Growth Rates of Primary-Forest and Pioneer Trees in Forest Understory

Parts of this exercise are excerpted from Murray et al. (2005).

Even though most trees live in forests, they’re not all well-adapted to the same ecological conditions. Some trees are best adapted to living in the deep shade of the forest understory even as seedlings; they can survive for many years there, growing slowly and biding their time until the canopy trees that shade them die or get blown over by wind. When that happens, they grow more rapidly, but it sometimes takes 3 or 4 such “releases” from suppression by shade for such trees to reach the canopy where they’re not overtopped by taller trees. Because trees that have the ability to survive and grow in the deep shade of the understory usually come to dominate a mature forest, they’re often called “primary forest” species, and in much of the eastern United States sugar maple (Acer saccharum) and American beech (Fagus grandifolia) are good examples. These trees often produce dense, sturdy wood, and can continue to grow and produce offspring for hundreds of years.

Some other species are adapted to survive and grow rapidly only in the high-light conditions in places where the forest canopy has been removed by some physical disturbance – e.g., where a tree has blown down or been killed by lightning or insect attack, or has been removed by human activity. Most of these species cannot grow or even survive for long in the deep shade under an intact canopy, and some species even have seeds that remain dormant in the soil until the canopy overhead is disturbed. These high-light conditions never last for very long, in part because branches of the trees surrounding the canopy opening grow rapidly to fill it, and in part because saplings in the understory of the “canopy gap” grow more rapidly while the light is available. As a result, near the forest floor it becomes as shady as it was before the canopy disturbance within a few years. For this reason, tree species that specialize on the high light levels in canopy gaps produce wood that is much less dense than that of most primary forest species, so that they can grow in height more rapidly and take full advantage of the light while it’s available. The downside of that strategy is that their low-density wood breaks more easily and is more easily attacked by insects than that of primary forest species, so that they come to be rare in a mature forest. Because these kinds of trees often dominate the early stages of ecological succession but become more rare as a forest matures, they’re often called “pioneer” species. They usually start reproducing at a relatively young age and produce lots of offspring over a short time period, since they’re unlikely to survive as long as primary forest species. Good examples in the eastern U.S. include sassafras (Sassafras albidum) and black cherry (Prunus serotina).

Step 1: Ask a research question

Are there differences in the growth rates of pioneer species and primary species trees in a forest?

Step 2: Design a study and Collect data

1. What is the variable of interest (response variable) and what type of variable is it?

2. How will you measure this variable?Since we will only be making one field trip to the forest to take measurements on trees, we cannot measure the height one year and then the height the following year. We also do not want to harm any of the trees, so we will not be cutting them down and measuring the distance between rings. Instead we will measure annual growth as the distance from the previous year’s bud scale scar to the end of this year’s terminal bud.

Measuring growth responses of tree seedlings or saplingsIf we know something about how plants grow, we can use a little trick to determine how much a given tree stem has grown during the current growing season. Every autumn when the tree is about to lose its leaves and go dormant for the winter, it forms several layers of bud scales around each of its terminal buds (the buds at the ends of each shoot). These scales protect the meristematic tissues in the buds from drying out over the winter. The next spring, the bud scales fall off as the bud starts to grow again, and the stem lengthens behind the bud.

Figure 1. Detail of sugar maple shoot, showing rings of bud scale scars left by successive year’s terminal bud scales.

If you look carefully, you can see the bud scale scars from the beginning of a growing season as several close-spaced rings around the stem (fig. 1). The current season's growth will be the distance between the tip of the terminal bud on a shoot and the most distal of last winter’s bud scale scars. With many species, you can even see the bud scale scars from several previous years, and measure those growth increments as well.

3. What other variables will you measure that might explain some of the variability seen in the different amounts of annual growth?

The variable we are most interested in as a predictor or explanatory variable to predict or explain the differences in tree growth is whether each measurement comes from a primary forest species or a pioneer species.

