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© Kristen Daniels Dotti 2005, 2009, 2015 AP® Biology Daily Lesson Plans (samples) [email protected] This product is licensed to a single user.

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AP® Biology Daily Lesson Plans

(samples)

This full-year curriculum includes:

• 142 sequential lesson plans covering the entire College Board curriculum including laboratory skills and test preparation

• A pacing calendar, a materials list, student handouts and grading rubrics

• 100% hands-on learning so the teacher can provide a student-centered classroom environment with no lecture

• Lab experiments, games, model building, debates, projects and other activities designed to promote critical thinking

• A curriculum that exceeds all the expectations of the AP College Board Redesign for 2012

Please visit our website at www.CatalystLearningCurricula.com to download additional sample lesson plans or to place an order. AP® is a registered trademark of the College Board, which was not involved in the production of, and does not endorse, this product.


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AP® Biology Daily Lesson Plans Curriculum

Table of Contents (with three sample lesson plans to follow)

I. Fostering Student-driven Learning in an AP® class II. Year Calendar and Adapting to Class Schedules

III. Materials List

IV. Daily Lesson Plans

A. Daily Lesson Plans – Biochemistry – 15 class days

B. Daily Lesson Plans – Cell Biology – 34 class days

C. Daily Lesson Plans – Genetics – 28 class days

D. Daily Lesson Plans – Evolution – 16 class days

E. Daily Lesson Plans – Anatomy and Physiology – 26 class days

F. Daily Lesson Plans – Ecology – 19 class days

H. Review for AP® Biology Exam


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AP® Biology Daily Lesson Plans Cell Biology Unit (A sample lesson plan)

Day 1

I. Topic: Using Statistics to Test a Null Hypothesis II. Warm-up: 5 minutes

Prior to class, write the following on the board: Set up a microscope with a transparent ruler on the stage. Focus the microscope to 400x magnification. What is the actual distance across the field of vision? (At 400x magnification the field of vision would be 0.45mm across or 450 micrometers in diameter.)

III. Activity One: Chi-squared Test and Test of Correlation 25 minutes

Objectives: a) The learner will (TLW) review the parts of the microscope and its

proper use. b) TLW observe and identify cells in various stages of the cell cycle. c) TLW analyze the data they collect using a chi-squared test and a

test of correlation calculations. Materials:

1 light microscope per student or pair of students; 1 prepared onion root tip slide or similar sample of cells in mitotic division; and 1 copy of the “Root Tip Cell Cycle Data Analysis” handout per student.

Procedure:

1. This activity lends itself to an extension experiment that students can

design and carry out themselves. If you choose to do this extension you will need to purchase pearl onions, garlic or other bulbs to allow them to grow their own root tips under the influence of a chosen treatment. Root tips can be grown by using toothpicks to suspend dry bulbs root side down in a few millimeters of water. Roots will take 3-7 days to emerge. Roots can be trimmed and treated with a nucleic acid stain such as Feulgen stain or acetocarmine stain. Look for “how to” videos on using either of these stains if the process is not familiar and practice prior to class to ensure you can adequately guide the students through their self-designed experiments. For treatment, students may want to use fertilizers or other stimulants, protease chemicals or other mitosis inhibition agents such as UV radiation, tobacco, caffeine, vinca alkaloids, etc. Avoid known spindle or DNA mutagens (such as colchicine or ethidium bromide).


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2. Allow students to follow the handout, counting cells in different phases of the cell cycle using prepared slides and contributing their data to a group data chart for all others to record.

3. Review and reinforce the use of scientific graphing skills with a peer review if needed.

4. Review and reinforce the purpose of the null hypothesis as a falsifiable statement that can be tested using statistical methods.

5. Guide the class through the chi-squared test and test of correlation calculations if the students are not already familiar with these techniques.

HW: Ask the students to complete their “Root Tip Cell Cycle Data Analysis” handout.


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Root Tip Cell Cycle Data Analysis Part 1: Collection of Cell Cycle Data

1. Focus your field of vision on the lowest portion of the root tip so the round lower arc of your field of vision lies just inside the first row of complete cells (just above the waxy root cap).

