# Evolution of bacteria specialization along an antibiotic dose gradient

Noémie Harmand, Romain Gallet, Thomas Lenormand

► To cite this version:

Noémie Harmand, Romain Gallet, Thomas Lenormand. Evolution of bacteria specialization along an antibiotic dose gradient. Evolution Letters, 2018, 2 (3), pp.221-232. 10.1002/evl3.52. hal-03118748v2

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# Experimental demonstration of the impact of hard and soft selection regimes on polymorphism maintenance in spatially heterogeneous environments

Romain Gallet,<sup>1,2,*</sup> Rémy Froissart,<sup>1,3,*</sup> and Virginie Ravigné<sup>4</sup>

Received November 16, 2017

Accepted May 6, 2018

Predicting and managing contemporary adaption requires a proper understanding of the determinants of genetic variation. Spatial heterogeneity of the environment may stably maintain polymorphism when habitat contribution to the next generation can be considered independent of the degree of adaptation of local populations within habitats (i.e., under soft selection). In contrast, when habitats contribute proportionally to the mean fitness of the populations they host (hard selection), polymorphism is not expected to be maintained by selection. Although mathematically established decades ago, this prediction had never been demonstrated experimentally. Here, we provide an experimental test in which polymorphic populations of *Escherichia coli* growing in heterogeneous habitats were exposed to hard and soft selection regimes. As predicted by theory, polymorphism was preserved longer under soft selection. Complementary tests established that soft selection slowed fixation processes and could even protect polymorphism in the long term by providing a systematic advantage to rare genotypes.

**KEY WORDS:** Bacteria, coexistence, density regulation, experimental evolution, frequency dependence, polymorphism.

Genetic variation is the fuel of evolution. Understanding the ultimate forces that shape the amount of genetic variation within populations is therefore a central issue of evolutionary biology. Beyond its fundamental interest, this topic is also crucial for a number of applied issues where evolutionary potential matters. In conservation biology for instance, preserving the adaptive potential of endangered species is now a primary goal of management policies (Crandall et al. 2000). Similarly, as pathogen evolution regularly ruins management attempts (e.g., antibiotic resistance, plant resistance breakdown), managing pathogen polymorphism is becoming a growing concern (Vale 2013).

The spatial heterogeneity in selection pressures among the different habitats composing an environment has early been

proposed as one of the mechanisms maintaining genetic variation in nature. The idea sounds intuitive, and yet it has been the subject of intense debate. Levene (1953) proposed a simple population genetic model showing conditions under which a stable local adaptation polymorphism could be maintained in a panmictic population. Dempster (1955) soon challenged this view with a very similar model, in which no polymorphism could ever be stably maintained. These two models have widely been recognized as prototypical cases of two different types of selection regimes: soft and hard selection, respectively. Soft selection appeared far more prone to polymorphism maintenance than hard selection (Christiansen 1975; Karlin and Campbell 1981).

Both authors assumed that a panmictic population inhabits a heterogeneous environment composed of several discrete habitats. At each generation, individuals disperse into a habitat and

<footnote n="1"><sup>1</sup>INRA, UMR 385 BGPI, Cirad TA A-54/K Campus International de Baillarguet 34398 Montpellier Cedex 5, France</footnote>

<footnote n="2"><sup>2</sup>E-mail: rgallet@gmail.com</footnote>

<footnote n="3"><sup>3</sup>CNRS, IRD, Université de Montpellier, UMR 5290 MIVEGEC, F-34090 Montpellier, France</footnote>

<footnote n="4"><sup>4</sup>CIRAD, UMR PVBMT, F-97410 Saint-Pierre, France</footnote>

<footnote>*These authors equally contributed to this work.</footnote>
face selection: they reproduce or survive differentially depending on their local adaptation genotype (i.e., local selection is modeled as density-independent and genotype-dependent survival). In Levene's model, density regulation occurs at the local scale, that is within habitats. The population grows until it reaches the carrying capacity of the habitat and only a fixed number of individuals will survive independently of their genotype (i.e., density regulation is modeled as density-dependent and genotype-independent survival). Surviving individuals from all habitats mix, reproduce panmictically, and the life cycle starts again. In Levene's model, the contribution of habitats to the next generation is fixed (i.e., a given number of individuals will reproduce). Consequently, individual fitness, which is the contribution of individuals to the next generation, depends on the fitness of all other individuals sharing the same habitat. The fitness of individuals is thus relative to the maximal fitness within the habitat. In contrast, in Dempster's model density regulation happens at the scale of the whole population, after global mixing, and before redistribution into habitats. Due to this assumption, the contribution of each habitat to the next generation is not fixed by some space or resource constraints at the local scale as in Levene's model. On the contrary, Dempster shows that individuals contribute to the next generation in proportion to their absolute fitness (i.e., their reproductive success is not affected by other individuals sharing the same habitat), so that the genotype adapted to the most productive habitat (i.e., the most frequent or with the highest carrying capacity) invades and no polymorphism can be maintained in the long run. The assumption of a fixed versus variable habitat contribution to the next generation later appeared so consistently important for the maintenance of stable polymorphism that it is its now what is referred to as "soft selection" and "hard selection," respectively (a definition we hereafter use, Christiansen 1975; Débarre and Gandon 2011).

