BIOL 208 Lab 8: Soil Respiration and Carbon Cycle (PDF)

# Lab 8: Soil Respiration and the Carbon Cycle (Weeks 9, 10 & 11 of Labs) ## Overview - Conduct a manipulative experiment to determine the effects of abiotic and biotic factors on soil respiration - Spanning Weeks 9, 10 and 11 of labs. - Part 1 (Week 9): Design and set up the experiment - Part 2 (...

# Lab 8: Soil Respiration and the Carbon Cycle (Weeks 9, 10 & 11 of Labs) ## Overview - Conduct a manipulative experiment to determine the effects of abiotic and biotic factors on soil respiration - Spanning Weeks 9, 10 and 11 of labs. - Part 1 (Week 9): Design and set up the experiment - Part 2 (Week 10): Analyze your data. Interpret and apply your results to the global carbon cycle. Conduct a multi-factor ANOVA analysis. - Part 3 (Week 11): Presentations. You will share your results as a verbal/visual group presentation. ## Objectives At the conclusion of this lab, participants will be able to: - Describe the role of soil respiration in the global carbon cycle - Identify several abiotic and biotic factors that affect soil respiration - Design a manipulative experiment to test how abiotic and biotic factors affect soil respiration - Calculate and interpret the results of a 2x2 ANOVA - Evaluate how multiple factors interact to cause synergistic effects - Apply your experimental results to how climate change may affect the global carbon cycle - Collaborare with a group to deliver a verbal/visual group presentation ## Connections to the lecture material - How does matter and energy move in an ecosystem? - What role does soil respiration play in the global carbon cycle? - How might climate change affect the global carbon cycle? ## The Carbon Cycle - The law of conservation of matter states that matter can neither be created nor destroyed within an isolated system. - Like all matter, carbon cycles through an ecosystem, changing form as it is assimilated, stored, and respired. - Carbon dioxide is assimilated by plants during the process of photosynthesis, and is converted to organic compounds such as simple sugars (e.g. glucose), or more complex carbohydrates (e.g. cellulose and lignin). - When plants are eaten, die, or decompose, the carbohydrates are broken down to CO2 and H2O through the process of respiration, which releases CO2 back into the atmosphere or water. - Respiration can be represented by the equation: C6H12O6 + 6 O2 → 6 CO2 + 6 H2O ## Major reservoirs - Rock (primarily sedimentary rock and fossil fuels such as oil and coal) - The atmosphere (carbon dioxide, methane, and water vapor) - The ocean (dissolved carbon) - All life (both living and dead, including broken-down matter in soil) ## Process of soil respiration and factors that affect it - Soil respiration is an important process in the carbon cycle. - Along with plant respiration, it is the primary way that carbon from organic matter (things that were once alive) is recycled back into CO2 to continue the carbon cycle. - If respiration is defined as the use of oxygen to break down organic compounds to release chemical energy, then **soil respiration** can be defined as the use of oxygen and/or the release of carbon dioxide by living organisms in the soil. - Soil respiration occurs when microorganisms in the soil decompose detritus, especially leaf litter. - **Decomposition of plant litter is a sequential process:** - **Leaching** of water-soluble minerals and simple sugars, such as glucose, from the material. - **Fragmentation of the litter** into smaller pieces, by mechanical or biological means. - **Mineralization**, the conversion of large organic compounds to simpler inorganic forms. ## Rate of soil respiration - Rates of soil respiration vary under a wide range of conditions. - Abiotic factors such as oxygen, temperature, and water availability all affect respiration rates. - Oxygen is a necessary molecule in the respiration equation, so the amount of oxygen present has a direct effect on the rate of soil respiration. - Temperature directly affects respiration, as it does for all metabolic processes. - An increase in temperature will increase the rate of metabolism for the microorganisms that break down detritus in the soil. ## The composition of the decomposing matter - Another factor that has a large effect on the rate of soil respiration is the composition of the decomposing matter. - Different types of carbon compounds will break down at different rates. - Simple sugars such as glucose are small and soluble in water, and are