Resource limitation affects productivity and heterocyst formation in nitrogen-fixing

cyanobacteria

Hansen Johnson

Semester in Environmental Science, Marine Biological Laboratory,

Woods Hole, MA

Bates College, Lewiston, ME

Mentors: Ed Rastetter and Zoe Cardon

December 2011

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Keywords: Resource optimization, substitutable resource, cyanobacteria, heterocyst, Anabaena,

Abstract

Cyanobacteria, of the species Anabaena circinalis, were used as model organisms to test

several ideas pertaining to the concept of substitutable resource optimization. These

cyanobacteria can acquire nitrogen through fixation or assimilation of nitrate. Fixation requires

the presence of heterocysts, which are specialized cells that are energetically expensive to make,

while assimilation is only possible when nitrate is available in sufficient concentrations. The

primary goals of this study were to compare Anabaena growth and heterocyst development over

a nitrogen and phosphorus gradient as well as determine if substitutable resource acquisition

strategies can differentially affect the environment. A regression design was used to establish

growth solutions with a nitrogen gradient from 0.3 to 300.3 mg/L nitrate and a phosphorus

gradient from 0.035 to 70 mg/L phosphate. Samples were incubated for 14 days before being

analyzed for chlorophyll a concentration, dry biomass, heterocyst density, filament length, 13-C

and 15-N isotope fractionation, pH, and alkalinity. The phosphorus gradient had no significant

effect on any treatment. Chlorophyll a concentration, filament length, and pH all agreed that

optimal growth occurred at 30 mg/L nitrate. Heterocyst density showed that heterocysts only

formed below 10 mg/L nitrate, and this concentration indicated the point at which the Anabaena

switched between fixing nitrogen and assimilating nitrate. These two processes had differential

effects on the environment as nitrate assimilation generated alkalinity while nitrogen fixation did

not. Because nitrate assimilation generated alkalinity, the total pool of dissolved inorganic

carbon actually increased as available nitrogen increased.

Introduction

Plants have evolved impressive strategies of compensating for conditions across the

globe in which the relative availability of essential resources often vary by more than two orders

of magnitude (Chapin et al 1987). The plants, and other organisms, that are confronted with this

variation survive by allocating their effort to acquire the resources necessary to maximize growth

and production in a process called resource optimization. This concept has developed over the

last thirty years and has been substantiated by both field and modeled observations and

experimentation (Rastetter et al 2001). Further study has revealed that organisms often have two

or more strategies that can be substituted for one another to fulfill the same resource requirement.

Strategic allocation of resources of this kind is more specifically referred to as substitutable

resource optimization (Tilman 1982; Rastetter forthcoming).

Nitrogen-fixing cyanobacteria, often referred to as perfect producers for their unique

ability to both fix nitrogen and perform oxygenic photosynthesis, consistently choose among

three sources of nitrogen; they can either fix nitrogen gas from the atmosphere or acquire nitrate

or ammonium in their environment (Kumar et al 2010). Cyanobacteria have specialized cells

called heterocysts that enable nitrogen fixation. These thick-walled, anoxic cells have the ability

to form and break down depending on environmental conditions (Mariscal and Flores 2010).

Depending on availability, some species of cyanobacteria can also either take up carbon dioxide

or bicarbonate as substitutable sources of carbon (Gundersen and Mountain 1973). The pathways

that cyanobacteria choose to acquire these substitutable nutrients affects both the organism and

its environment.

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One would intuitively believe that cyanobacteria’s ability to obtain the same resource

through multiple pathways would allow it to dominate many systems (Vitousek and Howarth

1991). Dierber and Scheinkman (1987) found nitrogen fixation by cyanobacteria often does play

an important role in nitrogen cycling, providing close to half of all nitrogen inputs to a

freshwater lake in Florida. Cyanobacteria also receive a lot of publicity for the dense and often

destructive blooms that occur periodically under the right circumstances. Blooms can form thick

mats that consume oxygen, block light, and even emit strong neurotoxins in addition to other

effects on the environment (Smith 1990; Lehtimaki et al 1997; Paerl et al 2001). Despite the

adaptability and periodic dominance of cyanobacteria, a blue-green carpet does not cover our

world’s oceans.

