Sampling designs for effective monitoring programs are often specific to individual systems and management needs. Failure to carefully evaluate sampling designs of monitoring programs can lead to data that are ineffective for informing management objectives. We demonstrated the use of an individual-based model to evaluate closed-population mark–recapture sampling designs for monitoring fish abundance in open systems, using Murray cod (Maccullochella peelii (Mitchell, 1838)) in the Murray–Darling River basin, Australia, as an example. The model used home-range, capture-probability and abundance estimates to evaluate the influence of the size of the sampling area and the number of sampling events on bias and precision of mark–recapture abundance estimates. Simulation results indicated a trade-off between the number of sampling events and the size of the sampling reach such that investigators could employ large sampling areas with relatively few sampling events, or smaller sampling areas with more sampling events to produce acceptably accurate and precise abundance estimates. The current paper presents a framework for evaluating parameter bias resulting from migration when applying closed-population mark–recapture models to open populations and demonstrates the use of simulation approaches for informing efficient and effective monitoring-program design.