Nurtured by this initial debate, numerous theoretical studies have attempted to identify the kind of life cycles and ecological settings that promote the maintenance of stable polymorphisms. Models have explored how spatial heterogeneity enables polymorphism maintenance in a wide array of settings. Usually one dimension of the niche such as temperature, nutrients, or a biotic feature varies within the modeled environment. It may vary gradually, as in niche breadth models (Kirkpatrick and Barton 1997; Case and Taper 2000; Mizera and Meszéna 2003), or in a discrete manner in the form of a set of habitats. In most models with discrete habitats, space is implicitly modeled. The earliest theoretical results stem from models with two patches, each corresponding to one habitat and connected by either full migration (Levene 1953; Karlin and Campbell 1981; Hedrick 1990; van Tienderen 1997; Day 2000; Doebeli and Dieckmann 2000; fine-grained environments *sensu* Levins 1968), limited migration (Maynard Smith 1966; Brown and Pavlovic 1992; Meszéna et al. 1997) or biased migration (i.e. with habitat choice, de Meeûs et al. 1993;

Egas et al. 2004; Ravigné et al. 2004). Now, more recent models include more complex settings. The modeled (meta)population may occupy a patchy environment, where the patches are connected by some form of migration and where each patch is characterized as belonging to one of two (rarely more) contrasted habitats (Parvinen and Egas 2004; Nurmi and Parvinen 2008). Finally, the case of two discrete habitats juxtaposed in a continuous space has been explicitly modeled (Débarre and Gandon 2010; Débarre and Lenormand 2011).

After decades of study, it appears that there is no simple relationship between spatial heterogeneity and stable maintenance of local adaptation polymorphisms (see Kassen 2002; Ravigné et al. 2009; Massol 2013; Vale 2013 for reviews). According to theoretical studies, whether selection leads to the stable maintenance of diversity depends on the interaction between four factors: (1) the existence of local adaptation trade-offs (i.e., negative genetic correlations in fitness across different habitats, Levins 1962), (2) the frequency and productivity of the different habitats in the environment (Levene 1953), (3) the amount of gene flow between habitats (Maynard Smith 1966; Christiansen 1975; Débarre and Gandon 2011), and (4) the way habitats contribute to the next generation (i.e., fixed vs variable habitat contribution, Levene 1953; Dempster 1955; Christiansen 1975; Ravigné et al. 2004).

Like all theoretical work, hard, and soft selection models are per se, very schematic and some intricate interactions may have been overlooked. For instance, while natural populations may have complex population dynamics, most models assume that dispersal, selection, density regulation, and mixing are distinct stages, or with very few exceptions (Egas et al. 2004), a ceiling type of population regulation (i.e., only a fixed number of individuals survive the regulation stage regardless of their genotype). In reality, migration may not be concentrated at one stage in the life cycle. Besides, selection and density regulation may occur simultaneously. Carrying capacity may vary over time either in relation to local adaptation or because of extrinsic factors. The regulation of population density, instead of resulting from contest competition (i.e., the survival of a few, Nicholson 1954) could take the form of scramble competition, where all individuals suffer from degraded demographic parameters (i.e., the suffering of all, Nicholson 1954). Some populations, as observed in host parasite systems, may even present cyclic population dynamics. All of these factors may have significant effects in the real world, and therefore limit the relevance of predictions made by hard and soft selection models. Verifying the validity of these predictions inherently calls for experimental testing.

Despite a vast consensus among theoreticians over the importance of the type of selection regime for polymorphism maintenance in heterogeneous environments, the concepts of hard and soft selection generally remain overlooked in the empirical literature (Roff 1993; Vale 2013). Previous studies tested the effect
of spatial heterogeneity on genetic variability (reviewed in Rainey
et al. 2000; Kassen 2002; see also Jasmin and Kassen 2007), most
of them concluding that populations confronted with a spatially
heterogeneous environment are more variable than those exposed
to homogeneous environments. Yet, these experiments did not
control for the selection regime imposed by serial passages and ex-
perimentally applied hard selection (except García-Dorado et al.
1991; Bell and Reboud 1997). The higher variability observed
under these heterogeneous treatments resulted from transient
polymorphism being less efficiently removed from heterogeneous
environments than from homogeneous environments. A possible
exception was the experiment by Bell and Reboud (1997) in
which, despite no experimentally imposed density regulation, lo-
cal density regulation was suspected to have occurred and to have
promoted higher genetic variance in heterogeneous environments
as compared to homogeneous ones. One study explicitly imposed
hard and soft selection regimes on a mixture of strains of the
unicellular algae *Chlamydomonas reinhardtii* maintained in a het-
erogeneous environment for 50 generations without sexual repro-
duction (Bell 1997). Contrary to theoretical predictions, genetic
variation remained similar regardless of the type of density reg-
ulation. This unexpected result was interpreted as a consequence
of the specific nature of the environmental heterogeneity–habitats
were composed of different mixtures of nutrients–that did not
impose a trade-off in local adaptation (Bell 1997). In fact, in the
absence of such trade-offs, polymorphism is not selected for,
even under soft selection. Thus despite the experimental attempts
cited above, the demonstration—through a proper experimental
test—that polymorphism observed under soft selection may be
maintained over the long term by selection has yet to be made
(Vale 2013).

In principle, demonstrating polymorphism maintenance over
the course of an experiment is not sufficient to conclude that envi-
ronmental heterogeneity selects for polymorphism. It is of prime
importance to discern whether observed polymorphisms are due
to the negative frequency dependence produced by environmental
heterogeneity or not. To do so, one must test for polymorphism
protection. Polymorphism is protected if both genotypes increase
in frequency when initially rare. Under polymorphism protection,
no genotype can ever disappear and polymorphism is expected to
be maintained (unless important drift causes the random loss of
one genotype).