2. Bring the magnification up to 400x and count 100 cells at the top of your field of vision, deciding which stage of the cell cycle each cell is in and making a tally mark in the appropriate box (interphase, prophase, metaphase, anaphase, or telophase).

3. Move the field of vision up the root so that the cells that were at the top of your field of vision are now at the very bottom of your field of vision, just out of sight.

4. Repeat steps 2 and 3 until you have collected data on 100 cells from each of four fields of vision in which you’ve moved progressively up the root.

5. Collect the class data and add it to the second chart. Do not include your own data in the class data chart and do not use duplicate data if some students collected data from the same root tip samples.

6. Calculate the diameter of the field of vision at 400x magnification for this microscope to the actual distance from the root tip in micrometers or millimeters (recall that 1mm = 1000 micrometers or µm).

My Raw Data Chart of Root Tip Cell Cycle Counts: Phase Lowest

part of the root tip, 1st field of vision

2nd field of vision from root tip

3rd field of vision from root tip

4th field of vision from root tip

Totals for each phase (add all totals across the row for all four fields)

Percentage of cells in each phase (total from previous column divided by the number of cells counted and then multiplied by 100)

My Data: Cells in Interphase

My Data: Cells in Prophase

My Data: Cells in Metaphase

My Data: Cells in Anaphase

My Data: Cells in Telophase


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Class Raw Data Chart of Root Tip Cell Cycle Counts: Phase Lowest

part of the root tip, 1st field of vision

2nd field of vision from root tip

3rd field of vision from root tip

4th field of vision from root tip

Totals for each phase (add all total across the row for all four fields)

Percentage of cells in each phase (total from previous column divided by the number of cells counted and then multiplied by 100)

Class Data: Cells in Interphase

Class Data: Cells in Prophase

Class Data: Cells in Metaphase

Class Data: Cells in Anaphase

Class Data: Cells in Telophase

Class total for cells in each phase

Part 2: Statistical Analysis of Mitotic Phases Using a Chi-squared Analysis

1. Using the chart below, calculate the percentage of time root tip cells spend in each phase of mitosis for both your data and the class data (do not include your own data in the class data).

My Data for Percentage of Time Spent in Each Phase of Mitosis: Interphase Prophase Metaphase Anaphase Telophase Total number of cells in this phase from 400 cells sampled

Mitotic Index (MI) = Number of cells in mitosis divided by the total number of cells counted

Use the total number of cells in mitosis for each column above to calculate the percentage of time root tip cells spend in each phase of mitosis (do not include the cells in Interphase for these calculations)

MI =

% of time spent in Prophase =

% of time spent in Metaphase =

% of time spent in Anaphase =

% of time spent in Telophase =


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Class Data for Percentage of Time Spent in Each Phase of Mitosis: Interphase Prophase Metaphase Anaphase Telophase Total number of cells in this phase from x cells sampled

Mitotic Index (MI) = Number of cells in mitosis divided by the total number of cells counted

Use the total number of cells in mitosis for each column above to calculate the percentage of time root tip cells spend in each phase of mitosis (do not include the cells in Interphase for these calculations)

MI =

% of time spent in Prophase =

% of time spent in Metaphase =

% of time spent in Anaphase =

% of time spent in Telophase =

2. Analyze your phase data against the class data for the time cells spend in each phase of mitosis. Use the class data as the “expected” and your data as the “observed.” Your null hypothesis (H0) in this case is that there is no difference in the percentage of time your root tip cells spent in each phase of mitosis compared to that of the rest of the class. You will need to perform a chi-squared (x2) analysis to test the null hypothesis and find out if your data are similar enough to the class data or if they are clearly different than the class data according to the p < 0.05 that is accepted in science. The instructions below will help guide you through this statistical test: The chi-squared (x2) test is performed using the following equation:

Chi-squared = Sum of (observed-expected)2/expected or x2 = Σ ((o-e)2/e)

where “o” is the observed (actual count or percentage) and “e” is the expected count or percentage of cells for each phase of mitosis. Important: If you use count data, perform all calculations using only count data; if you use percentage data, perform all calculations using only percentage data. Do not mix the two types of data.


