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Ecosystem Energy-Use Efficiency: Positive Effects of Predation on Productivity
Joseph Hakam
Brown University ‘13
Brown University Box 5588
69 Brown St.
Providence, RI, 02912
Advisor: Joseph Vallino, Brown University/Marine Biological Laboratory
December 18, 2011
Abstract
Ecosystem structure revolves around processing as much energy as possible within given
resource and growth constraints. Bottom-up and top-down forces have both been shown to exert
a significant control on ecosystem productivity. However, the traditional method of thinking of
top-down forces as limiting productivity may be offset by the additional nutrient availability that
occurs as a result of predation. To examine the importance of predators in an ecosystem’s ability
to utilize energy under nutrient-strained conditions, I used bacterial microcosms, some of which
contained bacterial predators, others which had mechanisms for predator exclusion.
Over the course of the experiment, some data showed that the systems with predators
were utilizing energy more efficiently than the systems without predators, however this difference
disappeared within 4 days of starting the experiment. The same trend was seen with nutrient
limitation, with the systems without predators showing much stronger evidence of nutrient
limitation until the 5th day of the experiment, at which point they were not differentiable from the
control system with predators. Unfortunately, the systems without predators seemed to become
contaminated with predators relatively rapidly. The results point to the importance of predation
in nutrient cycling, although it is possible that the extra productivity due to the nutrient cycling is
offset by the predation of primary producers. Systems with predators showed slightly more
energy utilization but only for a short period of the experiment.
Key words/phrases: Chemostats; Energy limitation; nutrient limitation; nutrient cycling; topdown effects; bottom-up effects; ecosystem productivity; predator exclusion.
Introduction
Species are structured to grow and consume resources until they hit a limit. In most cases,
the species becomes energy limited, nutrient limited, or is limited due to heavy predation. When
examining the top-down effects of predators on a system, it is typical to think of predators as
limiting growth. However, in some cases, the top-down control of a predator can actually have a
positive feedback effect on their prey by allowing nutrients to cycle through the system quicker
and promoting new growth (Sherr et al, 1986).
Ecosystems are generally structured to dissipate as much energy as possible with the
resources available to them (Schneider and Kay 1994). The famous Cedar Creek LTER
experiments used plots of land with different numbers of plant species to show how ecosystem
resilience and productivity improve with increasing biodiversity (Fargione et al. 2006). As
ecosystems lose functional groups in food webs, ecosystem productivity declines (Naeem et al.
1999). My experiment seeks to test this relationship by removing predators from a system and
seeing if the system responds with increased or decreased productivity. My hypothesis is that
while the presence of predators in a system limits the population and biomass of primary
producers, the predators actually help the ecosystem utilize more energy as a whole. By growing
bacteria in conditions with predators and without predators, it is possible to explore the
influences that predators have on the primary productivity of a system. The system I set up
utilized bacterial microcosms that were structured to be rich in energy, in the form of glucose, but
with limited quantities of available nutrients. The more efficient systems will be able to utilize
more of the primary energy source and display higher productivity.
While predators are viewed primarily as limiting the abundance and biomass of
producers, they play important, sometimes vital roles in nutrient cycling (Schmitz, Hawlena, and
Trussell 2010). In the system with predators, I was looking for a positive relationship between
predator abundance, bacterial growth rate, and ecosystem energy consumption. Meanwhile,
systems without predators should have lower bacterial growth rate and energy consumption as
they quickly use up all the available nutrients and are limited by the inputs of nutrients for
additional growth.
Methods
Bacterial microcosms were created in chemostats similar to those used in the ATB
Experiment 1 by Joseph Vallino and Julie Huber. In one treatment, I grew both bacteria and
bacterial grazers, while I attempted to exclude bacterial grazers in my other two treatments.