quickly lost by leaching or are immediately consumed by microbes. - Cellulose, a major plant carbohydrate, is a larger, more complex molecule, and is more difficult to decompose. - Cellulose digestion requires organisms that produce specific cellulose-degrading enzymes to break the chemical bonds and reduce it to simpler compounds that microbes are able to metabolize. - Breakdown of the cellulose molecule occurs extracellularly when enzymes are secreted to depolymerize the cellulose molecule. - An even larger and more complex plant polymer is lignin, which provides structural support in woody plants. - Lignin is usually only broken down by fungi by extracellular decomposition. - In leaf litter with a high lignin content, decomposition will occur over a long period of time. ## Carbon: Nitrogen (C:N) ratios - The ratio of carbohydrates to other compounds present in the organic matter, primarily nitrogen, can also affect the soil respiration rate. - This ratio is usually expressed as the C:N ratio. - Nitrogen is a necessary source of nutrients for decomposers. - Leaves with a low C:N ratio tend to be both higher in nutrients and softer, making them faster to decompose, while those with a high C:N ratio tend to be tough and slow to break down. - For example, leaves of the poplar tree have a C:N ratio of 40:1 and decompose in approximately one year, whereas pine needles, with a C:N ratio of 80:1 and a high lignin content, take at least 3 years to decompose. - Grass and clover have fairly similar C:N ratios (grass: 20:1; clover: 23:1) and would take about the same length of time to decompose. #### C:N Ratios in this lab - Poplar leaves: 40:1 - Pine needles: 80:1 - Grass: 20:1 - Clover: 23:1 In your experimental design, you can consider these leaf litters as representative species for different types of ecosystems. - Poplar leaves are representative of deciduous forest, pine needles for coniferous forest. - Grass can be representative of grassland ecosystems, important for livestock rearing and for maintaining wild populations of grazing animals, or can represent monocotyledonous agricultural crops (eg. Poaceae - maize, wheat, rice, barley). - Clover is a legume dicotyledonous agricultural crop, similar to peas, beans and alfalfa. ## Increasing CO2 levels and Climate Change - In the early Paleozoic Era (approximately 500 million years ago), the amount of CO2 in our atmosphere was actually much higher than it is today. - CO2 in the atmosphere acts as an insulating blanket, trapping solar heat in the earth's atmosphere. - Due to this **greenhouse effect**, even the polar regions of the earth had a tropical climate during the Paleozoic Era. - However, as land plants evolved, a dramatic decrease in the amount of atmospheric CO2 occurred with a corresponding increase in O2 levels. ## The Industrial Revolution and increases in atmospheric CO2 - Since the Industrial Revolution in the mid-1800s, the concentration of CO2 in the atmosphere has again begun to increase. - This increase in CO2 is primarily the result of burning coal, oil, gas, and wood at an ever-increasing rate to keep up with the demand for energy as our population multiplies. - Since the 1800s, the average annual temperatures have also begun to increase (currently by 0.8°C), and the last three decades have been the warmest in 400 years. - In addition, the rate of warming is also increasing. ## Arctic permafrost and climate change - To compound the problem, climate is not changing uniformly across the globe. - The current rate of warming in the arctic is greater than in other areas of the earth, happening at twice the global average. - As a result, the arctic permafrost is thawing at an increased rate. - Thawing soil, in turn, contributes to an acceleration of carbon transfer back into the atmosphere by releasing trapped methane gas and allowing previously frozen soil organic material to become accessible to decomposers. - The relationship between thawing soil and increased atmospheric carbon can largely be explained by changes in the rate of soil respiration. ## How climate change may affect soil - Increasing atmospheric CO2 is resulting in wide-ranging effects on climate with varied effects in different parts of the globe. - Some regions can expect increased drought, while some areas may have increased flooding. - In areas where the temperature is rising, soils are at risk of becoming more saline as the increased evaporation from the soil leaves behind salts. - The pH of the soil is predicted to change