The idea of evolution proceeding along some kind of scale from simple to complex also has pre-evolu-tionary roots. Aristotle, for example, ordered animals according to the degree of perfection of their eggs (see Gould, 1977). Later religious thinkers then described an elaborate scale of nature, or scala naturae, with inanimate materials on its bottom rung and archangels and God at the other extreme. The early evolutionists, such as Lamarck, transformed this static concept of a scala naturae into a dynamic phylogenetic scale that organisms ascended as they evolved. Darwin himself had doubts about arranging species on a scale, but most of his followers had no such qualms (Bowler, 1988). Even today, the phylogenetic scale is taught in many schools and it persists in medicine and academia. For example, the National Institutes of Health's (NIH) guide for institutional animal care and use still recommends that researchers, whenever possible, should work with "species lower on the phylogenetic scale'' (Pitts, 2002, p. 97). On the other hand, most contemporary evolutionists have pronounced as dead both the scala naturae and its postevolutionary cousin, the phylogenetic scale (Hodos and Campbell, 1969). What do those modern evolutionists cite as the scales' cause of death?
One fatal flaw in the idea that species evolve along a single scale is that, as we now know, evolution made at least some species simpler than their ancestors. Salamanders, for example, are much simpler, especially in brain anatomy (Roth et al., 1993), than one would expect from their phylogenetic position. Even more dramatically, the simplest of all animals, the placozoans, are now thought to have evolved from far more complicated ancestors (Collins, 1998). As more and more molecular data are used to reconstruct phylogenies, it is becoming apparent that such secondary simplification of entire animals has occurred far more frequently than scientists had previously believed (Jenner, 2004) - perhaps because they were so enamored of the phylogenetic scale. A second major problem with scala naturae thinking is that the order of species within the scale depends on which organismal features we consider. For example, many fishes would rank higher than mammals if we based our scale on skull complexity, which was reduced dramatically as early mammals evolved (Sidor, 2001). Similarly, dolphins rank high if we look only at brain size, but relatively low if we consider neocor-tical complexity, which was reduced as the toothed whales evolved (Morgane and Jacobs, 1972). Most people tacitly agree that 'higher animals' are warmblooded, social, curious, and generally like us, but once we try to be more objective, the single 'chain of being' (Lovejoy, 1936) fractionates into a multitude of different chains, none of which has any special claim to being true.
This multiple-chains idea becomes self-evident once we have grasped that species phylogenies are just like human family trees; they are neither ladders, nor trees with just a single trunk, but bushes or tum-bleweeds (Striedter, 2004) with branches growing in divergent directions. Within a given branch, or lineage, complexity may have increased at some points in time and decreased at others, but even if complexity increased more frequently than it decreased, the overall phylogeny would fail to yield a single scale, because complexity tends to increase divergently in different lineages. For example, bats, honeybees, and hummingbirds are all incredibly complex, compared to their last common ancestor, but they are each complex in different ways. Of course, we can pick one parameter and build a scale for that - we can, for instance, compare the ability of bats, honeybees, and hummingbirds to see ultraviolet (UV) radiation - but different parameters might well yield different scales. Simply put, changes that occurred divergently in different lineages will not, in general, produce a single overarching scale. This insight is old hat to evolutionary biologists, but news to many neuroscientists (Hodos and Campbell, 1969). In part, therefore, the persistence of scala naturae thinking in the neurosciences reflects a lack of proper training in contemporary evolutionary theory. In addition, I suspect that human minds possess a natural tendency for ordering disparate items linearly. Such a bias would be useful in many contexts, but it would make it difficult to comprehend (without training) the divergent nature of phylogeny.
Although scala naturae thinking persists in neuroscience generally, evolutionary neuroscientists have labored to expunge its ghost. For example, a consortium of 28 comparative neurobiologists revised the nomenclature of avian brains to replace the terms neostriatum, archistriatum, and paleostriatum - which suggested that brains evolved by the sequential addition of new brain regions - with terms devoid of scala naturae overtones (Reiner et al., 2004a, 2004b; Jarvis et al., 2005). Some of the replacement names are terms that were already used for brain regions in other vertebrates; they reflect our current understanding of homologies. However, some of the new terms - e.g., nidipallium and arcopallium - are novel and intended to apply exclusively to birds. These novel names were coined because bird brains, particularly bird forebrains, have diverged so much from those of other vertebrates (including reptiles) that strict one-to-one homologies are difficult, if not impossible, to draw for several regions (Striedter, 1998, 1999). Thus, the revised terminology reflects a new consensus view that avian brains did not evolve by the sequential addition of new brain areas, yet also reminds us that bird brains are full of features that evolved quite independently of those that feature in mammalian phylogeny. In other words, the new terminology avoids scala naturae overtones and, instead, combines the notion of a common plan with that of divergent complexity.
As comparative neurobiologists reject the notion of a scala naturae, they stand to lose a central part of their traditional justification for working on nonhuman brains. No longer can they argue that research on other brains must be useful because nonhuman brains are always simpler, and therefore easier to comprehend, than human brains. Instead, they must admit that some nonhuman brains are stunningly complex and, more importantly, that their phyloge-netic paths toward complexity diverged from the primate trajectory. That is, complex bird, fish, or insect brains are not mere steps along the path to human brains, but the outcome of divergent phylo-genies (see Evolution of the Nervous System in Fishes, Do Birds and Reptiles Possess Homologues of Mammalian Visual, Somatosensory, and Motor Cortices?, Evolution of Color Vision and Visual Pigments in Invertebrates). Does this suggest that research on nonhuman brains should cease to be funded? I do not think so, but the justification for working on nonhuman brains ought to be tweaked.
One obvious alternative justification is that all brains are likely to share some features, especially if they come from close relatives. Another good justification for research on nonhuman brains is that, compared to human brains, the former are much more amenable to physiological and anatomical research. This line of justification assumes that the model differs from the target system only in those respects that make the model easier to study, and not in the respects that are modeled - an assumption that sometimes fails. It now appears, for example, that the auditory system of owls, which was generally regarded as an ideal model for sound localization in vertebrates, exhibits some highly specialized features (McAlpine and Grothe, 2003). This finding, at first glance, suggests that research on bird brains is wasteful, but this is a simplistic view. Research on the owl's auditory system has taught us much about how neurons compute behaviorally relevant information and it serves as an invaluable reference against which we can compare sound processing in other species, including humans. Furthermore, some differences between a model and its target can lead to surprising discoveries. Much might be gained, for example, from studying why some nonhuman brains are far more capable than primate brains of repairing themselves (Kirsche and Kirsche, 1964). Thus, model systems research can be useful even if the model is imprecise. A third, less frequently discussed, justification for examining the brains of diverse species is that comparative research can bring to light convergent similarities, which in turn might reveal some principles of brain design. For example, the discovery that olfactory systems in both vertebrates and many different invertebrates exhibit distinctive glomeruli strongly suggests that those glomeruli are needed for some critical aspects of odorant detection and analysis (Strausfeld and Hildebrand, 1999).
Therefore, research on nonhuman brains need not be justified in terms of a presumed phylogenetic scale. Instead, comparative neurobiology is valuable because (1) all brains are likely to share some features, (2) nonhuman brains are more amenable to some types of research, and (3) the study of diverse nonhuman brains can lead to the discovery of design rules for brains. Historically, only the first of these alternatives has been widely discussed, but all are logically sound, and none depend on the existence of a scala naturae.
Was this article helpful?