Effects of Varying Condition and Temperature on L. odoratus
Design aspect 1: Research Question
Research Question: How does temperature(ºC+.5) with 10ºC increments(10ºC and 20ºC)
affect the final CO2 concentration(ppm+50), CO2 concentration difference(ppm+100), the rate of
CO2 production(ppm/s), and the mean rate of CO2 production(ppm/s) in germinating Lathyrus
odoratus(ppm/s)?
How does the condition of seeds(germinating/dormant) affect the final CO2
concentration(ppm+50) and CO2 concentration difference(ppm+100) and ultimately, the mean
rate of CO2 production in Lathyrus odoratus(ppm/s)?
Background Knowledge:
All kinds cells in living organisms do cellular respiration either aerobically or anaerobically
depending on the environment they are in. In this case, as the plant cells of the Lathyrus
odoratus(L. orodatus) are placed in water, which consists of oxygen, the plant cells go through
aerobic cellular respiration. In aerobic respiration, the cell first does glycolysis in the cytoplasm
where 2 ATP molecules are invested in the 6-carbon sugar in form of phosphorylation causing
the sugar to be lysed into two 3-carbon molecules. The 2 ADP molecules are phosphorylated by
the 3-carbon molecule to create 2 ATP per 3-carbon molecule with a total of 4 ATP and a net
gain of 2 ATP. As the phosphorylation of the ADP proceeds, an NAD+ molecule is reduced to an
NADH+H+ molecule for each 3-carbon molecule which creates a total of 2 NADH+H+ molecules.
As a result of glycolysis, the 6-carbon molecule is oxidized into pyruvate and 2 ATP(net gain)
and 2NADH+H+ molecules are created. After glycolysis, each pyruvate molecule(3-carbon
molecule) goes through a link reaction at the mitochondria(matrix) where the pyruvate is
decarboxylated(loses CO2) and an NAD+ molecule is once again reduced to NADH+H+ resulting
in the creation of 2 NADH+H+ molecules and an acetyl group that is picked up by coenzyme A.
This is followed with the Krebs Cycle where the coenzyme A releases the acetyl group to
combine with a 4 carbon molecule that resulted from the previous Krebs cycle to form citric
acid(6-carbon molecule). This citric acid goes through decarboxylation twice and reduces NAD+
into NADH+H+ 3 times with an additional reduction of FAD to FADH2. Since each acetyl group
goes through the Krebs Cycle, this results in the creation of 2 oxaloacetic acid(4 carbon
molecule), 6 NADH+H+ molecules, 2 FADH2 molecules, and 4 CO2 molecules. At this point,
there is a total of 6 CO2 molecules that are created. Hence it makes sense to measure CO2
concentration change in order to measure the rate of aerobic respiration. Once the Krebs cycle
is completed, there is a total of 10 NADH+H+ and 2 FADH2 molecules created as a result of the
Krebs cycle and glycolysis. Each of these NADH+H+ molecules are attracted to the inner
mitochondrial membrane to transport 2 protons or 2H+ molecules to the inter mitochondrial
space and release 2 electrons to the electron transport carriers(ETC). The 2 electrons go
through 2 more ETC to attract 2H+ molecules at each ETC and transport them to the inter
mitochondrial space. As a result of the 2 electrons passing a total of 3 ETC for each NADH+H+,
three 2H+ protons are transported to the inter mitochondrial space. Because one 2H+ molecule
results in the creation of one ATP, 1 NADH+H+ molecule results in the creation of 3 ATP. Since
the 2 electrons from a FADH2 molecule pass through only 2 ETC, only two 2H+ molecules are
transported. As a result, a total of 34 ATP are produced through oxidative phosphorylation.
Since 2 ATP are created at both glycolysis and at the Krebs Cycle, a total of 38 ATP are
produced as a result of aerobic respiration for one glucose molecule(6-carbon sugar) (Allot, &
Mindorff, 2012). The equation that best describes this is the following:
​
​
​
​
​
​
​(Biology Through Inquiry, 2009)
1 | P a g e Joel Hayashi December 12, 2013
Effects of Varying Condition and Temperature on L. odoratus
Hypothesis
Alternative Hypothesis(HA): As temperature(ºC+0.5) increases, the CO2 concentration
Effects of Varying Condition and Temperature on L. odoratus
Hypothesis
Alternative Hypothesis(HA): As temperature(ºC+0.5) increases, the CO2 concentration
difference(ppm+100) will increase, and therefore, the mean rate of CO2 production(ppm/s) will
also increase for the germinating L. odoratus. Meanwhile, when the condition of the L. odoratus
is germinating, the CO2 concentration difference(ppm+100) will be greater, and therefore, the
mean rate of CO2 production(ppm/s) will also be greater relative to that of a dormant/dry
condition.
-The germinating process basically involves the suspension of water and hence, the function of
enzymes in the seed needed for aerobic cellular respiration. On the other hand, theoretically,
seeds in a dry condition do not have those enzymes working and therefore, aerobic cellular
respiration shouldn’t occur at all. As a result, the germinating seeds will have a larger CO2
concentration difference(ppm+100) and higher mean rate of CO2 production(ppm/s).
