# 3.S: Introduction to Brownian Motion (Summary)

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In this chapter, I introduced Brownian motion as a model of trait evolution. I first connected Brownian motion to a model of neutral genetic drift for traits that have no effect on fitness. However, as I demonstrated, Brownian motion can result from a variety of other models, some of which include natural selection. For example, traits will follow Brownian motion under selection is if the strength and direction of selection varies randomly through time. In other words, testing for a Brownian motion model with your data tells you nothing about whether or not the trait is under selection.

There is one general feature of all models that evolve in a Brownian way: they involve the action of a large number of very small “forces” pushing on characters. No matter the particular distribution of these small effects or even what causes them, if you add together enough of them you will obtain a normal distribution of outcomes and, sometimes, be able to model this process using Brownian motion. The main restriction might be the unbounded nature of Brownian motion – species are expected to become more and more different through time, without any limit, which must be unrealistic over very long time scales. We will deal with this issue in later chapters.

In summary, Brownian motion is mathematically tractable, and has convenient statistical properties. There are also some circumstances under which one would expect traits to evolve under a Brownian model. However, as we will see later in the book, one should view Brownian motion as an assumption that might not hold for real data sets.

## Footnotes

1: More formally, the ball will move in two-dimensional Brownian motion, which describe movement both across and up and down the stadium rows. But if you consider just the movement in one direction - say, the distance of the ball from the field - then this is a simple single dimensional Brownian motion process as described here.

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2: Variance effective population size is the effective population size of a model population with random mating, no substructure, and constant population size that would have quantitative genetic properties equal to our actual population. All of this is a bit beyond the scope of this book (but see Templeton 2006). But writing *N*_{e} instead of *N* allows us to develop the model without worrying about all of the extra assumptions we would have to make about how individuals mate and how populations are distributed over time and space.

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3: In this book, we will typically consider variation among species rather than populations. However, we will also always assume that species are made up of one population, and so we can apply the same mathematical equations across species in a phylogenetic tree.

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4: In some cases in the literature, the magnitude of trait change is expressed in within-population phenotypic standard deviations, $\sqrt{\sigma_w^2}$, per generation (Estes and Arnold 2007; e.g. Harmon et al. 2010). In that case, since dividing a random normal deviate by *x* is equivalent to dividing its variance by *x*^{2}, we have *σ*^{2} = *h*^{2}/*N*_{e}.

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## References

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Estes, S., and S. J. Arnold. 2007. Resolving the paradox of stasis: Models with stabilizing selection explain evolutionary divergence on all timescales. Am. Nat. 169:227–244.

Hansen, T. F., and E. P. Martins. 1996. Translating between microevolutionary process and macroevolutionary patterns: The correlation structure of interspecific data. Evolution 50:1404–1417.

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