Data from a three-year baseline study was used to develop and evaluate monitoring designs for 10 different bird species occurring within an agroecosystem. I used an iterative approach employing several state of the art approaches for designing effective monitoring in order to illustrate how this approach that could be utilized by resource managers. I evaluated variance components to determine the allocation of sampling effort across habitats and through time. I used Monte Carlo resampling and simulations to calculate the precision and/or power for each design across the suite of target species. The design options evaluated included stratified sampling designs based on preferred habitat and revisit designs. Optimal design parameters were evaluated relative to a simple random sample by modeling a single year to year decline. My results indicate that habitat association and coherence, the potential for a species to be resighted at the same location, were the most important variance components. However, stratified sampling designs did not lead to an increase in power as expected, perhaps due to the coarse scale of the sampling unit. Revisit designs led to improved power for coherent species; but the improvement was a joint function of species prevalence and coherence. The prospective power analyses support the adoption of a single near-optimal design for the suite of target species. The strong performance of a single design was contrary to my understanding of the system based on the variance component analysis. This research highlights the value of combining variance partitioning with prospective power analysis to develop effective monitoring designs.