33.2: Exercise
Lab Experiment: Bean Beetles
Adapted from C. Beck and L. Blumer by Staci Forgey, TCC Biology Faculty
Bean beetles, Callosobruchus maculatus , are herbivorous pest insects that are found in Africa and Asia. Females lay their eggs on the surface of beans. Eggs are layed singly, and hatch into larvae (maggots) several days later. The larva then burrows into the bean and will form a pupa 21–30 days after the egg was deposited. They mature 24–36 hours after emergence from the pupa and do not need to feed.
Adults typically live for 1–2 weeks. Mating and oviposition occur during this time period. Females will choose the best substrate (bean) to lay their offspring on, since the larvae cannot move. By choosing a substrate for oviposition, the female chooses the food resource available to her offspring (Brown and Downhower 1988). This is a critical choice for the female, as it influences the growth, survival and future reproductive success of her offspring (Mitchell, 1975, Wasserman and Futuyma, 1981). Females can lay eggs on a wide range of bean species, but very few bean species will result in normal development and the successful emergence of adults. Some species of beans are toxic to larvae (Janzen 1977).
Materials
In class, you will be provided with live cultures of bean beetles containing adults that have been raised on mung beans ( Vigna radiata ).
Female beetles are easily identified in the live cultures because they have two dark stripes on the posterior of the abdomen, whereas the posterior abdomen of males is uniformly light in color.
You will also have access to petri dishes and several types of beans.
Experimental Design
Since the oviposition choices of females influences the survival and future success of their offspring, females may be very sensitive to the species and condition of the beans on which they are depositing eggs.
Each group should design a set of experiments to address whether female bean beetles discriminate between suitable species of beans. We will re-visit the experiment next week and collect our data set. We will choose one experiment to perform as a class.
As a group, list several ways we might examine female bean choice for oviposition. We will pick one experiment from the class to perform. Write your ideas below.
**Stop here. We will go over ideas for our experiment as a class and choose one to perform**
Outline the agreed on experiment using the criteria below.
- Formulate a hypothesis for this week’s experiment. Be specific!
- We will re-visit the experiment in a week to collect data on the number of eggs laid. Formulate a hypothesis for this experiment.
- Identify the independent variable(s).
- Identify the dependent variable(s).
- What variables will you keep standard?
- What is your control?
- Design data collection tables for both of your experiments on a separate sheet of paper.
For this data, it is most useful to perform a Chi Square analysis. Chi Square statistical test evaluate whether this is a significant difference between groups of data. The null hypothesis (H 0 ) is the hypothesis that states that there is no difference between the groups of data. The alternative hypothesis, (H a ) is the hypothesis you outlined above.
- Restate your H 0 hypothesis.
- H a hypothesis.
- If the null hypothesis is correct, what would we expect to see?
- If our alternative hypothesis is correct, what would we expect to see?
To do this statistical test, we need to calculate observed and expected values for the bean species and for our control. Our expected values are based on our null hypothesis (that there is no difference).
For example, if we had 40 eggs and four different treatment groups, we would expect there to be 10 eggs on each group.
| Treatment | Observed number of eggs | Expected number of eggs |
|---|---|---|
| Mung beans | 5 | 10 |
| Pinto beans | 25 | 10 |
| Chick peas | 10 | 10 |
| Control | 0 | 10 |
Fill in the data table below with our observed (what we actually found) and expected values.
| Treatment | Observed number of eggs | Expected number of eggs |
|---|---|---|
To calculate the chi-square value, or \(\chi ^{2}\), we simply add the square differences, divided by the expected, of all the observed and expected. In mathematical terms:
\(\chi ^{2} = \Sigma\frac{(O-E)^{2}}{E}\)
So for our example from the previous sample table, the first \(\frac{(O-E)^{2}}{E}\) would be \(\frac{(5-10)^{2}}{10}=2.5\)
We would then add to this value all of the other \(\frac{(O-E)^{2}}{E}\) in the table and then add to get the \(\chi ^{2}\) value.
| Treatment | Observed number of eggs | Expected number of eggs | Observed-Expected | (Observed-Expected) 2 | (Observed-Expected) 2 /Expected |
|---|---|---|---|---|---|
Compute the χ 2 value for your data by adding all of the (Observed-Expected)2/Expected values.