Each resource acquisition pathway has certain tradeoffs associated with it (Rastetter

forthcoming; Rastetter et al 2001). For example, the formation and maintenance of heterocysts

and their component parts makes nitrogen fixation an energetically costly process (Kumar et al

2010). However, this process theoretically becomes beneficial when nitrate and ammonia are so

rare that the organism will save energy by fixing its own nitrogen rather than exploit the small

amount of nitrogen available from its surroundings (Vitousek et al 2002; Rastetter et al 2001).

Heterocysts demand energy that the organism would have otherwise dedicated to growth or

reproduction. As a consequence of this redistribution of resources, changes in the productivity of

cyanobacteria with and without heterocysts should indicate the relative cost of nitrogen fixation.

I designed an experiment to test this concept of substitutable resource optimization in the

nitrogen-fixing cyanobacteria Anabaena. My primary goal was to understand how biomass and

productivity changed over a nutrient gradient and connect any variation in biomass back to

tradeoffs associated with heterocyst formation. Aldea et al (2008) pointed out that heterocysts

depend heavily on their neighboring cells for carbon and provide nitrogen in return. This is not

unlike the symbiosis between nitrogen fixing Rhizobium and legumes in which the energetic

demands of the bacteria actually restrict the growth of the plant (Ryle et al 1979). My hypothesis

was that the presence of heterocysts would cause a significant reduction in productivity because

of the added costs associated with nitrogen fixation.

I wanted to better understand what nutrient concentrations trigger heterocyst formation as

well as the nature of the transition from possessing heterocysts to lacking them and vice versa.

Ogawa and Carr (1969) found that Anabaena exposed to nitrate, ammonium, and atmospheric

nitrogen formed heterocysts with different densities. They did not, however, monitor heterocyst

density over a concentration gradient. Mickelson et al (1966) monitored heterocyst density over a

nitrogen gradient but did not carry out the experiment until heterocysts disappeared completely. I

expanded on their work by creating a large enough gradient to capture the transition to and away

from the presence of heterocysts. I believed that the cost of nitrogen fixation would cause the

transition to take place rapidly and at a fairly low nitrogen concentration.

My third and final goal was to investigate how the resource acquisition strategies

implemented by the cyanobacteria would alter their environment. I did not entirely know what to

expect, as this aspect of cyanobacterial life is poorly documented. Brewer and Goldman (1976)

demonstrated that nitrate assimilation by phytoplankton has the capacity to increase alkalinity

while nitrogen fixation does not. Gundersen and Mountain (1973) also mentioned that

bicarbonate uptake by nitrifying bacteria can have a slight but significant effect on alkalinity.

Many more environmental effects result from the formation of dense blooms but these are more

difficult to recreate and study in culture (Pearl et al 2001). I hoped that this study would increase

4

our understanding of substitutable resource optimization in cyanobacteria and how different

resource acquisition strategies alter the organism and its environment.

Methods

Preparing liquid culture

Anabaena circinalis in liquid culture was supplied by the Connecticut Valley Biological

Supply Company (model L 111LS). The cyanobacteria were sterilely transferred to four 250 mL

flasks containing sterile BG11 growth solution (Stanier et al 1971) and bubbled in a growth

chamber (Conviron Model No. PGW36DE) for ten days. All sterile work was done in a laminar

flow hood (Labconco Model No. 3612504). All the cultures were exposed to between 27+/-5 uE

of light for 16 hours per day at a temperature of 25° C. Carbon dioxide levels were slightly

elevated in the chamber and fairly constant at 495 ppm. After the ten day period I chose the flask

with the least contamination, determined visually and by microscope, and used it to inoculate

liquid cultures that varied in the relative availability of nitrogen and phosphorus.

I used a regression design to achieve the desired gradients of nitrogen and phosphorus

(Table 1). I made a large batch of BG11 without adding any nitrogen, in the form of NaNO3, or

phosphorus, in the form of K2HPO4. I divided this batch into 12 one litre bottles, each containing

800 mL of solution. To half of the bottles I added NaNO3 to yield approximate concentrations of

0, 6, 30, 60, 300 and 600 mg/L nitrogen from nitrate (designated as N1 through N6). A small

amount of ammonium, present in ferric ammonium citrate, contributed around 0.3 mg/L of

nitrogen to every solution. To the other half of the bottles I added K2HPO4 to yield approximate

concentrations of 0.07, 1.4, 7, 14, 70, and 140 mg/L phosphorus (designated as P1 through P6). I

saved approximately 200 mL of each of these 12 subsamples for future analysis and comparison.

