Calculate population growth using the logistic equation and carrying capacity (K). Enter initial population, growth rate, and K to model how a population changes over time.
Last updated: March 2026
| Time | Population |
|---|---|
| 0 | 1,000 |
| 5 | 1,142 |
| 10 | 1,300 |
| 15 | 1,478 |
| 20 | 1,675 |
| 25 | 1,892 |
| 30 | 2,131 |
| 35 | 2,391 |
| 40 | 2,672 |
| 45 | 2,974 |
| 50 | 3,294 |
| 55 | 3,632 |
| 60 | 3,985 |
| 65 | 4,348 |
| 70 | 4,719 |
| 75 | 5,094 |
| 80 | 5,468 |
| 85 | 5,837 |
| 90 | 6,197 |
| 95 | 6,545 |
| 100 | 6,878 |
Logistic Model: dN/dt = rN(1 - N/K). Population grows exponentially when small, then slows as it approaches carrying capacity K = 10,000.
Integration: Explicit Euler method with adaptive time step dt = 1.000 (adjusted for numerical stability when |r| is large)
Carrying capacity (K) is the maximum population size that an environment can sustain indefinitely given available resources like food, water, habitat, and other necessities. When a population exceeds K, resource depletion causes increased death rates and decreased birth rates, pushing the population back down. Below K, abundant resources allow rapid growth. The logistic growth model describes this density-dependent regulation mathematically.
The logistic equation dN/dt = rN(1 - N/K) captures this dynamic: when N (population) is small relative to K, the term (1-N/K) ≈ 1, giving approximately exponential growth. As N approaches K, (1-N/K) approaches zero, slowing growth. At N = K, growth stops entirely (equilibrium). The fastest growth occurs at N = K/2, where the product N(1-N/K) is maximized—this is why fisheries target harvest at K/2 for maximum sustainable yield.
Carrying capacity varies with environmental conditions and isn't fixed. Droughts reduce K, abundant rainfall increases it. Human activities dramatically alter K—deforestation reduces wildlife carrying capacity, while agriculture increases human carrying capacity. Exceeding K causes overshoot and crash cycles seen in many populations: rapid growth depletes resources, population crashes, resources recover, cycle repeats. Understanding K is critical for conservation, wildlife management, agriculture, and predicting human population dynamics.
As population approaches K, resources become scarce. Competition for food, water, territory intensifies. This increases death rates (starvation, disease, predation) and decreases birth rates (fewer resources for reproduction). The (1-N/K) term mathematically captures this density-dependent effect.
Overshoot! When N > K, the term (1-N/K) becomes negative, making dN/dt negative—population shrinks. Severe overshoot causes resource depletion and population crash. Examples: deer overbrowsing vegetation, locust swarms causing famine, algal blooms depleting oxygen.
Time to K/2 is how long it takes the population to reach half of carrying capacity from initial size. Unlike exponential doubling time (only valid when N << K), time to K/2 is meaningful for logistic growth—it marks when absolute growth rate is maximized and indicates how quickly the population approaches equilibrium.
Exponential (dN/dt = rN) assumes unlimited resources—growth never slows. Logistic adds (1-N/K) term for resource limitation. Small populations grow exponentially, but large populations slow. Exponential is unrealistic long-term; logistic is more realistic for bounded environments.
At K/2, both N and (1-N/K) are substantial (0.5 each), maximizing their product. Below K/2: few individuals, slow absolute growth despite high per-capita rate. Above K/2: many individuals but low per-capita rate. At K/2: optimal balance for maximum dN/dt.
Uses adaptive time stepping with Explicit Euler integration. When growth rate r is large (>0.5), smaller time steps (dt < 0.5/r) prevent oscillations and overshoot. Warns when r > 0.5 or initial population exceeds K, helping users avoid unrealistic parameters that cause numerical artifacts.
Absolutely! K varies with seasons (more food in summer), climate (droughts reduce K), habitat quality (human development), predator/disease dynamics, and technological change (for humans). Models can incorporate time-varying K: K(t), making predictions more realistic but mathematically complex.
The logistic growth equation is dN/dt = rN(1 − N/K), where K is the carrying capacity.
Carrying capacity is the maximum population size an environment can sustain. It can be estimated using the logistic growth model and observed population dynamics.
Simplifications: assumes stable K, immediate density response (no time lags), no age/genetic structure, no spatial heterogeneity, no stochasticity. Real populations show fluctuations, oscillations, chaos. Logistic is foundational but real dynamics are messier—it's a starting point, not complete description.
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