Treatments
I ran all microcosms by pulsed chemostat, at the rate of 1/d. This will involved replacing
media twice a day, once in the morning and once in the evening. Each replacement involved
removing 50% of the sample in each treatment. The treatments all used a basic filtered seawater
media with added glucose as a bacterial food source and additional nutrients. All seawater used
was filtered through a 305 µm filter in order to remove larger organisms that may skew results,
such as small fish or invertebrates and their larvae. 4000µM carbon in the form of glucose was
added to the seawater media to stimulate growth, along with a small amount of nutrients, similar
to the methods used in Sherr et al. 1986. The added nutrients were in the form of ammonium
chloride and potassium phosphate, monobasic. The concentrations of nitrogen and phosphorous
added to incoming seawater media were 40 µM and 2 µM, respectively. Each microcosm had a
250 mL volume and was kept in a 1-Liter Erlenmeyer flask for incubation. All samples were
incubated inside a shake table incubator, which kept the systems well-mixed, in the dark, and also
allowed me to incubate the samples at a constant temperature of 20oC. The incubation started at
8:00 p.m. on Saturday the 26th of November, 2011, and ran through Sunday the 4th of December.
As mentioned before, 3 treatments were run – a control and two grazer exclusion
treatments. These treatments were run in triplicate, so 9 separate one-Liter microcosms were
used. For the control treatment, I simply used the basic filtered seawater media with its
glucose/nutrient additions. For grazer exclusion treatments, I followed a modified version of the
treatments used in Fuhrman and McManus 1984. The first grazer exclusion treatment was a
simple filtration treatment. Using a filtration tower and 47 mm GFF filters with 0.7 µm mesh
size, I filtered the seawater media before adding it to the microcosms in an attempt to remove
predators and leave only microorganisms that were smaller than 0.7 µm in size – hopefully
allowing bacteria to pass through but not their predators. The second grazer exclusion treatment
used an eukaryote inhibition method modified from Newell et al 1983. 25 mg/L cycloheximide
was added to the seawater media entering the inhibited samples. As a potent eukaryote inhibitor,
the addition of cycloheximide should eliminate most predators of bacteria in the microcosms.
Bacterial and Predator Abundance Determination
In order to determine the abundance of bacteria and their predators in the microcosms, I
used the DAPI method of DNA staining to estimate microorganism abundance. The DAPI
method used was adapted from methods used in Porter and Feig 1980. Samples were taken daily,
12 hours after the last feeding, and analyzed using DAPI for bacterial abundance, and a subset of
samples were chosen to be analyzed for predator abundance, mostly from the middle to the end of
the experiment. For bacterial abundance, a 0.22 µm pore size black polycarbonate filter was used
and either 200 µl or 20 µl of sample was stained and filtered through it to determine abundance
of bacteria. 200 µl of sample was used earlier in the experiment, when bacterial populations were
low. However, when bacterial populations grew, it was necessary to filter a smaller amount of
liquid for ease of counting and to reduce error. For predator abundance, a 1 µm pore size black
polycarbonate filter was used and 5 mL of sample was stained and filtered through it to determine
abundance of nanoflagellates and ciliate predators.
Nutrient Analyses
To determine the extent of nutrient limitation, samples were collected from each
microcosm daily for ammonium, nitrate, and phosphate analyses. Samples were taken 12 hours
after the last feeding/nutrient addition. Additionally, since phosphate concentrations are often not
indicative of true extent of phosphorous limitation, daily enzyme assays on phosphatase
production were performed. The ammonium analyses were done according to the modified
Strickland and Parsons 1972 ammonium protocol. Nitrate analysis used a Lachat flow injection
analyzer and methods adapted from Wood, Armstrong, and Richards 1967. Phosphate analysis
was done using the adapted method from Murphy J. and Riley J.P. 1962. The enzyme assay for
phosphatase followed methods from Hoppe 1993 in Aquatic Microbial Ecology.
Energy Utilization/Productivity Analyses
I used three separate measures to determine productivity. I measured CO2 flux daily
throughout the experiment, stored samples for dissolved organic carbon analysis daily, and
measured bacterial productivity with radioisotopes on day 5 and day 8 of the experiment. All
samples were taken or measured 12 hours after the last feeding/addition of nutrients.