-Enzymes are usually affected by temperature and have an optimal temperature at which they
work most efficiently, carrying out the most reactions possible. However, once this optimal
temperature is exceeded, the enzymes start to work less efficient. Although this optimal
temperature differs for every single enzyme, assuming that 20ºC has not exceeded the
enzymes’ optimal temperature, the seeds at 20ºC should have a greater CO2 concentration
difference(ppm+100) and higher mean rate of CO2 production(ppm/s).
Null Hypothesis(H0): There is no relationship between condition(dry or germinating)
temperature(ºC+0.5) and CO2 concentration difference(ppm+100) and therefore, rate of CO2
production(ppm/s).
Figure 1: The predicted effect of seed condition(dormant/germinating) on mean rate of CO2
production(ppm/s) of L. odoratus at 20.0ºC+.5
2 | P a g e Joel Hayashi December 12, 2013
Effects of Varying Condition and Temperature on L. odoratus
Figure 2: The predicted effect of temperature(ºC+.5) on mean rate of CO2 production(ppm/s) of
L. odoratus in germinating condition
Independent Variables
Independent Variable: Temperature(ºC+0.5) and Condition(Dry/Dormant or Germinating)
The independent variable will be measured in celcius(ºC) with ranges of 10ºC(+.5) to 20ºC(+.5)
in 10ºC increments. The dry/dormant seeds will be seeds that are untouched. Meanwhile, the
germinating seeds will be prepared by having 20 seeds be The equipment used to measure
temperature is a non-mercury thermometer. Meanwhile, the condition of L. odoratus will be
manipulated(germinating process) by the following: First, leaving a total of 40 seeds
Dependent Variables
Dependent Variable:
Direct-Final CO2 Concentration(ppm+50)
Indirect-a) CO2 Concentration Difference(ppm+100)
​-b) Rate of CO2 Production(ppm/s)
​-c) Mean Rate of CO2 Production(ppm/s+95% CI)
The direct dependent variable will be measured with a CO2 sensor(Model Number: PS-2110).
The measurement will be done in SI units of ppm with an uncertainty of +50ppm.
The indirect dependent variable will not be measured but calculated using Microsoft Excel
Software(2011). Indirect DV a) will be calculated by subtracting the initial CO2
concentration(ppm+50) from the final CO2 concentration(ppm+50) in units of ppm with an
uncertainty of +100. Indirect DV b) will be calculated by dividing the CO2 concentration
difference(ppm+100) for each trial by 600 seconds(10mins) in SI units of ppm/s. In addition, the
Software(2011). Indirect DV a) will be calculated by subtracting the initial CO2
concentration(ppm+50) from the final CO2 concentration(ppm+50) in units of ppm with an
uncertainty of +100. Indirect DV b) will be calculated by dividing the CO2 concentration
difference(ppm+100) for each trial by 600 seconds(10mins) in SI units of ppm/s. In addition, the
uncertainty will vary depending on the percent uncertainty for each trial.
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Effects of Varying Condition and Temperature on L. odoratus
Finally, indirect DV c) will be calculated by finding the average of b) for each IV.
Average in this cause would indicate the division of the sum of all trials for a certain IV by its
sample size. The SI unit for c) will also be ppm/s but with a 95%CI as the uncertainty.
Formula:
a) CO2 Concentration Difference(ppm+100)
b) Rate of CO2 Production(ppm/s)
c) Mean Rate of CO2 Production(ppm/s+95% CI)
Control Variables
Table 1: Effects and method of controlling controlled variables
Variables
Effect
Control/Method
Number of Seeds If not controlled, number of seeds would vary, and
Controlled: The
because all seeds do cellular respiration, theoretically, number of seeds(L.
more seeds indicate greater CO2 concentration
odoratus) put in for
every trial for each IV
difference(ppm+100) and higher mean rate of CO2
production(ppm/s). This would inhibit the comparison will be set to 10
of data amongst IV, hence, if this variable is controlled, seeds.
the data can be compared with others as direct impact
of temperature and condition could be observed.
Time of Cellular Although dependent upon the condition, more time of Controlled: The time
Respiration/Data data collection/cellular respiration indicates more CO2 allowed for the seeds
Collection
production(theoretically for germinating) and a greater to do cellular
respiration will be set
CO2 concentration difference(ppm+100) and higher
mean rate of CO2 production(ppm/s). Hence, time must to 10 minutes or 600
seconds.
be controlled for temperature and condition’s impact
on data could be observed and compared.
Calibration CO2 As a result of going through the method of calibration, Controlled: Calibrate
the CO2 gas sensor is standardized to the CO2 in the
the sensor by pressing
sensor
atmosphere(approximately 400ppm). This allows for the calibrate button
and pressing it again
the recording of a valid measurement for CO2
after 3 seconds.
concentration(ppm+50)
Equilibration of
the L. odoratus
seeds
Starting the experiment right after the insertion of the Controlled: After
CO2 gas sensor is quite unreliable for data collection as putting the CO2 gas
the seeds themselves must be at the temperature of the sensor inside the
water bath(10.0ºC or 20.0ºC+.5). By controlling this, sample bottle, wait
the seeds are set at those particular IV temperatures.
for 3 minutes
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Effects of Varying Condition and Temperature on L. odoratus
Table 2: Effects of uncontrollable variables/Potential Sources of Error
Uncontrollable Variables
Uncontrollable VariableBiological Variation:
Effect
If the sizes and shapes of the L. odoratus seeds
have large variation, the number of cells and
Table 2: Effects of uncontrollable variables/Potential Sources of Error
Uncontrollable Variables
Uncontrollable VariableBiological Variation:
Quantity and rate of CO2 production of each L.
odoratus
Potential Source of ErrorInstrumental Error of CO2 gas sensor
Effect
If the sizes and shapes of the L. odoratus seeds
have large variation, the number of cells and
certainly, the number of enzymes would differ
for each seed. Consequently, the amount of
cellular respiration that occurs for each seed
would have large variations as it would lead to
the collection of unreliable data. This is
however, minimized by the usage of seeds with
similar sizes and shapes.
