Modeling yearly changes in a lionfish population
We can use mathematical models to study how populations of lionfish, and other species, change over time. You can learn more about these types of models in BioInteractive’s Population Dynamics Click & Learn.
One common population model is the logistic model, which describes how a population changes when its growth rate depends on the population’s current density. This type of growth is called density-dependent.
A discrete-time logistic model describes how a population with density-dependent growth changes over specific time periods. It is often used to model populations of organisms that reproduce at specific times — for example, once per year or season. An equation for this model is:
This equation can also be written as:
The table below describes the biological meaning of each symbol in the equations above.
|A specific point in time — in this case, the start of the time period over which we are measuring the population.|
|The size or density of the population at time .|
|The symbol (delta) means “change in,” so means the change in time for the population — in other words, the length of the time period we are using.|
|The change in the population’s size over the time period we are using.|
|The maximum per capita growth rate, which is the growth rate of the population per individual, in the discrete-time model. This rate is a constant, and it represents how fast a population can grow when it is not limited by density-dependent factors. Its value depends on the species’ physiology and life history features (rate of development, lifespan, number of offspring, frequency of reproduction, etc.).|
|The carrying capacity of the population. is a constant, and it is determined by factors like the availability of resources in the environment.|
The graph below shows how a population would grow according to the logistic model. As the population size () changes, the population growth rate () also changes. This makes the shape of the growth curve change over time.
If population size () is far below the carrying capacity (), the term will be close to 1, meaning it will have only a small impact on population growth. In that case, the population can grow faster and faster. If is close to , the term will be close to 0, drastically slowing population growth.