Population growth refers to the patterns governing how the number of individuals in a given population changes over time. These are determined by two basic factors: the birth rate and death rate. Patterns of population growth are divided into two broad categories – exponential population growth and logistic population growth.
Exponential Growth
Exponential growth of a population occurs when a population has a continuous birth rate throughout time, and is never hindered by the absence of food or the abundance of disease. To illustrate, imagine a bacterium divides in two, resulting in two bacteria. If these divide, the result is four bacteria. If these divide, the result is eight, then 16 and then 32. This is an exponential process that will continue until resources become scarce or run out.
Logistic Growth
In real world situations, it is very common for populations to be restricted by a lack of food, and the presence of predators and diseases. As conditions become crowded, the population approaches the upper limit of the number of individuals the environment can support. This upper limit is referred to as its “carrying capacity.” Thus, in logistic growth patterns, we can expect the population to increase exponentially up to a point, and then suddenly level off as resources become scarce.
Effective Birth Rate
In logistic population growth patterns, the carrying capacity of the environment alters the “effective birth rate.” The effective birth rate is the net birth rate once resource scarcity has been taken into account. When a population reaches its carrying capacity, the effective birth rate declines until it becomes 1.0. When the birth rate is 1.0, essentially each individual in the environment is replacing himself, resulting in virtually no change in the overall population.
Simulations
There are simulators available on the Internet that allow users to appreciate the difference between exponential and logistic population growth patterns. Exponential population growth simulators have one variable – the birth rate. Logistic population growth simulators have two variables – the birth rate and the carrying capacity. Users can play with these variables by entering different values for each. Try using a logistic population growth simulator to test how long it will take a population to reach its carrying capacity based on different values for the birth rate and carrying capacity.
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References
- Otherwise: Population Growth
About the Author
John Shields has written marketing materials and media releases since 2009. In 2010, he received a Master of Arts from York University. He currently works as an intern for a charitable criminological research organization. Shields is chiefly interested in writing on law, politics and public policy.
Population growth is the change in a population size over a certain time period. Population growth rate is the change in the number of individuals per unit time. This rate is basically determined by the birth rate (rate at which new individuals are added to the population), and the death rate (rate at which individuals leave the population). Population size never increases indefinitely due the limitation of resources such as light, water, space, and nutrients and the presence of competitors. The population growth can be explained by two simple growth models; exponential growth and logistic growth.
Exponential Growth
Exponential growth is defined as the population growth in which the number of individuals accelerate rapidly even when the rate of increase remains constant, finally resulting a population explosion. Here, the birth rate of a particular population alone determines its growth rate. Resource availability is the limiting factor for this growth. When we plot the number of individuals against time, the result will be a J-shaped characteristic curve for exponential growth. According to the curve, the growth starts slowly and then accelerates as the population size increases. In real populations, both food and space become limited as the population become crowded. Therefore, this model is more idealistic, unlike the logistic growth model and sometimes applies for bacterial cultures that have unlimited resources.
Logistic Growth
Logistic growth involves exponential population growth followed by a constant or steady state growth rate. When a population reaches its carrying capacity, its rate growth slows greatly due to the limiting availability of resources for each new individual. The carrying capacity is the size, in which a population ultimately gets stabilized. At this time, the growth rate of that population fluctuates slightly above and below the carrying capacity. This model is more realistic and can be applied for many population exist on earth.
What is the difference between Exponential Growth and Logistic Growth?
• Characteristic curve for exponential growth results in a J-shaped growth curve, while logistic growth results in a sigmoid or S-shaped growth curve.
• Logistic growth model applies to a population that approaches its carrying capacity, while exponential growth model applies to a population that has no growth limit.
• Logistic growth ends up with slightly constant population growth rate (when the population growth rate reaches its carrying capacity), whereas exponential growth ends up with the population explosion.
• Logistic growth can be seen in many population, and it is more realistic than exponential growth. Exponential growth is better suited for bacterial cultures that have unlimited resources such as space and food.
• There is no upper limit for exponential growth model, whereas carrying capacity of a population is the upper limit of logistic growth model.