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Evolutionary biologist who, with Martin Kreitman, compared synonymous and amino acid-changing differences within and between Drosophila species. Their 1991 analysis supplied a statistical test of neutral evolution: under the proposed neutral baseline, the two kinds of difference should occur in the same ratio within and between species.^[ref]

Evolutionary biologist who, with John H. McDonald, developed a comparison of within-species variation and between-species divergence to test neutral evolution. Their 1991 study compared synonymous and amino acid-changing differences at the alcohol-dehydrogenase locus in Drosophila, finding an excess of fixed amino acid changes consistent with natural selection.^[ref]

Neutral evolution is change in inherited variants whose differences do not affect biological fitness in the population and environment being modeled. Their allele frequencies can change through genetic drift even without a selective advantage. Neutrality concerns effects on reproductive success, rather than requiring that the underlying DNA sequences are chemically identical.

An idealized diploid population with $N$ individuals makes a useful prediction. If the neutral mutation rate per copy per generation is $\mu$, the expected value of the number of new copies is $2N\mu$. Each has fixation probability $1/(2N)$, so the neutral substitution rate isThe cancellation explains why a molecular clock can reflect a mutation rate rather than population size under these assumptions. Motoo Kimura's molecular argument motivated a major role for neutral evolution; it does not assert that every mutation is neutral.^[ref]

Weak selection is a regime of an evolutionary model in which the focal trait or interaction changes biological fitness only slightly relative to a baseline. It permits calculations close to neutral evolution. In one common evolutionary game theory convention, a payoff $P$ changes biological fitness according towhere $w$ controls the contribution of the game. Weak selection requires the payoff-dependent change to be small on the scale of baseline biological fitness.^[ref]

For example, take $w=0.01$ and two competitors with payoffs $P_1=2$ and $P_2=3$. Their fitnesses are $f_1=1.01$ and $f_2=1.02$. If their chances to fill a vacancy are proportional to biological fitness, the second competitor wins with probabilityonly slightly above the neutral value $1/2$. This invented calculation shows the meaning of a small selective bias. A small numerical $w$ would not ensure the same approximation if the payoffs were made enormously larger. Weak selection is also distinct from small biological population size: the strength of genetic drift and the cumulative effect of selection depend on additional features of the model.