TEMPORAL CYANOBACTERIA FLUCTUATIONS IN LAKE BALLARD
L. Brynn Davis Department of Ocean, Earth, and Atmospheric Science, Old Dominion University Field Studies 442 April 21, 2014
Abstract:
Cyanobacteria are a group of phytoplankton that strongly affect the biogeochemical cycling of nutrients in aquatic ecosystems. They provide the needed nitrogen compounds throughout the water column through the fixation of atmospheric nitrogen and have a competitive advantage in nitrate-depleted systems. To better understand the assemblage of the cyanobacteria community in
Lake Ballard, we identified the predominant genera based on morphological features and compared their distributions on two different days, 7 October 2013 and 19 March 2014. Abiotic factors such as nutrients, salinity, temperature, chlorophyll fluorescence, dissolved oxygen and extracted chlorophyll-a were considered in distinguishing the distributions observed. Planktothrix were the predominant genus making up 73% of cyanobacteria enumerated in October and
Aphancapsa were the predominant genus in March making up 64% of cyanobacteria enumerated. Cyanobacteria concentrations were much higher in October (26,028 ind./mL) than in March (3,855 ind./mL). Comparisons between the two days suggest a fall cyanobacteria bloom in October and a non-cyanobacteria bloom in
March due to the increased chlorophyll fluorescence and decreased cyanobacteria concentrations. The temporal cyanobacteria distributions emerged from these
results can help demonstrate how small-scale aquatic ecosystems model
cyanobacteria fluctuations worldwide.
Introduction:
Phytoplankton are the microscopic, photosynthetic foundation of the aquatic food web. As such, they are part of plant communities found in lakes all over the world
(Marshall 2009). Through photosynthesis, phytoplankton transform solar energy and nutrients from a physical water component to a usable energy source (Marshall 2009).
Phytoplankton groups differ in their nutritional and functional value for their grazers with some producing toxins that negatively affect the water quality and sustainability
(Litchman and Klausmeier 2008). Because of these characteristics and processes, phytoplankton play an important role in structure and ecosystem functioning on a wide range of scales from small lakes to the global oceans by serving as the principal control linking inaccessible nutrients to the upper trophic level predators.
The composition of the phytoplankton community strongly affects the biogeochemical cycling of nutrients (Litchman and Klausmeier 2008) in aquatic ecosystems. Many cyanobacteria (also called blue greens) produce the needed nitrogen compounds in the water column through the fixation of atmospheric nitrogen (Capone et al. 1997) and have a competitive advantage in low dissolved inorganic N (e.g. nitrate, nitrite, ammonium) waters (Nürnberg 2007). Heterocystous cyanobacteria (species including Cylindrospermum, Aphanizomenon, and Anabena) are nitrogen fixers as well as most filamentous cyanobacteria (Nürnberg 2007). However, species such as Oscillatoria,
Lyngbia and Planktothrix (Levine and Schindler 1999) are not nitrogen fixers and are expected to dominate in N-deficient systems (Blomqvist et al. 1994) because of their ability to out-compete other phytoplankton for benthic-ammonium (Nürnberg 2007;
Blomqvist et al. 1994). Vast amounts of research support conflicting hypotheses of the production of cyanobacteria, however on a seasonal basis, cyanobacteria tend to thrive in waters with low dissolved N (nitrate, nitrite, ammonium)(Nürnberg 2007). The biologically available proportion of nitrogen can become small over growing seasons and blue greens (with or without heterocysts) respond positively to nitrate deficient waters.
(Smith and Bennett 1999). Once more knowledge of these cyanobacteria distributions is available, general and detailed hypotheses can be tested within lakes and relationships can evolve connecting abiotic factors such as nutrients and climate to the annual variation of cyanobacteria blooms (Nürnberg 2007) even in small lakes such as Lake
Ballard.
Located in Portsmouth, Virginia, Lake Ballard began as a borrow pit in the late
1970’s and 80’s for neighboring construction companies, and is now a stratified brackish lake with nitrate limiting waters in the fall, and phosphate limiting waters in the spring
(Allen 2004; Cabatbat 2013). Many studies have been done on the abiotic factors of Lake
Ballard and once on the carbon biomass of the benthic microalgae (Pretty 2013). In 2013,
Davis began to look at the pelagic distribution of filamentous and non-filamentous phytoplankton. Additional analysis is required to further investigate the biological community of Lake Ballard. In this temporal investigation, cyanobacteria identification, abundance, and distribution were determined in samples taken from Lake Ballard in October 2013 and
March 2014. Environmental factors, such as nutrient availability, chlorophyll fluorescence, salinity, temperature, density, chlorophyll-a, and dissolved oxygen were evaluated to better understand the effects of chemical and physical factors on Lake
Ballard’s cyanobacteria distribution.
Materials and Methods
Study site- We took samples on the mornings of 7 October 2013 and 19 March 2014 from the deepest spot of Lake Ballard, located at N 36° 53.54 W 76° 23.92 (Figure 1).
CTD- An RBR XM-620 Conductivity, Temperature and Depth (CTD) was casted on both days from the surface to the bottom of the lake (12m). Salinity, temperature, dissolved oxygen and chlorophyll fluorescence data were used from the downcast to make profiles of Lake Ballard. Temperature and salinity were further used to construct density profiles with MATLAB.
Phytoplankton collection- Phytoplankton were collected using a peristaltic pump.
Unfiltered (125 mL) samples were collected at 1-meter depth intervals (0-8 meters) and transported in plastic bottles to the lab.
Lugol’s Acid- Samples were fixed with Lugol’s Acid, a staining preservative. Lugol’s Acid was added to samples to obtain a 1% solution or to the color of a “weak tea” (Zarauz and
Irigoien 2008). Samples were fixed for at least 24 hours before microscopy.
Inverted microscopy- We placed 200 μL from the fixed phytoplankton samples into separate chambers of the Lab-Tek Chambered Borosilicate Coverglass System. Each depth had two replicates (36 chambers total). The samples settled for one hour, and were then observed under the inverted microscope. Using 100x magnification, the phytoplankton were counted and categorized by morphological features such as shape, cell structure, and colonial form.
Phytoplankton Identification- We used A Key to the More Frequently Occurring
Freshwater Algae by (Bellinger and Siagee 2010) to help us categorize the phytoplankton into four genera based on morphological features: Aphanizomenon, Aphanocapsa,
Planktothrix, and Cylindrospermum. Aphanizomenon were identified by their circular heterocyst in the middle of the trichome (Figure 2a). Aphanocapsa were identified by their globular amorphous structure (Bellinger and Siagee 2010)(Figure 2b). Planktothrix displayed no heterocysts (Figure 2c). Cylindrospermum were identified as having a more ovular shaped heterocyst, compared to Aphanoizomenon, on the terminal end of the trichome (Figure 2d). The Shannon-Weaver Diversity Index was calculated for both days.
The cyanobacteria were then plotted against depth to display vertical distributions.
Nutrients- Simultaneous with the phytoplankton collection, filtered (0.45 μm) water samples (125 mL) were collected (0-12 meters) and transported to the lab where they were degassed for 30 minutes, then sent to Old Dominion’s Water Quality Lab for analysis.
Chlorophyll-a Water samples (125 mL) were collected at 1-meter depth intervals (0-
12m) using the peristaltic pump and taken to the lab for chlorophyll-a extraction according to the Welshmeyer (1994) method. The water samples were filtered through a
GFF filter, then placed into 15 mL Falcon tubes with 8 mL of 90% acetone, and stored in the freezer. After 48 hours, the Falcon tubes were sonicated with a Sonic 3000 dismembrator at 60% power in an ice bath for one minute in dim light to prevent damage to the chlorophyll pigment (Simon and Helliwell 1998). To prevent contamination between samples, the sonicator’s horn was cleaned with 90% acetone and the wash was collected with the corresponding sample. Thus, each sample held ~10 mL of 90% acetone. The sonicated samples were centrifuged with the IEC HN-SII centrifuge, for ten minutes (5600 x g), then were decanted into a cuvette and their fluorescence was read using a Turner Design 700 fluorometer.
Results
Shown in Figure 3 are the mean cyanobacteria concentrations against depth for
7 October 2013. Planktothrix was the most abundant genus making up 74%, (19,180 ind./mL) of the total cyanobacteria enumerated (Table 1, Appendix 1). They reached a maximum of 4,665 ind./mL (Appendix 1) at 4 meters. At 6m, they decreased to 500 ind./mL (Appendix 1) before becoming the most abundant genus again at 8m (Table 1,
Figure 3, Appendix 1). Aphanocapsa became the most abundant genus at 6m and 7m, making up 69%, (1,275 ind./mL) of cyanobacteria enumerated at 6m, and 72%, (622 ind./mL) of cyanobacteria enumerated at 7m (Table 1, Appendix 1). Aphanizomenon and
Cylindrospermum were the two least occurring genera in October, reaching a total of
2,268 ind/mL and 1,048 ind./mL respectively over the integrated water column
(Appendix 1). They decreased to 8 ind./mL and 5 ind./mL respectively at 7m and 8m
(Figure 3, Appendix 1) collectively making up only 5.2% of cyanobacteria enumerated at
7m and 1.1% of cyanobacteria enumerated at 8m (Table 1).
From the CTD data on 7 October 2013, the surface temperature was 25°C, then decreased with depth (Figure 4). With a rapid rate of change beginning at 2 – 3 meters, the thermocline occurred between 6-9 meters. At 10m, the water temperature remained at 10°C to the bottom of the lake. The halocline occurred between 8-10 meters.
Chlorophyll fluorescence increased with depth, peaked at 5m (0.32 μg/L), and then decreased with depth to 0.07 μg/L at 12m (Figure 4). Dissolved oxygen peaked twice to a relative 6.6 mg/L at 2m and 4m then decreased with depth (Figure 4). Dissolved oxygen dropped to a relative minimum of 0.09 mg/L at 9m then slightly increased at 12m.
The nutrient profiles on 7 October 2013, showed depleted waters until 7m where phosphate and ammonia concentrations began to increase (Figure 5). Phosphate reached a maximum of 1.1 mg/L at 12m and ammonia increased to a maximum of 16 mg/L at 12m. Nitrate began to increase at 11m, reaching a maximum of 0.5 mg/L at 12m
(Figure 5).
The cyanobacteria concentrations on 19 March 2014 are shown against depth in Figure 6. Overall the mean cyanobacteria concentrations over the integrated water column were less abundant (3,855 ind./mL) than October (26,028 ind./mL), (Appendix
1) and less diverse (Diversity index = 0.66 and 1.77 respectively). Excluding depths 1 and
2m, Aphanocapsa were the most abundant genus over the integrated water column making up 64%, (2,478 ind./mL) of the enumerated cyanobacteria (Table 1, Figure 6,
Appendix 1). Aphanocapsa reached a maximum of 465 individuals/mL (Appendix 1) at the surface making up 69% of the surface cyanobacteria enumerated (Table 1). The second most abundant genus on 19 March 2014 was Planktothrix making up 35%, (1,360 ind./mL) of cyanobacteria enumerated (Table 1, Figure 6, Appendix 1). Planktothrix were the most abundant genus at 1 and 2 meters making up 67%, (158 ind./mL) and 54%,
(165 ind./mL) of the enumerated population respectively (Table 1, Figure 6, Appendix 1). Not exceeding 18 individuals/mL (Appendix 1), throughout the integrated water column,
Aphanizomenon were only at the surface and 2m. Aphanizomenon made up 0.5% of the total cyanobacteria enumerated throughout the integrated water column (Table 1).
Cylindrospermum were not observed anywhere in the water column on 19 March 2014
(Figure 6, Table 1, Appendix 1).
There were notable differences in the CTD data from 19 March 2014 (Figure 7) relative to October (Figure 4). The water temperatures were overall colder, and did not change from 8.2°C for the first 6 meters. The temperature decreased to a minimum of 6°C at 8m, then rapidly increased to 7.6°C at 10m. A strong halocline occurred deeper at 9m, increasing rapidly to a maximum of 5 PPT at 10m. The greatest change in the CTD, relative to October, occurred in the chlorophyll fluorescence (Figure 7). Increasing with depth, fluorescence peaked to 47 μg/L at 8m. This large fluorescence peak was 146 times higher from the maximum of 0.32 μg/L observed on 7 October 2013 (Figure 4). The dissolved oxygen increased relatively with two maximums observed at 2m and 6m
(Figure 7).
Phosphate, ammonia and nitrate decreased from the surface to depletion at 2m
(Figure 8). Phosphate decreased from 0.5 mg/L, ammonia decreased from 3 mg/L, and nitrate decreased from 4 mg/L at the surface (Figure 8). While nitrate showed depletion to depth, phosphate and ammonia began to increase at 8m, maximizing at 12m to 1.5 mg/L and 16 mg/L respectively.
Chlorophyll α peaked at 9m to 8 fsu/mL on 7 October 2013 (Figure 9). In contrast, on 19 March 2014 the chlorophyll α reached a maximum of 9 fsu/mL at 7m. The chlorophyll fluorescence values from the CTD in March (47 μg/L) were not observed in the extracted chlorophyll-a values, however, after correlation analysis, both methods were significantly correlated (p=0.024).
The density profiles from both October and March are shown in Figure 10. The density profile from October was more stratified, increasing with depth, while the density profile in March was much more isopycnal (Figure 10).
Discussion:
In order to fully understand our temporal dynamics of the cyanobacteria at
Lake Ballard, it was important to consider past studies to gain any information that could assist with the new biological assemblages. Our data and past literature can suggest two things occurring in Lake Ballard relative to the cyanobacteria, a cyanobacteria bloom in the fall, and a non-cyanobacteria bloom in the spring.
From October’s nutrient depleted waters, and the predominant cyanobacteria genus, Planktothrix, over the integrated water column, we can suggest a fall cyanobacteria bloom. Species including Planktothrix, Oscillatoria, and Lyngbia (Levine and Schindler 1999) are not nitrogen fixers and are expected to dominate in N-deficient systems (Blomqvist et al. 1994) because of their ability to out-compete other phytoplankton for benthic-ammonium (Nürnberg, 2007; Blomqvist et al. 1994). We think this competitive advantage is what allows the Planktothrix to proliferate in Lake Ballard’s nutrient depleted waters and why the concentrations of nitrogen-fixing species such as
Aphanizomenon and Cylindrospermum are not as high. Lake Ballard is not the only lake to experience fall cyanobacteria blooms. The above patterns are shown in the hypereutrophic Fanshawe Lake in Ontario where cyanobacteria bloom with declining nitrate and nitrite concentrations throughout the summer and fall (Nürnberg 2007).
Vandermulen and Gemza (1991) found that cyanobacteria biomass and chlorophyll-a concentrations increased at the same time the nitrate concentrations were low. Lake
Ballard’s water’s are similar to the lake studied by Vandermulen and Gemza (1991), nitrate limiting in the fall (Cabatbat 2013) with increased cyanobacteria concentrations, occurring simultaneously with the fluorescence peaks.
Conversely in the spring, high chlorophyll fluorescence, high extracted chlorophyll-a concentrations and higher nitrate concentrations were observed while blue-green biomass concentrations were low (Vandermulen and Gemza 1992; Nürnberg
2007). These patterns are also seen within the late winter/ early spring of Lake Ballard.
Phytoplankton contain chlorophyll pigments which is why chlorophyll fluorescence and extracted chlorophyll-a are used as a proxy for the presence of phytoplankton. Since the cyanobacteria concentrations are less abundant in the spring, but the extracted chlorophyll-a and CTD reported chlorophyll fluorescence is much higher than in October, this suggests the presence of a non-cyanobacteria bloom. Located near Lake Ballard, Marshall (2009) showed phytoplankton follow a seasonal cycle in the
York River of Coastal Virginia. Beginning in the spring, several diatom species dominate the water column followed by a mixed algal composition in the summer and fall. We suspect the non-cyanobacteria bloom in the spring to be a diatom bloom based on past literature (Vanni and Tempte 1990; Marshall 2009). The diatoms could be the contributing factor to why the chlorophyll fluorescence observed by the CTD and extracted chlorophyll-a is much higher. The diatoms could be sinking due to their heavy silica frustules (Smetacek 1999) and become suspended by the halocline, thus, preventing them from reaching the bottom. The density profiles and the CTD suggest strong mixing occurring in the late winter creating an isopycnal profile. Small differences in mixing and abiotic factors such as light and nutrients can lead to switching between toxin producing cyanobacteria or others (Mischke 2003).
Estimates of detailed cyanobacteria abundances in eutrophic lakes are rarely available due to expense, and substantial expertise. Even imprecision and analytical errors during the general algal biomass estimates from chlorophyll a pigment analysis are skeptical due to temporal and spatial variation (Nürnberg 2007). Once more knowledge of these cyanobacteria distributions is available, general and detailed hypotheses can be tested within lakes and relationships can evolve connecting abiotic factors such as nutrients and climate to the annual variation of cyanobacteria blooms,
(Nürnberg 2007) even in small eutrophic lakes such as Lake Ballard. Understanding what abiotic factors affect the seasonality of the cyanobacteria in this small saline lake may help us understand what abiotic factors affect the seasonality of the foundation to the aquatic food web in the bigger the lakes and oceans.
Figures:
Figure 1: Satellite view of Lake Ballard in Portsmouth, Virginia. Forests and a suburban neighborhood directly surround Lake Ballard. The star indicates the location of the sampling location.
Figure 2: Images of cyanobacteria. A. Aphanizomenon cyanobacteria. B. Globular shaped Aphanocapsa (http://greenwaterlab.com/photo algal1.htm) C. Terminal heterocystic Cylindrospermum (http://protest.i.hosei.ac.jp/PDB/images/Prokaryotes/Nostocaceae/Cylindrospermum 3.html) and D. Planktothrix showing no visible heterocysts (http://www.cyanobacteria- platform.com/cyanobacteria.html).
Figure 3: 7 October 2013 cyanobacteria concentrations at depth from Lake Ballard.
Figure 4: CTD data from Lake Ballard on 7 October 2013. Values of dissolved oxygen are only relative due to technical complications with the CTD.
Figure 5: Nutrient profiles of phosphate, ammonia and nitrate on 7 October 2013 from Lake Ballard.
Figure 6: Cyanobacteria concentrations from 19 March 2014 at Lake Ballard.
Figure 7: CTD data from Lake Ballard on 19 March 2014. Values of the dissolved oxygen are only relative due to technical complications with the CTD.
Figure 8: Nutrient profiles of phosphate, ammonia and nitrate on 19 March 2014 from Lake Ballard.
Figure 9: Extracted chlorophyll-a depth profiles from both 7 October 2013 and 19 March 2014.
Figure 10: Density profiles from both October 2013 and March 2014 from Lake Ballard.
October(2013 March(2014
Depth AphanizoCylindroPlankto Aphano Mean(Conc.Aphanizo CylindroPlankto Aphano Mean(Conc. 0 20.8 7.3 69.8 2.1 1888 1.8 0.0 30.3 67.9 685 1 16.3 8.5 72.7 2.5 1703 0.0 0.0 67.0 33.0 235 2 10.9 3.6 85.5 0.0 3300 1.6 0.0 53.7 44.7 308 3 9.6 6.6 83.3 0.5 4578 0.0 0.0 32.1 67.9 490 4 6.5 2.7 88.6 2.2 5263 0.0 0.0 32.5 67.5 478 5 6.9 2.6 73.6 16.9 5458 0.0 0.0 33.0 67.0 455 6 2.4 1.9 27.0 68.7 1855 0.0 0.0 18.6 81.4 485 7 2.9 2.3 22.6 72.2 863 0.0 0.0 50.4 49.6 343 8 0.7 0.4 54.8 44.1 1123 0.0 0.0 27.8 72.2 378
Overall 8.7 4.0 73.7 13.6 26028 0.5 0.0 35.3 64.3 3855
Table 1: Depth integrated percentages of each genus and total cyanobacteria counted. Mean conc. equals number of cyanobacteria cells counted per mL of sample. Overall represents total percentage of each genus throughout the integrated water column. Aphanizo = Aphanizomenon. Cylindro = Cylindrospermum. Plankto = Planktothrix. Aphano = Aphanocapsa.