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Chi-squared Data Analysis Helper

Categories = Phases of Mitosis

Prophase Metaphase

Anaphase Telophase Total

Observed (o)

Expected (e)

Difference Squared (o-e)2

(o-e)2/e

The chi-squared value is equal to the sum of all differences squared:

X2 = Σ ((o-e)2/e)

3. Using a chi-squared data table (such as the one shown below) and the

degrees of freedom, determine whether or not the difference between your data and the class data is significant. Degrees of freedom would be defined as the number of possible categories minus 1. In this case there were 4 categories or phases of mitosis, so the degrees of freedom would be 3). In science, the 5% (p < 0.05) column is the standard to which all statistical tests are held. This data column reflects the assumption that there is a less than 5% probability of the null hypothesis being falsely rejected. In other words, if these data were gathered again 100 times using the same root tips, there is a 95% chance of the same results.

If your chi-squared value is smaller than the number found on this chart under the 5% column and across the row from 3 degrees of freedom, then you have failed to reject your null hypothesis, meaning your data are not significantly different from the class data. If your chi-squared value is larger (than the number found on this chart under the 5% column and across the row from the 3 degrees of freedom), then you can reject your null hypothesis because the amount of time your root tips spent in mitosis is significantly different from that of your classmates.

Degrees of Freedom

Probability

0.90 0.50 0.25 0.10 0.05 0.01

1 0.016 0.46 1.32 2.71 3.84 6.64

2 .0.21 1.39 2.77 4.61 5.99 9.21

3 0.58 2.37 4.11 6.25 7.82 11.35

4 1.06 3.36 5.39 7.78 9.49 13.28

5 1.61 4.35 6.63 9.24 11.07 15.09


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4. Based on your chi-squared value, do you reject the null hypothesis or do

you fail to reject the null hypothesis? Explain your response.

5. The main thing to note about the chi-squared formula is that the value of x2 increases as the difference between the observed and expected values increase. Explain the significance of getting a chi-squared value that is higher than the number on the chi-squared value chart at the p < 0.05 level?

6. If desired, you could conduct an experiment in which you treat your root tips with a chemical or subject them to some environmental pressure that disrupts the amount of time the root tip cells spend in each phase of the cell cycle. An additional chi-squared analysis of your new data would reveal whether the treatment you used on the root tips results in cells spending a significantly different percentage of time in each phase of mitosis. Use the Internet to research what treatments may have an impact on the cell cycle (i.e., changes in gravity; enzymes that break down spindle fibers, Cdk proteins or cyclins; etc.). Write an experiment with a root tip treatment that you suspect would alter the percentage of time cells spend in each phase of mitosis:

Null Hypothesis: Independent Variable: Dependent Variable: Procedure:


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Part 3: Statistical Analysis of Mitotic Indices Using a Test of Correlation

1. Calculate the mitotic index for each viewing—closest to the root tip, one field up from the root tip, etc.—for your own data and then for the class data. Be sure not to repeat data taken from the same root tip if more than one student collected their data from the same root sample. Distance from Root Tip (in mm or

µm) - these will be your x-axis data coordinates

Mitotic Index (number of cells in mitosis divided by total number of cells counted) – these will be your y-axis data coordinates

Cell count from 1st field of vision reading

Cell count from 2nd field of vision reading

Cell count from 3rd field of vision reading

Cell count from 4th field of vision reading

2. Create a graph of the mitotic indices using your best scientific graphing skills with the independent variable as the x-axis and the dependent variable as the y-axis.

3. Calculate the correlation coefficient between the mitotic index and the distance from the root tip by performing a test of correlation on these coordinates. The steps needed to perform this calculation follow: The equation used for a test of correlation: rxy = Σ (x-x) (y-y) when sx = Σ (x-x)2 and sy = Σ (y-y)2 (n-1) sx sy (n-1) (n-1) To begin this equation, you must first calculate the average of all x coordinates (the average of all x coordinates is represented with the letter x with a bar over the top) and the average of all y coordinates (the average of all y coordinates is represented with the letter y with a bar over the top) then use those averages to calculate the standard deviation of x (written as sx) and the standard deviation of y (written as sy) to be used in the ryx formula. The following chart will help you walk through all the steps of this calculation to reach the final r-value.


