SOURCES OF PHENOTYPIC VARIATION Learning Objectives After completing this laboratory, you should be able to perform the following: 1. List the two sources of variation that can cause differences between organisms. 2. Measure phenotypic variation 3. Measure genetic variation 4. Analyse data using chi-square tests and analyses of variance 5. Learn how to use hypothetico-deductive reasoning to test hypotheses. Introduction To Phenotypic Variation Understanding the sources of variation in phenotype in organisms is central to the understanding of natural variation and the adaptive responses of organisms to their environment. Variation within a species of plant or animal is very common over a geographic range. As Darwin documented in the first two chapters of “On the Origin of Species” (Darwin, 1859), individuals of all species exhibit within-species variation of almost all traits. Without such variation, evolution by natural selection would be impossible. Variation in size, shape, coloration, behavior and physiology may be a product of current environmental differences between sites (phenotypic plasticity), a product of heritable differences (genotype differences = ecotypes) between the subpopulations at different sites, or a combination of both. The classical methodology for determining the causes of variation is reciprocal transplants or transplants to a common environment. Transplanting individuals possessing different traits to a constant environment or performing cross transplants between natural sites is a means of evaluating the relative importance of environmental and genetic variation in producing the observed phenotypic variation. The finding of persistent differences between subpopulations independent of environmental conditions suggests that genetic variation underlies observed phenotypic variation. For example, a species of yarrow, Achillea millefolium, grows in a wide variety of habitats in California ranging from sea level to more than 3000m elevation. Plants at a given altitude have different height and biomass compared to plants from other altitudes even when seeds of plants from different sites are grown under the same conditions at sea level (Clausen, Keck, and Hiesey, 1948). This result indicates that the observed phenotypic variation among the California Achillea is ecotypic, caused by genotypic differences between populations. In this experiment, phenotypic variation in a vascular plant species will be evaluated for both morphological-structural traits (for example, total biomass, internodal length, and total plant height), and biochemical traits (for example, total chlorophyll and anthocyanin concentrations). We will work with a population of Brassica rapa, a rapidly growing mustard (Williams, 1989). We have collected individuals from three populations (from three locations) and which we know plants differ in phenotype, specifically with respect to the concentration of chlorophyll and anthocyanin, as well as their size. Today we will figure out whether the differences are environmentally or genetically driven. Chlorophylls-a and b, of course, are the principal photoreceptor pigments in plants, located in the chloroplasts. We can quantify the concentration of chlorophyll from different samples by evaluating the absorption of light at the specific wave lengths at which peak absorption occurs. The absorption of light increases with pigment sample concentration. Anthocyanins are a class of flavonoids, three ring secondary plant compounds, that produce orange to blue colors in leaves, stems, roots, flower petals, and fruits of many plants. There are more than 260 different anthocyanin compounds and these pigments may serve a wide range of roles such as protecting plant cells from ultraviolet light, attracting insect pollinators, and acting as anti-herbivore chemical defenses (Harborne, 1988). Anthocyanin concentrations can be quantified in the same manner as the chlorophylls using a spectrophotometer to evaluate light absorption (Harborne, 1973). Using Hypothetico-Deductive Reasoning To Test Hypotheses Hypothetico-deductive reasoning, one of the most common forms of scientific thinking, is useful for making sense of evolutionary processes (e.g., Platt, 1964; Feynman, 1984; Lawson, 2003). In brief, hypothetico-deductive reasoning tests hypotheses by determining what the hypothesis predicts should be observed in a test (Figure 1), and then performing the test to see whether the predicted result is observed. So, if we were to use the hypothetico-deductive method to test the hypothesis that a meteor impact caused the dinosaurs to go extinct, for instance, we might follow the logic described below: IF A meteor impact caused the dinosaurs to go extinct... AND ...we count the number