Connections between Water Chemistry
and Phytoplankton Populations on Racing
Beach
Eleanore Carson
Zachary Atkins-Weltman
2012 Ch. 4 pg. 1
Connections between Water Chemistry and Phytoplankton Populations on
Racing Beach
The objective of this project was to study the relationship between phytoplankton
population and seawater composition in three different locations: the northern jetty,
the freshwater inlet, and an open water location well south of the jetty and fresh
water areas. Each day the different locations were tested for silica, pH, phosphate,
ammonia and nitrate levels. Phytoplankton numbers were also counted each day.
The hypothesis for this project was that higher nutrient levels, particularly silica,
would result in higher numbers of phytoplankton in that area. The data, especially
the third day’s increase in phytoplankton population and lack of corresponding
chemical increases indicates that the populations may not follow chemical changes
at all but, rather weather or current patterns. It was also hypothesized that more
acidic water might cause a lower phytoplankton population, but a conclusion could
not be reached based on pH data.
Introduction:
The idea of the project is to study the effects of water chemistries on
phytoplankton. It stemmed mainly from previous research done at Racing Beach.
One such experiment was that of Travis Law and Scott Osborn who, in 2011, tried to
understand how water chemistries changed between the fresh water inlet, a place
on the beach where water from a swamp flows into the ocean, and the jetty. From
their data, they recommended doing this study because their productivity results,
pointed to the idea that the Phytoplankton were not acting as expected with respect
to water chemistries.1
Their experiment indicated that Lamotte Chemistry tests were very helpful
in testing the water, but this project also required finding the best possible way to
obtain and count phytoplankton. In 2005, Isabel Guenette and Rachel Perpignani
studied marine phytoplankton at the beach in order to help with future studies.
They used a ten-micron net and towed it around in a circle for a set amount of time
so that they weren’t dragging into the waves or parallel to them. They claimed
results of up to 5000 phytoplankton in 1 ml of water on one day.2
Another method involved taking a sample of water and straining a certain
amount of it, then taking a subset of that sample to look at the populations under a
microscope. This method would give a result that could be compared more easily to
other results because it would be more quantitative. Because of this conflicting
information on ways to collect phytoplankton, another aspect of this experiment
was to find the best method for collecting at Racing Beach. While this way would
2012 Ch. 4 pg. 2
give more quantitative and comparative data, it would limit the number of
phytoplankton and different species in the subset. The other procedure from the
earlier tests would give large amounts of plankton, but with an immeasurable
amount of strained water, the amounts would be hard to compare.3
Diatoms, the most abundant type of phytoplankton, depend on silica.4
Therefore the hypothesis of this experiment was that phytoplankton would
congregate in locations with high silica levels. Francisco A. Comin and Ivan Valiela
noted that the main control of plankton production is phosphorus in fresh water,
and nitrogen in seawater.5 It was therefore hypothesized that higher phosphate, and
nitrate levels would lead to higher populations of phytoplankton in that area. In
addition, experiments6,7 have shown that such an increase in carbon dioxide causes
a decrease in phytoplankton populations due to reduced pH. Populations should
therefore be higher at a more neutral pH.
The article by Hupert, Blasius, and Stone, A Model of Phytoplankton Blooms
further supports this hypothesis,8 but also notes that the phytoplankton population
will begin to decrease behind the decreasing nutrients. This could make it difficult to
interpret the relationship between phytoplankton and nutrients.
Materials and Methods:
Water was collected by scoping large bags underwater from three places on
Racing Beach, the north side of the jetty, the fresh water inlet, and from the beach
south of both other sample areas. Tests for Si, NO3, PO4, NH3, and pH were done for
each location using the Smart 2 Colorimeter system. The quantitative method
involved filtering 100ml of water through a piece of PVC with a 10 micron net on the
bottom. Three drops of the filtered water were added to a slide with one drop of
Iodine and the number of phytoplankton in 10 drops of each location was counted..
Because of the low plankton numbers using this method, the samples on the
16th and 17th were counted using the qualitative towing method. A 10 micron net.
was dragged in a spiral…….2 Water was taken for the slides and the chemical tests
directly from the bags of water after shaking them.
The last day water was taken separately from the tow for chemical testing.