Here, we conducted serial passage experiments reproducing
the key stages of soft and hard selection models with bacterial
populations (Fig. 1C). To test the effect of selection regimes on
the maintenance of diversity, a local adaptation trade-off was
required. Such a pattern was obtained by using two *Escherichia*
*coli* genotypes, one being resistant to tetracycline and the other
to nalidixic acid. Populations composed of two genotypes were
grown in heterogeneous environments each composed of two

different habitats, one containing a very low concentration of
tetracycline and the other a very low concentration of nalidixic
acid. Low antibiotic concentrations provided a selective advan-
tage to the resistant genotype over the susceptible one, but both
genotypes could grow in all conditions. Serial passages were
conducted in each heterogeneous environment, as in Bell (1997),
to simulate hard and soft selection (Fig. 1B). The maintenance
of already established polymorphisms (Experiment 1) as well as
polymorphism protection (Experiment 2) were assessed over the
course of the experiment by using flow cytometry. The duration of
the experiment was kept short enough to avoid the emergence of
de novo mutations (toward a generalist genotype resistant to both
antibiotics) and monitoring of selection coefficients a posteriori
confirmed that no such evolution occurred. Lastly, the diverse
effects of selection regimes on polymorphisms were dissected
by systematically confronting observed trajectories of genotype
frequencies to theoretical predictions. We discuss the potential of
such an experimental system to explore the contribution of soft
and hard selection to local adaptation polymorphisms.

Materials and Methods

BACTERIAL STRAINS

To test the effect of selection regimes on the maintenance of polymorphism, a local adaptation trade-off was required. Such a pattern was obtained by using two *E. coli* genotypes, one being resistant to tetracycline and the other to nalidixic acid. The *E. coli* B strains used in this study, REL4548 YFP-Tet<sup>R</sup> and REL4548 CFP-Nal<sup>R</sup> derive from the strain REL4548 kindly provided by R. E. Lenski. REL4548 was evolved in Davis minimal (DM) medium supplemented with 25 µg/mL glucose (DM25) for 10,000 generations as part of a long-term evolution experiment (Lenski et al. 1991). Gallet et al. (2012) then inserted YFP and CFP genes at the *rhaA* locus of REL4548 using a technique developed by Datsenko and Wanner (2000). A mini-Tn*10* derivative 104 (mini-Tn*10 tet/Ptac*-ATS transposase—Kleckner et al. 1991)—was introduced at the *insL-1* locus into REL4548 YFP (clone T121) (Gallet et al. 2012) to construct REL4548 YFP-Tet<sup>R</sup>. The strain REL4548 CFP-Nal<sup>R</sup> was then created by selecting a resistant REL4548 CFP colony on a LB plate (10 g/L NaCl, 10 g/L tryptone, 5 g/L yeast extract; 15 g agar, 1000 mL H<sub>2</sub>O) supplemented with 20 µg/mL of nalidixic Acid. These constructs permitted the association of specific antibiotic resistance with a specific fluorescent marker for easy identification. Bacterial strains were stored at –80°C in 15 % glycerol stocks.

SERIAL PASSAGES

To conduct the experiments, three heterogeneous environments
were created (see Fig. 1A and their detailed description in the
following section). A heterogeneous environment is composed
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Experimental design. Panel (A) illustrates the composition of the three environments (Env A, B, and C) used in our study. Each environment is composed of two habitats (i.e., two independent Falcon tubes), containing either tetracyclin (T-tagged circles) or nalidixic acid (N-tagged circles). Circles diameter represent habitats productivity (small and large circles for DM2 and DM50, respectively). Black and white dots inside of these circles represent the *E. coli* TetR and NalR genotypes, respectively. Panel (B) shows the transfer protocols used under hard (dark gray) and soft selection (light gray) for the asymmetric environment C (Tet2-Nal50). A transfer starts by the inoculation of a bacterial population mix in the two habitats composing the environment. Selection occurs within habitats during the 18 hours of bacterial culture at 37°C. The amount of cells transferred during the regulation step depends on the selection treatment. Under hard selection fixed volumes (50 µL) of each habitat are pooled together in the mixing tube, while under soft selection fixed numbers of cells (10⁷ cells) from each habitat are pooled. These mixes are then used to inoculate fresh habitats in the next transfer. Panel (C) shows the experimental design of both Experiments 1 and 2. Like in panel B, dark and light gray rectangles represent environments under hard and soft selection, respectively. Like in panel (A), the diameter of the circles represents habitat productivity while letters inside the circles symbolize the antibiotic present in the habitat. As Experiment 1 was designed to study the maintenance of a preexisting polymorphism and Experiment 2 to study polymorphism protection, these experiments were initiated with a 50–50% and a 2–98% mix of bacterial competitors, respectively. In each experiment, experimental treatments were replicated thrice. The entire Experiment 1 was also replicated twice.</figcaption></figure>

of two habitats, that is two independent 50 mL Falcon tubes containing distinct growth media and where bacteria are grown. For each heterogeneous environment, serial passages were conducted following the same protocol. Before the start of the experiment, REL4548 YFP-TetR and REL4548 CFP-NalR genotypes were grown separately overnight in 5 mL of DM25 (37°C, 215 rpm). At T0, the optical density (OD, 600 nm, Eppendorf spectrophotometer) of each culture was measured and a 50–50% mix (in Experiment 1) or a 2–98% mix (in Experiment 2) were made to inoculate the two habitats of each environment (Fig. 1B-transfer). After an 18 h incubation at 37°C and 215 rpm agitation (Fig. 1B-selection), fractions of the bacterial populations from the two habitats composing the environment were mixed (Fig. 1B-regulation and pooling). Depending on the selection regime, the regulation step was performed differently: either the same volume (50 µL-hard selection) or different volumes (containing 10⁷ cells per habitat-soft selection) from each habitat were pooled in DM0 (i.e., DM medium containing no glucose). During this step, cultures were diluted 10-fold regardless of selection regime. Part of the mix was used to make a glycerol stock (stored at –80°C) for subsequent flow cytometry analysis while the other part was used

to inoculate both habitats of the environment of the next passage (50 µL into 5 mL of fresh media—an additional 100-fold dilution).

