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### Course: AP®︎/College Biology>Unit 8

Lesson 3: Population ecology

# Exponential & logistic growth

How populations grow when they have unlimited resources (and how resource limits change that pattern).

## Key points:

• In exponential growth, a population's per capita (per individual) growth rate stays the same regardless of population size, making the population grow faster and faster as it gets larger.
• In nature, populations may grow exponentially for some period, but they will ultimately be limited by resource availability.
• In logistic growth, a population's per capita growth rate gets smaller and smaller as population size approaches a maximum imposed by limited resources in the environment, known as the carrying capacity ($K$).
• Exponential growth produces a J-shaped curve, while logistic growth produces an S-shaped curve.

## Introduction

In theory, any kind of organism could take over the Earth just by reproducing. For instance, imagine that we started with a single pair of male and female rabbits. If these rabbits and their descendants reproduced at top speed ("like bunnies") for $7$ years, without any deaths, we would have enough rabbits to cover the entire state of Rhode Island${}^{1,2,3}$. And that's not even so impressive – if we used E. coli bacteria instead, we could start with just one bacterium and have enough bacteria to cover the Earth with a $1$-foot layer in just $36$ hours${}^{4}$!
As you've probably noticed, there isn't a $1$-foot layer of bacteria covering the entire Earth (at least, not at my house), nor have bunnies taken possession of Rhode Island. Why, then, don't we see these populations getting as big as they theoretically could? E. coli, rabbits, and all living organisms need specific resources, such as nutrients and suitable environments, in order to survive and reproduce. These resources aren’t unlimited, and a population can only reach a size that match the availability of resources in its local environment.
Population ecologists use a variety of mathematical methods to model population dynamics (how populations change in size and composition over time). Some of these models represent growth without environmental constraints, while others include "ceilings" determined by limited resources. Mathematical models of populations can be used to accurately describe changes occurring in a population and, importantly, to predict future changes.

## Modeling population growth rates

To understand the different models that are used to represent population dynamics, let's start by looking at a general equation for the population growth rate (change in number of individuals in a population over time):
$\phantom{\rule{1em}{0ex}}\phantom{\rule{1em}{0ex}}\phantom{\rule{1em}{0ex}}\phantom{\rule{1em}{0ex}}\phantom{\rule{1em}{0ex}}\phantom{\rule{1em}{0ex}}\phantom{\rule{1em}{0ex}}\phantom{\rule{1em}{0ex}}\phantom{\rule{1em}{0ex}}\phantom{\rule{1em}{0ex}}\phantom{\rule{1em}{0ex}}\frac{dN}{dT}=rN$
In this equation, $dN/dT$ is the growth rate of the population in a given instant, $N$ is population size, $T$ is time, and $r$ is the per capita rate of increase –that is, how quickly the population grows per individual already in the population. (Check out the differential calculus topic for more about the $dN/dT$ notation.)
If we assume no movement of individuals into or out of the population, $r$ is just a function of birth and death rates. You can learn more about the meaning and derivation of the equation here:
The equation above is very general, and we can make more specific forms of it to describe two different kinds of growth models: exponential and logistic.
• When the per capita rate of increase ($r$) takes the same positive value regardless of the population size, then we get exponential growth.
• When the per capita rate of increase ($r$) decreases as the population increases towards a maximum limit, then we get logistic growth.
Here's a sneak preview – don't worry if you don't understand all of it yet:
We'll explore exponential growth and logistic growth in more detail below.

