* You can change the size of the animal.
* You can isolate and change single parts, keeping all else constant.
* You can reconstruct extinct animals.
* You can create animal behavior from the interaction of the agent and the world.
* You can test hypotheses about how animals function in terms of biomechanics, behavior, and evolution.
Now you can see why I got interested in physical models to study backbones. But there is one point I have completely ignored so far: the ability to reconstruct the evolution and behavior of extinct organisms.
Consider the complexity of the marlin backbone. It is unusual enough that Block mentioned it in her pitch to get me working on the organism. The point is, of the fifty-eight thousand vertebrates, if we looked at a variety of species, you’d see a great diversity of backbones. Some species have a continuous collagenous rod lacking bones, called a notochord. Some have a series of vertebrae, bones that form the vertebral column. Some have something in between, with what looks like partial vertebrae forming around or along the notochord.
What’s more, we know from the fossil record that our earliest vertebrate ancestors lacked vertebral columns themselves, instead having only a notochord. This continuous axial skeleton evolved earlier in a group of animals known as the chordates. In addition to vertebrates, chordates include living nonvertebrate species, like sea squirts and lancelets. From some group of long-extinct, notochord-bearing chordates, the first vertebrates arose over 530 million years ago. There must have been some problem that being a vertebrate and then having bony vertebrae solved. The question was, what?
I first got into this evolutionary question when I was studying blue marlin. At the time, in the lab of Serge Doroshov at the University of California, Davis, I was studying white sturgeon, big freshwater fish that keep the ancestral notochord as their backbone, even as adults. I would film living ones and subject the backbones of dead ones to the same tests I was using on marlin backbones. The basic hypothesis was simple as can be: vertebral columns, by virtue of possessing rigid bones, would be stiffer in bending than would notochords. Our data suggested that this was correct.
It still didn’t tell us much about the why this trait in marlins evolved or why it did not in sturgeon. As Steve Vogel likes to say: “Biomechanics is about tactics, not strategy.” In other words, biomechanics can tell us about the functional consequences of different structures but not why those different functions may have conferred behavioral and evolutionary advantages to the individuals that possessed them. To make the leap to having anything relevant to say about the evolution of vertebrates, I had to assume (here we go again) that what we learned from two species of fish applied not only to other species of fish but also, in particular, to ancient swimmers like Haikouichthys. These little inch-long jawless fish lived some 530 million years ago and had what looks like little bits of irregularly shaped cartilage blobs on and around its notochord. Revisiting an idea first proposed two centuries ago by Sir Everard Home, Karen Nipper, an undergraduate working in my lab, and I figured that increased stiffness ought to be what you need to swim faster. A stiffer backbone would be a bigger spring, storing more energy that could be used to power the tail.
Knowing that it would be terrifically difficult to measure speed and backbone stiffness in many species (just measuring marlin and sturgeon took me several years), Nipper came up with an easy proxy for backbone stiffness: the number of vertebrae. She also had to find a stand-in for maximum swimming speed, which is notoriously difficult to measure: the swimming fish’s “propulsive wavelength,” roughly the curviness of its body as it swims. Fish with a large propulsive wavelength, like tuna, tend to swim much faster than fish with a small propulsive wavelength, like eels. When we correlated the propulsive wavelength with the number of vertebrae, we found a weak but statistically significant relationship. As the number of vertebrae increased, the propulsive wavelength decreased. Converting this proxy-based result back into our variables of interest, we expected that stiffer backbones would allow their possessors to swim faster than those with floppier backbones.
A complementary approach, known as the phylogenetic approach, pointed us in the same direction. A phylogeny is the branching pattern of ancestor-descendent relationships that describes the evolutionary history of any group of organisms. You can reconstruct these relationships and the timing of evolutionary change by building what is known as a phylogenetic tree—a network that clusters species according to their genealogy. Strictly speaking, a phylogenetic tree is a hypothesis about evolutionary relatedness; it can be tested by collecting new data about the shared features as well as data from newly discovered features and new species. Once you have a tree that is well-supported by a variety of data, you can use it to answer questions about the pattern of evolution. You can map out related features, like notochords and vertebral columns, onto the branches of the tree. You can learn what feature came first, how many different times the feature evolved, and what other traits your feature of interest evolved alongside. This ability to map changes in features, what phylogeneticists call character state evolution, is what makes phylogenetic analysis such a powerful tool.
Using a phylogenetic tree of vertebrates, Tom Koob, a biochemist formerly of the Shriner’s Hospital for Children, and I correlated the pattern of vertebral evolution with changes in swimming behavior. When you map out just the evolution of vertebrae onto a phylogenetic tree of living vertebrates, you get a big surprise: vertebrae appear to have evolved from notochords at least three times. Vertebrae convergently evolved in elasmobranchs (sharks, skates, rays), ray-finned fishes, and tetrapods (amphibians, reptiles, bird, mammals). “Convergent evolution” is a fancy phrase for the same feature—in this case, vertebrae—having evolved independently in different species. Convergent evolution excites the heck out of biologists because it is like naturally repeating an experiment and seeing if you get the same result. Convergent evolution is thus taken as indirect evidence for similar kinds of selection pressures—in different species at different times and places—causing a similar outcome. In the case of vertebrae, they appear to be a good solution to a similar evolutionary problem. But still the question: what is the problem that vertebrae solve?
Thinking fish, fish, fish, Koob and I overlaid on this pattern of convergent vertebral evolution the pattern of changes in swimming behavior. Because we really know so little about swimming speeds and accelerations in vertebrates—which is the same problem that plagued us in the biomechanical analysis—the correlation was weak and, therefore, disappointing. First off, we had to leave out the land-based tetrapods because few adult tetrapods have retained their ancestral fish-like bodies and swimming behaviors. Second, with only elasmobranchs and ray-finned fishes to compare, we only have two large points on the map. Given those caveats, what we think we see on the tree is that vertebrae are correlated with faster swimming. Observations of single species appear to bear this out: frilled sharks with notochords are slow and plodding; mako sharks with vertebrae are some of the fastest fish in the sea; paddlefish with notochords cruise along but are not acrobatic; salmon with vertebrae leap over waterfalls. We were left with the same expectation our biomechanical analysis generated: stiffer backbones would allow their possessors to swim faster than those with floppier backbones.
But this expectation—this prediction—even though it is based on biomechanical and phylogenetic data, isn’t satisfying because it leaves so many questions unanswered. Are the proxies for stiffness and speed reasonable? Is the phylogenetic tree accurate? What other parts of the body, like muscles and shape, influence stiffness and speed? Do we find only a weak correlation because other parts of the species are different too? Would the correlation hold up if we could measure top speeds seen in the wild? Might stiffness also impact other parts of swimming performance, like acceleration and turning? What are the trade-offs in performance with increased speed? And worst of all, these questions don’t even speak to the evolutionary question of the dynamic process of adaptation.