You may want to gather your own data from a local forest, or you may use data that has already been gathered by students in nine lab sections of an introductory Biology course at Hope College. Students from nine sections of the class measured the 2017 growth increment on a large number of sugar maple and black cherry seedlings or saplings (between about 0.5 and 2.0 meters tall, so that the main vertical stem could be reached) at the Hope College Nature Preserve (HCNP) in Allegan County, Michigan during the last week of August 2017. Care was taken to have each lab section work in a different part of the forest, so as not to sample a given plant more than once. Since most of the HCNP is a pretty intact forest, we assumed that most of the seedlings and saplings sampled by these classes would be in the shaded understory.

A word here about how dark it really is in the forest understory: even though it seems pretty light to us, this is because our eyes are sensitive to a broad segment of the electromagnetic spectrum (the part that we call the “visible” spectrum, over a wavelength range of about 380 nanometers (violet) to 700 nanometers (red). The photosynthetic pigments in plants absorb well from about 400-500 nm (blue) and from about 650-700 nm (red), but not at all well from about 500-650 nm (green). That’s why plants look green, and because the green light isn’t absorbed by leaves in the canopy it penetrates all the way to the forest understory. Even though we can use these wavelengths to see, plants in the understory can’t use them. Only about 1% of the light that plants can actually use makes it to the forest understory, so it’s a very dark place for them indeed!

Even though ecologists recognize a broad distinction that characterizes what we call “pioneer” vs. “primary forest” species, we realize that these are just endpoints in a continuum of shade tolerance, growth rates, wood density, seed production, and other traits. Nevertheless, we can use this theoretical framework to propose testable hypotheses about the relative growth rates of tree seedlings under different conditions. In the forest understory, for example, we might predict that seedlings of a primary forest species like sugar maple would grow more rapidly than those of a pioneer species like black cherry, and that in treefall gaps the pioneer species would grow more rapidly than the primary forest species.

We posed the following null and alternative hypotheses in order to test the working hypothesis that primary forest species grow faster in the shaded understory:

H0: The mean growth increment of maple saplings is the same as the mean growth increment of cherry saplings and any difference is small enough to have resulted from sampling error alone. In other words, there is no association between annual growth and species.

Ha: The mean growth increment of maple saplings is greater than the mean growth increment of cherry saplings and this difference is not likely a result of sampling error alone. In other words, there is an association between annual growth and species; specifically sugar maple saplings grow more on average than do cherry saplings.

“Sampling error” isn’t any kind of mistake; rather, it’s just the variation in an estimate of a statistical population characteristic that results from taking measurements from a sample of the population rather than each and every member of the population. Here, for example, we’re really interested in the difference in mean growth increments of all sugar maples vs. all black cherries in the forest, but we’ve only measured a sample of 25 of each of them. And just by chance, each of the sample means is likely to differ from the “true” mean of the entire population of whichever species we happen to be sampling. And if we take several different samples even from the same species, they’ll likely differ from one another too. It follows that sampling error is greatest for small samples and decreases as sample size increases. When the size of the sample approaches the size of the entire population, sampling error approaches zero and the sample mean converges on the true population mean.

Step 3: Explore the dataLet’s first explore the data from one of the nine lab sections – section 3. In Figure 2, we examine descriptive statistics from the 50 trees sampled by lab section 3. As denotes measurements taken on sugar maple saplings and Ps denotes measurements taken on cherry saplings.

Figure 2. Descriptive statistics of tree growth (mm) from saplings at Hope College Nature Preserve taken in August, 2017.

4. Describe the distributions of sapling growth for both the sugar maples (As) and the cherries (Ps) by looking at the dotplots and boxplots in the output.

5. On average, did one species grow more than the other? Which species grew more? Calculate the difference in mean growth increment (As mean – Ps mean).

6. Do you think this difference could be due to sampling error (sample to sample variability)?

How different would these sample means have to be before we’d be willing to conclude that they resulted from a real, biological difference between the growth increments of sugar maples and black cherries, rather than from mere sampling error because we measured only 25 trees of each species? A good approach to this question might be to determine how large a difference between sample means could occur if there really were no difference between sugar maple and black cherry growth increments, i.e., if the null hypothesis (H0) above were known to be true. We could do this manually by writing down the 50 measured growths on separate index cards, shuffling well, and then dealing the cards randomly into two piles of 25 each - one that we’d call sugar maple measurements and the other that we’d call cherry measurements. Then we could calculate the mean growths of each pile and subtract one from the other. This is one difference in mean