October(2013 March(2014
Depth AphanizoCylindro Plankto Aphano Mean(Conc.Aphanizo CylindroPlankto Aphano Mean(Conc. 0 393 138 1318 40 1888 13 0 208 465 685 1 278 145 1238 43 1703 0 0 158 78 235 2 360 120 2820 0 3300 5 0 165 138 308 3 440 303 3813 23 4578 0 0 158 333 490 4 343 140 4665 115 5263 0 0 155 323 478 5 378 143 4018 920 5458 0 0 150 305 455 6 45 35 500 1275 1855 0 0 90 395 485 7 25 20 195 623 863 0 0 173 170 343 8 8 5 615 495 1123 0 0 105 273 378
Overall 2268 1048 19180 3533 26028 18 0 1360 2478 3855 Appendix 1: Averaged raw data of the cyanobacteria concentrations enumerated over the integrated water column and at each depth. Concentrations are number of individuals counted per mL. Overall represents total cyanobacteria counted per genus over the entire water column. Aphanizo = Aphanizomenon. Cylindro = Cylindrospermum. Plankto = Planktothrix. Aphano = Aphanocapsa
References
Allen, S. 2004. Understanding Lake Ballard in Portsmouth Virginia through the applications of various field data collection and GIS techniques. Geog497. http://sci.odu.edu/oceanography/academics/undergrad/441_442/Current/ Papers/LakeBallard.pdf
Bellinger, E. G., and D. C. Sigee. 2010. A key to the more frequently occurring freshwater algae. Freshwater Algae: Identification and Use as Bioindicators. 137-244.
Blomqvist, P., A. Pettersson, P. Hyenstrand. 1994. Ammonium-nitrogen – a key regulatory factor causing dominance of non-nitrogen-fixing cyanobacteria in aquatic ecosystems. Arch. Hydrobiol. 132:141-164.
Cabatbat, A. 2013. Nutrient limitation bioassay of a coastal stratified lake. http://sci.odu.edu/oceanography/academics/undergrad/441_442/12_13/ca batbat1.pdf
Capone, D. G., J. P. Zehr, H. W. Paerl, B. Bergman, E. J. Carpenter. 1997. Trichodesmium, a globally significant marine cyanobacterium. Science. 276:1221-29.
Davis, L. B. 2013. 50 shades of irradiance: A study of phytoplankton and light dynamics at Lake Ballard. Unpublished. 15 pgs.
Levine, S. N., and D. W. Schindler. 1999. Influence of nitrogen to phosphorus supply ratios and physiochemical conditions on cyanobacteria and phytoplankton species composition in the Experimental Lakes Area, Canada. Can J. Fish. Aquat. Sci. 56:451-466
Litchman, E., and C. A. Klausmeier. 2008. Trait-based community ecology of phytoplankton. Annu. Rev. Ecol. Evol. S. 39:615-639.
Marshall, H.G. 2009. Phytoplankton of the York River. J. Coastal Res. 57:59-65.
Mischke, U. 2003. Cyanobacteria associations in shallow polytrophic lakes: influence of environmental factors. Acta Oecol. 24:S11-S23
Nürnberg, G. K. 2007. Low-Nitrate-Days (LND), a potential indicator of cyanobacteria blooms in a eutrophic hardwater reservoir. Water Qual. Res. J. Can. 42:269-283.
Pretty, J. 2013. Carbon biomass of benthic microalgae in Lake Ballard. http://sci.odu.edu/oceanography/academics/undergrad/441_442/12_13/p retty1.pdf
Simon, D., and S. Helliwell. 1998. Extraction and quantification of chlorophyll a from freshwater green algae. Water Res. 32:2220-2223.
Smetacek, V. 1999. Diatoms and the ocean carbon cycle. Protist. 150:25-32.
Smith, V. H., and S. J. Bennett. 1999. Nitrogen: phosphorus supply ratio and phytoplankton community structure in lakes. Arch. Hydrobiol. 146:37-53.
Vandermulen, H. and A. Gemza. 1991. Fanshawe Lake: The need for water quality management in southern Ontario reservoirs. Ontario Ministry of the Environment, Toronto, log. 91:2345-058.
Vanni, M. J., and J. Temte. 1990. Seasonal patterns of grazing and nutrient limitation of phytoplankton in a eutrophic lake. Am. Soc. Limnol. Oceanogr. 35:697-709.
Welshmeyer, N. A.1994. Fluormetric analysis of chlorophyll a in the presence of chlorophyll b and phaeopigments. Limnol. Oceanogr. 39: 1985-1992.
Zarauz, L., and X. Irigoien. 2008. Effects of Lugol’s fixation on the size structure of natural nano-microplankton samples, analyzed by means of an automatic counting method. J. Plankton Res. 30:1297-1303.
Analysis of Groundwater Flow in Hoffler
Creek Wildlife Preserve
Robert Zielinski
Old Dominion University, Department of Ocean Earth and Atmospheric Sciences
1
Abstract
The purpose of this study was to analyze groundwater flow of the Hoffler Creek Wildlife
Preserve through computer modeling techniques. Using different methods, past studies have produced similar results, indicating that groundwater flows from the east end of the wildlife preserve, through the surficial aquifer and Lake Ballard towards Hoffler Creek at the west end of the preserve. To examine groundwater flow in this study, I created a three-dimensional model using software created by Groundwater Vistas. I used hydraulic conductivity values (k) determined in past studies, properties of the area’s surficial and confined aquifers, and atmospheric data to create and calibrate the model. The method of creating a model begins with gathering accurate topography data, followed by applying the known characteristics of the area and is completed by calibrating the model using head data. The results of this model confirm past studies’ work which indicated, in general, ground water moves from east to west through the surficial aquifer. After Hurricane Irene, however, flow direction on the eastern side of Lake
Ballard reversed, such that flow was outward from the lake in all directions. The results of the model indicated that the eastern end of the lake has again reversed its flow direction as of March,
2014. The scope of the model is simply to find flow direction and does not indicate quantity of flow. The benefit of using a computer model is its visual representation of results and accurate spatial analysis.
Introduction
Hampton Roads has undergone exponential growth in the past century and, as with the increase of any major urban area, the understanding of the area’s groundwater resources needs to grow proportionally. The first step in accounting an area’s groundwater resources is to examine the underlying stratigraphy, which for Hampton Roads can be summarized as multiple layers of
2 unconsolidated sediment (Cederstrom 1946) (Fig. 1). These massive sheets of sand with mud beds commonly found in between are the common package of layers for a depositional coastal environment (Cederstrom 1946). Each layer of the coastal plain has a different composition due to changes in sea level and, therefore, depositional environments. Lower energy environments allow sediment of smaller grain size, such as silt and clay, to settle out. Higher energy environments, such as beach faces, deposit and rework sand, keeping the silt and clay in suspension. Depending on grain size and sorting, some beds have low porosity, and do not let much water through, and are associated with beds with high clay content. For example, the bottom of the Columbia aquifer has a thin clay bed that starts the fining upward sequence that includes the Tabb formation. The beds with coarser grained, less compact sediments have a higher porosity and permeability, meaning they can hold more water and let it move more easily.
Some layers, such as the Yorktown formation in this area have a bed of very low permeability confining water from percolating downward into it at any significant rate. Within southeastern
Virginia, the Yorktown Formation consists of fossiliferous marine silty fine sand and cross bedded, biofragment and sand (Peebles 1984). Other beds are unconfined, meaning they are surficial with no confining bed above.
Figure 1: Stratigraphy of the Virginia coastal plain (Cederstrom 1946)
3
Hoffler Creek Wildlife Preserve (Fig. 2) is located in Portsmouth, Virginia. The preserve is constrained on the western and southern boundaries by Hoffler Creek with its eastern and northern boundaries bound by residential area. At the center of the preserve is Lake Ballard, a man-made lake, constructed as a borrow pit in the late 1970’s and early 1980’s. Construction took place in two phases, the first of which dug through the surficial aquifer of the Tabb formation, to a maximum depth of 8 meters. Pumping of the pit for excavation drew in saline water from the Lafayette, causing local private wells to run salty and complaints put a hold on pumping. The second phase excavated the borrow pit to a depth of 12 meters and reached the confined aquifer of the Yorktown formation. Pumping of the pit associated with the second phase caused many more private wells to run salty and promoted a groundwater survey of the area which found pumping had caused a saline intrusion from the surrounding salt water basin
(Whittecar et al. 2005). The pit has since filled with water and become a low salinity lake.
Freshwater flows through the surficial aquifer, mixing with saline water from the stratified lower level of the lake and exits the western end as brackish water (Whittecar et al. 2005).
Figure 2: Hoffler Creek Wildlife Preserve (Google Maps)
4
The hydraulic head data from the wells surrounding the lake suggests there is a hydraulic gradient sloping from the east end of the lake towards Hoffler Creek (Seger et al. 2012). Water moves from areas with high hydraulic head to areas with low hydraulic head and flows along the path of least resistance. The lake provides a path of extremely low resistance compared to flow through the sands of the Tabb and Yorktown formations. Water flows into the lake from the eastern boundary, and out of the lake along the western boundary, due to the eastern end having a higher hydraulic head. The western boundary of the lake is significant for its relatively higher elevation and coarser composition. The higher elevation of the land surfaces allows for a raised water table in the western boundary of the lake between the creek and the lake. Hydraulic conductivity is a key property involved in assessing an aquifer's potential yield as well as its susceptibility to contamination (Aller et al. 1987). Details of steady-state flow in regional groundwater basins can be investigated using digital computer solutions of appropriately designed mathematical models, such as Darcy’s Law (Freeze 2010). The goal of this study was to compare a modeled analysis of groundwater flow within the Hoffler Creek Wildlife Preserve to past empirical studies of the preserve’s groundwater.
In 2005, a study was conducted at Hoffler Creek Wildlife Preserve regarding the direction of groundwater flow in the surficial aquifer and it was determined through twenty-seven
Schlumberger soundings (Whittecar et al. 2005). Schlumberger soundings use electrical resistivity over varied distances to estimate the salinity of the water. These salinity values were then mapped and contoured. The salinity pattern indicated that fresh water was entering the system from the east and leaving the system more saline on the western end.
In 2012, another study of the groundwater surrounding Lake Ballard was conducted using an analytical method by Segar and colleagues. They gathered hydraulic head data from the
5 existing wells and two newly constructed wells. They used Darcy’s law, which allows calculation of hydraulic gradient between two wells, to calculate the hydraulic gradient.
Figure 3: Darcy’s Law (Wikipedia)
The results indicated groundwater was moving out of the lake in all directions (Segar et al.
2012). This result was explained by the higher head in the lake created when hurricane Irene caused overflow from Hoffler Creek to wash over the over the southern boundary of Lake
Ballard.
Methods
For coastal communities, understanding groundwater flow is essential to ensure adequate freshwater resources are available. One of the best ways to understand the groundwater resources in your area is to create a model. In general, models are conceptual descriptions or approximations that describe physical systems using mathematical equations; they are not exact descriptions of physical systems or processes (Kumar 2002). Models are created for two reasons.
The first is to analyze data collected in the past, and the second is to predict future effects from changing environmental factors. This analysis focused on the former. Groundwater data for the area surrounding Lake Ballard was collected by past OEAS 441/442 classes on a semi-regular basis. Data was taken monthly with the exception of a few months for the past 5 years. Summer data was most likely absent from the record due to lack of the field studies courses. Taking depth
6 to water measurements combined with the top of the well elevation data found in well construction reports, I calculated recent head levels for the surficial aquifer in the month of
March 2014.
With the computer modeling software, Groundwater Vistas, I created a steady-state representation of Hoffler Creek Wildlife Preserve. The software interface, shown in Figure 3, allows the user to input the grid size, number of layers, hydraulic conductivity values (k), and a recharge rate.
Figure 3: Software interface (Groundwater Vistas)
Hydraulic conductivity
Using the Hvorslev Method, Robert Murray conducted the hydraulic conductivity pump test on the Tabb formation in 2012-13, and I conducted the hydraulic conductivity pump test on the
Yorktown formation in fall 2013. In order to determine hydraulic conductivity values in the vicinity of Hoffler Creek Wildlife Preserve, Murray and I followed the methodology first
7 proposed by Hvorslev (1951). The idea behind the Hvorslev method is that one can estimate the hydraulic conductivity of an aquifer by recording the hydrostatic time lag in a well. Hydrostatic time lag is defined as the time required for water to flow into or out of the well until a desired state of equilibrium has been achieved after a disturbance to the level of the water within the well
(Hvorslev 1951). Once hydraulic conductivity has been determined, it is then possible to make such estimations as sediment grain size within the unconsolidated surficial aquifer that is the
Tabb formation (Whittecar et al. 2005) (Odong 2007). It is important to note that this method is designed to produce the horizontal component of hydraulic conductivity.
I performed both a bail down test and a slug test. In a slug test, a known volume was added to a well causing the water level in the well to rise. The rate of fall of the water level was recorded by a pre-staged Solinst levelogger pressure transducer. In a bail-down test, a volume of water is removed from the well causing the water level in the well to drop. The rate of water level rebound is recorded by the pre-staged Solinst levelogger pressure transducer (Hvorslev
1951). For this study, four wells were selected, providing spatial distribution within the study area.
Figure 5: The locations of the wells sampled at Hoffler Creek Wildlife Preserve 8
I then combined the averages of the Tabb and Yorktown formation’s hydraulic conductivity to estimate a hydraulic conductivity of the area as a whole, 1.85x10-4 m/s, using one of the functions in the software package. This was an iterative operation conducted until a graphical model, consistent with our own conceptual model, was produced. This approach took into account the understanding that regional package k values are higher than the parts due to much larger pore and fracture spaces regionally than locally.
Other Model Parameters
I overlaid an image taken from Google Earth on the model to insert the accurate dimensions of Lake Ballard in relation to the preserve. I set a manual offset of 50 meters to avoid problems computational software has surrounding the value of zero. I set the lake level to 51.25 meters, rather than 1.25 meters above sea level as indicated as a base level during a previous study (Whittecar et al. 2005). The river and surrounding wetlands were set as a constant head boundary of 50 meters, equivalent to sea level with the manual offset. Outside of the model I calculated the average monthly precipitation data for the last 10 years collected by the Norfolk
Naval Base, to calculate the recharge for the area. Recharge is the amount of water added to an aquifer over a period of time, which is the amount of precipitation minus the evapotranspiration.
Using the Thornthwaite equation, as shown in Figure 4, and the mean monthly temperature data from the Norfolk Naval Base station, I calculated the evapotranspiration.
9
Figure 4: Thornthwaite equation and variable legend (Thornthwaite 1948)
I subtracted the evapotranspiration value from the precipitation value and determined an average annual recharge in this area of 0.43 meters. I converted the average annual recharge to an average of 1.37x10-8 m/s of recharge.
Results
Hydraulic conductivity
In fall 2012 and spring 2013, Murray produced hydraulic conductivity values for eight wells screen in the Tabb formation (Table 1). The average k value for the Tabb formation was in the range 4x10-4 m/s.
Table 1: Tabb formation k results
In fall 2013, I compiled the k values into a table (Table 2). The highest k value (1.80x10-3
10 m/s) was found at well 22 followed by well 20 (well 20 value). Well 7 had the lowest k value,
2.52x10-4 m/s, of all four wells. The difference between the student constructed wells, 6 and 7, and the VDOT constructed wells, 20 and 22, was an order of magnitude. The average k value for the Yorktown formation was in the range of 3x10-4 m/s. K values of the same magnitude produced relatively the same results.
Trial Averages Trial 1 (m/s) Trial 2 (m/s) Trial 3 (m/s) (m/s) Well 6 Slug Test 2.01E-05 2.16E-05 2.01E-05 2.06E-05 Well 6 Bailer Test 1.29E-04 9.38E-05 1.03E-04 1.09E-04 Well 7 Slug Test 4.44E-05 3.56E-04 3.56E-04 2.52E-04 Well 7 Bailer Test 5.93E-05 4.44E-05 5.93E-05 5.43E-05 Well 20 Slug Test 5.93E-05 3.56E-04 5.93E-05 1.58E-04 Well 20 Bailer Test 8.88E-04 5.93E-04 1.80E-03 1.09E-03 Well 22 Slug Test 1.80E-03 1.80E-03 1.80E-03 1.80E-03 Well 22 Bailer Test 1.80E-03 1.80E-03 1.80E-03 1.80E-03 Table 2: Yorktown formation k results. 6&7 are student wells and 20&22 are VDOT wells
The final holistic k value for the models aquifer was 1.85x10-4 m/s. This eliminates the need for vertical components of hydraulic conductivity between the two formations.
Model Output
I generated a groundwater flow map, using Groundwater Vistas, for the area. Figure 6 shows the bitmap overlaid for a better spatial representation and Figure 7 is annotated with the direction of groundwater flow. When contour lines are close together, it indicates that there is a larger gradient and greater groundwater flow. Groundwater flows perpendicular to the contour lines. Using head data from the four wells, labeled on Figures 6 and 7, I calibrated the model to match the contour lines to the recorded head levels in the wells.
11
Figure 6: Dark blue indicates Hoffler Creek and the surrounding wetlands. Light blue is Lake
Ballard. Red contour lines connect points of the same hydraulic head values, that is, the elevation the water is in relation to sea level. Everything outside of the dark blue on the western and southern boundaries is part on another groundwater system and not scope of this model.
Figure 7: Groundwater flow map with black arrows indicating direction of flow, numbers indicate head level or elevation of groundwater above sea level plus the 50 ft offset
Discussion
The model results positively correlated with recent head levels for the months of
12
February and March 2014, indicating that the lake has returned to its previous pre-Irene flow pattern. Figure 8 shows the flow pre and post hurricane Irene, indicating that the east end of the lake reversed flow, but not to the extent it has in 2014 where flow from the Northeastern part of the lake is being drawn back West. The flow paths indicated in Figure 6 represent the paths with the highest gradients in and out of the lake. The area that has the highest flow rate is the west end of the lake with more tightly grouped contour lines. The area with highest flow rate into the lake corresponds with the area that has the greatest elevation change on the eastern end of the lake, near well 1.
Figure 8: The yellow arrows represent groundwater flow soon after Hurricane Irene and the
Blue arrows indicate the groundwater flow direction in March, 2012.
Future Work
Future analysis of the model could increase the detail by decreasing the size of the individual grid boxes. The results would not change the general pattern of the flow, but would increase the accuracy of the projected gradients. Alternatively, if the recharge was stepped through the model on a monthly basis, as opposed to an annual steady-state basis, it would allow for the reflection of season changes in the flow patterns.
13
References
Aller, L., J. H. Lehr, and R. Petty. 1987. Drastic: A standardized system to evaluate ground water pollution potential using hydrogeologic settings. U.S. Environmental Protection Agency.
Cederstrom, D. J. 1946. Genesis of ground waters in the Coastal Plain of Virginia. Econ. Geol. 41-3: 218-245.
Fetter, C. W. 2001. Applied Hydrogeology. 4th ed. Upper Saddle River, NJ: Prentice-Hall Inc.
Freeze, R. A., and P. A. Witherspoon. 1967. Theoretical analysis of regional groundwater flow: 2. Effect of water-table configuration and subsurface permeability variation. Water Resour. Res. 3-2:623–634.
Hvorselv, M. J. 1951. Time lag and soil permeability in ground-water observations. (Bulletin No. 36). Vicksburg, MS: U.S. Army Corps of Engineers.
Kumar, C. P. 2002. Groundwater flow models. Scientist ‘E1’National Institute of Hydrology Roorkee–247667 (Uttaranchal) publication.
Murray, R. 2013. Analysis of Hydraulic Conductivity of the Surficial Aquifer at Hoffler Creek Wildlife Preserve. OEAS 442.
Odong, J. 2007. Evaluation of empirical formulae for determination of hydraulic conductivity based on grain-size analysis. J. Am. Sci. :54-60.
Peebles, P. C., G. H. Johnson, and C. R. Berquist. 1984. The middle and late Pleistocene stratigraphy of the outer coastal plain, southeastern Virginia. Virginia Minerals. 30:13-22.
