Monitoring programs aimed at understanding the population trends of secretive marshbirds can be altered to benefit from the creative interplay between predictions, designs and models, and provide the template for doing so. Effective application of information to decision making typically requires integration of several types of information in a common framework, including “found” data and retrospective studies, innovative sampling designs and the use of hierarchical data structures. Hierarchical and state-space modeling provide a unified modeling structure for such designs and data. These ideas are illustrated with the problem of investigating and mitigating the effects of climate change on secretive marshbirds in coastal North America. How both ecological theory and available data can be used to provide predictions about the impacts of regional and local climate changes on these avian communities are illustrated.