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Is global population growth halving?
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While the global population is still increasing in absolute numbers, population growth peaked decades ago. In the chart, we see the global population growth rate per year. This is based on historical UN estimates and its medium projection to 2100.
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Mar 31, 2023 · Population by country, available from 10,000 BCE to 2100, based on data and estimates from different sources. Source. Gapminder - Population v7 (2022); Gapminder - Systema Globalis (2022); HYDE (2017); United Nations - World Population Prospects (2022) – with major processing by Our World in Data. Last updated.
Jun 1, 2023 · The chart shows that global population growth reached a peak in 1962 and 1963 with an annual growth rate of 2.2%; but since then, world population growth has halved. 4 For the last half-century we have lived in a world in which the population growth rate has been declining.
- Overview
- Key points:
- Introduction
- Modeling population growth rates
- Exponential growth
- Logistic growth
- What factors determine carrying capacity?
- Examples of logistic growth
- Summary
How populations grow when they have unlimited resources (and how resource limits change that pattern).
•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.
•When the per capita rate of increase (r ) takes the same positive value regardless of the population size, then we get exponential growth.
•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.
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 Island1,2,3 . 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 hours4 !
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.
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):
dNdT=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:
[How we get to the population growth rate equation]
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.
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.
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 rmax when it is very small, we can write the following equation:
dNdT=rmax(K−N)KN
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. (K−N)/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 (K−N)/K term will reduce the growth rate.
When the population is tiny, N is very small compared to K . The (K−N)/K term becomes approximately (K/K) , 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 .
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.
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 numbers5 . Since this program was shut down, seal populations have rebounded in a roughly logistic pattern6 .
•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:
dNdT=rmaxN
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:
dNdT=rmax(K−N)KN
Logistic growth produces an S-shaped curve.
Chart and table of World population from 1950 to 2024. United Nations projections are also included through the year 2100. The current population of World in 2024 is 8,118,835,999, a 0.91% increase from 2023. The population of World in 2023 was 8,045,311,447, a 0.88% increase from 2022.
Nov 15, 2022 · At present, the world population is growing by around 82.4 million people a year. The countries with the highest population growth in 2021 were Syria, Niger and Equatorial Guinea.
The ideal logistic growth curve shows population size leveling off as a flat line just below carrying capacity. However, a real population’s size typically oscillates around its carrying capacity.
