ncts

Organisms must juggle constantly changing environmental pressures, often needing to make trade-offs to maximize their shot at survival. Stressors like drought or resource availability can vary from year to year, causing an adaptation that was beneficial at one point to become deleterious later. When the only constant is change, it becomes imperative for organisms to evolve some way of reducing risk. One possible method for risk reduction is bet hedging, which reduces fitness variance across generations at the expense of also lowering arithmetic mean fitness. Classically, the benefit of bet hedging has been quantified using geometric mean fitness (GMF); bet hedging is expected to evolve if and only if it has a higher GMF than the wild-type. I build upon previous research on the effect of incorporating stochasticity in phenotypic distribution, environment, and reproduction to investigate the extent to which these sources of stochasticity impact the evolution of real-world bet hedging traits. I demonstrate that explicitly modeling stochasticity can alter the sign of selection for bet hedging compared to deterministic predictions. I apply this model to published data to show that our model can explain the evolution of real-world bet hedging traits, including variable germination phenology in poppies, and antibiotic persistence in Salmonella.