Lab 4 - Patterns of Natural Selection PDF

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Name Oyinda Adeyemo

LAB EXERCISE 4:

Patterns of Natural Selection Introduction Natural selection is one of the major forces of evolution acting on populations. The major requirements for selection to occur are that 1) traits (or phenotypes) vary among individuals in populations, 2) some of this variation among individuals is due to genetic differences among individuals, and 3) that variation in traits affects some aspect of fitness. If scientists know the distribution of a trait over time, they can identify changes in the population as a result of a change in the environment. Selective pressures can act on the variance of a population to result in stabilizing, disruptive, or directional selection. One way to visualize variance is using a histogram: a type of bar graph that looks at the abundance of a category. In the example to the right, the number of people who are different heights is graphed. In this case, we can look at the abundance of each phenotype of a trait. In today’s lab, we will look at the mean and variance of shark tooth length in a population of sharks. The length of a tooth can be related to many factors such as size of shark, typical size of prey, difficulty in catching prey, or sexual selection. We will then examine how natural selection can change the mean and variance of shark's teeth in a population and use these changes to interpret what type of selection may have occurred. Experiment: Activity 1

1. To get an idea of how our data looks, we will create a histogram. At first glance, a histogram looks just like a bar chart (and it is) but the bars are right next to each other. A histogram can look at the variation in a trait, which in this case is tooth length. 2. The first thing we need to do is identify the highest and lowest values of the dataset. Highlight the data values and select the sort button to arrange your values from smallest to largest. With the lowest number, round down to the next whole number and with the highest number, round up to the next whole number. This will give us a range of values for the Xaxis on our graph. Be sure to include units.

Lowest number (rounded): 22 mm Highest number (rounded): 36 mm

3. Using the values from the previous step, calculate the range of data (the highest value – the lowest value). Be sure to include units. Range: 14 mm

4. There is no general rule for how many bins, or bars, to create. Divide the range by the number of bins desired; in this case, we will use 8 bins. This will get you the bin size. Round this number to the next highest 0.1. Be sure to include the units. Bin size:

1.8 mm / bin

Now that we have the highest and lowest values for the graph as well as the bin size, we can figure out how many data values can be found in each bin to create our histogram. The first column “Bin Values” are the values for the bars. Using the example histogram on the previous page, the first bin is 60 – 65 (the bin size is 5). The conventional way of writing the bin size is [60, 65) where the first number has a bracket, then a comma, and then the second number has a parentheses. This notation helps in determining the inclusivity for the range. A bracket means that that value is included in that bin, whereas the parentheses is not included in that bin.

For example, if you have a data value of 65 and the two bins [60, 65) & [65, 70) the 65 would be included in the [65, 70) range because the 65 has the bracket. Your first minimum number should be the minimum calculated in step #5. Then in the second column, count the number of data points that fall within each bin. If your data isn’t already sorted from smallest to largest in Excel, do this now to make this step easier. Bin Values [min #, max #) [0)

Frequency (number of data points in bin) 0

[22,23.9)

4

[24,25.9)

6

[26,27.9)

9

[28, 29.9)

11

[30, 31.9)

13

[32,33.9)

9

[34,35.9)

7

[36,37.9)

1

5. Finally, let’s graph this data. Using the graph below, draw the x-axis using the bin values calculated in the previous question. Place the “Frequency” or the number of data points found in each bin on the y-axis. The max value on the y-axis should be close to the max value of the number of data points. Draw the bars that correspond with each bin, making sure that they touch. Include axes labels.