Seger et al. 2012. Hydrologic cycle in Lake Ballard: Rate of return to pre-hurricane Irene conditions. OEAS 442.
Whittercar, G. R., and A. A. Nowrozzi. 2005. Delineation of saltwater intrusion through a coastal borrow pit by resistivity survey. Environ. Eng. Geosci. 11:209-219.
14
Temporal dynamics of cyanobacteria in Lake Ballard
Mary Wolfrey Department of Ocean, Earth, and Atmospheric Science, Old Dominion University, Norfolk, VA Field Studies, Spring 2014 21 April 2014 Abstract
Cyanobacteria play a major role in the environment on a small ecosystem scale by impacting water quality of many lakes and reservoirs around the world, as well as a large global scale by regulating atmospheric carbon dioxide. Since phytoplankton are dependent on abiotic factors that can vary temporally, such as nutrients for photosynthesis, their concentrations can also vary in temperate latitudes. In October and March, phytoplankton were identified into different genera and counted under a microscope, chlorophyll-a concentrations were measured, nutrient levels were analyzed, and a CTD was cast to examine water properties. I hypothesized that cyanobacteria concentrations would follow the phytoplankton successional pattern that is seen in the coastal waters of Virginia. The nitrogen-limited waters and increased cyanobacteria concentrations suggest a potential cyanobacteria bloom in October. The phosphate-limited waters, increased chlorophyll fluorescence, and decreased cyanobacteria concentrations suggest a potential non-cyanobacterial bloom in March. The results from this study suggest that cyanobacteria in Lake Ballard follow a similar temporal pattern as larger environments in surrounding coastal waters.
Introduction
Phytoplankton are a diverse group of microscopic organisms that are able to produce their own biomass and chemical energy from carbon dioxide, water, and various inorganic
1 nutrients. Their utilization of carbon dioxide during the process of photosynthesis causes our oceans to act as a major sink for atmospheric carbon dioxide (Hopkins et al. 2010).
Phytoplankton are most commonly defined as either photosynthetic protists or cyanobacteria
(Litchman 2008). Diatoms are a major group of photosynthetic protists that have a great impact on the flux of carbon into the deep sea as a direct result of their fast sinking rates due to heavy silica based frustules. Some photosynthetic protists, such as dinoflagellates, can actually negatively impact their ecosystem by producing toxins.
Cyanobacteria are also a functional group of phytoplankton that are capable of producing useable forms of nitrogen from atmospheric N2. The ability to fix atmospheric nitrogen gives certain cyanobacteria, such as Cylindrospermum, an advantage in competition with other phytoplankton groups when dissolved inorganic nitrogen is depleted (Nürnberg 2007). However, not all cyanobacteria, such as the non-heterocystic Planktothrix, are capable of nitrogen-fixation.
These non-nitrogen-fixing cyanobacteria also have an advantage in low nitrogen systems due to their ability to out-compete for benthic ammonium through vertical migration (Nürnberg 2007).
Cyanobacteria blooms can often be toxic and create water quality issues for many reservoirs around the world, such as Fanshawe Lake in Ontario (Nürnberg 2007). Nitrogen is limiting in Fanshawe Lake in the summer and fall; therefore, cyanobacteria are out-competing other phytoplankton and are increasing into blooms. A 2007 study by Gertrud Nürnberg related days when the nitrate concentrations were below the lake-specific threshold to cyanobacteria blooms and found that those low-nitrate-days agreed with increased cyanobacteria biomass.
Since quantifying cyanobacteria abundances can be expensive and time intensive, understanding the nitrogen levels in an ecosystem could indirectly determine when a toxic bloom will occur.
2
Typically in the coastal waters of Virginia, the changing of seasons produces a general bi-modal pattern of phytoplankton abundance, having a large phytoplankton peak in the spring, followed by a decline into the summer, and then a smaller peak in the fall (Marshall 2009). Also in coastal Virginia, such as in the York River, there is a seasonal succession of phytoplankton.
The first spring peak in phytoplankton is dominated by diatom species and the second fall peak is dominated by cyanobacteria (Marshall 2009).
This pattern in phytoplankton succession agrees with local nutrient patterns. Joyce Strain determined that Hoffler Creek, a tidal estuary in coastal Virginia, is a nitrogen-limited system in the fall (Strain 2009). Then, Alex Cabatbat and Cory Shumate confirmed that Lake Ballard, a lake located adjacent to Hoffler Creek, is similarly a nitrate limited system in the fall and phosphate limited in the spring (Shumate 2009). These higher levels of nitrogen in the spring give phytoplankton, such as diatoms, an advantage, and the lower levels of nitrogen in the fall give cyanobacteria an advantage.
Lake Ballard is a brackish lake located within the coastal waters of Portsmouth, Virginia
(Figure 1). It is the result of an excavated borrow pit by the Virginia Department of
Transportation (VDOT) (Whittecar et al. 2005). Although Lake Ballard does not sustain higher aquatic trophic levels, it does support an abundant phytoplankton community. The objective of this temporal study is to identify the predominant genera of cyanobacteria in Lake Ballard and understand how various abiotic factors such as nutrients, temperature, and salinity affect their abundances and diversity in the early fall and late winter.
Methods
On the mornings of 7 October 2013 and 19 March 2014, phytoplankton samples were collected at the lake’s deepest spot in 125mL Nalgene bottles using a peristaltic pump at every
3 meter from 0m to 8m. In the lab, Lugol’s Acid was added to each sample to simultaneously fix and stain the phytoplankton. Enough Lugol’s Acid was added to each sample to obtain a 1% solution, or until it was the color of “weak tea” (Zarauz and Irigoien 2008). The samples were then stored in the dark until they were counted.
To count the phytoplankton, a fixed water sample was mixed by gently inverting it 5 times and 0.2mL of the sample was pipetted into a well of a Lab-Teck 8-chambered Borosilicate
Coverglass Settling System. Each well settled for one hour, and was visualized using an inverted microscope. Two wells were counted for each depth. The samples were observed under 100x magnification and phytoplankton were counted and classified into genera based on morphological features determined from A Key to the More Frequently Occurring Freshwater
Algae (Bellinger and Sigee 2010). Aphanizomenon were filamentous in shape and contained a circular heterocyst in the middle of the filament (Figure 2a). Cylindrospermum were also filamentous in form, but they contained an oval shaped heterocyst on the terminal end of the filament (Figure 2c). Planktothrix were filamentous but contained no heterocysts (Figure 2d).
Aphanocapsa were non-filamentous and formed globular gelatinous colonies (Figure 2b).
A Shannon-Weiner diversity index (H’) was then used to analyze the diversity of the cyanobacteria within the integrated water column between the two sampling days.
∑ ( )
H’: index of diversity : total # of species R: # species in community
: ratio of nth species to total species
4
Unfiltered water samples were simultaneously collected in 125 mL Nalgene bottles for chlorophyll-a analysis. In the lab, each sample was vacuum filtered onto a GF/F filter, then placed in a Falcon tube to which 8mL of 90% acetone was added. Analytical blanks consisted of an unused GF/F filter and 8 mL of 90% acetone. Each sample was sonicated for 60 seconds with a Sonic 300 Dismembrator probe. Next, the samples were centrifuged (5,600 x g) for ten minutes with an IEC HN-SII centrifuge to ensure that any extra material from the filter was packed into the bottom of the Falcon tube. Lastly, the liquid in the Falcon tubes was decanted into 9mL cuvettes and then measured for chlorophyll-a in a TD-700 Fluorometer (Welschmeyer 1994).
Water samples were collected at the deepest spot of the lake from 0m to 12m using a
0.45µm filter on a peristaltic pump. The filtered water samples were then degassed in the lab for
30 minutes to remove any hydrogen sulfide from the samples, frozen, and sent to Old Dominion
University’s Water Quality Lab to measure orthophosphate, nitrate, and ammonia.
An RBR XM-620 Conductivity, Temperature, and Depth (CTD) instrument was lowered, and the data from the downcast were used to obtain salinity, temperature, fluorescence, and dissolved oxygen profiles through the water column. Salinity and temperature was also used to establish density profiles of the water column using Matlab.
Results
In October, Planktothrix was the dominant genus in the water column from 0m to 5m making up 70 % to 89% of the total cyanobacteria counted (Figure 3 & 5). Below 5m,
Aphanocapsa was the dominant genus ranging from 44% to 69% of the total cyanobacteria counted (Figure 3 & 5). Cylindrospermum was consistently least abundant at all depths sampled and ranged from 0.4% at 8m to 9% at 1m (Figure 3 & 5). In March, Aphanocapsa was the most predominant genus at the surface and below 3m, ranging from 67% to 81% (Figure 4 & 5).
5
Planktothrix was the most abundant from 1m to 2m at 54% to 67%. Cylindrospermum was not present at any depths (Figure 4). Aphanizomenon was only present at 0m and 2m and was only
2% of the cyanobacteria counted at both of these depths (Figure 4 & 5). The mean cyanobacteria concentrations were overall greater through the integrated water column in October (26,028/mL) than March (3,855/mL) (Figure 5 & Appendix 1). October also had a higher calculated Shannon-
Weiner diversity index (1.77) than March (0.66).
Chlorophyll-a fluorescence was depleted at the surface on both sampling days. In
October, the chlorophyll maximum was 7.7 fsu/mL at 9m and in March the chlorophyll maximum was at 8.6 fsu/mL at 7m (Figure 6).
Ammonia, nitrate, and phosphate were all depleted at the surface of Lake Ballard in
October (Figure 7). Ammonia increased below 6m to a maximum concentration of 17 mg/L at depth, nitrate increased below 10m to a maximum concentration of 0.5 mg/L at depth, and phosphate increased below 7m to a maximum concentration of 1.1 mg/L at depth (Figure 7). In
March, ammonia, nitrate, and phosphate all had increased concentrations at the surface (0m)
(Figure 8). Below 0m, ammonia remained depleted until 8m where it increased until a maximum concentration of 16 mg/L at depth, nitrate remained depleted until depth, and phosphate remained depleted until 10m where it increased until a maximum concentration of 1.5 mg/L
(Figure 8).
On both days, there was a strong halocline in the water column, beginning at 8m
(October) or 10m (March), and both increasing in salinity from 3ppt to 5ppt (Figure 9 &10). The thermocline in October experienced a much larger change in temperature from 24ºC to 10ºC. In
March, the temperature only dropped from 8ºC to 6ºC (Figure 9 & 10). Dissolved oxygen had a minimum at 8m on in October, and in March dissolved oxygen concentration was greater overall
6 and had a minimum at 10m (Figures 9 & 10). Fluorescence only reached a maximum of 0.3μg/L at 4.5m in October, but in March, fluorescence reached a maximum of 45 µg/L at 8m (Figures 9
& 10). This drastic increase in fluorescence from the CTD was not present in the extracted chlorophyll-a samples (Figure 6). The two methods were analyzed for correlation and were significantly correlated (p: 0.024). In October, density gradually decreased until the pycnocline at 5m (Figure 11). In March, the density was isopycnal until the strong pycnocline at 9m (Figure
11).
Discussion
The presence of nitrogen-fixing cyanobacteria, Aphanizomenon and Cylindrospermum, in
Lake Ballard during October agrees with the lack of nitrate in the water column. Since these two genera are capable of the fixation of atmospheric nitrogen, their need for dissolved inorganic nitrogen in the water column is not as necessary as it is for phytoplankton that lack this ability, and are more likely to thrive in an environment that is nitrogen limiting. The presence of non- nitrogen-fixing cyanobacteria, Planktothrix and Aphanocapsa, in Lake Ballard agrees with the lack of ammonia in the water column. Since these two genera are more efficient at utilizing benthic ammonia through vertical migration, they are also more likely to thrive in an environment that is nitrogen limiting, such as Lake Ballard. The decrease in dissolved oxygen from 6m to 8m possibly indicates less oxygen production by photosynthesizing organisms such as cyanobacteria, which agrees with our decrease in cyanobacteria abundance below 5m. The pycnocline which also begins at this depth may be inhibiting the migration of cyanobacteria to deeper depths, as well as the mixing of oxygen in the water column.
The higher concentrations of cyanobacteria in October suggest that a bloom may have been occurring in Lake Ballard. Since inorganic nutrients are needed during the process of
7 photosynthesis, the exhausted nutrients in the water column also support that an increased amount of photosynthesis was happening in October. Specifically, because the lake is nitrogen- limiting in the fall (Shumate 2013), cyanobacteria were most likely the group of phytoplankton blooming during this time. Cyanobacteria blooms are a common occurrence in temperate latitudes and happen annually during the early fall in similar nitrogen-limiting lakes such as
Fanshawe Lake in Ontario and in surrounding areas such as the York River (Nürnberg 2007;
Marshall 2009).
In March, the lower cyanobacteria concentrations in the water column may indicate that a cyanobacterial bloom was not occurring in Lake Ballard. Nutrients, specifically nitrate, were higher in concentration at the surface. With greater levels of nitrate, cyanobacteria, especially nitrogen-fixers, lose their competitive advantage and can be out-competed by other types of phytoplankton. This unfavorable condition for cyanobacteria could also explain why their diversity in March was less than October. However, since the amount of chlorophyll in a system can be used as a proxy for phytoplankton abundance, the higher levels of chlorophyll-a fluorescence and total fluorescence from the CTD suggest that a non-cyanobacterial bloom was occurring in Lake Ballard. Also, the pycnocline did not occur until 9m, allowing for greater mixing of nutrients into the upper water column. However, since nutrients were depleted below the surface from 2m to 8m, this suggests that an excess of photosynthesis was resulting in the depletion of nutrients seen in the water column at these depths. The nearby York River experiences an annual spring bloom with the maximum levels phytoplankton abundance and biomass occurring in March (Marshall 2009). This bloom is largely composed of diatom species.
During 1999, Lake Ballard also saw its maximum levels of phytoplankton abundance during
March; however, this bloom was composed primarily of chlorophytes (Wolny 1999). Eukaryotic
8 phytoplankton, such as diatoms and chlorophytes, both have a more efficient nitrate reductase than prokaryotic cyanobacteria, giving them an advantage during higher nitrate concentrations as seen in Lake Ballard during the spring (Ferber et al. 2004).
From impacting the quality of water on a smaller scale to impacting the global atmospheric composition on a larger scale, phytoplankton play an essential role in our environment (Nürnberg 2007; Behrenfeld et al. 2008). The coastal waters of Virginia have a bi- modal pattern of phytoplankton abundance, having a large peak in phytoplankton in the spring and then a smaller peak in the fall (Marshall 2009). Specifically cyanobacteria are the dominant type of phytoplankton in that fall bloom. This temporal study suggests a similar pattern for cyanobacteria in Lake Ballard. Developing a more in depth study on the temporal dynamics of all types of phytoplankton in the lake would provide a small scale template for phytoplankton succession for similar brackish waters throughout temperate latitudes.
9
Figures
Figure 1. Lake Ballard with respect to surrounding areas. The Deep Spot pin shows the location of all samples collected for the study.
Figure 2. Identified cyanobacteria. (a) Aphanizomenon. (b) Aphanocapsa [http://greenwaterlab.com/photo_algal_1.htm]. (c) Cylindrospermum [http://protist.i.hosei.ac.jp/PDB/images/Prokaryotes/Nostocaceae/Cylindrospermum_3.html]. (d) Planktothrix [http://www.cyanobacteria-platform.com/cyanobacteria.html].
10
Figure 3. Phytoplankton concentrations against depth on 7 October 2013.
Figure 4. Phytoplankton concentrations against depth on 19 March 2014.
11
Figure 5. Percentage of each genus counted at specified depth. Aphanizo=Aphanizomenon. Cylindro=Cylindrospermum. Plankto=Planktothrix. Aphano=Aphanocapsa. Mean Conc is the total number of cyanobacteria per mL at the specified depth. Overall is the overall percentage of the specified genus through the integrated water column and the overall total number of individuals for Mean Conc.
Figure 6. Chlorophyll-a fluorescence against depth at Lake Ballard in October and March.
12
Figure 7. Ammonia, phosphate, and nitrate concentrations in Lake Ballard on 7 October 2013.
Figure 8. Ammonia, phosphate, and nitrate concentrations in Lake Ballard on 19 March 2014.
13
Figure 9. CTD data for 7 October 2013 plotted against depth.
Figure 10. CTD data for 19 March 2013 plotted against depth.
14
Figure 11. Density of Lake Ballard on both sampling days.
15
Appendix
Appendix 1. Average numbers of cyanobacteria counted per mL against depth. Aphanizo=Aphanizomenon. Cylindro=Cylindrospermum. Plankto=Planktothrix. Aphano=Aphanocapsa. Mean Conc is the total number of cyanobacteria per mL at the specified depth.
16
References Behrenfeld, M. J., K. H. Halsey, and A. J. Milligan. 2008. Evolved physiological responses of phytoplankton to their integrated growth environment. Philos. T. Roy. Soc. B. 363:2687- 2703. Bellinger, E. G., and D. C. Sigee. 2010. A key to more frequently occurring freshwater algae. Freshwater Algae: Identification and Use as Bioindicators. 137-244. Ferber L. R., S. N. Levine, A. Lini, and G. P. Livingston. 2004. Do cyanobacteria dominate in eutrophic lakes because they fix atmospheric nitrogen? Freshw. Biol. 49:690-708. Hopkins, F. E., S. M. Turner, P. D. Nightingale, M. Steinke, D. Bakker, P. S. Liss, and M. L. Bender. 2010. Ocean acidification and marine trace gas emissions. P. Natl. Acad. Sci. U.S.A. 107:760-765. Litchman, E., and C. A.Klausmeier. 2008. Trait-based community ecology of phytoplankton. Annu. Rev. Ecol. Evol. S. 39:615-639. Marshall, H. G. 2009. Phytoplankton of the York River. J. Coastal Res. 57:69-65. Nürnberg, G. K. 2007. Low-Nitrate-Days (LND), a potential indicator of cyanobacteria blooms in a eutrophic hardwater reservoir. Water Qual. Res. J. Can. 42:269-283. Shumate, C. 2013. Nutrient limitation in Lake Ballard. http://sci.odu.edu/oceanography/academics/undergrad/441_442/12_13/shumate.pdf Strain, J. A. 2009. Nitrogen dynamics in an estuarine system. http://sci.odu.edu/oceanography/academics/undergrad/441_442/08_09/J_Strain.pdf Welschmeyer, N. A. 1994. Fluorometric analysis of chlorophyll a in the presence of chlorophyll b and pheopigments. Limnol. Oceanogr. 39:1985-1992. Whittecar, G. R., A. A. Nowroozi, and J. R. Hall. 2005. Delineation of saltwater intrusion through a coastal borrow pit by resistivity survey. Environ. Eng. Geosci. 11:209- 219. Wolny, J. L. 1999. A study of the seasonal composition and abundance of phytoplankton and autotrophic picoplankton in a brackish water lake, Portsmouth, Virginia. Unpublished M.S. thesis, Old Dominion University, Norfolk, Virginia 23529. 54 pgs. Zarauz, L., and X. Irigoien. 2008. Effects of Lugol’s fixation on the size structure of natural nano-microplankton samples, analyzed by means of an automatic counting method. J. Plankton Res. 30:1297-1303.
17
Old Dominion University
Tidal Impacts on Temperature and Salinity in the Lafayette River.
OEAS 442: Field Studies II
John Plant
4/21/2014
Abstract
Studies on tidal influences on sub-estuaries are limited in number. Hampton Roads location at the southern edge of the Chesapeake Bay, in addition to its multiple rivers flowing into the bay, give an excellent opportunity to delve deeper into this research area. Two cruises were conducted on the lower Lafayette River during differing tidal periods of Spring 2014.
Analysis of measured temperature and salinity levels showed higher salinity stratification during an ebb tide period compared to the flood tide period, and limited temperature stratification during both periods. Comparison against historical averages from 2000-2005 showed that overall salinity levels were 3-4 PSU lower and temperature was several degrees lower, dependent on tidal condition.