Carbon dioxide flux was measured using a LI-COR 6200 analyzer modified with an extra
desiccant tube. The extra desiccant tube was placed in-line with the standard desiccant tube and
filled with Drierite®. This was done to prevent excess moisture from damaging the internal
detectors of the LI-COR. The measurements were taken by capping the Erlenmeyer flasks that
contained the microcosms with a rubber stopper. The rubber stopper had holes punctured through
it to fit the intake and output tubes of the LI-COR. With the samples still on the shake-table
incubator to maintain mixed conditions, the samples were given 3 minutes to stabilize with the
LI-COR analyzer and then change in CO2 concentration was measured over a 4-minute time
period.
I measured bacterial productivity following a modified version of the protocol laid out in
Simon and Azam 1989. Using C-14 enriched leucine, the bacteria were given a 30-minute period
to take up nutrients and grow. Then the bacteria were analyzed with a scintillation counter to
determine how much leucine they were able to take up in the time period and thus show how
productive the bacteria were.
Dissolved Organic Carbon was also measured as a method of productivity. 20 mL
samples were taken from each microcosm daily, filtered through a 0.7 µm GFF filter to exclude
microorganisms, and stored in glass DOC vials along with 50 µl of 80% phosphoric acid. After
the microcosm experiment ended, they were then analyzed in a OI Analytical Aurora model 1030
TOC analyzer.
Results
Bacterial and Predator Abundance
As seen in Figure 1, bacterial abundance remains low for the first 2 days in all
microcosms. After 2 days of incubation, bacterial populations in the filtered and inhibited
microcosms skyrocket. Control samples show a continuous, slow rise in bacterial population.
Populations in the filtered and inhibited samples drop after day 5 and 6.
Overwhelmingly, predators found in the microcosm experiment were nanoflagellates.
Predator populations in the control samples were large and seemed to rise over the course of the
experiment (table 1). In the inhibited samples, there was no significant colonization by predators
over the course of the experiment. The filtered samples started with a very small predator
population but the population seemed to expand quickly. Filtered 2 in particular, however, had
about ¼ the number of predators that the other samples had.
Other things happened in both the control and inhibited samples, however. As seen by the
photographs in figure 9, the control sample seemed to accumulate filamentous microorganisms
over the course of the experiment (fig 9B). Meanwhile, the inhibited samples seemed to be
colonized by unidentified microorganisms that fluoresced orange under DAPI staining (fig 9E
and 9F).
Nutrient Concentrations and Limitation
Nutrients seemed to be very limited in all samples. Phosphate concentrations (fig 2) were
so low that the analyzer returned negative numbers through the course of the experiment. With
the exception of the filtered samples on the first day, which appeared to be mostly inactive during
the first 12 hours of the experiment, all the concentrations were below the detection threshold. A
similar result was found for nitrate, where all concentrations were found to be below the
analyzer’s detection threshold with the exception of Filtered 2 on day 1, which returned a 3 µM
nitrate concentration. Ammonium analysis (fig 3) tells the same story, with concentrations in all
microcosms ranging between 0.2 and 0.6 µM, with the exception of the filtered samples on the
first day which still contained most of the originally added ammonium.
Unlike the nutrient analyses, the enzyme assay for phosphatase varied much more greatly
between treatments (fig 4). Between day 2 and day 6 of the experiment, both the filtered and
inhibited treatments showed a significant generation of phosphatase, while the control did not.
This generation of phosphatase stops abruptly, however, on day 6 of the experiment.
Energy Utilization
All treatments had similar CO2 efflux over the course of the experiment and no sample
was significantly higher or lower than the others (fig 5). Bacterial productivity determined by C14 leucine uptake showed no significant differences in productivity on the 5th day of the
experiment. On the 8th day, the control and inhibition treatments had high levels of productivity
and uptake, but the filtered treatments did not (fig 6). Dissolved organic carbon levels (fig 7) tell
a different story, with the control sample showing the highest energy utilization early in the
experiment, between days 1 and 4. After day 4, most samples show similar DOC uptake.
