Hypothetical relationships between correlates of fitness (<i>W_m</i>) and number of mutations (<i>m</i>).

<p>(<b>A</b>) Effects of lethal mutations measured as the natural log of the percent of mutants that are viable (<i>W_m</i>); curves were generated using the relationship ln(<i>W_m</i>) = −<i>αm^β</i>+ ln(100). (<b>B</b>) Effects of mutations on the phenotypic distance between the mutant and wild type; curves were generated using <i>W_m</i> = <i>αm^β</i>. For both A and B, <i>β</i> measures the strength and direction of epistasis. <i>β</i>>1 indicates negative directional (or synergistic) epistasis in which each subsequent mutation has a greater effect than the last, <i>β</i><1 indicates positive directional (or antagonistic) epistasis in which effects of mutations become weaker as they accumulate, and <i>β</i> = 1 indicates effects of mutations are the same as they accumulate. Each dashed lines corresponds to larger <i>α</i> and stronger effects of mutations relative to the paired solid line.</p

Epistatic effects of mutations on phenotypic distance and phenotypic robustness over time.

<p>Shown are mean responses (+/−SE) under the four selection regimes. (<b>A</b>) Epistatic effects on phenotypic distance (<i>β</i>) over time, measured as the Euclidean distance between post- and pre-mutation phenotypes. (<b>B</b>) Phenotypic robustness (1/<i>W₁</i>) over time measured as the inverse of phenotypic distance under a single mutation, 1/<i>W₁</i> = 1/exp(<i>α</i>). Results for the fluctuating environments are for red noise with a 50 generation period length and its corresponding white noise control.</p

Environmental Noise, Genetic Diversity and the Evolution of Evolvability and Robustness in Model Gene Networks

<div><p>The ability of organisms to adapt and persist in the face of environmental change is accepted as a fundamental feature of natural systems. More contentious is whether the capacity of organisms to adapt (or “evolvability”) can itself evolve and the mechanisms underlying such responses. Using model gene networks, I provide evidence that evolvability emerges more readily when populations experience positively autocorrelated environmental noise (red noise) compared to populations in stable or randomly varying (white noise) environments. Evolvability was correlated with increasing genetic robustness to effects on network viability and decreasing robustness to effects on phenotypic expression; populations whose networks displayed greater viability robustness and lower phenotypic robustness produced more additive genetic variation and adapted more rapidly in novel environments. Patterns of selection for robustness varied antagonistically with epistatic effects of mutations on viability and phenotypic expression, suggesting that trade-offs between these properties may constrain their evolutionary responses. Evolution of evolvability and robustness was stronger in sexual populations compared to asexual populations indicating that enhanced genetic variation under fluctuating selection combined with recombination load is a primary driver of the emergence of evolvability. These results provide insight into the mechanisms potentially underlying rapid adaptation as well as the environmental conditions that drive the evolution of genetic interactions.</p> </div

Appendix A. A supplemental model of food web assembly with stochastic sequential dispersal vs. simultaneous dispersal.

A supplemental model of food web assembly with stochastic sequential dispersal vs. simultaneous dispersal

Log fitness over time.

<p>Shown are means across populations (+/−SE) over 36000 generations of selection in the four selection regimes. Results for the fluctuating environments are for red noise with a 50 generation period length and its corresponding white noise control.</p

Appendix B. A table of assembly sequences used in the experiment and four supplemental figures displaying species diversity responses, time series of community dispersion distances, time series of temporal turnover, and temporal population variability.

A table of assembly sequences used in the experiment and four supplemental figures displaying species diversity responses, time series of community dispersion distances, time series of temporal turnover, and temporal population variability

Effects of selection on evolvability over time.

<p>Evolvability was measured as the rate of adaptation and the production of phenotypic variation assessed under selection in novel environments. Measures were determined using 100 networks randomly selected from each population every 2000 generations. (<b>A</b>) Mean evolvability (+/− SE) over time. (<b>B</b>) Mean production of phenotypic variation (+/− SE) over time. Results for the fluctuating environments are for red noise with a 50 generation period length and its corresponding white noise control.</p

Results of partial least squares regression examining the relationship between evolvability (the rate of adaptation) and measures of epistasis and robustness.

<p>Shown are results for the first component axis of the regression which accounted for 40.3% of variation in evolvability. With the exception of the initial pre-selection networks, data were based on time averages over the last 20000 generations. Loadings of explanatory variables on the component axis can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052204#pone-0052204-t001" target="_blank">Table 1</a>. Results for the fluctuating environments are for red noise with a 50 generation period length and its corresponding white noise control.</p

Appendix A. Environmental data for each study site and experimental period.

Environmental data for each study site and experimental period

Loadings of predictor variables on the first two components produced by the partial least squares regression analysis of evolvability.

<p>Predictors with squared weighted loadings >0.05 were treated as statistically significant (indicated by *).</p