The Eglin Air Force Base in Florida has significant coastal habitats which are sanctuaries to shoreline dependent birds. However, there were concerns about the effects of sea level rise associated with climate change as well as human activities on these important bird habitats. Recent studies used SLAMM (Sea Level Affecting Marshes Model) to simulate wetland conversion and shoreline modification for the purpose of habitat vulnerability assessment and decision making. Nonetheless, there are questions regarding the validity and suitability of the model due to the uncertainty involved in selecting many of the model's empirical input factors. The objectives of this study were to use a state-of-the-art screening and variance-based global sensitivity and uncertainty methods to: (1) identify the important input factors that control the model's output uncertainty and (2) quantify the model's global output uncertainty and apportion it to the direct contributions and interactions of the important factors. The screening method of Morris for a qualitative ranking of the input parameters was carried out followed by the variance-based method of Sobol for quantitative sensitivity and uncertainty analyses. Results showed that elevation, historical sea level rise trend, and accretion/sedimentation rate were the predominant factors that influenced the uncertainty in the prediction of changes in coastal habitats. Higher elevation habitats (swamps and inland fresh marsh) showed decrease in area over 100 years of simulation. Interestingly, for lower elevation habitats (salt marsh, tidal flat, and beach), results showed possible gain or loss of these habitats depending on the combination of input factors within their proposed uncertainty ranges.