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.

1. Formulate a hypothesis for this week’s experiment. Be specific!
2. We will re-visit the experiment in a week to collect data on the number of eggs laid. Formulate a hypothesis for this experiment.
3. Identify the independent variable(s).
4. Identify the dependent variable(s).
5. What variables will you keep standard?
6. What is your control?
7. 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 (H₀) 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.

1. Restate your H₀ hypothesis.
2. H_a hypothesis.
3. If the null hypothesis is correct, what would we expect to see?
4. 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 χ² 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.

1. 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.
   1. 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%).
2. 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?