## HETEROGENEOUS ENVIRONMENTS

Each of the three environments was composed of two habitats, that is two Falcon tubes containing different growth media: one habitat with a low concentration of tetracycline (Tet) and one habitat with a low concentration of nalidixic acid (Nal). Habitats also differed by their glucose concentration (habitat productivity). As two nutrient concentrations were used (DM2 and DM50 see below), four habitats (i.e., growth media) were used in this study (Nal2, Nal50, Tet2, Tet50). Environment A was composed of habitats Nal2 and Tet50. Environment B corresponded to the pair Nal50-Tet50. Environment C was composed of habitats Nal50 and Tet2. Since glucose concentrations of the two habitats were equal in Environment B, it was considered as a ‘symmetric’ environment and both bacterial genotypes were expected to have similar mean fitnesses in the habitat they were adapted to. The two other environments were considered “asymmetric” as one habitat had a higher glucose concentration than the other expectedly favoring one genotype over the other at the scale of the whole environment.
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Habitats, environments, and local adaptation trade-offs. Local fitness is defined as the rate of multiplication between transfers within one habitat (thereby corresponding to viabilities in Levene's model). Panels: (A) Distributions of the local multiplication rates of NaI<sup>R</sup> and Tet<sup>R</sup> genotypes in the long (white whisker charts) and the short (black whisker charts) trials of Experiment 1 for the four habitats used. (B) Mean local multiplication rates of NaI<sup>R</sup> (open circles and dashed line) and Tet<sup>R</sup> (filled circles and plain line) genotypes in the three environments used (with confidence intervals over all replicates and transfers of Experiment 1).</figcaption></figure>

All media were made on the base of Davis minimal (DM) medium (K₂HPO₄ monohydrate 5.34 g/L, KH₂PO₄ 2 g/L, ammonium sulfate 1 g/L, sodium citrate 0.5 g/L). Bottles were weighed before and after autoclaving and sterile milliQ water was added to compensate for evaporation occurring during sterilization. After autoclaving, media were supplemented with 806 µL/L of MgSO₄²⁻ [1 M], 1 mL/L Thiamine (vitamin B1) [0.2%]. Then, 40 µL/L or 1 mL/L of glucose [2.5%], were added to make DM2 and DM50 (2 and 50 µg/mL of glucose being present in the medium, respectively). These media were equivalent to the one used by Lenski et al. (1991), but with different glucose concentrations. Antibiotics were used at subinhibitory concentrations to provide a moderate fitness advantage to the resistant genotype. To take into account week-to-week variations (different medium batch, antibiotic dilution etc.), culture media were tested prior to the start of the experiments, and the relative fitness of bacterial genotypes was measured. Thus, tetracycline and nalidixic acid were added at final concentrations of 0.02 and 0.7 µg/mL, respectively for the first trial of Experiment 1 and 0.03 and 0.8 µg/mL, respectively for the second trial of Experiment 1 as well as Experiment 2. These antibiotic concentration adjustments resulted in similar fitnesses in the different experiments (Fig. 2A). Finally, to verify that the studied trade-off was not circumvented by the

selection of a generalist genotype resistant to both antibiotics, and thus fit to both habitats, we monitored the relative fitness of competitor strains at each transfer. Our results confirm that the relative fitness stayed constant during the entire experiment (Fig. S1), and therefore no generalist genotype was selected.

## EXPERIMENT 1: MAINTENANCE OF ESTABLISHED POLYMORPHISM

In Experiment 1, polymorphic populations with initially equal frequencies of both genotypes were grown under hard selection and soft selection regimes. In the first trial, flow cytometer measurements showed that the realized initial frequency of REL4548 YFP-Tet<sup>R</sup> was 0.508 and the experiment was conducted over five transfers. The second trial, conducted simultaneously with Experiment 2, started from an initial frequency of REL4548 YFP-Tet<sup>R</sup> of 0.437 and had to be interrupted after three transfers due to technical difficulties.

## EXPERIMENT 2: POLYMORPHISM PROTECTION

In Experiment 2, the genotype with a global disadvantage in the considered environment (i.e., the genotype with the lowest mean local multiplication rate at the scale of the environment) was initially rare. In Environment A (NaI₂-Tet50) the initial
frequency of REL4548 YFP-TetR was 0.975. In Environment C (Nal50-Tet2) the initial frequency of REL4548 YFP-TetR was 0.035. In Environment B (Nal50-Tet50), initially conceived as symmetric, both initial frequencies were tested. The experiment was conducted over two transfers only.

