Bodine High School for International Affairs
Department of Biology Laboratory Manual IB Biology – Higher Level Year 1
Fall, 2011 The FAR SIDE
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Commonly used metric system units and symbols Quantity measured Length, width, distance, thickness, girth, etc.
Mass (“weight”)* Time Temperature Area
Volume Speed, velocity
Unit
Symbol
micrometer millimeter centimeter meter kilometer milligram gram
μm
kilogram
mm cm m km mg g kg
second degree Celsius square meter hectare square kilometer milliliter cubic centimeter liter cubic meter meter per second
s °C m² ha km² mL cm³ L m³ m/s
Relationship 1 μm = 1 x 10-6 m 1 mm = 1 x 10-3 m 1 cm = 1 x 10-2 m 1 km = 1000 m 1000 mg = 1 g 1 kg = 1000 g
1 ha 1 km² 1000 mL 1 cm³ 1000 L
= 10 000 m² = 100 ha =1 L = 1 mL = 1 m³
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Density Concentration Energy Scientific Notation
kilometer per hour kilogram per cubic meter Parts per million kilojoule megajoule .0123 = 1.23 x10-2 .00123 = 1.23 x10-3 .000123 = 1.23 x10-4
km/h kg/m³ PPM kJ MJ * * *
1 km/h = 0.278 m/s
1 kj = 1000 J 1 MJ = 1000 kJ
* We use scientific notation when we are dealing with very small (as above) or very large numbers such as 1,630,000 = 1.63 x 106. Scientific notation allows us to retain significant digits easier
Academic Honesty Policy Do Your own work!! • Cite your academic sources in the report background section using APA format. •
Don’t • Copy and past tables, graphs or text from any source. This includes copying and pasting followed by “modification”. Do your own work from start to finish. • Email someone your finished or partially finished lab report for any reason. If you do, you will be as guilty as the person who uses your report to plagiarize. The penalties will be identical. * There are many instances in which you are using raw data in your lab reports that was collected by other people in the class. That’s fine. However, you must still process and present the data in tables and graphs yourself. The raw data itself may be the same, but the processing and presenting of the data must be your own work. You must write your own conclusion and evaluation.
Penalties for Plagiarizing lab reports • You will receive a zero for the lab report and possibly fail the marking period 3
• I will notify the IB coordinator and your parents of the plagiarism • You may be required to leave the IB program - that will be up to the IB coordinator to decide.
I ______________________________________promise to adhere to the academic policy as stated above. If I have any questions I will ask Mr. Herbstritt for clarification. Signed:…………………………………………………………
Laboratory Rules Science Safety Agreement
Mr. Herbstritt -
Biology I WILL:
1. Follow all written and oral instructions given by the teacher. 2. Ask questions, or state concerns before beginning a lab procedure. 3. Behave in a manner that will ensure the health and safety of myself and others in the laboratory or classroom at all times. 4. Use protective devices for my eyes, face, hands, body and clothing during laboratory activities. 5. Refrain from eating, drinking, chewing gum or applying cosmetics in the laboratory. 6. Keep my work area clean and free of clutter during lab class. I understand and realize that many accidents are caused by carelessness and being in a hurry. I will come to class prepared to be responsible so that the safety and welfare of myself and others is not jeopardized. I have read the set of written science safety rules prepared by my teacher and agree to follow these and any other rules. 4
Date _________________ Date _________________
Student_______________________________________ Parent/Guardian_______________________ _____
Please list any known allergies or health problems: Please Note: Contact lenses should not be worn in the laboratory when chemicals are being used as certain chemical fumes or small particulate may become lodged under the lens. Please be aware of the slight increase in the risk of eye damage for contact wearers as compared to students in similar situations without contact lenses. All students are required to wear goggles during procedures that involve the use of chemicals, dissection or projectiles.
Required Materials for Labs • • •
Marble Notebook- This will serve as your lab notebook during the course. You will write everything from the lab Centimeter Ruler Calculator
The Foundation of Modern Science: Reason Basic Logic: An appreciation of the benefits which can be accrued through the skillful use of Logic and Reason was the factor that led to the rapid development of the modern technological age over the last few several hundred years. Underlying this movement was a realization that the physical/natural world can be understood through experimentation and reason. In other words, what used to be called “Supernatural” or “Mystical” could now be substantially understood (although not perfectly or totally) through use of the Scientific Method. In this part of our IB Biology Course, we will explore the use of the scientific method in a deeper way than you ever have before. To begin, we must understand the two basic types of Reason. 1. Inductive Reasoning: This type of reasoning/logic is the result of repeated observations that lead to a logical conclusion. Ex. Every time I drop an object, it falls to the ground. Therefore, there must be some attractive force causing the object to fall. (we know this as gravity). This type of reasoning trends from specific observations to a general hypothesis or scientific law. 2. Deductive Reasoning: This type of reasoning/logic takes some known hypothesis or scientific law (found through inductive reasoning) and applies it to the prediction of specific events. Ex. If I drop my textbook, I predict- based on
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the law of gravity - that it will fall to the ground. This type of reasoning trends from general to specific. **Scientific Investigations use both of these types of reasoning at different times. Any time we are collecting observations with our senses about the natural world (whether in an experiment or not) in an attempt to create some overall hypothesis, we are using inductive reasoning. Any time we make a prediction about what will happen based on some knowledge that we think we have, we are using deductive reasoning. Which type(s) of reasoning is illustrated in the two examples below? 1. All the swans I have ever observed are white. Therefore, all swans are white 2. Based on the Theory of Evolution, the DNA of Polar Bears should be extremely similar to that of the American Black Bear. An appreciation of the Power of Reason will become very important in the experiments that you will design and the data which you will interpret in the next several months. One reason that the scientific method is such a powerful tool for understanding the natural world is it’s acknowledgement of the potential for error and uncertainty associated with empirical data. Empirical data is data that is collect through the senses (sight, hearing, smell, etc.) No other area of knowledge takes as much time to limit the negative affects of error and uncertainty associated with these “weaknesses” as does the scientific method
Laboratory Schedule Outline Page # Introduction to Lab Report Preparation………………………... 8 Lab 1 Measuring ad Recording Data…………………………… 12 Lab 2 Processing Data Using Statistics……………………….. 15 Lab 3 Presenting Data – Tables and Graphs………………… 18 Lab 4 Environmental Assessment- Wissahickon Cr……… 23 Lab 5 Using a light microscope …………………………………… 30 Lab 6 Using electron micrographs ……………………………… 36 Lab 7 Cell Fractionation Lab ………………………………………. 38 Lab 8 Error and Uncertainty Analysis …………………………. 41 Lab 9 Diffusion and Osmosis………………………………………. 45 Lab 10 Enzymes………………………………………………………… 54 Lab 11 Cellular Repiration…………………………………………… 57
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Lab 12 Photosynthesis………………………………………………… 63 Lab 13 Plant Transpiration (Internal Assessment)……… 69 Appendix A: Statistics in Biology…………………………………..73 Appendix B: How to Cite Sources………………………………….76 PLEASE NOTE:
THE LABS OUTLINED ABOVE ARE NOT THE ONLY LABS WE WILL BE PERFORMING DURING THE COURSE. HOWEVER, THEY ARE THE ONES THAT ARE THE MOST IMPORTANT IN PREPARING YOU TO COMPLETE YOUR INTERNAL ASSESSMENT FOR BIOLOGY. THIS ASSESSMENT CONSISTS OF 2 LAB REPORTS THAT WERE DESIGNED AND CARRIED OUT BY YOU. THE FIRST OF THESE IS THE PLANT TRANSPIRATION LAB IN THIS MANUAL
Other Lab Exercises that we will be completing throughout this course are outlined below. This list is not exhaustive but will give you a general idea of the remaining labs Ecology • Simulated Capture-mark Release Exercise We simulate a technique outside that is used to estimate the size of animal populations in the “wild” • Population Dynamics Computer Simulation http://www.cbs.umn.edu/populus • Carbon Cycling Computer Simulation : http://mvhs.shodor.org/coresims/carboncycle/index.php (Continued on Next Page)
Chemistry • Effect of pH on Enzyme Activity- We will use the pH computer probes to test the affect of pH on rate of pepsin activity • Protein Modeling - In this exercise we will use beads to model protein structure • Protein Modeling – Computer Simulation- Interactive Concepts in Biochemistry http://higheredbcs.wiley.com/legacy/college/boyer/0471661791/structu re/jmol_intro/jmol.htm Genetics and Biotechnology
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Mitosis and Meiosis Modeling – We will use yarn to model how chromosomes move in mitosis and meiosis and create genetic diversity • BioEyes Zebra Fish Experiment We will be mating these fish to observe genetic inheritence and embryological growth http://bioeyes.org/ • BLAST Computer Program labs T. Rex protein exercise to determine evolutionary relationships http://www.sciencebuddies.org/science-fairprojects/project_ideas/Genom_p018.shtml Identify 16 unkown genetic sequences at digitalworldbiology website http://www.digitalworldbiology.com/dwb/Tutorials/Entries/2009/1/26_BL AST_for_Beginners.html • Bacterial Transformation Lab: In this AP lab, we will insert genes from the Pacific Jellyfish into bacterial cells and make them “glow” • DNA Fingerprinting Lab – We will compare DNA from Potential Criminals as would be done in Forensics lab • Wards Science Protein translation models Anatomy and Physiology • Fetal Pig Dissection: We will spend several weeks dissecting a fetal pig and explore the respiratory, circulatory, digestive and excretory excretory systems • Heart Rate Experiment: We will use computer-based sensors to measure human heart rate. You will design and experiment to test the effect of some independent variable (such as stress or caffeine) on heart rate (the dependent variable)
Introduction to lab report preparation Introduction The basic format for a lab report in this class will be: • Research Question: Ex. What is the effect of light intensity on plant growth?