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Test of Correlation Data Analysis Helper

x coordinate calculations

y coordinate calculations

Average of all x coordinates ( x ) =

Average of all y coordinates ( y ) =

Using each x coordinate one at a time, subtract the average of x and square the difference, then add that value to the next Σ (x-x)2 =

Using each y coordinate one at a time, subtract the average of y and square the difference, then add that value to the next Σ (y-y)2 =

Divide the above number by (n-1)

Divide the above number by (n-1)

Take the square root of the above number. This is the standard deviation of x (written sx)

Take the square root of the above number. This is the standard deviation of y (written sy)

Write out the sum of all x coordinates minus the average of the x coordinates times all y coordinates minus the average of the y coordinates and solve Or Σ (x-x)(y-y)

Divide the above result by the number of coordinate points minus 1 multiplied by the standard deviation of x and multiplied by the standard deviation of y Or (n-1)sxsy The result will be your r-value

4. The “r-value” that is calculated using this equation will fall between 1.0 and

-1.0 indicating the relationship between the x and y data (i.e., the relationship between the independent and dependent variables). If the r-value is near either 1.0 or -1.0 the relationship is a strong positive correlation or a strong negative correlation. If the r-value is closer to 0.0 this indicates there is little to no relationship between the independent and dependent variable. What does the r-value of your data indicate? Explain your response.


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5. Research the anatomy of a root tip. Determine what factors may have contributed errors and how this experiment could be improved upon to make a more accurate statement of the results. Write a full conclusion about the mitotic indices of root tips based on your research and statistical analysis.


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AP® Biology Daily Lesson Plans Genetics Unit

(A sample lesson plan)

Day 14 I. Topic: Prokaryotic Genomes II. Warm-up: 5 minutes

Prior to class, write the following on the board: “Check your bacterial plates for results. (While students obtain their lab results, walk around the room questioning students individually to verify their understanding.)”

III. Activity One: Prokaryotic Operons 40 minutes

Objectives: a) The learner will improve (TLW) their understanding of gene regulation by

making models of inducible and repressible operons. b) TLW realize the usefulness of models in explaining scientific phenomena

and processes.

Materials: Each lab group will need: 2 foam pool “noodles” in different colors; 7 different colors of electrical tape or 7 different colors of Sharpie markers; 1 wire coat hanger; wire cutters; 2 racquet/tennis balls; 6 stick-on Velcro tabs.

Procedure:

1. Lead the lab groups through the process of making a model of a repressible

operon using the above supplies and the following sample diagrams of a prokaryotic tryptophan operon. For the repressible operon, use the prokaryotic tryptophan operon as an example:

er

Operator

Promoter trp E

trp D

trp C

trp B

trp A

trp R

Ball fits into extra noodle piece and acts as tryptophan co-repressor

The extra piece of noodle cut to fit operator and tryptophan acts as the repressor protein

Taped or colored gene domains with labels

Electrical-taped regulatory gene

Tryptophan operon


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a. Using a serrated knife, cut an 8-inch segment from the first noodle (steps 1a-i will apply to this noodle). This segment will be used as the repressor protein.

b. Each end of the noodle/operon should feature an unlabeled/ untapped section to show the continuation of the DNA strand.

c. Wrap spirals of colored electrical tape (or shade the noodle with colored Sharpies) where each of the five gene domain regions would be found (trpE – trpD – trpC – trpB – trpA), using a different color for each gene domain.

d. Tape or shade in the regulatory gene (trpR) region as far upstream of the promoter region as possible.

e. Using a Sharpie, draw the shape of the active form of the repressor protein onto the lower portion of the noodle/operon, in the operator region. Make the shape simple, like the one in the diagram, since you will need to carve it out using a serrated knife. Also, carve a matching shape into the regulatory repressor protein piece that you cut off in step “1a” above.

f. On the bottom side of the repressor protein, carve a “U” and wedge the racquet/tennis ball into the “U”.

