This paper presents an exposure assessment conducted as part of an ecological risk assessment for the Kennecott Southshore Wetlands, approximately 2,000 acres of jurisdictional wetlands between the northern Oquirrh Mountains and the Great Salt Lake in Utah.  Wetlands along the north, east and south shores of the Great Salt Lake are important habitat for migratory shorebirds and waterfowl, especially during the spring migration.  Selenium (Se) in the food and eggs of shorebirds exceeded a screening level for reproductive effects.  Field studies found highly variable Se bioavailability at the site, so uncertainty about Se bioaccumulation at individual shorebird feeding areas was high.  Our analysis of regional data obtained from published USFWS and USGS reports for 15 lentic sites (ponds, sloughs and wetlands) in the western United States confirmed that the ratio of water to bird egg Se concentration is highly variable and site-specific.  Therefore, we conducted a site-specific, probabilistic exposure assessment to estimate the spatial distribution of Se concentrations in bird eggs.  Our objective was to estimate the proportion of the nesting bird population potentially at risk from exposure to Se.  Black-necked stilt (Himantopus mexicanus) and American avocet (Recurvirostra americana) were selected as representative species.  We sampled aquatic macroinvertebrates across the wetlands to spatially characterize prey Se concentrations.  We used these site data and a bioaccumulation model built from the regional data to predict spatially variable egg Se concentrations (the “prior”).  We obtained site-specific egg Se and nest location data and used them in a Bayesian Monte Carlo (BMC) analysis to calibrate the predicted egg Se distribution.  BMC accounts for uncertainty in bioaccumulation predictions (from site-to-site variability) and uncertainty in prey and egg Se concentration observations (from sampling and measurement error).  The calibration improved the prior considerably by taking into account site-specific landscape and bioaccumulation information.  The paper demonstrates a practical way to rigorously combine landscape data, a regional bioaccumulation model and site-specific chemical concentration data to study population-level chemical exposures.