Underwater gliders can provide an alternative cost-effective platform for passive acoustic monitoring surveys, compared to boat surveys, for abundance estimation and to collect high resolution environmental data for habitat studies. Gliders are usually equipped with one acoustic sensor, which limits the methods available for abundance estimation from acoustic data. Estimation of parameters used in distance sampling methodology, such as the detection function and cue rates, must be estimated separately from the glider deployment. A methodology for deriving the acoustic detection function of vocal animals is demonstrated in chapter 2 with a combined biologging and passive acoustic experiment. The methodology consists of distance estimation of the clicks produced by the tagged animal and detected at acoustic receivers placed at different depths, using surface bounce detections to estimate range. In addition, different detection algorithms were tested for the detectability of Blainville's beaked whales. Detectability was found to vary with depth for Blainville's beaked whales in the area of El Hierro (Canary Islands). The depth dependent detectability for this species was tested further in chapter 3 with a wider dataset from two different geographic populations of Blainville's beaked whales, those of El Hierro and the Bahamas. Differences in detectability were found using depth and animal movement data as recorded on the DTAG in a simulated network of receivers placed at different depths. In addition, sequences of clicks, called click scans, were tested as an additional “cue” for cue counting methodology. The high directionality of beaked whale regular clicks leads to reduced detection ranges for receivers close to the surface or for receivers placed much deeper than the foraging depths of the wales and this reduction translates into varying lengths and numbers of detected click clusters as a function of distance and receiver depth. Chapter 4 presents a method for estimating density of animals from underwater gliders and tests the method in a simulated glider survey using different distribution and density scenarios using clicks and click scans as cue for density estimation.