a smaller decrease in cell concentration in the treatments with Rhodomonas sp., and even a small positive increase in cell concentration in one treatment. The K. veneficum that were provided with a food source in the absence of light would at least have been able to obtain nutrients for growth from the Rhodomonas sp., instead of relying only on photosynthesis for energy, which they were not able to carry out in the dark.

In the second feeding experiment, the treatments were kept in the same chamber that they were originally cultured in, so that they were kept on their usual diel cycle. Additionally, the experiment was allowed to run for 48 hours instead of 24, to see if K. veneficum required additional time to adjust, begin feeding, and show positive growth. During this experiment, the K. veneficum did show mostly positive growth in the controls and the treatments with

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Rhodomonas sp. added for the first 24 hours, and then either a small amount of positive growth or negative growth during the second 24 hours. However, having the Rhodomonas sp. available as an additional energy source apart from photosynthesizing did not seem to benefit the K. veneficum during this experiment, as none of the treatments showed a greater positive percent change than the control at the same N:P ratio, at least during the first 24 hours. However, three out of the four treatments with Rhodomonas sp. added did show positive growth during the second 24 hours, while the controls showed negative growth during this time. This could be because of the fact that while the controls began to run out of nutrients, the other treatments had access to a food source to acquire additional nutrients that could support growth for a longer time.

Interestingly, while none of the treatments with Rhodomonas sp. added did much better than the controls, there was a difference between the treatments that had K. veneficum cultured at an N:P of 4 and those that had K. veneficum cultured at an N:P of 24. Those with K. veneficum grown at an N:P of 4 did poorly with the addition of Rhodomonas sp., compared to the control. It is unclear why this is the case, but it appears that K. veneficum in these treatments was unable to handle the addition of Rhodomonas sp., as the Rhodomonas sp. experienced a fairly large positive percent change in these treatments (73.2% and 78.5%). However, in both of the treatments involving K. veneficum grown at an N:P of 24, the K. veneficum experienced a positive percent change that was greater than that of the Rhodomonas sp., and was similar to that of the control. In this case, the K. veneficum did not seem to be adversely affected by the addition of Rhodomonas sp., although it did not seem that the addition of the cryptophyte was of any benefit either.

Several things could help to explain the results of the second experiment. First, the Rhodomonas sp. was added to the K. veneficum in a 1:4 ratio (K. veneficum to Rhodomonas sp.) Because of this ratio, it is possible that the Rhodomonas sp. overwhelmed the K. veneficum more than it would have if it less of it had been added, and this caused the K. veneficum to do worse than or only as well as the controls. In a study by Li et al. (2011), the K. veneficum to Rhodomonas sp. ratio was only 1:3. Additionally, in their study, the cultures were allowed to grow and feed for longer than 48 hours (Li et al. 2011). It is possible that the K. veneficum requires longer than 48 hours to adjust and begin to feed phagotrophically, so that any growth advantage from having access to Rhodomonas sp. would not be seen until a later point. Additionally, as Glibert et al. (2009) and Li et al. (2012) point out, the reason that dinoflagellates like K. veneficum switch from being photosynthetic to feeding phagotrophically is still unclear. Mixotrophic activity may be stimulated by a decrease in available light or inorganic nutrients, or could be caused by high concentrations of available food items (Glibert et al. 2009; Li et al. 2012). For example, a study by Glibert et al. (2009) looked at the growth of another harmful dinoflagellate, Karenia brevis, under various light levels. At high light levels, the samples of K. brevis that were provided with a food source (Synechococcus) did not show much difference in growth when compared to K. brevis that was growing only photosynthetically. However, at low levels of light, the growth of K. brevis depended on how much Synechococcus was available, with the highest growth rates observed in the sample that was provided the most Synechococcus (Glibert et al. 2009). Therefore, the results of this experiment with K. veneficum could be explained in a similar way. Because the treatments were all kept at normal light levels, they may not have been inclined to switch to feeding on the Rhodomonas sp. right away, as they would be exhibiting the Type II mixotrophic tendency of preferring to carry out photosynthesis.

Additionally, another complication of this project was the fact that ideally the Rhodomonas sp. cells should have been completely removed from the media they were

193 cultured in before being added to the K. veneficum, in order to keep the K. veneficum in media of the correct N:P ratio. While the Rhodomonas sp. was initially gravity filtered in the first experiment, we were only able to remove about 25% of the media from the cells. Because of this, we did not filter at all during experiment 2, which could have caused misleading results.

Another limitation of studies like this is pointed out by Heisler et al (2008). While laboratory experiments like this are necessary to try to address questions about growth of harmful algal species under different conditions, there are also difficulties that arise because of the fact that the algal cultures are kept in a laboratory. If the algal species are kept growing for long periods of time in a laboratory environment, they most likely will adapt or change because of the environment in which they are grown. The ability of the algae to produce toxins might change, or there might be a change in the nutrients or nutrient ratio that the algae grow best on (Heisler et al. 2008).

However, despite the limitations of this experiment and while there did not appear to be clear enough trends to accept or reject the original hypothesis of the project, the results do raise more interesting questions about the autotrophic and mixotrophic behavior of K. veneficum under different nutrient conditions. In the future, it would be helpful to try different variations of this project, such as combining the two algal species in a different ratio of cell concentrations, and letting the experiment run longer than 48 hours.