I combined 100 mL of every concentration of nitrogen solution with 100 mL of every

phosphorus solution (Table 1). This combination diluted the initial nutrient concentrations by a

factor of two. It also achieved an approximate nitrogen to phosphorus molar ratio of 10 in N1P1,

N2P2, N3P3, N4P4, N5P5, and N6P6 treatments as well as a seven order of magnitude

difference from lowest to highest ratios (Table 2). The final 36 solutions were stored in 250 mL

Erlenmeyer flasks.

I inoculated these 36 flasks with 0.5 mL of well-mixed Anabaena culture under sterile

conditions. They were allowed to grow under the same conditions described above except they

were shaken, not bubbled, using a shaker table (Gyrotory Model G2) at approximately 75 rpm. I

also swirled the cultures by hand daily to ensure adequate mixing was taking place. They grew

under these conditions for two weeks before I harvested them for processing.

Chlorophyll A Determination

After ten days of growth I removed a small subsample, approximately 20 mL, of

Anabaena from culture under sterile conditions. I transferred half of the removed volume to test

tubes and run them through a 10-AU Fluorometer to determine the relative presence of

chlorophyll a. All vessels were adequately mixed prior to any transfer of culture or run through

the Fluorometer. Fluorometer measurements were taken immediately following removal of the

subsamples from culture and again after allowing the subsamples to adjust to the dark overnight.

I repeated these methods again after two weeks of total growing time.

5

To describe patterns in chlorophyll a, I created a model using the modified inhibition

equation below.

𝑃𝑥 ∗ 𝑒−𝛼𝑥𝑥

𝑘 + 𝑥+ 𝑓𝑜

Px = maximum chlorophyll a (mg/L)

k = half saturation coefficient (mg/L)

x = nitrogen concentration (mg/L)

a =coefficient of inhibition (L/mg)

fo =base chlorophyll a (mg/L)

I was able to successfully apply this model to both 10 and 14 day chlorophyll a data by only

altering the magnitude parameters of Px and fo.

Biomass and Stable Isotope Analysis

I measured the final biomass by filtering cyanobacteria onto a pre-weighed, ashed, and

dried 25 mm GF/F filter and recording the volume of solution required to saturate the filter. A

positive pressure filtration system that provided approximately 15 lbs/in2 of suction was used. I

rinsed the system with deionized water in between samples. Following filtering, each filter was

dried in a drying oven at 66°C for approximately 36 hours before they were weighed on Mettler

balance (Model AE-163). The same scale was used to measure initial and final masses. The mass

of the cyanobacteria divided by the volume of liquid filtered provided an estimate of total

biomass per unit of volume in each sample.

I selected a subsample of the N1P3, N2P3, N3P3, N4P3, N5P3, and N6N3 filters as well

as a sample of NaNO3 used to prepare the nutrient solution to analyze for carbon and nitrogen

isotopes. I packed half of each filter into a tin receptacle and submitted them to the MBL Stable

Isotope Laboratory for 15-N and 13-C analysis.

pH Analysis

I used an Orion 520A pH meter with a Ross Combination pH electrode (Model No.

8102BN) to measure pH directly from the 250 mL Erlenmeyer flasks after the full two week

growth period. Each flask was vigorously swirled prior to measurement and a stir bar was used

to keep the solution mixed during the measurement. I used the same method to measure the pH

of the initial 12 nitrogen and phosphorus solutions that had not been inoculated. I had to use a

smaller volume, approximately 50 mL held in 100 mL beakers, in the pH measurement of these

initial samples.

Alkalinity Titration and Dissolved Inorganic Carbon Calculation

I determined alkalinity in these samples using a sulfuric acid titration. Because of time

and resource limitations, alkalinity was only measured in initial and final N1P4, N2P4, N3P4,

N4P4, N5P4, and N6N4 treatments. Initial refers to treatments that were not inoculated with

Anabaena while final refers to those that were. I prepared the initial treatments by combining

approximately 12.5 mL of each nitrogen treatment with 12.5 mL of the P4 phosphorus treatment.

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Final treatments were removed from the shaker tables and allowed to settle overnight. I removed

approximately 30 mL of liquid from the surface of the solution and filtered it through 25 mm

GF/F filters using syringes fitted with swinex attachments. I ran 5 mL through the filter and

discarded it in an effort to remove any contaminants. I filtered and collected the remaining 25

mL for alkalinity analysis.