The variability of the instrument and the
variation in the variability of the instrument is
certainly a potential source of error especially
when the data is shared amongst groups.
Although it may in reality not be the variability
that is the source of error and instead, the high
sensitivity of the instrument, it is still a
potential source of error. However, since the
whole purpose is to record change in CO2
concentration, this potential source of error is
minimized up to a certain extent.
Figure1: General Setup of the Experiment
CO2 gas
sensor
Clamp
Water Bath Sample
Bottle
Germinating Seeds Dormant
for 2 trials
Seeds
Ring
Stand
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Effects of Varying Condition and Temperature on L. odoratus
Design aspect 2: Method
Equipment:
-Large water bath(1000mL)
-Sampling Bottle
-Ring Stand
-Clamp
-30 L. odoratus seeds
-Ice(crushed/cubed)
-CO2 gas sensor (PS-2110)
Part 1: Preparation of Germinating L. odoratus (Seeds)
1. Place 20 germinating seeds in water for 24 hours in an environment with no light for
the suspension of water in the seeds for the functioning of the enzymes
2. Rap 10 seeds each in moist paper towels and leave in environment with no light for
1. Place 20 germinating seeds in water for 24 hours in an environment with no light for
the suspension of water in the seeds for the functioning of the enzymes
2. Rap 10 seeds each in moist paper towels and leave in environment with no light for
another 24 hours to allow the seeds to be exposed to air for the purpose of having
the seeds do aerobic cellular respiration
For each of the three treatments:
1. Adjust the temperature of the water bath.
​
2. Calibrate the CO2 sensor with the sensor outside the sample bottle.
3. Add 20 (dormant or germinating)seeds (dry the germinating seeds) to the sample bottle.
4. Insert the sensor to the sample bottle and place sample bottle and sensor into water bath and
hold it using ring stand and clamp
5. Equilibrate the CO2 sensor for 3 minutes
6. Record the initial CO2 concentration(ppm+50) reading at t0; Record the final CO2
concentration (ppm+50) after 10 minutes (t10).
7. Rinse and dry the sample bottle between treatments.
Part 2: Respiration of Germinating Seeds at 20.0ºC+.5
1. Adjust the temperature of the water bath to 20.0ºC
2. Do steps 1 to 7 from “For each of the three treatments:”
Part 3: Respiration of Germinating Seeds at 10.0ºC+.5
1. Adjust the temperature of the water bath to 10.0ºC by adding ice to water bath
2. Do steps 1 to 7 from “For each of the three treatments:”
Part 4: Respiration of Dormant Seeds at 20.0ºC+.5
1. Adjust the temperature of the water bath to 20ºC
2. Do steps 1 to 7 from “For each of the three treatments:”
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Effects of Varying Condition and Temperature on L. odoratus
Part 5: Processing and Sharing
1. Collaborate with other groups and share data to produce sufficient(total 6) data/trial
for initial and final CO2 concentration(ppm+50)
2. Calculate the Change in CO2 concentration(ppm+100) by subtracting the initial CO2
concentration(ppm+50) from the final CO2 concentration(ppm+50) for each trial
3. Calculate the rate of CO2 production(ppm/s) for each trial by dividing the change in
CO2 concentration(ppm+100) for each trial by 600 seconds(10 minutes).
4. Calculate the mean CO2 production rate by taking the sum of the CO2 production
rate for each IV and dividing it by the number of trials(6).
Table 3: Variables and its unit of precision and error/uncertainty
Variable Type
Variable
Unit of
precision
Independent
Temperature
xx.x ºC
Variable(IV)
Condition(Dormant/Germinating) None
Dependent
Variable(DV)
Error / Uncertainty
+0.5 ºC
None
Final CO2 Concentration(Direct)
x ppm
+50 ppm
CO2 Concentration Difference
x ppm
+100 ppm
Rate of CO2 Production
x.xx
Processed and Indirect DV,
Variable(DV)
Final CO2 Concentration(Direct)
CO2 Concentration Difference
x ppm
+100 ppm
Rate of CO2 Production
x.xx
ppm/s
x.xx
ppm/s
Processed and Indirect DV,
hence no uncertainty included
Mean Rate of CO2 Production
Design aspect 3: Sufficiency of data:
There are two independent variables(IV) for this experiment. One being condition(dormant or
germinating), and the other being temperature(ºC+.5). Condition is manipulated as being either
dormant/dry or germinating. Meanwhile, temperature is manipulated to have a range of
10.0ºC+.5 to 20.0ºC+.5 with increments of 10.0ºC. The conditions were set to either dormant or
germinating with a controlled temperature of 20.0ºC+.5 for both IV because part of the purpose
of this experiment is to explore the effects of condition(dry/dormant or germinating) on the rate
of cellular respiration which is judged by mean rate of CO2 production(ppm/s). Meanwhile, the
lower limits of temperature was set to 10.0ºC+.5 because 10ºC would be a typical temperature
that plants would live through during a winter season. The upper limit was set to 20.0ºC+.5
because 20ºC is a typical temperature that plants would live through in other seasons such as
spring or summer. Ultimately, it is reasonable to set the range to 10.0ºC to 20.0ºC as these are
temperatures that plants experience in nature and it makes sense to compare the rate of cellular
respiration or growth that occurs in a temperature that is relatively similar to that experienced in
nature.