of dinosaur fossils and dinosaur species present in rock layers laid down before, during, and after dinosaurs went extinct... THEN ...we should observe that dinosaurs disappeared abruptly from the fossil record. To understand natural selection, we need to overcome several stumbling blocks common to most people not trained in evolutionary biology. Specifically, we need to think about the problems with typological thinking, we need to overcome some genetic misconceptions that are common, and we need to develop stronger scientific thinking skills. Many non-scientists have a tendency to dismiss variation or differences among individuals within species as unimportant and focus their attention on traits that individuals in a species share. We shall call this tendency “typological thinking.” Typological thinking arises in childhood and is useful for predicting the characteristics of individuals from their species identity but is an obstacle for understanding natural selection. Instead, we need to realize “that in biological populations. . . every individual is unique” Mayr (2004). The most common misconceptions that make it difficult to understand the significance of phenotypic variation and the process of natural selection is that evolution proceeds through the transformation of individuals. Many people believe, for example, that individuals evolve because they need to, because they use or do not use specific body parts, or because the environment directly induces changes. In fact, evolution proceeds via changing the relative reproductive success (and survival) of individuals based on their genetic composition. Hypotheses And Predictions H1: Variation between the plant varieties results from genetic differences between them. Prediction1: A given variety will have a constant phenotype between environments and will remain distinct from other plant varieties. H2: Variation between the plant varieties results from environmental differences between the sites where these varieties were growing in nature. Prediction2: Phenotypes will change with environmental conditions. H3: Variation between the plant varieties results from both genetic differences and environmental variation. Prediction3: Intermediate results of the two predictions above. Which Prediction is your leading favourite? _______________________ Methods Growing Plants From Seed Seeds from three populations of the mustard plant, Brassica rapa, will be used in this experiment (TA, TC, and SA). Information about the source habitat for each variety is not available, but the phenotypes of the source plants are known. Population TA is from plants that are known to grow tall and contain anthocyanin, population TC is from plants that grow tall and do not contain anthocyanin, and population SA is from plants that grow short and have anthocyanin pigments. We have sown seeds two - three weeks prior to the collection of data on the plant phenotypes. Seeds were planted in plastic quads containing four cells each. You and your group will use a total of 12 quads, 4 of each of the three varieties (Table 1). To plant, we placed two seeds on the soil surface in a quad cell, pressed them into the soil with the tip of a pencil to just below the soil surface, and covered each lightly with soil. One quad sown with seed of each population was be placed in each of two experimental treatments (see Table 1 below) and allowed to germinate and grow. FERTILIZER EFFECTS: TA, TC, and SA seeds were grown at no fertilizer (and no soil) and with fertilizer (and no soil) conditions with approximately the same light intensities under a grow light system. The fertilizer used was Miracle-Gro, all purpose water soluble Plant food, 24N-8P-16K. Liquid fertilizer was prepared according to the instructions where 2.8 g was added to 1L of stock. This was subsequently diluted 1:2 and added to plant growth containers. Over the duration of the experiment, we estimate 4L was added to all the plant growth containers. To the non-fertilizer containers, the same volume of water was added. The media in every quad was kept continually moist by using a self-watering wick system. Table 1. Quad planters assignments to treatments and populations. Treatment Population No fertilizer Fertilizer + Total TA 1 Quad 1 2 TC 1 1 2 SA 1 1 2 Total 3 3 6 One week after sowing, we thinned seedlings, leaving one seedling per cell. Morphological-Structural Data The 2-3 week old plants grown in the two fertilizer treatments will be evaluated for a variety of morphological characteristics. You will describe the plants from each population and treatment qualitatively: stem color (green, purple, white), petiole color (green, purple, white), and flowering and budding (yes or no) (see Figure 1 for protocol flow