g. Cut a piece of wire from a coat hanger and shove the wire into the repressor protein and bend it into a shape so that this piece will not fit the operator region if the co-repressor (tryptophan) is not in place.

h. Write the word “tryptophan” on one of the racquet/tennis balls. Write “repressor protein” on the carved foam piece. Now label the various parts of the noodle/operon using a Sharpie: “regulatory gene – trpR”, “promoter/operator”, “trpE”, “trpD”, “trpC”, “trpB” and “trpA”.

i. Place stick-on Velcro tabs on the parts of the operator and the repressor protein that fit together, so that they can stick together without being held in place. You may do the same for the repressor and the co-repressor/tryptophan ball.

2. For the inducible operon, use the prokaryotic lactose operon as an example:

a. Using a serrated knife, cut an 8-inch segment from the second noodle (steps 2a-i will apply to this noodle). This will be used as the repressor protein.

b. Again, each end of the noodle/operon should feature an unlabeled/untapped section, to show the continuation of the DNA strand.

c. Wrap spirals of colored electrical tape (or shade the noodle with colored Sharpies) where each of the three gene domain regions would be found (lacZ – lacY – lacA), using a different color for each gene domain.

Operator

Promoter lac Z

lac Y

lac A

lac I

Similar set-up as tryptophan, but with different labels and an inducer that distorts the repressor protein.

Repressor protein in distorted shape

Allolactose inducer


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d. Tape or shade in the regulatory gene (lacI) region which is immediately upstream of the promoter region.

e. Using a Sharpie, draw the shape of the active form of the repressor protein onto the lower portion of the noodle in the operator region. Make the shape simple, like the one in the diagram, since you will need to carve it out using a serrated knife. Also, carve a matching shape into the regulatory repressor protein piece that you cut off in step “2a” above.

f. On the bottom side of the repressor protein, carve a wide, semi-circle shape that is a little too wide to accommodate the racquet/tennis ball. You want the repressor protein to have two shapes, one that fits the operator shape perfectly when the inducer is NOT present and one that distorts the repressor so that the carved top shape appears to pop out of the operator when the inducer fits into the bottom (you can shove a piece of coat hanger wire into the repressor to make it hold two different shapes).

g. Write “allolactose” on one of the racquet/tennis balls. Write “repressor protein” on the carved foam piece. Write “regulatory gene – lacI”, “promoter/operator”, “lacZ”, “lacY” and “lacA” at the appropriate places along the noodle.

h. You may place stick-on Velcro tabs on both the operator and repressor protein parts so that they can stick together without being held in place. You may do the same for the repressor and the co-repressor/allolactose ball.

3. Use these models as props during class, when discussing the operon hypothesis. Have pairs of students use the props as they simulate and narrate the process of inducing or repressing an operon to regulate the genes. Make sure everyone has a chance to run through a simulation with each operon.

4. Ask the students to take notes on inducible operons and repressible operons. 5. Ask some questions to verify the depth of their understanding and clarify any

misconceptions: a. What is more common for each type of operon—the gene non-repressed

state or the repressed state? (inducible operons are more commonly found in the repressed state while repressible operons are more often actively transcribing, thus are not repressed)

b. Which type of operon would be used for anabolic reactions (reactions that make new molecules)? (repressible operons that are turned off when there is an excess of product)

c. Which type of operon would be used for catabolic reactions (reactions that break down other molecules)? (inducible operons that are only turned on in the presence of the metabolite)

d. Are operons examples of positive feedback or negative feedback? (negative feedback)

HW: Ask the students to write a FR essay to question #1 from the 2003 Form B AP Biology Exam.


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AP® Biology Daily Lesson Plans Ecology Unit

(A sample lesson plan)

Day 2 I. Topic: Population Growth II. Warm-up: 5 minutes

Prior to class, write the following on the board: What does the capital letter K represent in ecology? What does it mean to be a K-selected species?

