| Summary: | Since recordings of humpback whale (Megaptera novaeangliae) vocalizations were first heard 68 years ago off the coast of Oahu, scientists have been studying the complexity of whale song and the significance of vocalizations in whale behavior and culture. Over time, the methods for quantifying humpback whale song structure have solidified into the widely practiced approach of separating songs into nested hierarchical themes, phrases, and units. Despite a clear stratification in whale songs, analysis of structures and similarity between populations has largely remained a manual process, requiring substantial time and resources. This study seeks to test the viability of automated spectrogram cross-correlation (SPCC) in quantifying relationships between individual humpback whale songs, using the Rstudio package AMMonitor. Automated SPCC is frequently used in birdsong analysis but has not been incorporated into whale research, likely due to the complexity and length of whale songs. The main goal is to test SPCC in the comparison of whale songs from two geographically isolated populations, the Central American (CA) and Breeding Stock-G (BSG) populations that winters off the west coast of Costa Rica. No research has yet shown evidence for cross-equatorial acoustic connectivity, but literature suggests exchange can occur with very minimal acoustic contact. As a result, I hypothesize song elements are culturally transmitted between CA and BSG populations during the breeding season. Understanding the extent to which geographically isolated populations can transmit song elements, as well as testing the viability of automated analysis is paramount to future research by broadening the scope of research that is considered and paving the way for more efficient analysis strategies of humpback whale culture.
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