chart). FIGURE 1. Protocol flow chart for collection of morphological-structural data. Evaluate the plants from each quad separately. Working with one quad at a time, cut each plant from one quad at the soil level, then measure stem length (mm) above soil level, distance from soil to first node (mm), and internodal distance (mm between the two nodes closest to the soil) (Figure 2). Calculate the mean value for each of the measurements for the plants from each quad. Weigh the plants from one quad together. Record the total biomass and the number of plants (normally four) from each quad, so the mean biomass per plant stem can be calculated. A suggested data record format is given below (Table 2a and b). Record data in your laboratory notebook using this format, and later transfer the data to the class computer files (see section on Statistical Data Analysis). Keep the plants from each quad separate from the plants from other quads. Use labeled plastic weighing boats for the plants from each quad. You and your partner will have one quad of each variety per treatment (four quads of each variety total). You will be informed in class which pigment extraction to perform, half of the class will perform the chlorophyll analysis and the other half will perform the anthocyanin analysis. All four plants from one quad will be used to prepare a single pigment extraction. You and your partner will perform pigment extractions and quantify the pigment concentration from plants of each of the three varieties grown under each of the four environmental conditions (twelve combinations). FIGURE 2. Morphology of 13-day old Brassica rapa (after Williams, 1989). Note that individual plants may vary from this example. Scale units are in cm. TABLE 2a. Laboratory notebook data recording format for morphological data. The Student Group # will be the same for your entire data set, but that information will be required for the class data file. Note that this is identical to the format that will be used for the class data file (see section on Statistical Data Analysis). An example entry is shown in italics. Student Group # Treatment Population Stem Length First node Internodal Flowering? Stem Colour Petiole Colour 1 No Fertilizer TA 73 12 17 Yes Purple Purple TABLE 2b. Laboratory notebook data recording format for biomass and pigment extraction absorbance data. You and your partner will perform a pigment extraction and collect absorbance data on either anthocyanin (abs. antho. 530) or on chlorophyll (abs. chloro. 415 and abs. chloro. 662). Note that this is identical to the format that will be used for the class data file (see section on Statistical Data Analysis). An example entry is shown in italics. Student Group # Treatment Population Total Mass (g) # of plants Abs. Antho. 530 nm Abs. Chloro. 415 nm Abs. Chloro. 662 nm 1 No fertilizer TA 0.6 4 0.756 - - PIGMENT EXTRACTION AND ANALYSIS Wear safety glasses and gloves. Perform all pigment extractions in the fume hood. You will need to know the total biomass of the plants that you use to make each extraction. The following protocol flow chart (Figure 3) summarizes the extraction procedures for both anthocyanins and chlorophylls detailed below. Figure 3. Protocol flow chart for pigment extraction and analysis. Prepare a separate pigment extract for plants from each quad. ANTHOCYANINS: Cut four plants from one quad into small pieces, then grind plant material with a glass mortar and pestle containing some sand and 3 ml of 1% HCl methanol. Pipette or pour into two microfuge tubes (make sure the tubes are balanced, contain the same volumes, and place them in opposite positions in the centrifuge rotor, and spin 2 minutes (5000 rpm). Pipette the supernatant into 1 or 2 microcentrifuge tubes. Adjust the total extract volume in the test tube to 7 ml. Stopper the tube, label with tape, mix by shaking, and cover with foil to minimize exposure to light. Rinse the mortar and pestle with water and dry before preparing the next sample of plants. Disposable polycarbonate cuvettes are to be used to evaluate the anthocyanin extractions and methanol blank. Use 1% HCl methanol as the blank in the spectrophotometer. The spectrophotometer program ANTHO will measure absorbance in the visible range at 530nm. Be sure to turn-on the VIS lamp before attempting to read the blank. After you have obtained the absorbance values for your samples, turn lamp off. Rinse cuvettes with methanol and pour all methanol waste and methanol extractions in the Methanol Waste Bottle. You will be able to calculate the absorbance values per mg of fresh biomass used in each extraction after you enter your data in the class data file (see section on Statistical Data Analysis). Chlorophylls: Grind four plants with a glass