Introduction
The Lafayette River is a nine kilometer long river located in Norfolk, VA. Formerly known as Tanner’s Creek, it runs from 36°.83N, 76°.28W to 36°.90N 76°.32W, emptying into the Elizabeth River just south of Sewell’s Point (Yarsinske, 2007). In 1976, the Lafayette River was determined to have a water volume of 8.92x104 m3, with a tidal period of 12.4 hours and a mean tidal range of .79m (Blair, Cox & Kuo, 1976). Although Lafayette River’s dimensions are small relative to nearby rivers such as the Elizabeth, York and James River, its location in the heart of the city of Norfolk places increased emphasis on preserving this area for future generations. Studies of rivers in the Hampton Roads area are currently underway to preserve estuarine environments and to gain increased knowledge of environmental processes affecting the region’s waterways. Preservation efforts include a restoration project by the Chesapeake
Bay Foundation and other restoration projects such as the Elizabeth River Project, which aim to make the Lafayette “swimmable and fishable” by late 2014.
There have been many published studies of estuarine circulation that consider ocean- estuarine interaction (Pritchard 1952,1954, Hargis, 1981, Guo, Valle-Levinson, 2007). These studies focused on the impact that neighboring ocean waters have upon estuaries. In contrast, there are fewer publications that focus on the estuary-subestuary interaction, with most of these studies concentrating on the sub-estuaries’ effect on the estuary (Bowden, 1967, Yu-Chao, 1998,
Chua, 2013). Only Montgomery (1979) and Haas (1977) focused their efforts on the impact upon the sub-estuaries.
A common finding among many of the previous works is that, within the estuary, circulation and stratification are primarily influenced by winds, tides, and river discharge
(Bowden, 1967, Yu-Chao, 1998). In the Chesapeake Bay, a semi-diurnal tidal cycle is present with two similar high tides and two low tides each day (Browne & Fisher, 1988). Gou and
Valle-Levinson (2007) studied tidal effects on estuarine circulation and also demonstrated that the mixing caused by tidal currents weakens stratification. One study focused on the James,
York, and Rappahannock Rivers determined that “these estuaries oscillated between conditions of considerable vertical salinity stratification and homogeneity on a cycle closely correlated with the spring-neap tidal cycle (Haas, 1977).”
In order better understand the tidal impacts on sub-estuaries, further analysis of river characteristics must be conducted. This present study begins that process by comparing the physical properties of a smaller estuarine tributary, the Lafayette River, during a flood tide and ebb tide. The properties measured will be analyzed to determine the change in vertical temperature and salinity profiles in as well as stratification differences at various locations along the lower two-thirds of the river, including multiple cross sections of the river. By analyzing the river during these distinctly different periods, the overall level of knowledge of the river will be increased.
Methods and Materials
The Lafayette River flows east to west, emptying into the Elizabeth River which merges with the James River before emptying into the lower Chesapeake Bay (Figs. 1&2).
Fig 1. Ariel view of Hampton Roads tributaries. Fig 1. Ariel view of lower Chesapeake Bay. Lafayette River is outlined in red. General flow Lafayette River is outlined in red. Adapted direction is indicated by white arrows. Adapted from from Google Earth. Google Earth. In the spring of 2014, two separate cruises were conducted along a 5.6 km length of the western portion of the Lafayette River on February 17th and March 10th, extending 1km beyond the mouth of the Lafayette, into the Elizabeth River (36° 53.667N, 076° 19.394W). These cruises encompassed a flood and ebb tide period (Fig.3) within a spring and neap tide, respectively. (Fig. 4)
Fig.3. Sewell’s Point Tidal charts via NOAA’s Tides and Currents web page for February 17 and March 10. Gage located 6.5 km north of the mouth of the Lafayette River at 36° 57.32N, 076° 19.804W. Green line indicates observed values, blue indicates predicted values. (NOAA)
Fig.4. Sewell’s Point Tidal charts for February and March 2014. Gage located north of the mouth of the Lafayette River at 36° 57.32N, 076° 19.804W. Green line indicates recorded values, blue indicates predicted values. (NOAA)
CTD casts were conducted at pre-selected positions (Fig.5)(Table 1) to measure
temperature and salinity differences at different points along the length of the river. On the
March 10th ebb tide cruise, we added two additional CTD casts (stations 25 and 26) to create a
cross section allowing comparison of measured properties between the Lafayette and Elizabeth
Rivers.
Both cruises started at the Granby Street Bridge (36° 53.301N 076° 16.775W) and ended
at station 24 just beyond the mouth of the Lafayette River. Cross section locations were chosen
(Fig.5) based on the geography and bathymetry (Fig. 6) of the river to asses differences across
the width of the river, in addition to our length analysis.
3 5
4 4
6 2 1
Fig 5. CTD cast locations along Lafayette River. Cross sections are annotated by red boxes and neighboring numbers.
CTD Cast Locations Cast Latitude Longitude Cast Latitude Longitude Cast Latitude Longitude 1 36° 53.54N 076° 16.79W 10 36° 53.87N 076° 17.50W 19 36° 54.47N 076° 18.71W 2 36° 53.29N 076° 16.81W 11 36° 54.07N 076° 17.46W 20 36° 54.47N 076° 19.02W 3 36° 53.30N 076° 16.78W 12 36° 54.27N 076° 17.64W 21 36° 54.30N 076° 19.00W 4 36° 53.43N 076° 17.00W 13 36° 54.22N 076° 17.68W 22 36° 54.19N 076° 19.06W 5 36° 53.46N 076° 17.27W 14 36° 54.34N 076° 18.71W 23 36° 54.05N 076° 19.16W 6 36° 53.51N 076° 17.49W 15 36° 54.40N 076° 17.93W 24 36° 53.67N 076° 19.39W 7 36° 53.63N 076° 17.52W 16 36° 54.34N 076° 18.27W 25 36° 53.52N 076° 19.23W 8 36° 53.63N 076° 17.58W 17 36° 54.26N 076° 18.25W 26 36° 54.22N 076° 19.96W 9 36° 53.64N 076° 17.66W 18 36° 54.39N 076° 18.53W Table 1. CTD cast latitude and longitude coordinates for 17 February and 10 March cruises.
Fig. 6. Bathymetry of Lafayette River and CTD casts down center of the river, during 17 February cruise.
Historical data were compiled from previous research conducted by Old Dominion
University’s Center for Coastal Physical Oceanography. These data consisted of 968 CTD casts
from 2000-2005 at the Hampton Blvd Bridge (36° 54.344N, 076° 18.269W). These casts were
used to calculate monthly median temperature and salinity values over the five year period.
Similarly we tested the present study’s data from our CTD stations to determine precision
compared to the 2000-2005 values. In addition, temperature and salinity values were plotted via
MATLAB to provide a visual representation of the length profiles and cross section profiles for
each of the measured values. Density was calculated using the ‘gsw_rho_t_exact’ function
through MATLAB (McDougall & Barker, 2011). These density values were then plotted to
compare against temperature and salinity values to determine levels of influence of each
property. Temperature and salinity stratification was determined by a surface-bottom difference
(∆), the larger the ∆, the greater the stratification (Haas, 1977). Stratification values were then
compared along the length of the river, on both the surface and bottom utilizing data from
stations 3, 8, 16, 23, and 24. In addition, horizontal and vertical stratification analyses at all
cross sections were conducted, enabling comparison between the flood and ebb tide periods
Results
Analysis of cross section data revealed minimal differences across the width of the river
at all six locations, thus focus was shifted to vertical structure along the length of the river.
Historical data were averaged by month for temperature and salinity (Fig.7) and plotted for
comparison to current cruise data. Temperature data from 2000-2005 showed a strong
seasonality, with water warming during spring months and reaching its warmest, approximately
25°C, in late July through early August. The river then begins to cool down over the fall months
and reaches its coolest, 5°C, December through February. This pattern follows general air
temperature trends in the Hampton Roads region with warmer temperatures historically being in
July and August. Historical salinity values do not appear to follow a seasonal trend, but there
does appear to be a pattern to the recorded values.
Fig. 7. Historical temperature and salinity averages for the Lafayette River, from 2000-2005. Temperature patterns over the two cruises showed a 3-4°C difference between the flood
and ebb tides, with the warmer waters being observed on the ebb tide. (Figs.8,9) During the
flood tide, the vertical profile was almost completely homogeneous as compared to the
Fig. 8. Comparison of temperature changes over distance between flood and ebb tides.
Fig. 9. Comparison of vertical temperature changes from stations 3, 8, 16, 23 and 24. ebb tide results taken from the same five stations. Stratification values for temperature showed
limited differences along the length of the river, with no discernable trend visible between the
flood and ebb tide periods. (Fig. 10, Table 2)
Fig. 10. Change in vertical temperature stratification along the river between flood and ebb tide periods.
Temperature Stratification Station 3 Station 8 Station 16 Station 23 Station 24 17-Feb Surface 2.535 3.446 2.934 3.003 2.863 Flood Bottom 3.847 3.847 3.186 3.227 3.354 Tide ∆ 1.312 0.401 0.252 0.224 0.491 10-Mar Surface 7.533 6.887 6.024 6.31 5.574 Ebb Bottom 7.163 6.866 6.539 6.103 5.929 Tide ∆ 0.37 0.021 0.515 0.207 0.355 Table 2. Surface and bottom values for temperature (°C) taken at stations along length of Lafayette River. Highlighted ∆ value represents vertical stratification at that station.
As with the temperature results, there was a significant difference between the salinity
values observed on the two cruises. Overall values were higher during the flood tide by 3-4
PSU. (Fig. 11) The vertical profiles during flood tide, much like temperature profiles were
almost completely homogeneous. During the ebb tide period salinity increased with depth
(Fig. 12) showing weak stratification. A plot of vertical stratification (Fig. 13) showed one
significant difference between the two cruises that was not observable with the temperature data. During the ebb tide period, the vertical stratification appears to have increased downstream
along the , with greatest stratification where it empties into the Elizabeth River. (Table 3)
Fig. 11. Comparison of salinity changes over distance between flood and ebb tides.
Fig. 12. Comparison of vertical salinity changes from stations 3, 8, 16, 23 and 24.
Fig. 13. Change in vertical salinity stratification over distance comparison between flood and ebb tide periods.
Salinity Stratification Station 3 Station 8 Station 16 Station 23 Station 24
Upper 12.67 11.511 13.181 14.694 15.257 17- Lower 13.64 14.334 14.899 14.817 15.048 Feb ∆ 0.97 2.823 1.718 0.123 0.209 Upper 11.927 12.545 11.049 10.214 9.184 10- Lower 12.776 13.22 13.596 13.315 13.571 Mar ∆ 0.849 0.675 2.547 3.101 4.387
Table 3. Sample of surface and bottom salinity values taken at stations along length of Lafayette River. Highlighted ∆ value represents vertical stratification at that station.
From the temperature and salinity values, density was calculated and plotted (Figs 14
&15). Results showed that salinity was a much stronger determinent of density compared to
temerature. Density was almost completely vertically homogeneous during the flood tide and
increased with depth during the ebb tide.
Fig. 14. Comparison of salinity changes over distance between flood and ebb tides.
Fig. 15. Comparison of vertical salinity changes from stations 3, 8, 16, 23 and 24.
Discussion
In comparing data collected during flood tide to data collected during ebb tide, a tidal influence on the physical structure of the Lafayette River became apparent. During the flood tide period, which also coincided with a spring tide, more saline waters were being driven into the river. Higher saline waters during that period only extend approximately 2000 meters upstream and almost no vertical stratification is occurred. During ebb tide, minimal stratification was present with the greater stratification occurring closer to the mouth of the river. Temperature followed a similar trend during the flood tide with warmer waters extending 2000m upstream from the mouth of the river, again with limited vertical stratification. During the ebb tide period, however, tidal influence was downstream with no saline water being driven upstream.
Our initial expectation was to see a saline wedge present at the mouth of the river as freshwater flowing down the Lafayette was pushed over the top of the incoming saline water from the flood tide, causing significantly higher stratification at the mouth of the river compared to upstream. This anticipated phenomenon was not observed; instead the higher level of stratification was observed during the ebb tide cruise on March 10th.
Several factors not measured could explain our results, the first of which is wind. During the first cruise on 17 February, there was stronger wind present. NOAA’s Station CRYV2, located on South Craney Island (located 3km NW of the mouth of the Lafayette) recorded winds from the north at 9kts with gusts up to 11.7kts. During the March 10th cruise, winds were from the south at 0-2.6kts. The stronger wind during the flood tide period, coupled with the river’s shallow depths, most likely resulted in mixing in the water column. This wind mixing also explains the near homogeneous characteristics for both temperature and salinity. Another possible explanation is the heavy amount of precipitation in the weeks leading up to the 17 February cruise. The historical averages of for both temperature and salinity did not mirror the same measurements recorded during either flood or ebb tide. However, when comparing our data to known meteorological trends in the Hampton Roads area, a possible explanation is found.
Spring, the period from early March to late May, in Hampton Roads is normally a period of increased precipitation compared to the rest of the year. The salinity values in the Lafayette
River typically reach their lowest during this period and begin to increase again about the middle of May. In the winter of 2013-2014, Hampton Roads experienced abnormally high precipitation in the form of both snow and rain, a possible explanation for significantly low salinity values in both February and March and which were approximately 4 PSU lower and also for the lower temperatures recorded in February which were 2-3°C lower..
In future studies, extending measurement periods to include multiple tidal cycles and meteorological conditions, specifically wind and precipitation, would greatly increase our understanding of the processes effecting temperature and salinity in the Lafayette River. With the increasing need to protect our inland waterways, studies such as this are crucial to understanding the factors which impact their condition.
References
Blair, C.H., Cox, J.H., & Kuo, C.Y. 1976. Investigation of flushing time in the Lafayette River, Norfolk, Virginia. Final Technical Report 76-C4. Old Dominion University.
Bowden, K. F. 1967. Circulation and diffusion. Estuaries, 83:15-36.
Browne, D.R., & Fisher, C.W. 1988. Tide and tidal currents in the Chesapeake Bay. NOAA Technical Report NOS OMA 3. Rockville, MD. 84.
Chua, V.P. 2013. Modeling the variations of freshwater inflows and tidal mixing on estuarine circulation and salt flux. J. Coast. Res. 29:1391-1399.
Guo, X., Valle-Levinson, A. 2007. Tidal effects on circulation and outflow plume in the Chesapeake Bay. Cont. Shelf. Res. 27:20-42.
Haas, L. W. 1977. The effect of the spring-neap tidal cycle on the vertical salinity structure of the James, York and Rappahonnock rivers, Virginia, U.S.A. Estuar. Coast. Mar. Sci. 5:485-496.
Hargis, W. 1981. A benchmark multi-disciplinary study of the interaction between the Chesapeake Bay and adjacent waters of the Virginian sea. NASA Conf. P. 2188. 1-14.
Luo, L., Zhou, W., & Wang, D. 2012. Responses of the river plume to the external forcing. Aquat. Ecosyst. Health. 15:29-62.
McDougall, T. J., & P.M., Barker. 2011. Getting started with TEOS-10 and the gibbs seawater (gsw) oceanographic toolbox. SCOR/IAPSO Working Group 127. p.18.
Montgomery, J. 1979. Predicting level of dissolved reactive phosphate in the Lafayette river, Norfolk, Virginia, from information on tide, wind, temperature, and sewage discharge. Water Resour. Res. 15.5. 1207-1212.
Pritchard, D. 1952. Salinity distribution and circulation in the Chesapeake Bay estuarine system. J. Mar. Res. 11:106-123.
Pritchard, D. 1954. A study on the salt balance in a coastal plain estuary. J. Mar. Res. 13:133- 144.
Seitz, R. 1971. Temperature and salinity distributions in vertical sections along the longitudinal axis and across the entrance of the Chesapeake Bay. Chesapeake Bay Institute, The John Hopkins University. 99.
Sisson, G.M. 1976. A numerical model for the prediction of tides and tidal currents in the Lafayette River, Norfolk, Virginia. MS thesis. Old Dominion University.
Valle-Levinson, A., Li, C., Royer, C.T, Atkinson, L.P. 1998. Flow patterns at the Chesapeake Bay entrance. Cont. Shelf Res. 18:1157-1177.
Yarsinske, A. W. 2007. The Elizabeth River. Charleston: The History Press. 382.
Yu-Chao, S. 1998. River forced estuarine plumes. J. Phys. Oceanogr. 8:72-88.
Distribution of Microbial Biomass and Bacteria Concentrations in
Groundwater Surrounding Lake Ballard
Dennis McAlister OEAS 441 2013-2014
Abstract
I determined the microbial biomass in seven different groundwater-monitoring wells surrounding Lake Ballard in Portsmouth, VA. Phospholipid extraction using dichloromethane:methanol (2:1) solvents was used to find phospholipid membrane concentrations in the groundwater. The amount of phosphate attached to lipids were then used to calculate biomass per groundwater-monitoring well in grams of carbon. Bacteria concentrations were determined using epifluorescence microscopy. A comparison of these biomass and bacteria values with the hydraulic conductivity (K) values is shown with their respective distributions. I expected areas with lower hydraulic conductivity (K) values to have higher biomass and bacteria values. In contrast, groundwater-monitoring wells with low K values did not all show significantly higher biomass or bacteria values.
Introduction
Hydrology is an important field in geologic sciences that studies the movement, distribution and quality of water. More specifically, hydrogeology is very important for studies
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regarding access to clean water and water management. Hydrogeology appears to exert partial control over the spatial pattern of activities shown by microbial communities (Ayuso et al
2010). To aid in pumping plans for consumption, understanding the hydrogeological flow in the area can greatly improve expectations on the distribution of the microbial biomass and bacteria in aquifers.
In spite of great efforts during the last several decades, about 1.2 billion people lack access to safe drinking water, and half of the world’s diseases are transmitted through water
(Niemczynowicz 1999). Testing water quality for microbes and bacteria concentrations could impact quality of life for many, especially in developing countries. It is estimated twenty-five million deaths occur from water related illnesses each year, mainly in developing countries
(Niemczynowicz 1999). Pumping groundwater to use as a source of clean water is one of many ways hydrologists have planned to ease the stress on water resources. Many cities rely on groundwater for drinking water, irrigation, and sanitation. As populations increase and cities become developed the demand for clean water and stress on aquifers also increases. Nearly two billion people depend directly upon aquifers for drinking water and 40% of the world’s food produced by irrigation relies heavily on groundwater (Murial et al 2013). Organic carbon in groundwater can negatively interfere with treatment operations by contributing to disinfection by-product formation, membrane fouling, or biological regrowth in distribution systems (Rauch and Drewes 2005). If the groundwater is teeming with bacteria and has large microbial community, it would be beneficial to know before spending money to pump out for consumption.
2
Assessing the microbial biomass and bacteria concentration in groundwater is important for understanding the ecology in groundwater communities. Studies in groundwater ecosystems are still in early stages. For previous decades, it was widely assumed, even shallow aquifer were sterile habitats (Ayuso et al 2010). There has been microbial life found in the most extreme conditions and now, aquifers are considered as ecological systems with complex communities, rather than just inner reservoirs of water for consumption (Ayuso et al 2010).
These microbial communities are active and relevant in biogeochemical processes (Griebler and
Lueders 2008). Subsurface microbial, chemical, and physical soil processes directly influence groundwater chemistry through processes such as biodegradation, absorption, and dilution of organic and inorganic matter. These mechanisms are essential for purification processes associated with drinking water production (Schütz et al 2010).
To assess the microbial community in this study, an analysis of microbial biomass and bacterial concentrations in the groundwater were used. Biomass was determined using a phospholipid extraction revised from past methods beginning with Bligh and Dyer (1959).
Findlay et al. (1989) adapted these methods for microbes in sediment ranging from an intertidal zone to 30 meter deep sediment grabs of a river to increase the accuracy and precision of the extraction.
Located inside Hoffler Creek Wildlife Preserve, Lake Ballard is a man-made lake excavated through two geologic formations, the Tabb on top of the Yorktown. In a previous study, hydraulic conductivity in the Tabb formation was calculated (Murray 2013) (Figure 1). I hypothesized the wells with higher conductivity (K) values would have lower microbial biomass
3
and fewer bacteria. Wells with higher K values, due to the shorter residence times, would not have as much time to accumulate bacteria or biomass as wells with lower k values.