Experiments such as this one remain necessary as the global population continues to increase, along with sources of nutrient runoff that contribute to eutrophication of marine and freshwater systems and changes in the nutrient ratios of these systems. An important goal of this field of research is to create accurate models that can be used to predict when and where harmful algal species can be found, and when they might form HABs. Models could also help predict the harmful effects of a bloom in advance (Lindehoff et al. 2011). This is especially necessary because many of these harmful species can produce toxins that have serious consequences, including causing fish kills and potentially poisoning humans. The effectiveness of these models is reliant on data from experiments involving the effect of different environmental conditions on harmful algal species (Adolf et al. 2008, GEOHAB 2001).

Conclusions

The data collected from this experiment add to the current body of knowledge on the effect of eutrophication on growth of algal species and potential formation of HABs. While many studies have looked at the effect of nutrient limitation on growth rates and feeding of harmful species like K. veneficum, fewer have looked at the effect of altering the N:P ratio of a system on growth and feeding of K. veneficum in the absence of nutrient limitation. Additionally, results from experiments like this one can improve the models that are available for HABs that attempt to predict when and where HABs will occur in the environment. Research on mixotrophic species is also especially important, as algal species capable of being mixotrophic may react differently to eutrophication and changing N:P ratios than species that are only autotrophic or heterotrophic. These complex interactions and reactions to different nutrient ratios can have a large impact on the entire food web of a system, and eutrophication could lead to changes in the species composition of an ecosystem.

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Acknowledgements

I would like to thank Dr. Pat Glibert and Jeff Alexander for all of their help and guidance with this project. I would also like to thank Dr. Mike Allen, Dr. Fredrika Moser, and Maryland Sea Grant for providing me with the opportunity to participate in this summer research program.

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References

Adolf, J. E., T. Bachvaroff, and A. R. Place. 2008. Can cryptophyte abundance trigger toxic Karlodinium veneficum blooms in eutrophic estuaries? Harmful Algae 8: 119-128.

Anderson, D. M., P. M. Glibert, and J. M. Burkholder. 2002. Harmful algal blooms and eutrophication: Nutrient sources, composition, and consequences. Estuaries. 25: 704- 726.

GEOHAB. 2001. Global Ecology and Oceanography of Harmful Algal Blooms, Science Plan, P. Glibert and G. Pitcher, (eds.). SCOR and IOC, Baltimore and Paris, 86 pp.

GEOHAB. 2006. Global Ecology and Oceanography of Harmful Algal Blooms, HABs in Eutrophic Systems, P. Glibert, (ed.). IOC and SCOR, Paris and Baltimore, 74 pp.

Glibert, P. M., J. M. Burkholder, T. M. Kana, J. Alexander, H. Skelton, and C. Shilling. 2009. Grazing by Karenia brevis on Synechococcus enhances its growth rate and may help to sustain blooms. Aquatic Microbial Ecology 55: 17-30.

Glibert, P. and J. Burkholder. 2006. The complex relationships between increases in fertilization of the earth, coastal eutrophication and proliferation of harmful algal blooms, p. 341-354. In Anonymous Ecology of Harmful Algae. Springer.

Graham, E., M. Graham, and L. Wilcox. 2009. Algae, 2nd ed. Pearson Education, Inc.

Heisler, J., P. Glibert, J. M. Burkholder, D. Anderson, W. Cochlan, W. Dennison, Q. Dortch, C. Gobler, C. Heil, and E. Humphries. 2008. Eutrophication and harmful algal blooms: A scientific consensus. Harmful Algae 8: 3-13.

Li, J., P. M. Glibert, and J. A. Alexander. 2011. Effects of ambient DIN: DIP ratio on the nitrogen uptake of harmful dinoflagellate Prorocentrum minimum and Prorocentrum donghaiense in turbidistat. Chinese Journal of Oceanology and Limnology 29: 746-761.

Li, J., P. M. Glibert, J. A. Alexander, and M. E. Molina. 2012. Growth and competition of several harmful dinoflagellates under different nutrient and light conditions. Harmful Algae 13: 112-125, doi:10.1016/j.hal.2011.10.005.

Lindehoff, E., E. Granéli, and P. M. Glibert. 2011. Nitrogen uptake kinetics of Prymnesium parvum (Haptophyte). Harmful Algae 12: 70-76.

Place, A. R., H. A. Bowers, T. R. Bachvaroff, J. E. Adolf, J. R. Deeds, and J. Sheng. 2012. Karlodinium veneficum: The little dinoflagellate with a big bite. Harmful Algae 14: 179- 195, doi:10.1016/j.hal.2011.10.021.

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Figures and Tables

300000

250000

200000

150000 KV Tzero Cells/mL 100000 T24 Rep 1 T24 Rep 2 50000

0 KV 24 KV 4 KV 4/R KV 24/R KV 4/R 4 KV 24/R 24 24 4 Treatment

Figure 1. Concentration of K. veneficum (cells/mL) at 0 hour and 24 hour timepoints for each N:P ratio treatment. Treatments are labeled with the N:P ratio of the media in which K. veneficum and Rhodomonas sp. were grown (a ratio of 4 or 24).