mortar and pestle with some sand and 3 ml of acetone. Pipette or pour into two microfuge tubes (make sure the tubes are balanced, contain the same volumes, and place them in opposite positions in the centrifuge rotor), and spin 2 minutes. Carefully transfer supernatant (using a clean glass Pasteur pipette) to 2 microcentrifuge tubes. Adjust the total extract volume in the test tube to 5 ml. Stopper the tube, label with tape, mix by shaking, and cover with foil to minimize exposure to light. Rinse the mortar and pestle with water and dry before preparing the next sample of plants. Use the quartz cuvettes with your instructor supervising (handle the quartz cuvettes with care, they are very expensive). Acetone will melt disposable polycarbonate plastic cuvettes. Use acetone as the blank in the spectrophotometer. The spectrophotometer program CHLORO will measure absorbance in the visible range at 415nm and 662nm. Be sure to turn-on the VIS lamp before attempting to read the blank. After you have obtained the absorbance values for your samples, turn-off the VIS lamp. Rinse cuvettes with acetone and pour all acetone waste and acetone extractions in the Acetone Waste Bottle. You will be able to calculate the absorbance values per mg of fresh biomass used in each extraction after you enter your data in the class data file (see section on Statistical Data Analysis). Do the three populations exhibit differences in their phenotype? If phenotypic differences are observed among populations of individuals grown in different environments, does this indicate genotypic differences between the populations? If phenotypic differences are observed among populations of individuals grown in the same environment, does this indicate genotypic differences between the populations? STATISTICAL DATA ANALYSIS Enter your data in class data files that your instructor has created on Google Docs (Table 3a and b). This must be done within 24 hours of the laboratory in order to receive a grade for your work. In Lab 5, we will work together to perform the data analysis but this depends on you entering your data accurately. Table 3a. Sample Data File for Morphology. “Group” is a categorical variable to identify you and your group in the data file (type your full names and Student IDs). “Treatment”, “Population”, “Flowering”, “Stem Color”, and “Petiole Color” are all coded categorical variables. Stem “Length”, distance to “First Node”, and “Internodal” distance are all continuous variables measured in millimeters. Student Group Members Treatment Population Stem Length First node Internodal Flowering? Stem Colour Petiole Colour Full names and Student numbers Fertilizer TA 73 12 17 No Purple Purple Full names and Student numbers No fertilizer TA 15 5 0 No Purple Purple Full names and Student numbers Fertilizer TA 140 6 10 Yes Purple Purple Full names and Student numbers No fertilizer TA 58 15 19 No Green Green Table 3b. Data File for Pigments. “Group” is a categorical variable to identify you and your group in the data file (type your full names and Student IDs). “Treatment”, and “Variety” are coded categorical variables. “total mass”, “# of plants” per quad, “abs. antho. 530” (extract absorbance at 530nm), “abs. chloro. 415”, and “abs. chloro. 662” are all entered as continuous variables. Student Group # Treatment Population Total Mass (g) # of plants Abs. Antho. 530 nm Abs. Chloro. 415 nm Abs. Chloro. 662 nm 1 No fertilizer TA 0.21 3 0.754 • • 1 Fertilizer TA 0.60 4 0.378 • • 1 Fertilizer TA 0.94 3 0.756 • • 1 No Fertilizer TA 0.16 3 • 2.309 1.643 Together we will compare traits, physical and biochemical, between treatments within each variety, and between varieties within each treatment. We will walk through the analysis step-by-step together in Lab 5. First, you need to generate the questions to inspire your statistical tests (in reality, this has been done before we did the experiment, but here we make every single question explicit, comparing plant phenotypes between treatments within each variety: I strongly suggest phrasing this group of questions as one question in your introduction. Now, you should generate hypotheses. If you fail to detect differences among phenotypes of plants from the same variety when those plants are grown in different environments, what does this tell you about the trait (i.e., is the trait, at least partially, environmentally determined or is the trait exclusively genetically determined?)