The Effects of Varying Conductivities on Phytoplankton Concentration and Species Composition in the Presence of Nitrogen and Phosphorus in Lake Mattamuskeet


Lake Mattamuskeet, North Carolina. 

(Google Earth)

For the past four years I have researched the chemistry and phytoplankton of Lake Mattamuskeet, the 16,000-hectare centerpiece of Mattamuskeet National Wildlife Refuge in Hyde County, North Carolina (Waters et al.). I have grown up within a kilometer of the lakeshore; I have paddled its open waters and canals, identified the water birds that winter on the lake, and spied blue crabs through the wild celery plants that at times blanket the lakebed. When I was kayaking the lake a few years ago and noticed a change in the water clarity, my awareness of the lake heightened. Lake Mattamuskeet became a source of scientific wonder for me, and that intrigue has led to my research project, which focuses on the factors that could lead to the lake’s demise if they are not well understood and managed.

Reading past water quality notes while kayaking on Lake Mattamuskeet

Lake Mattamuskeet is an international migratory fish and wildlife habitat; hundreds of thousands of migratory waterfowl and other birds winter, stage, or nest on the lake. Additionally, several species of migratory fish spawn or develop in the lake. The value of the lake to these public trust resources, which travel between states and even nations, is threatened by nutrient input and sea level rise—factors that are increasingly worrisome to lakes and wetlands in environmentally sensitive regions across the globe.As a member of the U.S. Fish and Wildlife Service water quality working group for the lake, I analyze data collected by two water quality monitors. Last year I noticed that the lake’s conductivity had changed, probably because of changes made to four water-control structures that connect the slightly brackish lake with the more saline Pamlico Sound. I had studied the lake’s phytoplankton and was aware of an increase in its concentration. After an early August discussion with the refuge manager about the changes in the lake’s conductivity and phytoplankton, I wanted to design a project that would measure phytoplankton response to nutrient additions in the presence of varying conductivities.

Snow geese and mixed duck flock on Lake Mattamuskeet. (Click to enlarge)

The lake’s condition is impacted by cultural eutrophication caused by agricultural drainage as well as rising sea levels, which interfere with the lake’s wind-driven flow to nearby Pamlico Sound. The high concentration of nutrients in the lake stimulates the growth of phytoplankton, which cloud the water, preventing sufficient sunlight from reaching other photosynthetic organisms, including submerged aquatic vegetation (SAV) that provides both food and cover for other organisms (Janse et al.). When SAV does not receive enough sunlight, it dies, resulting in a decline in zooplankton and an increase in phytoplankton, exacerbating the blooms and turbidity. The problem is compounded by nutrient resuspension from the lake sediment, which occurs when winds exceed 12 kilometers per hour, as well by as sea level rise, which creates sound tides that block the gates through which excess lake water and nutrients flow to Pamlico Sound (Waters et al.).

One-way gates connect Lake Mattamuskeet with Pamlico Sound. (Click to enlarge)

Recent research shows that Lake Mattamuskeet is subject to increasingly high levels of phytoplankton, estimated by chlorophyll a, a light-absorbing pigment present in phytoplankton  (Egen 2013-14). Various sources state that chlorophyll a concentrations over approximately 10 μg/L are indicative of a eutrophic lake (Jones, Rast, and Lee). The comparatively high levels of chlorophyll a (averaging 60 to 120 μg/L) in Lake Mattamuskeet result in devaluation of the lake’s resources for migratory birds, fish, and other aquatic and wetland organisms (Egen).

Farm drainage pump near Lake Mattamuskeet.

Phosphorus is present in high amounts in Lake Mattamuskeet; the average phosphorus concentration in the lake is approximately 0.11 mg/L, while the maximum amount of phosphorus for a healthy lake, according to the Environmental Protection Agency, is 0.025 mg/L (Davis; Oram; Egen). Although the lake has high levels of phosphorus, its nitrogen levels (0.02 mg/L) are low (Egen). Like phosphorus, nitrogen is present in fertilizer, such as the 11-37-0 (nitrogen and phosphorus) and 30-0-0 (nitrogen) blends, commonly used on farmland, that drain into the lake. However, while nitrogen may enter the lake through runoff, it reacts with air and is not present in large quantities in the water column (Egen). Additionally, although nitrogen may enter lakes through bird excretion, this amount is usually insignificant and does not impact overall nitrogen concentration (Scheffer; Purcell).

Nitrogen plays a key role in cellular function; like phosphorus, nitrogen is a component of nucleic acids and other compounds critical for organismal development and survival. In shallow lakes that are not nitrogen-deficient, nitrogen often leads to phytoplankton growth. Although phosphorus is generally regarded as the primary limiting nutrient for phytoplankton growth, research indicates that nitrates, rather than phosphates, are correlated with chlorophyll a concentrations in a river system in the Eastern Cape (Prein; Babin et al.; Reece et al.; Dalu, Froneman, and Richoux). Such a relationship would indicate that, were nitrogen present in limited quantities with phosphorus present in much higher quantities, the sustainable level of phytoplankton would correspond to the amount of nitrogen, rather than the amount of phosphorus (Hecky and Kilham). Part of my experiment was designed to determine whether this relationship holds in Lake Mattamuskeet; I tested how increasing nitrogen and phosphorus separately and together increases or limits chlorophyll a concentrations.