with increasing temperatures, but the results can differ depending on the depth of the soil, the type of region, and whether warming is combined with changes in precipitation. - As a further downstream effect, there may be increased fertilizer run-off as farmers combat food insecurity and decreased yields due to these soil issues. ## Data Analysis: ### Comparison of means for multiple independent variables: Multi-Factor ANOVA - A one-way (single factor) ANOVA compares the means of more than two groups when you are examining only one independent (manipulated) variable. - When you have more than one independent variable, you will need to use a multi-factor ANOVA. - This is used when you want to examine the interaction between the two manipulated variables and their combined effects on the responding variable. - In Biology 208, the most complex test you will perform is a 2x2 ANOVA with 2 manipulated variables at 2 treatment levels. ### How to perform a 2x2 ANOVA - You should start by organizing your data into combinations of treatments. - For a 2x2 ANOVA you will have 4 different combinations of treatments. - You will need to collect the data for each replicate of each treatment combination, and use these data to generate means and variances for each treatment combination. - You can then use the Excel Data Analysis ToolPak to calculate the Multi-Factor ANOVA. - A caveat of the Excel Multi-Factor ANOVA is that all treatments must have the same number of samples. Ask your TA for help if you have different numbers of samples in your analysis. ### Using the Excel Data Analysis ToolPak to calculate a 2x2 Factor ANOVA Analysis: 1. Click the "Data" tab in Excel. 2. Click on "Data Analysis" Toolpak. 3. Select "ANOVA: Two Factor with Replication". 4. Input the range for your complete data set under "Input Range". Make sure your data is arranged as a matrix with labels for all rows/columns. 5. Enter the number of "Rows per Sample". 6. Leave the "New Worksheet Ply" selected, and leave the Alpha as "0.05". 7. Click "OK". 8. Retrieve the following data from the output table - "F", "df", "Within df", and "P-value". 9. You will have three separate statistical statements to interpret. One for the "rows" variable, one for the "columns" variable, and one for the "interaction" variable. 10. Format your statistical statement as shown below: - (F df, within df, p-value) - Eg. (F1,12 = 50.4, p = 1.25E-05) ## There are three p-values for a 2x2 ANOVA: - One for each independent variable (called the main effects) - One for the interaction effect between them (rxc) ## A statistical interaction occurs when the effect of one independent variable on the dependent variable changes depending on the level of another independent variable. ## Interpreting the results of a 2x2 ANOVA - To understand the results of a 2x2 ANOVA, you will need to graph your means. - You can use the Excel Descriptive Statistics function to calculate the means and confidence intervals for your data, or calculate it manually in excel using the “=AVERAGE”, “=CONFIDENCE.T” formulas. - Since this experiment has two manipulated variables, your graph should be a double bar graph. - You can then make three interpretations knowing that all effects were significant according to your statistical analysis (statistical analysis on this data is shown in the Appendix II for 2x2 ANOVA (Multi-Factor ANOVA). - **There are the two main effects**: - **First**, root growth is higher for annuals than perennials in the high water treatment. This result alone is strong enough to result in a statistically significant difference of life history strategy (annual/perennial), regardless of the fact there is very little difference between annual and perennial root length in the low water treatment. - **Second**, higher water availability increased root length in both annuals and perennials. - **There is an interaction effect about how the two main variables interact together**: - From your graph, you can interpret the directionality: that high water interacts with annual life strategies to produce extremely longer roots than any other condition alone. - In some cases, you may get a significant interaction with non-significant main effects. - In those cases, you can't reliably interpret your main effects because the interaction effect is informing (affecting) the main effects. - If you have a significant interaction and non-significant main effects, you should interpret the interaction and include a description of its effects on your data.