? ________________________________________________________________ If you see differences among phenotypes of plants from the same variety when those plants are grown in different environments, what does this tell you about the trait? ________________________________________________________________ Second, you need to generate questions to inspire the statistical tests that compare the plant phenotypes of varieties grown in the same environment (or treatment): I strongly suggest phrasing this group of questions as one question in your introduction. Now that we have the questions, you should generate hypotheses. If you fail to detect differences in the phenotypes of plants with different genetic backgrounds when those plants are grown in the same environment, what does this tell you about the trait (i.e., is the trait, at least partially, environmentally determined or is the trait exclusively genetically determined?)? ________________________________________________________________ ________________________________________________________________ If you see differences in the phenotypes different genetic backgrounds when those plants are grown in the same environment, what does this tell you about the trait? ________________________________________________________________ ________________________________________________________________ Now that we have expectations, you need to decide which type of test to perform. Typically, one performs chi-square tests on questions that use the categorical response variables, and an analysis of variance (ANOVA) with Scheffé comparisons on the questions that use the continuous response variables. So, which traits are the categorical response variables? ________________________________________________________________ ________________________________________________________________ And which traits are the continuous response variables? ________________________________________________________________ ________________________________________________________________ Once we have your results, you need to interpret your results. Do all the traits respond to environmental variation in the same way? What is the cause for the differences between the three varieties of plants, genotypic variation, environmental variation or both? You need to decide whether your results are expected or not. This will help you form your results section, by deciding what to emphasize. Then I would suggest creating your tables and figures for your paper. You may decide to show only a portion of the data through figures and tables, the remaining data you may decide to report only through text in the results section of your paper. Create at least two figures or tables for your manuscript. Creating a figure or table involves summarizing the data, rather than presenting each individual datum. Therefore, each of your figures should present summary statistics (like average values for continuous traits or frequency of a trait for categorical variables) on the y-axis and predictor variable (either genotype or environmental treatment) on the x-axis. In cases where you are evaluating two types of predictor variables (e.g., genotype and fertilizer environment), please also use different symbols to distinguish one factor (e.g., genotype) and the x-axis to distinguish the other factor (e.g., fertilizer environment). Furthermore, any table you include should present summary statistics of the response variables grouped by predictor variable. Again, we should not see individual data points in this table (or even data summarized by research group). So, to get you started, we would suggest including a table of summary statistics like this one: Table 1. Summary statistics for three genotypes of Brassica rapa grown under two fertilizer conditions. Genotype Fertilizer conditions Sample size (n) Percent with purple petiole (%) Percent with purple stems (%) Average Abs. Antho. (530 nm) Average Abs. Chloro. (415 nm) Average Abs. Chloro. (662 nm) Frequency of flowering plants Mean Stem length (cm) Mean Internodal distance (cm) Mean height of first node (cm) Mean Stem Density Mean Biomass (g) TA No Yes TC No Yes SA No Yes You could show one additional figure or another table. The figure you present might be two traits that show strong significant differences among genotypes or environments. Remember that you should not duplicate information between figures and/or tables. So, if you show average trait values in the figure, remember to remove the column of data associated with that particular trait from Table 1. Another table you could show could portray all of the ANOVA test results you collected. Your TA will show you how to do this in a lab demo. Now, write your results sections. Ensure that you refer to the figures and tables in the text of your results section like this (Table 1, Figure 2). However, the actual tables and figures are put at the end of the paper, after the literature cited section. Each table and figure should be on its own page. For the tables, the table legend (the sentence describing what the table