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Hildebrand, JA, Frasier KE, Baumann-Pickering S, Wiggins SM, Merkens KP, Garrison LP, Soklevilla 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   10.3389/fmars.2019.00066   AbstractWebsite

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.

Frasier, KE, Roch MA, Soldevilla MS, Wiggins SM, Garrison LP, Hildebrand JA.  2017.  Automated classification of dolphin echolocation click types from the Gulf of Mexico. Plos Computational Biology. 13   10.1371/journal.pcbi.1005823   AbstractWebsite

Delphinids produce large numbers of short duration, broadband echolocation clicks which may be useful for species classification in passive acoustic monitoring efforts. A challenge in echolocation click classification is to overcome the many sources of variability to recognize underlying patterns across many detections. An automated unsupervised network-based classification method was developed to simulate the approach a human analyst uses when categorizing click types: Clusters of similar clicks were identified by incorporating multiple click characteristics (spectral shape and inter-click interval distributions) to distinguish within-type from between-type variation, and identify distinct, persistent click types. Once click types were established, an algorithm for classifying novel detections using existing clusters was tested. The automated classification method was applied to a dataset of 52 million clicks detected across five monitoring sites over two years in the Gulf of Mexico (GOM). Seven distinct click types were identified, one of which is known to be associated with an acoustically identifiable delphinid (Risso's dolphin) and six of which are not yet identified. All types occurred at multiple monitoring locations, but the relative occurrence of types varied, particularly between continental shelf and slope locations. Automatically- identified click types from autonomous seafloor recorders without verifiable species identification were compared with clicks detected on sea-surface towed hydrophone arrays in the presence of visually identified delphinid species. These comparisons suggest potential species identities for the animals producing some echolocation click types. The network-based classification method presented here is effective for rapid, unsupervised delphinid click classification across large datasets in which the click types may not be known a priori.

Frasier, KE, Wiggins SM, Harris D, Marques TA, Thomas L, Hildebrand JA.  2016.  Delphinid echolocation click detection probability on near-seafloor sensors. Journal of the Acoustical Society of America. 140:1918-1930.   10.1121/1.4962279   AbstractWebsite

The probability of detecting echolocating delphinids on a near-seafloor sensor was estimated using two Monte Carlo simulation methods. One method estimated the probability of detecting a single click (cue counting); the other estimated the probability of detecting a group of delphinids (group counting). Echolocation click beam pattern and source level assumptions strongly influenced detectability predictions by the cue counting model. Group detectability was also influenced by assumptions about group behaviors. Model results were compared to in situ recordings of encounters with Risso's dolphin (Grampus griseus) and presumed pantropical spotted dolphin (Stenella attenuata) from a near-seafloor four-channel tracking sensor deployed in the Gulf of Mexico (25.537 degrees N 84.632 degrees W, depth 1220 m). Horizontal detection range, received level and estimated source level distributions from localized encounters were compared with the model predictions. Agreement between in situ results and model predictions suggests that simulations can be used to estimate detection probabilities when direct distance estimation is not available. (C) 2016 Acoustical Society of America.

Hildebrand, JA, Baumann-Pickering S, Frasier KE, Trickey JS, Merkens KP, Wiggins SM, McDonald MA, Garrison LP, Harris D, Marques TA, Thomas L.  2015.  Passive acoustic monitoring of beaked whale densities in the Gulf of Mexico. Scientific Reports. 5:16343.: Macmillan Publishers Limited   10.1038/srep16343   Abstract

Beaked whales are deep diving elusive animals, difficult to census with conventional visual surveys. Methods are presented for the density estimation of beaked whales, using passive acoustic monitoring data collected at sites in the Gulf of Mexico (GOM) from the period during and following the Deepwater Horizon oil spill (2010–2013). Beaked whale species detected include: Gervais’ (Mesoplodon europaeus), Cuvier’s (Ziphius cavirostris), Blainville’s (Mesoplodon densirostris) and an unknown species of Mesoplodon sp. (designated as Beaked Whale Gulf — BWG). For Gervais’ and Cuvier’s beaked whales, we estimated weekly animal density using two methods, one based on the number of echolocation clicks, and another based on the detection of animal groups during 5 min time-bins. Density estimates derived from these two methods were in good general agreement. At two sites in the western GOM, Gervais’ beaked whales were present throughout the monitoring period, but Cuvier’s beaked whales were present only seasonally, with periods of low density during the summer and higher density in the winter. At an eastern GOM site, both Gervais’ and Cuvier’s beaked whales had a high density throughout the monitoring period.

Wiggins, SM, Frasier KE, Henderson EE, Hildebrand JA.  2013.  Tracking dolphin whistles using an autonomous acoustic recorder array. The Journal of the Acoustical Society of America. 133:3813-3818.: ASA   AbstractWebsite

Dolphins are known to produce nearly omnidirectional whistles that can propagate several kilometers, allowing these sounds to be localized and tracked using acoustic arrays. During the fall of 2007, a km-scale array of four autonomous acoustic recorders was deployed offshore of southern California in a known dolphin habitat at ∼800 m depth. Concurrently with the one-month recording, a fixed-point marine mammal visual survey was conducted from a moored research platform in the center of the array, providing daytime species and behavior visual confirmation. The recordings showed three main types of dolphin acoustic activity during distinct times: primarily whistling during daytime, whistling and clicking during early night, and primarily clicking during late night. Tracks from periods of daytime whistling typically were tightly grouped and traveled at a moderate rate. In one example with visual observations, traveling common dolphins (Delphinus sp.) were tracked for about 10 km with an average speed of ∼2.5 m s−1 (9 km h−1). Early night recordings had whistle localizations with wider spatial distribution and slower travel speed than daytime recordings, presumably associated with foraging behavior. Localization and tracking of dolphins over long periods has the potential to provide insight into their ecology, behavior, and potential response to stimuli.

Soldevilla, MS, Wiggins SM, Hildebrand JA, Oleson EM, Ferguson MC.  2011.  Risso's and Pacific white-sided dolphin habitat modeling from passive acoustic monitoring. Marine Ecology-Progress Series. 423:247-267.   10.3354/meps08927   AbstractWebsite

Habitat characterization allows prediction of dolphin distributions in response to oceanographic processes and can be used to understand and predict effects of anthropogenic disturbances. Many habitat models focus on contemporary dolphin occurrence and environmental predictor data, but time-lagged oceanographic data may increase a model's predictive power due to ecological successional processes. Using hourly occurrence of Risso's dolphin Grampus griseus clicks and 2 types of Pacific white-sided dolphin Lagenorhynchus obliquidens clicks in autonomous passive acoustic recordings, we investigate the importance of time-lagged predictor variables with generalized additive models. These models relate dolphin acoustic activity from recordings at 6 sites in the Southern California Bight between August 2005 and December 2007 to oceanographic variables including sea surface temperature (SST), SST coefficient of variation (CV), sea surface chlorophyll concentration (chl), chl CV, upwelling indices, and solar and lunar temporal indices. The most consistently selected variables among the trial models evaluated during cross-validation were SST (100% of models) and SST CV (80%) for Risso's dolphin clicks; solar indices (100%) and SST and SST CV (60% each) for Pacific white-sided type A (PWS A) clicks; and SST CV (100%), solar indices (100%) and SST (80%) for Pacific white-sided type B (PWS B) clicks. Best predictive models for Risso's dolphins and PWS A clicks included time-lagged variables, suggesting the importance of ecological succession between abiotic variables and dolphin occurrence, while best models of PWS B clicks were for current conditions, suggesting association with prey-aggregating features such as fronts and eddies.