Conductivity, a measure of a solution’s conductive abilities and thus the total dissolved solids within the solution, affects the ecological viability of shallow freshwater and brackish lakes such as Lake Mattamuskeet. The average conductivity in Lake Mattamuskeet is approximately 2,100 μS/cm. Freshwater lakes are those with conductivities below approximately 2,000 μS/cm (Kemker). Some studies indicate that salts in fresh and brackish waters can decrease phytoplankton concentrations and affect species composition (Redden and Rukminasari; Dalu, Froneman, and Richoux; Flöder et al.; Larson and Belovsky).

Nielsen et al. suggest that conductivity and nitrogen fixation by cyanobacteria can interact in freshwater environments (Nielsen et al.). Examination of the microbes inhabiting a salinity gradient along the Neuse River, about 55 kilometers southeast of Lake Mattamuskeet, revealed that nitrogen-fixing properties of the cyanobacteria in the river appeared somewhat dependent on salt concentration (Affourtit, Zehr, and Paerl). Further research by Srivastava et al. suggests that nitrogen availability declines with increased salinity, resulting in increased nitrogen fixation (Srivastava et al.). Conductivity may also affect phosphorus availability in the water column; the data from a 2011 study indicate that increased conductivity allows more anion binding to phosphorus in the form of phosphate, resulting in a decreased concentration of total available phosphorus. Since phosphorus is a critical nutrient for phytoplankton, it is possible that this chemical interaction would prevent phytoplankton from accessing phosphorus, resulting in decreased phytoplankton populations (Chouyyok et al.; López-Flores). These potential relationships motivated me to use phosphorus, nitrogen, and conductivity as independent variables in this study.

Living near this valuable, remote, and at-risk resource gives me the unique opportunity to conduct field research on an important and relatively understudied subject. In 2014 and 2015, I studied the relationships between conductivity and phytoplankton concentrations and species composition in the presence of nitrogen and phosphorus. I chose these nutrients because both are crucial to phytoplankton growth and function, and are applied as fertilizer to farmland that drains into the lake. I included conductivity variations in my study because the lake is geographically positioned to potentially use the more saline water of Pamlico Sound as a tool to control phytoplankton concentrations. My data will enable the Fish and Wildlife Service to address watershed nutrient management and determine whether manipulation of the lake’s conductivity should be considered. On a more personal level, I was driven to conduct this research because my family’s livelihood is farming, and our farm contributes to the nutrient load in the lake. I designed my project to answer questions that would lead to improving the water quality of Lake Mattamuskeet and water bodies like it throughout the world—waters that support wildlife, fisheries, and human communities—and that are simultaneously impacted by nutrient input and sea level rise.



Lake Mattamuskeet, Hyde County, North Carolina. 

(Google Earth)

Lake Mattamuskeet is located in Hyde County, North Carolina, about 10 kilometers from Pamlico Sound and connects with it through four outlet canals, equipped with one-way structures that allow the lake to drain but prevent the backflow of brackish water from the sound. Sound water does enter the lake during hurricane tides and causes the lake to be slightly brackish. Approximately 4,000 hectares of cropland legally drain into the lake.



My project investigates how variations in conductivity affect phytoplankton concentration and species composition in the presence of nitrogen and phosphorus in Lake Mattamuskeet.



I hypothesized that increased conductivity will correlate with decreased chlorophyll a concentrations, and nitrogen-dosed samples will have higher chlorophyll a concentrations than phosphorus-dosed samples, but phosphorus- and nitrogen-dosed samples will have the highest chlorophyll a concentrations due to previously documented phytoplankton sensitivities to nitrogen in phosphorus-enriched systems (Dalu, Froneman, and Richoux). This hypothesis is further supported by research suggesting that increases in conductivity may result in decreased phytoplankton levels, and Dalu, Froneman, and Richoux, who determined that increased salinities in a river system in South Africa resulted in decreased chlorophyll a concentrations (Redden and Rukminasari; Dalu, Froneman, and Richoux). Further research shows that phytoplankton species composition is affected by salinity changes, which suggests that phytoplankton species composition in Lake Mattamuskeet may be changed by increased conductivities (Flöder et al.; Larson and Belovsky).


Collecting water samples from Lake Mattamuskeet for 2014/15 research.


Spiking Cubitainer samples.

Filtering samples through Buchner funnel.

Incubating samples in a floating corral.

Identifying phytoplankton.

Using the spectrophotometer.

Permission to conduct scientific investigation and water collections was obtained from the refuge manager. Past and current water quality characteristics of Lake Mattamuskeet were reviewed. Necessary field and lab equipment was purchased or borrowed. A preexisting floating corral was retrofitted to include a third chamber, using wire cutters and a hacksaw in addition to foam swimming pool noodles, 1.5-m lengths of 2.5-cm PVC pipe, PVC elbows, 450-cm chicken wire, PVC glue, 120-cm window screen, tie straps, and plastic twine.

Five hundred mL sodium nitrate and 500 mL monobasic potassium phosphate stock solutions (200 mg/L and 440 mg/L, respectively) were mixed using monobasic potassium phosphate and sodium nitrate reagents (both ≥ 99%). The solutions were mixed with a magnetic stirrer and stir bar and stored in 500 mL volumetric flasks (“Preparation of Nutrient Spike Solutions”; “Algal Growth Potential” 2011).