Methods & Materials
Study Site
Nine groundwater monitoring wells surrounding Lake Ballard were sampled for this study. A large amount of spatial data regarding several dynamics in Lake Ballard can be found in Allen (2004) and can be used to help understand the dimensions of the area where the samples were taken. Lake Ballard has a top surface area of 142,575 square meters and a circumference of 1583.01 meters. The groundwater monitoring wells are scattered in locations from the woods to the bank of the lake (Figure 2). The wells penetrate the poorly drained Tabb formation to a depth between 10-15 meters deep. The sediment is a fining upward sequence with clayey fine sand, over silty fine sand, over fine to medium sand (Whittecar et al. 2005).
Groundwater Sampling
This focus of this study is focused on biomass and bacteria in the groundwater and not the sediment, therefore to minimize the amount of sediment on filters being extracted, several steps were added to a simple sampling method. Using a bailer, the entire volume of the well was purged (removed) to allow fresh groundwater from the aquifer to recharge the well. A depth to water reading was taken each time before bailing any water to ensure the recharge occurred (Table 1). The wells were allowed to recharge overnight to allow most sediment to settle in the bottom of the well. A clean bailer was rinsed thoroughly using deionized water and 18 mega-ohm water between each sample. The second bail of water was collected in
4
500mL polypropylene screw-cap bottles. The samples were left to sit for several hours at 4°C to allow sediment to settle, then decanted separately into a graduated cylinder to determine the volume of groundwater used for analysis. The water was then vacuum-pressure filtered and the filters were stored at -20°C until the extraction was performed.
Phospholipid Extraction
I slightly modified the phospholipid extraction method from Findlay (1989) for all samples. Three types of samples were analyzed: groundwater-monitoring well samples, a standard made with 5.25 µg/µl phosphatidyl-serine, and one blank. The samples were vacuum- pressure filtered as mentioned earlier through a 47 mm combusted glass fiber filter, size F with
.7 µm pore size (GF/F). The filters alone were placed in separate 50mL test tubes and covered with solvent with dichloromethane: methanol (2:1). The test tubes were then sonicated for 20 minutes to further burst the membranes. By shaking the solution and adding a very small amount of 18 MΩ deionized water the solution separated into two phases, leaving the water and methanol on top of the organic layer with DCM on the bottom. Using a 4mL pipette, the bottom organic layer was extracted and placed into a round-bottom flask; the test tube was washed with DCM and extracted two more times to ensure the majority of phospholipids were removed. To rid the excess solvent the round-bottom flask was placed on a rotary evaporator, after which the smaller volume was then transferred back to smaller test tubes. The smaller volume test tubes were then placed in a nitrogen evaporator to completely dry out samples. A chromatography column was then used to finalize the extraction process and further separate phosphate from lipids. Glass columns filled with glass wool and silica gel were eluted with 1:1
DCM:methanol. The test tubes were rinsed with the solvents 2 more times and run through the
5
column to get all phosphate in the test tubes. The chromatography column volume equaled
1mL of silica and glass wool; therefore 5 mL of methanol was put through the column into smaller 25mL test tubes. The solvents were dried using the nitrogen evaporator and pipetted into smaller 4mL amber vials. The vials were dried with the nitrogen evaporator again and sent to Old Dominion University water quality lab for quantification of total phosphates that were bound to lipids. Using the phosphate results from the water quality lab the microbial biomass were calculated with the conversion factors from Findlay et al. (1989) expressed in grams of carbon. Findlay et al. (1989) states 191.7 µg of carbon are in every 100 nmol of phospholipid.
Bacteria Concentrations
Bacteria were quantified using methods adapted from Hobbie (1977). The same collection method was used as above, however using three 30mL screw-cap bottles per well.
Once collected, the samples were fixed (1 percent formalin final concentration). In the lab the samples were vacuum-filtered using 0.2 µm membrane black filters. The damp filter was then placed on a microscope slide, mounting medium Fluoroshield with DAPI was added to stain the
DNA, and produced a blue fluorescence between 360nm and 460nm. Using an epifluorescence microscope, I counted 5 random fields per slide, then calculated bacteria concentrations per milliliter.
Results
Using the ratio between carbon and phospholipids from Findlay (1989), the phosphate values were converted to grams of carbon biomass (Table 2). The mean biomass value for the groundwater was 5.84x10-7 g C/ml with a range from 2.5x10-7 g C/ml to 1.0x10-6 g C/ml (Figure
6
3). Using GIS I visualized an interpolation of biomass values in the groundwater. The border of
Lake Ballard indicates the extent of the interpolation due to the lake interface changing the type of water found at the same elevation (Figure 4). Bacteria counts were converted to concentrations of bacteria per milliliter. I made 3 microscopy slides per well for bacteria counting. By averaging the three concentrations, bacterial concentrations in each well can be estimated per reticle. I calculated the usable area of the filter that was analyzed, the number of reticles per filter, and by using the volume of water filtered I was able to determine the number of bacteria per ml (Table 3). Mean bacteria concentrations were 1.5x105 bacteria/ml with a range from 8.4x104 to 2.5x105 bacteria/ml (Figure 5).
Discussion
Most groundwater ecosystems experience strong carbon limitation that severely constrains the biomass, therefore converting biomass into carbon helps understand the microbial community in the groundwater (Foulquier 2011). The biomass in the groundwater is limited to energy-producing processes without the sun and heavily rely on breaking down carbon for energy. Due to the lack of solar energy one would expect bacteria to be the main source of biomass and they have a direct relationship. Biomass growth seems to be limited by the availability of organic carbon (Rauch and Drewes 2005).
Ayusco (2010) conducted a study on a similar environment, a coastal sandy aquifer in
Spain, and reported active biomass means equal to 2.54 ngC/ml and states the active biomass accounts from .02% to 6.36% of total biomass. My average biomass was 1080.5 ngC/ml and by
7
finding the .02% and 6.36% values I can see how they compare. The .02% and 6.36% values respectively are .216 and 68.72 ngC/ml. These values fall within the range of Ayuscos values and contain the mean. My values for bacteria appear to be low compared to Alfreider et al.(1996) where he reports a range of 4000 to 279000 respiring bacteria. My values for bacteria are in the same range as Alfreider et al.(1996) however, my values include all bacteria on the slide.
Contrary to my hypothesis, wells with low K-values did not significantly correlate with high biomass and bacteria values. For hydraulic conductivity and bacteria there was no correlation (r=.6656 n=6 p=.149). The hydraulic conductivity and biomass values no correlation
(r=-.1622 n=6 p=.7588). Biomass and bacteria did not show as strong a correlation with (r=-
.2568 n=6 p=.6244). Several factors may account for the lack of correlation: the amount of sediment on the analyzed filters can greatly increase the bacteria values, hydrologic conductivity values accuracy, and looking at the direction of hydrogeological flow in the area rather than only K values are potential sources of error. The bacteria in the groundwater are primarily attached to the sediment and in high concentrations, giving lots of variation between counts due to the amount of sediment in the reticle. It was observed wells 14a and 3 contained more suspended sediment than the other wells studied. However, lack of an increase in biomass, indicates the sediment is not affecting the biomass value as much as bacteria.
The distribution of biomass and bacteria varies between the wells for reasons yet to be determined; however, groundwater transports nutrients and organic matter. Consequenly, microbial activities are enhanced in those areas with high concentrations of nutrients and organic matter (Ayusco 2010). By converting to grams per ml, biomass of the aquifer potentially
8
could be estimated in the Tabb formation, therefore giving one a greater idea of the total biomass distribution in the aquifer. Investigating the relationship among bacteria, the microbial biomass (organic carbon), and the hydrogeological setting leads to a greater understanding of the microbial community. Repeating the study with further knowledge of the hydrogeological setting, and assessing the dissolved nutrients in the groundwater would provide more information on groundwater microbial community influences. -5
9
Well ID 11/4/2013 11/5/2013 2/5/2014 2/6/2014 3/5/2014 3/6/2014
3 7.8 7.5 6.2 6.5 6.8 7.0
4 13.4 13.3 12.2 12.3 12.8 12.9
5 No Data No Data No Data No Data 1.1 1.2
7 8.6 8.5 No Data No Data 8.2 8.3
8 No Data No Data 8.0 8.0 8.1 8.1
14A 5.6 4.4 4.0 4.4 4.6 4.8
16 10.7 10.5 10.8 9.9 10.4 10.3
18D 11.7 11.6 12.0 11.2 11.8 12.1
19D 10.6 10.5 10.0 10.0 10.4 10.2
Table 1: Depth to water values in Feet for two days showing recharge occurred in the aquifer, making sure water levels are near the previous day.
Hydraulic Conductivity 1.20E-02
1.00E-02
8.00E-03
6.00E-03 K (cm/s) 4.00E-03
2.00E-03
0.00E+00 3 5 7 8 16 18 19
Well ID
Figure 1: (Above) Hydraulic conductivity (K) in groundwater monitoring wells surrounding Lake Ballard (after
Murray 2013). Well 3 has a small value of 1.66x10-5cm/s and is not zero.
10
Figure 2: (above) Graduated circles showing differences in hydraulic conductivity. Wells listed on the left were used for comparison in this report (from Murray 2013).
Table 2: (above) Total lipid-bound phosphate conversion to carbon biomass in grams/ml. Conversion factor
191.7 µg C/ 100 nmol (Findlay 1989).
11
Well Average # #/ml #/ml * 1000
3 91.2 210586 211
4 84.2 194423 194 5 53.2 122842 123 7 107.7 248686 249 8 38.65 89245 89 18D 36.3 83819 84 19D 53.8 124227 124
Table 3: (above) Bacteria counts converted to average numbers per ml
Microbial Biomass 6000
5000
4000
3000
ng C/ml 2000
1000
0 3 4 5 7 8 14A 16 18D 19D STD Blank
Well ID
Figure 3: (Above) Microbial biomass in nanograms carbon/ml of the groundwater monitoring wells
surrounding Lake Ballard.
12
Figure 4: (Above) Interpolation of microbial biomass values using ArcGIS
Bacteria Concentrations 300
250
200
150
100
50
Average #/mL x1000 Average#/mL 0 3 4 5 7 8 18D 19D
Well ID
Figure 5: (Above) Bacteria concentrations per milliliter in groundwater monitoring wells
surrounding Lake Ballard.
13
References Cited
Alfreider, A., M. Krossbacher, R. Psenner. 1997. Groundwater samples do not reflect bacterial densities
and activity in subsurface systems, Water Res. 31:832-840.
Allen, A. E., 2004. Understanding Lake Ballard in Portsmouth, Virginia through the application of various
field data collection and GIS techniques, GEOG 497 Field Studies Report. Old Dominion
University, Norfolk, VA.
Ayuso, V. S., I. A. Lopez-Archilla, C. Montes, C.M. Guerrero. 2010. Microbial activities in a coastal
sandy aquifer system, Geomicrob. J. 27:5:409-423.
Bligh, E. G., W.J. Dyer. 1959. A rapid method of total lipid extraction and purification, Can. J. Biochem.
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Findlay, R., G. King, and Watling. 1989. Efficacy of phospholipid analysis in determining microbial
biomass in sediments, Appl. and Environ. Microb. 55:2888-2893.
Foulquier, A., F. Malard, F. Mermillod-Blondin, B. Montuelle, S. Doledec, B.Volat, and J. Gibert. 2011.
Surface water linkages regulate trophic interactions in a groundwater food web. Ecosystems
14:8:1339-1353.
Griebler, C., T. Lueders. 2009. Microbial biodiversity in groundwater ecosystems. Freshwater Biol.
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Harvey, R. 2013. Personal communication, Old Dominion University, Norfolk, VA.
Hobbie, J. E., Daley, R. J., Jasper, S., 1977 Use of nuclepore filters for counting bacteria by fluorescence
microscopy. Appl. And Environ. Microb. 33:5:1225-1228
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groundwater utilization in South Taluk of Coimbatore district, Tamilnadu, India. J. Appl. Sci. Res.
9:4:2455-2462.
Murray, R. 2013. Analysis of hydraulic conductivity of a surficial aquifer at Hoffler Creek Wildlife
Preserve. OAS 441 Field Studies Report. Old Dominion University, Norfolk, VA.
Niemczynowicz, J. 1999. Urban hydrology and water management-present and future challenges.
Urban Water 1:1:1-14.
Rauch, T., Drewes, J. 2005. Quantifying biological organic carbon removal in groundwater recharge
systems. J. Environ. Engin. 131:6:909-923.
Schütz, K., E. Kandeler, P. Nagel, S. Scheu, and L.Ruess, 2010. Functional microbial community response
to nutrient pulses by artificial groundwater recharge practice in surface soils and subsoils. FEMS
Microbiol. Ecol. 72:3:445-455.
Whittecar, G. R., A. Nowroozi, and J. Hall. 2005. Delineation of saltwater intrusion through a coastal
borrow pit by resistivity survey. Environ. Eng. Geosci. 11:209-219.
15
Transport and Atmospheric Deposition of Nitrogen and Sulfate to Lake Ballard
Akilah Matthews
Department of Ocean, Earth, and Atmospheric Sciences
Old Dominion University, Norfolk, VA
Field Study, Spring 2014
Transport of Atmospheric Deposition of Nitrogen to Lake Ballard
OEAS Field Study, Spring 2014
Matthews, Akilah N.
Abstract
Nitrogen and sulfate is one of the major causes of pollution in the Earth’s biosphere and lake eutrophication. The purpose of this study was to examine the wet and dry deposition of nitrogen within Lake Ballard. Wet and dry depositions were collected for using an atmospheric sampler (AEROCHEM Model 301). Samples of atmospheric deposition were sent to the Old
Dominion University Water Quality Lab. An air mass back-trajectory from HYSPLIT model free web based was utilized to discover the source of nitrogen deposited in the lake. An ion chromatograph was used to measure sulfate. Results of the air masses were classified by the path traveled and source direction; most of the air masses are continental polar. Wet deposition of nitrogen was 19.61 kg/ day higher than the dry deposition. These results were expected based on upon (Deal, 2012).
Introduction
Nitrogen enrichment is associated with increased primary production and eutrophication in coastal waters. This also exemplifies Chesapeake Bay which is still being researched because of its seasonal variability in nitrogen nutrient availability and plankton composition (Gilbert, et al., 1991). Based on Google Earth map length, the atmospheric sampler was approximately 10.52 km from the Chesapeake Bay, making Lake Ballard a perfect area to represent the effects in the
Chesapeake Bay on a broader scale ( Figure 1). Large input loads of nitrogen to the watershed is from atmospheric deposition (Linker , et al., 2013). Anthropogenic emissions, which release nitrogen and sulfate, has an influence on the atmosphere and biosphere (Aber, et al., 1989). The release of nitrogen and sulfate from power plants is a threat to water quality and forest ecosystem
(Aber, et al., 1989). Increase of nitrogen deposition from anthropogenic emissions, has been
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Matthews, Akilah N.
related to “phytoplankton blooms as a probable cause of decreased of dissolved oxygen concentrations (DOC)” (Russell, et al., 1998).
Sulfate deposition is a major concern to the world’s environment. Clean Air Act coal – fired electric utility plants dropped an average of 2.86 million tons in the United States of sulfur dioxide (Lynch, et al., 2000). Sulfate causes major health concerns including effects on the respiratory system, damage to lung tissue, cancer, and early death. Nitrogen oxides and sulfate dioxide is the main contributor to acid rain. Acid rain causes lakes to be acidified and changes the water chemistry in lakes and rivers.
There were four objectives this study. The first objective was to quantify both inorganic and organic forms of nitrogen. Secondly, air mass - back trajectories were calculated to see where the air masses were initiating from. Thirdly, I compare the relationship between the nitrogen levels, precipitation amounts, and the air mass back trajectories. The final objective was to determine the sulfate concentration within wet and dry deposition.
Methods
An atmospheric sampler (AEROCHEM Model 301) was placed near the pier at Lake
Ballard at 36°53’36. 99”N and 76°24’11. 90” W (Figure 2). There were two 3.5 gallon high- density polyethylene (HPDE) buckets inside the atmospheric sampler; the wet bucket collected the hydrometeor (rain, snow, fog droplets, and cloud droplets) and the dry bucket collected any debris that fall in the dry bucket ( Error! Reference source not found.). A rain sensor triggered the motor when precipitation occurred, moving the lid from the wet bucket to the dry bucket. Once
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Matthews, Akilah N.
the precipitation ended, the sensor heated and activated the motor to close the wet bucket, preventing atmospheric exposure. Before deployment, the buckets were treated overnight with
10% hydrochloric acid (HCl) and then rinsed three times with double deionized (DDI) water.
Immediately following each rain event, both the wet and dry buckets were transported to the laboratory to prepare subsamples for nutrient analysis. Both wet and dry buckets were deployed
Preparation of Subsamples
Before the preparation of the subsamples for the nutrient analysis, six 30 mL bottles and two 125 ml bottles were acid – cleaned. A Pyrex 1000mL flask was used to measure how much water was in the wet bucket to calculate how much double deionized water needed to be added in the dry bucket. 300 mL double deionized (DDI) water was added to the dry bucket. For fall semester, five 30ml subsamples were collected from both wet and dry bucket using a 20mL syringe with a 0.2 micrometer filter. Filtering the samples produced dissolved organic nitrogen.
However, during spring semester, three 30ml-unfiltered subsamples were collected from each bucket, two for total nitrogen and one for sulfate. One subsample from each bucket was filtered using a 20ml syringe with a 0.2 micrometer filter and designated for nitrate, nitrite, and ammonium analysis. The subsamples were sealed and labeled for future analysis. Samples were sent to the ODU Water Quality Lab in order to conduct analyses for total nitrogen, nitrate, and nitrite, and ammonium. After getting the results back from the Water Quality Lab; I then calculated the deposition rates of nitrogen and sulfate using the following formula:
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Transport of Atmospheric Deposition of Nitrogen to Lake Ballard
OEAS Field Study, Spring 2014
Matthews, Akilah N.
( ) ( )
( )
( )
Sulfate analysis
Ion chromatography was used for the sulfate determination. An ion chromatograph containing Dionex IonPac AG4A and Dionex AERS 500 RFIC elctrolytically regenerated suppressor were used to obtain a chemical profile of each water sample from the atmospheric sampler (AEROCHEM Model 301). At the beginning of each run, standards of ( )
1,000ppm with concentration range of 0 - 100µl were used to calibrate the ion chromatograph and create a daily calibration curve. The retention time were used to determine the peaked associated with( ) ion, and the area count of the peak used to determine the sulfate concentrations. Each sample were injected into a 100 µl injection loop where it was combined with 30mM of KOH eluent and the ( ) ion were allowed to separate from the sample detection. The computer displayed the area of each peak with an average of 2.72 min. Data were then entered into an Excel spreadsheet to calculate the concentration levels in each sample.
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Transport of Atmospheric Deposition of Nitrogen to Lake Ballard
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Matthews, Akilah N.
Air mass back – trajectory analysis
During the sampling period, Hybrid Single Particle Lagrangian Integrated Trajectory
(HYSPLIT) model software was used to calculate the air mass back - trajectories. This software was freely accessed through Air Resource Laboratory (ARL) of the National Oceanic and
Atmospheric Administration (NOAA). The HYSPLIT model reference archived meteorological data in to compute back trajectories. A new back – trajectory were run every 0 hour intervals with a maximum number were set at 1 trajectory. The HYSPLIT model produced the trajectories as a GIF plot so that the paths were easily visualized. The trajectories were set at an average of
72 hours at 500m above ground to study nitrogen compounds in the atmosphere (Strayer, et al.,
2006). The air mass initiating from northern Canada was classified as continental polar. Air masses coming from south into the Hampton Roads area were maritime tropical. Finally, air masses initiating from New York and Maine were classified as maritime polar (Alcorn, 1996-
2007).