I used an Accumet pH/conductivity meter (Model 20) to measure the conductivity in mV

of the sample as I added 0.16 N sulfuric acid solution with a Hach microtritrator. I added enough

acid to increase the conductivity to 195 mV and then repeatedly added 0.025 mL of acid and

recorded the resulting conductivity until I had ten data points. These data were entered into a

spreadsheet that calculated the alkalinity of the sample (Giblin, 2011).

I used the following equations, along with pH and alkalinity data, to calculate total

dissolved inorganic carbon, carbonate, bicarbonate, carbonic acid, and carbon dioxide in each

sample.

32

1

37

1 45.4COH

HCOHek

1

3

2

311

2 69.4

HCO

COHek

otherALKHOHCOHCOALK 112

3

1

3 2

111410 OHH

I solved the above equations simultaneously to calculate the four unknowns (HCO3-1

, CO3-2

,

H2CO3 and OH-1

). These calculations were made assuming ALKother was equal to zero and that

bicarbonate is primarily responsible for determining the alkalinity in freshwater systems

(Henriksen 1979). I then applied the stoichiometric balance outlined by Stumm and Morgan

(1995) to make a prediction of alkalinity based on nitrate assimilation.

106 CO2 + 16 NO3- + HPO4

2- + 122 H2O + 18 H

+ C106H263O110N16P1 + 138 O2

I assumed that half of biomass was carbon and used the carbon to nitrogen ratio of 106:16 to

predict the amount of nitrogen in biomass. The C:N ratio tends to fluctuate more in nitrogen-

fixing cyanobacteria than phytoplankton but Redfield ratio is still appropriate to use (Geider and

La Roche 2002). The 1:1 ratio of nitrate assimilated to protons released was used to calculate the

change in alkalinity in the system.

Heterocyst counts

I created a new method of quantifying heterocysts and filament length. I added 5 μL of

well-mixed culture to an Improved Neubauer Hemacytometer (Model No. 707e). I then

identified and photographed five filaments chosen at random at 10x on a light microscope. The

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ocular of the microscope had a rotatable grid pattern that was used to estimate filament length. I

selected only filaments greater than 4 grid squares long, or approximately 0.4 mm, to be

photographed. I assumed, based on observations, that filaments of this length were capable of

producing heterocysts. I photographed every sample in columns N1 to N4 but I only

photographed N5P3 and N6P3 in the last two columns because of the complete lack of

heterocysts, which I determined by inspection of all N5 and N6 cultures.

I used imageJ software to rotate and crop these images to allow accurate processing. The

total filament length, in ocular grid squares, and number of heterocysts on each filament were

counted and summed for all samples. I could then calculate the total number of heterocysts per

unit filament length and compare that across treatments. This analysis also provided an

indication of how average filament length changed across treatments.

Statistical Analysis

I performed an analysis of variance ANOVA test on the results from the chlorophyll a,

biomass, and pH analyses to determine if significant trends existed. I considered a p value of less

than 0.05 to be significant. The other data did not have enough replicates to justify statistical

analysis.

Results

No significant difference existed between any treatments across the established

phosphorus gradient (Table 3). As a result the different phosphorus treatments were used as

replicates to analyze the affects of nitrogen. A significant trend was evident in the chlorophyll a

content across the nitrogen gradient and was the same in both light and dark adapted

measurements. The concentration of chlorophyll increased up to and peaked in the fourth

nitrogen treatment, which had an initial nitrogen concentration of approximately 30.3 mg/L, and

then dropped off again in the higher nitrogen treatments. This trend was true of both chlorophyll

measurements taken at day 10 and day 14 of the growth period. Modeling results revealed that

the overall shape of the trend did not change significantly from day 10 to day 14 despite an

obvious change in magnitude (Figure 1).

While none of the biomass measurements were statistically significant (Table 3),

noticeable trends existed in samples over both a nitrogen and phosphorus gradient. Biomass over

a nitrogen gradient seemed to follow the same general pattern as chlorophyll a, but was not

inhibited as obviously at higher nitrogen levels. Instead peak biomass occurred in the fifth

nitrogen treatment, which had a nitrogen concentration of approximately 150.3 mg/L (Figure 2).

The only perceivable change across a phosphorus gradient was that biomass seemed to increase

as the concentration of phosphorus increased (Figure 3).