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Effects of Varying Condition and Temperature on L. odoratus
I will repeat the measurements just once at any given point in the range to make sure of the
reliability of the data. However, since the data will be shared as a class, we will have a total of 6
trials for each IV. Hence, we have a very small sample size of 6 where a very small sample size
is defined to be sample sizes between 5-10 samples. The total number of trials is set to 6
because this is the minimum number of trials needed for doing a statistical analysis of standard
deviation(s.d.) and therefore, 95% confidence intervals(95% CI). In addition, it was set to 6 for
the calculation of a more reliable mean value that is closer to the theoretical value as well. While
the direct DV is the final CO2 concentration, the indirect DV is the CO2 concentration
difference(ppm+100), rate of CO2 production(ppm/s), and mean rate of CO2 production(ppm/s).
The CO2 concentration difference(ppm+100) is calculated by subtracting the initial CO2
concentration(ppm+50) from the final CO2 concentration(ppm+50). The rate of CO2
production(ppm/s) is calculated by dividing each value of CO2 concentration
difference(ppm+100) by 600 seconds which is equivalent to 10 minutes(time given for cellular
respiration/data collection). The mean rate of CO2 production is calculated by taking the sum of
the rate of CO2 production for each IV and dividing it by the number of trials(6). The statistical
analysis carried out for this experiment is standard deviation(s.d.) and 95% confidence
interval(95% CI). The formulas for the statistical analysis will be presented later, but the general
explanation of the formula and reason for carrying out the statistical analysis will be done here
to explain the sufficiency of data. The s.d. will be calculated to measure the variability of the
data. It is calculated by calculating the square root of the quotient of the sum of the differences
between the change in CO2 concentration(ppm+100) and mean change in CO2
concentration(ppm) or between the rate of CO2 production(ppm/s) and mean rate of CO2
production(ppm/s) for a particular IV divided by a value that is one less than the sample size(61=5) of the IV. The 95% CI will be calculated in order to judge whether there is a significant
difference between two sets of mean values. In this case, the purpose of the 95% CI is to
determine whether the mean rate of CO2 production for germinating seeds at 10.0ºC+.5 and
20.0ºC+.5 are different and whether the mean rate of CO2 production for dormant seeds and
germinating seeds(at 20.0ºC+.5) are significantly different. If the error bars of the two points
overlap, it indicates that the two points are not significantly different whereas, if they do not
overlap, they are significantly different. Therefore, if all points’ error bars overlap, the alternative
20.0ºC+.5 are different and whether the mean rate of CO2 production for dormant seeds and
germinating seeds(at 20.0ºC+.5) are significantly different. If the error bars of the two points
overlap, it indicates that the two points are not significantly different whereas, if they do not
overlap, they are significantly different. Therefore, if all points’ error bars overlap, the alternative
hypothesis is rejected and the null hypothesis is accepted where if the all points on the error
bars do not overlap, the alternative hypothesis is accepted. However, if the error bars of some
points overlap and some do not, then the alternative hypothesis is accepted with limitations. The
95% confidence interval is calculated by adding or subtracting the product of t(on the t-chart
when p=0.05 and df=sample size-1) when multiplied with the quotient of s.d.(for the particular
IV) divided by the square root of the sample size to or from the mean rate of CO2
production(ppm/s) or mean CO2 concentration difference(ppm). In reality, this calculation is what
determines the upper and lower bounds of the error bars and so, the error bar itself is calculated
without adding or subtracting the mean(mean rate of CO2 production(ppm/s) or mean CO2
concentration difference(ppm)).
8 | P a g e Joel Hayashi December 12, 2013
Effects of Varying Condition and Temperature on L. odoratus
DPP aspect 1: Results – Raw Data
Qualitative Observations: The shape of the seeds were all round in a sphere like shape. They all
had similar sizes. However, the color of the germinated seeds were rather whitish or light green
due to the loss of pinkish color which was absorbed by the paper towels that were initially
rapped around the germinating seeds. The dry seeds had a pink color.
Raw Data:
Table 4: The effect of seed condition(dormant/germinating) and temperature(ºC+.5) on final CO2
concentration(ppm+50) of L. odoratus
Initial CO2
Final CO2
Seed
Temperature/ °C
Trial concentration/ ppm concentration/ ppm
Condition
+ 0.5
+50
+50
Dormant
20.0
1
505
541
2
261
285
3
534
610
4
423
412
5
220
234
6
420
395
mean
s.d.
95%CI
Germinated
20.0
1
2
594
930
1266
1561
3
629
1844
4
1153
2058
5
449
1562
6
670
1502
mean
s.d.