III. Activity One: Population Dynamics Game 45 minutes

Objectives:

a) The learner will (TLW) play a memorable game that clearly shows how a single population can fluctuate according to the condition and carrying capacity of its habitat.

b) TLW practice drawing and interpreting population growth graphs. Materials:

One “Population Dynamics” handout per student; enough small, wrapped hard candy to allow six per student; one pie pan to hold the candy; one large piece of sidewalk chalk to make a circle on cement, or a long piece of yarn to make a circle in grass/dirt.

Procedure:

1. Prior to class, find a concrete surface that is a large enough surface on which to draw a 20-ft diameter circle with chalk, or find a grass or dirt area on which to place a 20-ft diameter circle of string. Place 6 pieces of candy per student in the pie pan. Place the pie pan in the center of the circle when you start the game with your class (it helps to draw a chalk line around the pie pan, so that it stays in the correct place if the game gets rowdy).

2. Have the students space themselves out along the circle perimeter. 3. Tell the students that they are a population of elk and that each student

represents one elk family that lives in a habitat together with the others that are on the circle.

4. Ask them the following questions to generate a short discussion before you begin:

a. What do organisms in a population need? (resources: food, water, territory/space)

b. How do they get these resources? (by competing for them)


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c. How many organisms of a population can a given environment hold? (it depends on the amount of resources)

d. What do we call this number? (carrying capacity, or “K”) 5. Explain the guidelines of the game:

a. Each student represents one elk family. b. One round of play equals one year of time. c. The pie pan holds the resources that are available in a single year. d. Only one resource (piece of candy) can be collected at a time. The

student must return to the perimeter of the circle—touching it with both feet—after each resource has been collected to deposit it on the edge of the circle before returning to the pie pan for another resource.

e. Every adult elk needs two resources (candies) to live through one year and every juvenile elk needs one resource to live through one year.

f. For the first round of play every family has only one adult member. g. Each year, half of the families will each produce one offspring. h. The first year that the offspring is born it is a juvenile (and thus

needs one resource). i. The second year the family does not produce any offspring;

however the juvenile, if it lived through its first year, is now full- grown (and thus needs 2 resources).

j. Each family must collect the needed number of resources from the pie pan in order for the members of the family to survive for that year. If they collect less than that number, they need to determine how many individuals actually survived on the lower number of resources, with juveniles dying off first (ex: if a student/elk family has 3 adults and one juvenile, the student must collect 7 resources for the entire family to survive for the year; if the student only collects 4 resources, then the juvenile and one adult will have died, since the juvenile was the most vulnerable and the 4 resources can only sustain two adults).

k. The population will be counted at the end of every year/round of the game, when the students hold up the number of fingers that represent the elk in their family that survived that year.

l. If a family dies off completely, they will have to wait to come back into the game when it is their turn to reproduce a juvenile.

6. Begin the game by writing down the number of elk in the starting population at time 0 on the data chart. (This will be equal to the number of students in the class.)

7. Remind the students that they each need 2 resources, but they must collect them one at a time and return to the perimeter (with both feet touching the line) to drop off their resources one by one.

8. Start the first round by saying “Go!”. In the first round there is an abundance of resources, so all the elk should live. Tally the number of survivors and write that number on the data chart for year one.


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9. Divide the circle in half and tell the students on one half that they have reproduced this year and so they now must gather enough resources for one adult and one juvenile (3 pieces of candy in total). Start round two/year two.

10. Count and record the population totals for the end of year two/round two and repeat steps 9–10 over and over until the population plateaus for 3-4 rounds.

11. Ask the students why the population has hit a plateau? (because this is the carrying capacity of the environment with this amount of resources) Ask them why the elk continue to reproduce even though the carrying capacity has been hit? (biological potential is high even when it meets environmental resistance; Thomas Malthus pointed out that all organisms have a greater reproductive potential than can actually be sustained by their environment)

12. Tell the students that a fire has occurred in the forest and the amount of available resources has dropped. Explain that the fire has served as a density-independent population control. Now take 2 resources out of the pie pan for every student in the class. Ask the students what will happen to the carrying capacity. (it will be lower) Ask them if this change is permanent. (no, it will gradually go back up) Ask them to name another density-independent population control. (any abiotic change such as a flood, hurricane, freeze, change in humidity, or sunlight, etc.)

