BISC 111/113:Lab3assign2012.docx

Lab 3 assignment descriptive version

Lab 3: Assignment The Science Writing Guidelines in the BISC 111/113 WIKI describe the importance of effectively designing a figure that is easily evaluated by the reader and it is recommended that you refer to this resource for more detailed information about a results section. Modeling figure design and writing a results analysis based on peer reviewed published journal articles is highly recommended. Preparing a figure comparing mean±SD Transpiration Rates: For this assignment, you will create a portion of a results section to turn in at the beginning of lab 4. This week you collected water loss and leaf surface area data from 4 different species of plants and transformed those data into a measure of transpiration rate and resistance to transpiration. To summarize these results graphically, you will create one effective figure and its accompanying caption comparing the mean±SD transpiration rates for all four species in the two conditions (HLHW and LLLW. Use Chart: Source data: add series to add the LLLW data set). What experimental question would the transpiration figure address? Does the figure demonstrate the answer clearly? For example if you are interested in which plants transpire the most, in what order should the plants appear on your graph to show this most clearly? If the data is not arranged in that order, how can you adjust the figure? What if you are interested in whether or not the plants from drier environments, where water might be limited, will show lower transpiration rates? Can you organize the plants from least water availability to most water availability? Once you have a graphical representation of the mean±SD transpiration rates for each plant, you need to determine whether or not the means are significantly different from each other using statistics. T-Tests are used to compare means for two groups. For example your assignment asks you to conduct several T-Test comparing a. transpiration and b. resistance in the HLHW condition to the LLLW condition for the plant species you investigated in lab today. Refer to the Stats and Graphing Folder guide if you are not sure how to perform a T-Test in JMP. Brieflly: highlight the transpiration data for your three replicate plants in the light and in the dark, then create a TABLE: Subset. On this new JMP sheet there are only the 6 data points. Analyze: Fit Y by X: fill in the X and Y variable and click OK. On the graph that appears use the red triangle to select: Means/ANOVA/Pooled T. Is there a significant difference in mean transpiration in the light and dark? You can tell there is a difference if “P”≤ 0.05 (indicating a treatment effect). Consider following similar steps to compare transpiration rates in the sunflower and the water hyacinth in HLHW, and sunflower and Rhoeo in HLHW. What question are you trying to answer with each of these T-Tests? Since we actually measured four different plants (not two), you need to perform a one-way ANOVA to compare all four plants in one condition (e.g. HLHW OR LLLW). To perform the first of the assigned one-way ANOVA’s select only the HLHW transpiration data. Click Table: subset and then Analyze: fit Y by X. Is there a significant difference among the means being compared (P≤0.05)? If so find out which group(s) is/are significantly different by using the drop down red triangle to COMPARE: means: Tukey HSD. The test results will be added to the analysis. Notice how the letters identify which groups are significantly different from the others. Perform the remaining one way ANOVA’s assigned so you can evaluate differences among all four species for all the variables tested. If you forget how to use JMP, use the directions provided in the Stats and Graphing folder in SAKAI. Finishing the Graded RESULTS section: Write out a biological question (a hypothesis or thesis) you can answer using the data graphed in: 1. Prepare a figure with a caption comparing mean±SD Transpiration Rates. Place a caption under the figure, single-spaced. The caption should include: Figure number: A TITLE for the figure. And a brief summary explaining how the data in that figure was collected and/or calculated. A reader should be able to understand what you did to gather your data by examining the figure and reading the caption. Do not interpret the data in the caption (your instructor may modify these criteria and provide a model for you to follow, so take notes in lab). Write a results text summarizing how your data answers the biological question(s) addressed in your transpiration rate figure. A results text summarizes the main point(s) of a figure or table and analyzes the data to show how the biological question can be supported or not supported by the data. For novice science writers, it might be helpful for you to start each paragraph of the results text with “In order to (goal and context - what you wanted to learn) and (tool - how you tried to learn it). Followed by an interpretation of what you learned from the data and a summary of how the data supports (or doesn’t support) the biological question. Embed the stats in the results text as described in the science writing guidelines in the WIKI results text guide. You will NOT explain what a t-test or ANOVA or P is. Stats are used to evaluate if the means of any treatment effect are significantly different or due to chance; they do not tell you about the biology behind your experiment. Focus on the answer to your experimental question (the biology) and use the data to answer your question(s).