Population Growth Model
Environmental
scientists use two models to describe how populations grow over time: the
exponential growth model and the logistic growth model. Two important concepts
underlie both models of population growth:
- Carrying capacity: Carrying capacity is the number of individuals that the available resources of an environment can successfully support. In equations and models, the symbol K represents carrying capacity.
- Limiting resource: A limiting resource is a resource that organisms must have in order to survive and that is available only in limited quantity in their environment. Therefore, a limiting resource functions to limit population growth. Food and water are common limiting resources for animals.
Exponential population growth model
In
the exponential growth model, population increase over time is a result
of the number of individuals available to reproduce without regard to resource
limits. In exponential growth, the population size increases at an exponential
rate over time, continuing upward as shown in this figure.
The
line, or curve, you see in the figure shows how quickly a population can grow
when it doesn’t face any limiting resources. The line creates a shape like the
letter J and is sometimes called a J-curve.
Scientists
often describe models with equations. The exponential growth model equation
looks like this:
dN/dt
= rN
The
symbols in this equation represent concepts. Here’s how to translate the
equation into words: The change (d) in number of individuals (N) over a change
(d) in time (t) equals the rate of increase (r) in number of individuals (N).
Logistic population growth model
In
reality, the growth of most populations depends at least in part on the
available resources in their environments. To model more realistic population growth,
scientists developed the logistic growth model, which illustrates how a
population may increase exponentially until it reaches the carrying capacity of
its environment. When a population’s number reaches the carrying capacity,
population growth slows down or stops altogether. This figure illustrates the
logistic growth model.
In
the logistic growth model, population size levels off because the limiting
resources restrain any further growth. This model applies in particular to
populations that respond to density-dependent factors. As you can see in the
figure, the logistic growth model looks like the letter S, which is why
it’s often called an S-curve.
Scientists
describe the logistic growth model with the following equation, which uses the
same symbols as the exponential growth model (see the preceding section):
dN/dt
= rN (1 – N/K)
This
equation says that the change (d) in number of individuals (N) over a change
(d) in time (t) equals the rate of increase (r) in number of individuals where
population size (N) is a proportion of the carrying capacity (K).
The
best part about this equation is that it includes a way to factor in the
negative feedback effect of a larger population relying on the same resources
as a smaller population.
While
the logistic growth model is often more descriptive of what occurs in reality
than the exponential growth model, it still doesn’t accurately describe what
usually occurs in real life. What scientists have actually observed in nature
is that populations seldom reach the carrying capacity and remain stable.
Rather, they experience a pattern called overshoot and die off.
As
populations approach their carrying capacity, more offspring are born than the
current resources can support; as a result, the population exceeds, or overshoots,
the carrying capacity. When the population numbers exceed what the environment
can support, some individuals suffer and die off because of the insufficient
resources.
This
figure shows what the pattern of overshoot and die off looks like. A common
situation that leads to this pattern is the variation in resource availability
from year to year. For example, although plenty of food is available this
spring while a population is reproducing, by the time the offspring are born,
the food resources may have shifted enough that they can’t support all the new
offspring.
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