95%CI
10.0
1
400
612
2
1165
1580
3
457
833
4
253
473
10.0
1
400
612
2
1165
1580
3
457
833
4
253
473
5
399
688
6
587
931
mean
s.d.
95%CI
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Effects of Varying Condition and Temperature on L. odoratus
DPP aspect 2: Processing data
Processed Data:
Table 5: The effect of seed condition(dormant/germinating) and temperature(ºC+.5) on final CO2
concentration(ppm+50), ∆CO2 concentration(ppm+100), and rate of CO2 production(ppm/s) of L.
odoratus
Initial CO2/ Final CO2/ ∆CO2/
Rate of CO2
Seed
Temperature/ °C
Trial
ppm
ppm
ppm
Condition
+ 0.5
Production (ppm/s)
+50
+50
+100
Dormant
20.0
1
505
541
36
0.06
2
261
285
0.04
24
Germinated
20.0
10.0
3
534
610
76
0.13
4
423
412
-11
-0.02
5
220
234
14
0.02
6
420
395
-25
-0.04
mean
19
0.03
s.d.
36
0.06
95%CI
29
672
0.05
1
2
594
930
1266
1561
631
1.12
1.05
3
629
1844
1215
2.03
4
1153
2058
905
1.51
5
449
1562
1113
1.86
6
670
1502
832
1.39
mean
895
1.49
s.d.
234
0.39
95%CI
187
0.31
1
400
612
212
0.35
2
1165
1580
415
0.69
3
457
833
376
0.63
4
253
473
220
0.37
5
399
688
289
0.48
6
587
931
344
0.57
mean
309
0.52
s.d.
83
0.14
6
587
931
344
0.57
mean
309
0.52
s.d.
83
0.14
95%CI
0.11
67
*Initial CO2/ ppm+50, Final CO2/ ppm+50, ∆CO2/ ppm+100 indicates Initial CO2 concentration
/ppm+50, Final CO2 concentration/ ppm+50, ∆CO2 concentration/ ppm+100
Table 6: The effect of seed condition(dormant/germinating) on mean rate of CO2
production(ppm/s) of L. odoratus at 20.0ºC+.5
Seed Condition
Dormant
Germinating
Mean Rate of CO2
production(ppm/s)
0.03
1.49
+95%CI
0.05
0.31
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Effects of Varying Condition and Temperature on L. odoratus
Table 7: The effect of temperature(ºC+.5) on mean rate of CO2 production(ppm/s) of L. odoratus
in germinating condition.
Temperature/ ºC
+.5
10.0
20.0
Mean Rate of CO2
production(ppm/s)
0.52
1.49
+95%CI
0.11
0.31
Calculations:
1) ∆CO2(ppm+100) ---CO2 concentration difference
This calculation was done in order to find how much CO2 was produced over the 10 minutes of
cellular respiration in terms of concentration. This value is calculated also for the calculation of
rate of CO2 production for each trial where each of the value for CO2 concentration difference is
divided by 600seconds(10 minutes) to find basically, how much CO2 is produced in one second.
Formula:
​e.g. Trial 1 of IV: Dormant
Propagation of Uncertainty:
2) Rate of CO2 Production(ppm/s)
This value was calculated in order to compare the quantity of CO2 produced in one second for
each kind of seed in different conditions and temperature. Ultimately, this allows to make a valid
deduction of how condition and temperature affects the rate of CO2 production and therefore,
rate of cellular respiration.
Formula:
​e.g. Trial 1 of IV: Dormant
3) Mean Rate of CO2 Production(ppm/s)…+95% CI
This value was calculated in order to compare a single, generalized or simplified value of the L.
odoratus seeds with 6 trials of differing conditions. This allows for the comparison of a single
value rather than a multitude of these values.
Formula:
This value was calculated in order to compare a single, generalized or simplified value of the L.
odoratus seeds with 6 trials of differing conditions. This allows for the comparison of a single
value rather than a multitude of these values.
Formula:
​e.g. IV: Germinating(10.0ºC+.5)
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Effects of Varying Condition and Temperature on L. odoratus
4) Standard Deviation(.s.d.)
The standard deviation was determined in order to measure the variability in the data. s.d.
values that are large indicate data with great variability and therefore, low reliability where as
those with low values indicate low variability and hence, greater reliability.
