FIGURE 4.2. Selection, random processes, and history interact to create evolutionary change in a population. The dots represent the average position of the population in “morphospace,” the realm of evolutionary possibility represented by the ranges of phenotype 1 and phenotype 2. History determines where in the morphospace a population starts as well as the nature of the genetics underwriting the phenotypes. Evolutionary change is the movement of the population, over generational time, in response to either (1) selection and random processes (top population) or (2) random processes alone (bottom population). Selection is unlikely to act without random genetic processes because the continual creation of genetic variation is needed to make the phenotypic variations from which the fitness function selects.
For our population of Tadro3s, history covers a multitude of sins or, more accurately, assumptions. Our Tadro3s didn’t first evolve from single-celled Tadro0.001s, so they don’t have an explicit evolutionary history, but they do have an implicit one: the evolutionary history of the tunicate tadpoles after which Tadro3s are modeled. That tunicate history has bequeathed Tadro3s a notochord, a light-sensitive organ, a brain capable of linking sensor and undulating tail, and the genes that underwrite these phenotypes. History has also brought along environmental baggage—the water with a directional light source in which Tadro3 lives.
History’s most important legacy for Tadro3s is the blood line, the initial genetic conditions—essentially the genes and the variants of those genes that we decided Tadros had. Because we were interested in structural stiffness of the notochord and, by extension, the tail in which the notochord sits, we coded for that property and placed its initial value in the middle of a scale of biological stiffnesses.
Structural stiffness is a property that describes how a structure will change shape when an external force acts on it. Think about a cantilevered structure, like a flagpole hung horizontally. When you put a flag on the pole, it bends down slightly. Or, better yet, imagine that old Three Stooges movie short, Flagpole Jitters, in which Moe, Larry, and Shemp find themselves hypnotized and walking out on a flagpole high above the city streets. Through experiments with weights rather than clinging Stooges, engineers have figured out that the structural stiffness of a cantilevered beam like a horizontal flagpole is proportional to the beam’s flexural stiffness and inversely proportional to the cube of the beam’s length. This is one of those moments when, against the advice of my friends, I just can’t help but use an equation to summarize all of this:
To clarify, what this equation says is that structural stiffness, represented by the variable k (in units of Newtons per meter) is defined as the ratio of the flexural stiffness, the composite variable EI (in units of Newton square-meters), to the cube of length, L3 (units of cubic meters). What’s nifty about this equation is that you can see right away what matters. Want a stiffer beam? Increase the EI or decrease the L. A longer cantilever has a huge impact on k because of the cubing of the L. An equation like this also helps conjure up the genetics we’re after.
We could have just said that Tadros have genes that code for k directly and then left it at that. However, research on biological stiffness suggests that all three variables, E, I, and L can change independently during development and evolution. The variable E (in measurement units of Newtons per square meter) by itself is called by a variety of names: “modulus,” “elastic modulus,” “complex modulus,” “Young’s modulus,” or “Young’s modulus of elasticity.” Too many aliases! But wait—let me do my part for the witness protection of E. Because E is part of structural stiffness, k, and flexural stiffness, EI, and is caused by the kind and number of chemical bonds in the material you’re dealing with, I prefer to call it “material stiffness.”[37]
To allow for the likely possibility that selection might target the length of the tail for a variety of reasons, some of which may have to do with structural stiffness and some not, we created a genome that coded for L and E separately. By not coding for the variable I, we were holding that part of the geometry—and everything else about Tadro3 for that matter—constant. In the language of a geneticist, both L and E were quantitative characters, polygenes, multiple loci capable of producing smooth gradations in the phenotypes for which they code. All loci for L were located on a chromosome separate from the loci for E in order to allow for independent assortment. In other words, having quantitative traits means that the genome does not contain the simple on-off, wrinkled pea or smooth, kind of genes that we call “Mendelian.” Each set of genes is, instead, a continuous number scale, capable, within a given window, of producing a range of E values different from a range of L values.
You can see the independent changes in the proportion of E and L genes in the bottom panel of Figure 4.1. Notice that as the proportion of L genes increases from generation 7 onward, the structural stiffness, k, in the middle panel, plunges. This is exactly what we’d expect from our equation for k, on previous page. That L3 term in the denominator is increasing dramatically, and it is lowering k at the same time that the E term in the numerator is decreasing and also lowering k. Faced with this kind of genetic evolution, poor old structural stiffness doesn’t stand a chance.
Over the course of ten generations, the population’s average value of the structural stiffness of the tail, k, plummets from above 5 to below 1 Nm-1. We’ve seen what was happening genetically to cause the decreased value of structural stiffness. But these genetic changes don’t speak to how selection—which judges individuals by their behavior, not by their genetics—was interacting with randomness and history. We still have two bothersome itches to scratch: (1) Why did the structural stiffness decrease under selection for enhanced feeding behavior when we predicted that it would increase? (2) Why does feeding behavior seem at times to be unrelated to the structural stiffness of the notochord?
I want to warn you right now about a tempting siren who begins singing on the rocks at about this point in a study. When, as was the case with Tadro3, your experiment produces a result that appears to be the exact opposite of what you predicted, the immediate emotional response is to be disappointed and self-flagellating. My students and I certainly were. When we graphed the data in Figure 4.1, we had to have a group counseling session immediately to air concerns and responses. In the lightning reaction round, we heard: What went wrong? Our experiment didn’t work! These data suck! We stink as scientists! My line then, and I’m sticking to it now, is that if you design an experiment carefully, execute it well by tracking down mistakes as they occur and running controls, your data will always be great. Data just are. No matter what those data say about your predictions, they and the experiment that generated them stand on their own, with their total value determined by how well you measured what you set out to measure.
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For completeness, I should tell you that the variable