Spatial modeling of wetland condition in the U.S. Prairie Pothole Region

We propose a spatial modeling framework for wetland data produced from a remote-sensing-based waterfowl habitat survey conducted in the U.S. Prairie Pothole Region (PPR). The data produced from this survey consist of the area containing water on many thousands of wetland basins (i.e., prairie potholes). We propose a two-state model containing wet and dry states. This model provides a concise description of wet probability, i.e., the probability that a basin contains water, and the amount of water contained in wet basins. The two model components are spatially linked through a common latent effect, which is assumed to be spatially correlated. Model fitting and prediction is carried out using Markov chain Monte Carlo methods. The model primarily facilitates mapping of habitat conditions, which is useful in varied monitoring and assessment capacities. More importantly, the predictive capability of the model provides a rigorous statistical framework for directing management and conservation activities by enabling characterization of habitat structure at any point on the landscape.