Rahil Shah
Ecology - Food Web
Lab Report On Plant Population In
a Food Web
RQ - How does changing the number of dependencies (1-5 consumers) on plant A, affect the
population after 100 simulation days?
Background Information
Ecosystems are complex and unstable, with many levels of species and tangled relationships
between organisms. Inflating or removing any level from the ecosystem disrupts the delicate
balance that all organisms are accustomed to, and can be disastrous to the flora and fauna that
inhabit the ecosystem. Since an ecosystem is so interdependent, there are major effects for an
increased number of consumers. If there are many herbivores and omnivores present in an
ecosystem, with very few or no apex predators, the pressure put on vegetation usually increases
dramatically. Typically, this results in a quick collapse of the ecosystem or sometimes a slowly
deteriorating ecosystem that will eventually collapse. Another major problem that arises from
having a low autotroph population is competition between animals. If there are many species
depending on the same finite resource, competition between the species is unquestionably going
to occur. This results in the loss of population for the weaker species. In my experiment, since I
am only testing the effect on plant A with a different amount of species.
Hypothesis: The population of plant A will be the highest with 1 consumer, and
the lowest at 5. However, the rate of population decrease will not be the same from
1 consumer to 5. The reasoning is because as the population of consumers increases, the
dependency on the plant will increase, thus resulting in a lower population. However, from my
background research, I found that if many consumers all depend on one plant for nourishment,
competition between consumers will occur. This results in a lower consumer population which
in turn decreases the dependency on the plant. For example, if the difference in plant population
between 1 consumer and 2 is 25%, the difference between 2 consumers and 3 will only be 20%
because of the aforementioned reasons. Additionally, with the inclusion of the apex predator
who only consumes omnivores, the decrease of population of plants with 4-5 consumers will be
even less—around 10% in the above example. The rate of decrease of plant population with 4-5
consumers not be at the same level as 3,2, or 1 consumers.
Variables
Independent Variable
In this experiment, we will be changing the number of consumers.
These will be from 1 consumer to 5 consumers(it will start with only
herbivore A, then the addition of herbivore B, then herbivore C,
followed by adding omnivore A, and finally, omnivore B).
Dependent Variable
In this experiment, we will be measuring the population of Plant A.
We will measure this by checking the population after a controlled
amount of simulation days and compare the values to the initial
population values.
Controlled Variable
Same duration - The experiment will be measured at 100 simulation
days for every trial. This needs to be controlled because the results
change with more or fewer days. This will be controlled by keeping
the default duration settings on the simulation.
Same amount of plants - The amount of plants is very important
because changing this amount will result in the experiment being
unfair. This will be controlled by setting up the experiment once and
keeping the same setup for every trial.
Same organisms interaction - Every organism in the simulation has
an effect and keeping the same organisms consuming other
organisms is extremely vital. This means the top predator should not
2
be interacting with any organism. To keep the results fair, this top
predator should consume any possible omnivore for all trials.
However, if there are omnivores present for that trial, it needs to
consume only plant A and not other herbivores. Since, we are
increasing the dependency for only plant A, the omnivores should
only consume plant A. This should also apply for every trial.
Same browser/tab - Since the experiment works with a set program,
keeping the experiment on the same browser and tab can reduce the
chances of bugs or glitches that could affect the integrity of the
experiment. To ensure this doesn’t happen, the same browser and
tab will be used throughout the experiment.
Materials
-
A laptop with internet connection - x1 - this will be used to run the simulation.
Procedure
1. Go to the simulation
link:https://www.learner.org/wp-content/interactive/envsci/ecology/ecology.html and
start setting up the experiment.
2. The setup begins with only herbivore A consuming plant A. Plant B and C should be
turned on, but not interacting with any other organism. The simulation should look like
Figure-1
3. Next, run the simulation and measure the population of Plant A after 100 days. Since
there is no information of the amount of room for error in the simulation, three trials will
be to be taken.
4. Then, set up the next simulation with both Herbivore A and B eating only plant A. The
simulation should look like Figure-2. Repeat step 3, and write the data in a data table.
5. Again, repeat another setup with the third herbivore. In this setup, all three Herbivores
will be eating plant A. Refer to Figure-3. Again, repeat step 3 and collect the data in a
table.
6. Next, add omnivore A as a consumer. Make sure to only add plant A as the food source.
Look at Figure-4 for the setup. Repeat step 3 again and record the data in a data table.
7. Then, add Omnivore B, only interacting with plant A. Look at Figure-5. Repeat step 3
again and record relevant data in a data table.
8. Lastly, run the simulation without any consumers as a control group. Only keep the three
plants active. Regard to Figure-6.