On a per-bacteria basis, however, what is shown by the carbon dioxide flux is that there is
a huge uptake of energy by the bacteria in the filtered and inhibited samples during the first two
days of the experiment, followed by a huge drop in energy utilization as the bacterial population
becomes large and the bacteria stop growing and just maintain their population levels (fig 8). The
control treatments, on the other hand, show a consistently high level of energy utilization per
bacteria throughout most of the experiment.
Discussion
As far as experimental conditions are concerned, the treatments seemed to have worked
for eliminating predators. The control samples had the largest populations of nanoflagellates (fig
1), while the filtered samples had relatively few at the beginning of the experiment. However, the
filtered samples showed increasing numbers of nanoflagellates as the experiment went on.
Filtered sample 2 showed the lowest nanoflagellate density, about ¼ the density seen in the other
filtered samples and seems to have been contaminated to a lesser degree than either filtered 1 or 3
(table 1). Because of this, the results for filtered sample 2 are used in data analysis rather than the
results from all filtered samples. The inhibited samples showed very low nanoflagellate
populations throughout the experiment, however, as the experiment went on, it became apparent
that the microcosms were being colonized by nanoflagellate-sized, unidentified microorganisms
that fluoresced orange under DAPI staining (fig 9E and fig 9F). As these organisms proliferated
towards the end of the experiment, they seemed to have a negative effect on the bacterial
population in the inhibited samples (fig 1). The nanoflagellates that grew within the filtered
samples also seem to have prompted a population decline in the filtered samples, starting around
day 5 (fig 1).
Looking at the ammonium, nitrate, and phosphate concentrations that were present in the
microcosms (fig 2 and 3), it is clear that all the microcosms were nutrient limited as intended.
The extent of nutrient limitation seemed to vary across the microcosms, however. The
phosphatase assay data (fig 4) show that both the filtered and inhibited samples are showing a
much higher production of phosphatase enzyme than the control sample. Phosphatase is only
produced when a cell is likely to recoup the energy costs of creating and releasing the protein,
and it is likely that its production is inhibited by the presence of phosphate in the cell (Horuchi et
al. 1959). Thus based on the enzyme assay, it seems that though nutrient levels are low in all
samples, it is the filtered and inhibited treatments that are really feeling the strain of nutrient
limitation on their growth.
Seeing that the experiment did its desired portion of establishing nutrient limitation and
limiting predator populations in the filtered and inhibited samples, the next question is whether
predator exclusion proved to lower nutrient cycling in the microcosms and lower productivity.
We do see evidence of lower nutrient cycling in the phosphatase enzyme assay data. However,
when looking at CO2 data and bacterial productivity estimates (fig 5 and 6), there seems to be no
significant difference in energy utilization among the different treatments. These measurements
are extremely time sensitive, though, as the CO2 flux was measured over 4-minute incubation
times and the bacterial productivity estimates allowed for 30-minute incubations with
radiolabeled leucine. This makes these measurements very sensitive to the activity at the moment
of measurement, rather than an integrated measure of activity throughout the entire runtime of the
experiment.
Using a different measure, the DOC analysis, small differences can be seen in the
productivity of the different systems (fig 7). During the first 4 days of the experiment, the control
samples were able to utilize the largest portion of the added glucose energy source. After day 4,
the samples all converge into a similar level of energy usage. This indicates that the samples with
predators may have been able to use a larger fraction of the provided energy, though not much
more than the other treatments.
Comparing the carbon dioxide flux and the bacterial abundance, I was able to estimate a
carbon dioxide production rate per individual bacteria (fig 8). This information tells us that while
all treatments consumed similar levels of energy, they had vastly different community structures.
