Trade-off drives Pareto optimality of within- and among-year emergence timing in response to increasing aridity.

Adaptation to current and future climates can be constrained by trade-offs between fitness-related traits. Early seedling emergence often enhances plant fitness in seasonal environments, but if earlier emergence in response to seasonal cues is genetically correlated with lower potential to spread emergence among years (i.e., bet-hedging), then this functional trade-off could constrain adaptive evolution. Consequently, selection favoring both earlier within-year emergence and greater spread of emergence among years-as is expected in more arid environments-may constrain adaptive responses to trait value combinations at which a performance gain in either function (i.e., evolving earlier within- or greater among-year emergence) generates a performance loss in the other. All such trait value combinations that cannot be improved for both functions simultaneously are described as Pareto optimal and together constitute the Pareto front. To investigate how this potential emergence timing trade-off might constrain adaptation to increasing aridity, we sourced seeds of two grasses, and , from multiple maternal lines within populations across an aridity gradient in California and examined their performance in a greenhouse experiment. We monitored emergence and assayed ungerminated seeds for viability to determine seed persistence, a metric of potential among-year emergence spread. In both species, maternal lines with larger fractions of persistent seeds emerged later, indicating a trade-off between within-year emergence speed and potential among-year emergence spread. In both species, populations on the Pareto front for both earlier emergence and larger seed persistence fraction occupied significantly more arid sites than populations off the Pareto front, consistent with the hypothesis that more arid sites impose the strongest selection for earlier within-year emergence and greater among-year emergence spread. Our results provide an example of how evaluating genetically based correlations within populations and applying Pareto optimality among populations can be used to detect evolutionary constraints and adaptation across environmental gradients.