DSSS - Dynamics of adaptation and trade-off evolution in complex environments

Predicting evolution has been a long-standing goal in evolutionary biology. But due to experimental over-simplification, an important component of natural systems is often missed: Populations in nature rarely adapt to a single stress at a time. Instead, multiple biotic and abiotic factors come together to produce complex environments. Are the combined effects of multiple stressors additive? Or may they be antagonistic or synergistic due to trade-offs? And how do these trade-offs evolve over time as populations adapt? We used experimental evolution with yeast (Saccharomyces cerevisiae) to track the dynamics of adaptation in the presence of four stressors (heat, salt, starvation, antifungal). To test if we can predict evolution in complex environments from the effects of single stressors, we adapted populations to a full-factorial combination of these four stressors and used whole genome sequencing to identify SNPs with large fitness effects. We observed rapid increases in fitness paired with the accumulation of mutations associated with specific stressors. Populations in high complexity environments increased more rapidly in fitness than low complexity populations, but showed large variation between replicates. Trade-offs between stressors were abundant and evolved rapidly, but the direction of interactions varied and were difficult to predict. Overall, the degree of parallelism at the phenotypic level was modified by the degree of environmental complexity, while parallelism at the genic level was apparent between populations which shared stressors. While the strength of selection of individual components determined early adaptive dynamics, the genetic basis and ease of mutational accessibility determined the order and degree of trade-off evolution. Our results highlight that we need a deeper mechanistic understanding of contemporary stressor effects on organisms and the genetic basis of resistance in order to not only explain, but predict evolutionary outcomes in populations evolving in ecologically relevant environments.