In the filtered and inhibited treatments, we saw a quick uptake of energy and nutrients and a
population explosion. Once population reached a large size, a slowdown of growth occurred and
even some depopulation (fig 1). This population of bacteria was large and slow-growing, perhaps
even in a maintenance state rather than a growth state. Meanwhile, the control treatments
contained bacteria which maintained a high level of energy utilization and growth throughout the
whole incubation period.
While the bacterial population in the control samples was smaller, less nutrient limited,
and faster-growing, we did not see the system as a whole utilize much more energy than the other
two treatments, and, in fact, after day 4, the system energy use was indistinguishable between all
treatments.
There are many potential reasons for why the lower levels of nutrient limitation did not
translate particularly well to a higher energy uptake by the ecosystems. One reason is the simple
top-down hypothesis of control, as proposed by Silliman and Zieman in their 2001 paper
explaining the die-off of salt marsh grasses in Georgia due to overgrazing by the periwinkle snail.
The quicker cycling of nutrients through the control samples may allow the bacteria to grow
faster, but the low population that is controlled by grazers may limit the amount that they grow,
making them unable to use those additional nutrients and thus unable to utilize larger amounts of
energy.
Another possible reason why the treatments did not consume different amounts of energy
is the fact that the treatments could have been ineffective. As seen from the predator counts (table
1), the filtered samples displayed significant colonization and contamination with nanoflagellate
populations by day 5 or 6 in the experiment. Meanwhile, the inhibited samples became
contaminated with the orange, nanoflagellate-sized microorganisms. Furthermore, it seems that
cycloheximide may not be as effective an eukaryote inhibitor as previously thought. Smaller
protozoans were observed taking up to 48 hours to be fully inhibited in cycloheximide solution
(Tremaine and Mills, 1987), meaning that due to the fact that I was adding new seawater media
every 12 hours, it is possible that there was a small but active nanoflagellate population. The
contamination theory is supported by the data from the enzyme assay, which ceases to show rapid
production of phosphatase in the inhibited and filtered samples after day 4, at which point their
contamination grew more serious. Furthermore, day 4 is where the DOC values began to
converge on the same level of energy utilization between the three treatments.
Finally, there is the potential that viruses could have had a significant effect on bacterial
mortality, again pointing to a failure in the predator exclusion method. It is generally thought that
viruses are responsible for 10-50% of bacterial mortality in surface waters (Fuhrman 1999).
Because of this, it is possible that even had I achieved perfect eukaryote inhibition via
cycloheximide and perfect eukaryote exclusion via filtering, I would have only stopped 50-90%
of the predation that was occurring on the bacteria incubating in my microcosms. The potential of
still having over 50% of the predation occurring in the samples that were supposed to be devoid
of predators may be another factor that leveled the playing field between all the treatments.
Conclusions
From this experiment, there is a clear connection between predation and more rapid
nutrient cycling, however, this did not manifest itself in the form of increased ecosystem
productivity. Whether this is due to top-down limitations of ecosystem productivity or other
factors such as virus infection or contamination by bacterial predators is unclear. Due to the
short-term nature of this experiment and the rapidity of contamination, it may be preferable to
utilize different system structure to examine this question further. Perhaps using microcosms
utilizing fungi or plants as primary producers and insects like caterpillars as predators would be
more resilient to this kind of contamination, albeit working at longer timescales.
Acknowledgements
I would like to thank Joseph Vallino for being my very patient and helpful mentor in
setting up and planning this experiment. Richard McHorney, Stephanie Strebel, Laura Van der
Pol, and Carolyn Harris for all their help in running the experiment, taking measurements, and in
data analysis. Many thanks to the Marine Biological Laboratory and the Semester in
Environmental Science for giving me the opportunity to participate in the enriching program that
led up to this experiment and to work on this project itself. Finally, I would like to thank all the
students of the SES class of 2011 for their camaraderie and help both inside and outside of the
lab.
