Slower environmental change hinders adaptation from standing genetic variation

Abstract

Evolutionary responses to environmental change depend on the time available for adaptation before environmental degradation leads to extinction. Explicit tests of this relationship are limited to microbes where adaptation usually depends on the sequential fixation of de novo mutations, excluding standing variation for genotype-by-environment fitness interactions that should be key for most natural species. For natural species evolving from standing genetic variation, adaptation at slower rates of environmental change may be impeded since the best genotypes at the most extreme environments can be lost during evolution due to genetic drift or founder effects. To address this hypothesis, we perform experimental evolution with self-fertilizing populations of the nematode Caenorhabditis elegans and develop an inference model to describe natural selection on extant genotypes under environmental change. Under a sudden environmental change, we find that selection rapidly increases the frequency of genotypes with high fitness in the most extreme environment. In contrast, under a gradual environmental change selection first favors genotypes that are worse at the most extreme environment. We demonstrate with a second set of evolution experiments that, as a consequence of slower environmental change and thus longer periods to reach the most extreme environments, genetic drift and founder effects can lead to the loss of the most beneficial genotypes. We further find that maintenance of standing genetic variation can retard the fixation of the best genotypes in the most extreme environment because of interference between them. Taken together, these results show that slower environmental change can hamper adaptation from standing genetic variation and they support theoretical models indicating that standing variation for genotype-by-environment fitness interactions critically alters the pace and outcome of adaptation under environmental change.

Fig 1. Fitness reaction norms and experimental evolution design.

(A) Heritable genotype-by-environment (GxE) fitness variance implies that genotypes (colored lines) have different growth rates along the value of the environmental factor(s) considered [3, 13]. The function of each genotype’s growth rate with environmental value can be denominated its “fitness reaction norm”. Under density- and frequency-independent conditions, the relative magnitude of genotypic growth rates to the average population growth rate (thick line) will determine its deterministic frequency trajectory during evolution [25]. In general, fitness reaction norms of genotypes present in standing genetic variation will cross each other somewhere along the environmental value. A history of evolution in variable environments, with balancing selection, or population structure, for example, will determine the extent of GxE fitness variance in the ancestral population above that expected under a mutation-drift equilibrium. Crossing of fitness reaction norms means that genotypes will be favored at some environmental values while disfavored in others. Assuming no input from mutation or recombination during evolution, with a sudden change to an environmental value of 1 (vertical dotted line), from an ancestral environment 0, selection will favor the grey genotype, while under a gradual change selection will initially favor the red genotype, then the blue one, and only at a later period the grey genotype. Vertical lines broadly define the three population genetic stages expected under a gradual environmental change. In a first stage (I), the best genotype at the most extreme environment (i.e., the grey genotype) will be selected against and kept at such low frequency that its stochastic loss by genetic drift is likely under small population sizes. In a second stage (II), around where the fitness reaction norms cross, reduced fitness variance will slow down adaptive changes in allele frequency [16], and depending on the amount of standing variation [11], the best genotypes can be kept at such low frequencies that again their stochastic loss is likely. Under slower environmental change, therefore, a population will spend more time in stages I and II, reducing the chance that the best genotypes will be present at a third stage (III), when they can be selectively favored. Assuming no de novo mutation or recombination during gradual evolution the loss of the best genotypes will restrict adaptation to the most extreme environment [11]. At stage III, it is also possible, particularly in large populations with limited recombination between extant genotypes, that interference between the best genotypes (the grey and the blue genotypes for example) will transiently reduce selection efficacy on the best one [28, 29]. This interference process in turn will favor stochastic loss of the best genotypes if at the start of stage III they are at low frequencies. Note that without prior knowledge of fitness reaction norms, genotypes that increase in frequency and then decrease in frequency (for example the red genotype) would suggest the presence of negative-frequency dependence. (B) Experimental evolution design reported in [18] (S1 Table), used here to address whether slower environmental change hinders adaptation. A single 140-generation lab-adapted C. elegans population with abundant genetic diversity [19, 21], reproducing only by self-fertilization, was the ancestor for experimental evolution [18]. In the sudden regime, 4 replicate populations were faced from the first generation onwards to 305 mM NaCl in their growth media (high salt, black line). In the gradual regime, 7 replicate populations were faced with an 8 mM NaCl increase each generation until generation 35, being then kept at 305 mM until generation 50 (dark grey). In the control regime, 3 replicate populations were kept at 25 mM NaCl, the conditions to which the ancestor was adapted to (light grey). Here we genotype individual hermaphrodites at several time points during experimental evolution, at single nucleotide polymorphisms (SNPs) across pairs of chromosomes (vertical dashed lines, Fig 2 and Fig 3). Analysis of this data allow us to infer the fitness reaction norms of extant genotypes and predict with a deterministic model their frequency trajectories (Fig 4 and Fig 5), which are then empirically validated (Fig 6). To address the role of genetic drift and founder effects in the loss of the best genotypes during slower rates of environmental change we perform a second set of evolution experiments, where 7 of the gradual populations at generation 35 are kept in constant high salt for an extra 30 generations (Fig 7).