20

10 0 KV 24 KV 4 KV 4/R 24 KV 24/R 24 KV 4/R 4 KV 24/R 4 -10 -20

-30

-40

-50

Concentration Cell in % Change -60 -70 Treatment

Figure 2. Percent change of cell concentration of K. veneficum during the 0 to 24 hour time interval for each treatment. Treatments are labeled with the N:P ratio of the media in which K. veneficum and Rhodomonas sp. were grown (a ratio of 4 or 24).

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Figure 3. Average concentration of K. veneficum and Rhodomonas sp. (cells/mL) over time for each different N:P ratio treatment.

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Figure 4. Percent change of cell concentrations of K. veneficum and Rhodomonas sp. during the 0 hour to 24 hour interval and the 24 hour to 48 hour interval for each different treatment.

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The Evaluation of Basic Calculations Concerning the Effects of Non-storm Waves on Offshore Sediment at Jefferson-Patterson Park, MD

Nick Taylor, REU Fellow Maryland Sea Grant

Dr. Cindy Palinkas, Associate Professor Horn Point Laboratory, University of Maryland Center for Environmental Science

Introduction

Along coastlines around the world, waves are one of the dominant processes causing sediment transport (Short 2012). Water particles within a wave move in circular orbital patterns that attenuate in deep water. In shallow water, the orbits of the water particles are increasingly flattened ellipses that eventually become two-dimensional back-and-forth motions along the bottom (Sorenson 2006). Within the context of sediment transport, this motion creates a boundary shear stress that acts on the seafloor sediment (Coastal Engineering Manual 2008). A simplified way of determining the effect of , the boundary shear stress due to wave motion, on the sediment is to compare to the critical shear stress, , which opposes sediment 𝑤𝑤𝑤𝑤 movement. The determination of is usually𝜏𝜏 accomplished with a Shields diagram, or a 𝑤𝑤𝑤𝑤 𝑐𝑐𝑐𝑐 variation thereof (Guo 2002). In𝜏𝜏 this simplified situation, then, if𝜏𝜏 is greater than , the sediment is likely put into motion 𝜏𝜏by𝑐𝑐𝑐𝑐 wave action (Wiberg 2012). 𝜏𝜏𝑤𝑤𝑤𝑤 𝜏𝜏𝑐𝑐𝑐𝑐 In the Chesapeake Bay, waves have been found to play a significant role in erosion and sediment transport along shorelines (Erdle 2006). Previous studies on sediment transport with regard to wave action in the Bay have included the resuspension of mud in the northern Chesapeake, as well as the transport of sediment during winter storms (Sanford 1994; Boon et al. 1996). The Army Corps of Engineers has also performed several studies on island and shoreline erosion resulting from wave action (Lin et al. 2002).

This project will support the previously mentioned, and other, publications on waves and sediment transport within the Chesapeake Bay by looking at the effect of wind waves on sediment offshore of Jefferson-Patterson Park. Located approximately nine miles from the mouth of the Patuxent River, the park has two faces adjacent to the river, one is oriented in a northwest to southeast direction while the other is slightly northeast to southwest (Figure 1). The specific location for this study will be a short section of the northwest to southeast oriented park face as it is part of the living shorelines effort. Breakwaters, pocket beaches, and sills can be found at the park.

The goal of this project was to determine if a series of relatively simple calculations could reveal if (and if so, how) waves affect the sediment found offshore of the Jefferson-Patterson Park. This was accomplished by calculating ratios of the different shear stresses on the sediment for several sample transects taken offshore of the park, visual observation of

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bedforms at the site, along with a radioisotope analysis of 7Be decay provided a mechanism by which to judge the validity of the predictions made by the calculations.

Materials and Methods

The study began with the collection of sediment samples offshore of Jefferson-Patterson Park, MD. Push-cores were used to collect samples along four shore-normal transects; Transects 1 and 2 were located offshore of the beach face while Transects 3 and 4 extended from the riprap adjacent to the beach (Figure 2). Depth measurements were taken at each sample location using a meter stick, and the presence of bedforms was noted at the site as well.

The top centimeter of each core was wet sieved, to separate the mud and sand components, and placed in an oven until dry. The dry sand samples were run through a set of dry sieves, and each size fraction was weighed; this resulted in a cumulative size distribution. From the cumulative distribution, a frequency distribution was calculated using Microsoft Excel. Finally, the average sediment size (D50) was calculated through a linear interpolation between the adjacent sieve sizes with the sediment frequencies bracketing 50%. Assuming a common density of 2650 kg m-3, and using the calculated average sediment sizes for each sample, the critical shear stress was calculated using Yalin’s Method (Miller et al. 1977).

Wind data at the site, speed and direction, were obtained from Weather Underground for the day when field measurements were taken. After averaging the wind direction, a fetch length was established. With the fetch length, average wind speed, and duration, significant wave height (Hs) and period (Ts) were calculated using the SMB method of wave forecasting.

To calculate the maximum boundary stress due to wave action ( ) a Matlab code developed by Dr. Larry Sanford was used. This program required the depths at each core 𝑤𝑤𝑤𝑤 location, a roughness coefficient (ks) equal to the average grain size for 𝜏𝜏each point (this required the assumption that the sediment was uniform in size at each location), as well as the wave height and period calculated from the SMB plots.

Finally, portions of the dried sand samples were weighed in clear jars and placed in the gamma spectrometer to measure 7Be decay to look for evidence of sediment accumulation.