Citations
Fargione, J., Tilman, D., Dybzinski, R., Lambers, J., Clark, C., Harpole, W., Knops, J. Reich, P.,
and Loreau, M. 2006. From Selection to Complementarity: Shifts in the Causes of BiodiversityProductivity Relationships in a Long-Term Biodiversity Experiment. Proceedings of the Royal
Society of Biology. Published online: RSPB.2006.0351.
<http://cedarcreek.umn.edu/biblio/fulltext/t2135.pdf>
Fuhrman, Jed. 1999. Marine Viruses and Their Biogeochemical and Ecological Effects. Nature,
Vol. 399, pp. 541-548.
Fuhrman, Jed, and George McManus. 1984. Do Bacteria-Sized Marine Eukaryotes Consume
Significant Bacterial Production? Science, Vol. 224, No. 4654, pp.1257-1260
Phosphatase enzyme assay protocol modified from Hoppe, H. 1993. Use of Fluorogenic Model
Substrates for Extracellular Enzyme Activity (EEA) Measurement of Bacteria. Aquatic Microbial
Ecology, Lewis Publishing, Boca Raton, FL, USA.
Horiuchi, T., Horiuchi, S., and Mizuno, D. 1959. A Possible Negative Feedback Phenomenon
Controlling Formation of Alkaline Phosphomonoesterase in Escherichia coli. Nature, Vol. 4674,
pp. 1529-1530.
Phosphate analysis protocol modified from: Murphy J. and J.P. Riley. 1962. A modified single
solution method for the determination of phosphate in natural water and uses ascorbic acid
instead of stannous chloride. Anal. Chem. Acta 27, 31-36
Naeem, S., Chapin, F., Costanza, R., Ehrlich, P., Golley, F., Hooper, D., Lawton, J., O’Neill, R.,
Mooney, H., Sala, O., Symstad, A., and Tilman, D. 1999. Biodiversity and Ecosystem
Functioning: Maintaining Natural Life Support Processes. Issues in Ecology, Issue 4, Ecological
Society of America, Washington DC, USA.
Newell, S.Y., B.F. Sherr, E.B. Sherr, and R.D. Fallon. 1983. Bacterial Response to Presence of
Eukaryote Inhibitors in Water from a Coastal Marine Environment. Marine Envrionmental
Research, Vol 10, pp.147-157
DAPI count protocol for bacteria and bacterial predators adapted from Porter, K. and Feig, S.
1980. The Use of DAPI for Identifying and Counting Aquatic Microflora. Limnology and
Oceanography, Vol 25, Issue 5, pp. 943-948
Schmitz, Oswald, Dror Hawlena, and Geoffrey Trussell. 2010. Predator control of ecosystem
nutrient dynamics. Ecology Letters, Vol: 13, pp.1199-1209
Schneider, E.D and J.J. Kay. 1994. Life as a Manifestation of the Second Law of
Thermodynamics. Mathematical and Computer Modeling, Vol 19, No. 6-8, pp.25-48
Sherr, Barry, Evelyn Sherr, Tamara Andrew, Robert Fallon, and Steven Newell. 1986. Trophic
interactions between heterotrophic Protozoa and bacterioplankton in estuarine water analyzed
with selective metabolic inhibitors Marine Ecology, Vol. 32, pp.169-179
Silliman, B., and Zieman, J. 2001. Top-Down Control of Spartina Alterniflora Production by
Periwinkle Grazing in a Virginia Salt Marsh. Ecology, Vol. 82, Issue 10, pp. 2830-2845.
Bacterial productivity measurements via radiolabeled C-14 leucine modified from Simon, M. and
Azam, F. 1989. Protein Content and Protein Synthesis Rates of Planktonic Marine Bacteria.
Marine Ecology Progress Series, Vol. 51, pp. 201-213
Ammonium protocol modified from Strickland, J.D.H. and T.R. Parsons. 1972. A practical
handbook of Seawater Analysis. Fisheries Research Board of Canada 2nd. Ed. Ottawa, Canada.
