What Was I Thinking? Remembrances of My Dissertation

In 1987, I submitted my Ph.D. thesis, “Environmental Variability and Phenotypic Flexibility in Plants,” at the University of Illinois. What was I thinking? I was dealing with three variables: plants, the growth flexibility of those plants, and the variability of the environment—in this case, of sunlight intensity. I intended to write about all three of these things in this essay, but it quickly got out of hand, so I will just tell you about how I dealt with the complex problem of measuring environmental variability in three habitats: abandoned agricultural fields, tallgrass prairies, and forest floors.

One of the major problems with measuring environmental variability is construct validity—that is, how valid is a way of measuring something so that it is a believable construct of what you claim to be measuring? I explain this concept in my book Scientifically Thinking. There is no single valid way of measuring environmental variability. What I did instead was to measure it three different ways. If these three ways all gave the same results, then they were probably valid, and I could believe the results.

First, I made instantaneous measurements of light (that is, the colors of light that stimulate photosynthesis) with a “quantum sensor” designed to do exactly this thing. The problem is, this kind of light intensity varies from one moment to the next.

So I used a second method. What I really wanted to know was the spatial patchiness of shade caused by leaves. So I made three-dimensional profiles of leaf area in the three habitat types. To do this, I used nine pieces of wood, a string, and a nail. But neither of these methods could prove that variability of sunlight intensity would actually have an effect on plants.

So I used a third method as well. I used phytometers, that is, actual potted plants. I measured the weight of the plants (plus the soil, or course) in lots of different places in the three habitats in the morning and the afternoon. I measured environmental variability as the differences in how much water the plants transpired.

This was how I tackled the problem of construct validity. I measured what I wanted to know three different ways. I was taking a chance. What if the three ways gave three different answers? But I got lucky. All three methods agreed. Chalk one up for construct validity.

I had expected that weedy fields would be the most variable, the forest floor the least variable, and prairies intermediate between them. And this is what I found. I still marvel that it came out that way.

I announced my results triumphantly: weeds had the most phenotypic flexibility and lived in the most variable environment; forest floor plants had the least flexibility, and lived in the least variable environment; and prairie plants and their environment were intermediate. The results all lined up. Despite all of its limitations, this quixotic quest was successful. I believe the results, largely because I took a risk with construct validity, and it paid off. There is no way I could have gotten these results by sheer luck. Here is the plain English summary of my thesis.

A lot of the scientific method is about validity. You have to measure it, and every time you do, you are taking a risk. But if it works, you are closer to understanding the world.

Population Thinking and Why It Matters

According to the foremost philosopher of biology, the late Ernst Mayr, one of the most important advances in biological science has been population thinking. That is, we now understand that all the members of a population (or any other set of individuals) have individual variability. They are not all the same. Without this understanding, nobody could grasp the concept of natural selection. How could some individuals in a population (of bacteria, trees, or humans) have more offspring than others (aside from sheer luck) if they were all the same?

Here is one simple example. In this photo, I hold in my hand acorns from two post oak trees. The acorns from one tree are larger than those from another tree. I show that this difference in size is greater between trees than it is among the acorns from a single tree. The two on the left are from one tree, the two on the right from another, of the same species.

Darwin based his theory of natural selection on the work of Thomas Robert Malthus, the economist who said that there were always more people than resources, at least most of the time. Suffering and privation were, therefore, inevitable. Darwin did not simply apply this idea to plants and animals, as some people think. Darwin had to make the transition to population thinking. Even Malthus did not have population thinking; to him, all members of a human population were the same: they all worked, ate, had babies, and died, pretty much the same way.

The lack of population thinking is also why early experiments did not have replication. Bacon used only two chickens in his freezing experiment, and Redi had only two flasks for his spontaneous generation experiment. If all chickens, and all flasks with meat in them, were the same, then replication is not necessary.

Common sense, they say, is neither common nor sense. Many people, when they see an example of something, assume that it represents the entire picture. If some Republicans commit an act of domestic terrorism, as happened this last January 6, then all of them will, if given the opportunity. This is not true (I think). Because of individual differences, you need a sample. When I studied how much garbage was left along Oklahoma roadsides, I counted the garbage from many different stretches of roadside, and not just the ones that looked trashy. If I simply looked at the trashiest roadside, and assumed it was typical, I would have reached the incorrect conclusion that Oklahoma highways have over 300 pieces of garbage per mile. (Actually, it is “only” 100.)

How do we know that we have looked at enough samples of people, things, or places to draw a legitimate conclusion about them? That, my friend, is what the science of statistics is all about. And that is another story, though not one you will probably read in this blog.