Results

The wave calculations returned a wave period of 1.51 seconds and wave heights ranging from 0.1187 to 0.1253 meters. Wavelengths ranged from 2.24 to 3.43 meters.

After plotting the critical and boundary shear stresses and average sediment diameter relative to each sample’s position, some trends became apparent. For the most part, the values of each parameter decreased with distance from the shore (Figure 3). For the first transect, there was an increase in the boundary shear stress from the first to second core (Figure 3c).

Nine out of the 15 sample locations displayed a boundary shear stress that was greater than the critical shear stress (Table 1).

With regard to the radioisotope analysis, 7Be decay was detected in four of the 15 samples. Cores 29, 32, 34, and 37 all exhibited a detectable amount of 7Be decay (Table 2).

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Discussion

Although the SMB calculations are not often used for ocean waves, the scales involved in the Patuxent River made these equations well suited to the conditions for this project. The range of wave heights calculated was comparable to those measured at Jefferson Patterson Park in 2004 (Burke et al. 2005).

The trends seen in the shear stresses and the average sediment diameter make sense, for the most part, within the context of wave energy and sediment size. The decreasing wave heights with increasing distance offshore explain the general decreasing trend for , as depth is the primary factor controlling the wave heights in this situation. If we let stand as an 𝑤𝑤𝑤𝑤 indicator of wave energy, then the trends in average grain size and thus can be𝜏𝜏 explained as 𝑤𝑤𝑤𝑤 well. If wave energy decreases going further offshore, then it follows that that𝜏𝜏 the average sediment size would decrease as well, assuming wave action is the primary𝜏𝜏𝑐𝑐𝑐𝑐 factor in determining sediment distribution, simply because larger sediment grains would require more energy to transport. Finally, as is largely dependent on the size of the sediment grains, it makes sense that the critical shear stress decreases in a very similar fashion to the average grain size. This can be seen by𝜏𝜏 comparing𝑐𝑐𝑐𝑐 figures 3a and 3b; the shapes of the graphs for each transect are virtually identical.

The increase in the boundary shear stress from the first to second transect locations in transect 1 is likely due to the rise in the bottom elevation. This can be seen in Figure 3d.

To ultimately address the goal of this project, assessing the validity of the predictions made by our simple calculations, we compare the various ratios of to to the results of the 7Be radioisotope analysis. If we take the results of the radioisotope test to be accurate, we see that because 7Be decay was detected in only four samples, sediment𝜏𝜏𝑤𝑤 𝑤𝑤should𝜏𝜏𝑐𝑐𝑐𝑐 only be accumulating in these locations. A possible source of error at this point is the tendency for beryllium to resist adhesion to sand particles, which were the dominant sediment size of our samples. Although 7Be may have been present in more samples, it may not have existed in an amount that could have been detected by the gamma spectrometer. At any rate, since 7Be was only detected in four samples, some form of sediment transport should theoretically be occurring at the other 11 sample locations, in the form of either erosion or equilibrium (where the amount of sediment entering an area is equal to the amount of sediment leaving the same area). As a quick review, for the sediment to be transported, it must first be put into motion. This is where the ratios of to come in to play.

For nine out of𝜏𝜏 𝑤𝑤our𝑤𝑤 15𝜏𝜏 sample𝑐𝑐𝑐𝑐 locations, the predictions made by our equations correspond correctly to the presence or absence of 7Be. For seven of these nine situations, 7Be was absent and our equations predicted that sediment was being put into motion. The other two showed evidence of accumulation through the presence of 7Be, while the equations predicted that no sediment would be put into motion (Table 2).

Four out of our 15 predictions were not supported. Interestingly, in each case the equations predicted that sediment transport should not be occurring at those locations, while 7Be was not detected in each core (theoretically indicating that some transport was occurring). It is possible though, as mentioned earlier, that beryllium’s lack of adhesion to sand particles may explain this outcome.

Finally, two out of the 15 locations provided us with no useful information with which to gauge our predictions. In both of these cases, 7Be was present, and our equations predicted

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that sediment should be transported. Here, it is possible that the equations are correct and transport is occurring, with the amount of sand coming in equal to the amount of sand going out, or the equations are wrong and sediment is simply accumulating.

When we look at the results spatially, some interesting patterns emerge. For example, offshore of the beach, there are six instances where the results of the 7Be analysis seem to confirm the predictions that sediment transport is occurring. Offshore of the riprap, however, there is only one instance where the radioisotope analysis theoretically validates the possibility of sediment transport (Figure 4; gold stars). This may be interpreted to mean that the riprap is preventing the mobilization of sand, as is likely its intended purpose. The two seemingly anomalies are cores 29 and 35. Visually, it appears as if sample 29 should have exhibited a correlation between the 7Be analysis and the predictions made by the equations. Referring back to Table 2, we see that although the equations predicted that sediment was being mobilized at this location, 7Be was detected. A possible explanation for this situation might be that, as mentioned earlier, the amount of sediment coming into this location is equal to the amount of sediment going out. In other words, the location is in equilibrium.