Heterocyst frequency was highest at low nitrogen concentrations and then dropped by

more than half as nitrogen concentration increased from 1 to 10 mg/L. No heterocysts were

present above nitrogen concentrations of 150 mg/L. Filament length followed the same trend as

chlorophyll a. It increased with nitrogen concentration until it peaked in the fourth treatment and

then decreased in the two highest treatments (Figure 4).

The pH measurements revealed a statistically significant trend (Table 3) that was almost

identical to that of chlorophyll a. The average pH increased to a very basic maximum of 10.62 in

the fourth nitrogen treatment and then declined as nitrogen concentration continued to increase

(Figure 5). High nitrogen concentrations did not have the same inhibitory effect on alkalinity as

8

it did on pH. Instead alkalinity increased in the fifth nitrogen treatment and only decreased

slightly in the sixth. The total dissolved inorganic carbon pool increased as nitrogen

concentration increased. No carbon was present in the form of carbon dioxide or carbonic acid

(Figure 6). The estimate of alkalinity generated by nitrate assimilation was on the same order of

magnitude as the measured alkalinity. The calculation, which assumed only nitrate assimilation,

overestimated alkalinity at low nitrogen concentrations (Figure 7).

Both carbon and nitrogen fractionation increased as size of each of these pools increased.

Nitrogen fractionation remained low until the concentration of nitrogen in growth solution

reached approximately 10 mg/L. After this point the fractionation increased with the lighter

isotope taken up by the Anabaena; the lightest N-15 signatures in biomass were in the growth

solution with the highest nitrogen concentration (Figure 8). The carbon isotope values exhibited

the same steady and rapid increase in fractionation over nitrogen concentrations greater than 10

mg/L (Figure 9).

Discussion

Perhaps the most obvious initial result was the indifference the Anabaena showed

towards variation in phosphorus. The only semblance of a phosphorus effect I observed was in

the biomass measurement, and even that was not statistically significant. This result indicates

that the Anabaena was simply never limited by phosphorus because I did not extend the low

phosphorus end of the gradient sufficiently. It remains an interesting result that absolutely

flooding the organisms with phosphorus had little to no affect on their growth.

The nitrogen gradient had a profound effect on productivity compared to that of the

phosphorus gradient. The variation in chlorophyll a concentration across treatments indicated

that the cyanobacteria grow most efficiently at nitrogen concentrations of around 30 mg/L. The

reduced chlorophyll a concentration at low nitrogen levels nicely correlated with the presence of

heterocysts. The combination of the cost of forming and maintaining heterocysts as well as the

costs associated with assimilating scarce nitrate likely caused the decreased biomass at low

nitrogen concentrations.

The increase in fragment length, as well as chlorophyll a concentration, following the

abrupt decrease in heterocyst density supported the idea that the cost of maintaining heterocysts

siphons resources away from growth or reproduction. That same abrupt decrease in heterocysts

around a nitrogen concentration of 10 mg/L indicated the point at which it became more efficient

for the cyanobacteria to assimilate nitrate from their surroundings as opposed to fixing nitrogen

from the atmosphere. As predicted, the transition away from nitrogen fixation occurred quickly,

which indicated that heterocysts are too costly to maintain when they are not necessary.

Substantial evidence showed that tradeoff associated with maintaining heterocysts was

limiting growth at low nitrogen levels, but significant inhibition of growth occurred at the

highest nitrogen levels as well. None of the results of this study provided any evidence as to what

mechanism could be causing such inhibition. I was also unable to locate any previous studies that

had encountered or proposed a mechanism for this observation. Hopefully future studies will

pursue an answer to this question.

My ability to only vary the magnitude parameters of a single model and successfully

represent both rounds of chlorophyll measurements had interesting implications. This indicated

that, despite clear differences in standing biomass and drastically different nitrogen availability,

the cultures all grew at a fairly similar rate over the last four days of the growth period. One

possible explanation for why this occurred was because nitrogen was no longer a limiting

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nutrient in the system. Because the growth solution was not replenished during the growth

period, it is not unlikely that the Anabaena exhausted their supply of a necessary micronutrient.

Perhaps the differential growth depicted by variation in the chlorophyll a curves was caused by

an initial nitrogen limitation but then limitation by another nutrient restricted growth more

evenly across treatments. It is also possible that differential growth rates were simply too small

for the chlorophyll a measurements to pick up over four days. Further investigation is necessary

to support one explanation over another.