## FLOW CYTOMETRY

Flow cytometry allows counting small objects (typically cells) marked with various fluorescent colors. We used a Gallios flow cytometer (Beckman Coulter Inc)—designed to detect small objects such as bacteria—to estimate the relative frequencies of E. coli genotypes marked with CFP or YFP. Flow cytometers do not measure the volume of samples they use, precluding the calculation of cell concentrations directly from the counts. Estimating cell concentrations required the addition to the sample of fluorescent beads of a known concentration (AccuCount Fluorescent Particles, 7.0–7.9 µm, Spherotech). Counting fluorescent beads allowed estimation the samples volume used by the flow cytometer and so estimation of cell concentrations. Results were analyzed with the Kaluza 1.3 software (Beckman coulter Inc.). This procedure was performed on overnight cultures and on mixes.

## STATISTICAL ANALYSES

### Genotypic frequency trajectories

Observed genotypic frequencies were estimated by counting a minimum of 5.10⁴ cells using flow cytometry. The confidence interval due to measurement error around each frequency was therefore always smaller than 0.004, too small to be represented in Figures 3 and 4. Rather than comparing observed trajectories with time series models, we took advantage of the availability of theoretical predictions (Levene 1953; Dempster 1955) to conduct statistical analyses aimed at testing whether observed trajectories of genotype frequencies over transfers conformed to hard or soft selection models (or none). To do so, we established the predicted trajectories of genotype frequencies for each environment (A, B, C) and each selection regime (hard, soft). Changes in genotype frequencies over transfers under hard and soft selection can be predicted using previously established equations for Levene and Dempster's models (e.g., available in Appendices 1 and 2 of Ravigné et al. (2004) among others) assuming haploidy, complete and unbiased dispersal, equal habitat frequencies, and equal carrying capacities (see the theoretical Appendix of the supporting information section for details). For both models under such conditions, the change in frequency from one transfer to the other only depends on the viability coefficients Wᵢⱼ of genotype i in habitat j.

These viability coefficients, also called local fitnesses, correspond to rates of multiplication of each genotype between two transfers. To allow comparing the results with other studies and

checking that no generalist genotype was selected over the course of the experiments, the relative fitness (selection coefficients—sensu Chevin 2011) of competitor strains were calculated at each transfer (see Fig. S1 in the supporting information section). Selection coefficients (s) were calculated as follow:

$$s_{Tet} = \frac{\ln \frac{C_{Tet}^{final}}{C_{Tet}^{initial}} - \ln \frac{C_{Nal}^{final}}{C_{Nal}^{initial}}}{g}$$

where sTet is the selection coefficient of the TetR genotype, C Tet and C Nal correspond respectively to TetR and NalR genotype concentrations at the beginning (initial) and the end (final) of bacterial growth, and g corresponds to the number of generations.

Viability coefficients for each habitat and each transfer were obtained from flow cytometry measurements as the ratios of cell concentration at the end of the overnight culture to cell concentration at the beginning. Viability coefficients presented non-negligible experimental variability (Fig. 2A). To account for this variability, 10,000 trajectories of TetR frequency over transfers were simulated by sampling randomly at each transfer, the viability coefficient values W TetR ,Nal , W TetR ,Tet , W NalR ,Nal , and W NalR ,Tet among all corresponding values observed over all transfers of Experiment 1. These 10,000 trajectories represented the set of possible trajectories under each environment and each selection regime. The 2.5th and 97.5th percentile values of the distribution of TetR frequency at each transfer were used to define the 95% envelopes of genotype trajectories under each environment and each selection regime. Similarly, the equilibrium TetR frequency was estimated using the median, 2.5th and 97.5th percentile values of the distribution of predicted TetR frequencies after 100 transfers. Statistical analysis therefore amounted to checking whether each observed genotype trajectory fell inside or outside the 95% envelops defined for the corresponding environment.

## TIME POINT COMPARISONS

Punctual differences between averaged genotypic frequencies between two time points were tested with Generalized linear models (GLM), with “time” as a categorical explanatory factor, and a quasibinomial error type.

## Results

### THREE HETEROGENEOUS ENVIRONMENTS WITH CLEAR LOCAL ADAPTATION TRADE-OFF

Figure 2A shows the local fitnesses (i.e., between transfer multiplication rates) obtained for each bacterial genotype over all replicates in each of the four habitats. From this and the computation of selective coefficients (available in Appendix S1), the existence of three different local adaptation trade-offs could be verified (Fig. 2B). It was also confirmed that local fitnesses were
<figure><img src="image_5.png" /><figcaption>**Figure 3.** Evolution of genotype frequencies under hard and soft selection in Experiment 1. Left-hand panels show the 95% envelopes of theoretically predicted frequencies the $Tet^R$ genotype under hard (dark gray) and soft selection (light gray) in environments A (Nal2-Tet50), B (Nal50-Tet50), and C (Nal50-Tet2). Dashed gray lines show the medians of theoretical frequencies. At the right of the x-axis break, theoretically predicted equilibrium frequencies are shown. Central and right-hand panels confront these predictions to experimental results obtained under hard and soft selection regimes, respectively. Filled circles and lines show the frequencies observed in the three transfers of the short trial. Open circles and dashed lines correspond to the five transfers of the long trial. Note that initial frequencies slightly differ between the two trials.</figcaption></figure>

similar in the two independent trials of Experiment 1 and that multiplication rates–hence bacteria–did not evolve during the experiment (Fig. S1). Theoretical predictions showed that Environment A (Nal2-Tet50) was so asymmetric that the fixation of the $Tet^R$ genotype was expected under both hard and soft selection (Fig. 3A1 right-hand side of the x-axis). The expected dynamics of genotype frequency however differed clearly between hard and soft selection (gray areas in Fig. 3A1). In Environments B (Nal50-Tet50) and C (Nal50-Tet2), soft selection was expected to lead to polymorphism maintenance, while hard selection was expected to lead to the fixation of one of the two genotypes ($Tet^R$ in Environment B and $Nal^R$ in Environment C, Fig. 3B1 and C1). In Environment B, the dynamics of genotype frequencies over five transfers were hardly distinguishable between hard and soft selection (Fig. 3B1).

