Artificial Selection Lab

Report
Artificial Selection Lab:
An Example of Experimental
Evolution In Action
Dr. Tricia Radojcic (Bella Vista Middle School, Murrieta, California)
and
Dr. Theodore Garland, Jr. (University of California, Riverside)
(with assistance from Dr. Heidi Schutz)
Preparation Supported by the National Science Foundation,
the American Physiological Society, and the University of California, Riverside
1
Copy the definitions below and match
with the correct term.
 A process by which individuals that are better
suited to their environment survive and
reproduce more successfully (yielding higher
Darwinian fitness) than those that have less
favorable characteristics and behaviors.

Answer: Natural selection
 Who/what selects the ones that survive &
reproduce?

Answer: Nobody/nothing.
2
Artificial selection
Who/what selects the ones that survive &
reproduce?
People
3
Born to Run:
Experimental Evolution of
Hyperactivity in Mice
Professor Theodore Garland, Jr.
Dept. of Biology
4
Experimental Design
Starting (Base) Population:
224 mice of the outbred Hsd:ICR strain
Levels of genetic variation similar to populations
of wild Mus and to human populations;
Used for several other selection experiments
Design:
8 lines: 4 up, 4 control
10 mating pairs in each (litter size ~10)
Within-family selection (Ne ~35 for each line)
Selection Criterion:
Wheel revolutions on days 5 + 6
5
Wheels are Attached to Standard Housing Cages:
whether or not mice run is entirely voluntary
6
200 total
wheels,
100 in each
of two rooms
7
Important point!
● We did not select for low voluntary running
because it might not be the same trait (other
end of continuum) as high wheel running.
● For example, selection for low running might simply
increase fear of entering the wheels.
● Active behaviors may not be opposites of
sedentary behaviors (e.g., jogging is not the
opposite of watching TV).
● Also, selection for low performance might just
increase frequency of any deleterious
recessive alleles in the population, thus
leading to sickly mice from a variety of causes.
8
Revolutions/Day on days 5 + 6
17000
Wheel Circumference = 1.12 m
15000
Selected
13000
11000
Females from the 4 replicate Selected lines run ~3X
more than those from 4 replicate non-selected
Control lines
9000
7000
Control
5000
3000
1000
0
0
14FRUN56.DSF
55
10
10
15
15
20
20
25
25
30
30
Generation
35
35
40
40
45
45
50
50
9
Revolutions/Day on days 5 + 6
17000
Wheel Circumference = 1.12 m
15000
Males always tend to run less than females,
but the factorial difference between Selected
and Control is the same as for females.
13000
Selected
11000
9000
7000
Control
5000
3000
1000
0
0
14MRUN56.DSF
5
5
10
10
15
15
20
20
25
25
30
30
Generation
35
35
40
40
45
45
50
50
10
Show movie that accompanies:
Girard, I., M. W. McAleer, J. S. Rhodes, and T. Garland, Jr.
2001. Selection for high voluntary wheel running increases
intermittency in house mice (Mus domesticus).
Journal of Experimental Biology 204:4311-4320.
http://www.youtube.com/watch?v=RuqhC7g_XP0
http://www.biology.ucr.edu/people/faculty/Garland/Girard01.mov
Mice from the Selected lines (on
left in video) mainly run faster than
those from the non-selected
Control lines (on right), rather than
for more minutes per day.
11
What would you expect to be true about
the legs of a good runner?
True for other animals?
Cat
Horse
(http://www.youtube.com/watch?v=pWyrOuH-x7k&feature=related)
(http://www.youtube.com/watch?v=OcD1_jvhc_g)
True for extinct animals?
Tyrannosaurus rex
http://www.dublinphysio.com/blog
(http://www.youtube.com/watch?v=T8-gcXkNTAE)
True for human beings (Homo sapiens)?
Human
(http://www.youtube.com/watch?v=pgkWhcapWLU)
What about the bones of good runners?
Human skeleton
(http://www.youtube.com/watch?v=aASjtC-l_i8)
12
Would these characteristics also
be true for legs of mice bred for
wheel running?
List these characteristics:
13
Right and left femurs (thigh bones) of mice from
Selected and Control lines have been photographed:
Identification # of this
individual mouse
14
Various dimensions of these femurs might be important
during sustained, relatively high-speed running.
Femur seen from
front of mouse.
Femur seen from
back of mouse.
The round
head of the
femur fits into
the hip socket.
15
Various dimensions of these femurs might be important
during sustained, relatively high-speed running.
Femur seen from
front of mouse.
Femur seen from
back of mouse.
The bottom
end of the
femur (two
condyles)
articulates
with (touches)
the top end of
the calf (shin)
bones (tibia &
fibula).
Arrows point
to the medial
condyle.
16
What things could you measure on the
pictures of the thigh bones (femurs)?
List them on a piece of paper
As a group, decide which features you will measure
Write one or more hypotheses
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Artificial Selection Lab
Question: How would the legs of mice
from lines (populations) artificially selected
for wheel running differ from those of nonselected control mice?
Hypothesis: If legs from selected mice
are … then the leg bones will be ...
because …
18
Automated Measurement using ImageJ
Select File – Open: Click on the first image
Select Line tool on the tool bar
Draw a line on the ruler that is 15 mm (1.5 cm)
On menu bar: Select analyze – set scale
Draw a line on the femur
On the menu bar: Select analyze - measurement
19
Graph your data
Make a bar graph of the average values
for the mice from selected and control
lines
Use a ruler
Label axes
20
Conclusion
Write a paragraph that includes the following information:
• Explain your results. What effect did
selecting for the trait of wheel running have
on your measurements of the femurs?
• Restate the average measurements for
selected and control groups.
• Was your hypothesis supported or not?
• What parts of your method might have
resulted in inaccuracies?
• Suggest further questions to address.
21
Performing measurements manually
The following slide can be inserted in
place of slide 14 above if students are
using printed images instead of Image J
22
Selected vs. Control mice group data
Selected leg
measurement (cm)
Control leg
measurement (cm)
Average measurement
=
Average measurement
=
Collect the data from every member of your group.
Average the measurements.
Round to the nearest tenth.
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