The pH and chlorophyll a tests yielded very similar trends because pH was likely

generated by cyanobacterial production. The uptake of carbon in photosynthesis also consumes a

proton and causes pH to increase (Stumm and Morgan 1995). Other studies that grew

cyanobacteria in batch culture documented similar pH trends (Ward 1985). To grow at such

basic pHs, when carbon was only present as carbonate and bicarbonate, Anabaena must have be

capable of assimilating one of these forms. Bicarbonate had an inverse relationship with

chlorophyll a biomass, which indicated that Anabaena was using it as its primary source of

carbon. This provides strong evidence that Anabaena have substitutable strategies for

assimilating carbon.

This study found that cyanobacteria can have a drastic effect on the alkalinity of their

environment. The close comparison between the measured and calculated alkalinity provided

substantial evidence that nitrate assimilation was generating alkalinity. The calculation’s

overestimate of alkalinity at low nitrogen concentrations was most likely because it assumed

only nitrate assimilation was occurring. The presence of heterocysts at these low nitrogen

concentrations indicates that some nitrogen was being fixed from the atmosphere, which would

not have generated alkalinity. If this was factored into the calculation, not nearly as much

alkalinity would have been predicted at low nitrogen concentrations. Other reactions, such as

bicarbonate assimilation, also produce alkalinity and were also excluded from this calculation.

Unfortunately 15-N stable isotope analysis did not reveal the proportion of nitrogen

acquired through fixation versus assimilation. This was because the nitrate in solution did not

have an isotopic signature that was significantly different from that of nitrogen in the

atmosphere. The observed increase in fractionation occurred simply because the increasing size

of the nitrogen pool allowed Anabaena to more effectively select for lighter nitrogen. The

increase in 13-C fractionation occurred for the same reason. This was an important result,

however, because it confirmed that the pool of inorganic carbon increased across nitrogen

treatments.

The determination of environmental nitrate concentrations that optimize cyanobacterial

growth and trigger heterocyst production has broad implications. Because cyanobacteria achieve

their maximum growth under high nitrogen conditions of 30 mg/L, and are capable of fixing

nitrogen, destructive blooms can occur over a large gradient of nutrient conditions (Havens et al

2002). Bloom events in nitrogen depleted systems likely occur because nitrogen-fixing

cyanobacteria can thrive in the absence of many competitors that can only grow efficiently in

high nitrogen environments. Paerl et al (2001) suggests that dominance by Anabaena in nutrient

enriched systems is accomplished by outcompeting other alga with other methods such as

shading and toxin production. Both paths to blooms represent separate resource optimization

strategies in cyanobacteria. The generation of alkalinity by the assimilation of nitrate and

possibly bicarbonate illustrates how the use of these two substitutable resources can have an

effect on the environment. This finding emphasizes the importance of understanding what

10

conditions trigger a switch in substitutable resource use as well as the broader implications of

that switch.

Acknowledgments

I would like to thank Ed Rastetter, Zoe Cardon, and Suzanne Thomas for their help,

patience and support. Without them none of this work would have been possible. I would also

like to extend thanks to Claire Lunch, Rich McHorney, Marshall Otter, Anne Giblin, Stefanie

Strebel, Carrie Harris and Laura Van der Pol for their various contributions to this project.

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Appendix

Table 1. Concentration matrix used to generate nitrogen and phosphorus gradient. The NXPX

represents the treatment name while the number in black is nitrate concentration (mg/L)

and the number in red is phosphorus concentration (mg/L).

N1 N2 N3 N4 N5 N6

0.3 6.3 30.3 60.3 300.3 600.3

P1 N1P1 N2P1 N3P1 N4P1 N5P1 N6P1

0.3 3.3 15.3 30.3 150.3 300.3

0.07 0.035 0.035 0.035 0.035 0.035 0.035

P2 N1P2 N2P2 N3P2 N4P2 N5P2 N6P2

0.3 3.3 15.3 30.3 150.3 300.3

1.4 0.7 0.7 0.7 0.7 0.7 0.7

P3 N1P3 N2P3 N3P3 N4P3 N5P3 N6P3

0.3 3.3 15.3 30.3 150.3 300.3

7 3.5 3.5 3.5 3.5 3.5 3.5

P4 N1P4 N2P4 N3P4 N4P4 N5P4 N6P4

0.3 3.3 15.3 30.3 150.3 300.3

14 7 7 7 7 7 7

P5 N1P5 N2P5 N3P5 N4P5 N5P5 N6P5

0.3 3.3 15.3 30.3 150.3 300.3

70 35 35 35 35 35 35

P6 N1P6 N2P6 N3P6 N4P6 N5P6 N6P6

0.3 3.3 15.3 30.3 150.3 300.3

140 70 70 70 70 70 70

13

Table 2. N:P molar ratios in solutions. The highlighted diagonal line indicates treatments with an

N:P ratio of approximately 10.