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•
Background In this section you would do some research and write about light intensity and plant growth etc • Hypothesis with Justification A hypothesis is an “educated guess”. For example: Higher light intensity will increase plant growth because light is the source of energy for photosynthetic reactions. • Variables This is one of the most difficult sections of a lab report so there is a detailed explanation below • Materials (this section is simply a list of what was used) • Procedures (this section is a detailed list of what you did) • Data (this section will be discussed in Labs 2 and 3) • Conclusion (this section will be discussed in future science labs) • Evaluation and Suggestions for Improvement (this section will be discussed in future science labs) You will need to understand that there are 3 different types of variables 1. Independent Variable: The variable that is manipulated in an experiment. For example, in an experiment to test the affect of light intensity on plant growth, the light intensity would be the independent variable because you are manipulating (changing) the intensity to see what affect it has on plant growth. Some people call this the “manipulated” variable 2. Dependent Variable: This is the variable that is measured in an experiment. It is the variable that you are measuring to see what affect the independent variable has on it. In the example above, the Dependent Variable is Plant Growth. Some people call this the “responding variable”. 3. Controlled Variables: Even though some experiments don’t have a Control Group, all good experiments have Controlled Variables. These are the variables that could affect the Dependent Variable (in this example plant growth) other than the Independent Variable. In our plant example, controlled variables would include • Amount of water the plants receive • Temperature that the plants are exposed to • Nutrients in the soil These variables can be controlled in two ways.
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1. Physical Control: By keeping the controlled variables exactly the same, we control them. In the example above, we would water all the experimental and control groups with precisely the same amount of water. We would also use the same soils with the same nutrient content. 2. Making sure any variations are experienced equally: Some variables are difficult to control physically. In such cases, an acceptable substitute for physical control is to make sure that all plants experience the same fluctuations, thereby cancelling out the affects of the variation. In our example above, putting the plants in the same area of a room would ensure that any unavoidable temperature fluctuations in the room would be experienced by all plants in the experiment and thus the effect on the dependent variable would “cancel out”. There are two different “groups” in many scientific experiments 1. Experimental Group(s): All experiments have Experimental Groups. These are the organisms (in this case, individual plants) that are exposed to the same light intensity. For example, 10 plants exposed to 20 foot-candles of light intensity would constitute one experimental group. Ten different plants exposed to 40 foot-candles of light would be another experimental group and so on. 2. Control Group: Some experiments have a Control Group. If there is a Control Group, it is the group of organisms (in this case plants) that are not exposed to the factor being tested. For example, if you were going to test the affect of the tobacco mosaic virus on tobacco growth, the control group would be plants without the virus and the experimental group would be the plants that were exposed to the virus. The Control group always serves as a standard of COMPARISON to the Experimental Groups. In some cases, the control group is the “natural” situation that the organism is exposed to. For example, if you were doing an experiment on the effect of increased ozone concentration on plant growth, the control group would be the plants that are exposed to the usual or natural amount of ozone in the atmosphere. Objectives • Identify the variables in a scientific experiment • Identify the experimental groups in an experiment • Identify the control group (if present) in an experiment
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Procedures 1. Provide two examples of experiments, one with a control group and one without a control group. Identify the variables in each. Experiment Example #1- With a Control Group Describe the Experiment: This is an experiment that will test: …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… ….. Control Group …………………………………………………………………………………………… ……………………………… Independent Variable………………………………………………………………………………… ……... Dependent Variable………………………………………………………………………………… ……………. Controlled Variables: (list at least 3) …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …..
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Describe how you would control these variables in your experiment and whether each method would be a form of physical control or otherwise …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… ….. …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …..
Experiment Example #2- Without a Control Group Describe the Experiment: This is an experiment that will test: …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… ….. Independent Variable………………………………………………………………………………… ……... Dependent Variable………………………………………………………………………………… …………….
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Controlled Variables: (list at least 3) …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… ….. Describe how you would control these variables in your experiment and whether each method would be a form of physical control or otherwise …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… ….. …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …..
Lab 1
Measuring and Recording Data
Introduction
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Degrees of precision and uncertainty in data Students must understand that an important aspect of the scientific method is that there is always measurement uncertainty when data is collected. The scientific method is very careful in identifying this measurement uncertainty and, where possible, minimizing it. Examples of data collected in scientific investigations are such things as length, volume, pH and light intensity. General Rules for Estimating Measurement Uncertainty: •
For rulers and instruments with digital displays (like our digital mass meters): The degree of precision is plus or minus (±) the smallest division on the instrument . • For most other instruments such as thermometers, graduated cylinders, pipettes etc, the degree of precision is plus or minus (±) one half the smallest division For example, a pipette whose smallest unit is 0.1 ml has an uncertainty of ± 0.05 ml. Therefore, a measurement of 34.1 ml becomes 34.10 ml (± 0.05 ml). so that both are taken out to two decimal places. This measurement would then be written 34.10 ml ± 0.05 • For measurements that do not fall into the categories described above, such as reaction time in starting and stopping a timer or using a manual wind anemometer, students should do their best to quantify estimated uncertainty by best personal estimate. When you have data that is derived from 2 different variable you need to use the Upper and Lower Bounds Method for estimating uncertainty. For example, if you wanted to determine whether large insects absorb less insecticide per unit mass than smaller ones, you would have to measure the amt. of instecticide they absorbed (in milliliters) and divide it by their mass (in grams) and you would end up with an estimate in milliliters/gram of insecticide absorbed. Since we are dealing with two different units (volume and mass) we must use the upper and lower bounds method Insect #1: Absorbed 0.23 ml +/- 0.01ml with a body mass of 5.2 g +/0.1 g First, calculate the rate ignoring the uncertainties: 0.23ml/5.2g = 4.42 x 10-2 ml/g Next, calculate the Upper bound: 0.24 ml / 5.1 g= 4.70 x 10-2 ml/g Lastly, calculate the Lower bound: 0.22 ml / 5.3 g= 4.15 x 10-2 ml/g
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Therefore, the Answer would be 10-2 ml/g
4.42 x 10-2 ml/g +/- 0.28 x
Objectives Estimate the measurement uncertainty of a variety of measuring devices Calculate the measurement uncertainty for multi-variable measurements using the upper and lower bounds method
Procedures Measurement Uncertainty for Common Laboratory Measuring Devices Most Important Rule: Your estimates below and your uncertainties must always agree. For example, recording a value of 10.3 ml +/- 1 ml is incorrect. If the uncertainty is +/- 1, then is must be written as 10 ml +/- 1 ml. Similarly, it would be incorrect to record 10ml +/- 0.5 ml. This should be written as 10.0 ml +/- 0.5 ml. The easiest way to remember is that the number of significant figures should always be the same. Station 1: Wheel pipettes Use the wheel pipette to transfer 10 ml of water into beaker provided Using the rule outlined above, write the volume you transferred together with it’s uncertainty____________________ Station 2: 1 ml pipette What is the measurement uncertainty associated with this instrument? __________________ Why?______________________________________ Station 3: Micropipette. Use the pipette to transfer 10 micro-liters into the beaker provided Write the volume you transferred together with it’s uncertainty____________________ How did you know the uncertainty?__________________________________________ Station 4: Metric Rulers Measure the width of the desk. Record (with uncertainty) ___________________ Measure the diameter of the penny. Record (with uncertainty) How did you estimate the uncertainty?___________________________________ Station 5: Digital Mass Scale
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Record the mass of the penny together with it’s uncertainty____________________ How did you estimate the uncertainty?_______________________________________ Station 6: Metric Thermometers Record the temperature of the water with it’s uncertainty____________________ How did you estimate the uncertainty?___________________________________ Station 7 10 ml Syringe Use the pipette to transfer 8 milli-liters into the beaker provided Write the volume you transferred together with it’s uncertainty____________________ Station 8 Humidity Meter Record the humidity in the room (with uncertainty)________________________ Station 9 Light Intensity Meter Record the light intensity (with uncertainties) _______________________________ Station 10
Wind Speed Meter (anemometer)
Record the wind speed (with uncertainties) ________________________________ How did you estimate uncertainty?________________________________________ Station 11: Surface Area Estimation Record the Surface area of the leaf (with uncertainties) ________________________________ Station 12
Graduated “Cylinders
Record the volume of water in the cylinder with uncertainty____________________ Station 13 Beakers Record the volume (with uncertainties) ________________________________ Upper and lower bounds problem (Show your work) Germinating Pea Respiration Rate per gram of pea A pea respired oxygen at 0.120 ml +/- 0.005 ml for 6.2 g +/- 0.1 g Calculate Rate without Uncertainties__________________________________________________ 16
Upper Bound:________________________________________________________________ ___________ Lower Bound:________________________________________________________________ ___________ Final Answer________________________________________________
Lab 2 (Computer Lab) Processing Data using Statistics Introduction In order for “raw” data (like that collected in Lab 2) to be useful, it usually needs to be processed in some way. This involves the use of statistics. One of the most common statistics used to process raw data is to calculate a mean. Once you’ve calculated a mean, you can use the same data to calculate a Standard Deviation(SD) and the Standard Error of the mean (SEM) Objectives • Explain the importance of calculating a mean for biological data • Calculate the Standard Deviation and Standard Error for biological data • Explain the significance/importance of calculating SD and SEM Procedures The Standard Deviation (SD) essentially a measure of how “spread out” the data is around the mean. A high standard deviation like represented on the graph below left means that your data was very spread out and a low standard deviation like the graph below right means that most of the values were close to the mean.
