Recent advances in stochastic simulation algorithms create opportunities to study new ecological and eco‐evolutionary problems

Incorporating stochasticity into ecological modeling is vital for understanding the structure and function of ecological systems, but stochasticity has been challenging to consistently introduce into foundational ecological theory. The Gillespie stochastic simulation algorithm (herein SSA) provides a key method to represent demographic stochasticity, but due to its foundational assumptions is inadequate for problems involving intra‐population variation and extrinsically/environmentally‐driven rapid changes in demographic rates. Two recent extensions to the SSA address these limitations: one augments the SSA with individual‐linked trait distributions that let us track trait changes across individuals and populations and the other introduces a methodological innovation to allow demographic rates to change between events; in four case studies we demonstrate the utility of these advances and highlight that simultaneously integrating both extensions into a single framework allows us to tackle a new class of ecological and evolutionary questions that have typically not been explored in continuous time modeling.