Fitness increases were measured by competitions performed between Wild Type ara
+
and
Wild Type
 ara and between Mutator ara
+
and Mutator
 ara strains. To assay the fitness of evolved strains, the corresponding ancestral competitor was always used .
The competitions lasted 11 generations and were done in the same conditions as the evolution experiments described in the main text. The initial and final ratios were determined by plating the mix in TA-agar (as described in (Levin et al. 1977)) and counting colony forming units (CFUs). The ara markers were neutral on both genetic backgrounds and both environments (t-test p >0.05).
Fitness was calculated by the growth rate of the evolved strains relative to the ancestor, following (Lenski et al. 1991):
W a

Ln
Ln
Nf a
Nf b
Ni a


Ni b
, where W a
is the fitness of population a relative to ancestor b , Nf a and Nf b
are the CFUs of evolved ( a ) and ancestor ( b ) after the competition and Ni a
and Ni b are the numbers of evolved ( a ) and ancestor ( b ) CFUs before the competition.
Fitness was measured three times independently for each population.
In order to measure the adaptation rate, we estimated the increase in mean fitness of the population by competing a large sample (about a million cells) of the population with the ancestral clone, unlike previous studies where the fitness of particular clones was measured. Previous studies have also performed similar evolution experiments in both liquid and structured environments (fixed and mixed). These authors were interested to explore systematically how spatial structure affects both the emergence and maintenance of diversity (Habets et al. 2006). To this end, they isolated and analysed single clones from the evolved populations. This allows the study of specific properties of the individuals in the population, namely to test whether or not there are interactions between them. However, taking an average of the relative fitness of single individuals does not necessarily constitute a good estimate of the mean fitness of the population. This means that the estimates in Habets et al may not be directly compared with our estimates.

To model the evolution of the bacterial populations, we performed Monte-Carlo simulations of asexual populations of size of 7x10 7 . Deleterious mutations occur in each individual, drawn from a Poisson with mean 0.0002, as measured in E. coli and beneficial mutations occur at a rate U a
. Each deleterious mutation has an effect ( s d
) of 0.012 (Kibota
& Lynch 1996) and the effect of each beneficial mutation was drawn from an exponential distribution with mean E( s a
) . The probability of surviving to the next generation is proportional to the individual fitness, which was assumed to be multiplicative. Several parameter combinations were tested and, for each set of parameters, ten independent replicates were simulated for 275 generations. The mean fitnesses were then compared, using a Studen’t t-test, with the experimental data. Figure 1 shows the results of the parameters compatible with the data of evolution in the unstructured environment. To explain our results, either the mean effect of beneficial mutations (E( s a
)) is around 6% or
7% and U a
~ 10
-7
-10
-8
or E( s a
) ~ 2% and U a
~ 10
-6
(for the wild type strain). The latter values are compatible with previous estimates for E. coli adapting to a similar environment (Perfeito et al. 2007).
We also performed simulations with a fluctuating spatial structure following the methods described in Perfeito et al 2006. Briefly, individuals are arranged in a two-dimension regular lattice and the evolution follows a modified Wright-Fisher model to account for the spatial organization. In each generation, the individuals are all descendants from the previous one (non-overlapping generations) and compete with only their eight nearest neighbors. At periodic intervals, the structure is randomized. We did not try to compare the results of the evolution experiments in the petri dishes with the simulations of structured populations because these imply considering parameters that describe the size of the neighbourhood and the diffusion rates of bacteria for which we lack information.
Furthermore, we have not studied a model with frequency dependence, or other types of non-transitive interactions, which have been observed in an intact environment (Habets et al. 2006) because this type of interactions require the study of a more complex theoretical model which goes beyond the aim of the present study.

We used these results to show that, just like when the structure is fixed, there is an adaptation cost to evolving in a structure which fluctuates, and that this cost increases with U a
but has a limit for high mutation rates (Figure 2). This limit in the cost of adaptation happens because clonal interference becomes strong in both environments.
Despite the strong effect of clonal interference, we always observe a faster increase in fitness with the mutation rate.

0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
-9 -8 -7
a
-6
Figure 1 - Beneficial mutation rate ( U a
) and mean selective effect of beneficial mutations
(E( s a
)) compatible with the observed data for the unstructured population. Open symbols represent all the tested sets of parameters. Filled diamonds represent the parameters compatible with the data both from wild type and from mutator populations, the cross represents the set of parameters compatible with the wild type population and the triangle represents the parameters compatible with the mutator population.
-5

0.16
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0
0.0001
0.001
U a
Figure 2 - The predicted cost of adapting to a structured environment where the structure fluctuates, following the homogeneous environment model described in the text above and the changing structure model used by (Perfeito et al. 2006). The cost is measured as
W
 h
W s
W h
, where W h and W s are the mean population fitness after 300 generations in an unstructured and structured environments respectively. The structure was changed every
10 generations and the selective coefficient of each beneficial mutation was taken from an exponential distribution with mean 0.01. The crosses represent a population size of
160 000, the squares represent a population size of 40 000 and the circles 10 000. The points represent the means of 20 independent runs and the bars correspond to twice the standard error.

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Kibota, T. & Lynch, M. 1996 Estimate of the genomic mutation rate deleterious to overall fitness in E-coli. Nature 381 , 694-696.
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