Bushmeat markets can reflect the impact of hunting in large geographical areas, but efficient and adequate sampling strategies are needed. Five bushmeat markets in West and Central Africa were used to simulate the performance of different sampling regimes. The studied markets (n= 863 days) varied in animal carcasses and number of species recorded. In our simulations, we varied number of days sampled and their temporal distribution using a variant of the Monte Carlo methodology. Three sample strategies were considered: (1) unconstrained random sampling; (2) random sampling of start-points, where the n days sampled are a sequential block following the randomly selected start-point and (3) sampling blocked by season. No substantial differences between standard sampling theory and our simulations were present. However, only a large sample of markets will allow useful inferences on a regional scale and timing and coordination of sampling may be highly influential. Sampling in blocks of days was as efficient as simple random sampling in estimating species richness, but not carcass volume. This may indicate that, even with seasonality in market compositions, or irregular influences, the temporal pattern as described by presence/absence varies much less than does the volume of carcasses. Shorter sampling periods perform poorly in estimating species richness. The relationship between % species richness and overall carcass volume may predict the sampling effort required to estimate market species richness based on volume, when a large enough sample of markets becomes available. Similarly, a larger sample of markets would reveal how far the species composition in markets reflects the general organising principles of community structure in terms of frequency and abundance relationships.