## Exponential growth

Bacteria grown in the lab provide an excellent example of exponential growth. In exponential growth, the population’s growth rate increases over time, in proportion to the size of the population.
Let’s take a look at how this works. Bacteria reproduce by binary fission (splitting in half), and the time between divisions is about an hour for many bacterial species. To see how this exponential growth, let's start by placing $1000$ bacteria in a flask with an unlimited supply of nutrients.
• After $1$ hour: Each bacterium will divide, yielding $2000$ bacteria (an increase of $1000$ bacteria).
• After $2$ hours: Each of the $2000$ bacteria will divide, producing $4000$ (an increase of $2000$ bacteria).
• After $3$ hours: Each of the $4000$ bacteria will divide, producing $8000$ (an increase of $4000$ bacteria).
The key concept of exponential growth is that the population growth rate —the number of organisms added in each generation—increases as the population gets larger. And the results can be dramatic: after $1$ day ($24$ cycles of division), our bacterial population would have grown from $1000$ to over $16$ billion! When population size, $N$, is plotted over time, a J-shaped growth curve is made.
How do we model the exponential growth of a population? As we mentioned briefly above, we get exponential growth when $r$ (the per capita rate of increase) for our population is positive and constant. While any positive, constant $r$ can lead to exponential growth, you will often see exponential growth represented with an $r$ of ${r}_{max}$.
${r}_{max}$ is the maximum per capita rate of increase for a particular species under ideal conditions, and it varies from species to species. For instance, bacteria can reproduce much faster than humans, and would have a higher maximum per capita rate of increase. The maximum population growth rate for a species, sometimes called its biotic potential, is expressed in the following equation:
$\phantom{\rule{1em}{0ex}}\phantom{\rule{1em}{0ex}}\phantom{\rule{1em}{0ex}}\phantom{\rule{1em}{0ex}}\phantom{\rule{1em}{0ex}}\phantom{\rule{1em}{0ex}}\phantom{\rule{1em}{0ex}}\phantom{\rule{1em}{0ex}}\phantom{\rule{1em}{0ex}}\phantom{\rule{1em}{0ex}}\frac{dN}{dT}={r}_{max}N$

## Logistic growth

Exponential growth is not a very sustainable state of affairs, since it depends on infinite amounts of resources (which tend not to exist in the real world).
Exponential growth may happen for a while, if there are few individuals and many resources. But when the number of individuals gets large enough, resources start to get used up, slowing the growth rate. Eventually, the growth rate will plateau, or level off, making an S-shaped curve. The population size at which it levels off, which represents the maximum population size a particular environment can support, is called the carrying capacity, or $K$.
We can mathematically model logistic growth by modifying our equation for exponential growth, using an $r$ (per capita growth rate) that depends on population size ($N$) and how close it is to carrying capacity ($K$). Assuming that the population has a base growth rate of ${r}_{max}$ when it is very small, we can write the following equation:
$\phantom{\rule{1em}{0ex}}\phantom{\rule{1em}{0ex}}\phantom{\rule{1em}{0ex}}\phantom{\rule{1em}{0ex}}\phantom{\rule{1em}{0ex}}\phantom{\rule{1em}{0ex}}\phantom{\rule{1em}{0ex}}\phantom{\rule{1em}{0ex}}\frac{dN}{dT}={r}_{max}\frac{\left(K-N\right)}{K}N$
Let's take a minute to dissect this equation and see why it makes sense. At any given point in time during a population's growth, the expression $K-N$ tells us how many more individuals can be added to the population before it hits carrying capacity. $\left(K-N\right)/K$, then, is the fraction of the carrying capacity that has not yet been “used up.” The more carrying capacity that has been used up, the more the $\left(K-N\right)/K$ term will reduce the growth rate.
When the population is tiny, $N$ is very small compared to $K$. The $\left(K-N\right)/K$ term becomes approximately $\left(K/K\right)$, or $1$, giving us back the exponential equation. This fits with our graph above: the population grows near-exponentially at first, but levels off more and more as it approaches $K$.

### What factors determine carrying capacity?

Basically, any kind of resource important to a species’ survival can act as a limit. For plants, the water, sunlight, nutrients, and the space to grow are some key resources. For animals, important resources include food, water, shelter, and nesting space. Limited quantities of these resources results in competition between members of the same population, or intraspecific competition (intra- = within; -specific = species).
Intraspecific competition for resources may not affect populations that are well below their carrying capacity—resources are plentiful and all individuals can obtain what they need. However, as population size increases, the competition intensifies. In addition, the accumulation of waste products can reduce an environment’s carrying capacity.

### Examples of logistic growth

Yeast, a microscopic fungus used to make bread and alcoholic beverages, can produce a classic S-shaped curve when grown in a test tube. In the graph shown below, yeast growth levels off as the population hits the limit of the available nutrients. (If we followed the population for longer, it would likely crash, since the test tube is a closed system – meaning that fuel sources would eventually run out and wastes might reach toxic levels).
In the real world, there are variations on the “ideal” logistic curve. We can see one example in the graph below, which illustrates population growth in harbor seals in Washington State. In the early part of the 20th century, seals were actively hunted under a government program that viewed them as harmful predators, greatly reducing their numbers${}^{5}$. Since this program was shut down, seal populations have rebounded in a roughly logistic pattern${}^{6}$.
A shown in the graph above, population size may bounce around a bit when it gets to carrying capacity, dipping below or jumping above this value. It’s common for real populations to oscillate (bounce back and forth) continually around carrying capacity, rather than forming a perfectly flat line.