In order to find something to compare this number with, we need to calculate the degrees of freedom, or the number of different comparisons that can be made within the table. The degrees of freedom are the number of columns (M) minus one times the number of rows (N) minus one.
Degrees of freedom = (M − 1)(N − 1)
| A | B |
|---|---|
| C | D |
How many ways can this two by two table be broken into individual comparisons? Hint: Use the formula above.
Calculate the degrees of freedom in your experiment.
Now we can compare against the Chi distribution for the likelihood that our data is generated by chance. Remember, we are looking at the column labeled 0.05 for our p value. This means that at this level, we are 95% sure our results are real. The Chi distribution table gives us a critical value to compare our test value to.
- If test > critical value @ p level, reject null hypothesis
- If test < critical value @ alpha level, fail to reject null hypothesis
Compare to the Chi-distribution table(opens in new window) . Did your results come about by chance?
| DF/P | 0.995 | .990 | 0.975 | .950 | .900 | .750 | .500 | .250 | .100 | .050 | .025 | .010 | .005 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0.00004 | .00016 | 0.001 | 0.004 | 0.016 | 0.102 | 0.455 | 1.323 | 2.706 | 3.841 | 5.024 | 6.635 | 7.879 |
| 2 | 0.010 | 0.020 | 0.0506 | 0.103 | 0.211 | 0.575 | 1.386 | 2.773 | 4.605 | 5.991 | 7.378 | 9.210 | 10.597 |
| 3 | 0.072 | 0.115 | 0.216 | 0.351 | 0.584 | 1.213 | 2.366 | 4.108 | 6.251 | 7.815 | 9.348 | 11.345 | 12.838 |
| 4 | 0.207 | 0.297 | 0.484 | 0.711 | 1.064 | 1.923 | 3.357 | 5.385 | 7.779 | 9.488 | 11.143 | 13.277 | 14.860 |
| 5 | 0.412 | 0.554 | 0.831 | 1.145 | 1.610 | 2.675 | 4.351 | 6.626 | 9.236 | 11.070 | 12.833 | 15.086 | 16.750 |
| 6 | 0.676 | 0.872 | 1.237 | 1.635 | 2.204 | 3.455 | 5.348 | 7.841 | 10.645 | 12.592 | 14.449 | 16.812 | 18.548 |
| 7 | 0.989 | 1.239 | 1.690 | 2.167 | 2.833 | 4.255 | 6.346 | 9.037 | 12.017 | 14.067 | 16.013 | 18.475 | 20.278 |
| 8 | 1.344 | 1.647 | 2.180 | 2.733 | 3.490 | 5.071 | 7.344 | 10.219 | 13.362 | 15.507 | 17.535 | 20.090 | 21.955 |
| 9 | 1.735 | 2.088 | 2.700 | 3.325 | 4.168 | 5.899 | 8.343 | 11.389 | 14.684 | 16.919 | 19.023 | 21.666 | 23.589 |
| 10 | 2.156 | 2.558 | 3.247 | 3.940 | 4.865 | 6.737 | 9.342 | 12.549 | 15.987 | 18.307 | 20.483 | 23.209 | 25.188 |
| 11 | 2.603 | 3.053 | 3.816 | 4.575 | 5.578 | 7.584 | 10.341 | 13.701 | 17.275 | 19.675 | 21.920 | 24.725 | 26.757 |
| 12 | 3.074 | 3.571 | 4.404 | 5.226 | 6.304 | 8.438 | 11.340 | 14.845 | 18.549 | 21.026 | 23.337 | 26.217 | 28.300 |
| 13 | 3.565 | 4.107 | 5.009 | 5.892 | 7.042 | 9.299 | 12.340 | 15.984 | 19.812 | 22.362 | 24.736 | 27.688 | 29.819 |
| 14 | 4.075 | 4.660 | 5.629 | 6.571 | 7.790 | 10.165 | 13.339 | 14.114 | 21.064 | 23.685 | 26.119 | 29.141 | 31.319 |
| 15 | 4.601 | 5.229 | 6.262 | 7.261 | 8.547 | 11.037 | 14.339 | 18.245 | 22.307 | 24.996 | 27.488 | 30.578 | 32.801 |
| 16 | 5.142 | 5.812 | 6.908 | 7.962 | 9.312 | 11.912 | 15.339 | 19.369 | 23.542 | 26.296 | 28.845 | 32.000 | 34.267 |
| 17 | 5.697 | 6.408 | 7.564 | 8.672 | 10.085 | 12.792 | 16.338 | 20.489 | 24.769 | 27.587 | 30.191 | 33.409 | 35.718 |
| 18 | 6.265 | 7.015 | 8.231 | 9.390 | 10.865 | 13.675 | 17.338 | 21.605 | 25.989 | 28.869 | 31.526 | 34.805 | 37.156 |