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About 68% of the area under the curve falls within 1 standard deviation of the mean. About 95% of the area under the curve falls within 2 standard deviations of the mean. The standard deviation of data can be calculated manually (you’ll learn that in IB math) or on your calculator or using an excel spreadsheet. Once you have calculated the standard deviation (SD), we will be converting it to a similar statistic (because it measures the same “spread” of data) known as standard error of the mean which is: Standard Error of the Mean (SEM) = SD/sq rt. N where N is the number of data points Why do we need to calculate Standard Deviation and SEM? To explain why it is important to calculate the SEM in addition to the mean, let me give you an example. Two groups of people (ie 2 experimental groups) were involved in an experiment to test whether or not a certain experimental drug lowered their blood pressure. We would usually combine these into one group but I want to illustrate why knowing the mean of data is not enough to truly understand it. This was a controlled experiment, meaning that it followed all the “rules” we discussed in the Introduction to Lab Report Preparation Section of this Lab Manual. As shown below, both groups had exactly the same mean. However, their SD’s and SEM’s were significantly different. Which Experimental group data would you be more confident in – Group #1 or Group #2 and why??? _______________________________________________________________________ _ _______________________________________________________________________ _ _______________________________________________________________________ _ _______________________________________________________________________ _ Table 1: Decrease in Systolic Blood Pressure after Treatment with Accuretic ACE Inhibitor Medication Trials
Experimental Group #1
1 2
Decrease in Blood Pressure (mm/Hg) 5.5 6.4
Trials
Experimental Group #2
1 2
Decrease in Blood Pressure (mm/Hg) 4.0 4.5
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3 4 5 6 7 8 9 10 Mean SD SEM
3.5 0.0 0.0 7.5 8.3 4.5 7.3 2.2 4.5 3.0 0.95
3 4 5 6 7 8 9 10 Mean SD SEM
5.0 5.5 3.5 4.5 3.9 5.1 4.5 4.5 4.5 0.6 0.19
Now, Using your lap top computer and/or scientific calculator, calculate the mean, Standard Deviation, and Standard Error of Mean for the following data. These data are more typical than the Table 1 data because the experimental groups are being exposed to different independent variables (in this case, temperature) Table 2: Respiration Rate of White Mice (Mus musculens) at 25 degrees Celsius and 35 degrees Celsius Trials
Experimental Group #1 25 deg. Celsius (breaths/min)
Trials
Experimental Group #2 35 deg. Celsius (breaths/min)
1 2 3 4 5 Mean SD SEM
35 36 32 33 34
1 2 3 4 5 Mean SD SEM
42 50 44 40 48
Which of the Experimental Group data above would you have more confidence in and why? _______________________________________________________________________ _______________________________________________________________________ _______________________________________________________________________ 19
_______________________________________________________________________ _______________________________________________________________________ So, in conclusion, the reason that it is important to calculate the SD and SEM is that it helps the experimenter evaluate how “good” or “reliable” the data is. You would like your data to have a low SD and SEM because this means that your test subjects (trials) responded to the independent variable (in the first case the medicine) in similar ways. **Please Note: The statistics discussed in this section are not an exhaustive survey of all the statistics that can be applied to biological data There are many other ways to statistically process data other than mean, standard deviation and standard error of the mean. Examples of these are linear regression, correlation coefficients, and ttests. These will be covered later in this course and will also be covered in IB math. For a full treatment of these other statistical techniques, go to : http://www.utdallas.edu/~serfling/3332/Biology_statistics_made_simple_using_Excel. pdf Biology Statistics Made Simple Using Excel by Neil Millar
Lab 3 (Computer Lab) Presenting Data: Making Tables and Graphs on Computer Introduction It is important to be able to make tables and graphs on a computer so that your data can be presented in a clear and understandable manner in your Lab reports (Data Section). This lab will specifically show you how IB likes the data to be presented in your lab reports for your Internal Assessment Projects. This is only one of several possible means of presenting data and the type of presentation will depend on the type of data Objectives • Construct a raw and processed data table in Word • Construct column graphs in Excel with Error bars Procedures
Part A:
Tables in Word
To construct a table in Word, simply open up a word document and click on Table on the upper tool bar and then “Insert” and ‘Table” from the pull-down menu. You can then choose how many rows and columns you want. To practice this, open a Word document on your laptop now. Sometimes we separate out the raw data tables from the processed data tables. Raw data tables contain only the data that was collected
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during the experiment with no processing at all. They must contain several important elements such as: • • •
A descriptive title Variable Units and uncertainties for all data measurements (usually in column heading) A description under the table of how the uncertainties were determined
Table 1: Heart rates in humans (Homo sapiens) with and without meditation Heart Rates (BPM) +/- 2 Trial # 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.
No meditation
5 minutes meditation
10 minutes meditation
77 65 72 76 71 78 67 75 71 70
82 60 57 71 70 83 62 70 67 55
67 55 61 66 61 68 57 65 61 60
* Uncertainty in Heart Rate Measurement was determined by bestestimate of the researcher
**Hint: To merge cells as in the table above: Pallet – Table -Merge cells
View – Formatting
As you may have noticed, having raw and processed data on separate tables in this Lab is different than the tables shown in Lab 2. Neither one is always correct – it will depend on the type of data you are presenting and the best way to make it clear. Now try to recreate the following processed data table and calculate and enter the missing data. As indicated, this table has the following features: • A descriptive title • Variable Units and uncertainties for all data measurements • A notes indicating the equation for SEM Table 2 Mean, Standard Deviation, and Standard Error values for heart rates in humans (Homo sapiens) with and without meditation No
5 Minutes
10 Minute
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Meditatio n
Meditation
Meditation
Mean Heart Rate beats/min +/- ______ St. Dev SEM SEM = St. Dev/sq rt. n where n=#trials
Part B:
Graphs in Excel
Excel is only a tool to make graphs in- don’t print the graphs out in Excel. Always import your finished graph into the Word document lab report when finished . You can make line graphs or column graphs or pie graphs for example. In this example, we will make a column graph and a line graph. The example below uses the data from Table 2 above and explains how to make column graphs with error bars in excel. Try to recreate this table on your laptop. 1. Open an Excel spreadsheet and enter the mean values in either of the ways shown below. You will get the same graph either way. 2. Then highlight the cells
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No Meditation 5 Minute Meditation 10 Minute Meditation No Meditation 72.2
72.2 67.7 62.1 5 Minute Meditation 67.7
10 Minute Meditation 62.1
3. Click on Charts in the upper tool bar, then Columns (farthest one on left). 4. Click on Series 1 and delete it 5. Go to View - Formatting Palette and enter: Figure 1 (followed by a descriptive title shown below in Figure 1) All graphs are called Figures 6. Click on Verticle Axis and enter the Verticle Axis label in Figure 1 7. Now double click on one of the columns in the Graph and choose Error bars- Both-Custom- Specify Value 8. In the Specify Value box enter the SEM values for each of the Columns separate by commas. Enter the same values for both the + and – 9. Your graph should now appear as Figure 1 does below Please Note: The error bars on graphs are visual representations of how variable the data is spread around the mean that is represented. Therefore, which of the Groups (no med, 5 min, or 10 min) has the most variability in its mean value? ______________________________ How do you know? _______________________________________________________________________ _
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Error Bars are Standard Error of the Mean (SEM) In this Column graph, there were 2 experimental groups (5 and 10 min meditation) and one control group (no meditation). Sometimes you won’t have a control group, you’ll just have 3 or more experimental groups. You could have also graphed these same means using a line graph. Do that now – represent this same data as a line graph- do everything the same except instead of column graph, choose line graph. Click on the line to enter error bars instead of the column. Line graph or Column Graph? If there is an even gradient of temp, light intensity, humidity or any other indep. variable (like time of meditation) on the x axis you can do a line graph. If there is no clear gradient on the x axis (such as when you only have only 2 experimental groups you would have to use a column graph. This might occur – for example- if you were a drug company testing a new drug. One group of people (column) would be taking the drug and the other column wouldn’t be taking it. Incidentally, your x axis on a graph is always your indep. variable and your y axis is always your dep. variable.
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Write a Conclusion for the Meditation Data Above- does meditation affect heart rate? Justify your position and evaluate the error bars and what they suggest about the reliability of the data: …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… ………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………
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Lab 4 Environmental AssessmentWissahickon Creek and Environs Introduction Part A: Benthic Macroinvertebrate Analysis of Wissahickon Creek Introduction and Pre-lab Questions: It has been observed (First step of scientific method!) in the scientific community that certain benthic macroinvertebrates have specific tolerances to water pollution. While some are Very Sensitive, others are Not Sensitive at all.. Still others fall between these two extremes and are classified as Somewhat Sensitive. How could this observation be used to evaluate the water quality of Wissahickon Creek? _______________________________________________________________________ _______________________________________________________________________ _______________________________________________________________________ _______________________________________________________________________ ____________________________________
Research Question: Is the water quality of the stream from which these benthic macroinvertebrates were collected (Wissahickon Creek) poor, good, or excellent? Materials: •
Benthic macroinvertebrate sample from Wissahickon Creek,
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• • • • •
Plastic tray for observing the sample Forceps and tweezers Small vials Identification Key for Benthic Macroinvertebrates Pollution tolerance index values for Macroinvertebrates
Hypothesis with Justification: (you’ll have to do a little research for this) …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… ………………………………………………………………………… Procedure 1. Pour your sample into the container provided 2. Use your tools to sort the living from non-living material 3. Use your key to identify macroinvertebrates - place like-species in separate viles, keeping tract of their total numbers and their pollution tolerance 4. Add your data to the class data when finished 5. Compile the entire class data into a table for use in your own mini-lab report. Design your table in such a way that it is neat, easy to understand, has a descriptive title, and quickly gives the reader a view of how many species were collected at each tolerance level. This should be done in a word-processing document 27
6. Calculate the Macroinvertebrate Index using the guide on the next page 7. Create an appropriate graph for the class data 8. Write a conclusion
Mini-lab Report Format (DO THIS IN A WORD DOCUMENT) Research Question: (copy directly from what I gave you above) Materials
(copy directly from what I gave you above)
Results Insert the table and graph described above to the Word document Discussion 1. Discuss the results of this investigation and what they suggest about the water quality of Wissahickon Creek. (Use numbers of species as well as their pollution tolerance designation and Macroinvertebrate Index calculation and refer to the tables and/or graphs in your discussion. Include a discussion of the limitations of this data in making a definitive determination of water quality.
2. What are some possible advantages of analyzing stream quality using this type of benthic macroinvertebrate analysis vs chemical analysis (ie water testing)? (Warning! You must really think about this!)
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Macroinvertebrae Index Calculation
29
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Part B:
Using Simpson’s Diversity Index to Evaluate Biodiversity in a Bird Community at Wissahickon Creek Park. D = Sum of
n(n-1)
Simpson’s Diversity Index = 1-D N(N-1) (The greater the Index,
the greater the Diversity) Where n = Total # of organisms of a particular species N = Total # of organisms of all species 10/4/08 Species Blue Jay RB Woodpecker Carolina Wren Blue Warbler B&W Warbler 3 Wood Duck Mallard Duck R.T. Hawk Total
# 3 1 3
10/2/10 Species
_________#____
5 1
B.C. Chickadee 8 Mallard Duck Wood Duck 3 Turkey Vulture Downy Woodpecker
2
Carolina Wren Swallow
3 1 19
Total
4 1
2 3 25
10/10/09 R.T. Hawk B.C Chickadee Mallard Duck Wood Duck Warbler Swallow Total:
1 4 12 8 1 10 36
Calculate the Simpson’s Diversity Index for each of the years 20082011(show your work) 2008
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2009
2010
2011
Questions to Answer: (answer these on separate word document) 1. Did the Simpson’s Biodiversity Index change much in the four sampling efforts above? Which year(s) had the highest/lowest Biodiversity? Are there any noticeable trends?