Nitrogen

Treatment #

N1 N2 N3 N4 N5 N6

Concentration

(uMol

)

21 450 2164 4307 21450 42879

Phosphorus P1 2 9.49 199.3 958.47 1907.45 9499.29 18989.1

P2 45 0.47 9.96 47.92 95.37 474.96 949.45

P3 226 0.09 1.99 9.58 19.07 94.99 189.89

P4 452 0.05 1.00 4.79 9.54 47.50 94.95

P5 2258 0.01 0.20 0.96 1.91 9.50 18.99

P6 4516 0.005 0.10 0.48 0.95 4.75 9.49

14

Table 3. Significance determined using an ANOVA analysis of variance test

F Value P Value Significant?

10 Day Chlorophyll A Nitrogen 5.10 P < 0.005 yes

10 Day Chlorophyll A Phosphorus 0.87 P > 0.25 no

14 Day Chlorophyll A Nitrogen 3.92 P < 0.005 yes

14 Day Chlorophyll A Phosphorus 0.87 P > 0.25 no

Biomass Nitrogen 1.32 P < 0.25 no

Biomass Phosphorus 1.31 P < 0.25 no

pH Nitrogen 3.95 P < 0.005 yes

pH Phosphorus 0.97 P > 0.25 no

15

Figure 1. Chlorophyll a concentration of Anabaena measured at day 10 and day 14 of a 14 day

growth period in flasks of Anabaena. The points in each series represent phosphorus

treatments which were not significant and treated as replicates. Solid lines indicate

modeled trends.

0

5

10

15

20

25

0.1 1 10 100 1000

Ch

loro

ph

yll A

(m

g/L)

N Concentration (mg/L)

P1

P2

P3

P4

P5

P6

P1

P2

P3

P4

P5

P6

Day 14

Day 10

16

Figure 2. Dry biomass of Anabaena over a nitrogen gradient in flasks of Anabaena

0

20

40

60

80

100

120

140

160

0.1 1 10 100 1000

Bio

mas

s (m

g/L)

N Concentration (mg/L)

P1

P2

P3

P4

P5

P6

AVERAGE N

17

Figure 3. Dry biomass of Anabaena over a phosphorus gradient in flasks of Anabaena

0

20

40

60

80

100

120

140

160

0.01 0.1 1 10

Bio

mas

s (m

g/L)

P Concentration (mg/L)

N1

N2

N3

N4

N5

N6

AVERAGE

18

Figure 4. Heterocyst frequency and filament length over a nitrogen gradient in flasks of

Anabaena

19

Figure 5. pH over a nitrogen gradient in flasks of Anabaena

9.4

9.6

9.8

10

10.2

10.4

10.6

10.8

11

0.1 1 10 100 1000

pH

N Concentration (mg/l)

P1

P2

P3

P4

P5

P6

Average

20

Figure 6. Alkalinity and concentrations of dissolved carbon forms over a nitrogen gradient in

flasks of Anabaena

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

0.1 1 10 100 1000

Alk

alin

ity

(uEq

/L)

or

Co

nce

ntr

atio

n (

uM

)

N Concentration (mg/L)

CO3

HCO3

H2CO3 = CO2

DIC

ALK

21

Figure 7. Predicted alkalinity due to nitrate assimilation based on biomass compared to measured

alkalinity in flasks of Anabaena

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

0.1 1 10 100 1000

Alk

alin

ity

(mEq

/L)

N Concentration (mg/l)

Calculated

Measured

22

Figure 8. 15-N isotope fractionation over a nitrogen gradient in flasks of Anabaena

-9

-8

-7

-6

-5

-4

-3

-2

-1

0

0.1 1 10 100 1000

d1

5N

N Concentration (mg/l)

23

Figure 9. 13-C isotope fractionation over a nitrogen gradient in flasks of Anabaena

-22

-21

-20

-19

-18

-17

-16

-15

0.1 1 10 100 1000

d1

3C

N Concentration (mg/l)