The distribution and abundance of cetaceans has traditionally been investigated by conducting visual line transect surveys; however, visual detection and identification can be challenging because cetaceans spend much of their lives completely under water. Some limitations inherent to visual surveys may be overcome with the addition of passive acoustic methods. Many cetaceans produce distinctive sounds that propagate well under water and therefore acoustic techniques can be used to detect and identify them. This dissertation advances the role of passive acoustic monitoring during visual surveys by examining the whistling behavior of small odontocetes and developing methods for species identification of delphinid whistles. Chapter one provides an introduction to sounds produced by delphinids and prior research on acoustic species identification. Chapter two examines whistle use by small odontocetes. Data collected during visual and acoustic line transect surveys suggests that species in the eastern tropical Pacific Ocean whistle more frequently than species in the eastern North Pacific Ocean. Seven hypotheses to explain this trend are discussed. Group size seems to be an important factor in the whistling behavior of delphinid schools, however the distribution of whistling vs. non-whistling species does not likely have a simple univariate explanation. Whistling behavior and whistle structure are still largely unknown for many species. This is illustrated in chapter three, which provides the first description of the whistles of a seldom-recorded delphinid species in the Pacific Ocean, Lagenodelphis hosei. The remaining chapters focus on acoustic species identification. In chapter four, discriminant function analysis (DFA) and classification and regression tree analysis (CART) are used to classify the whistles of nine delphinid species. Overall, 41% of whistles were correctly classified using DFA and 51% were correctly classified using CART. Chapter five evaluates the effect of recording and analysis bandwidth on acoustic species identification. For the four species included in this chapter, an upper bandwidth limit of at least 24 kHz is necessary for an accurate representation of fundamental whistle contours. Finally, chapter six incorporates the classification techniques and bandwidth extensions discussed in chapters four and five into a software tool for real-time acoustic species identification in the fiel