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2. List the two major variables which affect the Simpson’s Diversity Index Equation?
3. Explain at least 2 environmental reasons that might lead to decreased bird biodiversity in the Wissahickon Valley over time.
4. Could this Index be used for plants? Why or Why not?
5. What are some weaknesses in the way that we have applied this Index over the past 4 years at Bodine. How could we improve the reliability of the data if time and money were no object? What would you do different next year?
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Lab 5 -Using a light microscope to observe eukaryotic cells and tissue Introduction :
Light Microscope Structure
A compound light microscope has two lenses, the ocular lens, and the objective lens. On the microscopes that you will use, the ocular lens will always be 10x. There are three objective lenses that can be rotated to change the magnification. The low power lens (short one) is 4x, the medium one is 10x and the high power lens (longest one) is 40x. To calculate the Total Magnification of what you are looking at, multiply the ocular lens magnification (10x) by the objective lens magnification (4x,10x or 40x). For example, if you are using the high mag. objective lens (40x) you would multiply that by the ocular magnification (10x) for a Total Magnification of 400x. Now calculate the total magnification for low and medium objective lenses. Show your work Low Power Total Magnifiation___________________________________________________________ ___ Med Power Total Magnifiation___________________________________________________________ ___
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Objectives • Prepare a wet slide and view it under low, medium and high power using a light microscope • Measure objects under a light microscope using a stage micrometer • Draw and properly annotate biological drawings
Procedures Part A: Practice using a light microscope to observe and draw cells/tissue
Letter “e” 1. Cut out the letter “e” and place it on the slide face up. 2. Add a drop of water to the slide. 3. Place the cover slip on top of the “e” and place on the microscope stage 4. Practice focusing using the coarse and fine adjustment knob (don’t break the slide!) 5. Draw what is on the slide in Fig. 1 under low power and Figure 2 under med. power.
Figure 1- Low Power Medium Power
Figure 2-
Cheek Cell 1. Place a small drop of methylene blue stain onto a clean slide. 2. Using a toothpick, gently scrape the inside of your cheek. 3. Place the toothpick tip into the stain and mix. The methylene blue stains the cells so you can see them. 4. “Search for a cheek cell using low power (4x). To do this, slowly move the microscope slide with one hand/finger while looking through
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the eyepiece. Hint: the cells will be very small under low power but will be round or oval with a dark spot in the middle. 5. When you find a cell position it at the end the pointer (the dark line you see when you look in the microscope. Then switch to med. power (10x on the objective lens) and draw and label what you see under Figure 3. 6. Switch to high power- draw and label the nucleus, cell membrane, and cytoplasm under Figure 4. (hint: you will probably need to fine focus again at high power)
Figure 3: Human cheek cell under cheek cell under medium power power
Figure 4: Human high
Onion Cell 1. Place a drop of iodine on a clean slide. 2. Place a small piece of onion membrane into the iodine; place a cover slip on top. 3. Observe under low power. Draw and label what you see in Figure 5. 4. Now switch to high power. Draw what you see in Figure 6. Label the following organelles: cell wall, nucleus, and cytoplasm.
Figure 5: Onion cells under
Figure 6: 36
Onion cells under medium power power
high
Analysis: 1. How was the onion cell different from the cheek cell? _______________________________________________________________________ _______________________________________________________________________ __ 2. Do onion cells have cell membranes? ____________________________________ 3. Why did we add iodine to our cheek cells? ___________________________________ _____________________________________________________________________ 4. What structure in the cheek cell was stained the darkest? ________________________ 5. Is your cheek cell an animal cell?___________________ Why?__________________________________________
The Elodea leaf 1. Place a drop of water on a clean slide. 2. Place an Elodea leaf in the drop of water, place a coverslip on top. 3. Observe under low power first (4x), then draw under medium and high power. 4. Label the following organelles: nucleus, cytoplasm, cell wall, and chloroplasts.
Figure 5: Elodea cells under
Figure 6: 37
Elodea cells under medium power power
high
Guard Cells in leaf tissue 1. “paint” the underside of your leaf with clear nail polish 2. Attach a piece of clear tape over the painted area and let it dry 3. Peel the tape off the leaf and place it under a light microscope 4. Look for the guard cells by scanning. If you’d like to see what the guard cells look like, review page 750 of Campbell Biology. Draw and label what you see. 5. Keep this slide intact because you will use it for Part B of this exercise
Figure 7: Guard cells under Figure 8: Guard cells under medium power high power Analysis: 1. What structures were you able to see in both the plant and animal cells? ___________________________________, _____________________________, ___________________________________ 2. What structures were only in the Elodea cell? _______________________________ 3. Why were these structures not found in the onion cell? _______________________________________________________________________ _______________________________________________________________________ __ 4. List several cellular structures that were too small to see in our cells, even under high power? ___________________________________, _____________________________,
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___________________________________,___________________________________ 5. Explain the relationship between the structure of the leaf guard cell and its function. Include a description of how the guard cell is able to change shape (page 750 in Campbell) _______________________________________________________________________ _______________________________________________________________________ _______________________________________________________________________ ___ _______________________________________________________________________ _ 6. Arrange the following cells and cellular structures in their correct order from smallest to largest: mitochondria, plant or animal cell, most bacteria, ribosomes, nucleus, viruses _______________________________________________________________________ __________________________ _______________________________________________________________________ __________________________ 7. What is the definition of tissue? Distinguish tissue from cells _______________________________________________________________________ __________________________ _______________________________________________________________________ __________________________
Part B: Using a stage micrometer to estimate the relative size of cells while using a light microscope
39
Introduction : Many times when working with cells under a light microscope, we would like to have general estimates of length of the cellular structures or density/concentration of cells. One way to do this to use a stage micrometer. 1. Set your microscope at 100x Total Magnification 2. Measure the field diameter (this is what you can see when you look into the microscope) To do this, position the 0 line of your stage micrometer on one end of the field and read the distance to the other edge of the field across the center diameter. 3. Once you determine the field diameter of your field of view in mm, you can then estimate the actual size of the cells 4. Place the slide you made of the guard cells in Part A on the microscope stage. 5. Focus on one of the guard cells at 100x magnification and visually estimate its length in relation to the total field diameter. Is it 1/3? 10%? 60%? Now calculate this dimension in mm. Total field width (mm) Size of cell in % or proportion of the field Calculated estimate of cell length (mm) Note: If you switch to a different magnification you would have to remeasure. The field diameter will change with magnification Analysis 1. Using your knowledge of mathematics, calculate the density of cells in the field of view for one of the slides you prepared in Part A (Such when viewed at 100x magnification (show your work) _______________________________________________________________________ _______________________________________________________________________ ____________________________________________________
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Lab 6 Viewing and measuring cells using electron micrographs Introduction :
Electron micrographs
As discovered in Lab 6, many Eukaryotic cellular parts (called organelles) are too small to see with the light microscopes we have. Our microscopes are also not able to view bacteria (prokaryotic cells) in much detail. Review page 95 in Campbell Biology to familiarize yourself with the size range within and among cells. To properly observe these smaller structures, we must learn to use the pictures that are generated from powerful electron microscopes (the pictures are called electron micrographs) Objectives • Estimate the magnification of micrographs using the scale bars • Determine the size of structures in micrographs using magnification Procedures Estimating the magnification of a micrograph using a scale bar 1. Measure the length of the scale bar in millimeters. In the micrograph below (letter b on the right) the length of the bar is 14mm. Verify this yourself 2. Convert the scale bar length as shown in the drawing from micrometers (1x10-6 meters) to millimeters (1x10-3 meters). This would be .0005mm
41
MICROGRAPH OF A BACTERIA 3. Divide the figure in #1 by the figure in #2. This would be 14mm/.0005mm = 28,000x magnification. How much higher is this magnification than our light microscopes were at their highest power?___________________________ Now, calculate the magnification of the micrograph below (The image to the right)
Show your work below
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Measuring size of structures in micrographs using magnification Once you have estimated the magnification of a micrograph using the scale bars as completed above, you can easily measure cells or cellular structures from the micrograph using a millimeter ruler. The technique for this is outlined below 1. Measure the object using a millimeter ruler. 2. To convert this measurement into a “microscopic measurement” simply use the following formula: (length in mm measured on the micrograph) (magnification)
= actual size in mm
Using the micrograph of the bacteria above, calculate the length of the bacteria using the equation above. Show your work below
Lab 7 Cell Fractionation Lab Introduction In lab 6 we viewed and described various cells (elodea leaf, onion root, human cheek, leaf guard) and realized that the structure of a cell is related to its function. For example, elodea leaf cells have lots of chloroplasts because they are responsible for photosynthesis whereas onion root cells have no choloroplasts. Also, leaf guard cells were long and curved and had the specialized ability change shape and regulate oxygen and carbon dioxide coming in and out of the leaf. In this lab, we are going to break open pea cells and observe the parts of these cells through a process called cell differential centrifugation. The process separates the heaviest parts of the cell from the lightest Objectives • •
Separate cell parts by blending, filtering, and centrifuging cellular material Use the microscope to examine cell parts found in each layer of centrifugred cellular material
Materials • •
100 ml 0.58 M Sucrose Solution 50 ml fresh, green peas
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• • • • • • • • • • •
Blender Cheesecloth 250 ml beaker Rubber Band Stirring rod Centrifuge tubes and Centrifuge Microscope slides and coverslips Toothpicks Iodine solution Light microscopes Pipettes
Procedure 1. Pour 100 ml of sucrose solution into a blender. Add about 50 ml of peas. Blend at highest speed for 3 minutes. 2. Loosely stretch cheesecloth over a beaker. Secure the cheesecloth with a rubber band. Pour the blended mixture through the cheesecloth. The liquid that passes through is called filtrate. The solid material in the cheesecloth is called residue. 3. Stir the filtrate with a stirring rod. Fill a centrifuge tube ¾ full with filtrate and repeat on opposite side of the centrifuge. Spin on highest speed for 10 minutes 4. While spinning, make a wet mount of the residue and stain with iodine. View this under a microscope. The blue black color indicates starch. Record what is seen in the table below. 5. After 10 minutes, stop the centrifuge. Sketch the centrifuge tube using colored pencils. Number each layer to conform to the table below. 6. Using a pipette, carefully remove several drops of the material from the lightest portion at the top of the centrifuge tube. Place a drop of this material on a microscope slide. Stain with iodine and add a cover slip 7. Observe the material under low and high power. Record results below 8. Repeat steps 6 and 7 for the other layers Sketch Below
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Table Layer
Labeled sketch of cell parts observed
Resul ts of starc h test
Cell Part
Residue
Fibers
1 (top)
CellWall
Function
2 3 4 (bottom)
Leucopla st
Analysis 1. Which plant parts were not separated using this technique? Give reasons why you might not have been able to see these cell parts.