Water was collected when phytoplankton concentrations were peaking, during late summer (Havens; Pilkaitytë and Razinovas). One hundred fifty liters of water were collected at the USGS east gauge location (35°30'23", 76°11'02") and stored in five-gallon jugs in a cooler on ice. A YSI meter was used to measure the lake conductivity (approximately 2,100 μS/cm). 

One hundred eighty one-liter Cubitainers were filled with 750 mL of collected water each. In 65 Cubitainers, no Instant Ocean was added. These samples were controls, with the baseline lake salt level of approximately 2,100 μS/cm. In 60 Cubitainers, 0.99 g Instant Ocean were added to produce 2X samples (4200 μS/cm). In 60 Cubitainers, 2.97 g of Instant Ocean were added to produce 4X samples (8400 μS/cm). Plastic weighing boats and a 0.01 g digital scale were used to measure Instant Ocean. After addition of reagent, Cubitainers were shaken gently to mix. 2.5 mL phosphorus stock solution were added to spike 15 1X samples, 15 2X samples, and 15 4X samples. Additionally, 3.0 mL nitrogen stock solution were added to spike 15 1X samples, 15 2X samples, and 15 4X samples; and 2.5 mL of phosphorus stock solution and 3.0 mL of nitrogen stock solution were added to spike 15 1X samples, 15 2X samples, and 15 4X samples. A micropipette controller and 200 10 mL serological pipettes were used to add nutrients. Phosphorus-spiked samples were 20 times lake concentration (0.11 mg/L), while nitrogen-spiked samples were 60 times lake concentration (0.02 mg/L), in accordance with standard spiking protocol (“Preparation of Nutrient Spike Solutions”; “Algal Growth Potential”). Nutrient combinations used in this experiment are located in Table 1. After being spiked, samples were shaken gently to mix. 

Five of the samples without Instant Ocean were immediately filtered through a Buchner funnel with a Size 8 rubber stopper, 3/16” ID, 3/8” OD PVC laboratory tubing, and a one liter filtering flask using a vacuumpump and 90 mm glass fiber Whatman filters; filters were wrapped in foil, labeled, and frozen. These samples served as controls for both conductivity and nutrient content. YSI and Hanna phosphate and nitrogen meters and reagent packs were used to confirm that actual nutrient concentrations matched calculated concentrations in unfiltered samples.

One hundred eighty Cubitainers were incubated in a floating corral in a canal near the lake to simulate lake conditions for Day 5, 10 and 15 collections. The corral was secured to the bank using nine meters of rope and two cinder blocks.

One third of the samples were retrieved every five days for 15 days. A light microscope, Wipple grid, lens oil, lens cloth, eyedropper, slides, and coverslips, in addition to How to Know the Freshwater Algae, were used to count and identify phytoplankton strains present in the samples before filtration (Prescott). A random number generator was used to choose the coordinates of five transects per slide on which phytoplankton were identified. A Canon EOS 50D camera and adapter were used to photograph phytoplankton. Samples were filtered as above; each filter was wrapped in foil, labeled, and frozen. 

Two liters of 90% acetone solution were mixed using lab-grade acetone and deionized water. Filters were removed from freezer and placed in labeled 15 mL high-clarity polypropylene conical tubes. Ten mL of acetone solution were added to each tube, and tubes were frozen for 24 hours to extract pigment. Centrifuge tubes were placed on ice and taken to the Coastal Studies Institute for use of UV-VIS spectrophotometer. Approximately 2 mL of sample from each centrifuge tube were poured into clean cuvette, which was placed in spectrophotometer. Absorbance data were recorded for 750, 665, 663, 645, and 630 nanometer wavelengths according to standard chlorophyll a measurement protocol (“ESS Method 105.1”). Cuvette was removed from spectrophotometer, contents were discarded, and cuvette was cleaned. This process was repeated for each sample. When acetone was handled, safety goggles and latex gloves were worn. The chlorophyll a concentration of each sample was calculated (Appendix A).


On 11 September 2014, 150 liters of lake water were collected. Table 1 shows nutrient and time combinations used. Five samples of pure lake water were filtered at the beginning of the experiment; 45 separate samples of pure lake water also served as controls (C), 15 of which had no additional Instant Ocean (1X), 15 of which had double the conductivity of pure lake water (2X), and 15 of which had quadruple the conductivity of pure lake water (4X). Forty-five more samples of lake water were dosed with 20 times the average concentration of phosphorus in the lake; as with the control samples, 15 of these samples were designated as 1X, 15 as 2X, and 15 as 4X. The same designations were used for samples dosed with 60 times the amount of nitrogen present in the lake, and for samples dosed with both 60 times the amount of nitrogen in the lake and 20 times the amount of phosphorus in the lake. The five replicates of every dosage combination were collected every five days for 15 days.


Table 1: Nutrient Combinations Used in Experiment

table 1

When the samples were collected, they were filtered through a Buchner funnel and the filters were frozen. On 9 October 2014, chlorophyll a levels in the samples were measured with a spectrophotometer. Raw data are located in Appendix B.