Results
The results of the nutrient analyses have been consolidated to show all the analyses for fall and spring semesters separated from both wet and dry deposition including the amount of precipitation that occurred for the following collection dates (Table 1 and Table 2). The total nitrogen results were in the units of kg/day of N. Nitrate and nitrite results were in units of kg/day of . The values of ammonium are in the units of kg/day of . Based on (Table 1)
October 2, 2013 – October 7, 2013 had higher amounts of TN with a value of 19.16 kg/ day whereas on December 14, 2013 – December 16, 2013 had lower amounts of TN with a value of
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Transport of Atmospheric Deposition of Nitrogen to Lake Ballard
OEAS Field Study, Spring 2014
Matthews, Akilah N.
0.599 kg/ day. Then wet and dry components drop tremendously throughout the sampling period. (Table 3) shows the types of air masses that made contact into the atmospheric sampler during each sampling period. Majority of the air masses were continental polar. A separate table is shows the sulfate deposition with the air masses and amounts of precipitation for October 2,
2013 – March 8, 2014 (Table 4). The results of the total nitrogen, nitrate and nitrite, ammonium, and sulfate have been organized into stacked bar graphs showing the total wet and dry deposition in kg/days applied to Lake Ballard (Figure 4 – Figure 7). (Figure 4 – Figure 7) the total deposition rates were mostly influenced by wet deposition rather than dry deposition. Air mass back trajectories were run and classified based on the path travelled to the atmospheric sampler in (Figure 8 – Figure 17). Based on my results of the air mass back – trajectories, the majority of the air mass is continental polar initiating from Canada travelling south east to the atmospheric sampler (Table 4).
Discussion
I expected higher amounts nitrogen coming from the Northwest or west continental and since majority of coal – burning power plants are in Midwest region of the United States whereas the maritime tropical would have lower amounts of nitrogen since the air is clean this is also the same in (Southwell, et al., 2010). However for October 2, 2013 – October 7, 2013 has higher amount of nitrogen levels coming from the maritime tropical. This is to be expected since most of our heavy precipitation comes from maritime tropical (warm and moist) air mass. Fluctuations occurred in total nitrogen rates in the month of October; the rate for October 2, 2013 – October 8,
2013 was 19.16 kg/day. While the deposition rate for the same duration, October 16 – October
23, 2013 produced rate of 3.68 kg/day. Even though, sampling period were long the precipitation
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Transport of Atmospheric Deposition of Nitrogen to Lake Ballard
OEAS Field Study, Spring 2014
Matthews, Akilah N.
were low for October 16 – 23 due the air masses were continental polar producing less amounts of rainfall.
Acknowledgements
I would like to extend my gratefulness to the professors of my Field Studies class, Dr.
Fredrick Dobbs, Dr. Richard Whittecar, also TA Ben Hiza. Thanks also to Chris Powell, Joyce
Strain, Dr. Burdige and his lab assistant Jeremy Bleakney. Finally, thank you to Hoffler Creek
Wildlife Preserve Foundation.
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Transport of Atmospheric Deposition of Nitrogen to Lake Ballard
OEAS Field Study, Spring 2014
Matthews, Akilah N.
References
Aber, J. D., Nadelhoffer, K. J., Steudler, P. & Melillo, J. M., 1989. Nitrogen Saturation in Nothern Forest Ecosystems. Amer. Institute of Bio. Sci., pp. 378-386.
Alcorn, M., 1996-2007. Air masses and surface charts. [Online] Available at: http://www.met.tamu.edu/class/atmo202/Dir-surface/surface-stu.html [Accessed 20 April 2014].
Deal, C., 2012. Trends in atmospheric deposition of nitrogen compounds to Lake Ballard. [Online] Available at: http://sci.odu.edu/oceanography/academics/undergrad/441_442/12_13/deal1.pdf [Accessed 4 September 2013].
Gilbert, P. M., Garside, C., Fuhrman, J. A. & Roman, M. R., 1991. Time-dependent coupling of inorganic and organic nitroigen uptake and regeneration in the plume of the Chesapeake Bay estuary and its regulation by large heterotrophs. Limnol. Oceanogr. , pp. 895-909.
Linker , L. C. et al., 2013. Computing atmospheric nutrient loads to the Chesapeake Bay watershed and tidal waters. Amer. Water Resources Assoc., pp. 1-7.
Lynch, J. A., Bowersox, V. C. & Grimm, J. W., 2000. Changes in sulfate deposition in eastern USA following implementation of Phase I of title IV of the Clean Air Act Amendments of 1990. Atmos. Enviro., pp. 1665-1680.
Russell, K. M. et al., 1998. Sources of Nitrogen in Wet Deposition to the Chesapeake Bay Region. Atmos. Enviro., pp. 2453-2465.
Southwell, M. W. et al., 2010. Seasonal variability of formaldehyde production from photolysis of rainwater dissolved organic carbon. Atmos. Enviro., pp. 3638-3643.
Strayer, H., Smith, R., Mizak, C. & Poor, N., 2006. Influence of air mass origin on the wet deposition of nitrogen to Tampa Bay, Florida-An eight-year study. Atmos. Enviro., pp. 4310-4317.
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Transport of Atmospheric Deposition of Nitrogen to Lake Ballard
OEAS Field Study, Spring 2014
Matthews, Akilah N.
Figures and Tables
Figure 1 Google Earth image southeastern Virginia with a red line showing how far the atmospheric sampler was from the Chesapeake Bay.
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Transport of Atmospheric Deposition of Nitrogen to Lake Ballard
OEAS Field Study, Spring 2014
Matthews, Akilah N.
Figure 2 Arc Map image of southeastern Portsmouth, Virginia with an
overlay image of Lake Ballard and also showing the atmospheric sampler
noted on the west side of the lake. Figure shows that Lake Ballard is
located on the mouth of the James River and the Chesapeake Bay. Picture
adapted from (Deal 2012)
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Transport of Atmospheric Deposition of Nitrogen to Lake Ballard
OEAS Field Study, Spring 2014
Matthews, Akilah N.
Figure 3 this is an atmospheric sampler set up on the west bank of Lake
Ballard. One of the buckets was labeled Wet that collects the precipitation. The
other bucket was labeled Dry which collects gaseous and particles. When
precipitation occurs a sensor activates the motor, which moves the lid from the
wet bucket to the dry bucket.
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Transport of Atmospheric Deposition of Nitrogen to Lake Ballard
OEAS Field Study, Spring 2014
Matthews, Akilah N.
Nitrate and Ammonium Collection Date TN Nitrite NH 3 Precipitation (in.) & Time (kg/day) (kg/day) (kg/day)
October 2, 2013 8:00 am – October 4, 2013 8:00 12.26 am and October 4, 2013 8:00am – October 7, 2013 19.16 3.3111 5.291355 4 pm
October 16, 2013 at 4:00 pm – October 18, 4:00 0.13 pm 3.685252 0.744 2.349925 October 18, 2013 4pm – October 23, 2013 4:00pm
December 7, 2013 8:00 am – December 8, 2013 0.83 4pm 0.599312 0.1651 0.246186 4:00 pm
December 8, 2013 4:00pm - December 10, 2013 1.56 and December 10, 2013 – December 12, 2013 0.747831 0.1223 0.322166 4:00 pm
0.62 December 14, 2013 4:00pm – December 16, 2013 0.509183 0.1356 0.121149 4:00 pm
January 21, 2014 4:00 pm - January23, 2014 4:00 7.3 pm 0.2292 0.74125 0.293449
February 12, 2014 4:00pm – February 14, 2014 2.26 4:00 pm 0.93096 0.1695 1.155848
Table 1 fall and spring semester wet deposition results from Old Dominion University Water
Quality Lab including precipitation amounts for the October 2, 2013 – February 14, 2014 sampling event.
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Transport of Atmospheric Deposition of Nitrogen to Lake Ballard
OEAS Field Study, Spring 2014
Matthews, Akilah N.
Total Precipitation Nitrate and Nitrite Ammonium Nitrogen (in.) Collection Date & Time (kg/day) (kg/day) (Kg/day)
October 2, 2013 8:00 am – October 4, 2013 8:00 am and October 4, 2013 8:00am – 2.624109842 0.446 0.756327 12.26 October 7, 2013 4 pm
October 16, 2013 at 4:00 pm – October 18, 4:00 pm 1.532395912 0.4465 0.73102 0.13 October 18, 2013 4pm – October 23, 2013 4:00pm
December 7, 2013 8:00 am – December 8, 0.208196212 0.503 0.07583 0.83 2013 4:00 pm
December 8, 2013 4:00pm - December 10, 2013 and December 10, 2013 – December 0.874065131 0.1394 0.273706 1.56 12, 2013 4:00 pm
December 14, 2013 4:00pm – December 16, 0.344241668 0.9672 0.108047 0.62 2013 4:00 pm January 21, 2014 4:00 pm - January23, 2014 0.862937403 0.2925 0.337242 7.3 4:00 pm February 12, 2014 4:00pm – February 14, 0.316781305 0.3434 0.125097 2.26 2014 4:00 pm
Table 2 fall and spring semester dry deposition results from Old Dominion University Water
Quality Lab including precipitation amounts for the October 2, 2013 – February 14, 2014 sampling event.
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Transport of Atmospheric Deposition of Nitrogen to Lake Ballard
OEAS Field Study, Spring 2014
Matthews, Akilah N.
Sulfate Sulfate (wet) (dry) Precipitation Collection Date & Time (in.) (kg/day) (kg/day)
October 2, 2013 8:00 am – October 4, 2013 8:00 am and October 4, 2013 13.26712413 4.145976291 12.26 8:00am – October 7, 2013 4 pm
October 16, 2013 at 4:00 pm – October 18, 4:00 pm 2.772958169 1.206102194 0.13 October 18, 2013 4pm – October 23, 2013 4:00pm
December 7, 2013 8:00 am – December 8, 2013 4:00 pm 0.988632875 0.169009856 0.83
December 8, 2013 4:00pm - December 10, 2013 and December 10, 2013 – 2.74304492 1.881543353 1.56 December 12, 2013 4:00 pm
December 14, 2013 4:00pm – December 16, 2013 4:00 pm 1.18276986 0.585103148 0.62
January 21, 2014 4:00 pm - January23, 2014 4:00 pm 1.651211337 1.527370486 7.3
February 12, 2014 4:00pm – February 14, 2014 1.173795885 1.069697779 2.26 4:00 pm
1.921627106 2.148967798 1.07 March 5, 2014 4:00pm – March 8, 2014 4:00pm
Table 3 shows both wet and dry deposition of sulfate (kg/day). Including precipitation amount for the October 2, 2013 – March 8, 2014 collection period.
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Transport of Atmospheric Deposition of Nitrogen to Lake Ballard
OEAS Field Study, Spring 2014
Matthews, Akilah N.
Collection Date Direction of Path
& Time
October 2, 2013 8:00 am – October 4, 2013 8:00 am and Maritime Tropical and Continental Polar October 4, 2013 8:00am – October 7, 2013 4 pm
October 16, 2013 at 4:00 pm – October 18, 4:00 pm Continental Polar and Continental Tropical
October 18, 2013 4pm – October 23, 2013 4:00pm
December 7, 2013 8:00 am – December 8, 2013 4pm Continental Polar
4:00 pm
Continental Polar and Tropical December 8, 2013 4:00pm - December 10, 2013 and December 10, 2013 – December 12, 2013 Continental Artic and Continental Polar 4:00 pm
December 14, 2013 4:00pm – December 16, 2013 4:00 pm Continental Polar
January 21, 2014 4:00 pm - January23, 2014 4:00 pm Continental Polar February 12, 2014 4:00pm – February 14, 2014 Continental Polar 4:00 pm March 5, 2014 4:00pm – March 8, 2014 4:00pm Continental Polar and Maritime Polar Table 4 the types of air masses that were distinguished using NOAA’s HYSPLIT Model for
October 2, 2013 – March 8, 2014.
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Transport of Atmospheric Deposition of Nitrogen to Lake Ballard
OEAS Field Study, Spring 2014
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Figure 4 Stacked bar graph showing wet and dry components of total nitrogen
deposition (kg/day), along with air masses associated with each collection
date. Precipitation is shown on graph inlay.
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Transport of Atmospheric Deposition of Nitrogen to Lake Ballard
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Matthews, Akilah N.
Total Deposition Ammonium
30
25
20
15 Total Ammonium (dry) Total Ammonium (wet) 10
5 Total Deposition Ammonium(kg/day)
0 Oct 2-7 Oct 16-18 Dec 7-8 Dec 8-12 Dec 14- Jan 21-23 Feb 12-14 2013 2013 2013 2013 16 2013 2014 2014 Collection Date
Figure 5 Stacked bar graph showing wet and dry components of total ammonium (kg/day) with air masses associated with each collection date. Precipitation is shown on graph inlay.
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Transport of Atmospheric Deposition of Nitrogen to Lake Ballard
OEAS Field Study, Spring 2014
Matthews, Akilah N.
Total Deposition Nitrate and Nitrite
80
70
60
50
40 Nitrate and Nitrite (DRY)
30 Nitrate and Nitrite (WET)
20
10 Total Deposition Nitrateand Nitrate (kg/day)
0 Oct 2-7 Oct 16- Dec 7-8 Dec 8-12 Dec 14- Jan 21-23 Feb 12- 2013 18 2013 2013 2013 16 2013 2014 14 2014 Collection Date
Figure 6 Stacked bar graph showing wet and dry components of total nitrate and nitrite (kg/day) with air masses associated with each collection date. Precipitation is shown on the graph inlay..
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Transport of Atmospheric Deposition of Nitrogen to Lake Ballard
OEAS Field Study, Spring 2014
Matthews, Akilah N.
Total Sulfate Deposition
90 80 70 60 50 40 30
(kg/day) Sulfate Total 20 10 Total Sulfate (dry) 0 Total Sulfate (wet)
Oct 2-7 2013 2-7 Oct Dec 7-8 2013 7-8 Dec Mar 5-8 2014 5-8 Mar Dec 8-12 2013 8-12 Dec Jan 21-23 2014 21-23 Jan Oct 16-18 2013 16-18 Oct Feb 12-14 2014 12-14 Feb Dec 14-16 2013 14-16 Dec Collection Date
Figure 7 Stacked bar graph showing wet and dry components of total sulfate (kg/day) with air masses associated with each collection date. Precipitation is shown on graph inlay.
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Transport of Atmospheric Deposition of Nitrogen to Lake Ballard
OEAS Field Study, Spring 2014
Matthews, Akilah N.
Figure 8 Mean air mass back trajectory from October 2, 2013 –
October 4, 2013. The source air mass (maritime tropical), moved north
– east from North Carolina into the Hampton Roads area.
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Transport of Atmospheric Deposition of Nitrogen to Lake Ballard
OEAS Field Study, Spring 2014
Matthews, Akilah N.
Figure 9 Mean air mass back trajectory from October 4, 2013 – October 7, 2013. The
source air mass (maritime tropical), moved west - northwest from the Mid Atlantic
Ocean into the Hampton Roads area.
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Transport of Atmospheric Deposition of Nitrogen to Lake Ballard
OEAS Field Study, Spring 2014
Matthews, Akilah N.
Figure 10 Mean air mass back trajectory from October 16, 2013 – October 18, 2013. The source air mass (continental polar), moved southeast from South Dakota into the Hampton Roads area.
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Transport of Atmospheric Deposition of Nitrogen to Lake Ballard
OEAS Field Study, Spring 2014
Matthews, Akilah N.
Figure 11 Mean air mass back trajectory from December 7, 2013 – December 8, 2013. The source air mass (continental polar), moved south east from the Great Lakes into the Hampton
Roads area.
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Transport of Atmospheric Deposition of Nitrogen to Lake Ballard
OEAS Field Study, Spring 2014
Matthews, Akilah N.
Figure 12 Mean air mass back trajectory from December 8, 2013 – December
10, 2013. The source air mass (continental polar), moved southeast from
Canada into the Hampton Roads area.
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Transport of Atmospheric Deposition of Nitrogen to Lake Ballard
OEAS Field Study, Spring 2014
Matthews, Akilah N.
Figure 13 Mean air mass back trajectory from December 14, 2013 – December 16, 2013. The source air mass (continental polar), moved southeast from Montano into the Hampton Roads area.
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Transport of Atmospheric Deposition of Nitrogen to Lake Ballard
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Matthews, Akilah N.
Figure 14 Mean air mass back trajectory from January 21, 2014 – January 23, 2014. The source air mass (continental polar), moved from southeast North Dakota into the Hampton Roads area.
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Transport of Atmospheric Deposition of Nitrogen to Lake Ballard
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Matthews, Akilah N.
.
Figure 15 Mean air mass back trajectory from February 12, 2014 – February 14, 2014. The source air mass (continental polar), moved southeast from Alaska into the Hampton Roads area.
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Transport of Atmospheric Deposition of Nitrogen to Lake Ballard
OEAS Field Study, Spring 2014
Matthews, Akilah N.
Figure 16 Mean air mass back trajectory from March 5, 2014 – March 6. The source air mass continental polar, moved southeast from North Dakota into the Hampton Roads area.
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Transport of Atmospheric Deposition of Nitrogen to Lake Ballard
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Matthews, Akilah N.
Figure 16 Mean air mass back trajectory from March 6, 2014 – March 8, 2014. The source air mass (maritime polar), moved southwest and then east from Pennsylvania into the Hampton
Roads area.
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Vertical distribution and phylogenetic diversity of the two prokaryotic domains, Bacteria and Archaea, in a brackish lake with oxic and anoxic zones
Amanda Laverty Field Studies I Instructor: Dr. Fred C. Dobbs Department of Ocean, Earth, and Atmospheric Sciences Old Dominion University
Abstract
In this study, we analyzed the variation with depth in the community composition of the two prokaryotic domains Bacteria and Archaea in Lake Ballard, a stratified brackish lake. Samples were collected from the oxic- and anoxic-zone as well as from the oxic-anoxic interface and the communities compositions were assessed using high throughput DNA pyrosequencing of the
V1-V3 (Bacteria) and V3 (Archaea) region of the16S rRNA gene. 30,251 high quality bacterial sequences were retrieved with a mean of 5,041 sequences per sample, and 1,070 OTUs in total and 33,680 high quality archaeal sequences with a mean of 4,210 sequences per sample, and 441
OTUs in total, respectively. Phylogenetic analysis showed that Cyanobacteria dominated the upper sunlit portions of the lake while unclassified Bacteria were present in second highest abundances in all three depths in the lake. Within defined taxa, the oxic-zone and oxic-anoxic interface communities were more closely related and consisted of representatives of
Cyanobacteria, Actinobacteria, Bacteroidetes, Betaproteobacteria and Alphaproteobacteria.
Taxa were distributed in a similar manner for the oxic zone, but their proportions varied much more at depth. These communities also shared a high number of OTUs, in contrast to the anoxic- zone water, which shared few OTUs with the interface water and even fewer with the oxic,
Laverty, 1 surface water. Chlorobi was the predominant phylum identified in the anoxic-zone of Lake
Ballard, where other phyla absent from the shallower waters were also detected, such as
Chloroflexi and Lentisphaerae. The detection of the archaeal 16S rRNA gene was feasible only after nested amplification of all sampling sites except for the anoxic 12m samples, implying low
Archaea abundance in the upper water column. Communities retrieved from DNA for replicate
12m samples showed similar phylogenetic distribution, in contrast to those retrieved after subsequent (nested) PCRs, suggesting uneven amplification of replicate PCR samples. All archaeal OTUs that were identified belonged to the kingdom Euryarchaeota, apart from one
Crenarchaeota found in the 12m sample, with all phyla consisting of methanogens. The most abundant Archaea in the 12m sample consisted of Methanomicrobiales, unclassified Archaea, and Methanosarcinales, with the most abundant classes being Methanospirillum and
Methanosaeta.
Introduction
Microbial life plays a vital role in the regulation of many global biogeochemical systems and yet it is estimated that we know less than 1% of all microbial species on Earth (Heywood, 1995).
Examining microbial diversity can correlate community structure with function and improve understanding of global microbial community processes. This understanding can be applied to global epidemiology of human, animal, and plant pathogens, applications in marine biotechnology and bioremediations, as well as numerous other beneficial purposes (Ramette and
Tiedje, 2007).