Formula:
x=rate of CO2 production
=mean rate of CO2 production
n=sample size
​e.g. Dormant
5) 95% Confidence Interval(95% CI)
The 95% confidence interval was measured in order to judge whether two sets of points on the
graph are significantly different or not. This is done by judging whether the errors bars of the
data points, the 95% CI, overlap to each other or not. If the error bars of the data points overlap,
this signifies that the two set of points are not significantly different. On the other hand, if the
data points do not overlap, the two set of points are significantly different. Ultimately, it allows to
make a judgment of whether to accept or reject the hypothesis. If, on the graph, some of the
error bars overlap and some do not, the alternative hypothesis is accepted with limitations. If all
the error bars overlap, then the null hypothesis is accepted. The calculation of 95% CI is done
12 | P a g e Joel Hayashi December 12, 2013
Effects of Varying Condition and Temperature on L. odoratus
using Microsoft Excel(2011). However, since the formula presented below is not the same as
the one used in Excel, the value calculated will not be the same as the one calculated in the
table 5 and 6. Instead, the upper and lower bounds of the error bars will be presented
Formula:
=Mean rate of CO2 production
t=Degree of freedom(df) for n-1
n=number of samples/trials
s=standard deviation
t=Degree of freedom(df) for n-1
n=number of samples/trials
s=standard deviation
e.g. Dormant
---As all IVs have 6 as their sample size(n), the value for t will be for when the degrees of
freedom is 5. Hence, t=2.571
​
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Effects of Varying Condition and Temperature on L. odoratus
DPP aspect 3: Presentation of processed data
Figure 3: The effect of seed condition(dormant/germinating) on mean rate of CO2
production(ppm/s) of L. odoratus at 20.0ºC+.5 (Error Bars: +95% CI)
Figure 4 :The effect of temperature(ºC+.5) on mean rate of CO2 production(ppm/s) of L. odoratus
in germinating condition (Error Bars: +95% CI)
14 | P a g e Joel Hayashi December 12, 2013
Effects of Varying Condition and Temperature on L. odoratus
CE aspect 1: Conclusion
Based on table 5, 6, and figure 3, which show that the mean rate of CO2 production(ppm/s) is
higher for germinating L. odoratus(seeds) relative to the dormant seeds at 20.0ºC+.5, we can
conclude that when the condition of the seeds are set to be germinating, the mean rate of CO2
production(ppm/s) and therefore, the rate of cellular respiration is higher than that of the
dormant condition. In addition, based on table 5, 7, and figure 4, which show a higher mean rate
of CO2 production(ppm/s) when temperature(ºC+.5) is 20.0ºC+.5 relative to than that of
10.0ºC+.5, we could come to a conclusion that as temperature(ºC+.5) increases, the mean rate
of CO2 production(ppm/s) also increases and therefore, the rate of cellular respiration is higher
at a higher temperature relative to the rate at a low temperature. Based solely on the data, we
could deduce that the independent variable of temperature(ºC+.5) is directly proportional to the
DV of mean rate of CO2 production(ppm/s) and therefore, the rate of cellular respiration. At the
same time, based solely on the data, we could deduce that the independent variable of
condition when germinating results in a higher DV of mean rate of CO2 production(ppm/s) and
could deduce that the independent variable of temperature(ºC+.5) is directly proportional to the
DV of mean rate of CO2 production(ppm/s) and therefore, the rate of cellular respiration. At the
same time, based solely on the data, we could deduce that the independent variable of
condition when germinating results in a higher DV of mean rate of CO2 production(ppm/s) and
therefore, the rate of cellular respiration.
From this conclusion, we can clearly state that the data certainly supports my alternative
hypothesis(HA). In the data from table 5, there are some outliers. This includes the 4th and 6th
trials of the IV of dormant condition at 20.0ºC+.5. With the assumption that the cork on the CO2
gas sensor does not allow the exit of CO2 or any substances, it is impossible to get a negative
value. Hence, it makes sense to deduce that this outlier is due to a systematic, instrumental
error. Otherwise, for my first research question which seeks to investigate the effect of the
condition(germinating or dormant) on the rate of cellular respiration, all other data(table 5, 6,
and figure 3) fully supports the conclusion and alternative hypothesis. In fact, all other values of
IV for dormant seeds at 20.0ºC+.5 are lower than that of the IV for germinating seeds at
20.0ºC+.5. Even when observing the values from table 6, the dormant seeds have a mean rate
of CO2 production of 0.03ppm/s while the germinating seeds(from table 6, which are at
20.0ºC+.5) have a mean rate of CO2 production of 1.49ppm/s. From this set of data, we could
calculate that the germinating seeds produce CO2 about 50 more times efficiently relative to the
dormant seeds. In addition, we could come to the same conclusion by observing the mean
values projected for the difference in CO2 concentration(ppm+100). While the value for IV of
dormant is 19+100, the value for germinating(at 20.0ºC+.5) is 895+100. This data also supports
my conclusion because it shows that the values for the germinating seeds are greater than that
of the dormant as it proves the germinating seeds to have gone through more cellular
respiration. As stated in my reasoning for the alternative hypothesis, the biological reasoning or
scientific explanation for the germinating seeds to have a higher mean rate of CO2
production(ppm/s) is because the suspension of water in the cells of the seeds is a requisite for
the functioning of enzymes to carry out the process of aerobic respiration. Since the germinating
seeds were placed in water and the dry seeds were not, the germinating seeds were the ones
with a higher mean rate of CO2 production and therefore, faster rate of cellular respiration.
Meanwhile, the dry seeds are theoretically supposed to not have any enzymes functioning to
carry out aerobic respiration, hence, theoretically, no CO2 difference should even be observed
although in reality, they have rates of CO2 production(ppm/s) with just 2 digits. Perhaps reasons
for this would be because some of the cells in the seeds picked up water in the air causing
some of the enzymes to be able to carry out aerobic respiration and ultimately, production of
small molecules of CO2.