| 19 | 6.844 | 7.633 | 8.907 | 10.117 | 11.657 | 14.562 | 18.338 | 22.18 | 27.204 | 30.144 | 32.852 | 36.191 | 38.582 |
| 20 | 7.434 | 8.260 | 9.591 | 10.851 | 12.443 | 15.452 | 19.337 | 23.848 | 28.412 | 31.410 | 34.170 | 37.566 | 39.997 |
| 21 | 8.034 | 8.897 | 10.283 | 11.591 | 13.240 | 16.344 | 20.337 | 24.935 | 29.615 | 32.671 | 35.479 | 38.932 | 41.401 |
| 22 | 8.643 | 9.542 | 10.982 | 12.338 | 14.041 | 17.240 | 21.337 | 26.039 | 30.813 | 33.924 | 36.781 | 40.289 | 42.796 |
| 23 | 9.260 | 10.196 | 11.689 | 13.091 | 14.848 | 18.137 | 22.337 | 27.141 | 32.007 | 35.172 | 38.076 | 41.638 | 44.181 |
| 24 | 9.886 | 10.856 | 12.401 | 13.848 | 15.659 | 19.037 | 23.337 | 28.241 | 33.196 | 36.415 | 39.364 | 42.980 | 45.559 |
| 25 | 10.520 | 11.524 | 13.120 | 14.611 | 16.473 | 19.939 | 24.337 | 29.339 | 34.382 | 37.652 | 40.646 | 44.314 | 46.928 |
| 26 | 11.160 | 12.198 | 13.844 | 15.379 | 17.292 | 20.843 | 25.336 | 30.435 | 35.563 | 38.885 | 41.923 | 45.642 | 48.290 |
| 27 | 11.808 | 12.879 | 14.573 | 16.151 | 18.114 | 21.749 | 26.336 | 31.528 | 36.741 | 40.113 | 43.195 | 46.963 | 49.645 |
| 28 | 12.461 | 13.565 | 15.308 | 16.928 | 18.939 | 22.657 | 27.336 | 32.620 | 37.916 | 41.337 | 44.461 | 48.278 | 50.993 |
| 29 | 13.121 | 14.256 | 16.047 | 17.708 | 19.768 | 23.567 | 28.336 | 33.711 | 39.087 | 42.557 | 45.722 | 49.588 | 52.336 |
| 30 | 13.787 | 14.953 | 16.791 | 18.493 | 20.599 | 24.478 | 29.336 | 34.800 | 40.256 | 43.773 | 46.979 | 50.892 | 53.672 |
We will now use this data and information to do a lab write up.
Field Study: Campus Bird Ethology
A number of bird species live at our campus pond, providing a natural experimental setting.
Procedure:
Identify a species of bird at the pond. Write down its common and scientific name.
Watch an individual or group of individuals of the same species for ten minutes and list every behavior you see (examples: rest, peck object, dip head in water, social interaction).
A full list of all the activities or behaviors observed in an animal is called an ethogram , and can be an excellent tool for study of that species.
Once you have your list, you will do two kinds of measurements for behavioral observations of your species.
-
Time budget: watch an individual of your species for 5 minutes, keep track of each behavioral change and how long the animal is doing that behavior.
- Calculate a percentage of time for each activity you saw. For example, if your bird spent 2 minutes and 10 seconds of the 5 minutes resting it would have spent 43% of its time resting. Convert the time to seconds, then divide time of activity by total time (in this example 130/300 = .4333, x100 for percentage = 43%).
- Total occurrences: watch an individual of your species for 10 minutes. Using the ethogram/list you made, put a tick mark next to the activity at every occurrence. If you individual was resting, then scratched its head, then went back to resting that would be two tick marks next to "rest" and one next to "scratched head". If you encounter a new behavior during this time add it to your list.
Which type of measurement do you think is better for measuring behaviors in a species? Why? What kinds of information might you want to study using time budget or total occurrence data?