2012 Ch. 4 pg. 3
Results:
2012 Ch. 4 pg. 4
Figure 1: NO3 vs Population
350
Phytonplankton/ml
300
250
200
Open Water
150
Fresh Water
N. Jetty
100
50
0
0
0.1
0.2
0.3
NO3 levels (PPM)
0.4
0.5
Fig 1: There is no significant correlation between nitrate levels and phytoplankton
populations. The other chemicals have similar graphs with no trends. Nitrate in open
water however related in a correlation of .98.
Figure 2: Average Phytoplankton
Population
120
(Phytoplankton/ml)
100
80
Open Warer
60
Fresh
N. jetty
40
20
0
Fig 2: The average phytoplankton population per ml of water was highest in the open
water and lowest in the northern jetty area. None of the values were significantly
different.
2012 Ch. 4 pg. 5
Figure 3: Average Chemical levels
1.60
1.40
1.20
(ppm)
1.00
0.80
Average Open Water
0.60
Average Fresh
0.40
Average N. jetty
0.20
0.00
-0.20
Si(ppm)
NH3(ppm)
-0.40
NO3(ppm)
PO4(ppm)
Chemicals
Fig 3: The chemical levels overall were highest in the northern jetty area. The only
significant difference was between the northern jetty Si and fresh water outflow or
open water Si.
Figure 4: Population
350
(Phytoplankton/ml)
300
250
200
Open Water
150
Fresh Water
Jetty
100
50
0
5/13/08
5/14/08
5/15/08
Day
5/16/08
5/17/08
Fig 4: The overall open water and jetty values increased over the 5 days with a large
spike on day three. Fresh water also went up with a spike on day three, but went down
on the last day.
2012 Ch. 4 pg. 6
Conclusion:
This project is about possible connections between phytoplankton
populations and water chemistry and the thought that phytoplankton would
congregate in areas where there were higher nutrient levels. Furthermore, silica
levels would have the highest correlation since diatoms, the most abundant type of
phytoplankton, rely on it.4 None of the chemicals produced a correlation to
phytoplankton populations , but limited time and data make it hard to decisively
disprove our hypothesis. While concluding that whether phytoplankton correspond
to rising and falling chemical levels cannot be done, certain trends in the data
indicate that chemistry is not the underlying cause of high or low population, but
that perhaps weather or currents may be the cause of the dramatic changes.
Figure 1 indicates just how scattered all the plots are, with no significant
correlation at all with Si, NO3, NH3, or PO4. Though the open water nitrate suggests a
significant correlation, this is most likely due to chance because none of the other
locations or chemicals have a significant or suggestive trend line that could indicate
a pattern. With more accurate data, some correlation may be proven; however, the
currents or weather might have a bigger affect on phytoplankton populations.
Since there wasn’t much of a correlation between phytoplankton population
and the chemical levels, the northern jetty area may have had the least amount of
average phytoplankton (Figure 2) due to the fact that it is protected from the long
shore current. This experiment suggests that currents are more important than
originally thought because they may carry nutrients and phytoplankton. Unlike the
hypothesis, the open water, which had the lowest average Silica levels, had the most
phytoplankton. However, the jetty, with the highest silica levels had the lowest
phytoplankton population. This could be because the phytoplankton in the open
water are using the silica making its levels lower, and the lower population in the
jetty would cause a higher amount of silica in that area. Figure 3 indicates that
overall the fresh water outflow has the lowest levels of chemicals other than silica,
when the over range NH3 is added to the chart. This location also has a relatively
low population of phytoplankton. From this, there really isn’t much evidence of a
2012 Ch. 4 pg. 7
connection between the chemical levels and the populations because the
populations are low at both relatively high and low chemical levels.
It is impossible to attribute any change in phytoplankton populations to the
different areas or the differing chemical levels between those areas, because the
populations are too similar. The nutrient levels also proved to be too similar to each
other with the exception of silica. While this exception may be due only to chance, it
might also be an interesting chemical change that may affect the ecosystem on the
north side of the jetty, which has never been studied before.
