Left completely unchecked, a population of almost any organism grows exponentially — each generation produces more offspring than the last, and the growth rate itself accelerates over time, producing the classic steep, upward-curving explosion familiar from population graphs. No real population actually sustains that curve indefinitely, because no real environment offers unlimited food, space, or resources. Logistic growth is the model ecologists use to describe what happens once a growing population runs into that ceiling, and it produces a strikingly different shape from unchecked exponential growth.
Carrying capacity puts a limit on the curve
The logistic growth model introduces a concept called carrying capacity — the maximum population size a given environment can sustain indefinitely, given its available resources. Early in a population's growth, when numbers are still small relative to that capacity, resources remain abundant and growth proceeds at close to the exponential rate, the population climbing steeply. As the population approaches carrying capacity, though, competition for increasingly scarce resources intensifies, birth rates tend to fall, death rates tend to rise, and the growth rate itself slows correspondingly — not because individual organisms are behaving differently, but because the environment's constraints are increasingly limiting how much further growth it can actually support.
An S-shaped curve, not an ever-climbing one
Plotted over time, logistic growth traces a distinctive S-shaped, or sigmoid, curve: a slow start, a steep exponential-like climb through the middle, and then a gradual levelling off as the population approaches and eventually stabilises near carrying capacity, rather than continuing to climb indefinitely the way an unconstrained exponential curve would. This model captures something genuinely different from simple exponential growth, and it's borne out reasonably well in many real populations under laboratory or otherwise controlled conditions, giving ecologists a useful baseline for understanding how population growth actually behaves once a fixed environment's limits start to bite.
What we're still unsure about
The basic mathematical logic of logistic growth, and its S-shaped curve, are well established and match many controlled experimental populations closely. What's genuinely more complicated in real, uncontrolled ecosystems is that carrying capacity itself often isn't a fixed, stable number the way the simple model assumes — it can shift with seasonal resource availability, climate variation, predator and competitor populations, and other dynamic factors, and many real populations overshoot carrying capacity and crash, or oscillate around it rather than settling smoothly, which is why ecologists treat the basic logistic model as a useful starting point rather than a precise description of any given real-world population's dynamics.
This sits inside Population Dynamics & Logistic Growth, one of seven topics in Ecology, one of six domains in Biology, one of seventeen subjects the app can quiz you on.