Conclusions

As it stands, there are several drawbacks to this procedure. Although this procedure is not terribly time consuming or difficult, it does not provide a complete picture of the processes involved. Tides and boat waves were not taken into consideration, and they may have even more of an effect on sediment than wind waves. Storms were not considered, either, and they are usually the driving force behind most sediment transport. In addition, this method does not describe where the sediment is being transported. Therefore, while it may be tempting to assume that if sediment is being mobilized, then the shoreline must be eroding, this may not be the case. Theoretically, this procedure could be used as a simple way to decide the mobilization potential of sediment at a particular shoreline, with the purpose of determining that shoreline’s risk for erosion. To become even slightly practical, though, this method should probably be applied to tides and measured boat waves to establish a more sound basis for its own predictions.

Acknowledgements

I would like to thank Debbie Hinkle for all of her help in the lab and the field, Cindy Palinkas for her mentorship and patience, and Mike Allen and the Maryland Sea Grant REU.

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References

Boon, J.D., M.O. Green, and K.D. Suh. 1996. Bimodal wave spectra in lower Chesapeake Bay, sea bed energetics and sediment transport during winter storms. Continental Shelf Research 16: 1965–1988.

Burke, D.G., Koch, E.W., and J.C. Stevenson. 2005. Phases I & II. Assessment of Hybrid Type Shore Erosion Control Projects in Maryland’s Chesapeake Bay.

Erdle, S.Y., J.L.D. Davis, and K.G. Sellner. 2006. Management, policy, science, and engineering of nonstructural erosion control in the Chespeake Bay. Proceedings of the 2006 Living Shoreline Summit. http://www.dnr.state.md.us/irc/docs/00013969.pdf.

Guo, J. 2002. Hunter rouse and shields diagram. Advances in Hydraulics and Water Engineering 2:1096-1098.

Lin, W. L.P. Sanford, and S.E. Suttles. 2002. Wave measurement and modeling in Chesapeake Bay. Continental Shelf Research 22:2673-2686, doi: 10.1016/S0278-4343(02)00120-6.

Miller, M.C., McCave, I.N., Komar, P.D. 1977. Threshold of sediment motion under unidirectional currents. Sedimentology 24: 507-528.

Sanford, L.P. 1994. Wave-forced resuspension of upper Chesapeake Bay muds. Estuaries 17:148-165.

Short, A. D. 2012. Coastal processes and beaches. Nature Education Knowledge. http://www.nature.com/scitable/knowledge/library/coastal-processes-and-beaches- 26276621.

Sorenson, R.M. 2006. Basic Coastal Engineering, 3rd ed. Springer.

Wiberg, P. 2012. EVGE 5840 Lecture Materials. University of Virginia.

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Figures and Tables

Figure 1. Location of site along Patuxent River. Photo credit: Google Earth

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Figure 2. Aerial view of shoreline and transects of core samples taken. Each color corresponds to a separate transect. Photo credit: Google Earth

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Figure 3. Plots of factors as listed vs. position. From top left, going clockwise figures are: a, b, c, d. Blue = Transect 1, Red = Transect 2, Green = Transect 3, Purple = Transect 4.

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Figure 4. Gold stars represent locations where results of 7Be analysis seem to confirm prediction that sediment transport is occurring. Photo credit: Google Earth

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Table 1. Calculated and measured metrics of each sample location. D50 is average grain size, τcr is critical shear stress, and τwm is boundary shear stress. Position is the transect specific location of the sample relative to the shoreline, with position 1 being closest and position 5 being farthest away.

Transect Core no. Position τcr τwm D50 (m) Depth (m) 1 33 1 0.2796 0.8017 0.0004898 0.37 29 2 0.2569 0.9810 0.0004304 0.27 30 3 0.2514 0.3443 0.0004161 0.74 31 4 0.2391 0.2445 0.0003847 0.92 32 5 0.2277 0.1590 0.0003563 1.16 2 34 1 0.2755 0.3685 0.0004790 0.75 35 2 0.2503 0.2680 0.0004133 0.89 36 3 0.2249 0.1966 0.0003495 1.02 37 4 0.2213 0.1548 0.0003405 1.16 3 38 1 0.2709 0.2442 0.0004667 0.99 39 2 0.2510 0.1572 0.0004150 1.22 4 40 1 0.2552 0.7456 0.0004260 0.36 41 2 0.2610 0.4647 0.0004408 0.59 42 3 0.2546 0.2812 0.0004243 0.87 43 4 0.2357 0.1668 0.0003763 1.15

Table 2. Ratios of critical to boundary shear stress for each sample, presence of Beryllium at each sample, and prediction of movement by equations

7 transect core no. position τ wm/ τcr Be? Transport? 1 33 1 2.867 N Y 29 2 3.818 Y Y 30 3 1.369 N Y 31 4 1.023 N Y 32 5 0.698 Y N 2 34 1 1.338 Y Y 35 2 1.071 N Y 36 3 0.874 N N 37 4 0.699 Y N 3 38 1 0.901 N N 39 2 0.626 N N 4 40 1 2.921 N Y 41 2 1.780 N Y 42 3 1.104 N Y 43 4 0.707 N N

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Hypoxic Impacts on Egg Respiration Rates of the Copepod Acartia tonsa

Cristina Villalobos, REU Fellow Maryland Sea Grant

Dr. Jamie Pierson, Research Assistant Professor Horn Point Laboratory, University of Maryland Center for Environmental Science