15 | P a g e Joel Hayashi December 12, 2013
Effects of Varying Condition and Temperature on L. odoratus
For the other research question which seeks to investigate the effect of temperature(ºC+.5) on
the rate of cellular respiration in germinating seeds, we could also see that all data supports my
alternative hypothesis as well. My alternative hypothesis being, that the increase in
temperature(ºC+.5) causing the mean rate of CO2 production(ppm/s) and therefore, rate of
cellular respiration to increase. Referring to the mean values of the rate of CO2
production(ppm/s) on table 7, we see that the value for the germinating seeds in 10.0ºC+.5 is
0.52ppm/s where as the value for 20.0ºC+.5 is 1.49ppm/s. In addition, when referring back to
the mean values of the difference in CO2 concentration(ppm+100), the data supports my
alternative hypothesis as well. While the mean value for germinating seeds at 10.0ºC+.5 is
309ppm+100, the value for the germinating seeds at 20.0ºC+.5 is 895ppm+100. As the CO2
production is greater for the IV of 20.0ºC+.5 relative to the 10.0ºC+.5 over the identical given
time of 10minutes, it is clear that more aerobic respiration occurs at 20.0ºC+.5. This basically
shows that when the germinating seeds are in environments of 20.0ºC+.5, the rate of cellular
respiration is about 3 times faster than when it is in an environment of 10.0ºC+.5. However,
what is interesting is that as the temperature(ºC+.5) increased by twice the original temperature,
the mean rate of CO2 production(ppm/s) increased by 3 times more. This signifies that the rate
of increase is not identical to each other. In other words, from this, we could imply that the
relationship between temperature(ºC+.5) and mean rate of CO2 production(ppm/s) is either
linear or partially exponential. Though overall, this relationship makes sense because in terms
of a biological analysis, enzymes tend to function more efficiently when the temperature of its
environment is at the enzyme’s optimal temperature at which it functions best. However, once
relationship between temperature(ºC+.5) and mean rate of CO2 production(ppm/s) is either
linear or partially exponential. Though overall, this relationship makes sense because in terms
of a biological analysis, enzymes tend to function more efficiently when the temperature of its
environment is at the enzyme’s optimal temperature at which it functions best. However, once
this temperature is exceeded, the function of the enzyme starts to become highly inefficient. As
10~20ºC is a temperature that is experienced in nature, it made scientific sense to judge that
neither are temperatures exceeding the seed’s enzyme’s optimal temperature. Therefore, the
data makes sense for the mean rate of CO2 production(ppm/s) and therefore, the rate of aerobic
cellular respiration to be greater at a higher temperature(ºC+.5).
Now we could also comment that the strength of my conclusion itself is quite high as well. This
could be stated based on the 95% CI error bars. When observing the bar graphs for figure 3 and
4, we observe that the error bars, despite having to be large for the IV of germinating seeds in
20.0ºC+.5, do not overlap for both bar graphs. This signifies, that each of these data points or
bars, are significantly different to each other and therefore, allowing the alternative hypothesis to
be accepted without limitations. There are certain outliers as mentioned previously and
variability is also a factor that must be taken into consideration when commenting on the overall
reliability of data but that will be done in the next evaluation.
Based on this published data from PASCO, we could see
that the green, which is the germinating seeds, have a
higher rate of increase in rate of CO2 production(ppm/s)
where as the dormant seeds have a small rate of increase.
(PASCO, 2013)
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Effects of Varying Condition and Temperature on L. odoratus
CE aspects 2 & 3: Evaluation and Improvements
When referring back to tables 5, we see values for s.d. and 95% CI which are statistical test that
are significant for the purpose of evaluating the reliability of the data. Although the s.d. and 95%
CI for the difference in CO2 concentration, since it is a value used for the calculation of the mean
rate of CO2. However, as its uncertainty is also huge, this will be mentioned as well. Although it
is difficult to comment on the 95%CI as the data for difference of CO2 concentration(ppm+100)
is not graphed, the s.d. is something that is quite significant. As we see from table 5 that the s.d.
for the dormant IV is 36. For most data for this IV, the s.d. is basically greater than or equal to
50% of the actual data. In other words, it shows that each of these data points are highly
unreliable. Since some of these values even are unreliable to the extent that a negative value
was observed, this unreliability is evident. Similarly, the s.d. for the germinating seeds at
20.0ºC+.5 is 234 which is in fact, greater than or equal to 25% of the actual data sets or
samples in this IV. Hence, we could deduce that this set of data is not as unreliable as the IV for
the dormant seeds, however, is still a relatively unreliable set of data. Likewise, the s.d. for the
IV of the germinating seeds at 10.0ºC+.5 is 83 as this is also more than or equal to about 17%.
Since there are other points in which the s.d. is actually about 45% of the actual data, it cannot
be stated that these values are reliable. This goes the same for the s.d. of the rate of CO2
production(ppm/s) as well. The s.d. of the IV for dormant seeds, germinating(20.0ºC+.5),
germinating(10.0ºC+100) was the following: 0.06, 0.39, 0.14. Once again, each s.d. relative to
the actual data collected, is a large portion of the data. In other words, there is a large variability
for each set of IV data. And this makes sense because the error of the instrument(CO2 gas
sensor) was +50. Since there is a huge uncertainty, there definitely is a high variability in all
data. As a result, the s.d. would tend to potentially, give a huge impact on the evaluation of the
data. Though in terms of 95% CI, which would be used to assess the validity of the conclusion
or alternative hypothesis, was quite supportive for the alternative hypothesis. In other words, as
I previously mentioned, the 95% CI did not overlap with each other at all for both graphs. Hence,
it was only reasonable to accept the alternative hypothesis without limitations and reject the null
hypothesis. This supports my perception that the results or conclusions were quite valid and in
fact, also supports the biological interpretation, theories, and concepts which underlie the
alternative hypothesis.
hypothesis. This supports my perception that the results or conclusions were quite valid and in
fact, also supports the biological interpretation, theories, and concepts which underlie the
alternative hypothesis.
