Modeling the Tangled Bank: A Users Guide to Gillespie Eco-evolutionary Models using the Julia Programming Language

O_LIEco-evolutionary processes drive patterns in species abundances and traits. However, modeling complex ecological systems is challenging because of the many species and traits involved and because processes unfold in stochastic and non-equilibrium conditions. Gillespie Eco-evolutionary Models (GEMs) were created to simulate the eco-evolutinary dynamics in such complex scenarios. C_LIO_LIGEMs bridge the gap between ecological and evolutionary timescales, enabling tracking of trait dynamics and abundances simultaneously, and allowing real-time feedback between the two processes. Designed to capture demographic stochasticity and individual trait variation, GEMs enable tracking of multi-trait and multi-species evolution. Originally designed for a quantitative genetics approach, GEMs have now been expanded to handle both quantitative traits and discrete traits (genotypes or strains). C_LIO_LIIn this manuscript, we introduce an optimized and accessible framework for GEMs developed in the Julia programming language. We have simplified the simulation setup process by placing many of the information containers and design structures into functions that adapt to the model configurations chosen by the user. We use a birth-death logistic growth model as an example to illustrate the GEM setup. C_LIO_LIThis users guide is designed to make building GEMs for new models more streamlined and approachable. In addition to compartmentalized internal workings, the new framework also improves computational speed and efficiency. This lets users focus on model development rather than algorithmic complexities. C_LI