EFFECTS OF SELECTION REGIMES
ON THE MAINTENANCE OF POLYMORPHISM

Under hard selection, in both environments with asymmetric habitat productivities (Environments A—Nal2-Tet50 and Environment C—Nal50-Tet2), polymorphism was almost completely lost over the experiment (Fig. 3A2 and C2). In environment A, $Tet^R$ genotype frequency reached an average of 0.991 ± 0.001 after three transfers in the two Experiment 1 trials ($n = 6$ replicates) and 0.999 ± 0.0002 after five transfers in the long trial ($n = 3$). In environment C, $Tet^R$ genotype frequency decreased to 0.032 ± 0.027 after three transfers in the two Experiment 1 trials ($n = 6$) and 0.002 ± 0.003 after five transfers in the long trial ($n = 3$). The trajectories of genotype frequencies fit well with predictions obtained assuming hard selection and fell outside the 95% envelope of theoretical predictions obtained for soft selection (Fig. 3A2 and
<figure><img src="image_6.png" /></figure>

**Figure 4.** Polymorphism protection. All panels show the dynamics of the frequency of the TetR genotype over successive transfers in Experiment 2. Frequencies observed under hard and soft selection are represented with filled circles connected by solid lines and opened circles connected by dashed lines, respectively. Dark and light gray areas show the 95% envelopes of theoretically predicted frequencies under hard and soft selection, respectively. Panels: (A) Environment A (Nal2-Tet50) with initial high TetR genotype frequency. (B) Environment C (Nal50-Tet2) with initial low TetR genotype frequency. (C) Environment B (Nal50-Tet50) with initial high TetR bacteria genotype frequency. (D) Environment B with initial low TetR genotype frequency. In each panel, the inset shows a magnification of the frequency trajectories.

C2). In the symmetric environment (Environment B), as predicted, polymorphism was almost unchanged at the end of the experiment with only a slight increase of TetR frequency (Fig. 3B2). On average, TetR genotype frequency reached 0.595 ± 0.055 after three transfers in the two trials of Experiment 1 (n = 6 replicates) and 0.593 ± 0.059 after five transfers in the long trial (n = 3).

Under soft selection, genetic polymorphism was maintained throughout the experiment regardless of habitat productivities (Fig. 3A3, B3, and C3). In Environment A (Nal2-Tet50), the frequency of the TetR genotype increased at a rate compatible with predictions obtained under soft selection and not with predictions obtained under hard selection (Fig. 3A3). In this environment, although the expected final outcome of selection was the same under hard and soft selection regimes (fixation of the TetR genotype), the rate of evolution was much slower under soft selection than under hard selection.

In Environments B and C where stable polymorphism was expected, genotype frequencies fit well with predictions obtained under soft selection (Fig. 3B3 and C3). In the five-transfer trial, the frequency of the TetR genotype finally attained 0.484 ± 0.045 (expected value: 0.488 with 95% envelope [0.353–0.628]) in Environment B and 0.284 ± 0.033 (expected value: 0.333 with 95% envelope [0.206–0.470]) in Environment C.

## POLYMORPHISM PROTECTION OBSERVED IN SOFT BUT NOT IN HARD SELECTION

The trajectories of genotype frequency observed in Experiment 2 were again in agreement with theoretical expectations (Fig. 4). In Environment A (Fig. 4A), as observed previously, neither hard selection nor soft selection produced an advantage for the rare genotype. In Environment B the TetR was predicted to go to fixation under hard selection whatever its initial frequency, suggesting
a global fitness advantage over the NaI<sup>R</sup> competitor (Fig. 3B1). Under soft selection, Tet<sup>R</sup> frequency nevertheless significantly decreased from a high initial starting value (Fig. 4C, GLM with quasibinomial error comparing Tet<sup>R</sup> frequency at T0 and T1: T value = -3.017, P value = 0.0393). Similarly in Environment C (Fig. 4B), where the Tet<sup>R</sup> genotype had a global fitness disadvantage, Tet<sup>R</sup> frequency significantly increased when initially rare under soft selection only (GLM with quasibinomial error comparing Tet<sup>R</sup> frequency at T1 and T2: T value = 3.458, P value = 0.0259). In these two environments the observed polymorphism was therefore protected under soft selection.

## Discussion

From the seminal debate between Levene (1953) and Dempster (1955), theory has suggested that the manner in which populations redistribute among habitats of a given environment is crucial for the long-term maintenance of local adaptation polymorphisms. Under some conditions, soft selection, in which habitat contribution to the next generation is constant, can protect polymorphism by producing negative frequency-dependent selection. In contrast, in the same conditions, hard selection—in which habitat contribution to the next generation varies with habitat genetic composition—do not have such a protective effect on polymorphism and low frequency genotypes are lost (e.g., Christiansen 1974, 1975; Karlin and Campbell 1981; de Meeûs et al. 1993). The present experiment is a proof of concept testing for conditions under which the maintenance of a local adaptation polymorphism can be attributed to soft selection.

