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

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 km 2) than those that have been derived for Kogia from line transect visual surveys in the same region (0.5 animals/1000 km 2). 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.

Monitoring of a Nearshore Small Dolphin Species Using Passive Acoustic Platforms and Supervised Machine Learning Techniques

“…Passive acoustic monitoring surveys are increasingly being adopted as a key sampling approach to acquire novel information about ocean ecosystems, including their biodiversity (Ricci et al, 2016), habitat degradation (Gordon et al, 2018), the distribution and abundance of elusive species (Hildebrand et al, 2019), benthic communities (Freeman and Freeman, 2016), coral reef health (Bertucci et al, 2016), macroalgae production (Freeman et al, 2018), the status of MPAs (Buscaino et al, 2016;Gabriele et al, 2018), and the effect of human activities (Lammers et al, 2017). In this study, we described the use of multiple PAM sites to investigate spatiotemporal patterns in the distribution and acoustic behavior of an IPHD population.…”

“…Spectrogram resolution could be considered a limiting factor during the human-operated checks of the data, especially when low intensity clicks were recorded (when the animals were more than 500 m from the platform or traveling and echolocating in other directions) or in states of high background and broadband noise generated by ship traffic and/or adverse climatic conditions. However, the manual check of the automatic results was imperative as a validation technique (Hildebrand et al, 2019). In addition, the spectrogram analysis was an important complementary monitoring technique which produced information on soundscape composition.…”

“…In the first stages of the development of automatic detection or classification algorithms for a specific and complex underwater habitat, it is imperative to test and confirm their reliability using manual spectrogram analysis (Caruso et al, 2017;Hildebrand et al, 2019). After applying the trained classifier to the data from the six PAM platforms, each file with pulses classified as "IPHD" was manually checked using the program Raven Lite.…”

“…In comparison to visual and photo-identification surveys which only provide information on surface activities over limited time periods, passive acoustic monitoring (PAM) can be applied continuously over extended periods of time, even in rough weather conditions (Mellinger et al, 2007). PAM is a non-invasive and reliable method for surveying mobile and phonating marine organisms, which can provide information regarding species distribution and activity at high spatiotemporal resolutions (Wang et al, 2016;Hildebrand et al, 2019;Monczak et al, 2019). It is especially useful for marine mammals that are otherwise difficult to visually monitor in the natural environment (Cato and McCauley, 2001).…”

“…Our improved ability to acquire acoustic data continuously from multiple recording stations over extended periods of time has increased the use of automatic signal identification techniques (Zimmer, 2011) and machine learning approaches (Bianco et al, 2019) for detecting marine mammal vocalizations (Shiu et al, 2020). Several detection and classification algorithms have been developed to identify cetacean sounds, particularly the echolocation clicks of toothed whales (Soldevilla et al, 2008;Roch et al, 2011Roch et al, , 2015Giorli et al, 2016;Caruso et al, 2017;Giorli and Goetz, 2019;Hildebrand et al, 2019). Compared to manual analysis methods (spectrogram analysis), such algorithms have the advantage of being able to quickly and reliably analyze large amounts of data and produce standardized measures of sound characteristics that can be used for statistical analysis (Giorli et al, 2016;Caruso et al, 2017).…”