Fig 2. Standing genetic variation.

(A) Each of the individuals sampled from the various populations during experimental evolution were genotyped for biallelic SNPs located in a pair of chromosomes. This approach was employed due to the limited amount of genomic DNA available for the genotyping method used. The pairs of chromosomes are referred to as: region 1 (chromosomes I and II), region 2 (chromosomes III and IV) and region 3 (chromosomes V and X). The haplotypes defined by the SNPs genotyped in a certain chromosome are referred to as chromosome-wide haplotypes (CWHs). In this way, the CWHs in the chromosomes that were not assayed in an individual can be conceptualized as missing data, denoted by the interrogation marks. Shown are the number of SNPs assayed in each chromosome, and the total number of CWHs that were measured, after quality control. Details on the SNPs density and number of individuals genotyped in each of the populations can be found in S1 Fig. (B) Given the genotyping data, a CWH consists of the alleles observed in each of the target sites in a single chromosome. The alleles observed in the target sites in the respective pair of chromosomes define the region-wide haplotype (RWH) for the individual, equivalent to concatenating the two corresponding CWHs. The figure shows the number of distinct CWHs and RWHs observed within each region, computed considering the full dataset (across time points and populations sampled). Since a RWH is defined by the CWHs in each of the two chromosomes in a region, the number of distinct RWHs is bounded below by the minimum of the number of distinct CWHs observed in the two respective chromosomes (reflecting the existence of linkage disequilibrium), and bounded above by the product of these two numbers (no linkage). The data indicate that linkage is strong since the number of distinct RWHs is comparable to those of distinct CWHs but not complete. Similarly, the number of lineages present in the lab-adapted ancestral population is bounded below by the product of the number of distinct RWHs in each of the three regions, indicating that the number of lineages in the ancestral population must be greater than or equal to 212. Data available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.76n6f7c [54].

Fig 3. Observed experimental population genetic dynamics.

(A-C) Observed frequency of region-wide haplotypes (RWH) during experimental evolution, for region 1 (A), region 2 (B) and region 3 (C). Upper rows show the responses in the control populations, middle rows for the gradual populations and bottom rows for sudden populations. Left columns show the class of minor frequency RWH that were grouped into a single class (named H0A, H0B and H0C). They indicate that the majority of haplotypes are quickly selected against under all experimental evolution regimes. Middle and right columns show the two haplotypes showing the greatest frequency change during experimental evolution. Points and error bars are the mean and one standard error of the observed haplotype frequencies among replicate populations. Line and shaded grey area are the frequency RWH trajectories inferred by modeling linear fitness reaction norms (see next Fig 4 and Fig 5). Data available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.76n6f7c [54].

Fig 4. Overview of the model used for reconstructing the lineage fitness reaction norms and their frequency dynamics.