Since the maintenance of our planet relies largely on microbial life, it is of utmost importance to recognize anthropogenic impacts on aquatic processes and identify how predicted
Laverty, 2 changes to pH, temperature, and ultraviolet radiation will affect microbes (Coelho et al. 2013).
This vital understanding will influence our ability to appropriately respond to climate change, ocean acidification, and marine pollution. Our future conservation and utilization of microbial diversity first requires extensive research of species distributions in the environment.
Lake Ballard is a thermally stratified, brackish lake located in Portsmouth, Virginia.
Originally excavated as a borrow pit in the late 1970’s and early 1980’s, Lake Ballard is now an established aquatic ecosystem used by the surrounding public for recreational and educational activities (Allen, 2004). The Lake is part of the Hoffler Creek Wildlife Preserve which is located in the Chesapeake Bay Watershed. This preserve is dedicated to protecting 142 acres of land surrounding and including Lake Ballard. The southern and western sides of the Lake are bound by Hoffler Creek, which flows into the mouth of the James River only a few hundred meters away. Although Lake Ballard has been previously studied on a geochemical basis over the past decade, no data are available about the microbial populations that probably drive and sustain the
Lake’s chemical and biological equilibrium.
Understanding the composition and community structure of Bacteria and Archaea at different depths in the water column is fundamental in elucidating the role these populations carry out in biogeochemical processes. In this study, bacterial and archaeal communities were characterized at three depths in the water column of Lake Ballard by 454-pyrosequencing
(Schloss et al., 2011). The three sample depths were in the oxic zone, anoxic zone, and oxic- anoxic interface. Examining microbial communities at these specific zones should show how microbial distributions typically mirror oxygen stratified environments by colonizing microniches (Brune et al., 2000). This information can then be compared against other
Laverty, 3 environments to more thoroughly understand community structure and function as it is related to environmental conditions.
As observed in many similar studies, physical, chemical, and biological environmental conditions dictate, but can also be a result of, habitat selection by microbial communities
(Comeau et al. 2012; Galand et al. 2011; Koskinen et al. 2010; Labrenz et al. 2007). The aim of this study was to examine bacterial and archaeal composition and community structure within the stratified water column of Lake Ballard.
Methods
On October 7th, 2013, a peristaltic pump was used to collect two independent, replicate water samples in the deep spot of the lake at 3 meters (oxic zone), 6 meters (oxic-anoxic interface), and
12 meters (anoxic zone). Sample depths were chosen solely on the oxygen profile in the water column, measured using a YSI probe (85/100 FT) at the time of sampling. A CTD (RBR XM
620) was also used at the time of sampling to determine a vertical profile of temperature, salinity, and dissolved oxygen concentration in the lake. Before collection, the dead volume of water in the pump tubing was flushed for 1 minute at each depth. After collection, water samples were taken to the lab and 250 ml of each water sample from depths three and six meters were filtered through a series of four polycarbonate and poly-ether sulfone membrane filters with decreasing pore size (8.0 m, 5.0 m, 1.0 m, and 0.2 m). The 250 ml water sample from 12 meters was filtered in the same way, however, after the 5.0 m filter, the water was split into two 125 ml fractions and sent through two separate 1.0 m filters. Then the entire 250 ml was sent through the 0.2m filter. Filters were placed into separate, sterile Petri dishes and frozen (-20oC).
Laverty, 4
DNA was extracted with the MoBio PowerWater DNA Isolation Kit (cat # 12888,
Carlsbad, USA), according to the manufacturer’s instructions with the following modifications.
PowerBead tubes were replaced with 15ml Falcon tubes to allow the beads to effectively beat the entire surface of each filter. Two Falcon tubes were used for each depth for each independent sample. The four filters from each depth were split into two Falcon tubes with two filters in each tube. Since the twelve meter depth had five filters, the 8.0 m filter was cut in half using a sterile razor and each half was placed into separate tubes, resulting in 2 and a half filters in each tube for this sample. Control samples were also extracted with clean filters to account for any DNA contamination during the process. Supernatant from each depth was loaded onto separate spin filters and at this step the two tubes from each depth were combined until all supernatant was sent through one spin filter. At the end, the two tubes for each sample were collected in the same spin filter so that DNA from all filters used was eluted.
DNA yield and purity were measured using a BioRad SmartSpecTM Plus spectrophotometer (Biorad, Hercules, CA). In order to check quality of the extracted DNA, a 16S
PCR was run using primers BAC-8F (5'-AGAGTTTGATCCTGGCTCAG-3') (Turner et al.,
1999) and 1492R (5′-GGTTACCTTGTTACGACTT-3′) to amplify an approximately 1,500 bp long fragment of bacterial 16S rRNA gene (Lane 1991). This PCR consisted of an initial denaturation step at 94◦C for 1 min followed by 30 PCR cycles (95◦C denaturation for 1 min; primer annealing at 55◦C for 1 min; and primer extension at 72◦C for 2 min), and a final 7 min elongation step at 72◦C.
A nested PCR was run for archaeal 16S DNA amplification using primers ARC-8F (5'-
TCCGGTTGATCCTGCC-3') (Teske et al., 2002) and ARC-1492R (5′-GGTTACCTT
GTTACGACTT-3′). Amplification was feasible only for DNA extracted from the 12m.
Laverty, 5
Nevertheless, the reactions of this PCR above were used as template for a subsequent PCR reaction using primers ARC-344F (5'-ACGGGGYGCAGCAGGCGCGA-3') (Raskin et al.,
1994) and ARC-915R (5'-GTGCTCCCCCGCC AATTCCT-3') (Stahl and Amann, 1991) to amplify an approximately 572 bp fragment of the archaeal 16S rRNA gene. This reaction was also tested directly with DNA samples from the 3m and the 6m but direct amplification was still not feasible. In addition, primer pair ARC-340F (CCCTACGGGGCGCAGCAG) and ARC-
1000R (GAGA GGAGGTGCATGGCC) (Gantner et al., 2011) were tested for the 3 and 6 m
DNA samples but also failed to amplify DNA directly. Each PCR consisted of an initial denaturation step at 94◦C for 3 min followed by 30 PCR cycles (94◦C denaturation for 45 sec; primer annealing at 52.5◦C for 45 sec; and primer extension at 72◦C for 1 min), and a final 2 min elongation step at 72◦C. Cycle optimization was determined for each sample, where PCRs were repeated to find the lowest number of cycles that yielded product.
PCR products were visualized on a 1.0 % agarose gel under UV light and then were cleaned up using a PCR Clean-up kit, according to the manufacturer’s instructions. Once DNA and PCR product quality were confirmed to meet the standards of the pyrosequencing facility, the DNA samples and PCR products were shipped to MR DNA (www.mrdnalab.com) for a barcoded pyrosequencing analysis of the bacterial communities [amplification of the V1-V3 hypervariable region of the 16S rRNA gene (27Fmod 5’-AGRGTTTGATCMTGGCTCAG-3’,
519Rmodbio 5’-GTNTTACNGCGGCKG CTG-3’)] and the archaeal communities
[amplification of the V3 hypervariable region of the 16S rRNA gene - arch344F
ACGGGGYGCAGCAGGCGCGA and arch915R GTGCTCCCCCGCCAATTCCT], following protocols originally described by Dowd et al. (2008).
Laverty, 6
Pyrosequencing data was received in the form of an SFF file (standard flowgram format) and analyzed using the MOTHUR pipeline v.1.32.1 by Schloss et al. (2009). Sequences were depleted of barcodes and primers, then low quality sequences or sequences < 200bp, sequences with ambiguous base calls, or sequences that had multiple barcode or primer motifs, and sequences with homopolymer runs exceeding 8 bp were removed (Schloss et al 2011).
Sequences were subsequently aligned using the SILVA (http://www.MOTHUR.org/w
/images/9/98/Silva.bacteria.zip) reference database and were then denoised (“pre.cluster” command/MOTHUR) (Huse et al. 2010). Chimeras were removed with UCHIME algorithm implemented in MOTHUR (http://drive5.com/uchime) (Edgar et al. 2011). High quality sequences were classified (domain to genus level) using the Ribosomal Database Project (RDP)
Naïve Bayesian Classifier (Wang et al. 2007) and contaminants (e.g. mitochondria, Eukarya, and unknown domain) were removed. DNA distance matrices were calculated and used to define the number of operational taxonomic units (OTUs) at sequence divergences of 3% (97% similarity)
(Schloss and Westcott 2011).
Bacterial and archaeal diversity richness were calculated using rarefaction curves,
Invsimpson and Shannon, while species richness was estimated with ACE and Chao 1 nonparametric estimators (Table 1 & 2). Dendrograms for both Archaea and Bacteria were generated using the thetaYC (θYC) and the Jclass coefficients to determine similarities among the depths.
All unclassified OTUs greater than 1% of the total number of sequences were compared to known sequences using BLAST (http://www.ncbi.nlm.nih.gov/BLAST/). Sequences obtained from the database were aligned using ClustalX (http://www.ebi.ac.uk/Tools/clustalw2/ index.html), trimmed to similar size (approx. 250 bp) and then compiled using MEGA5 software
Laverty, 7
(Tamura et al., 2011) where phylogenetic analysis could be performed. Neighbor joining tree topology was implemented as determined by distance using the Jukes-Cantor model and bootstrapping was performed with 1,000 replicates to assign confidence percentages.
Results
The vertical distribution of water column parameters at Lake Ballard on October 7th is shown in
Figure 1. The oxycline was apparent between 4 and 7m with a high dissolved oxygen concentration of 5.9 mg/L and a low of 0 mg/L (determined by YSI). The thermocline extended roughly between depths 3 and 9 m, with an oxic zone temperature of 25C and an anoxic zone temperature of 10C. Samples collected from each depth varied in temperature, salinity, and dissolved oxygen concentrations.
Highest bacterial diversity is seen in the 12 meter samples (Fig. 2, A, B & C). The average coverage values for duplicate samples at each depth were 96%, 95.5%, and 90% for oxic, oxycline, and anoxic samples, respectively (Table 1), indicating that the sequence population from the anoxic sample was more diverse than the other two samples. This conclusion is supported by a statistical analysis of diversity in Table 1, where at 12 meters the diversity indices are greater for both diversity and richness estimators. Thirty different bacterial classes were found throughout the water column and 62 genera were identified; Cyanobacteria
(42%) constituted the majority of the total sequences with the next most retrieved bacterial classes being unclassified Bacteria (26%), Actinobacteria (8%), and Bacteroidetes (7%). At all depths, unclassified bacteria were present in proportionately high abundances: 23% at 3 meters,
30% at 6 meters, and 34% at 12 meters (Fig. 3). After unclassified bacteria, the most abundant bacterial classes varied with depth. At 3 meters (oxic zone) the largest relative abundances
Laverty, 8 showed Cyanobacteria (35%), Actinobacteria (14%), Bacteroidetes (11%), Betaprotobacteria
(10%), and Alphaprotobacteria (5%). At 6 meters (along oxycline), Cyanobacteria (57%),
Alphaproteobacteria (4%), Actinobacteria (3%), and Bacteroidetes (3%) were most frequently retrieved. At 12 meters (anoxic zone), the largest bacterial abundances showed Chlorobi (23%),
Deltaproteobacteria (12%), Cyanobacteria (12%), Bacteroidetes (11%) and Chloroflexi (3%).
At all depths, a total of 51.6% sequences at the genus level and 43.5% sequences at the family level of all sequence reads could not be classified.
Analysis of unclassified bacterial OTUs greater than 1% of the total number of sequences was performed by constructing a phylogenetic tree. The two most abundant unclassified OTUs were located at the 3 and 6 meter depths and accounted for Cyanobacteria (OTU 1) and
Actinobacteria (OTU 2) (Fig. 4). The most abundant OTU clustered well with two extensively studied Cyanobacterium species, Synechococcus and Cyanobium, signifying very close relatedness among these sequences. The second most abundant OTU, found in equal proportions among 3 and 6 meter samples, was branched amongst Actinobacteria, however, it clustered well with another unclassified OTU found in this study (OTU 24). All unclassified OTUs used in this tree were finally assigned a phylum based on closely clustered species, all of which were found to be representative of the sample location along the oxygen gradient.
Dendrograms were created for using two different calculators which represent similarities among samples. The ThetaYC calculator returns the Yue & Clayton measure of dissimilarity between the structures of two communities and the Jclass calculator returns the traditional
Jaccard index which describes the dissimilarity between two communities (“tree.shared” command/MOTHUR). Based on the bacterial and archaeal dendrograms’ clustering (Fig. 5 & 6,
A &B), the 3 meter and 6 meter samples were more closely related to each other than the 12
Laverty, 9 meter samples, which formed a separate branch. Venn diagrams (Fig. 7 & 8, A & B) are in accordance with the dendrograms, showing that 3 meter and 6 meter samples have more OTUs in common than either have with 12 meter samples.
The detection of the archaeal 16S rRNA gene was feasible only after nested amplification of all sampling sites except for the anoxic 12m samples, implying low Archaea abundance in the upper water column. Communities retrieved from DNA for replicate 12m samples showed similar phylogenetic distribution, in contrast to those retrieved after subsequent (nested) PCRs, suggesting uneven amplification of replicate samples (Fig. 9).
Five different archaeal classes were found throughout the water column and nine genera were identified; unclassified Archaea (52%) constituted the majority of the total OTUs with the next most retrieved archaeal classes being Methanospirillum (31%) and Methanosaeta (8.8%).
All Archaea OTUs identified belonged to the kingdom Euryarchaeota, apart from one
Crenarchaeota found in the 12m DNA sample, with all phyla consisting of methanogens. The most abundant Archaea in the 12m DNA samples consisted of Methanomicrobiales (46%), unclassified Archaea (30%), and Methanosarcinales (22%) (Fig. 9). In contrast, for nested samples, unclassified Archaea were the least present in the anoxic waters (0.06%) and instead dominated the upper water column community: 45% at 3 meters, 59% at 6 meters;
Methanomicrobiales were present in the highest abundances at all depths of nested samples: 45% at 3 meters, 59% at 6 meters, and 0.06% at 12 meters. The only depth exclusive community found was Methanocellales, present in the 3 meter nested community (10%). As with Bacteria, the full archaeal diversity of each sample depth was not revealed by the amount of sequencing carried out (Fig. 10)
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Discussion
Microbial life is of great importance for all ecosystems on Earth due to their roles in biogeochemical cycling. In this study, 454-sequencing was used to illustrate the bacterial and archaeal diversity and structure with depth in Lake Ballard. Similar studies on community structure and diversity with depth have been done in many diverse locations on Earth including the North Pacific Ocean, Baltic Sea, Mediterranean Sea, Arctic Ocean, and tropical and subtropical oceans, among others (Brown et al., 2009; Galand et al., 2011; Kirchman et al., 2010;
Koskinen et al., 2010; Labrenz et al., 2007; Pommier et al., 2010; Jing et al., 2013).
Since oxygen abundance, sunlight, and warm temperatures are important factors for many communities, it was hypothesized that greater bacterial diversity would be seen in oxic surface waters than in anoxic bottom waters. In this study, however, highest bacterial diversity was found with increasing depth (Fig. 2, A, B & C). This result is consistent with other studies done on lakes with similar physical factors (Humayoun et al., 2003; Galand et al., 2011). In one such study, Galand et al. found that the deeper monimolimnion community in the Clipperton
Lagoon, a stratified water column with oxic and anoxic zones, had the highest diversity, while the upper mixed layer and pycnocline had similar diversities. Other studies’ results such as one by Pommier et al. (2010) in the Mediterranean Sea also show parallels with this study, i.e. a decrease in richness and evenness from bottom to surface. Although we see higher bacterial diversity with depth, since the rarefaction curves from each sampling depth did not reach a plateau, the full diversity of each sample depth was not revealed by the amount of sequencing carried out (Koskinen et al., 2010). This suggests that the coverage was adequate to provide a full inventory of the dominant, but probably not rare, taxa.
Laverty, 11
The most abundant bacterial taxa retrieved from the oxic-zone and the oxic-anoxic interface consisted of Cyanobacteria, Actinobacteria, Bacteroidetes, Betaproteobacteria and
Alphaproteobacteria, phyla which are commonly found in freshwater environments
(Lymperopoulou et al., 2012). Interestingly, the two most abundant OTUs found in the anoxic zone were affiliated with Cyanobacteria, which is likely a result of sinking phytodetrital aggregates from the upper euphotic water (Lochte and Turley, 1988). Unclassified bacterial
OTUs were also present in large proportions at all three depths and the construction of a phylogenetic tree showed that the most abundant of these OTUs belonged to Cyanobacteria and
Actinobacteria. This tree provided good phylogenetic resolution for all unclassified OTUs above
1% of total sequences that were used for its construction.
Within Archaea, low diversity was observed at all depths in addition to decreased distribution among the water column (Fig. 9 & 10). Archaeal 16S rRNA was only amplified at 3 and 6 meters by using a nested PCR approach. This approach only captures presence of archaeal communities, therefore might be unrepresentative of relative abundances due to re-amplification of higher abundant sequences and “masking” of the rarer ones. Unsuccessful amplification of archaeal 16S rRNA from surface waters in this study is consistent with results observed in other similar studies (Durisch-Kaiser et al, 2005; Galand et al., 2011). Archaea were, however, amplified effectively from DNA at the 12 meter sample depth. Characteristic of methanogen populations, who produce methane as a metabolic byproduct (Durisch-Kaiser et al, 2005), our samples consisted of strictly methanogen populations.
Since methane oxidizing Archaea are frequently observed in association with sulfate- reducing bacteria (Valentine, 2007) and due to presence of Deltaproteobacteria as well as evident sulfur gases, it was hypothesized that Archaea would be present with higher abundances
Laverty, 12 in the anoxic zone of the lake. This study showed high abundance only in the anoxic water, with very low abundance in the upper water column (Fig.10). All archaeal sequences found, from
DNA and nested PCR combined, belonged to the kingdom Euryarchaeota, apart from one
Crenarchaeota. Cultured members of Crenarchaeota include thermophiles, while Euryarchaeota include methanogens, halobacteria, and some extreme thermophilic aerobes and anaerobes
(Valentine, 2007). The most abundant Archaea in the 12m DNA samples consisted of
Methanomicrobiales, which are commonly recovered in freshwater and marine sediments
(Lymperopoulou et al., 2012). High abundance of Methanomicrobiales found in the anoxic zone is consistent with a study done by Galand et al. (2011) in the Clipperton lagoon, a water column with similar physical conditions.
To describe the similarities in membership and structure of the six samples, dendrograms using the Jaccard coefficient (jclass) and the thetaYC (θYC) community structure similarity were constructed (Fig. 5 & 6, A &B). The closely clustered samples in these dendrograms are replicates (e.g. 3A & 3B; 6A & 6B; 12A & 12B), showing distinct separations between all three depths. Although the dendrograms have similar topologies, the terminal branch lengths of the
Jaccard coefficient dendrogram are longer. This indicates that while these samples have different memberships (jclass), the relative abundances of the shared OTUs are similar. Thus, the differences between the communities at different depths are likely attributed to the rarer OTUs
(Schloss et al. 2009).
Bacterial and archaeal community structures observed in this study are consistent with other studies showing changes in diversity along stratified water columns (Comeau et al, 2012;
Durisch-Kaiser et al, 2005; Massana et al, 1997; Labrenz et al, 2007). Our results show clear bacterial community distinctions between the upper oxic water and lower anoxic water, most
Laverty, 13 probably in response to optimal physical, chemical, or nutritional requirements, and may also be a result of competition and predation stressors. Additional diversity studies executed in tandem with investigation into their functions will establish a better understanding of the microbial dynamics in Lake Ballard and show how these communities may be functionally related to the physical and chemical structure of the water column.
Application of ‘next generation’ sequencing, such as 454 pyrosequencing, has allowed much greater sequence reads than conventional sequencing approaches (Kirchman et al., 2010).
