Goldenrod+Gall+Lab

GOLDENROD GALLS (from student course pack)

Host/parasite and interspecies interactions: Goldenrod galls, gall insects, their parasitoids and predators.

Objectives: - Apply the scientific method to evaluate the interactions of various aspects of gall phenotypes (e.g. height on stem & gall diameter) - Develop testable hypotheses and predictions - Learn additional presentation skills

INTRODUCTION:

The local goldenrod species of the genus Solidago are extremely common in the United States, and often grow in dense fields. Shoots develop in the spring from over-wintering rhizomes, and flower throughout much of the summer, eventually producing large numbers of seeds. True to its name, goldenrod has dense clusters of yellow flowers. In this lab we will examine galls formed by insect larvae on Goldenrod plants.

Gall forming insects (Parasites of goldenrod) Insect larvae are able to stimulate abnormal plant growth in its host leading to structures that are referred to as galls. Galls provide food, protection from the elements and predators to the insect larva. Goldenrod plants are subject to attacks by three different types of gall-forming insects:

1. Common round galls are caused by the larva of the gallmaker fly, Eurosta solidaginis. Adult flies emerge in May from galls and lay eggs in the terminal buds of the goldenrod. The larva hatches and bores into meristematic stem tissue and a gall forms around it. The larva is full grown by mid-September, but remains in the gall until spring when it pupates.

2. Elliptical galls are caused by the larva of the moth, Gnorimoschema galloesolidaginus. This elliptical-shaped gall is usually found lower down the stem of the goldenrod than a gall ball, indicating earlier oviposition by the moth.

3. Rosette galls are caused by the larva of the midge, Rhopalomyia solidaginis. This midge causes proliferation of leaves at the tip of the growing stem forming a dense rosette of leaves.

Parasitoids of gall forming insect larvae A number of parasitoids are able to exploit the larvae as a food source.

1. A large component of this mortality is caused by the larvae of two parasitoid wasps Eurytoma obtusiventris and Eurytoma gigantea.

• E. obtusiventris larva causes the fly larva to pupate in mid-August, consumes the larva, and remains inside the puparium until spring. • E. gigantea larva consumes the fly larva and also eats some of the gall but does not pupate (stage of insect development when it changes from a larval body-form to an adult form) until the spring. The success of E. gigantea is dependent upon the wasp's ability to oviposit through the toughened walls of the gall, and not surprisingly this species is larger than the closely related E. obtusiventris.

Predators of gall forming insect larvae Larvae mortality due to predation can be as high as 96%.

1. A beetle larva, Mordellistena unicolor, bores into the gall and usually, but not always, eats the fly larva.

2. Bird predators (e.g. downy woodpecker and black-capped chickadee) often attack the gall and eat the larva during the winter months.

Note: Small larva of different fly species may live in gall walls without harming the larva inside the galls.

Vocabulary list Symbiosis – An ecological relationship between organisms of two different species that live together in direct contact. Mutualism – A symbiotic relationship in which both, the host and the symbiont benefit. Parasitism – A symbiotic relationship in which the symbiont (parasite) benefits at the expense of the host. Commensalism – A symbiotic relationship in which the symbiont benefits but the host is neither helped nor harmed. Ovipositioning – The laying of eggs, especially by means of an ovipositor (= special organ in female insects). Parasitoid – A parasite that ultimately destroys its host, as any wasp larvae, which feed progressively on the tissue of an immature stage of a host species. Parasitoids spend weeks feeding on a single larva of the gall forming parasite inside the gall. Parasitoids only eat one parasite in their lifespan. Predator-Predators rapidly eat the gall forming parasite inside the gall and have no long term interactions with this parasite. Predators can eat more than one parasite in their lifetime.

ACTIVITY

I. Brain storming session:

1. Formulate one question your group is interested in based on the information given and introduction of this laboratory exercise. You may want to think about how gall size, shape, or parasite species may influence the mortality of the gall's parasite. Is there any reason why you would expect one predator to be more prevalent in the goldenrod population on campus? Be creative!

