Modelling Population Growth
INTRODUCTION
Understanding and tracking populations are important. Knowing and understanding pupation changes allows us to prevent extinction, habitat destruction and environmental damage. Unlike humans, we cannot just take a census, so scientists and mathematicians have developed different models in order to predict and model population growth for organisms.
A population growth rate is the change in a population over a specified period of time. Species that reproduce continuously ( no breeding season) are shown by exponential models of population growth. These species have the potential to increase exponentially, by a constant rate. A big example of this is bacteria, but there are some multicellular organisms that fit this model as well.
The growth of bacteria is a great example of the aplication of the exponontial growth model
Logictic model
A major problem with the exponential growth model is that they don't factor in predation, starvation, or any negative factors for growth therefore, they are only realistic under perfect conditions. To account for these things, the logistic model was created. This model uses extra variables to account for the carrying capacity for that particular environment.
The logictic model is much better suited for more complex populations such as rabbits and mice
The carrying capacity involves many different factors including: Food availability, predation, and space.
The logistic growth model has its own problems. It assumes that all individuals reproduce, die, and use up resources at the exact same rate. Another assumption is that the carrying capacity always stays the same. This would mean that there would be no environmental variations that would cause fluctuations. But form a statitistical and simplicity point of view, it works.