The span of the caudal fin (bottom graph) shows different patterns in both runs, with an initial decrease followed by an increase in the first run, and a longer, stronger decrease followed by a longer, stronger increase in the second run. The correlation of the span of the caudal fin with Nover the first five generations is positive in the first run and negative in the second. This inconsistent pattern of correlations with N is consistent with a hypothesis of mosaic evolution for these two characters with respect to each other.
We had also predicted an in-phase pattern of concerted evolution for the character pairing of number of vertebrae and the span of the caudal fin. Here, though (Figure 6.9, bottom graph), the direction of the correlation reverses from one run to the next, at least over the first five generations. Because we don’t see the same pattern in the two runs, this seems like a clear case in which we have refuted the hypothesis of concerted evolution. The default or “null” hypothesis is that the two characters show mosaic evolution, at least with respect to each other.
Mosaic evolution was a surprise. We thought for sure that these two characters, number of vertebrae and the span of the caudal fin, would show a functional connection because they are tightly connected in terms of anatomy and physiology. Again, this is why you run the experiments! This result highlights the fact that we always have to test our assumptions. I want to point out, though, that you could imagine different situations in which you might see a tight and consistent correlation between the two characters. For example, if we were just looking at swimming speed alone, devoid of any evolutionary environment, I’d predict that we’d see a relationship. So there!
It’s time to put our Tadros to bed. Tadro3, Digi-Tad3, and PreyRo (Tadro4) have done their jobs. They have evolved. Their characters—tail stiffness and the number of vertebrae—evolved and, in so doing, tested our hypotheses about what kinds of selection pressures may have driven the evolution of early vertebrates. We’ve learned that whereas selection for improved feeding behavior seems unlikely to have been the sole driver of increased number of vertebrae, when coupled with fleeing from predators, it becomes a much more powerful selective pressure.
With Tadros, we’ve seen how to start with the simplest autonomous agents you can imagine and then add in only the smallest bit of complexity needed to make a new and/or improved model of your biological system. Moreover, the simplicity of Tadros as embodied brains gave us an opportunity to understand the physical basis of the intelligence and behavior of feeding and fleeing. Finally, Tadros serve as working examples of this special category of robots that we’ve come to call Evolvabots.
As the Tadros sink into slumber, we still have much to explore.
Chapter 7
EVOLUTIONARY TREKKERS
REMEMBER: NO MATTER WHERE YOU GO, THERE YOU ARE.[135] That’s one of the most frustrating things about the otherwise wonderful Evolvabots. They visit just a few places in a vast morphospace of evolutionary possibilities (Figure 7.1). As we’ve seen, a population of Tadro3s or PreyRos take but a single path out of a huge number of possible trajectories. Which path they take and how quickly they travel it depends on those three all-encompassing classes of evolutionary mechanism that we introduced in Chapter 2: selection, randomness, and history. Those mechanisms determine what we observe at any time and place in the population’s evolutionary journey: a generation of individuals playing the game of life. Although the game of life is fascinating to watch, sometimes the “there” you are observing is not the “there” where you want to be.
We were curious about the travels of our two different populations of PreyRos. Why had the populations never evolved more than an average of 5.7 vertebrae? Our guess, based in part on the behavior of many populations of Digi-Tad3s, was that the PreyRos had reached an equilibrium or, possibly, an optimum number of vertebrae. But what, if anything, makes 5.7 optimum? Why not 8 or 10 vertebrae? What if PreyRos evolved 12 vertebrae: would the world end?
Why not? What if? Questions about the counterfactual dogs anyone interested in just about anything with a history, including the process of evolution. Whether we are studying the evolution of life-forms,[136] engineering solutions, or artificial intelligence, our curiosity about what didn’t happen[137] or what might have happened motivates much of what we do. A central curiosity-driven question regarding any evolutionary system is: Why did some forms evolve while others haven’t?[138] From this question springs a host of related queries:
* Why didn’t the population evolve along a different path?
* What if the population were to evolve again, from the same starting point? Would it evolve along the same path?[139]
* Why haven’t all imaginable forms evolved?[140]
How to proceed? Curiosity leads us to a classic forbidden-fruit conundrum, presenting us with at least three possible actions. First choice: don’t bite the apple. We could simply let our findings be, content that PreyRo, in two different runs, increased the number of vertebrae under selection for enhanced feeding behavior and predator avoidance. We’ve learned much. Move on to the next study.
FIGURE 7.1. (facing page) There you are. Evolution samples only a small portion of the morphospace, the area of all possible trait combinations. The entire second evolutionary run of PreyRo is shown here, with the points representing the average values of the population for the pair of traits that we found to be evolving in an uncorrelated, mosaic pattern, the span of the caudal fin, b, and the number of vertebrae, N. The thin vertical and horizontal gray lines intersecting each point represent the ranges of each trait for a given generation. The rectangles that those range lines touch represent the population’s total phenotypic footprint, the area that the population has sampled.
Top: The population of PreyRos evolves over a very small region of morphospace in ten generations. The rectangle encloses all individual PreyRos ever evolved.
Bottom: A close-up of the evolution of the population in b-Nmorphospace shows a shrinking phenotypic footprint as selection removes variation, here represented by the ranges, from the population. Loss of variation in traits is a sign of selection.
But no, for our curiosity remains. Remember this inscription that taunts Digory in C. S. Lewis’s The Magician’s Nephew:
Madness, surely, is not something that we desire. If wonder we must, then adventure we take. Strike the bell! Here’s an adventurous choice: employ “directed evolution” and then try to force the system to evolve toward a prescribed goal or along a particular path. Although this approach to engineering might seem new—it has garnered much attention recently because of its success in the synthesis of novel proteins—you could argue that directed evolution is what breeders have been doing for millennia to domesticate the likes of cattle, rice, and corn. It works, but only to a certain point and in certain cases. No matter how much I want flying cows or talking corn, it ain’t gonna happen given the limits genetics and physics impose. Just because you have a target doesn’t mean that you’ll be able to hit it or, for that matter, that it’s even hittable.
135
This opening line is from the movie,
136
Even though we spoke at length in Chapter 2 about evolutionary theory, you may hunger for more (or for a refresher). Consider starting at this website, “Understanding Evolution,” \evolution.berkeley.edu/evolibrary/home.php.
137
You may recognize this problem from our discussion of scientific inference in Chapter 2. We only ever see a limited number of cases of all of the phenomena we seek to understand. Once we infer some property of all possible cases from witnessing just a few, we are always worried, with good reason, about the other cases. What if one of those unseen cases falsifies my idea of how the system is working? With that very real concern in mind, we conduct additional tests, make sure that we sampled the study cases within the system in a way that best represents all of the possible cases, and “prove” by being unable to disprove after repeated attempts to do so.
138
Some folks argue that this question is the motivation for most of evolutionary biology. It was addressed by Sewall Wright, who, in the first part of the twentieth century, extended individual genetics to the genetics of populations. In so doing, he helped propel the modern synthesis of evolutionary theory, which includes his concept of an “adaptive landscape,” wherein a population has an evolutionary path that is determined by, you guessed it, history, selection, and random genetic effects. Check out his paper: Sewall Wright, “The Roles of Mutation, Inbreeding, Crossbreeding and Selection in Evolution,”
139
This question springs from Steven J. Gould’s point about the importance of historical contingency. He argues that chance events in life make it highly unlikely that any species would evolve along the same path given a second opportunity to do so. Steven J. Gould,
140
This question is a variant on these: Why is morphospace clumped? Why is biodiversity limited? What kinds of life-forms are physically possible? Genetically possible?