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 studentcentered 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,
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© Kristen Daniels Dotti 2005, 2009, 2015
AP® Biology Daily Lesson Plans (samples)
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1
AP® Biology
Daily Lesson Plans Curriculum
Table of Contents
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 – 11 class days
B. Daily Lesson Plans – Cell Biology – 28 class days
C. Daily Lesson Plans – Genetics – 29 class days
D. Daily Lesson Plans – Evolution – 17 class days
E. Daily Lesson Plans – Anatomy and Physiology – 29 class days
F. Daily Lesson Plans – Botany – 20 class days
G. Daily Lesson Plans – Ecology – 9 class days
H. Review for AP® Biology Exam
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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:
Tryptophan operon
Electrical-taped
regulatory gene
trp R
Promoter
Operator
trp E
trp D
trp C
trp B
trp A
er
The extra piece of noodle
cut to fit operator and
tryptophan acts as the
repressor protein
Taped or colored gene
domains with labels
Ball fits into extra noodle piece and
acts as tryptophan co-repressor
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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 corepressor/tryptophan ball.
2. For the inducible operon, use the prokaryotic lactose operon as an example:
lac I
Promoter
Similar set-up as
tryptophan, but with
different labels and an
inducer that distorts the
repressor protein.
Operator
lac Z
lac Y
lac A
Repressor protein in
distorted shape
Allolactose inducer
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.
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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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5
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
st
tip, 1
field of
vision
nd
2
field
of
vision
from
root
tip
rd
3
field of
vision
from
root
tip
th
4
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
st
tip, 1
field of
vision
nd
2
field
of
vision
from
root
tip
rd
3
field of
vision
from
root
tip
th
4
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
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
MI =
Prophase
Metaphase
Anaphase
Telophase
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)
% of time spent
% of time
% of time
% of time
in Prophase =
spent in
spent in
spent in
Metaphase =
Anaphase =
Telophase =
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Class Data for Percentage of Time Spent in Each Phase of Mitosis:
Interphase
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
MI =
Prophase
Metaphase
Anaphase
Telophase
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)
% of time spent
% of time
% of time
% of time
in Prophase =
spent in
spent in
spent in
Metaphase =
Anaphase =
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
Metaphas
e
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.
Probability
Degrees of
Freedom
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
st
Cell count from 1
field of vision reading
nd
Cell count from 2
field of vision reading
rd
Cell count from 3
field of vision reading
th
Cell count from 4
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)
(n-1) sx sy
when
sx = Σ (x-x)2
(n-1)
and sy = Σ (y-y)2
(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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13
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
2
Σ (x-x) =
Divide the above
number by (n-1)
Using each y
coordinate one at a
time, subtract the
average of y and
square the difference,
then add that value to
the next
2
Σ (y-y) =
Divide the above
number by (n-1)
Take the square root of
the above number.
This is the standard
deviation of x (written
sx)
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
Take the square root of
the above number.
This is the standard
deviation of y (written
sy)
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 rvalue 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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15
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 Kselected 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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5.
6.
7.
8.
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”)
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 fullgrown (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.
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.)
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.
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 densitydependent 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.
© Kristen Daniels Dotti 2005, 2009, 2015
AP® Biology Daily Lesson Plans (samples)
[email protected]
This product is licensed to a single user.
18
Population Dynamics Game
Results:
Adults need 2 resources per year to survive.
Juveniles need 1 resource per year to survive.
Year
1
Population
Notes
Year
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
Population
Notes
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AP® Biology Daily Lesson Plans (samples)
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19
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?
© Kristen Daniels Dotti 2005, 2009, 2015
AP® Biology Daily Lesson Plans (samples)
[email protected]
This product is licensed to a single user.
20
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?
← Exponential Growth Curve
← Logistical Growth Curve
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?
© Kristen Daniels Dotti 2005, 2009, 2015
AP® Biology Daily Lesson Plans (samples)
[email protected]
This product is licensed to a single user.
21
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.
Elk Population
Dynamics of a Elk Population Over a 30-year Period
Reflection Questions:
Time (in years)
© Kristen Daniels Dotti 2005, 2009, 2015
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This product is licensed to a single user.
22
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).
© Kristen Daniels Dotti 2005, 2009, 2015
AP® Biology Daily Lesson Plans (samples)
[email protected]
This product is licensed to a single user.
23
← Exponential Growth Curve
← Logistical Growth Curve
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.
© 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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