growths that could have happened if there was no difference in the annual growth of the two different species. If we repeat this shuffling and re-randomizing of the growth measurements into two piles of 25 measurements many times, each time calculating the difference in mean growth, we can build a distribution of differences in mean growth that could have happened if there really was no difference between the annual growths of these two species. Luckily for us, the Multiple Means applet does exactly this. This process of first combining the measurements from the two groups of interest by shuffling them together and then dividing them randomly into samples and comparing them is called constructing a null model, because we know at the outset that any difference between the samples has resulted from sampling error rather than any real, biological difference between the groups. We know this because we assigned each of the measurements to a group randomly, without respect to the species that it originally came from.

Step 4: Draw inferences from the data

7. We can use simulation to see how different the means from our two groups of 25 measurements might be if were just as likely to have come from sugar maple saplings as from cherry saplings.

a. Clear out the data in the Multiple Means applet. Copy and paste the data from lab section 3 into the applet. Click use data. You should see the same output that is displayed in Figure 2. Now click show shuffle options. Click on the plot button. Now click shuffle responses. You should have seen the growth measurements shuffled and distributed back into two groups of 25 measurements each. New means have been calculated and the difference between these means has been plotted. This is one difference in mean growths that you could have seen if there was no association between species and annual growth. Report this difference in means.

b. Shuffle 4 more times, each time recording the difference in means.

c. Is the observed difference in mean growth larger than any of these simulated differences in mean growth?

d. In the box number of shuffles, change this number to 995 and click shuffle responses. The resulting distribution shows differences in mean growth between the two groups under the null model – that is, if there was no association between species and annual growth. Describe the shape, center and variability of this simulated null distribution.

e. Is the observed difference between mean growth increments, 15.08 mm, something we might have expected to see if there was no association between species and annual growth, or is it unlikely? Just how likely is it to get a difference as great as 15.08 under the null model?

f. Calculate this probability (commonly called a “p-value) by putting the observed difference (15.08) into the count samples box and making sure that “greater than >” is chosen in the box to the left of the difference. The shaded portion of the distribution corresponds to values under the null model that produced mean differences > 15.08, and the number of such occurrences out of 1000 is given below the count samples box. Dividing the number of occurrences by 1000 gives the probability of a difference > 15.08 when the null hypothesis above is known to be true.

g. What is your strength of evidence against the null hypothesis?

8. This was a descent sample size (50), but we do have data from all 9 lab sections. Larger sample sizes should give a more accurate idea of what is going on in the broader population (i.e., less sampling error). Clear out the data from section 3. Copy & paste the data from all of the sections (451 saplings sampled) into the applet. Report sample means and SDs. Calculate the difference in mean growths (As mean – Ps mean). How do these values compare to the ones calculated from the sample of 50 saplings (questions 4 & 5)?

9. Do 1000 shuffles on these data to see differences that could have happened under the null model of no association between species and annual growth. Describe the shape, center, and variability of this distribution. How do they compare to the shape, center, and variability

generated by the null model using only data from section 3 (question 7.d)?

10. Find the observed difference in mean growth (As – Ps) on the simulated null distribution. Is a value this large likely to have occurred if there was no association between species and annual growth, or is it unlikely?

11. Find the p-value associated with this result. How does the p-value from the test of significance on the data including all 9 sections compare to the p-value from the test of significance on the data from just section 3? What is the strength of evidence against the null when using the full data?

Step 5: Formulate conclusions

12. State your conclusions from the test of significance. Do you think these conclusions can be generalized to the whole HCNP forest? To any forest in a climate similar to Michigan?

Step 6: Look back and ahead

13. What things were well-designed in this study? What things could be changed to improve the study? What type of study do these results lead you to conduct next?

Literature Cited

Murray, K.G., K. Winnett-Murray and L. Hertel. 2005. Photosynthetic Strategies and their Consequences for Plant Community Structure. Pages 233-254, in Tested studies for laboratory teaching, Volume 26. (M.A. O’Donnell, Editor). Proceedings of the 26th Workshop/Conference of the Association for Biology Laboratory Education (ABLE), 452 pages.

Other screen shots that could use.