includes) should be at the top of the page whereas the figure legend (the sentence describing what the figure includes) is at the bottom of the figure. Once those sections are put together, you need to write a discussion (~ 4 paragraphs long). In this section, you might consider 1) summarizing the results of your experiment in a single coherent paragraph; 2) exploring how your results are similar/different to other studies that have measured genotypic response to fertilizer (in other words, can you find other studies that measure the amount of variation in response to those variables among various plant populations?); 3) Adaptive implications of differences among populations (or lack thereof); 4) Future studies that should be done, now that we have done this research. In the discussion, we expect you to use at least three additional references (from the primary literature, not from textbooks or websites). Finally, create an abstract and literature cited section to your manuscript (minimum 6 unique citations). The style of your literature cited section should conform to the style described Evolution as described in Lab 1. Hints for the Scientific Manuscript The following three scientific articles will be useful for this experiment: • Reed & Frankham (2001) Evolution 55: 1095-1103. (http://www.jstor.org/stable/2680276) • Stearns (1989) BioScience 39: 436- 445 (http://www.jstor.org/stable/1311135) • Rebecca Klaper, Steven Frankel, May R. Berenbaum. 1996. Anthocyanin Content and UVB Sensitivity in Brassica rapa. Photochemistry and Photobiology 63, 811–813, DOI: 10.1111/j.1751-1097.1996.tb09635.x (http://onlinelibrary.wiley.com/doi/10.1111/j.1751-1097.1996.tb09635.x/pdf) If you choose to write about this experiment in your Scientific manuscript, I encourage you to have a look at the the three publications listed above. You are welcome to write your introduction as you wish, but we have provided some suggestions about the structure and content of your introduction. A reasonable introduction may include: a) 1 paragraph on “The role of genetic variation in determining a quantitative trait”. This will be your first paragraph in your introduction and you might describe first how allelic variation contributes to evolution. Second, you might explore how allelic variation translates into phenotypic variation (or in some cases, how genetic variation is not used in phenotypes – refer to Reed & Frankham for a description of why this might be true). Finally, you should probably mention that a phenotype is the product of both the genetic composition of an individual as well as their environment. b) Further, 1 paragraph on “The role of phenotypic plasticity in survival and fitness”. To write this paragraph, I would suggest reading the Stearn’s (1989) paper. Here, I hope you will really explain to the reader how a genotype interacts with the environment to produce a phenotype. c) 1 paragraph on “Brassica rapa as a model organism in the study of phenotypic response to fertilizer”. Here, we hope that you will justify the use of this particular species and the treatments. While preparing your introduction, we encourage you to practice your citation skills. At the end of every sentence, we expect you to provide at least one academic citation (no textbooks, no websites – only journal articles for this exercise). You are welcome, but not required, to cite papers other than those listed above (remembering, of course, that a minimum of 5 citations are required to get a 1/3 on Citation section of your grade). Literature Cited Blumer, L. 1996. Phenotypic variation in plants. Pages 231-247, in Tested studies of laboratory teaching, Volume 18 (J. C. Glase, Editor). Proceedings of the 18th Workshop/Conference of th Association for Biology Laboratory Education (ABLE), 322 pages. Brown, L., and J.F. Downhower. 1988. Analyses in Behavioral Ecology: A Manual for Lab and Field. Sinauer Associates, Sunderland, Massachusetts, 194 pages. Clausen, J., D.D. Keck, and W.M. Hiesey. 1948. Experimental studies on the nature of species, III: Environmental responses of climatic races of Achillea. Carnegie Institute of Washington Publication, 581: 1-129. Goldberg, D., K. Gross, A. Snow, and J. Garvey. 2010. A quick introduction to statistics. In Laboratory Manual for EEOB 413.02 – Ecology Lab. Ohio State University. Harborne, J.B. 1973. Flavonoids. Pages 344-380 in Phytochemistry. Volume II (L.P. Miller, editor). Van Nostrand Reinhold, New York, 445 pages. Harborne, J.B. 1988. The Flavonoids: Recent Advances. Pages 299-343, in Plant Pigments (T.W. Goodwin, editor). Academic Press, New York, 362 pages. Williams, P.H. 1989. Wisconsin Fast Plants Growing Instructions. Carolina Biological Supply Company, Burlington, North Carolina, 47 pages. .