2. Rank the cell parts you observed in order of density from least dense to most dense. Explain how you knew the relative density of the cell parts. 45
Lab 8 Error and Uncertainty Analysis (a) Normal or Random Variation
In biological investigations, errors can be caused by changes in the material used, or by changes in the conditions under which the experiment is carried out. Variation in biological materials: Biological materials are notably variable. For example, the water potential of potato tissue may be calculated by soaking pieces of tissue in a range of concentrations of sucrose solutions. However, the pieces of tissue will vary in their water potential, especially if they have been taken from different potatoes. Pieces of tissue taken from the same potato will also show variations in water potential, but they will probably show a normal variation that is less than that from samples taken from different potatoes. Random errors can, therefore, be kept to a minimum by careful selection of material.
46
Higher Variation
Lower Variation
Changes in the conditions under which the experiment is carried out: Random errors can also be kept to a minimum by careful control of variables. For example, you could use a water bath to reduce the random fluctuations in ambient temperature during the experiment. (b) Human errors (mistakes)
Human errors can occur when tools, instruments or protocols are used or read incorrectly. For example, a temperature reading from a thermometer in a liquid should be taken after stirring the liquid and with the bulb of the thermometer still in the liquid. Thermometers (and other instruments) should be read with the eye level with the liquid in the thermometer (reading needle) to prevent parallax error. Human errors can be systematic, because the experimenter does not know how to use the apparatus properly, or they can be random, because the power of concentration of the experimenter is fading. These random may occur when people have to make a large number of tedious measurements. Automated measuring, using a data logger system, can help to reduce the likelihood of this type of error. Alternatively, the experimenter can take a lot of breaks (c) The act of measuring
When a measurement is taken, this can affect the environment of the experiment. For example, when a cold thermometer is put into a test tube with only a small volume of warm water in it, the water will be cooled by the presence of the thermometer, or when the behaviour of animals is being recorded, the presence of the experimenter may influence the animals’ behaviour. (d) Systematic errors
Systematic errors can be reduced if equipment is regularly checked or calibrated to ensure that it is functioning correctly. For example, a thermometer should be placed in an electronic water bath to check that the thermostat of the water bath is correctly adjusted. A blank
47
should be used to calibrate a colorimeter to compensate for the drift of the instrument. Another way to minimize the effect of systematic errors from equipment is to use the same piece of equipment for measuring. This will eliminate any potential discrepancies between similar measuring devices. (e) Degrees of precision and uncertainty in data Students must choose an appropriate instrument for measuring such things as length, volume, pH and light intensity. This does not mean that every piece of equipment needs to be justified, and it can be appreciated that, in a normal science laboratory, the most appropriate instrument may not be available. For the degrees of precision: •
For rulers and instruments with digital displays: The degree of precision is plus or minus (±) the smallest division on the instrument (the least count). • For most other instruments such as thermometers, graduated cylinders, pipettes etc, the degree of precision is plus or minus (±) one half the smallest division For example, a pipette measurement of 34.1 ml becomes 34.10 ml (± 0.05 ml). Note that the volume value is now cited to one extra decimal place so as to be consistent with the uncertainty. • For measurements that do not fall into the categories described above, such as reaction time in starting and stopping a timer or using a manual wind anemometer, students should do their best to quantify estimated uncertainty by best personal estimate. (f) Replicates and samples
Biological systems, because of their complexity and normal variability, require replicate observations and multiple samples of material. As a rule, the lower limit is five measurements, or a sample size of five. Very small samples run from 5 to 20, small samples run from 20 to 30, and big samples run from 30 upwards. Obviously, this will vary within the limits of the time available for an investigation. Some simple investigations permitting a large sample, or a large number of replicate measurements, could be included in the scheme of work to reinforce this point. It is also possible to use class data to generate sufficient replicates to permit adequate processing of the data. However, each student must have been personally involved in the data collecting process, and their own set of raw data should be presented and clearly identified.
48
Where sufficient replicates have been carried out, then the calculation of the standard deviation of the mean is expected. Another statistic, the standard error of the mean to derive confidence limits, may also be calculated. The standard error is not expected, but it would be an acceptable alternative to the standard deviation. In order to establish the significant difference between two samples, it may be possible to calculate a student’s t-test. However, this would not be systematic as it is only appropriate to use this statistic when certain conditions apply (interval data, sample sizes greater than five, normal distribution of the population). Where these statistics are calculated from a preset menu on a calculator or computer, a worked example will not be expected, but the data should be presented in such a way that the steps in the processing can be clearly followed. Students should be made aware that, if a reading is particularly different from the others, it may be left out of the processing and analysis. However, students must always justify why they have chosen to do this.
Error and Uncertainty Analysis Lab Activity: Error Analysis
49
In the activity designed to test the affect of mediation on heart rate, discuss possible sources of error for each of the types of error and describe could minimize the error.
50
Normal V a ri a ti o n
Possible E r r o r
Way(s) t o M i n i m i z e
Human e rr o rs ( m is t a k e s)
Possible E r r o r
Way(s) t o M i n i m i z e
The act of m e a s u ri n g
Possible E r r o r
51 Way(s)
Lab 9 Diffusion and Osmosis Key Terms Diffusion: The spontaneous tendency of a substance to move down its concentration gradient from a more concentrated to a less concentrated area. Osmosis is the diffusion of water. Hypotonic solution: In comparing two solutions, it is the one with the lower solute (and therefore higher water) concentration Isotonic solution: Having the same solute (and water) concentration as another solution. Hypertonic solution: In comparing two solutions, it is the one with the higher solute (and therefore lower water) concentration. Objectives • Measure the effects of various concentrations of solutes on the process of osmosis • Differentiate between hypotonic, isotonic, and hypertonic environments • Examine the effects of osmosis on plant cells Materials (for each group) • 6 plastic cups (marked: pure water, 0.2, 0.4, 0.6, 0.8, 1.0) • 100 ml each of distilled water, 0.2 Molar, 0.4 Molar, 0.6 Molar, 0.8 Molar and 1.0 Molar Sucrose solutions • 24 potato cylinders Procedures Part A: Estimate Cell Solute Concentration 1. Pour 100 ml of distilled water into the cup marked “ pure water” 2. Repeat this for the remaining Sucrose solutions in the remaining 5 cups 3. Record the temperature of each of the cups and record in Table 1 under “Initial Temperature. 4. Cut 24 potato cylinders to a length of 3 cm each. (be as accurate as possible!) Remove any skin from the cylinders 5. Carefully weigh 4 of the cylinders together and record their mass in Table 1 under Water - Initial Mass 6. Place these cylinders in the cup marked “Water” 7. Repeat Steps 5 and 6 for each of the remaining Sucrose cups 8. After 24 hours, record the temperature of the 6 cups on Table 1
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9. Take the cylinders out of the Water cup and carefully blot them dry with a paper towel. 10. Weigh them and record their mass under Water - Final Mass Table 1 11. Repeat Steps 9 and 10 for each of the remaining Sucrose cups 12. Calculate the change in mass and % change in mass for each of the solutions 13. Graph the class average data from Table 2 on the bottom graph. Draw a best fit regression line and show SEM statistical error bars also Table 1: Mass Change in Potato (Solanum tuberosum) Tissue in Various Sucrose Solutions for Trial Group #________ Solutio n (Molar Sucros e)
Initial Temp (deg. C) +/______
Final Temp (deg. C) +/______
Initial Mass (grams) +/______
Final Mass (grams) +/______
Change in Mass(gra ms)
% Change in Mass
+/- ______
Water 0.2 Molar 0.4 Molar 0.6 Molar 0.8 Molar 1.0 Molar Table 2: Mean % Change in Mass in Potato (Solanum tuberosum) tissue in various Sucrose Concentrations Trials
Water
0.2 Molar
0.4 Molar
0.6 Molar
0.8 Molar
1.0 Molar
1 2 3 4 5 6
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7 Mean St.Dev SEM SEM = Standard Error of Mean = St. Dev/ square root n
Analysis Questions: 1. Based on the X-intercept of your graph from the best fit line, what is your estimate of the cytoplasm solute concentration of these potato cells? ……………………………………………………………………………………………… 2. Based on your Standard Error of the Mean Values, how confident are you in the reliability of these data? Why? …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… ……………………………………………………………………………………… 3. Using the Errors and Uncertainties/Data Processing Handout I gave you as a guide, discuss in as much detail as possible, any errors you made or observed others making while collecting data. Also discuss briefly the measurement uncertainties associated with your experiment. …………………………………………………………………………………………… …………………………………………………………………………………………… ……………………………………………………………………………………………
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…………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… ……………………………………………………………………..………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………..………… 9.
Discuss in as much detail as possible, ways that the errors and uncertainties discussed above could be minimized or eliminated in future experiments. Also discuss any other ways you can think of that the reliability of the data could be improved.
…………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… ……………………………………………………………………………………………
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…………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………..………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………..………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………..………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………..…………
Procedures Part B: First Complete Lab Report - Compare Cell Solute Concentration of 2 types of potato’s The purpose of this lab is to give you a template for writing up your first full lab report in IB Biology. The actual Lab Report will be typed up in a Word document. Use this worksheet as a way of organizing what you will write about in the report. The bold headings below represent
56
the headings you must have in your report. Blank spaces and ……. are left for you to determine. Research Question: Which has a higher cytoplasm solute concentration, sweet potato cells or Yukon gold potato cells? Background: • • •
(approx. 1-2 paragraphs)
Include information about what diffusion is and how it works (including tonicity and the role if plays in diffusion and movement of water in and out of cells) Try to find information about sweet potatos and Yukon gold potatos that will enable you to justify a hypothesis Cite the Campbell textbook at least once and cite one internet source using APA method (See Appendix of this lab manual for direction on this)
Hypothesis My hypothesis is…….. The justification for my hypothesis is…….. Variables My independent variable is…. I will vary my independent variable by… My dependent variable is…. I will measure my dependent variable by…. (Be careful to explain this in sufficient detail!!!!)