## Summary

• Exponential growth takes place when a population's per capita growth rate stays the same, regardless of population size, making the population grow faster and faster as it gets larger. It's represented by the equation:
$\phantom{\rule{1em}{0ex}}\phantom{\rule{1em}{0ex}}\phantom{\rule{1em}{0ex}}\phantom{\rule{1em}{0ex}}\phantom{\rule{1em}{0ex}}\phantom{\rule{1em}{0ex}}\phantom{\rule{1em}{0ex}}\phantom{\rule{1em}{0ex}}\phantom{\rule{1em}{0ex}}\frac{dN}{dT}={r}_{max}N$
Exponential growth produces a J-shaped curve.
• Logistic growth takes place when a population's per capita growth rate decreases as population size approaches a maximum imposed by limited resources, the carrying capacity($K$). It's represented by the equation:
$\phantom{\rule{1em}{0ex}}\phantom{\rule{1em}{0ex}}\phantom{\rule{1em}{0ex}}\phantom{\rule{1em}{0ex}}\phantom{\rule{1em}{0ex}}\phantom{\rule{1em}{0ex}}\phantom{\rule{1em}{0ex}}\frac{dN}{dT}={r}_{max}\frac{\left(K-N\right)}{K}N$
Logistic growth produces an S-shaped curve.

## Want to join the conversation?

• Why can we just say that the carrying capacity of the seals is 7500? Just because the data seems to imply that?
• Yes! It's an interpretation of field observations. When someone analyzes real world data, the trends that appear can usually be fit to a known mathematical function. In this case, the logistic curve of the data had a carrying capacity of 7500 so that is the inferred capacity for that population. It's a great question though, and considering the spread of that data it might have a significant standard deviation (so 7500 might not be the "exact" carrying capacity).
• My textbooks says that "The intrinsic rate of natural increase is biotic potential." what does it mean?
• I believe "biotic potential" refers to the availability of resources.
• In Exponential growth there is a line:
"the number of organisms added in each generation—increases as the population gets larger" and
"In exponential growth, a population's per capita (per individual) growth rate stays the same regardless of population size, making the population grow faster and faster as it gets larger."
• No, if you have a growth rate of 1 per every 10 people. If you have a population of 100 people then the number of people added to the next generation is 10 giving a population of 110, the next generation no adds 11 people for a population of 121. If you continue this table you get this:
# added  Total         100.0010.00    110.0011.00    121.0012.10    133.1013.31    146.4114.64    161.0516.11    177.1617.72    194.8719.49    214.3621.44    235.7923.58    259.3725.94    285.3128.53    313.8431.38    345.2334.52    379.7537.97    417.7241.77    459.5045.95    505.4550.54    555.9955.60    611.59

Each of these generations adds 1 person for every 10 people of the previous generation but since the generations get larger the number of people added get larger as well.
• Is there any way to include the bounces into an equation? I am talking about the bounces in the last graph.
• You could add error bands to the graph to account for the deviations of the observed values from the values the model predicts. These would not tell the viewer whether a given observation was above or below the predicted value, but they would remind the viewer that the equation only gives an approximation of the actual values.
• My textbook mentions "Geometric Growth" in addition to Exponential and Logistic growth. Could you explain this? Thank you!
• Geometric growth is a situation where successive changes in a population differ by a constant ratio. So while exponential growth is a drastic amount of growth in a short amount of time and logistic is growth that practically stops at some point, geometric growth would be a growth rate that almost never changes. For example, a growth of 2x per hour is geometric growth; every hour, a population doubles, with that rate never changing. So if that population starts with 2, the next hour is 4, then 8, then 16. Exponential growth would be more like 2x^y of growth. Does that make sense? That's the clearest I can think to explain it. Sorry if it's a little confusing.
• If an organism has higher growth pattern which feature support their growth
• It is then exponential growth.

Environmental factors I suppose. Plenty of food and other resources, lack of predators, immunity to diseases, etc.
(1 vote)
• When would we expect the exponential growth and logistic growth both to occur at the same time?