We have another choice that helps satisfy our curiosity. We can hit the target by knowing exactly where we want the robot to be in the evolutionary landscape. Unlike chemists trying to engineer enzymes of unknown structure with a targeted function, we, as mentioned in Chapter 3, take the reverse-engineering course, asking about the function of a given structure (rather than seeking a particular function). For example, we could just build a PreyRo with ten vertebrae and see how it functions. This is like dropping a paratrooper behind enemy lines. We can put individual robots at specific points on the evolutionary map and ask them to report back. These robots serve as probes, allowing us to go where no one has gone before. In homage to Star Trek, let’s use eponymism (see Chapter 3) and call these agents Evolutionary Trekkers, or ETs for short.
As we’ve defined them, ETs are not Evolvabots. They don’t evolve. Sorry. Just like the rules for Starfleet officers, the prime directive (General Order 1) applies, and ETs don’t participate in or alter the evolutionary trajectories of the prewarp life-forms that they study or encounter. Even with this limitation, they are a powerful complement to Evolvabots. ETs are the crew members testing the functional waters, so to speak, of different bodies and brains without the historical constraint that Evolvabots drag with them. Keep in mind that ETs cannot test different selective conditions that might drive a system to evolve from one place to another on the adaptive landscape. For this reason, ETs are a separate class of robots, testing hypotheses about the outcomes of evolution rather than the process itself.
Before we explore ETs I need to clarify some terms that I’ve tossed around willy-nilly. The term “evolutionary landscape” is also known as a “fitness landscape” or, in the original concept Sewall Wright created, an “adaptive landscape”: I use these terms interchangeably. The metaphor of a landscape gives us a way to conceptualize the hills of fitness heights and the valleys of fitness despair. Fitness is represented by contours lines on a two-dimensional map (Figure 7.2).
You’ve probably noticed that I didn’t show you all three of PreyRo’s evolving traits in Figure 7.2. That’s for the simple reason that maps with more than two traits are difficult to make and interpret visually. For example, for three traits in a three-dimensional surface, you need to be able to rotate the surface so that you can view the selection vectors from different angles. On top of that, literally, you then have to somehow visually code the fitness gradients. Folks more talented than me with visual graphics can manage. But we all fall down when it comes to illustrating maps with more than three dimensions.
Even though adaptive landscapes, as a visual tool, have severe limits, at least for the two characters shown here, they can be very instructive (Figure 7.2). To wit: we had previously decided that the number of vertebrae, N, and the span of the caudal fin, b, evolved independently with respect to each other, a pattern of character interaction that we called mosaic evolution (see Chapter 6). Because this N-b character pair is mosaic and thus uncorrelated, we can’t tell what will happen to one by simply looking at the evolutionary changes of the other. Instead, we make sense of the combined evolutionary history that they share, even if it is uncorrelated, by looking at the population’s evolutionary trajectory and the adaptive peaks in the N-b landscape.
What we see on the map is wild (Figure 7.2). From a bird’s-eye view we see multiple adaptive peaks, a chain of misty fitness mountains running from north to south. In between the peaks we find what looks like valleys and then a whole bunch of white space labeled “terra incognita.” All of the white space on the map, and probably some of the gray hilly parts too, is unknown territory.
Here we return to our main problem: adaptive topography can only be mapped if the population has been in that area—to that “there”—and played the game of life. Only when each individual gets a fitness score can we then calculate the population’s selection vector. For PreyRos my colleagues and I determined where the vector pointed (say, to having five vertebrae and a caudal fin span of 22.25 millimeters).[141] The selection vector represents the direction and magnitude of evolutionary change that selection alone would cause.
FIGURE 7.2. (facing page) Mapping the adaptive landscape. Top: We can use two of PreyRo’s evolving traits, span of the caudal fin, b, and number of vertebrae, N, to create the two-dimensional “morphospace” of the evolutionary map. The points represent the population’s average values for the traits at each generation, numbered 1 to 6 for the first evolutionary run. The black arrows represent the actual evolutionary change of the population from generation to generation. The gray arrows are the selection vectors. Each selection vector has a direction and a strength, with strength represented by the length of the arrow. The random evolutionary mechanisms (mutation, mating, and genetic drift) cause the difference between the selection vector (gray arrow) and the evolutionary vector (black arrow).
Bottom: Because the selection vectors point toward a local fitness peak, they can be used to map the adaptive landscape. The points here are the same average values of the population from above (generations 1 to 5). The arrows are the same selection vectors. Adaptive peaks and ridges can be of any shape. The shape and placement of the fitness features that I’ve drawn here are wildly speculative, given that we have only five selection vectors. Terra incognita (any white area) refers to areas that are unknown and therefore unmapped. Note that the lack of selection pressure (short arrow) means that the population is on an adaptive peak.
But selection does not act alone. You can see in the top diagram that those selection vectors don’t predict exactly where the population moves on the landscape. Deviations between the selection vector and the actual evolutionary trajectory, which can be thought of as another vector, are caused by random processes (mutation, mating, drift). What the selection vectors do, though, is point uphill toward the closest fitness maximum or “adaptive peak.”
Selection maps evolutionary terra incognita. With that in mind, look at our population of PreyRos in generation 3 (find the generation number in the top diagram and then look for the corresponding point in the bottom diagram, Figure 7.2). That population sits in what I’ve labeled as a valley, an area of low fitness compared to two or more close-by regions of higher fitness, the adaptive peaks to the north and the south.[142] The selection vector for generation 3 is small and points due south. The small magnitude of the vector means that the population is nearly sitting right on top of an adaptive peak, with just a little bit of climbing in the b-dimension to reach the local summit. The fact that this generation-3 population never summits but instead shifts off of this peak in generation 4 illustrates one of the great ironies of evolution: random factors like mutation and mating can displace a well-situated population, adaptationally speaking.
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We calculate the selection vector as follows. First, the fitness scores determine who gets to mate. For PreyRo, the top three out of six get to mate, with the first-, second-, and third-place winners contributing to six, four, and two gametes, respectively, to the mating pool. Second, before we mutate those gametes or allow them to join to make offspring, we calculate the average values of the traits of the pre-mutation and pre-mating offspring. Third, this average of the traits is the position of the head of the selection vector’s arrow, with the vector’s tail anchored at the average of the parental population.
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Warning: I made the peaks on this map in an intuitive and qualitative manner. In other words, I guessed. Well, it’s a bit better than guesswork, but not much, given how little data we’ve got here. Knowing that the selection vectors point uphill, I knew where at least some peaks or ridges needed to be. The guesswork comes in as follows. For the selection vectors from generations one and two, I assumed that they were pointing to a ridge. I could have assumed that they pointed to two separate peaks. I didn’t, though, because the direction that they point is similar, south-south-east for generation one and south-east for generation two, and I took that to mean that they were pointing at the same adaptive structure. This guesswork shows you how much data you would need to create a comprehensive adaptive landscape.