Tremaine, S. and Mills, A. 1987. Inadequacy of the Eucaryote Inhibitor Cycloheximide in
Studies of Protozoan Grazing on Bacteria at the Freeshwater-Sediment Interface. Applied and
Environmental Microbiology, Vol. 53, Issue 8, pp. 1969-1972
Vallino, J., and Huber, J. 2009. Biological Systems Organize to Maximize Entropy Production
Subject to Information and Biophysicochemical Constraints.
<http://dryas.mbl.edu/MEP/Documents/ATB_2009_core.pdf>
LACHAT Nitrate analysis modified from: Wood, E.D., F.A.G. Armstrong, and F.A. Richards.
1967. Determination of nitrate in seawater by cadmium-copper reduction to nitrite. J. Mar. Biol
Assoc. U.K. 47:23
Figure 1: Bacterial Abundance via DAPI
200
Bacterial Abundance
180
Million Bacteria per mL
160
140
120
C Average
100
F2
80
I Average
60
40
20
0
1
2
3
4
Days
5
6
7
8
Table 1: Table of Selected Predator Abundances via DAPI
Sample
Day
Nanoflagellates/mL
Filtered 2
3 847
Filtered 2
4 2,118
Filtered 2
6 15,249
Filtered 2
7 26,262
Filtered 3
3 424
Filtered 3
6 80,481
Control 1
4 77,939
Control 1
7 125,663
Control 2
4 160,538
Inhibited 1
3 478
Inhibited 1
7 797
Inhibited 2
4 4,304
Figure 2: Phosphatase Concentrations in Microcosms
1.6
Phosphate Concentrations
1.4
Phosphate Concentration (µM)
1.2
1.0
0.8
C Average
F2
0.6
I Average
0.4
0.2
0.0
0
-0.2
2
4
6
Day of Experiment
8
10
Figure 3: Ammonium Concentrations in Microcosms
30.000
Ammonium Concentration
Ammonium Concentration (uM)
25.000
20.000
C Average
15.000
F2
I Average
10.000
5.000
0.000
0
1
2
3
4
5
Days
6
7
8
9
Figure 4: Phosphatase Enzyme Assay Results
12
Phosphatase Assay Results
Rate of MUF-P Production (ug/L hr)
10
8
6
Control
Filtered 2
4
Inhibited
2
0
0
-2
1
2
3
4
5
Days
6
7
8
9
Figure 5: Carbon Dioxide Efflux from Microcosms Over a 4-Minute Incubation Period
Water CO2 Efflux (ug C/mL
sample hr)
1.200
1.000
0.800
Control
0.600
Filtered 2
Inhibited
0.400
0.200
0.000
0
2
4
6
Days
8
10
Figure 6: Bacterial Productivity via C-14 Leucine Uptake
60
Carbon Uptake (umols C/liter day)
50
40
Control
30
Filtered 2
Inhibited
20
10
0
Day 5
Day 8
Figure 7: Dissolved Organic Carbon After Filtration Through a 0.7 µm GFF filter
55
Dissolved Organic Carbon
DOC Concentration (ppm Carbon)
50
45
40
35
C Average
30
F2
I Average
25
Initial Media Carbon
20
15
10
0
2
4
6
Days
8
10
Figure 8: Carbon Dioxide Production per Bacteria
attograms (10-18) CO2 Carbon Production per Bacteria
45
Bacterial CO2 Production
40
35
30
25
Control
20
F2
15
Inhibited
10
5
0
0
1
2
3
4
Days
5
6
7
8
Figure 9: Bacterial Predator Count Pictures
Fig 9A) Control 1 Day 4
Fig 9B) Control 1 Day 7:
Fig 9C) Filtered 2 Day 4
Fig 9D) Filtered 2 Day 7 vs. Filtered 3 Day 6
Filtered 2 Day 7:
Filtered 3 Day 6:
Fig 9E) Inhibited 1 Day 4:
Fig 9 F) Inhibited 1 Day 7: