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. Author manuscript; available in PMC: 2014 Feb 1.
Published in final edited form as: J Evol Biol. 2012 Dec 17;26(2):451–456. doi: 10.1111/jeb.12058

The effect of spontaneous mutations on competitive ability

Sarah Schaack 1,✉, Desiree E Allen 2, Leigh C Latta IV 1, Kendall K Morgan 3, Michael Lynch 4
PMCID: PMC3548015  NIHMSID: NIHMS418591  PMID: 23252614

Abstract

Understanding the impact of spontaneous mutations on fitness has many theoretical and practical applications in biology. Although mutational effects on individual morphological or life-history characters have been measured in several classic genetic model systems, there are few estimates of the rate of decline due to mutation for complex fitness traits. Here, we estimate the effects of mutation on competitive ability, an important complex fitness trait, in a model system for ecological and evolutionary genomics, Daphnia. Competition assays were performed to compare fitness between mutation-accumulation (MA) lines and control lines from 8 different genotypes from two populations of D. pulicaria after 30 and 65 generations of mutation accumulation. Our results show a fitness decline among MA lines relative to controls as expected, but highlight the influence of genomic background and genotype on this effect. In addition, in some assays MA lines outperform controls providing insight into the frequency of beneficial mutations.

Keywords: Mutation, competition, Daphnia, fitness

Introduction

Although the phenotypic effects of spontaneous mutation are thought to be, on average, deleterious (Mukai 1964), empirical estimates of the fitness consequences of spontaneous mutation are primarily limited to classic model organisms about which little is known ecologically ( e.g., Dobzhansky et al. 1952, Kibota and Lynch 1996, Keightley and Caballero 1997, Schultz et al. 1999; reviewed in Eyre-Walker and Keightley 2007) and the variability among genotypes and species is largely unknown (but see Baer et al. 2007 and Keightly et al. 2009). Previous work indicates that the average effects of spontaneous mutations may, indeed, differ markedly among strains and species (Simmons and Crow 1977, Fry et al. 1996, 1999, Fernandez and Lopez-Fanjul 1996; Wayne and Mackay 1998; reviewed in Lynch et al 1999; Baer et al. 2005; Haag-Liautard et al. 2007). Despite the typical decline in trait values that is observed under standard laboratory conditions due to mutation accumulation (Houle et al. 1994), differential performance in more natural or variable environments reveals a broader distribution of fitness effects that can include beneficial effects (Kondrashov and Houle 1994, Roles and Connor 2008, Rutter et al. 2010). Lastly, the relationship between the rate of decline for individual morphological, performance, or life-history traits to an overall decline in fitness is difficult to disentangle (Huey et al. 2003). In part, this is because little is known about the ecology of many model species, but also because a change in one trait, e.g., feeding rate, can have beneficial or deleterious consequences for fitness depending on conditions.

Here, we assay competitive ability, a composite fitness trait, between mutation-accumulation (MA) lines and control lines from multiple genotypes from two different populations of Daphnia pulicaria. MA lines provide a powerful approach for estimating the rate and effects of mutation because individual lines are propagated by single progeny, thereby minimizing the influence of selection and capturing the spontaneous mutations as they occur (Lynch 1985; Halligan & Keightley 2009). Further, members of the genus Daphnia (Branchiopoda) are among the best understood model systems in ecology in which it is also possible to estimate mutation rates and effects (Lynch et al. 1998, Vassilieva and Lynch 1999). The recent completion of the whole genome sequence for D. pulex (DGC 2011) has accompanied the growing utility of this group for ecological and evolutionary genomics (Schaack 2008). By comparing the relative fitness of MA and control lines of D. pulicaria, we can estimate the mean and distribution of effects of accumulated mutations on competitive ability. Such estimates allow us to better understand how evolutionary forces, such as natural selection and genetic drift, shape genetic variation over time.

Methods

Mutation-Accumulation Experiment

Ten Daphnia pulicaria genotypes from each of two permanent populations, Klamath Lake and Lake Marie (Oregon, USA), were collected and isolated into individual beakers. All animals were maintained under uniform environmental conditions (18 °C, 12L:12D light cycle, in filtered lake water, fed Scenedesmus spp. maintained in culture in the lab) in a controlled environmental chamber. Each genotype was allowed to reproduce asexually (producing clonal offspring) until 10 genetically identical individuals were available. Single individuals were then placed in beakers to start 10 lines per genotype (2 populations × 10 genotypes × 10 lines = 200 lines total). Lines were genotyped (Morgan et al. 2001) and maintained by single-progeny descent by randomly choosing individual offspring from the second clutch produced each generation. Previous generations were kept as backups until the subsequent generation was propagated in case individuals died or were sterile. In this manner, the 10 lines starting from each genotype diverged due to the accumulation of non-lethal and non-sterilizing mutations resulting from the minimization of selection and maximization of genetic drift due to single-offspring propagation of the lines. Control representatives of each genotype were maintained in large populations at 7 ° C and 12L:12D light cycle, conditions designed to slow the generation time and reduce mutational divergence.

Competition Assays

After approximately 30 generations, MA lines from five genotypes (A, B, C, D, and E) from Lake Marie were randomly chosen (between 1 and 9 MA lines per genotype, n = 21; see Figure 1A) for competition assays against control lines that had been initiated from each of the starting genotypes and maintained in parallel (see previous Methods). For the assays, all MA and control lines were maintained under constant conditions in beakers for two generations prior to initiating the competition experiments to ensure that maternal environmental effects would not contribute to differences in competitive performance. Two replicates of each pair were assayed. The same type of paired competition assays were conducted using randomly selected lines (n = 16) from three genotypes from the Klamath Lake experiment (X, Y, and Z; see Figure 1B) after approximately 65 generations of mutation accumulation. Two replicates each from a subset of lines from a single Lake Marie genotype (n = 4) were assayed after an average of 30 generations of mutation accumualtion and after an average of 69 generations of mutation accumulation to compare the competitive performance of MAs to controls.

Figure 1.

Figure 1

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Mean per-generation selection coefficient estimates for competitive ability based on comparisons of MA and control lines for D. pulicaria from Lake Marie (A) and Klamath Lake (B). Estimates for each MA line-control pair are plotted, with each color representing lines initiated from the same starting genotype (A, B, C, D, and E from Lake Marie and X, Y, and Z from Klamath Lake). Lake Marie lines that were also assayed in generation 69 are denoted by asterisks. Error bars are ±SE.

Competition assays were conducted by placing 10 genetically identical individuals from a given line, derived from either MA or control conditions, in each of two paired 3 L jars. Animals were maintained in 2.5 L of filtered lake water with a food resource (Scenedesmus algae) at a starting density of approximately 250,000 cells/mL. All jars were kept in a controlled environmental chamber at 18° C and a 12 L:12 D light cycle. No additional food was added during the course of the experiment. Thus, resources were initially abundant and gradually declined over the course of the experiment. Under these conditions Daphnia reproduce asexually, with populations initially increasing in size as they utilize the available resources, and declining as the resources are depleted.

Within each pair, the entire population of individuals from the MA or control line were isolated and switched to their respective paired jar (e.g. the MA population was removed from its jar and placed in the jar that previously contained the control population) every other day, such that the total resources were shared without actual mixing of the MA and control Daphnia. Under this design, the resource use of the control animals impacts the resources available to the MA animals, and vice versa, with a brief time delay. Every other day, all Daphnia were strained out of each jar using a fine mesh screen. While on the screen, all individuals were counted using a dissecting scope before being moved to their competitor’s resource in the paired jar. In the experiment comparing MA and control lines from genotypes derived from Lake Marie, jars were maintained indefinitely until one jar of each pair went extinct (maximum of 120 days). Because there were very few survivors at the tail end of this experiment, we truncated the second experiment (using Klamath Lake genotypes) after 35 days of competition.

