Ecosystem Balancing Act: Predators, Prey, and Population Dynamics

NGSS Alignment:

• Performance Expectations: HS-LS2-1, HS-LS2-2
• Evidence Statements: HS-LS2-1 Evidence Statement 1.a, 3.a; HS-LS2-2 Evidence Statement 1.a, 2.a
• Science and Engineering Practices (SEPs): Using Mathematics and Computational Thinking
• Disciplinary Core Ideas (DCIs): LS2.A Interdependent Relationships in Ecosystems, LS2.C Ecosystem Dynamics, Functioning, and Resilience
• Crosscutting Concepts (CCCs): Scale, Proportion, and Quantity, Cause and Effect

Introduction

Have you ever wondered how ecosystems maintain a delicate balance? Why don’t prey populations explode and consume all available resources? Why don’t predators eat all the prey and then starve? In this activity, you will use a mathematical computational model to investigate the intricate dance between predator and prey populations. You will manipulate environmental factors and species traits to observe their effects on carrying capacity and ecosystem stability over time.

Access the Simulation Here: Predator-Prey Ecosystem Simulation

Part 1: Exploring Carrying Capacity (HS-LS2-1)

In this section, you will use the computational model to identify factors that affect the carrying capacity of an ecosystem.

1. Set the Stage: Open the simulation and ensure the “Isolated Environment (Island)” toggle is unchecked. Set “Initial Predators” to 0.
2. Baseline Observation: Set the “Prey Carrying Capacity” to 1000. Run the simulation for several minutes. Observe the population graph.
   • Question 1: Describe the shape of the population growth curve for the prey. What happens as the population size approaches 1000? Use mathematical reasoning to explain this pattern based on the concept of carrying capacity.
3. Changing the Limits: Pause the simulation. Change the “Prey Carrying Capacity” to 500, then reset and run the simulation again.
   • Question 2: How did the mathematical representation (the graph) change? What real-world environmental factors might cause an ecosystem’s carrying capacity to decrease from 1000 to 500?

Part 2: Predator-Prey Dynamics (HS-LS2-2)

Now, let’s introduce a predator to see how interdependent relationships affect population sizes.

1. Initial Balance: Reset the simulation. Set “Initial Prey” to 200, “Initial Predators” to 10, and “Prey Carrying Capacity” to 1000. Ensure the environment is not isolated. Run the simulation.
   • Question 3: Observe the graph for several population cycles. Describe the relationship between the peaks and valleys of the predator and prey curves. Which curve changes first, and why? Support your explanation with evidence from the graphical representation.
2. Hunting Efficiency: Pause and reset. Increase the “Predator Hunting Efficiency” to the maximum value (1.0). Run the simulation.
   • Question 4: How does this change affect the population dynamics? Does the ecosystem reach a stable cyclical balance, or does it crash? Explain the cause-and-effect relationship between predator efficiency and biodiversity in this modeled ecosystem.

Part 3: The Impact of Isolation (HS-LS2-1 & HS-LS2-2)

Habitat fragmentation can isolate populations, turning continuous environments into “islands.”

1. The Island Effect: Reset the simulation to the initial balanced state (Prey: 200, Pred: 10, Capacity: 1000, Efficiency: 0.6). Check the “Isolated Environment (Island)” box. Run the simulation.
   • Question 5: Compare the long-term stability of the isolated environment to the continuous (unchecked) environment you observed in Part 2. What happens to the populations over time?
   • Question 6: Based on your observations and the mathematical model provided by the graph, construct an explanation for why isolated populations (like those on islands or in fragmented habitats) are more vulnerable to extinction events.

Synthesis

• Question 7: Imagine a scenario where a new disease significantly increases the “Predator Starvation Rate.” Using your understanding of the computational model, predict what would happen to both the predator and prey populations in the short term and long term. Justify your prediction using evidence gathered from your previous simulations.