= Δp.
Isn’t that tidy? To be careful about what we have wrought, we would say that in our population of robots any phenotypic change equals a proportional genetic change. Tidy, indeed.
There is a problem with all of this, a perceptual one: usually the Δ from generation to generation is so small that we, as observers, don’t recognize the changes. The goldfinches in my garden this year look just like the goldfinches in my garden last year. We are blind to slow and steady changes, even those that happen over the course of a few minutes right in front of us. This phenomenon has been called “change blindness,” and the fact that it happens predictably is a startling testament to the fact that we have to be told to pay attention to most things in order to notice when they change. Misdirect attention and you have a magic trick. Thus, it’s no wonder that we don’t automatically track the evolutionary changes happening around us all the time. For example, unless you’re a gardener, you are unlikely to have noticed the Oriental bittersweet vine that has slowly crept into the shrubs and bushes of your midwestern and northeastern US yards since it was introduced in the 1860s.
Fortunately for us, Darwin—having trained with the best naturalists of the day, having traveled the world collecting samples, and having bred pigeons—was well placed to see variation and change on a small scale. Combined with his knowledge of Charles Lyell’s geology, he knew that the world was old enough to have let that kind of variation build up over time to become the huge changes that differentiate whales from hippopotamuses or tuna from trout. In today’s parlance microevolutionary changes cause macroevolutionary changes.[6]
Most biologists looking to measure evolution tend to focus on specific traits, or characters. John Lundberg, a biologist famous for his work on the evolutionary relationships of catfishes and the freshwater fishes of South America, told the graduate-student version of me that a character was any feature of an organism that you can observe or measure. In practice, then, you end up counting the number of spines in the dorsal fin in a population of bluegill sunfish and pumpkinseed sunfish. Or you measure whether or not the males in each species make and defend nests on the edges of the lake. Or you sample and sequence DNA to compare the alleles that make the little colored flap that sticks off the back of the gill cover. The result is that we tend to focus our analytic efforts not on the evolution of the population or species but on the evolution of one or two traits.
We do this even though we know that selection does not compartmentalize traits: the “whole animal interacting with the world and creating behavior” is really what is being selected at any given time and place, so some traits evolve not because they are the specific target of selection but because they just happen to be part of the whole animal. Changes in some traits may help the animal play the game of life whereas changes in other traits may hinder. Some changes may be neutral, but if selection on one trait is strong enough, the rest just get dragged along for the ride. For now, however, we’ll just think about traits as isolated evolutionary units. To do this oversimplification, we have to perform the convenient assumption called ceteris paribus, Latin for “all else being equal.” Under ceteris paribus thinking, we pretend that when we change the one thing that we are interested in, like a trait, nothing else changes or is influenced by that change. The logic of ceteris paribus is that we isolate one variable and understand how it influences the behavior of the whole system.[7]
We use ceteris paribus thinking all the time: eliminating one kind of food at a time to see if we have allergies, trying high-octane gasoline in our car to see if that improves mileage, altering our posture to see if that makes our back feel better, or testing a new drug for the treatment of multiple sclerosis in a clinical trial. Ceteris paribus is a great approach if all other variables remain constant and you have the discipline not to change other variables at the same time. If you remove wheat and dairy from your diet together, and that muscle soreness disappears, then you still have to go back and test each separately to know which one is causing the problem—or if it is the interaction of the two.
Using ceteris paribus, then, we can ask if any single trait is an adaptation. This is the equivalent of asking if a trait has evolved because it was the target of natural selection. Keep in mind that this use of “adaptation” as a noun is different from the verb of “adapting,” which refers to the process of natural selection in action. In addition, if we are being careful, we’ll always ask if a trait is an adaptation for a specific situation.
To answer this kind of question, we need information, and lots of it. Fortunately, Robert Brandon has carefully analyzed the kinds of information that are necessary and sufficient to provide what he calls a “how-probably” explanation of adaptation (Figure 2.2).[8] A how-probably explanation of adaptation, by the way, is rarely accomplished because we usually are missing one or more pieces of evidence. We miss loads of evidence when we are dealing with adaptation in extinct life-forms, and then the best we can do with our partial set of information is to claim that we have a “how-possibly” explanation.
FIGURE 2.2. Got adaptation? To show that natural selection created a trait—in other words, that the trait is an adaptation—you need hard, physical evidence. You need to know about the trait and the population of organisms in which the trait exists. Collecting all of this information is difficult enough when we have the population right in front of us. Doing so when the population is extinct is impossible because we can’t dig up the genetics, population structure, or selection environment. The beauty of simulating evolution with autonomous robots is that we can choose the genetics, population structure, and the selection environment. Once those features of the trait and population are chosen, we can then put our robotic population in motion and watch, over generational time, as the population evolves. Robert Brandon’s 1990 book, Adaptation and Environment, inspired this perspective.
The evidence that we need to test a how-probably hypothesis of adaptation begins with understanding the trait of interest. First, we need to know that the trait is heritable, how it is genetically coded, and how it interacts, at the level of DNA, with other heritable traits. You can see how this evidence fits in with the definition of natural selection from earlier in this chapter. Second, we need to understand “polarity” of the trait—that is, what did the trait evolve from? What did the trait look like in its ancestral form, and what does it look like in its derived form? In the case of a jointed vertebral column, we know that it evolved from an unjointed notochord. Third, we need to understand how the ancestral and derived forms of the trait—and all the intermediate forms in-between—functioned in a living individual.
6
This insight—that slow, small changes happening right in front of us are sufficient to drive large-scale and dramatic changes over long periods of time—is the basis for explaining any kind of evolutionary change. When we’ve published evolutionary simulations with robots that measure change over generational time, we claim that we are learning something about the evolutionary processes that have occurred over millions of years to help create new traits and new species.
7
John Stuart Mill created methods for inferring causal relations that includes
8
Robert Brandon,