Analysis

To measure the competitive ability of each MA line relative to its control, each pair of jars was analyzed separately by weighted least squares regression of log(p/(1-p)) on days of competition (up to 120 days for Lake Marie pairs and up to 35 days for Klamath Lake pairs) , where p is the proportion of MA individuals in the competing pair of MA and control individuals (p=Nm/(Nm+Nc), with Nm = the number of MA individuals and Nc = the number of control individuals counted every other day (Crow and Kimura 1970). Data points were weighted by the expected sampling variance of the population (1/[Nm + Nc × p(1-p)]), such that data points including greater numbers of individuals carried more weight. This was necessary, as the number of individuals was small early and late in the experiments, making the ratio sensitive to small changes in the number of individuals at these time points.

In the absence of a selective advantage or disadvantage of mutations, the proportion of MA and control animals in each competing pair should remain constant over time. The regression slope value is an estimate of the selection coefficient on the MA line and a regression slope that differs significantly from zero indicates a difference in competitive performance between the MA line and its control (defined as relative fitness on available resources). Negative slopes signify reduced MA fitness and positive slopes signify enhanced MA fitness, relative to control lines. We estimated the per-generation selection coefficient for each line by dividing the slope by the line-specific number of generations of mutation accumulation that had occurred. The per-generation selection coefficients were averaged across the lines assayed from each population and a sign test was used to test whether the number of negative versus positive slopes was greater than expected by chance alone.

To assess the potential for variability in selection coefficients among genotypes within a population, we used a mixed-effects nested analysis of variance (NANOVA) with lines (treated as a random effect) nested within genotypes (treated as a fixed effect; Bates et al. 2012). Estimating the significance of fixed effects under restricted maximum likelihood is difficult because the denominator degrees of freedom used to penalize certainty are unknown. Therefore, we determined the significance of the fixed effect of genotype by estimating upper- and lower-bound p-values, which provide anti-conservative and conservative p-value estimates, respectively (Tremblay & Ransijn 2012). To test for differences among the two populations, we used a two sample t-test assuming unequal variances based on the genotype-specific estimates of selection coefficients estimated from the NANOVA. To test for differences in selection coefficients between the Marie Lake lines that were assayed at both 30 and 69 generations, we used a paired t-test to compare the average of two replicates for each subline assayed at each time point.

Results

The average per-generation selection coefficient for the Lake Marie MA lines was −0.0020 (±0.0006 SE) and the distribution of selection coefficients was negatively skewed (Sign Test: 16 of 17, P = 0.0001), indicating MA lines were typically outcompeted by control lines (Figure 1A). There were significant differences in selection coefficients among genotypes within Lake Marie (NANOVA: upper bound, F4,39 = 2.86, P = 0.0361; lower bound, F4,29 = 2.86, P = 0.0412). For the subset of lines from Lake Marie for which assays were performed after 30 and and 69 generations, a difference was observed in per-generation selection coefficients (−0.00003 ±0.00011 and −0.00052 ±0.00027, respectively; paired t-test, t3 = 2.70, P = 0.0373 [one-tailed] and P = 0.0737 [two-tailed]; Figure 2).

Figure 2.

Figure 2

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Mean per-generation selection coefficient estimates for competitive ability from the four Lake Marie lines assayed after 30 and 69 generations. Error bars are ±SE.

In Klamath Lake-derived MA lines the same pattern is observed (Figure 1B),with a mean per-generation selection coefficient of −0.0009 (±0.0006 SE) and the majority of competition pairs having negative slopes (Sign Test: 9 of 11, P = 0.0327). There were also significant differences in selection coefficients among Klamath Lake genotypes (NANOVA: upper bound, F2,29 = 5.56, P = 0.0090; lower bound, F2,21 = 5.56, P = 0.0115). A difference in selection coefficients between the two populations overall was observed with a one-tailed (Two-sample t Test: t6 = 2.05, p = 0.0430), but not a two-tailed test (Two-sample t Test: t6 = 2.45, p = 0.0862). Among lines derived from both populations, there were some assays during which MA lines had positive selection coefficients relative to controls, indicating better performance after mutation accumulation.

