Phenotypic plasticity in evolutionary rescue experiments

Abstract

Population persistence in a new and stressful environment can be influenced by the plastic phenotypic responses of individuals to this environment, and by the genetic evolution of plasticity itself. This process has recently been investigated theoretically, but testing the quantitative predictions in the wild is challenging because (i) there are usually not enough population replicates to deal with the stochasticity of the evolutionary process, (ii) environmental conditions are not controlled, and (iii) measuring selection and the inheritance of traits affecting fitness is difficult in natural populations. As an alternative, predictions from theory can be tested in the laboratory with controlled experiments. To illustrate the feasibility of this approach, we briefly review the literature on the experimental evolution of plasticity, and on evolutionary rescue in the laboratory, paying particular attention to differences and similarities between microbes and multicellular eukaryotes. We then highlight a set of questions that could be addressed using this framework, which would enable testing the robustness of theoretical predictions, and provide new insights into areas that have received little theoretical attention to date.

Figure 1.

Evolving plasticity and evolutionary rescue. Evolutionary rescue caused by an increase in plasticity (a) or by adaptation with little change in plasticity (b). We assume linear reaction norms, with plasticity quantified by reaction norm slope, while the non-plastic component of the phenotype is described by the intercept, or elevation, in a reference environment. The environment changes from 0 to 3 on generation 0, and the optimum phenotype from 0 to 6, causing maladaptation and population decline (for more details see [13]). Whether evolutionary rescue is caused by the evolution of plasticity, or by genetic evolution of the phenotype with little change in plasticity, depends notably on the relative genetic variances of reaction slope and elevation. The initial and final reaction norms are plotted in figure 2. A quantitative genetic model was used, with genetic variance of reaction norm elevations V_a = 0.1 (a) or V_a = 0.4 in (b), and genetic variance of slopes V_b = 0.05 (a) or V_b = 0.005 (b), and no covariance between slope and elevation measured in a reference environment.

Figure 2.

Evolved reaction norms for the trait and fitness. The initial (dashed) and final reaction norms for the trait (a,b) and for absolute fitness (tolerance curves, c,d) are shown for the two scenarios shown in figure 1 (a,c versus b,d).

Figure 3.

Plasticity, predictability and evolutionary rescue in a fluctuating environment. Evolutionary rescue following an abrupt change in environmental predictability is illustrated. (a) Relative plasticity, that is, the mean reaction norm slope scaled to the slope of changes in the optimum phenotype with the environment (environmental sensitivity of selection). The temporal autocorrelation of the environment during the time lag between development and selection on the plastic trait is also represented (dashed line). (b) The reaction norm elevation, i.e. its intercept in a reference environment. (c) Represents population size, under density-independent population growth. Autocorrelation of environmental fluctuations drops from 0.8 to 0.2 at generation 100 (a), with no change in the average environment. This less predictable environment causes the high level of plasticity that was previously optimal to become detrimental, and the population starts declining (b). In this example, evolutionary rescue is afforded by the evolution of a decreased level of plasticity that matches the current environmental predictability (a), with little change in reaction norm elevation (b). Quantitative genetic simulations were used as in figure 1, but with fluctuations in the optimum, with variance 1.5 and autocorrelation as given above. All genetic parameters are as in figure 1a.

Figure 4.

Competition and evolutionary rescue. (a) Demographic dynamics with negative density-dependence of population growth is represented, together with (b) the evolutionary dynamics of a single mutation causing evolutionary rescue. Density regulation follows the Ricker model (discrete time analogue to logistic growth [89]). Selection, in contrast, is density-independent, such that both the environmental stress and the beneficial mutation only affect the intrinsic rate of increase r₀ of the population, while the density-dependent component of population growth does not evolve (as described in [13,90]). After the onset of stress (generation 0), the population starts declining because the lowered r₀ no longer allows maintenance of a large equilibrium population size. A beneficial mutant restoring higher r₀ appears in one copy in generation 100, causing the population to increase back to a high equilibrium size. Note that the first phase of the apparent population recovery (until the population size stabilizes at a lower value) occurs without any genetic evolution. Parameters are, before the onset of stress: intrinsic rate of increase r₀ = 0.1, carrying capacity K = 1000; stress-induced reduction in r₀: s₀ = −0.11; selection coefficient of the rescue mutation: s = 0.077.