2. State one hypothesis that may explain the question you are interested in.

3. Describe each of your predictions for this hypothesis in one sentence and create graphs that depict the data you expect to collect in support of your hypothesis. What are your dependent and independent variables?

4. Write on the chalkboard which measure (= dependent variable) you need to gather from each goldenrod specimen in order to test the predictions of your hypothesis. Possible measures may include: gall type, gall diameter, gall wall thickness, gall height, gall content such as empty or presence of pupae or larvae, damage of gall.

II. Data collection:

1. Each group will collect data from approximately 5 goldenrod stalks with galls that have been collected for you. To increase the sample size, all the groups will share the data they gather from their set of goldenrod plants with the rest of the class. Therefore, it is crucial that ALL groups measure ALL the variables listed on the chalkboard to ensure that everyone can benefit from a large data set.

2. Record all external features BEFORE you start dissecting the galls.

3. Carefully dissect each gall, record all parasitoids, predators, commensal species or unspecified mortality. On the following page is a list of descriptions that should help you with this task. Don't forget that gall type can indicate parasite too.

Terminology: remember that any goldenrod that has a gall was therefore parasitized. The larva that makes the gall is thus the parasite of the goldenrod. This larva, which makes the gall, can itself become the host to other larvae that are called parasitoids. Those parasitoids feed on the host (which is the parasite of the goldenrod and the maker of the gall).

Parasite. a) Eurosta solidaginis larva: yellowish-white, football-shaped, and fleshy with black mouthparts and no legs. This is the fly larvae, i.e. the maker of the round gall, i.e. the host to potential parasitoids.

Parasitoids. b) Eurytoma obtusiventris larva: inside small brown Eurosta puparium (~2.2 x 7.4 mm), resulting from forced early pupation. Host has been consumed. This is a parasitoid: feeds on the parasite (host/fly larvae that made the gall).

c) Eurytoma gigantea larva: white, teardrop shaped, often surrounded by large black frass pellets. Host has been consumed by the end of August. This is also a parasitoid: feeds on the parasite (host/fly larvae that made the gall).

Predators. d) Beetle larva, Mordellistena unicolor: a slender, elongated, white larva with small appendages. May or may not consume host pupa. This is a predator: rapidly feeds on the parasite (host/fly larvae that made the gall).

e) Birds, break through gall wall and consume host pupa. This is a predator: rapidly feeds on the parasite (host/fly larvae that made the gall) Downy woodpecker creates a narrow hole. Black-capped chickadee creates a cruder, conical-shaped hole.

Natural Mortality. f) Natural mortality, including all larvae found dead for reasons such as improper gall development, or imprisonment of larva in resins.

ACTIVITY Gall Lab Part 1: Descriptive Statistics and Spreadsheets Put together by : Allison Rober

1. Enter your gall data into an excel spreadsheet a. Label column headings with variable name b. Assign units to each variable in the column heading (cm, mm, inches, etc) c. Highlight row 1 by selecting “1” in grey (left hand column) i. Right click (apple and space bar on a mac), select “format cells”. ii. Choose “alignment tab” iii. Under text control select “wrap text” This will make your column heading text wrap around, which is neater. iv. While the row is highlighted center the text using the alignment button on your top tool bar (insert picture). d. Sort data by gall height i. In order to properly sort data, click on the blank square in the corner of your spreadsheet in the upper left hand corner. On a Mac this will have a diamond in it. This will highlight the entire spreadsheet. ii. Select “Data” from the tool bar. The first option is sort. 1. Select the “sort” button. A menu will appear, at the bottom of the menu under “My list has” select Header row. This will allow you to sort by your column headings. 2. Under the “sort by” drop down menu choose “gall height (cm)” 3. Under the “Then by” drop down menu choose “external hole present (y/n)”


 * be sure your whole spreadsheet is selected while sorting or your data will not match!***