This new technology has also helped uncover detailed understanding of microbial diversity, abundance, community structure, and distribution profiles along environmental gradients (Jing et al., 2013). Greater awareness of microbial diversity as well as community structure in various aquatic environments is fundamental to understanding ecological roles played by marine microbial communities. Developing this greater understanding can be implemented towards more effective bioremediation processes, pollution management, and potentially new medical advancements. In addition, evidence of microbial community stability in aquatic systems has been inferred by few natural experiments (e.g. Jones et al., 2008; Yannarell et al., 2007) and still little is known about the response of microbial communities to environmental disturbances. In this context, Lake Ballard may serve as a stable system where the structure of the microbial community on a temporal basis remains to be defined and associated to any future disturbances as a point of reference, and then may as well serve as a useful model for “microbial disturbance ecology” (Shade et al. 2011).
Laverty, 14
Acknowledgments
I would like to thank Dr. Despoina Lymperopoulou for instruction and development of the final project as well as guidance in sequence data and phylogenetic analysis and editing of the final paper; Dr. Fred C. Dobbs for instruction and support in the Field Studies course; Joyce Strain and Ben Hiza for support and supervision in field work; Chris Powell for technical support and
Matlab lessons; Old Dominion University’s department of Ocean, Earth, and Atmospheric
Sciences for funding; Hoffler Creek Wildlife Preserve for the study site; and the rest of the supportive researchers in the Field Studies 441 class of 2013-2014.
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Table 1
Number of bacterial sequences analyzed, observed diversity richness (OTUs), estimated OTU richness (ACE and Chao 1), diversity index (Invsimpson, Shannon), and estimated sample coverage (EST) for 16S rRNA libraries of water samples at different depths in Lake Ballard. “A” and “B” correspond to replicates of the same depth.
Number Richness Number of reads ESC Diversity Index of OTUs estimators Cleaned Number Good’s Group Initial reads 1Invsimpson 1Shannon(H`) 2Chao1 2Ace sequences of OTUs C LB 3A 4150 2787 199 95.95% 21.44 3.74 365 321 LB 3B 11839 6206 268 96.00% 19.53 3.66 404 381 LB 6A 5349 2497 220 95.71% 11.61 3.60 325 292 LB 6B 6742 2307 213 95.32% 9.52 3.49 432 320 LB 12A 2961 2029 330 91.25% 17.07 4.01 477 477 LB 12B 2521 1764 341 89.34% 22.59 4.29 679 619
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Table 2
Number of archaeal sequences analyzed, observed diversity richness (OTUs), estimated OTU richness (ACE and Chao 1), diversity index (Invsimpson, Shannon), and estimated sample coverage (EST) for 16S rRNA libraries of water samples at different depths in Lake Ballard. “A” and “B” correspond to replicates of the same depth.
Diversity Index Richness estimators
Group 1Invsimpson 1Shannon(H`) 2Chao1 2Ace LB 3A.N 6.30 2.15 100 117 LB 3B.N 2.20 1.40 105 222 LB 6A.N 2.49 1.04 51 52 LB 6B.N 2.01 0.96 35 49 LB 12A.N 3.15 1.64 182 299 LB 12B.N 3.12 1.57 151 197 LB 12A 4.22 1.75 107 174 LB 12B 4.08 1.70 59 83
Laverty, 22
Figure 1
CTD data of vertical profile of the water column in the deep spot of Lake Ballard on October 7th,
2013 (date of sampling) indicating temperature in blue and salinity in red.
Laverty, 23
A. 400
350
300
250
200 3A
Number OTUs of 150 3B 6A 100 6B 12A 50 12B
0 0 2000 4000 6000 8000 10000 12000 Number of sequences
B.
400 3A 350 3B 6A 300 6B 12A 250 12B 200
150 Number of OTUs of Number 100
50
0 0 500 1000 1500 2000 Number of sequences
Laverty, 24
C.
400 3m 350 6m 12m 300
250
200
150 Number of OTUs of Number
100
50
0 0 500 1000 1500 2000 Number of sequences
Figure 2
Rarefaction analysis of V1/V2 pyrosequencing reads indicating the observed number of OTUs at a genetic distance of 97% similarity for water samples collected from the oxic-zone (3m), oxic- anoxic interface (6m) and anoxic-zone of Lake Ballard. A) Full dataset, B) Normalized (sub- sampled) dataset with n=1764 sequences and C) similar to “B” but averaged for each depth with standard deviation calculated.
Laverty, 25
100%
90%
80%
70%
60%
50%
40% Relative Abundance (%) 30%
20%
10%
0% 3A 3B 6A 6B 12A 12B Samples Cyanobacteria Un. Bacteria Actinobacteria Bacteroidetes β-proteobacteria α-proteobacteria Chlorobi δ-proteobacteria Un. Proteobacteria γ-proteobacteria Chloroflexi Others
Figure 3
The relative abundance of partial sequences of the bacterial 16S rRNA gene were estimated by classification at the phylum level, using MOTHUR with a modified 16S rRNA database from the
Ribosomal Database Project (RDP). Numbers (“3”, “6”, “12”) designate depth and letters (“A”,
“B”) designate replicate. Bacteria labeled with “Un.” indicate unclassified.
Laverty, 26
Figure 4
Phylogenetic tree of the unclassified bacterial partial 16S rRNA (ca.250 bp) of OTUs (in bold) retrieved from Lake Ballard based on the neighbor-joining method as determined by distance using the Jukes-Cantor model. Numbers of sequences represented by each OTU are shown in parentheses. Bars next to OTUs represent their relative abundances at corresponding depths. One thousand bootstrap analyses were conducted, and percentages ≥50% are indicated at nodes.
Numbers in brackets are GenBank accession numbers and numbers in parentheses correspond to sequences. Scale bar represents 2% estimated distance.
Laverty, 27
A.
LB6B LB6A LB3B LB3A LB12B LB12A
0.1
B.
LB6B LB6A LB3B LB3A LB12B LB12A
0.1
Figure 5
Distances between bacterial communities were calculated with the thetaYC (A) and the jaccard
(B) coefficients and each community was clustered using the UPGMA (Unweighted Pair Group
Method with Arithmetic Mean) algorithm in MOTHUR.
Laverty, 28
A.
LB6B.N LB3B.N LB6A.N LB3A.N LB12B.N LB12A.N LB12B LB12A
0.1
B.
LB6B.N LB6A.N LB3B.N LB3A.N LB12B.N LB12A.N LB12B LB12A
0.1
Figure 6
Distances between archaeal communities were calculated with the thetaYC (A) and the jaccard
(B) coefficients and each community was clustered using the UPGMA (Unweighted Pair Group
Method with Arithmetic Mean) algorithm in MOTHUR.
Laverty, 29
A.
B.
Figure 7
Venn diagrams at distance 0.03 (97% similarity) depicting the common OTUs (in numbers) retrieved among the different depths for each replicate series. OTUs in overlapped areas represent the number of OTUs in common between those depths.
Laverty, 30
A.
B.
Figure 8
Venn diagrams at distance 0.03 (97% similarity) depicting the common OTUs (in numbers) retrieved among the different depths for each replicate series. OTUs in overlapped areas represent the number of OTUs in common between those depths.
Laverty, 31
100%
80%
60%
40% Relative abundance (%) Relative
20%
0% 3A.N 3B.N 6A.N 6B.N 12A.N 12B.N 12A 12B Samples Methanomicrobiales Methanosarcinales Un. Archaea Methanocellales Others
Figure 9
The relative abundance of partial sequences of the archaeal 16S rRNA gene were estimated by classification at the phylum level, using MOTHUR with a modified 16S rRNA database from the
Ribosomal Database Project (RDP). Numbers (“3”, “6”, “12”) designate depth and letters (“A”,
“B”) designate replicate. “Un.” indicate unclassified Archaea . DNA could not be amplified for the archaeal 16S rRNA gene at 3 and 6 m and “N” designates the use of nested PCR amplification prior to pyrosequencing. For comparison, 12 m DNA was also subjected to nested
PCR, as well as to direct pyrosequencing.
Laverty, 32
A.
200 180 160 140 LB12A 120 LB12A.N 100 LB12B 80 LB12B.N LB3A.N Number of OTUs of Number 60 LB3B.N 40 LB6A.N 20 LB6B.N 0 0 2000 4000 6000 8000 10000 Number of sequences
B.
90 LB12A LB12B 80 LB12A.N 70 LB12B.N 60 LB3A.N LB3B.N 50 LB6A.N 40 LB6B.N 30 Number of OTUs of Number 20
10
0 0 500 1000 1500 2000 2500 Number of sequences
Laverty, 33
C.
90
80 LB 12 LB 12.N 70 LB 6.N 60 LB 3.N
50
40
30 Number of OTUs of Number 20
10
0 0 500 1000 1500 2000 2500 Number of sequences
Figure 10
Rarefaction analysis of V3 pyrosequencing reads indicating the observed number of OTUs at a genetic distance of 97% similarity for water samples collected from the oxic-zone (3m), oxic- anoxic interface (6m) and anoxic-zone of Lake Ballard. A) Full dataset, B) Normalized (sub- sampled) dataset with n=2300 sequences and C) similar to “B” but averaged for each depth with standard deviation calculated. DNA could not be amplified for the archaeal 16S rRNA gene at 3 and 6 m and “N” designates the use of nested PCR amplification prior to pyrosequencing. For comparison, 12 m DNA was also subjected to nested PCR, as well as to direct pyrosequencing.
Laverty, 34
1
Andrew Foor
OEAS 442
Historical Comparison of Temperature and Salinity in the Lafayette River
during a Flood and Ebb Tide.
Abstract:
I studied the Elizabeth River’s impact on the distribution of salinity and temperature throughout the Lafayette River during a flood and ebb tid e. Historical CTD data from the Hampton
Boulevard Bridge in Norfolk Virginia and two cruise days in, which 24 and 26 CTD casts were taken on the first and second day, respectfully; from this data, averages of the monthly change in temperature and salinity at the Hampton Bridge were calculated and compared to recently measured surface values at approximately the same location. Due to the Elizabeth’s higher salinity (13 PSU – 15 PSU), the stratification of the Lafayette is dependent on the input of the
Elizabeth. Further research is needed to analyze the impact of wind on the stratification of the
Lafayette. 2
Introduction:
The Lafayette River is one of the tributaries of the Elizabeth River located in Norfolk,
Virginia that is considered fairly urbanized compared to the surrounding watershed. Some of the issues facing this tidal estuary today are the impacts from pollution, rising sea levels, too many algae blooms and too little oxygen (Hassett et al. 2005). The Lafayette has a low volume of freshwater compared to the salinity input from the Elizabeth River and is a small watershed
(Blair et al. 1976). The Lafayette is vertically mixed due to its shallow depth and tidal currents
(Montgomery 1972). “In the York and Rappahannock Rivers tributaries of Chesapeake Bay,
Haas (1977) and Sharples et al. (1994) indeed found that salinity oscillated between conditions of considerable vertical salinity stratification and homogeneity on a cycle that was closely related with the spring–neap tidal cycle” (Li et al. 2007). This is consistent with findings in the Lafayette
River, were the water column oscillated between homogeneity and stratification.
The focus of this study was to analyze salinity and temperature profiles of the Lafayette River and determine the impact of the Elizabeth River on the distribution of these properties throughout the Lafayette during a flood and ebb tide.
The salinity exerts a control over the water quality of the river (MacCready and Geyer, 2001).
The water quality in the Lafayette River could provide valuable information used in oyster, as well as other Chesapeake Bay ecosystem, restoration projects
Methods:
Research cruises aboard the R/V Riptide were conducted on February 17th and March
10th, 2014, cruise 1 was conducted during a flood tide and cruise 2 was during an ebb tide.
Twenty four and twenty six casts were made during the first and second cruises, respectfully, 3 using a Conductivity, Temperature, Depth profiler (CTD). We added two casts on the second day
(casts 25 and 26) to determine the temperature and salinity values in the Elizabeth River.
Stations in the channel from figure 1 were chosen to examine the rivers temperature and salinity on the two days. Due to the change in depth along the channel, each casts recorded measurements were interpolated from the surface with an interval of 0.1 meters to the maximum depth of each cast. This interpolation is needed in order to concatenate each CTD cast into a single matrix. Using these interpolated values, temperature and salinity were contoured across depth and distance.
At station 16, CTD measurements had been made from 2000 to 2005 by Dr. Arnoldo
Valle-Levinson, formerly a professor at CCPO. This historical data was also interpolated to an interval of 0.1 meters to a maximum depth of 7 meters. In reference to the historical data, numerous measurements were inaccurate. Some of these inaccuracies were temperature values ranging from 70° to 120° Celsius on several CTD casts and human error for not assimilating the
CTD to the water before lowering the CTD in the water column. This causes the first meter of water to be inaccurate with the regards to the water column. These measurements and the inaccurate temperature values were removed. Data from 6 meters to 7 meters was inaccurate due to sediment that impacted the conductivity and temperature sensor readings from the CTD touching the river bottom. This data was removed and the values analyzed will be from the surface to 6 meters depth. Temperature values recorded at 1 meter below the surface were used for the deleted surface values. These measurements were then averaged by month to determine seasonal variability of the Lafayette River. The averaged surface values were plotted against time for temperature and salinity along with the maximum and minimum surface values from the cross section at the Hampton Boulevard Bridge (HBB). 4
Results:
For this study, the cross sections were ignored because measurements at each cross section did
not show a statistical difference for both cruises. Due to this finding, temperature values across
channel are assumed equal as well as salinity. (Fig. 2) shows bathymetry of the channel from the
Granby Street Bridge (leftmost on the horizontal axis) to the HBB (rightmost) and the location of
multiple casts along the channel. This plot depicts the total distance of both cruises as well as the
depth of each CTD cast.
For both cruise days, the water column in (Fig. 3 & Fig. 4) was homogeneous except at
the mouth of the Lafayette River. There was an impact of the Elizabeth River on the Lafayette
during a flood tide as indicated by the consistent horizontal and vertical temperature values from
1,500 meters to the 7,000 meter mark in (Fig. 3). Salinity in (Fig. 4) shows the change in salinity
between a greater saline value (13 PSU – 16 PSU) during a flood tide and a lower saline value
(10 PSU – 13 PSU) during an ebb tide.
The seasonal average of temperature within the water column shown in (Fig. 5) at the
HBB is vertically homogeneous in the winter and summer months, then becomes more stratified
in the spring and fall. The salinity average has a more stratified water column compared to
temperature average. These plots give an accurate representation of the historical record in the
Lafayette River for temperature and salinity values, which can then be compared to the casts at
station 16 to determine differences from the HBB averages.
From (Fig. 5), we can assume that recorded surface temperature values can be integrated
across depth for the water column due to the homogeneity of the thermocline. Due to this
generalization, a comparison between station 16’s surface measurements on the two cruises to 5 that of the seasonal averages shows both cruises’ surface values at station 16 were lower than the seasonal average.
Discussion:
Given the homogeneity of its water column, the Lafayette River would be classified as a well-mixed estuary with tidal forcing (Robinson et al. 2007). Results show differences in temperature and salinity between a flood and ebb tide in the Lafayette River. From (Fig. 4), the increase in the salinity of cruise 1 shows there is an impact from the Elizabeth River on the
Lafayette during flood tides. A comparison between (Fig. 3) and the temperature contour from
(Fig. 5) show the temperature values recorded from cruise 1 and 2 were statistically same, but also the homogeneity of the water column in cruises 1 and 2 is also seen in the historical data during the same time frame. However the salinity contour in (Fig. 5) shows there was a decrease in salinity during the month of October. When I looked at the historical record, between years
2004 and 2005, the average salinity dropped by approximately three PSU. There is a decrease in surface temperature and surface salinity of the cruise measurements compared to the historical data in (Fig. 6). The cause is related to a colder winter Hampton Roads has experienced compared to recent years.
There are several errors with the figures and several factors that were not taken into account. In (Fig. 3) and (Fig. 4), there are inconsistences in the first two casts from cruise 1 to 2.
The second cruises depth profile is correct, while the first cruise has missing data values on the figure from 2.5 to 5.5 meters. This is due to an error in a function in Matlab that concatenates different size arrays into a matrix. However because of homogeneity of the water column during cruise 1, the error from missing data at the bottom depths is minimal. Precipitation and wind 6 were not considered during the study, as well as weather phenomena that could impact the seasonal average.
To state that there is an impact of the Elizabeth River on the Lafayette River is evident.
However, the exact impact on the Lafayette is still unanswered from this study. There is a difference in the water column between cruise 1 and 2, however the exact impact cannot be quantified without further cruises. This difference could possibly be due to strong wind gusts during the first cruise, which may have caused strong wind-driven mixing in the channel as compared to tidal mixing (Chen, et al, 2012). Further research is needed to look at the lower surface temperature and salinity values compared to the historical data in reference to an impact of the 2014 winter season in Hampton Roads, as well as the impact of tides and other factors to see the possible change in this ecosystem.
Acknowledgements:
Would like to thank Dr. John Klinck, Dr. Chet Grosch, Dr. Fred Dobbs, Dr. Arnoldo Valle- Levinson, Dr. Larry Atkinson, Chris Powell, Curtis Barnes, Ben Hiza, and Joyce Strain.
7
Works Cited:
Blair, C. H, J. H. Cox, and C. Y. Kuo. 1976. Investigation of flushing time in the Lafayette River, Norfolk, Virginia.
Chen, S., W. R. Geyer, D. K. Ralston, and J. A. Lerczak. 2012. Estuarine exchange flow quantified with isohaline coordinates: contrasting long and short estuaries. J. Phys. Oceanogr. 42.5: 748-763.
Haas, L.W., 1977. The effect of the spring-neap tidal cycle on the vertical salinity structure of the James, York and Rappahannock Rivers, Virginia, USA. Estuar. Coast. Mar. Sci. 5, 485–496. Hassett, B., M. Palmer, E. Bernhardt, S. Smith, J. Carr, and D. Hart. 2005. Restoring watersheds project by project: trends in Chesapeake Bay tributary restoration. Front. Ecol. Environ. 3(5), 259-267.
Li, M. and L. Zhong. 2009. Flood–ebb and spring–neap variations of mixing, stratification and circulation in Chesapeake Bay. Cont. Shelf. Res. 29(1), 4-14.
Montgomery, J. R. 1979. Predicting level of dissolved reactive phosphate in the Lafayette River, Virginia, from information on tide, wind, temperature, and sewage discharge. Water. Resour. Res. 15.5: 1207-1212.
Robinson, C., L. Li, and D. A. Barry. 2007. Effect of tidal forcing on a subterranean estuary. Adv. Water Resour. 30.4: 851-865.
Sharples, J., J. H. Simpson, J. M. Brubaker. 1994. Observations and modelling of periodic stratification in the upper York River estuary, Virginia. Estuar. Coast. Shelf. S. 38, 301– 312. NOAA Chart (12253) Norfolk Harbor and Elizabeth River. 2014. http://www.charts.noaa.gov/OnLineViewer/12253.shtml.
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Figures:
Figure 1. Chart showing the location of the CTD casts made on the two cruises. Casts 25 and 26 were only conducted on the second day in order to determine the Elizabeth River’s Temperature and Salinity measurements.
Figure 2. Distance versus depth plot shows the length of the channel surveyed, as well as the depth of each CTD used in the study. The 0 on the x axis represents Granby Street Bridge (GSB). The x axis is from GSB to station 24.
9
Figure 3. Temperature of the channel contoured over depth and distance. The temperature ranges from 3°C to 7°C. The top plot is cruise 1 and plot below is cruise 2. Both figures show warmer water near GSB, colder water near the mouth of the Lafayette River.
Figure 4. Salinity of the channel contoured over depth and distance. Salinity ranges from 8 PSU to 16 PSU. The top plot is cruise 1 and plot below is cruise 2. The first cruise shows less saline water at GSB compared to more saline water at the mouth of the river.
10
Figure 5. Seasonal temperature and salinity at the HBB. Temperature values range from 5°C to 25°C. Salinity values range from 14 PSU to 22 PSU. Seasonal pattern shows a warm summer with a cold winter temperatures. The salinity decreases in the spring due to increase in precipitation during the spring season.
Figure 6. Seasonal surface average at HBB, with the minimum and maximum surface values at the cross section of HBB from cruise 1 and cruise 2. The black line represents the seasonal average and the red and blue stars represent cruise 1 and 2, respectively. Temperature and salinity values for both cruises were both below the average.