Over the last century or so, humans have greatly increased the amount of nitrogen (N) released into the atmosphere. For instance, combustion of fossil fuels such as gasoline and coal results in emission of N containing gases such as nitrous oxide, nitric oxide and ammonia. Some of this N returns to earth in rainfall and snow. You are probably familiar with the term "acid rain"; the nitric acid we now see in rain is largely the result of fossil fuel combustion and other human activity.

Both the northeast US and central Europe have been especially impacted by this long-term deposition of N due to wind patterns and population density. While N is a fertilizer that we apply to gardens and agricultural fields, it seems likely that the continuous addition of N over time to temperate forest and grassland ecosystems will have negative consequences because these systems evolved under low N conditions. The Chronic Nitrogen Amendment Study was established in 1988 at the Harvard Forest in central Massachusetts to evaluate how long-term N additions influence soil processes (such as nitrogen cycling), growth of trees and other plants, and the chemical composition of leaves and needles. Over the last 15+ years, two types of common forests — hardwood and pine — have been fertilized with two levels of N. You will be working with data collected in two adjacent forest stands. One, a pine stand, is dominated by mature red pine (Pinus resinosa). The other is a hardwood stand of black (Quercus velutina) and red (Quercus rubra) oak mixed with black birch (Betula lenta), red maple (Acer rubrum), and American beech (Fagus grandifolia).

Three plots measuring 30 x 30 m were established in each stand and have been treated annually with 0, 5, or 15 g N m-2 (control, low N, and high N, respectively). There is one 30 x 30 m plot per treatment in each stand for a total of six plots. Nitrogen fertilizer is applied at 4-week intervals from May to September as a concentrated solution of ammonium nitrate (NH4NO3). The amount of N applied to the low N plots is similar to the amount of N deposited in some European ecosystems. The N addition in the high N plots is not intended to be representative of current N deposition levels, but rather to push the system to saturation, the point at which the forest can no longer use and retain the added N. This study method is called a "dose for time substitution." As Alison Magill, the plot manager, notes "We don't have time to put on 12 kilograms per year and wait 60 years to see the results because this type of research is, hopefully, oriented toward getting results that can be used to bring about changes in environmental policy."

The objectives of this exercise are for you to 1) increase your understanding of N deposition and its impact on forest ecosystems and 2) learn to ask questions and then make and interpret figures with actual scientific data from a Long-term Ecological Research (LTER) site. The ecosystem properties and processes that you will examine are net primary productivity (net rate of primary production), tree photosynthetic capacity (photosynthesis of leaves and needles), soil respiration, and soil microbial biomass. The data you will work with are published in a special issue of the journal Forest Ecology and Management (Aber and Magill 2004). This special issue reports the results from the first 15 years of N additions at Harvard Forest. Throughout the exercise you should keep in mind the following question: How have the pine and hardwood stands responded to long-term N fertilization?


Dataset 1

Methods and Rationale

Forest decline has been seen in Europe as a result of long-term N additions. One goal of the Chronic Nitrogen Amendment Study at Harvard Forest is to determine how tree growth in northeast forests will respond to long-term N fertilization. All trees greater than 5 cm in diameter in the control, low N, and high N plots were tagged at 1.5 m above ground level in 1988 with numbered aluminum tags (Magill et al. 2004). Tree diameters were measured at tag height during fall/winter of 1990, 1993, 1996, 1999, and 2002.

Ring for measuring DBH (diameter at breast height) used for assessing tree growth. The tree diameters were used to estimate year-end aboveground tree biomass. Net primary productivity is the change in total cumulative tree biomass between time periods. The data are expressed as grams of tree biomass per square meter (g m-2).
(Photo courtesy Serita Frey, University of New Hampshire. Used with permission.)

Open-ended Questions

Think about the following questions and how you will go about answering them given the data provided.

  1. How did tree productivity change over time in each of the forest stands, and how was productivity altered by N additions? Consider possible reasons to explain the pattern that you see in the data.
  2. How is the effect of N additions different for the pine and hardwood stands? Why might there be a difference?

Detailed Graphing Instructions

Using Excel (instructions are for Excel 2003), make a figure of tree productivity from 1988 to 2002. To do this:

  1. Click on the chart wizard icon in the toolbar.
  2. Choose "XY (Scatter)" for the chart type and "Scatter with data points connected by lines" for the chart sub-type.
  3. Click next.
  4. Select data for the graph by clicking on the "Collapse Dialog Button" at the end of the "Data range" box. You will now be able to select the data from the worksheet. Highlight the entire dataset including headers (i.e., A6 — G11). Click the "Collapse Dialog Button" again to return to the chart wizard.
  5. Select "Series in: columns"
  6. Click next and label the chart and axes appropriately.
  7. Customize the figure and click "Finish" when through.

