Part 3: Measuring Ring Width to Determine Climate Change in the Past
Now that we have explored several important research sites, let's take a look at some of the tree-ring data from those sites. In this activity, you will use a digital software program called ImageJ to measure the width of digital tree-ring samples from the three sites that you already explored in Part 2. You will produce a time-series graph from this data and combine your measurements with those of your classmates to produce a preliminary it's preliminary record because we will not be doing some of the statistical steps needed to convert the data into a tree-ring chronology. tree-ring record for each of the three tree-ring sites. You will be looking at just three cores from each site, but scientists at such a site would typically core and process 30 to 40 trees or more to be sure they are capturing ring patterns that represent the entire forest and not just a handful of trees. Once the samples have been taken back to the lab and prepared for microscope work, scientist cross-date a technique that ensures each individual tree ring is assigned its exact year of formation by matching patterns of wide and narrow rings between cores from the same tree, and between trees from different locations each sample to ensure that exact calendar dates are accurately assigned to each ring. This is accomplished by matching patterns of wide and narrow rings between cores from the same tree, the same site, and between trees from nearby locations. In this lab, the cores have been marked, or cross-dated for you.
To see this process in action, watch historical footage of A.E. Douglas, the founder of tree-ring science, at work cross-dating samples. Right click on the link to open the video in a new window.
Show caption
HideYou will work in pairs. You will be assigned a core from one of the three study sites to focus on. If different students pairs are working on the same core it is ok (repetition in science is a good thing). Work together to complete the Students Activity sheet and answers all Think About it Questions.
Instructions
Activity Instructions
1. Open ImageJ. (If you don't have it on your desktop, you can download an "all platforms" version here. If you've never used ImageJ before, and would like a review of its basic features, view go to the ImageJ Beginner's Tutorial. Note: to run ImageJ, you may have to also download a Java Bundle, if so, you will be prompted to do this. Some computers may also require you to go into System Preferences/Security and Privacy and allow apps from "App store and identified developers" in order to allow the download.
2. Before you begin work, look at the nine core samples shown below. Can you tell which of the nine trees is the oldest? The youngest?
3. Each core sample tells a story of the environmental conditions the tree endured from year-to-year. Occasionally however, due to very harsh or stressful conditions, a tree may not produce a ring all the way around its perimeter. These are called missing or locally absent ringsare growth rings that are discontinuous around the stem so that it is absent at certain points; also referred to as a partial ring or missing ring. Trees can also produce what scientists call a false ring a layer of wood less than a full season's growth and sometimes not all around the trunk when compared to a normal annual ring. False rings usually occur during a warm snap after the tree starts to go into dormancy. Note: Samples with tricky rings (e.g. faint or missing rings) have been marked accordingly, so you may see some notes on your core to guide you through the measuring process.
4. Your instructor will assign your group a sample from one of the sites that you explored in Part 2. Right click on your designated core sample from the list below, then click on it again to get to the highest resolution image. Download this image to your desktop. In ImageJ, go to File/Open and open your sample image in the program.
Wright Mountain, Gulf of Alaska (near Mendenhall Glacier)
![[creative commons]](/images/creativecommons_16.png)
Ondur Zuun Nuruu, Northern Mongolia
Kamchatka Peninsula, Russia
5. Click on the straight line measuring tool. 
Draw a line from the left hand edge of the image, parallel to the reference dots, to the right edge of the image (to the bark). Note: If you would like to see an example of how to measure in ImageJ, watch the first portion of the ImageJ tutorial.
6. Go to the Analyze Menu/Set Scale. The length of the line you drew should be displayed. Enter the known distance listed next to the sample core you are working on (you can find the length in mm of each core in small print below the image of the cores above). Set the unit to "mm." The scale of the image should now be displayed.