13. Play several more rounds of the game, adding a few resources back to the pan with each round, until they are up to their original carrying capacity.

14. Simulate a density-dependent population control by telling the students that their area has been hit by a disease. The resources stay the same but every elk family loses a certain number of members that year. Ask the students to predict what will happen to the population over time. (it will drop and then, eventually, rise again slowly) Since the availability of resources has not decreased, how will the rest of the ecosystem be affected? (other organisms will prosper due to a decrease in competition for resources) Ask the students to give you examples of other density-dependent population controls. (any biotic factor, such as a new predator population in the area, another organism that competes for the food, water or space resources, etc.)

15. Allow several rounds to occur so that the elk population recovers from the disease losses.

16. When you stop playing, have the students graph the population dynamics and answer the reflection questions on their handout to turn in tomorrow.

HW: Ask the students to complete the “Population Dynamics Game” handout.

HW: Ask the students to complete the “Population Dynamics of the Kaibab Deer” handout.


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Population Dynamics Game Results:

Adults need 2 resources per year to survive. Juveniles need 1 resource per year to survive.

Year Population Notes Year Population Notes

1 21

2 22

3 23

4 24

5 25

6 26

7 27

8 28

9 29

10 30

11 31

12 32

13 33

14 34

15 35

16 36

17 37

18 38

19 39

20 40


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Graph the data collected during the game.

Reflection Questions: 1. What is the carrying capacity of the elk habitat in this game?

2. How much was the population able to fluctuate around the carrying capacity? What were the constraints?

3. How was the elk population affected by the fire? By disease?


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4. How do density-independent and density-dependent population controls

differ?

5. How could the carrying capacity of this environment increase?

6. How could the carrying capacity of this environment decrease?

7. Above are graphs that show two common patterns of population growth. What type of growth pattern most closely resembles the elk population growth prior to the fire?

8. Consider the exponential growth curve. Is it possible that a population growing exponentially might eventually level off and have a logistical growth curve?

9. What would it take for this to happen?

� Logistical Growth Curve

� Exponential Growth Curve


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Population Dynamics Game Teacher’s Version

Results: The students’ data charts and graphs will vary some, however the

population should be depicted climbing to reach carrying capacity, holding steady at carrying capacity until the fire, going down after the fire and then climbing again slowly to return to carrying capacity. The simulated disease will cause the graph line to dip and return to carrying capacity slowly, just as with the fire. Check that all students are graphing correctly—labeling axes with units, depicting steady increments on each axis and smooth curves, and providing their graph with a title. Dynamics of a Elk Population Over a 30-year Period

Reflection Questions: Time (in years)

Elk

Popu

latio

n


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1. What is the carrying capacity of the elk habitat in this game?

Answers will vary, but should match the plateau of the student’s graph. 2. How much was the population able to fluctuate around the carrying

capacity? What were the constraints?

Very little, because the food resources were limited. 3. How was the elk population affected by the fire? By disease?

In both circumstances the carrying capacity of the habitat dropped, so the population dropped significantly. As more food became available, the population climbed again.

4. How do density-independent and density-dependent population controls

differ?

Density-independent controls affect all the organisms in an ecosystem, while density-dependent controls usually affect organisms unequally.

5. How could the carrying capacity of this environment increase?

Answers may include: the elimination of competing species, expansion of the size of the habitat, a reduction in the individual needs of each organism, or an increase in the availability of resources.

6. How could the carrying capacity of this environment decrease?

Answers may include: an increase in the needs of the organisms, an increase in the populations within the ecosystem, an introduction of more species or another population, a reduction in the amount of resources needed at the bottom of the food chain (such as caused by drought).


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7. Above are graphs that show two common patterns of population growth. What type of growth pattern most closely resembles the elk population growth prior to the fire? A logistical growth curve. 8. Consider the exponential growth curve. Is it possible that a population

growing exponentially might eventually level off and have a logistical growth curve?

Yes. 9. What would it take for this to happen?

The population growth would have to slow as it approaches the carrying capacity and then level off, to plateau around the carrying capacity.

� Logistical Growth Curve

� Exponential Growth Curve


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