Table 2 displays the strains identified through light microscopy, and the qualitative density of the specimens. “Single” denotes low-density, discrete, countable individuals not contained in mats; “clumped” denotes such a high density of phytoplankton that individuals are uncountable, as shown in the photographs below.

image 14 15

Single Sample (C/2X/10), Mag 400X (left)

Clumped sample (NP/1X/10), Mag 400X (right)

Sample Strains Approximate Density
C/1X Microcystis, Anabaena, Microcoleus Single
C/2X Microcystis, Anabaena, Microcoleus, Schroederia setigera, Lyngbya wollei Single
C/4X Anabaena, Microcystis, S. setigera, Microcoleus Single
P/1X Anabaena, L. wollei, Microcoleus, S. setigera Single
P/2X Anabaena, Microcoleus, Microcystis, L. wollei, S. setigera Single
P/4X Microcoleus, Microcstis, Anabaena Single
N/1X Anabaena, Microcoleus, S. setiera Clumped
N/2X Anabaena, Microcoleus, S. setigera, Microcystis, L. wollei Clumped
N/4X Anabaena, Microcystis, Microcoleus, L. wollei, S. setigera Clumped
NP/1X Anabaena, Microcystis, Microcoleus, L. wollei, S. setigera Clumped
NP/2X Anabaena, Microcystis, Microcoleus, L. wollei, S. setigera Clumped
NP/4X Anabaena, Microcystis, Microcoleus, L. wollei, S. setigera Clumped 

Table 2: Strains identified in samples and qualitative densities.

Figure 1 shows a comparison of average chlorophyll a samples for all samples sorted by nutrient type. One-way ANOVA for all samples showed no significant difference between average chlorophyll a concentration for C and P samples; however, the rest of the samples were significantly different (P < 0.01).

graph 1

Figure 1: Comparison of average chlorophyll a concentrations for control, phosphorus, nitrogen, and nitrogen/phosphorus samples over all conductivities; error bars are respective standard deviations.

Figures 2–5 show average chlorophyll a concentrations over the 15-day incubation period for control, phosphorus, nitrogen, and nitrogen and phosphorus samples, respectively. For control samples, ANOVA analysis indicated no significant change in concentration over the collection period. For phosphorus samples, ANOVA analysis and a subsequent Tukey HSD test indicated a significant peak in concentration in the Day 10 samples (P < 0.05 for Day 5 vs. Day 10 and for Day 10 vs. Day 15), but no significant difference between concentrations for Day 5 and Day 15 samples. For nitrogen samples, ANOVA analysis and a subsequent Tukey HSD test indicated a significant difference between Day 5 and Day 10 concentrations (P < 0.01), but no significant difference between concentrations for Day 5 vs. Day 15 samples, or for Day 10 vs. Day 15 samples. For nitrogen and phosphorus samples, ANOVA analysis and a subsequent Tukey HSD test indicated a significant peak in concentration in the Day 10 samples (P < 0.05 for Day 5 vs. Day 10 and for Day 10 vs. Day 15), but no significant difference between concentrations for Day 5 and Day 15 samples.

graph 2

Figure 2: Average chlorophyll a concentrations over time for control samples over all conductivities; error bars are respective standard deviations.

graph 3

Figure 3: Average chlorophyll a concentrations over time for phosphorus samples over all conductivities; error bars are respective standard deviations.

graph 4

Figure 4: Average chlorophyll a concentrations over time for nitrogen samples over all conductivities; error bars are respective standard deviations.

graph 5

Figure 5: Average chlorophyll a concentrations over time for nitrogen/phosphorus samples over all conductivities; error bars are respective standard deviations.

Figures 6–9 compare the average chlorophyll a concentrations corresponding to different conductivities for control, phosphorus, nitrogen, and nitrogen and phosphorus samples, respectively. One-way ANOVA analysis indicated no significant differences among concentrations for different conductivities in any samples.

graph 6

Figure 6: Average chlorophyll a concentrations for 1X, 2X, and 4X control samples; error bars are respective standard deviations.

graph 7

Figure 7: Average chlorophyll a concentrations for 1X, 2X, and 4X phosphorus samples; error bars are respective standard deviations.

graph 8

Figure 8: Average chlorophyll a concentrations for 1X, 2X, and 4X nitrogen samples; error bars are respective standard deviations.

graph 9

Figure 9: Average chlorophyll a concentrations for 1X, 2X, and 4X nitrogen/phosphorus samples; error bars are respective standard deviations.



According to one-way ANOVA on all samples, the data show a significant difference between phytoplankton concentration for samples dosed with nitrogen and those dosed with phosphorus, and a significant difference between those dosed with nitrogen and those dosed with no excess nutrients; in both scenarios, nitrogen-dosed samples had higher chlorophyll a concentrations. Furthermore, samples dosed with both nitrogen and phosphorus had significantly higher phytoplankton concentrations than other samples. Samples dosed with phosphorus had significantly lower chlorophyll a concentrations than those dosed with nitrogen or nitrogen and phosphorus, but were not significantly different from control samples, indicating that phosphorus is not a limiting nutrient for phytoplankton in Lake Mattamuskeet (Figure 1). The portion of the hypothesis stating that nitrogen samples would have higher concentrations of chlorophyll a than phosphorus or control samples was supported by the data, as was the portion stating that the samples with both nitrogen and phosphorus would have significantly higher chlorophyll a concentrations than any other samples. However, the portion of the hypothesis stating that the phosphorus samples would have higher chlorophyll a concentrations than the control samples was not supported by the data.

According to one-way ANOVA on all samples, conductivity changes did not affect chlorophyll a concentrations for any samples. The portion of the hypothesis stating that increased conductivity would correspond to decreased chlorophyll a concentrations was not supported by the data (Figures 6–9). The hypothesis that conductivity variations would affect species composition was not supported (Table 2). Additionally, for nitrogen, phosphorus, and nitrogen and phosphorus samples over all conductivities, chlorophyll a concentrations peaked significantly at Day 10 and tended to decline towards Day 15. One reason for this trend could be that the average lifespan of a phytoplankton bloom is approximately two weeks, and so phytoplankton were experiencing normal senescence at the end of the sampling period (Havens).

Phytoplankton require approximately ten times as much nitrogen as phosphorus to function (Scheffer). Thus, the increased phytoplankton concentrations in the nitrogen-spiked samples can most likely be explained by the lake’s relatively low nitrogen levels compared to its phosphorus levels, and so nitrogen, rather than phosphorus, is a limiting nutrient for phytoplankton. Additionally, Anabaena individuals, prevalent in this project’s samples, are particularly sensitive to nitrogen increases (Ganguly et al.). A 2007 study showed that phytoplankton are more sensitive to nitrogen deficiencies during the summer than other seasons, which could further explain the phytoplankton increases in nitrogen-spiked samples (Pilkaitytë and Razinovas). The reason for the significantly higher concentrations of phytoplankton in the nitrogen/phosphorus samples than samples with nitrogen alone is likely that with a nitrogen concentration 60 times that of the lake, phytoplankton were phosphorus-deficient. Thus, samples with both nitrogen and phosphorus had higher concentrations of phytoplankton than samples with only nitrogen or phosphorus.

Five strains of phytoplankton were identified in the samples; there were no apparent differences in species composition for any samples. Four of these strains, Anabaena, Microcystis, Microcoleus, and L. wollei, are members of the Cyanobacteria phylum, while the fifth strain, S. setigera, is a member of the Chlorophyta phylum (Prescott; Stiller). In the spring of 2013, the lake had 18 distinct strains of phytoplankton. This number declined to 13 strains in the summer of 2013, to five strains in the summer of 2014, and continues to hover at five strains (“Seasonal Species Richness and Phosphate”; “Species Richness and Phosphorus”).

One reason increased conductivity had no effect on phytoplankton concentration or species composition regardless of nitrogen and/or phosphorus presence could be that some strains of phytoplankton are somewhat resistant to conductivity changes; the increments used in this study may have been too small to affect the phytoplankton. For instance, some species of the Anabaena genus are saltwater resistant, while others are more sensitive to conductivity changes (Apte and Baghwat; Flöder et al.). A 2011 study suggests that cyanobacteria are generally more resistant to solute increases than other phyla such as Chlorophyta (Chakraborty et al.). Many of the cyanobacteria individuals could not be identified as a particular species without more sophisticated equipment, but it is possible that they were members of a species tolerant of increased conductivities. Another possibility for the lack of a relationship between conductivity and phytoplankton concentration is that the conductivity changes resulted in decreased zooplankton populations. Since zooplankton naturally predate phytoplankton, it is possible that a reduction in zooplankton would result in an increased phytoplankton population, perhaps compensating for the phytoplankton that may have be unable to tolerate a conductivity increase.

Nitrogen and phosphorus dynamics appeared to be unaffected by changes in conductivities, as phytoplankton concentrations did not respond. This suggests that artificial or synthetic methods may be needed to reduce nutrient concentrations in Lake Mattamuskeet. Research by Ou et al. indicates that phosphate can be efficiently removed from water using lanthanum-doped mesoporous silicon dioxide (Ou et al.). Likewise, my previous research on the lake has shown that addition of aluminum potassium sulfate can decrease phosphate concentrations; however, decreased phosphate concentrations did not affect phytoplankton, which further suggests that phosphorus is not a limiting nutrient for phytoplankton (“Effects of Phosphorus Interception”). As implied by the data from this project, nitrogen is more likely a limiting nutrient in Lake Mattamuskeet; thus, chemical methods of nitrogen binding may be needed to decrease phytoplankton concentrations.

Possible sources of error include a human-operated microscope, which could have led to slightly inaccurate phytoplankton counts and identifications. A more sophisticated data collection system, such as a DNA analyzer, could have improved this study’s accuracy by reducing user error. Other sources of error could have been imprecise or incorrectly calibrated equipment, although these possibilities are unlikely, as most of the equipment was in excellent condition and calibrated as necessary, and all measuring equipment was precise.

This project sparks many questions:

  • Are the phytoplankton in Lake Mattamuskeet affected by conductivity changes larger than those used in this experiment?
  • Do conductivity changes decrease zooplankton populations and consequently increase phytoplankton populations?
  • Would the addition of nitrogen-binding agents to lake water decrease phytoplankton concentrations?
  • Can this study be replicated with similar results at fresh or slightly brackish lakes along the environmentally sensitive Atlantic and Gulf coasts?



Cultural eutrophication has resulted in harmful phytoplankton blooms that degrade Lake Mattamuskeet’s value to fish and wildlife populations and recreational usage. My research shows that nitrogen may need to be controlled to manage phytoplankton growth; this finding builds the evidence that in some eutrophic systems, nitrogen is the primary nutrient to target for reduction. Additionally, my project reveals the ineffectiveness of using low-level conductivities to control phytoplankton in Lake Mattamuskeet and perhaps other fresh or marginally brackish systems where rising sea levels increase saltwater presence. These two discoveries are valuable for managers of Lake Mattamuskeet, and are also likely to be useful for those charged with the protection or remediation of near-coastal lakes and wetlands in other parts of the world.

My research prompts managers to continue testing water quality factors that correlate with phytoplankton concentrations so that an effective water quality improvement plan for Lake Mattamuskeet can be devised. With a better understanding of the relationship among the lake's nutrient load, conductivity, and phytoplankton response, a plan can be implemented that preserves the lake's value as a resource for wildlife and fish populations and the public that enjoys them.



Thanks to the Fish and Wildlife Service and Mattamuskeet National Wildlife Refuge manager Pete Campbell and his staff for supporting this project with funds and lake access. Thanks to Dr. Michael Piehler and Dr. Matthew Waters for advising me on project logistics and providing access to a spectrophotometer, and to Stephanie O’Daly for helping me set up the spectrophotometer. Thanks to Dr. John Stiller for teaching me phytoplankton identification, and to Dr. Michelle Moorman for providing me with USGS lake water quality data. Finally, thanks to my parents, Blythe and Kelly Davis, for helping fund this project and for their assistance in the water collection, sample transport, and in converting an air conditioner vacuum pump into a lab aspirator.



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Chlorophyll a concentrations were calculated using the formula below:

anne formula

where E is the volume of acetone solution used (10 mL), F is the dilution factor (irrelevant in this study), V is the volume of water filtered (0.750 L), and L is the cell path length (1 cm) (“ESS Method 105.1”).



