Modeling the detection range of pulsed calls from resident killer whale in nearshore waters of British Columbia, Canada

Passive acoustic monitoring (PAM) using underwater listening stations is widely employed to assess the presence and movements of marine mammals. Accurate interpretation of PAM data requires knowledge of vocalization detection ranges, which vary spatially and temporally with ambient sound levels and sound propagation conditions. This study presents a Monte Carlo framework for estimating call detection probabilities as a function of distance incorporating variability in frequency dependent source levels, ambient sound levels and caller depths. This methodology was applied to underwater listening stations deployed by Fisheries and Oceans Canada in the Salish Sea to monitor endangered Southern and threatened Northern Resident Killer Whales (Orcinus orca ater). The approach integrates in situ ambient sound measurements and modeled propagation losses to account for variability in source levels and vocalizing depths. To reflect frequency-dependent detectability by an automated detector, the analysis was performed independently across consecutive 300 Hz frequency bands. Median estimated detection ranges for Southern Resident Killer Whale pulsed calls varied from 650 m under the worst conditions (high ambient noise levels, low source level, high propagation loss) to 7.9 km under the best conditions (low ambient noise levels, high source level, low propagation loss). Maximum detection ranges were generally greater in summer than in winter, primarily due to higher ambient noise levels in winter associated with increased weather activity. Calls from Northern Resident Killer Whales were detectable at shorter ranges than those from Southern Residents, reflecting their lower source levels and weaker low-frequency components. Sensitivity analysis showed that the frequency distribution of source levels was the primary factor influencing detection range estimates, while seasonal changes in propagation loss had comparatively limited impact. Although developed for killer whales, this approach can be adapted to other vocal species to quantify species-specific detection range probabilities, guide optimal hydrophone placement to maximize coverage in noisy environments (with application to noise impact mitigation strategies), and provide needed inputs for passive acoustic density estimation models.