A prerequisite to study the effect of selection regimes on polymorphism maintenance was to use several well-characterized reproducible trade-offs. In the absence of a local adaptation trade-off, selection is expected to favor a single generalist genotype and environmental heterogeneity cannot lead to stable polymorphism maintenance. The absence of a clear local adaptation trade-off had led previous experimental tests of hard and soft selection to inconclusive results (reviewed in Vale 2013). Here, the use of very low antibiotic concentrations provided a small fitness advantage to resistant genotypes and created a local adaptation trade-off. Since the considered trade-off was not constitutive of bacterial metabolism, it could have been circumvented by the evolution of nalidixic acid resistance in Tet<sup>R</sup> resistant bacteria. Keeping the experiments short (≈50 generations) avoided the selection of a generalist genotype (the monitoring of selection coefficient throughout the experiment confirmed that no generalist genotype evolved in our experiment, see Fig. S1), but also precluded the monitoring of polymorphism on the long term. This latter difficulty, which is actually always a limitation of experimental selection, was overcome by testing for polymorphism protection in a separate experiment (Experiment 2).

In mathematical models of soft and hard selection, population mixing and redistribution among habitats are assumed to happen at each generation and local selection within habitats is assumed to act on viability, that is either fecundity or survival rate. Here, transfers were controlled to reproduce the density-regulation steps characteristic of hard and soft selection. But no control was applied during bacterial growth (i.e., between transfers). Thus, for 8–10 generations, complex population dynamics or density-dependence could occur within habitats. For instance, priority effects, where the most frequent genotype transforms the environment in a way that is not favorable to the competitor genotype, could have established and led to frequency dependence within habitats. Furthermore, bacterial populations reached their carrying capacity before transfers, so that competition could be at work not only during the exponential growth phase, but during the stationary phase as well. Congruently, although habitats were all made simultaneously from the same medium preparation, important variability in local multiplication rates was observed. Hence, contrary to original models of hard and soft selection, local multiplication rates (but not fitnesses) varied across transfers and replicates. For instance, the variability of local multiplication rates across transfers made it impossible to use the local multiplication rates observed at the first transfer to predict the dynamics of genotype frequencies over the whole experiment. It also impeded the distinction between trajectories produced under hard and soft selection in an important range of observations (superposed 95% envelopes). Lastly, it translated into unexpected dynamics in Experiment 2 testing for polymorphism protection. Indeed, we observed (i) a transient decrease of the genotype with the highest mean local multiplication rate under hard selection, and symmetrically (ii) an initial decrease of the rare genotype under soft selection (Fig. 4C and B). This variability, not present in the models, is actually expected to be even greater in natural conditions. Here, we showed that it is possible to account for variable local multiplication rates by combining the models with a bootstrap procedure. This statistical approach revealed that experimental results were in remarkable agreement with theoretical predictions in all treatments. Thus, we can conclude that the impact of hard and soft selection on polymorphism maintenance is robust to a variety of biological processes occurring between regulation steps.

Under the conditions investigated, hard selection never protected polymorphism. The fixation of the genotype with the highest mean local multiplication rate at the scale of the environment was observed within three transfers in the two asymmetric environments. Importantly, in the symmetric environment, polymorphism was still observed after five transfers under hard selection (Fig. 3B2). But deviations to frequencies theoretically expected under soft selection (Fig. 3B2) and the polymorphism protection experiment (Fig. 4C) confirmed that such polymorphism
consisted of transient polymorphism not being easily removed
because of very similar initial frequencies and local multiplica-
tion rates. In contrast, under soft selection, polymorphism was
never lost over the course of the experiment, even in asymmetric
environments where the low fitness genotype was inoculated at a
very low relative frequency. The results from Experiment 2 and
our theoretical predictions concurred that such polymorphism (1)
was only transient in one of the two asymmetric environments
(Environment A), and (2) was effectively protected by the ex-
istence of a systematic advantage of the rare (i.e., negative fre-
quency dependence) in the two other environments (Environment
B and C). Interestingly, in the environment C under soft selec-
tion (Fig. 3C3), the genotypic relative frequencies seem to have
reached the equilibrium point predicted by theory. Lastly, Exper-
iment 1 confirmed that with all else being equal, even when soft
selection is expected to lead to the fixation of a single genotype
(i.e., when its mean fitness at the scale of the environment is very
high-environment A in Fig. 3), soft selection leads to a slower
rate of evolution (sensu modification of genotype frequencies)
than hard selection (as shown by Whitlock 2002).

These results illustrate that observing a polymorphism
over long periods of time is not sufficient to conclude that it
is maintained by soft selection. Under hard selection, transient
polymorphism may take long to be removed when habitats
are symmetric but it cannot be maintained indefinitely. If one
genotype has a slightly higher mean local multiplication rate
across habitats than the other, its frequency will increase transfer
after transfer up to fixation. The results also confirm that under
soft selection, negative frequency-dependent selection emerges
even when a genotype has a higher mean fitness at the scale of the
environment (Environment C). Indeed, the local regulation step
occurring at each transfer opposes the effect of within-habitat
selection and hampers invasion of the whole environment by
the genotype adapted to the most productive habitat. For very
asymmetric trade-offs (and when habitats have different carrying
capacities), regulation is not sufficient to generate negative-
frequency dependence, which results in polymorphism loss.
Overall, the present experiment therefore confirmed the main
theoretical predictions on the effects of hard and soft selection
on a system that departs from theoretical models by allowing
complex population dynamics within habitats between transfers.