None of the averages in population or chemicals were significantly different
from one another in the three separate locations. This would suggest that with more
data the hypothesis could be proven or disproven if the populations or chemistry
made a significant change over a longer time period. Though not significant,
observations showed such a jump on May 16th. The influx in population and stability
in chemicals indicates a potential for the hypothesis to be disproven, but it is not
enough data to say conclusively. Also, the large thunder storm on the 16th with
winds that pushed outer waters into the beach area and might have mixed up the
phytoplankton from the bottom, accounted for the sharp increase in phytoplankton
population, especially in the open water and freshwater outflow, since the north
side of the jetty is protected. If the population changed so dramatically without
some sort of significant change in chemicals over the two days, then that indicates
that phytoplankton populations really do not follow chemical levels.
The first two days recorded had a separate procedure from the following
days and this could have caused the slightly lower results on these days compared
to the last three days. Identifying Marine Phytoplankton talked about the two
different procedures tried for this experiment: the quantitative method, and the
qualitative method. As mentioned earlier, the book talks about the pros and cons of
each method, but while at the beach both methods were tested to see which worked
the best, starting with the quantitative, but ending up with the qualitative half way
through the week of sampling. An overall note is that each graph’s data is not strictly
comparable because it comes from multiple procedures. As a recommendation for
the future, the quantitative procedure is better for knowing specific amounts of
2012 Ch. 4 pg. 8
water, but the qualitative is better for getting a general amount of phytoplankton in
a certain area.
Another difficulty was with the chemistry test. The colorimeter would give
inconsistent readings for the same sample. This indicated a possibility that the
colorimeter was misreading floating particles in the filtered water, making its
readings questionable. This problem was fixed on the last day by taking a separate
bag of water for chemistry. It is therefore possible that on May 16 and 17 (the days
without the separate unfiltered samples for chemistry testing) there was faulty
chemical data.
Another error in the data is part of Figure 4. The ammonia levels for open
water were extremely high on May 14 and 15, so high that the colorimeter could not
read them. Therefore this data could not be included in our graph. This caused the
data to be less varied and the average ammonia level in the open water to go down.
This means the ammonia average would be much higher if the over range points
were taken into consideration.
Another aspect of the hypothesis that turned out to be full of errors was the
pH. Outside sources had indicated that more acidic water would cause lower
populations of phytoplankton. 6,7 In this project LaMotte pH tests were used, and all
three range tests were tried each day. The problem was that all three tests gave
readings outside of the values that the colors were made for. These readings seemed
very disturbing and untrustworthy, so the data could not be used to analyze the
hypothesis. Therefore, the LaMotte pH tests are not recommended for future tests
on pH or the tests should be tested before attempting to find the pH of the water.
One interesting future study would be to relate population to wind and
current patterns or to compare phytoplankton at different depths. A further study
of silica levels and the differences in the population and nutrients north of the jetty
could also be an interesting direction for future work.
2012 Ch. 4 pg. 9
Work Cited:
1. Law, Travis and Osborn, Scott. (2011). An Overview of Surface Current
and Water Chemistry Including Productivity, at Racing Beach. CSW
Marine Biology Project Chapter 3.
2. Guenette, Isabel and Perpignani, Rachel. (2005). Abundance and Diversity
Patterns in Plankton. CSW Marine Biology Project Chapter 9.
3. Identifying Marine Phytoplankton, Academic Press. 332. Tomas, Carmelo
R. (1997).
4. An Introduction to the Biology of Marine Life, Fifth, Wm. C. Brown
Publishers. Sumich, James L. (1992).
5. Comin, Francisco A. and Valiela, Ivan. (1993). On the Controls of
Phytoplankton Abundance and Production in Coastal Lagoons. Journal of
Coastal Research Volume 9 No. 4.
6. Kunshan, Gao et al. (2012). Rising CO2 and Increased Light Exposure
Synergistically Reduce Marine Primary Productivity. Nature Climate
Change.
7. Flynn, Kevin J. et al. (2012) Changes in pH at the Exterior Surface of
Plankton with Ocean Acidification. Nature Climate Change.
8. Huppert, Amit and Blasius, Bernd and Stone, Lewi. (2002) A Model for
Phytoplankton Blooms. The American Naturalist Volume 159 No. 2.
2012 Ch. 4 pg. 10