During the course of this particular lab, we worked together in groups to calculate the data of humans who had survived in the last couple years due to . Our ultimate goal was to describe how human mortality had changed in the past 200 years, to create a data chart with population data, and to generate mortality and survivorship graphs depicting that change.
With every study of the population, there is a model that all species must follow, and that is as follows: (Births - Deaths) + (Immigration - Emigration). If the various rates represented in this equation remain equal, the population will remain constant, but if any factor goes through a significant change, then the population will be affected and decreases. The immigration and emigration can tell us if the population goes up or down depending on the number of people who stay or leave the particular population.
We used a hypothetical data sheet of ages of human death, with four different categories. With our survivorship curves, a graphical representation of the likelihood that an individual will survive from birth to a certain age, we could determine which trends in which years accounted for the number of deaths at that time.
Based on these results, we can confirm the three main survivorship curves, which can determine the lifespan of an organism and show how an organism lives or dies.
The first type of graph is that of the human population, as well as several other large species, living and then gradually dying out as they age.
The second type usually sits in between types one and three, takes the form of a diagonal line from one end of the graph to the other.
The third of these types of graphs appears to be the opposite of a type 1 graph. It depicts most of the organisms that die first, and the organisms that live a longer life at the end.
There are two main factors that influence population growth, and these are known as density independent and dependent factors.
Density independent factors affect the size of a population, but are not influenced by the changes in population density. For example, an earthquake event will alter population density, sometimes drastically, but don't occur because of a change in density. By contrast, density dependent factors are able to have effects on population change as the population density changes. An example of this would be if a disease were to kill off many producers in a specific habitat, such as grass in a field. If all the grass were to die out, then there would be hardly any consumers, such as cows, to graze in this area, thereby decreasing the population of cows in that particular area.
In order to properly show a population's logistic growth, there are two main types of graphs; S-type and J-type.
S-type's curve shows a population that starts at a lower capacity, growing higher and higher before stopping at the limit of the environment's carrying capacity.
J-type graphs show a period of continuous, exponential growth, that never stops growing and continues on for a longer period of time, without any limit to the carrying capacity.
Source: Environment, Wiley Books
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