Discussion

Our results show that Daphnia pulicaria with accumulated spontaneous mutations experience a significant decline in competitive ability relative to their respective controls. The difference in this decline between genotypes from the two starting populations, Lake Marie and Klamath Lake, may be related to differences in the mutation rate of simple life-history traits, such as fecundity, which have been reported between these two populations (Latta et al. 2012). Specifically, the fitness assays of MA lines from genotypes isolated from Klamath Lake revealed a rate of decline for clutch size that was an order of magnitude lower than in the Marie population (Latta et al. 2012). Thus differences in genomic background among populations may explain the smaller selection coefficients found for Klamath lines in the competition assays performed here.

An alternative explanation for the difference in mean per-generation selection coefficients is the timing of the assays for each population (after 30 and 65 generations, respectively for Marie and Klamath lines). Older MA lines could exhibit lower mean per-generation selection coefficients if extinction bias (loss of most mutated lines over times) reduces the mean over time. On the other hand, older MA lines may exhibit a lower mean fitness if the interacting effects of accumulated mutations magnify fitness declines, often referred to as mutational meltdown (Lynch and Gabriel 1990). To distinguish among these hypotheses (differences due to genomic background versus the timing of the assays) we can compare selection coefficients based on assays with genotypes from Lake Marie that were conducted after both 30 and 69 generations of mutation accmulation. In this subset of lines, the mean per-generation selection coefficient from the assay on older lines is 17X greater than the early assay, suggesting synergistic epistasis may lead to mutational meltdown over time (Figure 2). These data are limited, but they support the hypothesis that differences are more likely due to genomic background than timing of the assays.

In addition to the trends indicating overall differences in the effect of mutations on competitive ability among the two populations, within each population there were significant differences in per-generation selection coefficients among genotypes. This variation supports several studies that indicate major differences in mutation rates within species (e.g., Haag-Liautard et al. 2007, Sharp and Agrawal 2012), but contrasts with the majority of studies in which little intraspecific variation has been found (Keightley et al. 2009, Solomon et al. 2009, Xu et al. 2012, and Denver et al. 2012).

The net effect of accumulated mutations in D. pulicaria across all lines assayed was deleterious in 67.6% of the lines (proportion of significant negative slopes), neutral in 24.3% (proportion of non-significant slopes) and beneficial in 8.1% (proportion of significant positive slopes). The observation that a small proportion of mutant lines exceeded the performance of the original genotype (control lines) is congruent with recent studies demonstrating fitness benefits of mutation when composite fitness traits are measured and assays are performed in competitive environments outside the laboratory (Roles and Connor 2008, Rutter et al. 2010). It is worth noting that regardless of their positive or negative effect, all mutations initially arise in the heterozygous state. In Daphnia, mutations in the heterozygous state cannot become homozygous until sex occurs. Thus, the fate of new mutations, and those in this experiment, depends on their heterozygous effect, and will be observed only to the extent that mutations are partially dominant (Simmons and Crow 1977; Charlesworth and Hughes 1996).

Acknowledgments

We would like to thank Emilia Martins and John Postlethwait for comments on the manuscript. Thanks to Casse Weaver for conducting a pilot study. This work was funded by fellowships from National Institute of Health Training (5 T32 GM07413) and National Science Foundation (NSF) grant (DBI-9413223) to KKM. Funding also provided by M.J. Murdock Charitable Trust Grant and NSF (MCB-1150213) grants to SS and NSF grant (DEB-9903920) and NIH grant (R01 GM036827) to ML.

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