2. Now that we have organized our data properly we will do some basic statistics. a. Label the next available columns “Sum”, “Mean”, “ Median”, “ Mode”, “Standard Deviation”, “Standard Error”, and “Variance” b. Select the first cell under the column labeled “Sum” c. Select the function drop down menu i. Select “SUM” ii. =SUM will appear in the chosen cell. iii. Highlight all the cells in the column labeled Gall Height (cm), hit Enter. d. Select the first cell under the column labeled “Mean” i. Follow the directions above except under the function drop down menu, select “average” and highlight all the cells under the column labeled Gall Height (cm), hit Enter. e. Select the first cell under the column labeled “Median” i. Select the function drop down menu ii. Select “more functions”

iii. Next select “statistical” under category and scroll down to “median” and select OK.

iv. Highlight all the cells under the column labeled Gall Height (cm), hit Enter

f. Select the first cell under the column labeled “Mode” iv. Follow the instructions above but instead select “mode” from the statistical drop down menu. g. Repeat the above operations for standard deviation, standard error, and variance (see below illustrations for statistical abbreviations).

3. You may also choose to use the shortcut method to descriptive statistics. a. Select “tools” on your toolbar b. Select “data analysis” This is a tool pack that may need to be installed on your computer, directions for installation are in your lab manual.

c. Under data analysis, choose “descriptive statistics” and hit OK

d. In the “input range”, highlight all the cells under the column labeled Gall Height (cm), hit Enter.


 * You did it! You now have descriptive statistics for Gall Height (cm). Now you can calculate the same statistics for your other variables of interest and graph the differences***

Goldenrod Galls Lab (Part 2) Developed by Jorge Celi and Stephen Thomas

In the last part of the Goldenrod Gall Lab, you hypothesized about relationships among goldenrod and its parasites, and you collected data that you used to describe characteristics of these populations. In this part, you will learn some statistical analyses that will help you to address your earlier hypotheses.

Before you dive into testing your hypotheses, there are a couple of things we need to clarify. Below are some basics that you should be familiar with before starting the second part of the Goldenrod Gall Lab.

Basic concepts of hypothesis testing Reprinted with permission from the Handbook of Biological Statistics, by John McDonald http://udel.edu/~mcdonald/stathyptesting.html

Null hypothesis The null hypothesis is a statement that you want to test. In general, the null hypothesis is that things are the same as each other, or the same as a theoretical expectation. For example, if you measure the size of the feet of male and female chickens, the null hypothesis could be that the average foot size in male chickens is the same as the average foot size in female chickens. If you count the number of male and female chickens born to a set of hens, the null hypothesis could be that the ratio of males to females is equal to the theoretical expectation of a 1:1 ratio.

The alternative hypothesis is that things are different from each other, or different from a theoretical expectation. For example, one alternative hypothesis would be that male chickens have a different average foot size than female chickens; another would be that the sex ratio is different from 1:1.

Sometimes the null and alternative hypotheses are a little more complicated than just "Things are the same" and "Things are different." For example, if you are looking for a breed of chickens in which males have larger feet than females, and you have no interest in breeds in which females have larger feet, you might want to use the alternative hypothesis "The average foot size in males is larger than in females." Your null hypothesis would then be "The average foot size in males is the same as, or smaller than, the average foot size in females."

Biological vs. statistical null hypotheses It is important to distinguish between biological null and alternative hypotheses and statistical null and alternative hypotheses. "Sexual selection by females has caused male chickens to evolve bigger feet than females" is a biological alternative hypothesis; it says something about biological processes, in this case sexual selection. "Male chickens have a larger average foot size than females" is a statistical alternative hypothesis; it says something about the numbers, but nothing about what caused those numbers to be different. The biological null and alternative hypotheses are the first that you should think of, as they describe something interesting about biology; they are two possible answers to the biological question you are interested in ("What affects foot size in chickens?"). The statistical null and alternative hypotheses are statements about the data that should follow from the biological hypotheses: if sexual selection favors bigger feet in male chickens (a biological hypothesis), then the average foot size in male chickens should be larger than the average in females (a statistical hypothesis).