I will form a conclusion to the research question by….. My controlled variables are…… I will control each of these variables by……. Materials •
Distilled Water OR Tap Water (your choice!!)
•
List all other materials used in the experiment
Procedures • • •
You may use the former procedures and simply revise them These procedures must be numbered – 1, 2, 3….. They must resemble a good cake recipe in their level of detail
57
Data Table 1 – Raw Data (This will combine the Yukon Gold data your group already collected with the new data) • Use former Table #1 as template and revise this table to clearly express all the raw data you collected for both Yukon and Sweet Potatoes (inc. change in mass) • Include the units and measurement uncertainty. • Measurement uncertainties must always “agree” with data • Include a note under each table explaining how uncertainties were determined . Table 2 – Processed Data (this will include the entire classes data as before) • • •
Use former Table #2 and revise this table to clearly express all the processed data, including means, standard dev, and SEM Include the units and measurement uncertainty. Measurement uncertainties must always “agree” with data
Figure 1: Scatter Plot with 2 Regression lines on Graph Paper • • • • • •
Your x axis should be sucrose molarity Your y axis is % change in mass Show the equation for both regression lines on the graph Show SEM error bars for each mean Provide units and uncertainties (uncertainties on y axis only) Give a descriptive title
Conclusion ( Should be approx. 1 paragraph) • • •
State whether the data did/did not support the hypothesis. Do not say that the data “proves” or “disproves” the hypothesis Cite the actual data to justify the above statement Make any comments you deem appropriate regarding the reliability of the data (in other words, do any of your data points seem to be outliers? Do any of the means seem to have a higher than average SEM?
Evaluation (Should be approx 1-2 paragraphs)
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• • • •
In general, refer back to Lesson 4 in Lab Exercise Packet to evaluate errors and uncertainties that may have affected the accuracy of your results In addition to this, try to think of any variables that you may have failed to control that may have negatively affected the results. If there were aspects of your experimental procedure that could have been improved upon, discuss these. Discuss how significant were each of the errors and uncertainties discussed above in affecting the quality of your data and your ability to form a strong conclusion about your results?
Improving the Investigation (Should be approx 1-2 paragraphs) • •
Practical suggestions for improvements should be based on the weaknesses and limitations identified above The modifications proposed should be realistic and clearly specified.
Checklist for Design Portion of Labs Score__________/40
Total
Experimental Question ___ Stated a clear Experimental Question? (1) Background/Introduction ____ Adequately explained the mechanisms associated with the experiment? (0.5) ____ Provided some information on the organisms/substances used? (0.5) ____Included at least two in-text sources using APA method? * (0.5) ____Sources in a bibliography? * (0.5) * Must have in-text reference and Reference at end of lab
Hypothesis ____Stated a valid hypothesis? (2)
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____Justified in a scientific way why hypothesis is believed to be correct? (1) Materials ____Listed all materials used in the experiment? (2) ____All metric measurements? (1) ____Included any drawings necessary? (2) Variables ____Listed Independent/Manipulated and Dependent/Responding variables? (2) ____Clearly explained how to precisely vary indep. variable? (3) ____Clearly explained how to precisely measure dep. variable? (3) ____Clearly described all my controlled variables? (4) _____Clearly explained how to control these variables * (6) * Be detailed here. Indicate which are physically controlled and which are controlled by allowing them to vary together
Methods/Procedures ____Described exactly how to repeat the experiment? * (4) ____Specified adequate sample size for each experimental group? (1) ____Included in the method all necessary instructions for controlling unwanted variables as described in the variables section? (5) ____Explained how to precisely collect data for the dependent variable? (4) * Use command phrases. No interpretation or analysis needed – just what you did during the experiment. Don’t forget to say collect and record date etc.
Checklist for Data Processing/Presentation and Conclusion/Eval. Total Score _______/ 35 Tables ____ table headings are clearly labeled with units and uncertainties (2) ____method for determining uncertainty is clearly noted under each table (1) ____any outliers removed from raw data table were noted and justified (1) 60
____tables have clear descriptive titles (1) ____ table clearly expresses all the data collected (2) ____measurement uncertainties always “agree” with data (2) Graph ____ Data points were accurately and neatly plotted (1) ____ Regression line(s) is accurate (1) ____ x axis and y axis clearly labeled with variable and units (uncert. on y axis only) (2) ____descriptive title (1) Conclusion ____clear statement of whether the data did/did not support the hypothesis.(not prove/disprove) (2) ____ actual data is cited to justify the above statement including trends when appropriate (3) ____ if necessary, comments made regarding the reliability of the data (1) Evaluation ____Errors and uncertainties were rigorously identified and discussed (2) ____uncontrolled variables were identified and evaluated (2) ____weaknesses in experimental procedure were identified and discussed (2) ____ The significance of each of the above errors/uncert., uncontrolled vars and procedure weaknesses were realistically assessed.(1) Improving the Investigation ____Practical suggestions for improvements were suggested based on the weaknesses and limitations identified above (4)
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____The modifications proposed were realistic and clearly specified. (4)
Lab 10 Enzymes Introduction An extremely important enzyme reaction that occurs in your blood constantly is the conversion of hydrogen peroxide (H2O2) a cellular waste, into water and oxygen, harmless byproducts. This reaction is outlined below H2O2 -------------catalase-------------- H2O + ½ O2 (cellular waste) Objectives • Observe the reaction of the enzyme catalase with its substrate hydrogen peroxide • Test the affect of extreme temperature on enzyme activity rate • Test various types of tissue for the presence of catalase • Measure the rate at which the enzyme catalase converts hydrogen peroxide to its products Procedures Class Demonstration:
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I will add some catalase to hydrogen peroxide in a cup. What did you observe? What is the gas being emitted? …………………………………………………………………………………………… ………………………………. Part 1: Effect of Extreme High Temperature on Enzyme Activity 1. Using the syringe marked “H”, add 10 ml of hydrogen peroxide to one of the small plastic cups. 2. Add one ml of the boiled catalase to the cup 3. Mix the contents by swirling and observe for 30-60 seconds. Record your observations below. Explain your observation …………………………………………………………………………………………… ………………………………. …………………………………………………………………………………………… ……………………………….
Part 2: Testing Enzyme Activity in Potatoes 1. Macerate (cut and crush) the potato in the glass beaker using the metal instrument 2. Using the syringe marked “H”, add 10 ml of hydrogen peroxide to the glass beaker/potato 3. Record your observations in the space below. Suggest what might happen if the potato was boiled before adding the hydrogen peroxide …………………………………………………………………………………………… ………………………………. …………………………………………………………………………………………… ……………………………… …………………………………………………………………………………………… ………………………………
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4. What does this reaction suggest about the presence of catalase in potato cells? Do you think catalase is present in pineapple cells or earthworm cells? Why? …………………………………………………………………………………………… ……………………………… …………………………………………………………………………………………… ……………………………… Part 3: Establishing a baseline – Determining the amount of hydrogen peroxide in a 1.5% solution Titration Basics: The basic idea behind titration is that you slowly add a certain chemical to a solution until the chemical you are trying to measure is all used up. This is always accompanied by some visual change in the solution. Therefore, the amount of titration chemical (in this case potassium permanganate) you add before the chemical is used up (in this case hydrogen peroxide) is an indirect measure of the amt. of hydrogen peroxide present. The visual change that indicates all the hydrogen peroxide is used up is that the solution stops being clear.T
The baseline hydrogen peroxide concentration in a 1.5% solution is: …………………………………………………………………………………………… ……………………………… Part 4:
Rate of hydrogen peroxide decomposition by enzyme
catalysis 1. Using the syringe marked “H”, add 10 ml of hydrogen peroxide to one of the small plastic cups. 2. Using a pipet, add 1 ml of catalase and swirl gently for ______ seconds 3. Immediately add 10 ml of sulfuric acid using the syringe marked “S” This will stop the reaction. 4. Using the syringe labeled “T”, transfer 10 ml of the mixture into the larger plastic cup. 5. Fill a titration syringe to the 10 ml mark with potassium permanganate. 6. Slowly add on drop of potassium permanganate and swirl the solution to mix Continue to add potassium permanganate until the solution turns pink or brown. The amt. of potassium
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permanganate used is proportional to the amt. of hydrogen peroxide that was present. Record the amt used below. …………………………………………………………………………………………… ……………………………… Reaction Time
Ml of KMnO4
10 Sec 30 Sec 60 Sec 90 Sec 120 Sec 150 Sec 180 Sec
Lab 11 Cellular Respiration Introduction Research Question: What affect does temperature have on the rate of cellular respiration?
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Group #___________ What you need to write up in your “abbreviated” Lab Report 1. Hypothesis section for the research question above which includes a scientific justification for the hypothesis 2. Variable section which outlines the following • Independent Variable and how this variable was manipulated • Dependent Variable and how this variable was measured • All the controlled variables that we controlled and how they were controlled. 3. Tables and Graphs (as described below) 4. Conclusion Section which discusses whether the data supports or does not support the hypothesis using the t-test analysis Objectives • • • • •
To measure the effect of temperature on the rate of respiration in germinating peas To accurately identify and describe the independent and dependent variables in the experiment and how they are varied and measured respectively. To identify all the controlled variables in the experiment and how they were controlled To accurately collect the data and present it in table and graph form To form an appropriate conclusion from the data
Procedures 1. Choose 10 germinating peas and record their mass. 2. Record the volume of these peas by putting them in a graduated cylinder with water. (dry them off when done) 3. Place a cotton ball in one of the respirometer vials 4. Add 1 ml of potassium hydroxide to the cotton ball 5. Place a rayon ball on top of the cotton ball 6. Add the peas to the respirometer and assemble (make sure rubber stopper is tight) 7. Using the graduated cylinder, determine how many glass beads equal the volume of the 10 peas. 8. Repeat steps 3-6 using glass beads instead of peas
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9. Repeat steps 1-8. You should now have 4 respirometers, 2 with germinating peas and 2 with glass beads. 10. Place metal washers over the end of each of the respirometers 11. Place one respirometer with peas and one respirometer with beads in the room temperature water bath. (rest tip on edge) 12. Place the remaining 2 respirometers in the ice bath (rest tip on edge) 13. Wait 10 minutes 14. After 10 minutes, place a drop of red dye on the opening of each of the respirometer and carefully submerge them 15. Wait 5 minutes 16. Record your initial respirometer reading and water temperature and continue recording respirometer and temperature readings every 5 minutes for 30 minutes. Check list for Variables Section Variables ____Listed Independent/Manipulated and Dependent/Responding variables? ____Clearly explained how the indep. variable was precisely varied? ____Clearly explained how the dependent variable was measured? This discussion must include how the units of the dependent variable were processed and why they had to be _____ Clearly described all the controlled variables? _____Clearly explained how these variables were controlled Tables Required in the report Table 1: Create a Raw Data Table Below that enables you to clearly record all the data that you collected on the day of the experiment Table 2: Create a Processed Data Table that contains the corrected values for respirometer readings that you collected for Time 0min, 5min, 15 min etc. This is done by adding or subtracting the values from the glass bead respirometer if they varied during the time interval. Table 3 Create another Processed Data Table that contains corrected hourly respiration rates per gram of pea for all 6 groups for both room temperature and icebath as well as the mean values, St.Dev and SEM values. This table should also contain the t-test results and a description
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*Use the upper and lower bounds method to calculate uncertainty using ml and gram uncertainties (assume no uncertainty for minutes)
Figures Figure 1: Graph the oxygen use for your peas in both the room temperature bath and the ice bath over time using the corrected values above in Table 2. You should have two lines on your graph. Be neat!!!!!!! Figure 2: Graph the mean values for room temp. and ice bath respiration rate for all the groups. Create a bar graph of the means with SEM bars Check lists for Data Section Tables ____ table headings are clearly labeled with units and uncertainties (2) ____ method for determining uncertainty is clearly noted under each table (1) ____any outliers removed from raw data table were noted and justified (1) ____ upper and lower bounds method is accurately used (2) ____ tables have clear descriptive titles (1) ____ tables clearly express all the data collected (4) ____measurement uncertainties always “agree” with data (2) Figures ____ data points were accurately and neatly plotted and graphs neat and legible (2) ____ data is appropriately expressed in the 2 Figures (4) ____ x axis and y axis clearly labeled with variable and units (uncert. on y axis only) (2) ____descriptive titles (1) T-test Analysis ____ T-test analysis was correct (4)
The t Test In biology you often want to compare two sets of replicated measurements to see if they are the same or different. For example 68
are plants treated with fertilizer taller than those without? If the means of the two sets are very different, then it is easy to decide, but often the means are quite close and it is difficult to judge whether the two sets are the same or are significantly different. The t test compares two sets of data and tells you the probability (P) that the two sets are basically the same. P varies from 0 (not likely) to 1 (certain). The higher the probability, the more likely it is that the two sets are the same, and that any differences are just due to random chance. The lower the probability, the more likely it is that that the two sets are significantly different, and that the differences are real. Where do you draw the line between these two conclusions? In biology the critical probability is usually taken as 0.05 (or 5%). This may seem very low, but it reflects the facts that biology experiments are expected to produce quite varied results. So if P > 0.05 then the two sets are the same, and if P < 0.05 then the two sets are different. For the t test to work, the number of repeats should be as large as possible, and certainly > 5. In Excel the t test is performed using the formula: =TTEST (range1, range2, tails, type) . For the examples you'll use in biology, tails is always 2 (for a "two-tailed" test), and type can be 1 or 2 depending on the circumstances.
The usual form of the t test is for "unmatched pairs" (type=2), where the two sets of data are from different individuals. For example the number of white blood cells of 5 patients infected with a parasite were compared with 5 unaffected individuals. The results are shown in the spreadsheet on the right, and the means and confidence limits have been worked out as usual. The infected patients certainly have a smaller mean white blood cell count, but is it significantly smaller? Cell B9 has the t-test probability, which is 0.014. This probability is smaller
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than the critical value of 0.05, so the two groups are significantly different, and the infected patients really do have significantly fewer white cells than normal.
The other form of the t test is for "matched pairs" (type=1), where the two sets of data are from identical individuals. A good example of this is a "before and after" test. For example the pulse rate of 8 individuals was measured before and after eating a large meal, with the results shown in the left. The mean pulse rate is certainly higher after eating, but is it significantly higher? Cell B12 has the t-test probability, which is a tiny 0.000065, and indicates that the difference between the before and after results is very highly significant. If we had used the normal unmatched pairs t-test, we would have obtained a P of 0.225, which is higher than 0.05, so indicates that the apparent increase in pulse rate with eating is not significant! This shows the importance of choosing the right test. Checklist for Conclusion Section Conclusion ____clear statement of whether the data did/did not support the hypothesis.(not prove/disprove) ____ the above statement must include a correct interpretation of the t-test results ____ In support of the conclusion, actual data is cited to justify the conclusion including trends and means when appropriate ____ Using SEM calculations, comment on the reliability of the data
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Analysis Questions 1. Why did we need to calculate the respiration rate per gram of pea mass and not just present the respiration rate per pea? 2. Why did we have to use the respirometers with the glass beads? An Explanation of how to correct your respiration data Peas Beads Readin Diff. Corr. Readin Diff. g Diff. g
Roo m Tem p Time 0 .83 5 .825 10
.785
15 20
.75 .71
25
.685
-----. 005 . 045 .08 .12
-----.00
.88 .875
-------.005
.04
.875
-.005
.075 .155
.875 .915
-.005 +.03 5 +.05
. .195 .93 145 30 .645 . .245 .94 +.06 185 Weight of the peas __________ grams Temp._______ Cold Tem p Tim e 0 5 10
Peas Beads Readin Diff. Corr. Readin Diff. g Diff. g
.850 .830 .830
------ ------
.840
-------
.840 .860
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15 20 25 30
.840 .840 .830 .830
.880 .890 .880 .880
Weight of the peas __________ grams Temp__________ ** Students: If the corrected values goes negative, enter it as 0.000
Lab 12 Photosynthesis Introduction Photosynthesis is arguably the most important biological process in existence simply because – without photosynthesis – life as we know it would not exist. The process of photosynthesis is the chemical pathway by which all plants and some protists and monerans make food from carbon dioxide, water and sunlight. The entire photosynthetic pathway is a complex series of enzyme transformations that take place in chloroplasts. During the transformation, hydrogen from water is added to molecules of carbon dioxide to make carbohydrates. The generalized equation for this process is given below: 6CO2+ 6H2O>>>>>>>>>>>>>>>>>>>>>C6H12O6+ 6O2 carbon dioxide + water --------------> carbohydrate + oxygen Aquatic plants and photosynthetic microorganisms release the oxygen into the water where it dissolves, forming dissolved oxygen. There is a limited amount of O2 that water can hold, sowhen photosynthesis have been classified into two main sets: the light-dependent reactions and the light independent reactions. The set classified as the light-dependent reactions at its most basic level is involved in capturing light energy into energized molecules that are then used in the set of light-independent reactions to fuel the synthesis of sugars from carbon dioxide and water. The light-dependent reactions are so named the concentration reaches a 72
certain level (saturation), the O2 diffuses (or out-gases) into the air.The series of biochemical events that comprise Objectives • To isolate various chlorophyll pigments from leaf tissue • To calculate an Rf value for the pigments using paper chromatography • To measure the rate of photosynthesis at various light intensities • To explain the relationship between light intensity and photosynthetic rate Procedures Part A: Isolating Photosynthetic Pigments using Paper Chromatography 1. Obtain a glass cylinder which has about 1 cm of solvent at the bottom. 2. Obtain a piece of chromatography paper and draw a light pencil line about 2.0 cm from the bottom of the paper. 3. Place a small section of leaf on the top of the pencil line. Use the ribbed edge of a coin, press and lightly rub a line of green pigment onto the pencil line.. Be sure the pigment line is on top of the pencil line. Use a back and forth movement exerting firm pressure through out. 4. Place the chromatography paper in the cylinder and replace the cap. Do not allow the pigment to touch the solvent. 5. Cover the beaker. When the solvent is about 1 cm from the top of the paper, remove the paper with tweezers and lay it on a paper towel. Immediately mark the location of the solvent front before it evaporates. 6. Mark the bottom of each pigment band. Measure the distance each pigment migrated from the bottom of the pigment origin to the bottom of the separated pigment band. Record the distance that each front, including the solvent front, moved in the table below. Depending on the species of plant used, you may be able to observe 4 or 5 pigment bands.
Distance moved by Pigment Band (millimeters) Band Number Distance (mm) Band Color 1 (top) 2 3 4
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5 Distance Solvent Front Moved _________________ Analysis of Results: The relationship of the distance moved by a pigment to the distance moved by the solvent is a constant called Rf . It can be calculated for each of the pigments using the formula: Rf =
Distance pigment traveled Distance Solvent traveled
Record your Rf values in the table below. ____________________ = Rf for carotene (yellow to yellow -orange) _______ ____________________ = Rf for xanthophyll (yellow) _______ ____________________ = Rf for Chlorophyll a (bright green to blue _______ green) ____________________ = Rf for Chlorophyll b (yellow green to olive _______ green)
Topics for Discussion 1.
What does the Rf value represent? If you were to perform your experiment on a chromatography strip that was twice the length of the one you used, would your Rf values still be the same?
…………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …During the summer, leaves are generally green. What would you hypothesize that this indicates about the role of green light wavelengths and the photosynthetic process?
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…………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… ……………………………………………………………………………………………… 2.
Why do plant pigments consist of 3 different types of pigments and not just one type?
…………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… ……………………………………………………………………………………………… 3.
Design an experiment to test the hypothesis that plants use light from the blue and red wavelengths and does not use light from the green wavelengths in photosynthesis
…………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… ……………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… ………………………………………………………………………………………………
Part B: Measuring the rate of Photosynthesis at different light intensities Materials 1 PASPORT Xplorer 1 Dissolved Oxygen Sensor 1Temperature Probe 1 Photosynthesis Tank 75
1 1 1 1 1 1 1
Cloth, opaque, about 50 cm by 50 cm sprig Elodea (or equivalent aquatic plant) L Ice, crushed or cube (optional) Lamp, 100 W (or equivalent) Magnetic stirrer and stir bar Rubber stopper, #3 (included with Photosynthesis Tank) L water
Experimental Setup
Lab Report -: Rate of Photosynthesis for an Aquatic Plant Name ________________________________ Date ___________ Pre-Lab Questions 1. What will happen to the concentration of dissolved oxygen in the water if the plant is exposed to bright light? ___________________________________________________________ 2. What will happen to the concentration of dissolved oxygen in the 76
water if the plant is put into a dark environment with no light? ___________________________________________________________
Data
Make a sketch of one run of dissolved oxygen concentration versus time, including labels for the y- and x-axes. Label the section of the graph that shows the light-dependent reaction of photosynthesis.
