Bio100 Appendix G Assignment

Bio100 Appendix G Assignment Words: 1028

Interpreting Your Data Plots Although basic trends in your data can sometimes be estimated by simply looking at the data points on your scatter plots, quantitative measures of the effects you are studying can only be determined by fitting a curve to your data. Curve fitting involves producing a statistically derived best-fit line of data points on the graph; not a hand-drawn or estimated line connecting data points. Once you have plotted your data, a Plot # tab will appear at the top of the Plot Data screen.

Clicking on this tab will take you to the curve-fitting functions of LeafLab and allow you to switch between plots that you generate. 1. Click on the Plot 1 tab to enter the curve-fitting view. •An enlarged view of the plot should now appear with a series of curve-fitting controls to the left of the plot. 2. The purpose and instructions for manipulating each control are described in following steps: •Curve: generates a best-fit curve based on the data points selected. You will be generating a best-fit curve by following the steps listed next. •y–Intercept: indicates the rate of dark respiration (light compensation point) To input the y-intercept: return to the data table by clicking on the Data tab. Look at the zero light measurement in the table and use the P value for this measurement as the initial measurement of the intercept. oReturn to the curve-fitting view and enter this P value directly into the intercept box. •Slope (of the line): photochemical efficiency; indicates the rate at which photosynthesis increases as light intensity increases. oTo manipulate the slope: click on the up arrow next to the slope function (you will see the line rise up and begin to form a curve). Increase the slope of the line until the curve looks like it is matching (fitting) the data points. •Asymptote (where the curve forms a straight line indicating that the data has leveled off): indicates photosynthetic saturation (maximum rate of photosynthesis). oLook at the plot line and estimate where the data levels off. This is the asymptote. oClick on the line, next to the data point that you think represents the asymptote. Two sets of numbers in parentheses will appear. The first number is light intensity and the second number is photosynthetic rate (P). Enter this P value into the asymptote box. •Error SS Value: is an expression of the calculation of the distances of each data point from the fit line. The lower the Error SS value, the more accurate the line in representing the data. oTo find the lowest possible Error SS value, use the up or down arrows to adjust each parameter: the slope, asymptote, or intercept. oStart with the slope; adjust the slope until you have the Error SS value number as low as possible. Stop when your adjustments cause the Error SS value number to increase. oThen, follow the same process with the asymptote and the intercept. Continue to adjust the parameters until you get the absolute lowest possible Error SS value. The values that give the smallest Error SS value produce the best-fit line for your data points. Note: this value could be as low as 0. 2. 3. Save your plot by clicking on the Export Graph button at the left of the screen. A separate window will now open showing your plot and a table with the intercept, slope, asymptote, and Error SS values. You can save this page by going to File and using the Save As feature of your browser. Summary: What Did This Experiment Tell Us?

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The experiment you just performed is representative of other experiments that you will conduct. A lot of information can be learned from studying the curves that you generate. Study the curve of Photosynthetic Rate vs. Light in Tomato to answer the questions below. The following is a representative plot and table from this experiment: SeriesInterceptSlopeAsymptoteError Sum of Squares 1-1. 30. 03817. 10. 198 1. Answer the following questions: •What is the relationship between an increase in light intensity and photosynthetic rate in tomato leaves? Does this relationship support the hypothesis that you formulated? An increase in light intensity increases photosynthetic rate, supporting the hypothesis. •Photosynthetic saturation is the maximum rate of photosynthesis. What was the value for photosynthetic saturation in tomato leaves? •What value of light intensity produced photosynthetic saturation in tomato leaves? •Based on what you know about photosynthesis, provide possible reasons for what causes photosynthetic saturation (these cannot be determined from the plot).

Photosynthetic saturation in tomato leaves will occur at a light intensity of approximately 1600 mol/m2/s. At saturation, photosynthetic rate is approximately 15-17 (as indicated by the asymptote). Exploration Experiment: Light Intensity and Photosynthetic Rates in Corn 1. Follow the steps detailed in the first experiment to test the effects of an increase in light intensity on photosynthetic rates in corn (a C4 plant). •The only modification to the experiment is that you will need to use a high rate of gas flow. Keep all other parameters the same as you did for tomato. 2.

When calculating P and plotting your data, make sure that you select only those values that you recorded for corn and not previously recorded values for tomato. 3. Plot photosynthetic rate versus light intensity and fit a curve to the data as you did for tomato. 4. Add your graph to Appendix H: LeafLab Report by clicking on Export Graph. 5. Copy and paste your graph under the Data section of Appendix H: LeafLab Report. 6. Answer the following questions in Appendix H: LeafLab Report: •What is the relationship between an increase in light intensity and photosynthetic rate in leaves from a corn plant?

How does this relationship compare with what you observed for tomato plants? •Photosynthetic saturation is the maximum rate of photosynthesis. What value of light intensity produced photosynthetic saturation in corn leaves? References LeafLab assignments and answers were adapted with permission from Pearson Education, Inc. Biology Labs On-Line is a collaboration between the California State University system and Benjamin Cummings. © 2002 California State University and Benjamin Cummings, an imprint of Pearson Education, Inc. Development was partially supported by a grant from the U. S. National Science Foundation.

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