Testing the null hypothesis The primary goal of a statistical test is to determine whether an observed data set is so different from what you would expect under the null hypothesis that you should reject the null hypothesis. For example, let's say you are studying sex determination in chickens. For breeds of chickens that are bred to lay lots of eggs, female chicks are more valuable than male chicks, so if you could figure out a way to manipulate the sex ratio, you could make a lot of chicken farmers very happy. You've tested a treatment, and you get 25 female chicks and 23 male chicks. Anyone would look at those numbers and see that they could easily result from chance; there would be no reason to reject the null hypothesis of a 1:1 ratio of females to males. If you tried a different treatment and got 47 females and 1 male, most people would look at those numbers and see that they would be extremely unlikely to happen due to luck, if the null hypothesis were true; you would reject the null hypothesis and conclude that your treatment really changed the sex ratio. However, what if you had 31 females and 17 males? That's definitely more females than males, but is it really so unlikely to occur due to chance that we can reject the null hypothesis? To answer that, you need more than common sense, you need to calculate the probability of getting a deviation that large due to chance.

P-values Graph of binomial probabilities Probability of getting different numbers of males out of 48, if the parametric proportion of males is 0.5.

In the figure above, the BINOMDIST function of Excel was used to calculate the probability of getting each possible number of males, from 0 to 48, under the null hypothesis that 0.5 are male. As you can see, the probability of getting 17 males out of 48 total chickens is about 0.015. That seems like a pretty small probability, doesn't it? However, that's the probability of getting exactly 17 males. What you want to know is the probability of getting 17 or fewer males. If you were going to accept 17 males as evidence that the sex ratio was biased, you would also have accepted 16, or 15, or 14,… males as evidence for a biased sex ratio. You therefore need to add together the probabilities of all these outcomes. The probability of getting 17 or fewer males out of 48, under the null hypothesis, is 0.030. That means that if you had an infinite number of chickens, half males and half females, and you took a bunch of random samples of 48 chickens, 3.0% of the samples would have 17 or fewer males.

This number, 0.030, is the P-value. It is defined as the probability of getting the observed result, or a more extreme result, if the null hypothesis is true. So "P=0.030" is a shorthand way of saying "The probability of getting 17 or fewer male chickens out of 48 total chickens, IF the null hypothesis is true that 50 percent of chickens are male, is 0.030." Significance levels

Does a probability of 0.030 mean that you should reject the null hypothesis, and conclude that your treatment really caused a change in the sex ratio? The convention in most biological research is to use a significance level of 0.05. This means that if the probability value (P) is less than 0.05, you reject the null hypothesis; if P is greater than or equal to 0.05, you don't reject the null hypothesis. There is nothing mathematically magic about 0.05; people could have agreed upon 0.04, or 0.025, or 0.071 as the conventional significance level.

The significance level you use depends on the costs of different kinds of errors. With a significance level of 0.05, you have a 5 percent chance of rejecting the null hypothesis, even if it is true. If you try 100 treatments on your chickens, and none of them really work, 5 percent of your experiments will give you data that are significantly different from a 1:1 sex ratio, just by chance. This is called a "Type I error," or "false positive." If there really is a deviation from the null hypothesis, and you fail to reject it, that is called a "Type II error," or "false negative." If you use a higher significance level than the conventional 0.05, such as 0.10, you will increase your chance of a false positive to 0.10 (therefore increasing your chance of an embarrassingly wrong conclusion), but you will also decrease your chance of a false negative (increasing your chance of detecting a subtle effect). If you use a lower significance level than the conventional 0.05, such as 0.01, you decrease your chance of an embarrassing false positive, but you also make it less likely that you'll detect a real deviation from the null hypothesis if there is one.

