Carl Lipo and I were recently talking about a recent paper by Kevin Laland and Michael J. O’Brien, Niche Construction Theory and Archaeology, and it stimulated me to think about why I buy the argument that niche construction theory (NCT) is important for the future development of evolutionary theory.
After all, scientific theory has “fads” like anything else, one could argue (in parallel to the argument recently developed by Nowak and colleagues concerning inclusive fitness theory) that any “niche construction” argument can also be formulated in a different framework by simply writing standard natural selection models, with appropriate values or operators for fitness values.
I believe that while Nowak et al. are absolutely on the right track with respect to population structure, cooperation, and eusociality, that NCT arguments cannot always be reduced to an equivalent “traditional selection model.” To see why, we need to follow Richard Lewontin’s argument from a 1982 and 1983 paper originally defining NCT.
Lewontin, as he so often has in evolutionary biology, stripped the argument down to its essentials and provided a very simple skeleton. In this case, he boils down evolutionary biology to an ansatz or generic model as follows:
\begin{eqnarray}
\frac{dO}{dt} &=& f(O, E) \\
\frac{dE}{dt} &=& g(E) \\
\end{eqnarray}
The first equation describes the evolution of a population by natural selection as a dynamical system, in which rate of change of the population state (O), is given by a function f whose values depend both upon the population state itself and the nature of the environment (E). This dynamical system is fully generic and can describe constant selection (when the function f ignores O and only depends upon E, for example), or frequency-dependent selection (when the function f depends mostly upon the population state, with the environment providing “background fitness” to the payoffs of a particular evolutionary game. And so on….density dependence fits in this model as well.
Simple or toy models of evolutionary processes might focus only on the first equation. But we also know, in the real world, that the environment itself is changing. The second equation in our dynamical system accounts for this, “coupling” change in the environment with the first equation. Evolutionary dynamics in this “full” model of evolution thus requires solving this system of differential equations (keeping in mind that these are a deterministic ansatz to what is ultimately an underlying set of stochastic processes).
The second equation thus specifies a function, g which describes how the environment changes over time. But notice that in neo-Darwinian evolutionary theory, according to Lewontin, we usually consider models in which environmental change is exogenous, and does not depend upon population state. Environment is external to the system of organisms and interactions being studied. We can study systems where selection is dependent upon rapidly changing, random environments, systems where selection is frequency-dependent, and systems where it is both. But we cannot, with this overall model of evolution, study systems where change in organisms depends upon the state of the population and the environment, and where change in the environment depends both upon the state of the environment and the state of the population of organisms.
And yet, the latter “reflexive” or “internalist” model is how much of the organic and cultural worlds really do evolve. We construct environments which suit us, but then we are subject to competition within those environments, which determine which folks flourish to construct the next environment we’re subject to, which define the competitive environment for the next generation, and those winners largely determine the environment, and so on….
So again, following Lewontin, a better “overall” or generic model for evolution is the following:
\begin{eqnarray}
\frac{dO}{dt} &=& f(O, E) \\
\frac{dE}{dt} &=& g(O, E) \\
\end{eqnarray}
Obviously, in the second model the function g which describes environmental change, is now fully dependent upon the state of the population. As the population evolves, it changes its environment, which leads to different dynamics in the future change of the population itself. This is “niche construction,” and when you strip it down to this level, it’s pretty apparent why some version of NCT must be true of evolving populations.
We can, of course, recover nearly any evolutionary model from this expanded ansatz. If the function g gives no, or little, weight to the parameter O, then we lose niche construction as a driver of the overall dynamical system. There are situations where we might imagine this to be the case. If we’re describing the evolution of particular traits relate only to direct solar energy flux, and the organisms have no ability to enhance or shield themselves from this flux, then there isn’t much potential for niche construction and while organismal change might still be related to both population state and environment, environmental change is fairly constant and unrelated to what organisms “do.”
The point of highlighting NCT as a major component of evolution, however, is that situations like this are rare. Most of the time, we need the full ansatz model to describe real populations and their evolution. In fact, I’d argue given the immense amount of recent work on population structure (in, say, the last decade or 15 years), that an even better ansatz is as follows:
\begin{eqnarray}
\frac{dO}{dt} &=& f(O, S, E) \\
\frac{dE}{dt} &=& g(O, S, E) \\
\frac{dS}{dt} &=& h(O, S, E) \\
\end{eqnarray}
This final ansatz, of course, points out the nearly orthogonal role that population structure plays in evolution, leading to different outcomes for any given environment or population state, depending upon the spatial and social structure of interaction. We have only to classify the hundreds of papers concerned with variations on the Prisoner’s Dilemma or Snowdrift models, to see that the same payoff matrices (i.e., the O parameter to function f) lead to different evolutionary dynamics given different spatial or topological structures to interaction. Given this, it stands to reason that niche constructions will have different fitness effects depending upon the population structure of organisms which are constructing and inhabiting those niches. Right?
I certainly think so, and I’m betting that the third ansatz model here brings together the NCT insights of Lewontin/Laland/Feldman, with the insights of Nowak and others who study evolution on complex interaction structures, to form the core of evolutionary theory for the 21st century.
