One of the central components of current evolutionary theory, particularly the
modern evolutionary synthesis, is population thinking [1]. Of the oft-cited four forces of evolution (drift, selection, recombination, and mutation), two of these are explicitly linked to populations. And the most commonly used species concepts (reproductive isolation) is also dependent on population-level phenomena. Population thinking has not only provided sufficient explanations for understanding the distribution of natural variation, but has also provided us with advanced computing tools such as
genetic algorithms.
Yet any individual organism in a population can exhibit traits that are deviate from the population norm. For example, mutation and recombination occur within individuals, and genetic drift (or
neutrality) can often involve very small subpopulations. While the modern synthesis does a very good job of describing phenomena that define a species or variation across phylogeny, I propose that a model that unifies aberrant, individualistic behaviors with more normative and aggregate population-level phenomena is needed.
What is the missing piece then? A natural fit to this is
emergence theory, which comes in a number of varieties [2]. One of the main predictions of emergence theory is self-organization, or the notion of order from chaos. Self- organization should be expected in natural populations because individuals act both in competition and collectively to produce unsupervised patterns at the population level. These are the very same patterns that we identify when we say that a population has undergone selection or some other form of differential reproduction.
In his book "The Origins of Order", Stuart Kauffman [3] uses the term "order for free". Order for free refers to the seeming lack of thermodynamic cost to the spontaneous generation of order observed in self-organizing systems. Of course, self-organizing processes must conform to thermodynamic constraints, but nevertheless result in highly ordered patterns. Two common examples of self- organization in biology are morphogenesis (a developmental process - [4]) and insect nest building (a behavioral process - [5]). In the former example, highly-parallel gene expression and intercellular signaling result in a highly patterned and repeatable cellular architecture across organisms. In the latter example, highly-parallel interactions between organisms produces a highly patterned but variable nest architecture across subpopulations in the same species.
Figure 1. Examples of self-organization that involve animal populations. UPPER LEFT: examples of a honeybee hive and a wasp's nest, UPPER RIGHT: example of epidermal morphogenesis in worm (Courtesy, wormbook.org - Chapter on Epidermal Morphogenesis), LOWER LEFT: example of emergent states of activation in the human brain (Courtesy, Figure 2, Chialvo, Nature Physics, 6, 744–750 -- 2010), LOWER RIGHT: example of fish shoaling (Courtesy, Wikipedia).
The question that naturally arises from this is
how evolution by natural selection over multiple generations is related to these examples, since development and behavior both shape and constrain evolution. And while the answer is not straighforward, we can learn much from the structure of these examples. The first lesson is that while evolution is a population-dependent process, it is also dependent upon highly-parallel interactions between individuals. We can see this in many of the competitive and cooperative processes that define mating and social interaction. While this may seem to require no paradigm shift, the role of these processes in regulating the aggregate properties of the population is not a consideration of modern theory.
What are the regulatory processes that influence the evolution of a population? To do this, we will take a complex adaptive systems approach [6] to emergence. This implies that there are
top-down, bottom- up, and hierarchical components of evolution in addition to the four factors proposed by current theory. An example of a top-down force in evolution is constraint by
historical contingency. Historical contingency acts upon all members of a population more or less uniformly, and provides a organizational scheme to developmental and physiological processes such as gene expression. By contrast, a bottom-up force of evolution is embodied in the randomness produced by mutation and recombination. This is brought to the population by individual organisms. This randomness is not totally independent across individuals, but does provide a mechanism for individuality.
Neither top-down nor bottom-up mechanisms are particularly different from what is accounted for in current perspectives on evolution. Yet there is a third component (hierarchy) that unifies top-down and bottom-up components into the context of a complex system. The hierarchical component of evolution by natural selection is related to hierarchical structure of a population. By this I not only mean relationships between individuals in a population, but also the trophic levels of organismal organization (e.g. cells, tissues, organs - [7]). Hierarchical organization, particularly the multiscale nature (e.g. relationship between scales of organization) of evolving populations, is key to driving self- organization in individual animal body [8], and may provide a means to understand variability across instances of emergence in long-term evolution.
In considering the difference between developmental emergence (in which deviations are often deleterious) and social insect emergence (where deviations across nest and hive designs are often observed), evolution is much more like the latter than the former. This may be due to robustness mechanisms specific to biological evolution (e.g. modularity and evolvability) which are somewhat beyond the scope of the current essay, but have interesting implications on the emergence of evolutionary systems.
Besides acting to regulate the current population, these alternate components of evolution also operate on multiple generations of individuals. However, to observe the emergence of features in long-term processes such as evolution, we must consider a time scale between that of a single reproducing organism and the traditional signatures of evolution by natural selection. Think of emergent natural selection as a series regulatory processes as acting upon a small number of generations. This allows us to see the origins of long-term evolutionary changes.
Understanding the links between ultimate outcomes and proximal events in this way allows us to talk about "evolution for free", a play on the coinage "order for free" and a phrase I have encountered informally amongst colleagues. Specifically, an emergent view allows us to place evolution for free in a less evanescent context. In addition, placing evolution in this emergent context allows us to build sets of models that more explicitly link complex behaviors, brain function, and developmental processes to evolutionary outcomes.