Data Write a description of the pattern of the graph: • Light: • Darkness: Data Table Light Condition
Dissolved Oxygen (mg/l)
Ambient Light Bright Light Darkness
Questions 1. What happens to the level of dissolved oxygen when the plant is in bright light? Explain why this happens.
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2. What happens to the level of dissolved oxygen when the plant is in darkness? Explain why this happens.
Lab 13 Plant Transpiration- Internal Assessment Introduction Part A: Write a design for a plant transpiration lab that evaluates the effect of one factor on plant transpiration Basic Information You need to know to design your experiment
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• • • •
• • • • •
You will be using privet plants (Ligustrum vulgare) You need to evaluate one environmental condition for your independent variable (light intensity, temp, humidity or wind speed or another variable of your choosing) You will have 5 trials in each experimental group (15 plants total) I have the following instruments to measure variables: humidity meter, anemometer (measures wind speed), thermometers, potometer, light intensity meter (measures light intensity in footcandles), thermometer I have large plastic enclosures that will fit 5 potometers inside to control conditions easier The device for measuring transpiration rate is called a potometer (a sketch or picture of this should be included as Figure 1 in your design- this should be accurately annotated) You need to think a lot about how you are going to make the conditions the same in each experimental group and maintain them for at least 20-30 minutes- this is not easy You need to think a lot about what variables can affect transpiration rate and how you are going to control them You need to think about how you will minimize experimental errors and uncertainties (review your errors and uncertainties handout for this)
Checklist for Design Portion of Labs Experimental Question ___ Stated a clear Experimental Question? (1) Background/Introduction ____ Adequately explained the mechanisms associated with the experiment? (0.5) ____ Provided some information on the organisms used? (0.5) ____ Included at least two in-text sources using APA method? * (0.5) ____ Sources in a bibliography? * (0.5) * Must have in-text reference and Reference at end of lab
Hypothesis ____Stated a valid hypothesis? (2)
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____Justified in a scientific way why hypothesis is believed to be correct? (1) Materials ____ Listed all materials used in the experiment? (2) ____ All metric measurements? (1) ____ Included any drawings necessary? (2) Variables ____Listed Independent/Manipulated and Dependent/Responding variables? (2) ____Clearly explained how to precisely vary indep. variable? (3) ____Clearly explained how to precisely measure dep. variable? (3) ____Clearly described all my controlled variables? (4) _____Clearly explained how to control these variables * (6) * Be detailed here. Indicate which are physically controlled and which are controlled by allowing them to vary together
Methods/Procedures ____Described exactly how to repeat the experiment? * (4) ____Specified adequate sample size for each experimental group? (1) ____Included in the method all necessary instructions for controlling unwanted variables as described in the variables section? (5) ____Explained how to precisely collect data for the dependent variable? (4) * Use command phrases. No interpretation or analysis needed – just what you did during the experiment. Don’t forget to say collect and record date etc. Just like a Cookbook!!
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Part B: Processing and Presentation of Data from Transpiration Lab, Conclusion, Evaluation, and Suggestions for Improvement General Points • Do not turn in the design that you’ve already turned in –only turn in your Data, Conclusion, Evaluation, and Suggestions for Improvement. • Clearly note which data you collected personally. • In the Evaluation and Suggestions for Improvement Sections, critique your original design. This evaluation should include revelations of variables that – in hindsight – you failed to control or could have been controlled better and/or ways that you could have minimized errors and/or uncertainties. It can also include procedures that you may have changed or done differently than you described in your design – decisions that you made “on the spot” during the experiment that you thought would improve the results. Checklists Data Tables • Tables must be labeled Table 1, Table 2 etc. and have descriptive titles • Tables must be done in Word, not in Excel and must clearly express all data • All Raw Data (pipettes, leaf area, humidity, temp etc.) must have measurement uncertainty • Uncertainties must always agree with the data in the raw or processed data tables • Each table must have a clear note below it explaining how measurement uncertainties were determined (pipette readings, leaf area estimates and transpiration rates etc.) • Processed data table must calculate final transpiration rate measurement uncertainty with the Upper and Lower Bounds Method (See instructions on this handout) • Mean and SD and SEM should be on the processed data table with a note below the processed data table explaining how SEM is calculated • Your individual data must be CLEARLY indicated • If you remove any outliers from the mean and SEM calculations, keep them in raw data and have a note in processed data saying why they were removed from the mean and SEM calculations. Ex. Trial 2 for the 80% humidity trial was removed because…….
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Graphs *** I will try to allow time in class to make your graphs with the computers in cart*** • Graph or graphs should be labeled as Figures (ex. Figure 1, Figure 2 etc) and must have descriptive title • X and Y axis of graph has variables and units clearly indicated ex. Wind Speed (RPM) • note on or under graph that error bars are Standard Error of the Mean (SEM) Conclusion • • • •
clear statement of whether the data did/did not support the hypothesis.(not prove/disprove) actual data is cited to justify the above statement including trends when appropriate comment on the reliability of the data based on SEM bar width Use one other way to statistically analyze the data. Include a description of how you calculating this statistic (Excel)
Evaluation • • • •
Errors and uncertainties were rigorously identified and discussed uncontrolled or poorly controlled variables were identified and evaluated Based on the above, any weaknesses in experimental procedure were identified and discussed The relative significance of each of the above errors/uncert., uncontrolled vars and procedure weaknesses were realistically assessed (ie. how much of an affect will they have on the results and why)
Improving the Investigation • •
Practical suggestions for improvements were suggested based on the weaknesses and limitations identified above The modifications proposed were realistic, detailed, and clearly specified.
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Appendix A: Statistics in Biology The best guide I’ve ever found for how to use statistics in biology is Biological Statistics Made Easy using Excel by Neil Millar. It was published in the School Science Review, December 2001, 83(303) and can be found on the following website: http://www.utdallas.edu/~serfling/3332/Biology_statistics_made_simple _using_Excel.pdf Standard Deviation and Error Bars • • •
•
Error bars are a graphical representation of the variability of data You must be able to calculate the mean and St.Dev of a set of values Must know that the term Standard Deviation is used to summarize the spread of values around the mean and that 68% of values fall within one St.Dev. from the mean- 95% of values fall within two St. Deviations from the mean. Know that a small St. Dev indicates that the data is clustered closely around the mean and a large St. Dev means it is widely spread
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Graph A
Graph B Graph A has a higher standard deviation than Graph B
T-Test
One form of the t test is for "matched pairs" (type=1), where the two sets of data are from identical individuals. A good example of this is a "before and after" test. For example the pulse rate of 8 individuals was
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measured before and after eating a large meal, with the results shown in the left. The mean pulse rate is certainly higher after eating, but is it significantly higher? Cell B12 has the t-test probability, which is a tiny 0.000065, and indicates that the difference between the before and after results is very highly significant. The other form of the t-test is for “unmatched pairs” (type=2) This test would be used if the two sets of data are not from the same individuals. All t-tests in biology that we will use are two-tailed tests
The Null Hypothesis The Null Hypothesis (H0) is the hypothesis which says that there is no difference between two values. Therefore, if you accept the null hypothesis, there is no statistical difference, if you reject the null hypothesis, there is a statistical difference between the means
Pearson’s Correlation Coefficient Strong Positive Correlation Between Temp and Transpiration Rate
Plant Plant Plant Plant Plant Plant Plant Plant Plant Plant Plant Plant Plant
1 2 3 4 5 6 7 8 9 10 11 12 13
Temperature (deg. C) 20 20 20 20 20 30 30 30 30 30 40 40 40
Transpiration Rate (ml/m2/hr) 22.3 28.5 27.5 23.1 26.9 38.6 34.1 39.7 40.5 41.6 55.4 57.4 55.5
Pearson's Correlation Coeff.
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Plant 14 Plant 15
40 40
54.8 53.7
0.982266321
Strong Negative Correlation between seed size and time of germination for Corn seed
Seed Seed Seed Seed Seed Seed Seed Seed Seed Seed Seed Seed Seed Seed Seed Seed Seed Seed Seed Seed
#1 #2 #3 #4 #5 #6 #7 #8 #9 #10 #11 #12 #13 #14 #15 #16 #17 #18 #19 #20
Germination time (Days) 14 20 13 22 23 19 19 17 13 12 11 12 13 12 22 21 23 18 19 11
Seed Mass (grams) 1.2 0.7 1.3 0.6 0.6 0.7 0.7 1.8 1.2 1.3 1.5 1.2 1.3 1.2 0.5 0.7 0.6 0.7 0.7 1.5
Pearson's Correlation Coeff. -0.835605714
Appendix B: Report
How to Cite Sources in Your Lab
There are two places that you need to cite your sources 1. At the end of the lab report in the sources cited section (aka bibliography) 2. In the text of the lab report How to do the Sources Cited Section EASY BIB is the website that is easiest to use : http://www.easybib.com/ In general, Sources are cited in the following manner: Article in an Internet Periodical Author, A. A., & Author, B. B. (Date of publication). Title of article. 86
Title of journal, volume number (issue number if available). Retrieved: month day, year, from http://Web address. Nonperiodical Internet Document (e.g., a Web page or report) Author, A. A., & Author, B. B. (Date of publication). Title of article. Retrieved month date, year, from http://Web address. An article in a periodical (e.g., a journal, newspaper, or magazine) Author, A. A., Author, B. B., & Author, C. C. (Year). Title of article. Title of periodical, volume number, pages. Other General Rules: • If you can’t find the author’s name, Use the website name or, if it is a government agency use that • If you can’t find the date, put (nd.) where the date should appear. How to cite sources in the text In the text of the lab report, there is rarely a need to quote directly from an author in a science lab report as you might in an English paper. So don’t use quotation marks. Just paraphrase the idea or data you are citing and write it as described below. How citation should appear in the text of the Lab Report a. In a recent study of student performance (Jones, 1998), ... b. Jones (1998) compared student performance ... c. In 1998, Jones compared student performance ...
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