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Communications play a key role in collaborative navigation algorithms. A better understanding of the ability to send and receive messages permits greater navigational flexibility and system robustness. This paper focuses on the building of an underwater acoustic communications map for collaborative navigation. The emphasis is in two areas - a local and global communications map. The local communications is defined with respect to a single destination reference point. Using a sample set of a priori signal to noise ratio acoustic modem data, Kriging techniques are used to create mean and variance map estimates. The global communications map is a compendium of local maps and is defined within a bounded survey space. Bayesian Inferencing is used for building the global map. It is based on REML parameter estimation of an anisotropic covariance function. The paper analyzes acoustic communication signal to noise datasets recently collected in Monterey Harbor, Monterey, CA and is used to demonstrate the above-described techniques. [ABSTRACT FROM PUBLISHER]