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ABSTRACT Many insects and other animals host heritable endosymbionts that alter host fitness and reproduction. The prevalence of facultative endosymbionts can fluctuate in host populations across time and geography for reasons that are poorly understood. This is particularly true for maternally transmittedWolbachiabacteria, which infect roughly half of all insect species. For instance, the frequencies of severalwMel‐likeWolbachia, includingwMel in hostDrosophila melanogaster, fluctuate over time in certain host populations, but the specific conditions that generate temporal variation inWolbachiaprevalence are unresolved. We implemented a discrete generation model in the new R packagesymbiontmodelerto evaluate how finite‐population stochasticity contributes toWolbachiafluctuations over time in simulated host populations under a variety of conditions. Using empirical estimates from naturalWolbachia‐Drosophilasystems, we explored how stochasticity is determined by a broad range of factors, including host population size, maternal transmission rates, andWolbachiaeffects on host fitness (modeled as fecundity) and reproduction (cytoplasmic incompatibility; CI). While stochasticity generally increases when host fitness benefits and CI are relaxed, we found that a decline in the maternal transmission rate had the strongest relative impact on increasing the size of fluctuations. We infer that non‐ or weak‐CI‐causing strains likewMel, which often show evidence of imperfect maternal transmission, tend to generate larger stochastic fluctuations compared to strains that cause strong CI, likewRi inD. simulans. Additional factors, such as fluctuating host fitness effects, are required to explain the largest examples of temporal variation inWolbachia. The conditions we simulate here usingsymbiontmodelerserve as a jumping‐off point for understanding drivers of temporal and spatial variation in the prevalence ofWolbachia, the most common endosymbionts found in nature. 

The turbulent channel flow database is produced from a direct numerical simulation (DNS) of wall bounded flow with periodic boundary conditions in the longitudinal and transverse directions, and no-slip conditions at the top and bottom walls. In the simulation, the Navier-Stokes equations are solved using a wall {normal, velocity {vorticity formulation. Solutions to the governing equations are provided using a Fourier-Galerkin pseudo-spectral method for the longitudinal and transverse directions and seventh-order Basis-splines (B-splines) collocation method in the wall normal direction. De-aliasing is performed using the 3/2-rule [3]. Temporal integration is performed using a low-storage, third-order Runge-Kutta method. Initially, the flow is driven using a constant volume flux control (imposing a bulk channel mean velocity of U = 1) until stationary conditions are reached. Then the control is changed to a constant applied mean pressure gradient forcing term equivalent to the shear stress resulting from the prior steps. Additional iterations are then performed to further achieve statistical stationarity before outputting fields.