Human populations grow and shrink due the balance between births and deaths. The number of births per 1000 people is called the birth rate, and the number of deaths per 1000 people is the death rate.
The Population Growth Rate
Population growth rate = birth rate – death rate
It’s really not a complicated equation, you must admit. Say the birth rate of Switzerland was 23/1000 and the death rate 28/1000. The population growth rate works out as -5/1000 (23/1000 – 28/1000). As a percentage, this is -0.5% growth. This means that there is a net decrease of 5 people per thousand.
The Demographic Transition Model
This is a model used to classify different populations depending on their birth rate, death rate, overall population size, and the factors behind these numbers. There are 5 different stages:
Birth rate is high due to lack of contraception and low education.
Death rate is also high due to poor sanitation, high infant mortality and starvation.
Therefore, overall population size remains low.
In stage 2, the causes of high death rate ameliorate, but not those behind high birth rate, therefore:
Birth rate is high for all the reasons in stage 1.
Death rate decreases as more food becomes available and healthcare improves.
Overall population size increases steeply.
Birth rate decreases steeply due to contraception availability and family planning.
Death rate decreases more slowly.
Overall population size increases.
Birth rate is low due to more money being directed towards assets, and the lack of need for child labour.
Death rate is low.
Overall population size is high and stable.
Birth rate decreases as children are expensive to have.
Death rate is stable as many older people die, counteracting the effect of advancing healthcare.
Overall population size decreases for the first time.
Non-human populations have other factors affecting their population sizes, including competition. All individual actions between organisms form a web which impacts on all populations in an ecosystem, therefore determining their sizes.
Interspecific competition refers to competition between members of different species for the same resources (food, light, water. etc.). Often when a new species is introduced in a habitat, say the American ladybird to the UK, if the invader species is better adapted, then the host population decreases in size. This may lead to extinction in some cases of the host species.[Can’t remember the difference between interspecific and intraspecific? Interspecific is like the internet – different things come together.]
Intraspecific competition refers to competition between members of the same species. If a population of apple trees all compete for a source of light, then each apple tree is taking up some light that has now become unavailable to a different apple tree. There are only so many apple trees which that habitat can sustain. The maximum population size sustainable indefinitely in a habitat is called the carrying capacity.
Life expectancy and Survival Rates
Life expectancy graphs refer to the numbers of people who are expected to survive to a certain age.
Here, it can be seen that for 1981 (triangles) 25% conceived survived up to 70 years. In this case, very few people reached 80 years old.
To find out any figure from the graph, just choose a percent first on the y axis, e.g. 50%, then draw a parallel line to the x axis until it meets the curve. Now draw a line from that crossing point down to the x axis (parallel to the y axis) until it crosses it, and read off the corresponding value (for 1900 it’s about 50 years).
These are special graphs which represent the numbers of males and females in different age brackets. Any significant sex differences can be clearly seen at each age range, as well as the life expectancy of the population, and the proportions of younger to older people.
You can see above that, with time, the proportion of children in Japan (below 15 years old) decreased from 35.4% to 13.5%. From this data it can be projected (predicted based on past trends) that by 2050 this percentage will drop further to 8.6%.
It can also be seen that the percentage of people aged 65+ increased from 4.9% to 21.5% between 1950 – 2007.
Also, in 2007 there can be seen significant numbers of people aged 90+ appearing, of which most correspond to females.
Life expectancy also rose very significantly, with many more people surviving to an older age in 2007 than in 1950.
Population regulation through density dependent and density independent factors
Factors affecting populations are diverse and span environmental aspects as well as biological factors specific to a given population. These varied factors can be classified based on whether they affect populations because of the population density or they affect them regardless of the population density.
For example, weather affects a population equally, whether its density is high or low (whether there are many individuals in a given area, or very few). The availability of space or food, on the other hand, doesn’t. A low density population might be OK with the available food and space, but if it booms in density, these factors will suddenly become an issue – a population density-dependent factor.
Another key density dependent factor is reproduction, in terms of which individuals might cross paths and how often.
Sometimes, such as in the case of some parasitic worms, fecundity decreases as the worm population expands. This could be a function of fewer nutrients being available, or stressful conditions caused by overcrowding. This is an example of an inverse density dependent factor because as the population increases, the factor (reproduction) decreases.
The same graph could look reversed in another population. For example, if we were looking at free-roaming animals, reproduction could be unlikely or even impossible at a low density. As the density of the population goes up, more individuals are available for reproduction, and the rate would increase. This is an example of a density dependent factor.
In the case of a constant death rate caused by a predator in a population, this might not depend on the density of the population at all. This is an example of a density independent factor.
The same factor can be density dependent, inversely density dependent, or density independent in different scenarios, populations or at different times, so only the actual data for a specific case can be used to determine which one it is.