Abstract

-1 The Chesapeake Bay has experienced extensive areas of hypoxia (< 2 mg O2 L ) in the past half-century as a direct result of eutrophication. The copepod Acartia tonsa, serves as a valuable prey item to higher trophic levels in the Chesapeake Bay and past studies have detected negative hypoxic effects on reproductive success and egg development rates. Little is known, however, on the physiological mechanism causing increased egg hatching time by hypoxia in A. tonsa. The goal of this study was to examine if lowered A. tonsa respiration rates may be a potential physiological mechanism impacted by hypoxia in the Chesapeake Bay. Egg development rates of female A. tonsa and egg hatching success were measured in hypoxic (< 2 -1 -1 mg O2 L ) and fully oxygenated (> 5.99 mg O2 L ) waters. Egg respiration rate and development were measured in two-hour intervals for a total of eight hours using a Pro2030 Professional Series YSI probe. We expected lowered respiration rate in hypoxic conditions to contribute to decreases in A. tonsa egg production, sinking rates, and ultimately egg hatching success. Results of this study may inform how future A. tonsa populations will be impacted in a changing environment and how those changes impact the future health of their predators.

Keywords: Respiration, Hypoxia, Acartia tonsa, Egg hatching

Introduction

Hypoxia in coastal and estuarine ecosystems is traditionally defined as dissolved oxygen (DO) concentrations less than 2 mg L-1 (Roman et al. 1993). It is a result of an increase in nutrient inputs or eutrophication (Hagy et al. 2004) that leads to an excess of productivity from phytoplankton biomass, which causes a significant depletion in available oxygen for marine organisms (Diaz and Rosenberg 2008). Areas with high rates of hypoxia are known to form oxygen minima or “dead” zones where there is little to no marine life (Diaz and Rosenberg 2008). The Chesapeake Bay, the largest estuary in the US (Kemp et al. 2005), has experienced extensive areas of hypoxia, particularly in the summer months in the past half-century as a direct result of eutrophication (Hagy et al. 2004). This increase of hypoxic zones in the Chesapeake Bay may cause considerable ecological damage (Hagy et al. 2004). In particular, many studies have investigated the harmful consequences of hypoxia on marine zooplankton (Roman et al. 1993; Invidia et al. 2004; Marcus et al. 2004; Roman et al. 2005; Elliot et al. 2013).

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Marine zooplankton serve as valuable prey to higher trophic levels and a decrease in zooplankton biomass may lead to significant changes in predator-prey interactions (Marcus et al. 2004). Acartia tonsa are one of the most abundant and important copepod species in the food web (Figure 1) (Heinle 1966). In the Chesapeake Bay, A. tonsa are primarily preyed upon by several species of planktivourous fishes and gelatinous zooplankton (Jung and Houde 2004; Purcell and Decker 2005), one economically important planktivourous fish being the bay anchovy, Anchoa mitchilli.

The impacts of hypoxia on A. tonsa have been discovered to impair development and survivorship of their earlier life stages (Roman et al. 1993; Invidia et al. 2004; Marcus et al. 2004; Elliott et al. 2013). Acartia tonsa are broadcast spawning copepods, releasing fertilized eggs at the water surface (J. Pierson, pers. comm.). This behavior has the potential to aid in the survival of nauplii if eggs are set free and hatch in non-hypoxic or anoxic conditions (White and Roman 1992). If the eggs are exposed to long-term hypoxic conditions, egg-hatching success severely decreases; laboratory experiments with oxygen concentrations < 0.09 mg L-1 demonstrated A. tonsa eggs failed to hatch all together (Lutz et al. 1992; Roman et al. 1993).

Although past research has supported that hypoxia increases A. tonsa egg hatching time, little is known of the physiological mechanism impacted by hypoxia (Marcus et al. 2004). The focus of this study will be to investigate how respiration rate affects egg-hatching time of A. tonsa. Lowered respiration rate may have the potential to increase egg-hatching time and cause eggs to sink into hypoxic or anoxic waters. Therefore, it is important to examine how respiration rate influences egg-hatching time to help determine the future health of A. tonsa populations and their predators in a changing environment.

This study will compare egg development rates and hatching time of A. tonsa in fully -1 -1 oxygenated (>5.00 mg L O2) and hypoxic (< 3.50 mg L O2) waters. The oxygen concentrations were chosen based on the hypoxic conditions A. tonsa are likely to encounter in the Chesapeake Bay during summer months. We will investigate if respiration rate may be a potential physiological mechanism of A. tonsa impacted by hypoxia in the Chesapeake Bay. Egg developmental stages will also be examined using a staining technique outlined in Zirbel et al. (2007). Lowered respiration rate in hypoxic conditions may contribute to decreases in A. tonsa egg production and developments rate and ultimately increase hatching time.

Materials and Methods

Breeding and Egg Collection

Adult A. tonsa samples used in this study were obtained from laboratory cultures kept inside an environmental chamber (~22°C) at Horn Point Laboratory in Cambridge, MD. Samples were gently rinsed using a 200 µm sieve to retain adult A. tonsa. Approximately 25-30 A. tonsa females and 1-2 males were collected, per treatment. Samples were organized with a Nikon dissecting microscope using a 100 µL glass transfer pipette and placed inside breeding chambers (Figure 2). The oxygen concentrations for the breeding chambers varied between 5-6 mg L-1. After a time interval of 24 hours, the breeding chambers were filtered through a 35 µm sieve to retain the eggs.