In addition to a histogram, we can describe data using descriptive statistics. Specifically, we will look at the mean and the standard deviation of our data set. The mean is the average of the data set. We can calculate it by adding all the values together and dividing it by the number of values. Another way we can measure the difference between datasets is looking at the standard deviation, which is a measure of the spread of the data. The standard deviation gives us an idea of how close or far apart data is from the mean. 6. Let’s use Excel to calculate both the mean and the standard deviation. In the cell to the right of on the word “Mean”, write the following formula: =AVERAGE(range_of_data) Write your formula and the mean of this dataset in the space below. Round to 2 decimal places. = AVERAGE (A3:A10)= 29.79 7. Draw a vertical line through the X-axis on the graph to denote the mean. How does the data look in comparison to the mean? Is the mean also where the highest frequency of values is? Is the mean elsewhere? The mean is not where the highest frequency of value is

8. the cell to the right of the word “Standard Deviation”, write the formula: =STDEV(range_of_data) Write the formula and the standard deviation of this dataset in the space below. Round to 2 decimal places.

=STDEV(A3:A10) = 3.59 9. We now have three metrics to look at our data: a histogram, the mean, and the standard deviation. Let’s put all of these values in the same place. Next to your graph on the previous page, write the mean and standard deviation.

Experiment: Activity 2 10.In the table below, hypothesize how the mean and standard deviation will change if this shark population undergoes various types of selection.

Mean: Type of Selection Directional (larger) Directional (smaller) Disruptive Stabilizing

Hypothesis (circle one) Increase same Increase same Increase same Increase same

Decrease

Stay

Decrease

Stay

Decrease

Stay

Decrease

Stay

Actual results (circle one) Increase same Increase same Increase same Increase same

Decrease

Stay

Decrease

Stay

Decrease

Stay

Decrease

Stay

Standard deviation: Type of Selection Directional (larger) Directional (smaller) Disruptive Stabilizing

Hypothesis (circle one) Increase same Increase same Increase same Increase same

Decrease

Stay

Decrease

Stay

Decrease

Stay

Decrease

Stay

Actual results (circle one) Increase same Increase same Increase same Increase same

Decrease

Stay

Decrease

Stay

Decrease

Stay

Decrease

Stay

11.Describe a scenario that uses natural selection to explain why the length of sharks’ teeth would change to reflect the selective pressures listed below. a) Stabilizing Selection Sharks with little teeth, couldn’t grip on fishes when trying to eat them. Sharks with large teeth had a tough time chewing on fishes when eating them.

b) Disruptive Selection Sharks with medium sized teeth had a hard time biting through things

c) Directional Selection Sharks with either little sized or medium sized teeth couldn’t eat bigger fishes and there are no more little fishes around the area for them to feed on.

Create a new worksheet (tab) in Microsoft Excel and rename it to “Exercise 2”. Copy and paste the shark tooth data into this new worksheet. 12.Now choose one of the three selective pressures and change this data to simulate the selective pressure that you chose to occur in this population. To do so, delete 20% of the data points (if you choose disruptive selection, delete 30% of the data points). What is the range for this new data set? Round up to the next whole number. Show your work.

Disruptive selection Range = 36-22 = 14 13.Calculate the mean and standard deviation of this data set. Color these cells in yellow so they are easier to find. Round to 2 decimal places. Mean = 28.58 Standard Deviation =

5.78

14.Calculate the percent change in the mean and standard deviation from the original dataset. Report your values in the appropriate location on the board. Values that are greater than 10% or smaller than -10% are considered significant changes. Do the changes that you see make sense for your change in population? Why or why not?

% change =

( new value – old value )∗100 new value

=

4.23% We will make the histogram of this data in Microsoft Excel instead of doing it by hand. We will use the same highest and lowest values, bin size, and bin values as before (#5-8). Create a table similar to question #8 in Microsoft Excel using the same bin intervals. Then, count and record the number of data points (frequency) in your manipulated data set that are contained in each bin. Remember to use the sort tool, that will make this step a lot easier! To create a histogram in Excel using your data, highlight ALL the cells of your table, including the column titles. Click on “Insert” along the top menu bar, then choose the first bar graph icon in the Charts section, and then select the first bar graph (2D Column - Clustered).

A new bar graph should appear. In this introductory lab, do not spend time playing with colors or the sort. Just be sure to add axes labels and explore how to get the bars to touch. Add your histogram to the bottom of this page....