3
Figure-1
Figure-2
Figure-3
Figure-4
Figure-5
Figure-6
Safety - Risk Assessment
While doing this simulation online, there are no physical and extremely minimal environment
risks with this simulation. This is because of the high energy efficiency of my laptop and the low
cpu/gpu requirements of the simulation. The risk assessment of the reliability of the data can
also be prevented by having the experiment on the day/time - Since the experiment is being
done on a simulation, possible bugs or updates to the simulation can occur. In order to minimize
these possibilities, the experiment should be done on the same day and quickly.
Raw data table
Trials
Trial 1
Trial 2
4
Initial
Plant A
populati
on
0
1
2
3
4
5
5000
10000
3335
3334
3334
3333
3334
5000
10000
3335
3334
3334
3333
3334
Population with a certain number of consumers
Trial 3
5000
10000
3335
3334
3334
3333
3334
Processed Data Table
Average
Plant A
population
Population with a certain number of consumers
1
2
3
4
5
3335
3334
3334
3333
3334
Graph:
Analysis:
There aren’t many different trends that can be observed from the data. However, I have
observed that we were mostly getting inconsistent results. From the processed data table and
graph, it can be seen that all the data for 1-5 consumers was similar and there were no outliers.
From the looks of it, in the graph, it seems like there are no relationships that can be described
except that the population change is a straight line.. Despite this, there are some changes in the
data presented as the amount of consumers increases. The first big change is from 0-1
consumers. Here, the population changed from 10,000 with zero consumers to only around
5
3335. The decrease is massive. The next change is from 1-2 consumers. This is a minor change
that only decreases the plant population from 3335 to 3334. The plant A population stays the
same until 3 consumers. The next change, also minor, is from 3-4 consumers where the
population decreases by 1 again(from 3334-3332). The last change that can be observed is from
4-5 consumers, where the population increases by 1.
Evaluation:
My hypothesis states that: The population of plant A will be the highest with 1 consumer, and
the lowest at 5. However, the rate of population decrease will not be the same from 1 consumer
to 5. My hypothesis was somewhat correct although not entirely. In my hypothesis, I wrote that
the reasoning behind this is that as the population of consumers increases, the dependency on
the plant will increase, thus resulting in a lower population. Although this is true in theory, the
same did not apply to the data collected from simulation. However, my hypothesis did presume
that the overall plant population would decrease as the amount of consumers went up—which
was true. This does confirm that the greater the amount of consumers in an ecosystem, the lower
the plant population will be. the density, the higher the pressure must be because the variables
are being multiplied.
My hypothesis was also correct with another factor. I predicted that the rate of decrease of plant
population with 4-5 consumers would not be at the same level as 3,2, or 1 consumers. This also
proved to be true, as the trials with 4-5 consumers together didn’t decrease the population at
all(compared to with 3 consumers). This is likely because the simulation takes into account that
the apex predator is consuming the omnivores.
In my experiment, my method was accurate, although not the most realistic. Firstly, since I used
a simulation, many controlled variables were controlled to the highest extent—for example,
temperature. The simulation doesn’t have an option for temperature, which means the results
are not influenced by temperature. Temperature plays a quite important role in the pressure of a
liquid. Assuming in the simulation the liquid is in a closed container, as the liquid temperature
increases, the liquid tries to expand but is prevented by the walls of the container. Since liquids
are incompressible, the results are a significant increase in pressure for a slight temperature
change. The warmer the liquid, the more space it takes up, thus becoming less dense. The
temperature would need to be controlled in real life, which would be much more complex and
not as accurate. Similarly, using the simulation method, all the independent variable values(the
liquid density) were consistent. One example of this is gasoline having a density of 700. Using a
real-life method, it would be hard to accurately find a consistent gasoline supply with an exact
density of 700. Impurities in the gasoline can cause slight changes in density.
The simulation is also good because it has a minimal margin of error. Since the simulation is a
formula running in the background with set values, the margin or error between each trial is
minimal. In the graph and data table, It can be seen most of the trials were very similar values,
making the simulation method quite accurate. Another advantage of this method is that it
6
doesn’t waste materials. The experiment, because it is a simulation, doesn’t need 3 meters cubed
of any liquid. Using a real-life method would be impractical for this scale. Additionally, there is
no way to scale down this experiment because the pressure difference in a small volume of liquid
would be inconsequential. The final benefit of this method is how easy the setup is and how
cost-effective the method is. Since the only cost for using the simulation is electricity, the cost
and environmental damage compared to a real-life method are minimal. It is also effortless to
set up the experiment on a simulation and can be done in a fraction of the time.
Conclusion:
https://sciencing.com/happens-top-predator-removed-ecosystem-8451795.
html
Raw data table
Works cited
7