Evolution of Competitive Ability: An Adaptation Speed vs. Accuracy Tradeoff Rooted in Gene Network Size

Ecologists have increasingly come to understand that evolutionary change on short time-scales can alter ecological dynamics (and vice-versa), and this idea is being incorporated into community ecology research programs. Previous research has suggested that the size and topology of the gene network underlying a quantitative trait should constrain or facilitate adaptation and thereby alter population dynamics. Here, I consider a scenario in which two species with different genetic architectures compete and evolve in fluctuating environments. An important trade-off emerges between adaptive accuracy and adaptive speed, driven by the size of the gene network underlying the ecologically-critical trait and the rate of environmental change. Smaller, scale-free networks confer a competitive advantage in rapidly-changing environments, but larger networks permit increased adaptive accuracy when environmental change is sufficiently slow to allow a species time to adapt. As the differences in network characteristics increase, the time-to-resolution of competition decreases. These results augment and refine previous conclusions about the ecological implications of the genetic architecture of quantitative traits, emphasizing a role of adaptive accuracy. Along with previous work, in particular that considering the role of gene network connectivity, these results provide a set of expectations for what we may observe as the field of ecological genomics develops

Smaller Gene Networks Permit Longer Persistence in Fast-Changing Environments

The environments in which organisms live and reproduce are rarely static, and as the environment changes, populations must evolve so that phenotypes match the challenges presented. The quantitative traits that map to environmental variables are underlain by hundreds or thousands of interacting genes whose allele frequencies and epistatic relationships must change appropriately for adaptation to occur. Extending an earlier model in which individuals possess an ecologically-critical trait encoded by gene networks of 16 to 256 genes and random or scale-free topology, I test the hypothesis that smaller, scale-free networks permit longer persistence times in a constantly-changing environment. Genetic architecture interacting with the rate of environmental change accounts for 78% of the variance in trait heritability and 66% of the variance in population persistence times. When the rate of environmental change is high, the relationship between network size and heritability is apparent, with smaller and scale-free networks conferring a distinct advantage for persistence time. However, when the rate of environmental change is very slow, the relationship between network size and heritability disappears and populations persist the duration of the simulations, without regard to genetic architecture. These results provide a link between genes and population dynamics that may be tested as the -omics and bioinformatics fields mature, and as we are able to determine the genetic basis of ecologically-relevant quantitative traits

Quantitative trait heritability after 250 generations, given network (A) or linear (B) genetic architectures.

<p>The mean heritability (±95% CI) of the ecologically-critical trait when the genetic architecture is defined as a network, as a function of network size and topology; smaller networks and scale-free topology increase heritability (Panel A). The mean heritability (±95% CI) of the trait when the genetic architecture is purely linear, as a function of number of genes and the width of the fitness function (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0014645#s4" target="_blank">Methods</a>). Contrary to the network architecture, when the architecture is purely linear heritability is (weakly) positively related to the number of underlying genes.</p

Factors influencing quantitative trait heritability in a stable environment.

<p><i>Effect direction</i> refers to whether the relationship between heritability and the predictor is directly or inversely proportional. Additional interaction terms not presented here accounted for the remaining 5% of variance, but each at <1%.</p

Population recovery times given network genetic architectures.

<p>Mean time (±95% CI) required for a population to recover to pre-impact population size after a sudden environmental change when the genetic architecture is defined as a network, as a function of network size and the degree of environmental change (dE; arbitrary units). Population recovery takes long if either network size or the degree of environmental change is greater.</p

Epistasis in the Boolean gene networks used in these models.

<p>Panel A shows the distribution of 24000 epistasis estimates across all network sizes and topologies. Directional epistasis is prevalent (94% of all single- versus double-mutants). Panel B shows mean weighted epistasis (±95% CI) as a function of network size and topology. The weighting, with weights calculated as the standard deviation of epistasis within network size, was required to achieve homoskedasticity for statistical analysis. Without weighting, a strong negative relationship between epistasis and network size is observed (data not shown).</p

The rates of change of genotypic and phenotypic variance over 250 generations in a constant environment.

<p>On the left, the mean (±95% CI) rate of change of additive genetic variance (dV_A/dt; Panel A) and phenotypic variance (dV_P/dt; Panel C), given a network genetic architecture, as a function of network size and recombination rate. On the right (Panels B and D), the same parameters given a linear genetic architecture. The results are generally consistent with the analytical solutions assuming additivity of Crow and Kimura <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0014645#pone.0014645-Crow1" target="_blank">[9]</a> and Bürger <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0014645#pone.0014645-Brger1" target="_blank">[10]</a>.</p

Primary factors influencing population recovery time following a sudden environmental impact.

<p><i>Effect direction</i> refers to whether the relationship between population recovery time and the predictor variable (Factor) is directly or inversely proportional.</p