Using Stella Online for Modeling Predator-Prey Dynamics

This model builds off of Stella Online - Population Modeling where you can find instructions for using Stella Online For this model, you will start with a population of fish preyed upon by a second population of sharks. Open up a Stella Online model workspace window to begin your model. Because you have had so much practice with this already, we won’t give you as much help.

Before you begin...

  • You will need a pencil and paper as you work through this activity. You have the option of printing this webpage or recording your findings in a notebook.

  • If you haven't yet, take some time to read about modeling dynamic systems and their behavior over time.

1. Start out by drawing the stocks for your predator and prey with the appropriate flows and links to produce a growth curve for each. Give each component a name but don’t worry about assigning values or equations yet, you can do that later. Assuming organisms are subject to similar factors (births and deaths), you can save some time by duplicating your first organism model (octopuses) through copy and paste then changing the names to fit your new organism. Note: The free version of Stella Online allows a maximum of two stocks in a model and will give a warning that you have reached your limit. Just close the warning window and continue.

2. How should the two populations be connected? Because you want to keep the model relatively simple, assume that the fish die only when eaten by a shark, this means you can delete the fish death rate.

3. The number of fish that die depends on two variables: the shark population and the fish population. More predators mean that more prey will be captured and high prey populations will generally make it easier for a predator to find and capture the prey, increasing the prey deaths. Connect those two stocks to the fish deaths flow.

4. Despite their best efforts, no predator ever succeeds 100% of the time; some prey will always escape. The ratio of attempts to successes defines the predator efficiency; the greater the efficiency, the more frequent the success. Add a variable called predator efficiency and connect it to fish deaths.

5. The fish population death rate is now dependent on the shark population. But doesn’t the amount of prey the sharks have affect their birth-rate? The fish population and the predator efficiency both affect the shark births so you can link them as well.

Can you now name each piece and add the formulas?

  • Fish birth rate: 0.01

  • Fish: 1000

  • Fish births: Fish * fish birth rate

  • Sharks: 10

  • Shark efficiency: 0.0003

  • Fish-deaths: Fish * Sharks * shark efficiency

  • Shark birth rate: 0.6

  • Shark births: shark birth rate * sharks * shark efficiency * fish

  • Shark death rate: 0.15

  • Shark deaths: Sharks * shark death rate

Here is where each one goes:

2. Now you need to set up a graph to record model output. Remember how to do that? Click on the graph icon along the menu bar at the top of the model window, then click once inside the model window. If you forgot how you can find it earlier in this worksheet. The only change would be to create a graph showing two variables, one called Sharks and another called Fish. Remember that you can choose any variable that you wish to graph by choosing its name from the pull down menu in the Settings Panel.

  1. There is one last thing you need to do with this model before simulating. It is currently set to run for 50 months, but to see the real dynamics in this model we need to extend the time frame to 1000 months. To do this you must make adjustments in the Settings Panel.

  2. Be sure that the Settings Panel is open. If it isn’t, click on the small triangle in the right hand margin of the model window.

  3. Check to make sure that none of the components of the model are selected. You can de-select all components by clicking once inside the model window, but not on any of the components.

  4. When no components are selected, the Settings Panel will allow for adjustments to the simulation, rather than the individual components.

  5. Set the start time to zero and the stop time to 1000.

  6. Check to see that the time units are set to be in months.

  7. Set the Sim Speed to 20 seconds (so you don’t fall asleep during the simulation).

  8. If all changes were made correctly, the graph should look like the one above, but with an X-axis that extends to 1000 months.

Before you run the model make a prediction of the BOTG (behavior over time graph) you think you will see on the chart below. Make sure to add a line for both the sharks and the fish. If you want to refresh on the different types of BOTGs, take another look at the background information.

Now it is time to actually run the model! Press the Run button at the lower left of the model window.

Record your results on the graph below:

Review Questions:

  • Does your resulting graph look like the one below?

  • How do your results compare to your prediction on the BOTG on the previous page?

  • What are some reasons for the differences?

It is important to remember that this is a model of an unspecified shark and an unspecified fish. As such, the model does not describe the specifics of any particular interactions, say great white sharks and tuna, or basking sharks and plankton. Because the model is generalized, a great deal has been left out of it.

  • How might you change your model if you were looking at the interaction of great white sharks and tuna?

  • How about basking sharks and plankton?

Photo Credits: Basking shark by Dan Burton, plankton by Amy Moran, great white shark by Eric Hanauer/Alamy, blue fin tuna by National Geographic

Sharing your Models

Feel free to share your Stella Online models with The isee Exchange, an online modeling community. By sharing your models on this platform, you can help others learn and gain insight into the mysteries of our world.

Please take this 1-minute survey, now that you've completed this activity. We are interested in learning about your experience so we can improve these resources. All responses to this survey are anonymous, all questions are optional, and your feedback is much appreciated.

Curriculum Contributors and Supporters

Students Anna Farrell-Sherman, William Wick, Michael Huang and Andrew Liu collaboratively created this resource with ISB scholars

Dexter Chapin, Teacher Scholar, ISB; Seattle Academy of Arts and Sciences
David Reiss: Senior Research Scientist, Computational Systems Biology, ISB 2001-2016
Anne Gillies: Teacher Scholar, 2019, ISB
Rachel Calder, Education Coordinator of SEE, ISB
Claudia Ludwig: Director of Systems Education Experiences, ISB
Nitin Baliga: SVP and Director, Founder of Systems Education Experiences, ISB

Funding to support the development of this lesson was provided by National Science Foundation Award DBI-1565166 & 0640950. The content of these pages was created by students for students with the help of teachers and scientists. The views expressed herein are those of the authors and do not necessarily reflect the views of NSF or ISB.