Sample ID Abs 665 nm Abs 663 nm Abs 645 nm Abs 630 nm Chlorophyll a (µg/L)
C/1X/1/A 0.118 0.119 0.025 0.034 17.79413333
C/1X/1/B 0.161 0.15 0.031 0.03 22.4272
C/1X/1/C 0.214 0.192 0.04 0.028 28.68373333
C/1X/1/D 0.098 0.099 0.015 0.013 14.95013333
C/1X/1/E 0.146 0.14 0.028 0.028 20.95893333
C/1X/5/A 0.18 0.168 0.034 0.033 25.1384
C/1X/5/B 0.189 0.18 0.035 0.035 26.97466667
C/1X/5/C 0.149 0.14 0.033 0.059 20.85626667
C/1X/5/D 0.19 0.187 0.038 0.034 27.97333333
C/1X/5/E 0.187 0.179 0.036 0.036 26.792
C/2X/5/A 0.327 0.319 0.065 0.061 47.71813333
C/2X/5/B 0.183 0.172 0.034 0.035 25.76186667
C/2X/5/C 0.127 0.124 0.024 0.023 18.58426667
C/2X/5/D 0.217 0.207 0.042 0.044 30.97546667
C/2X/5/E 0.128 0.126 0.025 0.024 18.8672
C/4X/5/A 0.182 0.158 0.027 0.032 23.78666667
C/4X/5/B 0.131 0.125 0.024 0.026 18.74346667
C/4X/5/C 0.186 0.182 0.037 0.037 27.23013333
C/4X/5/D 0.148 0.136 0.022 0.024 20.5056
C/4X/5/E 0.0206 0.195 0.056 0.061 28.73253333
C/1X/10/A 0.282 0.269 0.057 0.057 40.1832
C/1X/10/B 0.161 0.182 0.034 0.03 27.3072
C/1X/10/C 0.155 0.154 0.095 0.065 21.25146667
C/1X/10/D 0.192 0.186 0.054 0.052 27.38133333
C/1X/10/E 0.169 0.161 0.035 0.034 24.02453333
C/2X/10/A 0.171 0.164 0.033 0.032 24.54506667
C/2X/10/B 0.223 0.218 0.045 0.044 35.59626667
C/2X/10/C 0.172 0.166 0.037 0.038 24.74826667
C/2X/10/D 0.093 0.09 0.016 0.015 13.5272
C/2X/10/E 0.21 0.192 0.035 0.031 28.83173333
C/4X/10/A 0.178 0.165 0.031 0.29 24.75386667
C/4X/10/B 0.16 0.151 0.033 0.033 22.5288
C/4X/10/C 0.089 0.088 0.029 0.029 12.86106667
C/4X/10/D 0.105 0.094 0.016 0.02 14.15466667
C/4X/10/E 0.243 0.238 0.053 0.049 35.47653333
C/1X/15/A 0.156 0.0131 0.018 0.018 19.8368
C/1X/15/B 0.234 0.25 0.123 0.122 35.42026667
C/1X/15/C 0.133 0.118 0.021 0.022 17.73813333
C/1X/15/D 0.18 0.179 0.041 0.033 26.644
C/1X/15/E 0.128 0.119 0.013 0.008 18.10506667
C/2X/15/A 0.191 0.185 0.042 0.042 27.5584
C/2Z/15/B 0.125 0.103 -0.006 -0.005 16.15173333
C/2X/15/C 0.21 0.21 0.048 0.043 31.26693333
C/2X/15/D 0.18 0.167 0.032 0.034 25.04213333
C/2X/15/E 0.145 0.142 0.038 0.031 20.98533333
C/4X/15/A 0.302 0.293 0.065 0.057 43.6776
C/4X/15/B 0.195 0.194 0.045 0.046 28.87413333
C/4X/15/C 0.12 0.112 0.021 0.028 16.81493333
C/4X/15/D 0.059 0.059 0.012 0.013 13.76
C/4X/15/E 0.271 0.265 0.072 0.065 39.14106667
P/1X/5/A 0.173 0.163 0.033 0.033 24.3912
P/1X/5/B 0.183 0.176 0.037 0.035 26.29626667
P/1X/5/C 0.093 0.089 0.035 0.033 12.8488
P/1X/5/D 0.143 0.139 0.029 0.029 20.31066667
P/1X/5/E 0.089 0.084 0.017 0.018 12.5712
P/2X/5/A 0.91 0.089 0.016 0.018 13.376
P/2X/5/B 0.145 0.141 0.029 0.028 21.08533333
P/2X/5/C 0.094 0.092 0.024 0.028 13.62453333
P/2X/5/D 0.08 0.076 0.015 0.013 11.38053333
P/2X/5/E 0.232 0.22 0.045 0.043 32.90533333
P/4X/5/A 0.059 0.057 0.011 0.011 8.544266667
P/4X/5/B 0.077 0.075 0.017 0.014 11.16906667
P/4X/5/C 0.056 0.053 0.009 0.01 7.979733333
P/4X/5/D 0.175 0.17 0.036 0.035 25.39386667
P/4X/5/E 0.06 0.057 0.011 0.012 8.5456
P/1X/10/A 0.271 0.259 0.064 0.06 38.4336
P/1X/10/B 0.106 0.103 0.032 0.031 15.10533333
P/1X/10/C 0.123 0.121 0.27 0.024 18.0336
P/1X/10/D 0.108 0.102 0.023 0.044 15.22666667
P/1X/10/E 0.107 0.096 0.015 0.016 14.48853333
P/2X/10/A 0.193 0.186 0.04 0.038 27.76586667
P/2X/10/B 0.256 0.246 0.052 0.049 36.74693333
P/2X/10/C 0.135 0.13 0.026 0.026 19.46186667
P/2X/10/D 0.274 0.259 0.048 0.047 38.87706667
P/2X/10/E 0.087 0.082 0.02 0.022 12.17973333
P/4X/10/A 0.183 0.173 0.037 0.037 25.83333333
P/4X/10/B 0.211 0.207 0.043 0.038 30.93866667
P/4X/10/C 0.113 0.107 0.021 0.022 16.03093333
P/4X/10/D 0.2 0.193 0.04 0.037 28.85093333
P/4X/10/E 0.285 0.283 0.061 0.049 42.23013333
P/1X/15/A 0.016 0.016 0.003 0.003 2.4008
P/1X/15/B 0.009 0.009 0.001 0.001 1.369333333
P/1X/15/C 0.069 0.067 0.012 0.01 10.06613333
P/1X/15/D 0.098 0.095 0.022 0.018 14.1344
P/1X/15/E 0.168 0.164 0038 0.033 24.4024
P/2X/15/A 0.083 0.079 0.019 0.019 11.73893333
P/2X/15/B 0.04 0.04 0.008 0.008 5.988266667
P/2X/15/C 0.216 0.214 0.058 0.051 31.6104
P/2X/15/D 0.036 0.034 0.007 0.008 5.085866667
P/2X/15/E 0.197 0.195 0.045 0.037 29.01733333
P/4X/15/A 0.183 0.177 0.039 0.036 26.3952
P/4X/15/B 0.144 0.141 0.03 0.028 21.05653333