From a theoretical perspective it is understood that the con-
ditions for polymorphism maintenance under soft selection are
rather stringent (Prout 1968; Christiansen 1974; Maynard Smith
and Hoekstra 1980). Various processes, such as drift and mutation,
may reduce the range of parameters (trade-off shapes and habi-
tat frequencies) where polymorphism is protected. Polymorphism
protection also depends on an equilibrium between local fitnesses
(i.e., trade-off shape) and habitat frequencies and carrying ca-
capacities. When habitats have contrasted frequencies or carrying

capacities even fewer trade-offs shapes allow polymorphism pro-
tection. This suggests that soft selection may not be that frequent
in nature and that most observed polymorphism is either transient
or maintained by other frequency dependence mechanisms (De
Meeûs and Goudet 2000).

In natura, soft selection can be suspected to occur based
on species' life cycle. For instance, in some solitary insect par-
asitoids, a fixed number of individuals, often one, can emerge
from each host individual where eggs have been laid (Mackauer
1990). Density regulation is local and the contribution from each
infected host to the next generation is fixed. On similar bases,
Chao et al. (2000) argued that soft selection could be the rule
rather than the exception in pathogens that kill their host. The ra-
tionale is as follows. Pathogens infecting the same host (habitat)
constitute a local population. Pathogen genotypes with highest
within-host accumulation (highest absolute local fitness) kill the
host too quickly to transmit efficiently. The contribution of these
genotypes to the next generation is thus not proportional to their
absolute fitness but to their fitness relative to other genotypes
infecting the same host. Following the same reasoning, hard se-
lection should be common in pathogens in which virulence is
disconnected from transmission rate, for instance for those that
either transmit after host death (e.g., Ebola infecting humans or
baculoviruses infecting insects), or that do not kill their host, such
as the plant pathogenic fungus *Mycobotryum violaceum*, which
replaces plant pollen grains by its own spores. Hard selection is
also likely frequent in serial passage experiments when parasite
transmission is simulated by experimenters. Logically, most se-
rial passage experiments lead to a decrease or disappearance of
the initially present polymorphism (for review, see Ebert 1998).
Based on our work and previous studies, we advocate that the
importance of soft selection in shaping standing genetic varia-
tion should not be overlooked (Agrawal 2010; Reznick 2016). In
recent experiments, the “softness” of selection (i.e., the contribu-
tion of soft selection) was measured in experimental populations
of both *Drosophila melanogaster* with different genes (Laffafian
et al. 2010; Ho and Agrawal 2012) and on seedling emergence
in *Brassica rapa* (Weis et al. 2015). In addition to highlighting
an unexpected sensitivity of softness to genes, individuals and
population densities, it was found in both cases that the softness
of selection was generally high, cementing the idea that soft se-
lection shapes natural variation at local adaptation loci (Agrawal
2010; Reznick 2016).

Examining the softness of selection is not an easy task as
it requires time-consuming experiments implying the manipula-
tion of genotype frequencies, something difficult to apply in the
wild. The present work shows that this difficulty can be relieved
by modeling the trajectories of genotype frequencies in some
parameter ranges and when the variability in local multiplica-
tion rates is properly accounted for. Such an approach may help
distinguishing between trajectories of genotype frequencies that are compatible with hard selection from those that are compatible with soft selection.

Finally, the present experimental work provides new perspectives for further testing theoretical predictions about the effect of spatial heterogeneity on polymorphism maintenance. The genetics of local adaptation used (two resistance alleles at two different loci) hampers the study of the emergence of local adaptation polymorphisms by gradual evolution. But the experimental system is likely relevant for questions regarding the maintenance of already existing polymorphisms, as observed for species diversity in communities established in heterogeneous environments. For instance, one could test more systematically for the effect of habitat carrying capacities and trade-off intensities on polymorphism maintenance. The present experiment conservatively considered full migration between the two habitats. Migration intensity (Maynard Smith 1966), timing (Ravigné et al. 2004; Débarre and Gandon 2011; Massol 2013), and bias (density-dependent migration or habitat selection, de Meeûs et al. 1993) affect the range of conditions favorable to polymorphism maintenance and could be tested through a similar experimental design. Estimating the relative importance of spatial and temporal variability of the environment in shaping polymorphism could also help our understanding of ecological specialization (Massol 2013).

## AUTHOR CONTRIBUTIONS

R.F. had the original idea of the study; R.F., R.G., and V.R. designed the experiments; R.F. and R.G. carried out the experiments; R.G. analyzed experimental results; V.R. provided theoretical predictions; R.F., R.G., and V.R. wrote the article.

## ACKNOWLEDGMENTS

We are grateful to R. E. Lenski for providing *E. coli* strains. We thank C. Duperray (IRMB-Montpellier) and the Montpellier RIO Imaging platform for hosting the flow cytometry measurements, F. Débarre and B. Facon for helpful comments on the manuscript, and C. Prator for careful English editing. This study received financial support by the French Agropolis Fondation (Labex Agro-Montpellier, BIOFIS Project Number 1001-001 and E-SPACE project number 1504-004), the European Union (ERDF), and the “Conseil Régional de La Réunion.”

## DATA ARCHIVING

The doi for data https://doi.org/10.5061/dryad.261553q.

## CONFLICT OF INTEREST STATEMENT

The authors declare no conflict of interest.

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Associate Editor: T. Cooper

Handling Editor: M. Noor

Supporting Information

Additional supporting information may be found online in the Supporting Information section at the end of the article.

**Table S1.** Statistical analysis of the relative genotype frequency variations.

**Figure S1.** Evolution of selection coefficients ($s$) during the experiment.