Abstract
Performance tradeoffs are ubiquitous in both ecological and evolutionary modeling, yet they are usually postulated and built into fitness and ecological landscapes. However, tradeoffs depend on genetic background and evolutionary history and can themselves evolve. We present a simple model capable of capturing the key feedback loop: evolutionary history shapes tradeoff strength, which, in turn, shapes evolutionary future. One consequence of this feedback is that genomes with identical fitness can have different evolutionary properties shaped by prior environmental exposure. Another is that, generically, the best adaptations to one environment may evolve in another. Our simple framework bridges the gap between the phenotypic Fisher's Geometric Model and the genotypic properties, such as modularity and evolvability, and can serve as a rich playground for investigating evolution in multiple or changing environments.
Highlights
Performance tradeoffs, caricatured by “you can’t be good at everything”, are ubiquitous in both ecology and evolution
We propose a minimally structured model capturing some key experimentally observed behaviors: namely, the model exhibits performance tradeoffs, but their strength evolves and depends on evolutionary history. This minimal setting proves sufficient to observe non-trivial ways in which tradeoff strength shaped by evolutionary past can predictably influence evolutionary future; in particular, we identify a mechanism that makes the path towards the highest fitness in one set of environments is via exposure to a different set
What are our expectations for the behavior of the mutational tradeoff χ? First, any notion of tradeoff strength is expected to depend on the difference between environments
Summary
The fitness of genome G in environment E measures how well the basis (the “tools” available) can approximate the target To motivate this setup, consider the L-dimensional space as the space of traits (phenotype space). We caricature a genome by its pattern of trait co-regulation, a set of K basis vectors {gμ}, and posit that an organism can adopt any phenotype realizable as a linear combination of its {gμ} with positive coefficients — loosely “expression levels”. Target phenotypes outside this K-dimensional subspace can only be approximated, and we define fitness F (G, E) as the (Euclidean) norm of the residual:. This protocol accepts one mutation per exposure epoch; the validity of this approximation is discussed in the SI (Fig. S1)
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