Date post: | 13-Aug-2015 |
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Education |
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Food choice
• Foraging: the act of an animal searching for food
• Animals seek out food sources that will give them the most energy reward for the least amount of energy expended
• Today: examine foraging behaviors of birds in the quad
Three types of seeds
• Black oil (soI hulled)• Safflower (small, soI hulled)• Striped sunflower (large, thick hulled)
• Come up with a hypothesis as to why birds will choose or not choose certain seeds.
• What is your alterna@ve hypothesis?• Would your hypothesis change with different
birds?• If your hypothesis is correct, what do you
expect to observeat the feeders?
Variables
•What is the independent variable?– Manipulated
•What is the dependent variable?– Measured
Observa@ons: 20 minutes
• Try to be quiet and s@ll• What species of birds appear?• What types of seeds does each species eat?• How long does it take an individual bird to eat a
seed and return to the feeder?• Do different species of birds have different seed
handling techniques?• Record all of your data!
Data Analysis: Chi2 Test
• Sta@s@cal test to test our bird feeding hypothesis
• Test based on differences between the observed results and the expected values (based on null hypothesis)
• The formula for X2 is as follows:
– o is the observed frequency– e is the frequency expected under the null hypothesis of
no difference between groups.
Example: Snail sediment type preference
• 20 snails• 2 sediments: mud and sandNull hypothesis: There is no difference in
sediment preference for this species of snail.• Expected results: 10 in mud, 10 in sand• Actual results:– Trial 1: 13 in mud, 7 in sand– Trial 2: 11 in mud, 9 in sand
Chi2 Test on snail results
Expected value Mud: (24 x 20)/40= 12 Expected value sand (16 x 20)/40= 8
Chi2= [(13f12)2/12] + [(11f12)2/12] + [(7f8)2/8] + [(9f8)2/8]= .416
• Now you compare your experimental value (0.416) to a cri@cal chi2 value.
Mud Sand Total
Observed 1 13 7 20
Observed 2 11 9 20
Total 24 16 40
Chi2 Test comparison• Degrees of Freedom: the number of independent
cases(Mf1)(Nf1)
M= # rows N=# columns
(3f1)(3f1)=4• p level: level at which the given sta@s@c is on the
border between rejec@ng or not the null hypothesis– Probability that values outside this set level are due to
chance sampling errors rather than real differences• Use p=0.05– At p=0.05 you are 95% sure your results are real