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Logistic Growth for a predator-prey cycle

Simulate a predator-prey cycle live in your browser. This runs the real Logistic Growth solver — adjust the inputs, watch it respond instantly, and export the result. No install, no account.

Logistic Population GrowthLive

Controls

Presets

Exponential growth assumes unlimited resources and explodes without bound. Logistic growth adds a carrying capacity K: as the population nears K, growth slows and levels off in a characteristic S-curve. It is the foundation of ecology, epidemiology, and resource management.

▶ Run in Python

Data Inspector

Carrying capacity1000
Max growth atN = 500
Growth rate r0.50

Governing equation

Reading this result: Classic S-curve: from N₀=20 the population grows fastest as it crosses the inflection point N=K/2=500, then decelerates and levels off at the carrying capacity K=1000.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

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About this simulation

The full Logistic Growth tool models a predator-prey cycle with the same numerics engineers and scientists use — running entirely client-side. Change any parameter and the result updates in real time, so you can build intuition, check a design, or teach the concept without spreadsheets or installs.

More you can do with Logistic Growth

Other ways to simulate a predator-prey cycle

Frequently asked questions

How do I simulate a predator-prey cycle?
Open this page and use the live Logistic Growth tool below — set your inputs and the simulation runs instantly in your browser using real numerics. No install, no account needed.
Is it free?
Yes. The simulation runs free in your browser. A one-time unlock or a Pro plan adds advanced parameters, saved presets, data import, and clean exports.
Can I use my own numbers?
Absolutely — every input is adjustable, and with data import you can drive a predator-prey cycle from your own measurements.