You must choose your significance level before you collect the data, of course. If you choose to use a different significance level than the conventional 0.05, be prepared for some skepticism; you must be able to justify your choice. If you were screening a bunch of potential sex-ratio-changing treatments, the cost of a false positive would be the cost of a few additional tests, which would show that your initial results were a false positive. The cost of a false negative, however, would be that you would miss out on a tremendously valuable discovery. You might therefore set your significance value to 0.10 or more. On the other hand, once your sex-ratio-changing treatment is undergoing final trials before being sold to farmers, you'd want to be very confident that it really worked, not that you were just getting a false positive. Then you might want to set your confidence level to 0.01, or even lower. ..

One-tailed vs. two-tailed probabilities The probability that was calculated above, 0.030, is the probability of getting 17 or fewer males out of 48. It would be significant, using the conventional P<0.05 criterion. However, what about the probability of getting 17 or fewer females? If your null hypothesis is "The proportion of males is 0.5 or more" and your alternative hypothesis is "The proportion of males is less than 0.5," then you would use the P=0.03 value found by adding the probabilities of getting 17 or fewer males. This is called a one-tailed probability, because you are adding the probabilities in only one tail of the distribution shown in the figure. However, if your null hypothesis is "The proportion of males is 0.5", then your alternative hypothesis is "The proportion of males is different from 0.5." In that case, you should add the probability of getting 17 or fewer females to the probability of getting 17 or fewer males. This is called a two-tailed probability. If you do that with the chicken result, you get P=0.06, which is not quite significant.

You should decide whether to use the one-tailed or two-tailed probability before you collect your data, of course. A one-tailed probability is more powerful, in the sense of having a lower chance of false negatives, but you should only use a one-tailed probability if you really, truly have a firm prediction about which direction of deviation you would consider interesting. In the chicken example, you might be tempted to use a one-tailed probability, because you're only looking for treatments that decrease the proportion of worthless male chickens. But if you accidentally found a treatment that produced 87 percent male chickens, would you really publish the result as "The treatment did not cause a significant decrease in the proportion of male chickens"? Probably not. You'd realize that this unexpected result, even though it wasn't what you and your farmer friends wanted, would be very interesting to other people. Any time a deviation in either direction would be interesting, you should use the two-tailed probability. In addition, people are skeptical of one-tailed probabilities, especially if a one-tailed probability is significant and a two-tailed probability would not be significant (as in the chicken example). Unless you provide a very convincing explanation, people may think you decided to use the one-tailed probability after you saw that the two-tailed probability wasn't quite significant. It may be easier to always use two-tailed probabilities. ..

Reporting your results In the olden days, when people looked up P-values in printed tables, they would report the results of a statistical test as "P<0.05", "P<0.01", "P>0.10", etc. Nowadays, almost all computer statistics programs give the exact P value resulting from a statistical test, such as P=0.029, and that's what you should report in your publications. You will conclude that the results are either significant or they're not significant; they either reject the null hypothesis (if P is below your pre-determined significance level) or don't reject the null hypothesis (if P is above your significance level). But other people will want to know if your results are "strongly" significant (P much less than 0.05), which will give them more confidence in your results than if they were "barely" significant (P=0.043, for example). In addition, other researchers will need the exact P value if they want to combine your results with others into a meta-analysis. Further reading

Sokal and Rohlf, pp. 157-169.

Zar, pp. 79-85. Reference

ACTIVITY In this activity, you will be using three statistical tests that we will use throughout the rest of the semester. Instead of doing the calculations by hand, you will be using Excel and the internet. We will not be going into great detail behind the derivation of the statistics, but we expect you to understand when you could use these tests, how to set them up, and how to interpret the information you get from them.

1. From the questions that you developed from the first Goldenrod Gall Lab and the data you collected. Choose three questions and determine what statistical tests you might use.

2. Run either a correlation, t-test, or chi-square on your data. Discuss with your group whether the data supports your predictions and come to a general conclusion.

3. Present your question, test, and conclusions to the rest of the class.

4. Turn in your overhead, data sheets and any calculations to your TA. You will be graded on the material you turn in to your TA in addition to your group presentation.

Data Sheet for Goldenrod Laboratory

Hypothesis:

Prediction(s):

Variable(s):

Prediction graph(s):