7. You will see a series of dots on your image. Scientist place single dots to represent each decade, two dots to mark every 50 years, and three marks at the century mark. You will begin your data collection at the ten-year or century mark on the left side of your image. Note: It is OK to omit the first couple of rings of the core sample (all the way left) if they are distorted. Choose the magnifying glass icon and zoom in the first section of your core on the left side of the core sample. Since the rings are narrow, it will make measuring easier if you work on just 10 to 20 years/rings at a time.
8. Go to Analyze/Set Measurements and remove (uncheck) area or any other measurements listed.
9. Click on the straight-line tool. Draw a line across the first tree ring in your sample. Make sure the line is perpendicular to both sides of the ring. See illustration at the right. Hit "Command M" (or Control M on a PC). The width of the ring (in mm) will be displayed in a results box.
10. Measure each tree-ring width in your sample in succession from the inner most ring (left side of the image) toward the outermost one just inside the bark (right side of the image). Re-position the straight line measuring tool each time to measure with a line perpendicular to both sides of the ring. You do NOT have to follow the line of marked dots. You will need to click on the hand tool and re-position your image as needed to complete your measurements. Leave your last measurement on the screen when you do this so you can return to the next ring in the sequence. Note: Samples with tricky rings (e.g. faint or missing rings) have been marked accordingly, so you may see some notes on your core to guide you through the measuring process.
11. When you are finished, save your data table to your desktop. When you save this file it should default to an ".xls" format, so you can graph your data in Excel.
12. The dots representing the decades and centuries on your core samples were derived by carefully cross-dating samples from the same site.
13. Look at the image of your core sample. The first group of three dots found in your core samples (left side of the core) represents the century mark (This could be 1700, 1800 or 1900 depending on the core that you were assigned), the last group of three dots near the right edge of the core represents the year 2000. Count backward and forward from this point to place actual dates on your excel table instead of 1 - X.
14. Open your data table in Excel or other similar program. Create graphs of each core with "year" on the x-axes and "width (mm)" on the y-axis. You will use this graph for comparison below.
15. Analysis
Keep in mind that the data you derived was "raw" —it hasn't been processed to removed growth patterns that are related to how a tree grows over time, called age-trendsa pattern of differing tree ring widths that result from early tree rings tending to be wider than later growth tree rings. The growth ring of a very young tree is spread over the small diameter of the tree and is proportionally larger than the ring you observe in trees that are much older that have the growth ring spread over a larger diameter tree. You may have noticed how large the rings in the first ten years of growth in your sample were compared to the last ten years. Scientist use conservative statistical methods, called detrending the process of removing the age effects, or age-trends, in the data by dividing the actual tree ring width by those predicted from a statistically derived equation that relates tree growth over time to tree age , to remove the age trends in samples, while preserving climate information. You won't be removing age-trends in this lab so when you look at the graph of your data, you have to consider that some of the patterns are related to this age trend.
16. Looking at the big picture: Compare your tree-ring data with the master chronology a tree-ring chronology that has a large number of samples that have been processed and checked by a dendrochonologist, and has been statistically processes to remove age-trends for your site.
The following graphs show tree-ring chronologies from the sites that you worked on, but a much larger number of samples were used, and this data has been de-trended so that the age-trends of the tree have been removed. Remember that due to the harsh climate of the northern latitudes the annual rings of these trees reflect summer, or growing season temperature conditions. For example, during a colder than normal summer, a tree would only grow a little bit, resulting in a smaller than normal ring, and during an unusually warm summer the tree would grow better than normal, so the resulting ring for that year would be wider.
To see how your core sample data fits into a longer term record for each region, compare your tree-ring graph to the master tree-ring chronology for your group's study site shown below.
Stop and Think
3.1 Compare the graph of your sample to the related master tree-ring chronologies above. Does the data from the core/cores that you measured look similar to the related master chronology from the same region? Why/why not?
3.2 Looking at the master chronologies above, describe the patterns that you see. What are the extreme narrow (reflecting cold) and extreme wide (reflecting warm) years?
3.3 Are there any other patterns that you notice that last ten-twenty years or more? List any extended periods of extremely narrow (cold) or wide (warm) rings in the data.