Dataset 2

Methods and Rationale

One of the most consistent responses of forests to N fertilization is increased leaf tissue N (Magill et al. 2000). At Harvard Forest, pine needles (pine stand) and oak leaves (hardwood stand) collected from the N fertilized plots showed increased levels of N in both green and senesced (i.e., dead) leaf tissue, especially for the high N plots. You might predict that an increase in leaf N content would stimulate photosynthesis. Bauer et al. (2004) measured leaf photosynthesis of pine needles in the control and high N plots. The measurements were carried out in the tree canopies using two 25 m tall walk-up towers. Photosynthesis was measured for needles with ages of 0 to 3 years. Note that there were no 3-year needles in the high N plots. The data are expressed as the amount of CO2 consumed in nmol per gram of leaf tissue per second (nmol g-1 s-1).

Open-ended Questions

Think about the following questions and how you will go about answering them given the data provided.

Detailed Graphing Instructions

Prepare a bar graph that shows how photosynthetic capacity differed between the control and high N plots for needles of different ages. Follow the instructions given for dataset 1 except select "Column" for chart type and "Clustered Column" for chart sub-type. Highlight only the mean values and their headings to start. You will need to label the bars. To do this:

  1. Select the "Series" tab in the second dialog box of chart wizard (i.e., the box where you select the data for the graph.)
  2. Click on the "Collapse Dialog Button" at the end of the "Category (x) axis labels" box.
  3. Select the labels column (A8-A11).

Label the graph and axes appropriately. Looking at the figure, you will notice differences between the control and fertilized treatments, but you have no way of determining whether these differences are significant. To determine whether the control and fertilized plots are significantly different, you need to examine the amount of variability associated with each mean value. This information is provided in the dataset as the standard error. Use these data to add y-axis error bars to your graph. To do this:

  1. Right click on the first bar in the graph and select "Format Data Series…"
  2. Click on the "Y Error Bars" tab.
  3. For the Display select "Both" and for the Error Amount select the Custom box.
  4. Use the Collapse Dialog Buttons as before to select the standard errors associated with the highlighted bars (e.g., control data). Do not select the heading. You will need to select the same data twice, once for the "+" box and once for the "-" box.
  5. Click OK.
  6. Repeat these steps for the next set of bars (i.e., high N data).

As a general rule, if the error bars do not overlap, a significant difference exists between the mean values being compared.

Dataset 3

Methods and Rationale

In the first two datasets you explored how N additions have impacted tree growth. The next two datasets focus on belowground (soil) responses. With dataset 3 you will specifically evaluate how soil respiration has responded to N fertilization. Globally, soils contain more carbon stored as organic matter than is contained in aboveground plant biomass and the atmosphere combined. Soil respiration is the primary way that carbon, as carbon dioxide (CO2), leaves the soil and enters the atmosphere. The metabolic activity of both roots and soil organisms (e.g., fungi, bacteria) contribute to soil respiration. Small changes in soil respiration may dramatically alter the concentration of CO2 in the atmosphere. Thus there is great interest in how environmental change, including N deposition, is altering the amount of carbon stored in soil and the amount that enters the atmosphere as CO2.

Soil respiration measurements were made from three areas within each control, low N, and high N plot at Harvard Forest using plastic chambers placed over the soil for a 24 hour period (Bowden et al. 2004). A sample of air inside the chambers was removed every 4 hours and CO2 concentrations were determined using a gas chromatograph. These measurements were made once or twice monthly from March to December in 1988, 1989, and 2001. The data in this dataset represent the amount of carbon respired as CO2 per square meter of forest (g C m-2) during the measurement period for each of the three years. Standard errors are also provided.

Collar used for measuring soil respiration.
(Photo courtesy Serita Frey, University of New Hampshire. Used with permission.)

Open-ended Questions

Think about the following questions and how you will go about answering them given the data provided.

  1. How does soil respiration vary from year to year?
  2. Does soil respiration differ between pine and hardwood stands?
  3. Is soil respiration affected by nitrogen fertilization? If so, how?

Graphing Instructions

Using the skills you learned in the first two datasets, prepare a bar graph for each stand (hardwood and pine) that compares the cumulative soil respiration amongst the three treatments (control, low N, high N) for each of the three years that data were collected (1988, 1989, and 2001). Be sure to include error bars and to label the axes appropriately.

Dataset 4

Methods and Rationale

Soil biota in many terrestrial ecosystems evolved under low N conditions and thus one anticipated consequence of elevated N inputs is a change in the soil biotic community as ecosystems move toward N saturation. Until recently, little work had been done at Harvard Forest to determine how the soil microbial community has responded to increased N availability or how changes in the microbial community might be linked to the observed changes in tree growth and soil respiration. Frey et al. (2004) collected soil samples in fall 2002 to examine how N enrichment has affected microbial biomass, microbial functional capacity, and the activities of cellulose-degrading and lignin-degrading enzymes produced by soil microorganisms. In this activity, you will analyze the bacterial and fungal biomass data.

