Given that we know the Tadro3 world inside and out, we sure as heck-fire better be able to explain the unexpected directions in the evolution of the population Tadro3s.
Let’s jump-start the process of interpretation by revisiting the big picture. We were interested in creating Evolvabots that could test a hypothesis about the evolution of early vertebrates: natural selection for enhanced feeding behavior drove the evolution of vertebrae in early vertebrates. From this hypothesis we came up with a primary prediction: selection for enhanced feeding behavior will cause the population of Tadro3s to evolve stiffer tails. Implied in that prediction is another: the evolutionary change will be directional, moving the population from flexible to stiff tails in concert with ever-improving feeding behavior.
What’s clear is that these predictions are too simple. Take a close look at the reality of the hard data (Figure 4.1). The average score for feeding behavior[32] increases greatly from generation 1 to 2 while the standard deviation,[33] represented by the length of the bars emanating from the filled circles, decreases. This initial change sure looks like the direction we predicted: enhanced feeding behavior. The decrease in variance might be expected, too, because selection was just picking individuals with the highest feeding behavior to reproduce. But in the very next generation, 3, the average score for feeding behavior drops and then continues downward through generation 4. What’s up, Doc?
At first glance, this downward trend looks crazy-wrong: how can we select for improved feeding behavior and get just the opposite? The short answer is this: in generation 2 differences in feeding behavior among individuals were not large enough to cause differences in reproduction. All three individuals contributed the same number of gametes (egg and sperm)—two—to the mating pool of six gametes. Our mating algorithm assigned differences in reproductive output according to the differences in fitness between individuals. Even though the fitness function assigned slightly different numbers to each individual—based on their abilities to swim quickly, reach the light target quickly, stay and feed near the light target, and move around smoothly—in generation 2 those fitness differences were just too small to matter.[34]
When individuals contribute equally to the next generation, we have an evolutionary tie. This tie means that the parents are likely to make a generation of offspring that looks, on average, like themselves. In evolutionary terms, there was an absence of selection, or no selection pressure. Either phrase might sound a bit inaccurate, as we had selection judging individual Tadro3s in a given generation using our fitness-function scorecard. But keep in mind that ultimately it’s differential reproduction among individuals that makes evolution by natural selection work.
In the absence of selection, how, then, do we get evolutionary change? Recall Darwin’s lament from Chapter 2: “Great is the power of steady misinterpretation.” He was referring to the fact that many scientists overplayed the power of selection, to the extent that they ignored other evolutionary mechanisms and, as a result, saw adaptations in every cranial bulge and fingerprint, when sometimes randomness is what’s at work.
Darwin had little hard evidence to counter this misconception because he lacked our understanding of genetics, which, ironically, was developed by a contemporary of Darwin’s, Gregor Mendel, but languished in an obscure journal until the early twentieth century. With our current understanding of genetics, of course, we know that random genetic changes are always occurring, both in the germ-line and other cells. More randomness can come into play during mating. Although some mating is decidedly not random, for many organisms it is. We set up our Tadro3 population to engage in random mating. After each gamete was given a certain probability of mutating or not, we took all six of the gametes the parents produced, and combined the gametes into pairs randomly. Those pairs of haploid gametes combined to give us the new diploid genome for each new baby Tadro3.
At this point, my colleague Rob Root, a mathematician and central collaborator on many of our Tadro projects, would want me to remind you that we have a problem with our randomness because we are operating in the realm of “the mathematics of small numbers.” Our population size of three is simply too small for the statistical assumptions of flip-a-coin randomness to hold. The result, if you were flipping coins, is that you could easily hit heads, heads, and heads, three in a row. You’d say that you were “on a roll,” and it sure would not look like a random process until you flipped the coin about twenty more times. Geneticists call this mathematical description of what happens in populations of small size “genetic drift.”[35]
Genetic drift, in the absence of selection, produces evolutionary change in directions that are random with respect to both phenotype and genotype. Because drift is random, neither it nor any other random mechanism will produce the kind of long-term directional pattern that we recognize as the signal of adaptation. Only selection is equal to the task.
For our tiny population of Tadro3s, the absence of selection in some generations allows the combined random chance of two sources of randomness—mutation and genetic drift—to be the prime movers. Over a generation or two, we can even be fooled. Our population can be “on a roll” that may look like a directional and evolutionary trend. This is what happened to the average feeding behavior from generations 2 through 4. Random changes just happen to have combined to decrease the feeding behavior. Chance favors no one.
What’s really important to keep in mind, and to keep Darwin from being on a roll in his grave, is that over generational time random changes—which occur with or without selection—are, indeed, mechanisms of evolutionary change. This fact is worth restating: you can have evolution with or without selection. We’ve shown this to be true with our population of Tadro3s. It’s also worth pointing out that the random genetic processes are always occurring and that they operate independently from selection. There is a third mechanism influencing how populations evolve, however: history (Figure 4.2).[36] The independence of these three kinds of mechanisms was beautifully demonstrated by Professor Rich Lenski, of Michigan State University, in his work first on bacteria and then on what he calls “digital organisms.” Lenski showed that the genetic and phenotypic variation that exist in your population at any time constrain the population’s future evolutionary possibilities. Any finite population can only evolve in certain directions, directions that are constrained by the underlying genetic coding of the phenotypes and the responses of individual organisms to the particular environment. Another way to think of this history effect is that selection can only select, as we saw in the Tadro3 generations 2 through 4, when individuals vary. Selection can only result in evolution-by-selection when genes code, at least in part, those individual variations.
32
This single number that measures feeding behavior is a composite from the fitness function that we developed in Chapter 3. We defined better “relative fitness” for an individual in a given generation, relative to other individuals in that generation, as the sum of their scaled values for increased swimming speed, decreased time to the light target, reduced distance from the light target over the course of the whole experiment, and reduced wobble as they moved. These relative fitness values only make sense within a generation relative to other competing individuals at that time and place: they can’t be compared across generations. To make those cross-generation comparisons for Figure 4.1, we compared any individual’s performance to the average of all individuals over all ten generations scaled by the standard deviation of the particular sub-behaviors, speed, time, distance, and wobble. In statistical terms, we summed up the z-scores of each sub-behavior for each individual.
33
In statistics one standard deviation, which changes in value depending on the situation, is a measure of how far away from the average most numbers in a group of numbers fall. A small standard deviation means that most numbers in the group are close to the average of the group.
34
If you are interested in the mathematics of the mating that we used, you can find the details in our paper on the evolution of Tadro3: J. H. Long Jr., T. J. Koob, K. Irving, K. Combie, V. Engel, N. Livingston, A. Lammert, and J. Schumacher, “Biomimetic Evolutionary Analysis: Testing the Adaptive Value of Vertebrate Tail Stiffness in Autonomous Swimming Robots,”
35
In John Gillespie’s
36
Students and scholars of evolutionary theory will be quick to interject, what about sexual selection, gene flow, genetic drift, epistasis, mating, and developmental processes as evolutionary mechanisms? True. Those are other identifiable mechanisms of evolutionary change. Lenski’s point, which I follow here, is that any mechanism fits into a category of either being deterministic or random. Natural selection is deterministic in that once you identify all of Brandon’s information (see Chapter 2), you can predict evolutionary outcome. Random factors like mutation or assortative mating have outcomes that are not predictable. I continue to be influenced by this fascinating and illuminating paper: M. Travisano, J. A. Mongold, F. Bennett, and R. E. Lenski, “Experimental Tests of the Roles of Adaptation, Chance, and History in Evolution,”