Predator-Prey Simulator

What you just saw

Wolves hunt sheep, sheep graze grass, grass slowly regrows after being eaten. The three populations rise and fall in offset cycles — exactly the Lotka-Volterra oscillation. When sheep are abundant, wolves proliferate; when wolves peak, sheep crash; when sheep are scarce, wolves starve; grass recovers; sheep bounce back. No equations — just individual animals deciding whether to eat, reproduce, or starve.

The science behind it

The predator-prey pattern was first described mathematically by Alfred Lotka (1925) and Vito Volterra (1926) as coupled ODEs. The agent-based version here (wolf-sheep-grass) is one of NetLogo's flagship demonstrations and has become a canonical teaching example. Unlike the ODE version, the spatial agent-based model exhibits extinctions, refugia, and fronts — effects invisible to the well-mixed equations.

Try these experiments

1. 1. Classic oscillation

   Settings: defaults

   What to look for: Offset sine-like waves in sheep and wolf populations.

2. 2. Wolf extinction

   Settings: wolf_reproduce_rate=0.02

   What to look for: Wolves die out, sheep explode to carrying capacity limited by grass.

3. 3. Total collapse

   Settings: wolf_reproduce_rate=0.08 sheep_reproduce_rate=0.02

   What to look for: Wolves over-hunt, sheep crash, wolves starve. Both populations extinct.

from scirouter import SciRouter
client = SciRouter(api_key="sk-sci-...")
result = client.simulation.run(
    model="predator_prey",
    params={"grid_size": 500, "initial_sheep": 10_000,
            "initial_wolves": 2_000,
            "sheep_reproduce_rate": 0.04, "wolf_reproduce_rate": 0.05},
    steps=1000, seed=42,
)