We also need information about the population in which the trait is evolving. First, we need to know about the structure of the population, things like number of individuals, age at sexual maturity, and rates of immigration and emigration, to name a few. Second, we need to know about what Brandon calls the “selection environment”—what I think of as the world in which the population exists. This world includes both physical and biological factors. Most importantly, the world contains other individuals very much like you, and because of that similarity, you are likely to interact and compete with those other members of your population. All of these features in the world make up the “selection pressure.” Third, we need to know how the population responds to selection. This gets us back to how we measure evolutionary change, with Δ
and Δp.
If you can muster all of that information, you have what Brandon considers to be an “ideally complete” explanation of adaptation. But you can see the problem with these how-probably explanations: you basically need to know everything there is to know about the trait and the population! This is what makes the Grants’ work on the ground finches in the Galapagos so impressive: they have over twenty years of data on the genetics and function of multiple phenotypic traits and over twenty years of data on the demography, selection environment, and responses to selection of the population of medium ground finches on the island of Daphne Major.
Keeping Brandon’s necessary and sufficient information in mind (Figure 2.2), you can see that one of the brilliant decisions that the Grants made was to select a population that was isolated (very little immigration and emigration), small, and in a simple selection environment (open habitat with only a few other animal and plant species). As Wake Forest University’s David Anderson, another bird expert working in the Galapagos says, the birds on those geologically new and ecologically simple islands suffer out in the open.
What Anderson means by “suffering out in the open” is that humans who spend the time to observe carefully in the Galapagos can actually watch many events that have huge evolutionary impacts. For example, Anderson watches in lean years as Nazca boobies can only produce a few or feed some of their chicks. Reproductive success or failure is there, out in the open, for him to observe.
Make babies and help them make babies. If you are a Galapagos finch and you do this better than other Galapagos finches, then you are a winner in the game of life. Your score is based on how well you do relative to others in your population. If you are the best, you get a score of 1.0. If you are the worst and don’t produce any offspring, you get a score of 0.0. This score is called your “evolutionary fitness.”
Scoring the game of life is just the beginning. Once you have the score, the natural question to ask is, why do some individuals play the game better than others? And then, what about the individual and its interactions with its world matter? When you can answer these questions, then you’ve got a handle on which traits are important, how those traits function, what in the world selects individuals, and how the population responds to those selection pressures.
Anderson and the Grants were both lucky and smart—they managed to find an environment in which this scoring is, if not easy to do, at least possible. Most biologists don’t have this advantage. Thanks to our decision to study evolving robots, my colleagues and I suddenly found ourselves in a position a lot like that of the biologists studying Galapagos finches: we could watch a population that suffered out in the open. We can create our own simplified world, create individuals whose genetics we know, create a population whose structure is predetermined, and then carefully observe behavior and evolution as the individual robots interact with their world. Because we also set up what is called the “fitness function,” we are also the judges of the behavior of individuals. We become the agents of selection.
The idea of evolving robots is not new to my laboratory. Stefano Nolfi and Dario Floreano brought the concept to the general academic world with their book, Evolutionary Robotics, which was published in 2000. From the context of artificial intelligence, cognitive science, and engineering, they helped create a framework in which researchers could harness evolutionary processes—randomness, selection, and differential reproduction—to create without their guidance new kinds of behaviors and intelligence in mobile robots.
What we’ve done is to take Nolfi and Floreano’s evolutionary robotics framework and apply it to biology (Figure 2.3). Whereas Nolfi and Floreano weren’t originally trying to build biologically realistic robots, that’s where we start. And the inspiration for that approach came from Barbara Webb, an invertebrate neuroscientist and behaviorist who figured out that she could use robots to test hypotheses about the neural underpinnings of animal behavior.[9] When this approach—using physical robots to test hypotheses about biological systems—is thought of in general terms, Webb calls the field biorobotics. The combination of these two approaches creates evolutionary biorobotics.
FIGURE 2.3. People evolve robots for two main purposes: to test ideas about evolution and to design new kinds of robots. In our laboratory at Vassar College we create evolving robots in physically embodied or digital form to test ideas about animals, evolution, and behavior. We also create evolving robots to make new designs for intelligent machines.
So if we’re going to build robots that can really play the game of life, they must be able to reproduce, have behaviors and other traits that are genetically heritable, and have limits placed on the number of offspring that can be reproduced. Putting these features into a robotic system gives us what we like to call the lifecycle of evolving robots (Figure 2.4).
To be frank, evolutionary biorobotics has four important limitations when it deals with extinct species and their evolution. First, as we discussed earlier when talking about the kinds of evidence that you need to explain an adaptation (Figure 2.2), analyses of past selection are fraught with potentially crippling and untestable assumptions about the genetic structure of the population; the genetics of traits in question; and the pattern, strength, and phenotypic targets of selection. Second, what you can reconstruct and test is only the ecological function of the character, the selection environment, and the response of the population to selection. Third, because we create model simulations with our robots, our reasoning is by analogy. So as we set out to explore the evolution of backbones in robotic fish, the best we could hope for was robust support—in digital and embodied populations—for the prediction that selection for swimming abilities drove the evolution of the backbone in real fish. In the worst case, the best we’d be able to say is the obvious: that different selection environments can produce different results in different robot-world systems. Fourth and finally, our use of digital and embodied robots interacting in constructed worlds grossly simplifies the animal, its environment, and the animal-environment interaction.
Still, there is much to be excited about: at the minimum, if varying our robotic backbones changes robotic behavior, at least we’d have a proof of concept that we were studying an important variable that may or may not have been under selection at some point. Second, the fact that robots evolve can give us insight into how the process of adaptation works, whether in robots or biological organisms. And at least we knew we were in good company: model simulations with digital agents have already been used, most notably by Charles Ofria and Richard Lenski at the Digital Evolution Laboratory at Michigan State, to test a range of biological hypotheses about evolution.
9
Barbara Webb, “Can Robots Make Good Models of Biological Behaviour?”