P/4X/15/C 0.15 0.146 0.033 0.03 21.7488
P/4X/15/D 0.026 0.026 0.006 0.008 24.36
P/4X/15/E 0.177 0.173 0.04 0.036 25.7456
N/1X/5/A 0.293 0.288 0.054 0.041 43.19706667
N/1X/5/B 0.209 0.255 0.079 0.049 37.36613333
N/1X/5/C 0.282 0.262 0.066 0.063 38.8456
N/1X/5/D 0.33 0.301 0.04 0.055 45.63653333
N/1X/5/E 0.299 0.272 0.058 0.068 40.63466667
N/2X/5/A 0.222 0.209 0.042 0.049 31.29253333
N/2X/5/B 0.185 0.178 0.034 0.036 26.6944
N/2X/5/C 0.168 0.18 0.073 0.06 25.9136
N/2X/5/D 0.182 0.163 0.02 0.019 24.74693333
N/2X/5/E 0.179 0.167 0.027 0.035 25.18746667
N/4X/5/A 0.198 0.149 0.033 0.029 22.21306667
N/4X/5/B 0.199 0.196 0.059 0.056 28.79466667
N/4X/5/C 0.148 0.139 0.046 0.026 20.28266667
N/4X/5/D 0.112 0.109 0.019 0.022 16.39893333
N/4X/5/E 0.265 0.258 0.06 0.054 38.3856
N/1X/10/A 0.217 0.204 0.045 0.046 30.42613333
N/1X/10/B 0.22 0.198 0.045 0.05 29.50026667
N/1X/10/C 0.287 0.28 0.046 0.046 42.19253333
N/1X/10/D 0.358 0.339 0.068 0.057 50.7304
N/1X/10/E 0.43 0.424 0.089 0.079 63.34693333
N/2X/10/A 0.545 0.524 0.114 0.106 78.18293333
N/2X/10/B 0.628 0.612 0.126 0.116 91.50826667
N/2X/10/C 0.233 0.217 0.043 0.044 32.49866667
N/2X/10/D 0.322 0.308 0.064 0.063 46.0424
N/2X/10/E 0.44 0.423 0.091 0.088 63.14613333
N/4X/10/A 0.49 0.48 0.102 0.094 71.68373333
N/4X/10/B 0.475 0.467 0.101 0.092 69.69226667
N/4X/10/C 0.28 0.264 0.058 0.058 39.37973333
N/4X/10/D 0.44 0.436 0.1 0.087 64.9032
N/4X/10/E 0.031 0.028 -0.032 -0.026 53.831
N/1X/15/A 0.504 0.499 0.105 0.096 74.5488
N/1X/15/B 0.259 0.243 0.053 0.062 36.26986667
N/1X/15/C 0.24 0.234 0.059 0.06 34.6976
N/1X/15/D 0.374 0.359 0.079 0.082 53.55093333
N/1X/15/E 0.369 0.345 0.08 0.076 51.34133333
N/2X/15/A 0.267 0.247 0.059 0.059 36.71386667
N/2X/15/B 0.257 0.253 0.047 0.043 37.96933333
N/2X/15/C 0.085 0.084 0.024 0.024 12.3776
N/2X/15/D 0.432 0.408 0.091 0.091 60.82213333
N/2X/15/E 0.427 0.421 0.087 0.085 62.94693333
N/4X/15/A 0.236 0.229 0.06 0.059 33.89146667
N/4X/15/B 0.207 0.19 0.017 0.014 29.01706667
N/4X/15/C 0.31 0.3 0.017 0.074 44.61386667
N/4X/15/D 0.263 0.242 0.04 0.046 36.46773333
N/4X/15/E 0.276 0.259 0.056 0.061 38.66533333
NP/1X/5/B 0.333 0.313 0.067 0.066 46.736
NP/1X/5/C 0.188 0.181 0.043 0.038 26.90346667
NP/1X/5/D 0.235 0.226 0.051 0.049 33.67173333
NP/1X/5/E 0.451 0.422 0.096 0.096 62.8576
NP/2X/5/A 0.657 0.626 0.137 0.126 93.3776
NP/2X/5/B 0.842 0.789 0.38 0.146 111.2378667
NP/2X/5/C 0.38 0.366 0.085 0.078 54.4592
NP/2X/5/D 0.212 0.203 0.036 0.031 30.51013333
NP/2X/5/E 0.6 0.568 0.122 0.112 84.78933333
NP/4X/5/A 0.488 0.471 0.102 0.091 70.28293333
NP/4X/5/B 0.724 0.695 0.151 0.136 103.6965333
NP/4X/5/C 0.613 0.584 0.144 0.113 86.64026667
NP/4X/5/D 0.541 0.524 0.132 0.112 77.67253333
NP/4X/5/E 0.468 0.453 0.096 0.086 67.65546667
NP/1X/10/A 0.073 0.07 0.009 0.008 10.61546667
NP/1X/10/B 0.909 0.877 0.175 0.146 131.2650667
NP/1X/10/C 0.877 0.832 0.178 0.165 124.22
NP/1X/10/D 1.287 1.259 0.267 0.227 188.0098667
NP/1X/10/E 1.438 1.4 0.305 0.259 208.8413333
NP/2X/10/A 0.656 0.638 0.148 0.127 94.92453333
NP/2X/10/B 0.564 0.54 0.116 0.106 80.60853333
NP/2X/10/C 0.691 0.655 0.143 0.134 97.71626667
NP/2X/10/D 0.689 0.645 0.138 0.128 96.30026667
NP/2X/10/E 0.707 0.675 0.146 0.134 100.7338667
NP/4X/10/A 0.843 0.799 0.173 0.157 119.2317333
NP/4X/10/B 0.888 0.843 0.178 0.156 125.9152
NP/4X/10/C 0.588 0.561 0.12 0.109 83.75653333
NP/4X/10/D 0.821 0.789 0.168 0.148 117.8117333
NP/4X/10/E 0.802 0.773 0.165 0.147 158.65
NP/1X/15/A 0.233 0.228 0.049 0.041 34.02906667
NP/1X/15/B 0.45 0.441 0.092 0.076 65.89493333
NP/1X/15/C 0.331 0.325 0.069 0.057 48.5288
NP/1X/15/D 0.26 0.257 0.053 0.043 38.41733333
NP/1X/15/E 0.063 0.063 0.013 0.012 9.4192
NP/2X/15/A 0.447 0.438 0.091 0.078 65.4608
NP/2X/15/B 0.332 0.325 0.064 0.056 48.67146667
NP/2X/15/C 0.567 0.537 0.115 0.107 80.17306667
NP/2X/15/D 1.027 0.995 0.207 0.171 148.6904
NP/2X/15/E 0.176 0.17 0.036 0.033 25.3912
NP/4X/15/A 0.241 0.234 0.054 0.048 34.8256
NP/4X/15/B 0.12 0.113 0.026 0.021 16.8168
NP/4X/15/C 0.528 0.498 0.113 0.1 74.16853333
NP/4X/14/D 1.084 1.06 0.246 0.196 157.6885333
NP/4X/15/E 0.603 0.562 0.126 0.112 83.74293333

Raw spectrophotometry data.