| Summary: | Marine mammals face numerous anthropogenic threats, including fisheries interactions, ocean noise, ship strikes, and marine debris. Monitoring the negative impact on marine mammals through the assessment of population trends requires information about population size, spatiotemporal distribution, population structure, and animal behavior. Passive acoustic monitoring has become a viable method for gathering long-term data on highly mobile and notoriously cryptic marine mammals. However, passive acoustic monitoring still faces major challenges requiring further development of robust analysis tools, especially as it becomes increasingly used in applied conservation for long-term and large-scale studies of endangered or data deficient species such as sperm or beaked whales. Further challenges lie in the translation of animal presence into quantitative population density estimates since methods must control for variation in acoustic detectability of the target species, environmental factors, and for species-specific vocalization rates. The main contribution of this thesis is the advancement of the framework for long-term quantitative monitoring of cetacean species, applied to deep-divers like sperm and beaked whales. Fully-automated methods were developed and implemented to different populations of beaked whales in different conditions. This provided insight into generalization capabilities of these automatic techniques and best practices. However, implementing these tool kits is not always practical, and alternative methods for additional data processing were developed to expeditiously serve multiple purposes including annotation of individual sounds, evaluation of data in order to provide a highly dynamic technique, and classification for quantitative monitoring studies. This work also presents the longest time series of sperm whale presence using passive acoustic monitoring for over seven years in the Gulf of Mexico. Echolocation clicks were detected and discriminated from other sounds to understand the ...
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