Assessing Seasonality and Density From Passive Acoustic Monitoring of Signals Presumed to be From Pygmy and Dwarf Sperm Whales in the Gulf of Mexico

Citation:
Hildebrand, JA, Frasier KE, Baumann-Pickering S, Wiggins SM, Merkens KP, Garrison LP, Soldevilla MS, McDonald MA.  2019.  Assessing Seasonality and Density From Passive Acoustic Monitoring of Signals Presumed to be From Pygmy and Dwarf Sperm Whales in the Gulf of Mexico. Frontiers in Marine Science. 6(66):1-17.

Date Published:

2019-February-27

Abstract:

Pygmy sperm whales (Kogia breviceps) and dwarf sperm whales (Kogia sima) are deep diving cetaceans that commonly strand along the coast of the southeast US, but that are difficult to study visually at sea because of their elusive behavior. Conventional visual surveys are thought to significantly underestimate the presence of Kogia and they have proven difficult to approach for tracking and tagging. An approach is presented for density estimation of signals presumed to be from Kogia spp. based on passive acoustic monitoring data collected at sites in the Gulf of Mexico (GOM) from the period following the Deepwater Horizon oil spill (2010-2013). Both species of Kogia are known to inhabit the GOM, although it is not possible to acoustically separate the two based on available knowledge of their echolocation clicks. An increasing interannual density trend is suggested for animals near the primary zone of impact of the oil spill, and to the southeast of the spill. Densities were estimated based on both counting individual echolocation clicks and counting the presence of groups of animals during one-min time windows. Densities derived from acoustic monitoring at three sites are all substantially higher (4–16 animals/1000 km2) than those that have been derived for Kogia from line transect visual surveys in the same region (0.5 animals/1000 km2). The most likely explanation for the observed discrepancy is that the visual surveys are underestimating Kogia spp. density, due to the assumption of perfect detectability on the survey trackline. We present an alternative approach for density estimation, one that derives echolocation and behavioral parameters based on comparison of modeled and observed sound received levels at sites of varying depth.

DOI:

10.3389/fmars.2019.00066