Examples from the Nervous System:
One example of how this might be useful is in what is typically referred to as
exaptation. Evolution for free might explain the evolution of color vision in primates on top of an existing circuit [9, 10]. This may also be true in cases where the primate color vision system utilizes existing cortical areas for purposes of processing. In terms of a fitness landscape, this could lead to a fitness amplifier, or perhaps an evolutionary “ratchet” that moves a population towards fitness peaks more quickly. Another example may involve the evolution of
ocular dominance columns, which self-organize in development but are also similar across phylogenetically-distant taxa.
The authors of [11] refer to the self-organization of ocular dominance columns as canalization, a concept which has many analogies with evolutionary dynamics.
Canalization [12] occurs when organisms are constrained to the same developmental pathway, and common developmental pathways are roughly equivalent to canals (hence canalization). These canals might be thought of as a series of minima with respect to energy required or changes in gene expression, or as linkages in an
nk-boolean network [13]. This can be short-circuited through stresses such as heat shock, which uncover a lot of deleterious variants. Self-organization, on the other hand, is quite different. Self-organization is related to emergence, which is the production of higher-order patterns from disorderly interactions among cells or organisms (e.g. "stripes" from white noise). The way in which you might get to an emergent structure (such as a termite's nest) is not constrained in "development". Rather, there are many alternate pathways and patterns of interaction to the same structure (in this case, it is the structure which is "canalized", not the means of getting there).
You might say that while canalized phenotypes are products of path-dependence (e.g. developmental contingency), self-organized aspects of the phenotype are path-invariant but structure-dependent. In the visual cortex, interactions between inputs might produce an interference pattern that creates spatial boundaries and, yes, maximally efficient patterns of information storage. Much like a box of Neopolitan ice cream (which has NOT undergone canalization), there is competition for space among multiple types of output. As long as those inputs are mapped to a cortical-like structure, self- organization is the predominant driving force.
Figure 2.
LEFT: Neopolitan ice cream, an intentionally striped form.
MIDDLE: Striping in an ocular dominance column (courtesy of [14]),
RIGHT: D-Stat mRNA expression in
Drosophila (courtesy of [15]).
Conclusion
I have provided a very rough outline of what I believe to be a necessary component of evolutionary theory largely overlooked by contemporary theorists. It is not so much a matter of being "overlooked" as is a more explicit grounding of complexity theory in the relationship between individuals and populations. There has been much spirited discussion regarding the merits and shortcomings of
group vs. individual selection, but that is not what I am proposing here. This alternative view still champions population thinking -- but is done so in a way that does not obscure the role of individualistic, non-normal events that occur in the course of natural history. Based on observations of
assortative mating and
differential reproduction in nature, we might ask: if a trait is rare in the population is it also rare with regard to the individual? As with most posts on this blog, this is a work in progress. Suggestions for future directions are welcome.
References:
[1] see Sober, E. (1980). Evolution, population thinking, and essentialism. Philosophy of Science, 47(3), 350-383 for more information on population thinking.
[2] Reid, R.G.B. (2007). Biological Emergences: evolution by natural experiment. MIT Press, Cambridge, MA.
[3] Kauffman, S.A. (1993). Origins of Order: self organization and selection in evolution. Oxford University Press, Oxford, UK.
[4] Wartlick, O., Mumcu, P., Julicher, F., and Gonzalez-Gaitan, M. (2011). Understanding morphogenetic growth control: lessons from flies. Nature Reviews Molecular Cell Biology, 12, 594-604.
[5] Camazine, S., Deneubourg, J-L., Franks, N.R., Sneyd, J., Theraulaz, G., and Bonabeau, E. (1992). Self-Organization in Biological Systems.
[6] Holland, J.H. (1992). Adaptation in Natural and Artificial Systems. MIT Press, Cambridge, MA.
[7] Alicea, B. (2008). Hierarchies of Biocomplexity: modeling life's energetic complexity. arXiv Repository, arXiv:0810.4547.
[8] Hunter, P.J. and Borg, T.K. (2003). Integration from proteins to organs: the Physiome Project. Nature Reviews Molecular and Cellular Biology, 4(3), 237-43.
[9] Surridge, A.K., Osorio, D., Mundy, N.I. (2003). Evolution and selection of trichromatic vision in primates. Trends in Ecology and Evolution, 18(4), 198-205.
[10] Barton, R.A. (2010). Evolutionary specialization in mammalian cortical structure. Journal of Evolutionary Biology, 20(4), 1504-1511.
[11] Kaschube, M., Schnabel, M., Lowel, S., Coppola, D.M., White, L.E., and Wolf, F. (2010). Universality in the Evolution of Orientation Columns in the Visual Cortex. Science, 330, 1113-1116.
[12] Waddington, C.H. (1960). Experiments on canalizing selection. Genetical Research, 1, 140-150.
[13] Bassler, K.E., Lee, C. and Lee, Y. (2004). Evolution of developmental canalization in networks of competing boolean nodes. Physical Review Letters, 93(3), 038101.
[14] Stiles, J. and Jernigan, T.L. (2010). The Basics of Brain Development. Neuropsychology Review, 20(4).
[15] Yan, R., Small, S., Desplan, C., Dearolf, C.R., Darnell, J.E. (1996). Identification of a Stat gene that functions in Drosophila development. Cell, 84(3), 421-430.