Measuring Respiration Rates

Hypoxic water concentrations used in this study were created by bubbling nitrogen gas into a large glass jar filled with artificial seawater. Normoxic water concentrations were created

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by using an aquarium air bubbler (Figure 3). Acartia tonsa eggs were counted and distributed into four Biological Oxygen Demand (B.O.D.) bottles with an oxygen concentration of 5.00 mg L- 1 and 6.50 mg L-1 (normoxic treatment) and four B.O.D. bottles with an oxygen concentration of ~3.00 mg L-1 (hypoxic treatment) and four additional B.O.D. bottles with an extreme hypoxic concentration of ~1 mg L-1. A control experiment was also employed with four B.O.D. bottles not containing A. tonsa eggs at an oxygen concentration of ~6.00 mg L-1.

Each B.O.D. bottle represented a developmental time step of two hours for a total developmental period of eight hours. Respiration rate was determined via changes in dissolved oxygen before and after each time interval using a Pro2030 Professional Series YSI probe. Due to limited production of eggs, three eggs were distributed per B.O.D. bottle. After each two-hour interval, eggs were rinsed through a 35 µm sieve, placed inside a 10 mL plastic bottle with approximately 5 mL of deionized water and 1 mL of 37% formaldehyde solution. Samples were then refrigerated overnight.

Staining

To examine developmental stages, eggs were stained using the staining technique described in Zirbel et al. (2007). Staining involved the use of 60 µL of 5 µg mL-1 DAPI solution, as described in Ludt (2008), with 20 µL of deionized water. Samples were placed in depression slides and incubated overnight. Longer incubation periods allow DAPI to further penetrate through the egg chorion in order to be stained. The slides were then viewed under a Nikon epiflourescence compound microscope to detect egg developmental stages by counting the nuclei in each egg.

Expected Results

-1 In the normoxic treatments (5 and 6 mg L O2), we expect A. tonsa eggs to respire at a much higher rate since more oxygen will be available (Figure 4). Throughout the 8-hour time period, we also expect egg development rate and egg-hatching success to be the greatest in the normoxic treatments.

-1 Conversely, in the hypoxic treatments (1 and 3 mg L O2) we expect A. tonsa eggs to respire at a slower rate since oxygen will be severely limited (Figure 4). We also expect egg development and egg-hatching success, or the presence of nauplii, to be absent in both hypoxic treatments.

Results

Across each treatment, mean dissolved oxygen concentrations decreased (Figure 5) and observed respiration rate varied widely during each time interval and treatment (Figure 7). Due to time constraints and other complications, data collection for staining egg development and hatching success did not occur. An analysis of covariance (ANCOVA) revealed the slope lines are not significantly different from one another (p = 0.2956) (Figure 6).

Discussion

The data collected during the length of this investigation does support a general trend in that A. tonsa egg respiration is higher in normoxic concentrations rather than hypoxic concentrations. This study was originally going to be carried out with a high precision

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respirometer. Unfortunately, the respirometer was not available for the duration of this investigation.

The data collected in the observed respiration rate contained considerable variation for changes in dissolved oxygen. Respiration rate gradually decreased throughout the eight hour time period for the majority of trials. Interestingly enough, respiration slightly increased during -1 -1 the 1 mg L hr O2 trial (Figure 7). This may have been caused by additional introduction of atmospheric oxygen into the B.O.D. bottles when taking measurements after each time period. Air bubbles may have also been introduced when pouring the desired oxygen concentration into the B.O.D. bottles in the beginning of the trial. A noticeable trait of A. tonsa eggs are their adhesive behavior to petri dishes and other materials they come into contact with. This behavior may interfere with the transfer process from a petri dish to a B.O.D. bottle to a microscopic slide. The data collected during the length of this investigation do support a general trend in that A. tonsa egg respiration is higher in normoxic concentrations rather than hypoxic concentrations.

Complications in staining for egg development and hatching success also arose during multiple attempts. Eggs could often not be located underneath the Nikon epiflourescence compound microscope. This occurrence hindered viewing the full egg developmental process in both normoxic and hypoxic concentrations. Respiration rate may possibly relate to egg hatching success and future studies are needed to investigate if respiration can be a predictive factor for egg hatching success.

Conclusion

Based on this study, many factors can affect the well-being of A. tonsa copepods. Past research has greatly demonstrated how hypoxia can disrupt reproduction and developmental processes that may ultimately lead to the decline of future populations of these zooplankton. Given A. tonsa’s significant contribution to the Chesapeake Bay’s food web, declines in zooplankton biomass will likely cause considerable ecological harm to the Bay. Therefore it is vital to understand the physiological mechanisms influenced by hypoxia and other factors of climate change. These findings could possibly aid the scientific community in predicting the health of A. tonsa populations and the Chesapeake Bay. Although the findings of this study were rather inconclusive, similar experimental designs may be employed with the use of a respirometer, which may detect finer changes in egg respiration.

Acknowledgments

I thank Dr. Jamie Pierson and Catherine Fitzgerald for their significant contribution in helping make this research possible. I also thank the HPL community for use of their facilities, Maryland Sea Grant, and the National Science Foundation for funding. A special thank you to my HPL family.

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References

Decker, M.B., D.L. Breitburg, and J. E. Purcell. 2004. Effects of low dissolved oxygen on zooplankton predation by the ctenophore Mnemiopsis leidyi. Marine Ecology Progress Series 280:163-172.