Four soil samples were collected to a depth of 10 cm from each of the control, low N, and high N plots in both the hardwood and pine stands. The amount of bacterial and fungal biomass in the samples was estimated by the substrate-induced respiration method. In this method, glucose is added to the soil and the amount of soil respiration due to glucose metabolism is measured over a four hour period. Glucose is a readily usable food source for many soil organisms. Previous studies have shown that this glucose-induced respiration is well correlated to the amount of microbial biomass present in the soil. The data are reported in terms of the amount of CO2 respired (μg CO2 g-1 soil) in response to glucose addition and thus represent an index of microbial biomass. Bacteria and fungi were differentiated with the use of antibiotics that are inhibitory to one or the other group of organisms. Respiration from soil amended with a bacterial inhibitor is attributed to fungi, and respiration from soil amended with a fungal inhibitor is likewise attributed to bacteria. In this way, the relative contribution of bacteria and fungi to total soil microbial biomass can be determined.

Open-ended Questions

Think about the following questions and how you will go about answering them given the data provided.

  1. Which microbial group, bacteria or fungi, represent the largest proportion of the total microbial biomass?
  2. How does bacterial and fungal biomass vary across treatments?
  3. Does the microbial response differ between the hardwood and pine stands? If so, how?
  4. How do bacteria and fungi respond differently or similarly to N additions?
  5. Compare these results to the soil respiration data above. How might changes in bacterial and/or fungal biomass influence soil respiration?

Detailed Analysis and Graphing Instructions

In this dataset, you are given the raw data (i.e., the individual data points for each sample collected). First orient yourself to how the data are presented. Now get started by calculating the means and standard errors. You should do this in such a way as to make preparing graphs as easy as possible. To do this:

  1. Click the cursor on cell H8 of the spreadsheet. In this cell, calculate the mean value for bacterial biomass for the control samples from the hardwood stand using the function "=average(D8.D11)."
  2. Move to the next cell in that column (H9) and repeat this instruction for the pine stand. The function in this case will be "=average(D20.D23)."
  3. Now move to the next column (I) and repeat these instructions for the low N treatment, changing the function accordingly.
  4. Do the same in column J for the high N treatment.
  5. Finally, repeat these instructions in columns K-M to calculate the mean values for fungal biomass for the control, low N, and high N samples.

You are halfway done.

Excel does not have a function for calculating the standard error; however, you can make this calculation by taking the standard deviation and dividing it by the square root of n, where n is the number of sample replicates (four in this case). To do this:

  1. In cell N8 calculate the standard error for bacterial biomass in the control samples from the hardwood stand using the following function: =stdev(D8.D11) / sqrt(4).
  2. Modify this function as necessary to calculate the standard error associated with each mean value.
  3. To make graphing easy, organize the standard errors in the same way as the means. Thus you should have 6 columns of mean values (columns H-M) and 6 columns of standard errors (columns N-S). There should be only two rows of data, one for the hardwood stand and one for the pine stand (rows 8 and 9).

Use the skills you learned previously to prepare two bar graphs, one for bacterial biomass and one for fungal biomass. Each graph should compare the three treatments (control, low N, high N) within each of the hardwood and pine stands. Change the scaling on the Y-axis to be the same for each graph so that you can easily compare the relative amount of bacterial and fungal biomass. To do this, double click on the Y axis and select the "scale" tab. Input the minimum and maximum value for that axis.

Synthesis Activity

Choose one of the following:

  1. Prepare a written report that synthesizes the information provided in the above datasets. The report should include an introduction, data summary, conclusion section, and references. The introduction should discuss the problem of N deposition in general terms, clearly state the objectives of the Chronic Nitrogen Amendment Study at Harvard Forest, and give the specific questions addressed by each of the datasets. The data summary should include the graphs that you prepared as well as a written summary of your interpretation of each graph. That is, describe in writing what each graph is depicting and the main conclusion to be drawn from each one. The conclusion section should summarize how chronic N additions have impacted the pine and hardwood stands at Harvard Forest.
  2. Prepare a 15-minute PowerPoint presentation (approximately 15 slides) that synthesizes the information provided in the above datasets. In the presentation, you should introduce the problem of N deposition, clearly state the objectives of the Chronic Nitrogen Amendment Study at Harvard Forest, cite the specific questions addressed by each of the datasets, interpret the graphs you prepared, and give the conclusions you have reached from the data. The last slide should provide a list of any reference materials that you used to prepare the presentation. You will give the presentation to your instructor at a mutually agreed upon time.