The common loon nests on land near large lakes. Some loon nesting places have been taken over by human development and the loon population has decreased. Pollution can also hurt animal and plant populations. Sometimes hunting can impact animal populations. Whale populations have been lowered because of over hunting.
If the balance between predator and prey is changed, populations are changed. The white-tailed deer population in some areas has grown too large because there are no natural predators. Mountain lions and wolves are the natural predators of the white-tailed deer. Wolf and mountain lion populations have been lowered due to overhunting and habitat loss.
Describe what r, K, and N 0 are in a population. Analyze how r, K, and N 0 can change population sizes over time both individually and together. Devise a way to stabilize a hypothetical fish population by varying r, K, and N 0 and propose a set of management strategies using these concepts.
Justify this proposal in a group presentation. Activity Procedure Part 1: Changing r We will first focus on understanding r the intrinsic growth rate of a population and how it affects population dynamics or the size of populations over time. For full page, click here. Use the interactive app above to complete the activity. First, become acquainted with the graph. What are the x and y axes?
Use the slidebar to slowly increase the value of r, noting when the dynamics in the plot changes. Describe the pattern of how the dynamics change as r increases. Note any changes in scaling of the y axis. Use the slidebar to change the value of K the carrying capacity. Explain why this observed pattern is happening in terms of growth rate and carrying capacity.
Describe what happens to the population size over time. How much does the population size vary? Bacteria are prokaryotes that reproduce largely by binary fission. This division takes about an hour for many bacterial species. If bacteria are placed in a large flask with an abundant supply of nutrients so the nutrients will not become quickly depleted , the number of bacteria will have doubled from to after just an hour.
In another hour, each of the bacteria will divide, producing bacteria. After the third hour, there should be bacteria in the flask. The important concept of exponential growth is that the growth rate—the number of organisms added in each reproductive generation—is itself increasing; that is, the population size is increasing at a greater and greater rate.
After 24 of these cycles, the population would have increased from to more than 16 billion bacteria. When the population size, N , is plotted over time, a J-shaped growth curve is produced [Figure 1] a. The bacteria-in-a-flask example is not truly representative of the real world where resources are usually limited. However, when a species is introduced into a new habitat that it finds suitable, it may show exponential growth for a while.
In the case of the bacteria in the flask, some bacteria will die during the experiment and thus not reproduce; therefore, the growth rate is lowered from a maximal rate in which there is no mortality.
The growth rate of a population is largely determined by subtracting the death rate , D , number organisms that die during an interval from the birth rate , B , number organisms that are born during an interval.
The growth rate can be expressed in a simple equation that combines the birth and death rates into a single factor: r. This is shown in the following formula:. The value of r can be positive, meaning the population is increasing in size the rate of change is positive ; or negative, meaning the population is decreasing in size; or zero, in which case the population size is unchanging, a condition known as zero population growth. Extended exponential growth is possible only when infinite natural resources are available; this is not the case in the real world.
The successful ones are more likely to survive and pass on the traits that made them successful to the next generation at a greater rate natural selection.
To model the reality of limited resources, population ecologists developed the logistic growth model. In the real world, with its limited resources, exponential growth cannot continue indefinitely. Exponential growth may occur in environments where there are few individuals and plentiful resources, but when the number of individuals gets large enough, resources will be depleted and the growth rate will slow down.
Eventually, the growth rate will plateau or level off [Figure 1] b. This population size, which is determined by the maximum population size that a particular environment can sustain, is called the carrying capacity , or K.
In real populations, a growing population often overshoots its carrying capacity, and the death rate increases beyond the birth rate causing the population size to decline back to the carrying capacity or below it.
Most populations usually fluctuate around the carrying capacity in an undulating fashion rather than existing right at it. The formula used to calculate logistic growth adds the carrying capacity as a moderating force in the growth rate. Thus, the exponential growth model is restricted by this factor to generate the logistic growth equation:. A graph of this equation logistic growth yields the S-shaped curve [Figure 1] b.
It is a more realistic model of population growth than exponential growth. There are three different sections to an S-shaped curve. Initially, growth is exponential because there are few individuals and ample resources available. Then, as resources begin to become limited, the growth rate decreases. Finally, the growth rate levels off at the carrying capacity of the environment, with little change in population number over time.
However, as population size increases, this competition intensifies. Yeast, a microscopic fungus used to make bread and alcoholic beverages, exhibits the classical S-shaped curve when grown in a test tube a. Its growth levels off as the population depletes the nutrients that are necessary for its growth. In the real world, however, there are variations to this idealized curve. Examples in wild populations include sheep and harbor seals b.
In both examples, the population size exceeds the carrying capacity for short periods of time and then falls below the carrying capacity afterwards. This fluctuation in population size continues to occur as the population oscillates around its carrying capacity. Still, even with this oscillation, the logistic model is confirmed. Logistic population growth : a Yeast grown in ideal conditions in a test tube show a classical S-shaped logistic growth curve, whereas b a natural population of seals shows real-world fluctuation.
Population regulation is a density-dependent process, meaning that population growth rates are regulated by the density of a population. In population ecology, density-dependent processes occur when population growth rates are regulated by the density of a population.
Most density-dependent factors, which are biological in nature biotic , include predation, inter- and intraspecific competition, accumulation of waste, and diseases such as those caused by parasites.
Usually, the denser a population is, the greater its mortality rate. In addition, low prey density increases the mortality of its predator because it has more difficulty locating its food source. An example of density-dependent regulation is shown with results from a study focusing on the giant intestinal roundworm Ascaris lumbricoides , a parasite of humans and other mammals.
The data shows that denser populations of the parasite exhibit lower fecundity: they contained fewer eggs. One possible explanation for this phenomenon was that females would be smaller in more dense populations due to limited resources so they would have fewer eggs.
This hypothesis was tested and disproved in a study which showed that female weight had no influence. The actual cause of the density-dependence of fecundity in this organism is still unclear and awaiting further investigation. Effect of population density on fecundity : In this population of roundworms, fecundity number of eggs decreases with population density.
Many factors, typically physical or chemical in nature abiotic , influence the mortality of a population regardless of its density. They include weather, natural disasters, and pollution. An individual deer may be killed in a forest fire regardless of how many deer happen to be in that area. Its chances of survival are the same whether the population density is high or low.
In real-life situations, population regulation is very complicated and density-dependent and independent factors can interact.
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