Diaz, R.J., and R. Rosenberg. 2008. Spreading dead zones and consequences for marine ecosystems. Science 321: 926–929.

Elliott, D.T., J.J. Pierson, and M.R. Roman. 2013. Predicting the effects of coastal hypoxia on vital rates of the planktonic copepod Acartia tonsa Dana. PLoS ONE 8: e63987. doi:10.1371/journal.pone.0063987

Hagy, J.D., W.R. Boynton, C.W. Keefe, and K.V. Wood. 2004. Hypoxia in Chesapeake Bay, 1950-2001: Long-term change in relation to nutrient loading and river flow. Estuaries 27: 634-658.

Heinle, D.R. 1966. Production of a calanoid copepod, Acartia tonsa, in the Patuxent river estuary. Chesapeake Science 7: 59-74.

Invidia, M., S. Sei, and G. Gorbi. 2004. Survival of the copepod Acartia tonsa following egg exposure to near anoxia and to sulfide at different pH values. Marine Ecology 76: 187- 196.

Jung, S., and E.D. Houde. 2004. Production of bay anchovy Anchoa mitchilli in Chesapeake Bay: application of size-based theory. Marine Ecology Progress Series 281:217-232.

Kemp, W.M., W.R. Boynton, J.E. Adolf , D.F. Boesch, W.C. Boicourt, G. Brush, J.C. Cornwell, T.R. Fisher, P.M. Glibert, J.D. Hagy, L.W. Harding, E.D. Houde, D.G. Kimme, W.D. Miller, R.I.E. Newell, M.R. Roman, E.M. Smith, and J.C. Stevenson. 2005. Eutrophication of the Chesapeake Bay: Historical trends and ecological interactions. Marine Ecology Progress Series 303:1-29.

Ludt, W. 2008. Determining egg development rates of Acartia tonsa in the Chesapeake Bay using a DAPI staining technique and its significance. [REU Intern Thesis] Horn Point Laboratory, Maryland Sea Grant Undergraduate Research Program.

Lutz, R.V., N.H. Marcus, and J.P. Chanton. 1992. Effects of low oxygen concentrations on the hatching and viability of eggs of marine calanoid copepods. Marine Biology 114: 241- 247.

Marcus, N.H., C. Richmond, C. Sedlacek, G.A., Miller, and C. Oppert. 2004. Impact of hypoxia on the survival, egg production and population dynamics of Acartia tonsa Dana. Journal of Experimental Marine Biology and Ecology 301: 111-128.

Roman, R.R., A.L. Gauzens, W.K. Rhinehart, and J.R. White. 1993. Effects of low oxygen waters on Chesapeake Bay zooplankton. Limnology and Oceanography 38: 1603-1614.

Roman, M., X. Zhang, C. McGilliard, and W. Boicourt. 2005. Seasonal and annual variability in the spatial patterns of plankton biomass in Chesapeake Bay. Limnology and Oceanography 50: 480-492.

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Uye, S. 1980. Development of neritic copepods Acartia clausi and A. steuri. 1. Some environmental factors affecting egg development and the nature of resting eggs. Bulletin of the Plankton Society 27: 1-9.

White, J.R. and M.R. Roman. 1992. Egg production by the calanoid copepod Acartia tonsa in the mesohaline Chesapeake Bay: the importance of food resources and temperature. Marine Ecology Progress Series 86: 239-249.

Zirbel, M.J., C.B. Miller and H.P. Batchelder. 2007. Staging egg development of marine copepods with DAPI and PicoGreen®. Limnology and Oceanography: Methods 106-110.

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Figures and Tables

Figure 1. Image of the copepod A. tonsa.

Figure 2. Breeding chamber for A. tonsa

Figure 3. Experimental set-up of creating desired hypoxic and normoxic water concentrations.

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0.4

0.3

1mg/L/hr 0.2 3mg/L/hr 5mg/L/hr 0.1 6mg/L/hr

0 Dissolved Oxygen (mg/L/hr)

Time 2 hours 4 hours 6 hours 8 hours

Figure 4. Expected change in dissolved oxygen over time in normoxic (5, 6 mg/L/hr) and hypoxic (1, 3 mg/L/hr) treatments.

0.8

0.6

0.4 Normoxic

0.2 Hypoxic

0

Mean Dissolved(mg//L) OxygenMean 1 2 3

-0.2 Treatments

Figure 5. Gradual decrease in mean dissolved oxygen for both normoxic and hypoxic treatments.

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1.4

1.2

1

0.8 y = 0.1403x - 0.1446 R² = 0.3375 Control 0.6 Treatment

0.4 Linear (Control) y = 0.0444x + 0.2115 0.2 R² = 0.0836 Linear (Treatment) Change in Respiration

0 0 2 4 6 8

-0.2

-0.4 Starting Dissolved Oxygen

Figure 6. Linear regressions for the treatments and control.

0.4

0.3 1mg/L/hr 0.2 3mg/L/hr

0.1 5mg/L/hr 6mg/L/hr 0

Dissolved Oxygen (mg/L/hr) -0.1 2 hours 4 hours Time 6 hours 8 hours

Figure 7. Observed change in dissolved oxygen over time in normoxic (5, 6 mg/L/hr) and hypoxic (1, 3 mg/L/hr) treatments.

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