17 | P a g e Joel Hayashi December 12, 2013
Effects of Varying Condition and Temperature on L. odoratus
Table 8.1: The effect of errors and limitations on data described with its type and potential
modifications
Errors and
Significance:
Type
Modification to
Limitations
Effect on data
of
method
error
Limitation in
As a result of using the CO2 gas sensor,
D
A potential solution is
DesignE
to measure pressure
each group realized that the gas sensor
Instrumental Error: was either inaccurate or was highly
S
using a PASCO
CO2 gas sensor
Xplorer-GLX and
sensitive to change. Evidently, the
calculating the
projected random
uncertainty of the sensor is +50. This
amount of CO2
variation in ppm
limitation is the most likely cause for
value
outliers such as that of trial 4 and 6 of IV
produced by using
for the dormant seeds.
the real gas law
rather than the ideal
so that the result of
the initial pressure
could be compared
with the final.
Limitation in
Due to a very small sample size of 6,
D
We could conduct the
Design-Number of
where in reality, each group is only
E
experiment in
Trials/Sample Size: supposed to do 1 trial per IV. As the
S
individuals rather than
Only 6 trials(very
variability of each CO2 gas sensor varied
groups with each
small sample size, amongst all groups, this could have
conducting at least 2
at least 6, less than been the cause for such large s.d.
trials so that we could
11) were done
have at least a total of
values for all IV.
20 trials and
therefore, a small
sample size that is
between 20-30 as we
are indeed limited
with resources.
Limitation in
When investigating upon the effects of
D
We could add one or
Design-Range of
temperature on the rate of cellular
E
two more IV of 30.0ºC
IV:
respiration for germinating seeds. It
S
and 40.0ºC so that we
Specifically for
would have been better if we could also
could investigate on
investigation on
investigate on the effects of exceeding
the effects of going
temperature(ºC+.5) the optimal temperature of the enzymes
over the optimal
effect, only 2 IV
inside the L. odoratus seeds. And it
temperature.
where both are
could have also helped to make a more
under optimal
valid deduction on whether the
temperature for
relationship is exponential or linear.
effect, only 2 IV
where both are
under optimal
temperature for
enzyme
inside the L. odoratus seeds. And it
could have also helped to make a more
valid deduction on whether the
relationship is exponential or linear.
temperature.
18 | P a g e Joel Hayashi December 12, 2013
Effects of Varying Condition and Temperature on L. odoratus
Table 8.2: The effect of errors and limitations on data described with its type and potential
modifications
Limitation in
As a possibility of variability in the
P Perhaps instead of a cork, we
Performance- instrument, the limitation in
H could use a cap that would
Cork not
performance of not firmly fixing the cork R firmly cover up any kind of
firmly fixed to of the gas sensor into the sample bottle
space that can potentially be
not allow loss could be raised. If the gas sensors
created as a result of the
of CO2,
were actually just highly sensitive, then
usage of a cork and hence,
the cause for the instrumental error
prevent any loss of CO2.
perhaps CO2
would be the small loss of CO2 through
gas sensor
the opening of the sample bottle.
reacted due
to this
Limitation in
Referring back to the qualitative data
D In order to minimize more of
Designabout the shape and size of the L.
E the random biological
Biological
odoratus seeds of all trials. We could
S variation, we could simply use
Variation:
see that the random biological variation
seeds in which have small
Number of
is minimized at least with the
biological variation. Since the
enzymes/cells assumption that shape and size has a
best way to minimize biological
judging by
relationship with the number of cells
variation in this case would be
size and
and enzymes. Hence, it is ranked low
size and shape, we could
shape of L.
in terms of significance. However, it is
select a seed which do not
odoratus
still a source of error as this could also
vary in those attributes as
contribute to the variability of the data
much as the L. odoratus. For
as well as each trials’ differing initial
example, a basil seed or
and final CO2 concentrations
pumpkin seed is very small
and is associated with very few
(ppm+50).
differences amongst each
other.
Key for types of error: D = Design, P = Performance; H = Human, E = Equipment; R = Random,
S = Systematic
*Ordered in significance or importance
References
Allot,, A., & Mindorff, D. (2012). OXFORD IB Diploma Progamme: Biology course companion
(2nd Edition ed.). OXFORD, UK: Oxford University Press.
Biology Through Inquiry (2009). ''Respiration of Germinating Seeds''. Roseville, California:
Pasco Scientific
The College Board, Educational Testing Service. (1997). Biology Laboratory Manual for
Students''. New York, New York: The College Board. New York: The College Board, Educational
Testing Service.
PASCO. (2013). PASCO. Retrieved from Respiration of germinating peas:
http://www.pasco.com/biology/cell-biology/respiration-of-germinating-peas.cfm
19 | P a g e Joel Hayashi December 12, 2013