Adaptive Phenotypic Plasticity Stabilizes Evolution in Fluctuating Environments

Abstract

Fluctuating environmental conditions are ubiquitous in natural systems, and populations have evolved various strategies to cope with such fluctuations. The particular mechanisms that evolve profoundly influence subsequent evolutionary dynamics. One such mechanism is phenotypic plasticity, which is the ability of a single genotype to produce alternate phenotypes in an environmentally dependent context. Here, we use digital organisms (self-replicating computer programs) to investigate how adaptive phenotypic plasticity alters evolutionary dynamics and influences evolutionary outcomes in cyclically changing environments. Specifically, we examined the evolutionary histories of both plastic populations and non-plastic populations to ask: (1) Does adaptive plasticity promote or constrain evolutionary change? (2) Are plastic populations better able to evolve and then maintain novel traits? And (3), how does adaptive plasticity affect the potential for maladaptive alleles to accumulate in evolving genomes? We find that populations with adaptive phenotypic plasticity undergo less evolutionary change than non-plastic populations, which must rely on genetic variation from de novo mutations to continuously readapt to environmental fluctuations. Indeed, the non-plastic populations undergo more frequent selective sweeps and accumulate many more genetic changes. We find that the repeated selective sweeps in non-plastic populations drive the loss of beneficial traits and accumulation of maladaptive alleles, whereas phenotypic plasticity can stabilize populations against environmental fluctuations. This stabilization allows plastic populations to more easily retain novel adaptive traits than their non-plastic counterparts. In general, the evolution of adaptive phenotypic plasticity shifted evolutionary dynamics to be more similar to that of populations evolving in a static environment than to non-plastic populations evolving in an identical fluctuating environment. All natural environments subject populations to some form of change; our findings suggest that the stabilizing effect of phenotypic plasticity plays an important role in subsequent adaptive evolution.

Artificial selection methods from evolutionary computing show promise for directed evolution of microbes

“…Each population comprises digital organisms (self-replicating computer programs) that compete for space in a well-mixed virtual environment. Both the digital organisms and their virtual environment are inspired by those of the Avida Digital Evolution Platform ( Ofria et al, 2009 ), which is a well-established study system for in silico evolution experiments (e.g ., Lenski et al, 1999 ; Lenski et al, 2003 ; Zaman et al, 2014 ; Lalejini et al, 2021 ) and is a closer analog to microbial evolution than conventional evolutionary computing systems. However, we note that our model’s implementation is fully independent of Avida, as the Avida software platform does not allow us to model laboratory setups of directed microbial evolution (as described in the previous section).…”

Newly Sequenced Genomes Reveal Patterns of Gene Family Expansion in Select Dragonflies (Odonata: Anisoptera)

“…Hypothesis 6: highly tolerant dragonflies have expanded gene families to allow for greater phenotypic plasticity. Expansions related to both methylation, and response to ATP could result in increased phenotypic and molecular plasticity, which are a necessity for evolutionary stability in fluctuating environments [59], and could be another molecular adaptation to fluctuating environments.…”

Artificial selection methods from evolutionary computing show promise for directed evolution of microbes

“…Both the digital organisms and their virtual environment are inspired by those of the Avida Digital Evolution Platform (Ofria et al, 2009), which is a well-established study system for in silico evolution experiments ( e.g ., A. Lalejini et al, 2021; Lenski et al, 1999; Lenski et al, 2003; Zaman et al, 2014) and is a closer analog to microbial evolution than conventional evolutionary computing systems. However, we note that our model’s implementation is fully independent of Avida, as the Avida software platform does not allow for us to model laboratory setups of directed microbial evolution (as described in the previous section).…”