In our genotyping scheme (Fig 2), the combination of region-wide haplotypes (RWHs, Fig 2 and Fig 3) define standing ancestral variation in genome-wide haplotypes or “lineages”. Fitness reaction norms are inferred from the fitness data (on the ancestral population, see Fig 6) and the genotyping data (Fig 3) assuming that the log-transformed RWH fitness reaction norms follow specific functional form, for example a linear function of the environmental value. The lineage fitness reaction norm of a lineage is the sum, in log space, of the component RWH reaction norms.

Fig 5. Fitness reaction norms of the two lineages explaining most population genetic dynamics.

(A) From Fig 3, RWHs H1A1, H1B1 and H1C1 define lineage L28, while RWHs H2A2, H2B2 and H2C3 define lineage L11 (see also S5–S7 Figs and S2 Table). The figure shows the inferred lineage fitness reaction norms of the L28 (red) and L11 (blue) lineages, when sampling standing genetic variation of the ancestral population 20 times. (B) We modeled selection assuming infinite population sizes and that the lineage frequency dynamics were a deterministic function of the previous generation. Predicted L28 and L11 frequency trajectories under our model with the linear reaction norms of (A). Trajectories were evaluated over 100 generations, assuming that the gradual populations would be kept under constant high salt from generation 35 onwards (left vertical dashed line). The second set of evolution experiments (described below, Fig 7), were run for 30 generations from the 7 replicate gradual populations at generation 35 (right vertical dashed line). Detailed trajectories for other inferred lineages are shown in S8 Fig. Shaded colors correspond to the credible intervals obtained when sampling the ancestral population 20 times, with the line showing the median. S9 Fig shows the RWHs trajectories and S10 Fig shows lineage trajectories under quadratic fitness reaction norms. S11 and S12 Figs show the expected mean and variance in population fitness under linear and quadratic functions. Data available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.76n6f7c [54].

Fig 6. Evaluating the model predictions using fitness data.

(A) Absolute fitness of the ancestor lab-adapted population at three salt concentrations (mean ± SE). The point and dashed error show the expected absolute fitness of the ancestral population, when modelling linear fitness reaction norms of all segregating lineages (from S11 Fig). At 25 mM and 305 mM NaCl the predicted values exactly match the observed values since they were used for inference. (B) Absolute fitness of the lines L28 and L11 at three salt concentrations (mean ± SE), with points and dashed lines as in (A) (from Fig 5A). (C) From (B), estimates (mean ± SE) of the expected relative fitness of L28 to L11 at three salt concentrations. (D) Similar to (C), but estimates from competitive fitness assays between L28 and L11. From ref. [21], the two inferred L28 and L11 lineages were identified after genome-wide sequencing of 100 lines derived from two gradual populations at generation 50 (S13 Fig and S2 Table). For the competitions shown in (D), frequency estimates of L28 and L11 were obtained using pooled-genotyping data on 18 SNPs that differ in L28 and L11 (see S14 Fig for calibration curves on these SNPs, and S15 Fig for our ability to differentiate L28 from L11). Data available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.76n6f7c [54].

Fig 7. Prior gradual evolution can restrict future adaptation to high salt.

(A) Experimental evolution design at different population sizes. Seven gradual populations at generation 35 become the ancestors (colored dots) for continued evolution in constant 305 mM NaCl for an extra 30 generations under large (green) and small (blue) population sizes. Populations were pool-genotyped for 18 SNPs differentiating L28 from all other segregating lineages, after 15 and 30 generations (arrows’ heads). (B-E) Trajectories for the replicate populations under large and small population sizes, from the seven ancestor populations. These trajectories are based on principal component (PC) analysis of allele frequency data, with the two first axis accounting for more than 70% of the variance (see S16 and S17 Figs). Red crosses indicate the likely position of the L28 lineage in this PC space. Analysis of the probability of a sweep by L28 in each population is shown in S17 Fig. Data available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.76n6f7c [54].