Click Types Research Articles

Biogeographic patterns of Pacific white‐sided dolphins based on long‐term passive acoustic records

AbstractAimThis study investigates the biogeographic patterns of Pacific white‐sided dolphins (Lagenorhynchus obliquidens) in the Eastern North Pacific based on long‐term passive acoustic records. We aim to elucidate the ecological and behavioural significance of distinct echolocation click types and their implications for population delineation, geographic distribution, environmental adaptation and management.LocationEastern North Pacific Ocean.Time Period2005–2021.Major Taxa StudiedPacific white‐sided dolphin.MethodsOver 50 cumulative years of passive acoustic monitoring (PAM) data from 14 locations were analyzed using a deep neural network to classify two distinct Pacific white‐sided dolphin echolocation click types. The study assessed spatial, diel, seasonal and interannual patterns of the two click types, correlating them with major environmental drivers such as the El Niño Southern Oscillation and the North Pacific Gyre Oscillation, and modeling long‐term spatial‐seasonal patterns.ResultsDistinct spatial, diel and seasonal patterns were observed for each click type. Significant biogeographical shifts in presence were observed following the 2014–2016 marine heatwave event.Main ConclusionsDistinct spatial distributions of the two click types support the hypothesis that Pacific white‐sided dolphins produce population‐specific echolocation clicks. Seasonal and diel patterns suggest spatiotemporal niche partitioning between the populations in Southern California. Interannual changes, notably initiated during the 2014–2016 marine heatwave, indicate climate‐driven range expansions and contractions related to gradual tropicalization of the Southern California Bight.

Structure-Based Identification of Organoruthenium Compounds as Nanomolar Antagonists of Cannabinoid Receptors.

Metal complexes exhibit a diverse range of coordination geometries, representing novel privileged scaffolds with convenient click types of preparation inaccessible for typical carbon-centered organic compounds. Herein, we explored the opportunity to identify biologically active organometallic complexes by reverse docking of a rigid, minimum-size octahedral organoruthenium scaffold against thousands of protein-binding pockets. Interestingly, cannabinoid receptor type 1 (CB1) was identified based on the docking scores and the degree of overlap between the docked organoruthenium scaffold and the hydrophobic scaffold of the cocrystallized ligand. Further structure-based optimization led to the discovery of organoruthenium complexes with nanomolar binding affinities and high selectivity toward CB2. Our work indicates that octahedral organoruthenium scaffolds may be advantageous for targeting the large and hydrophobic binding pockets and that the reverse docking approach may facilitate the discovery of novel privileged scaffolds, such as organometallic complexes, for exploring chemical space in lead discovery.

Contact influence in the Tjhauba variety of Kgalagadi

Tjhauba, spoken in northwestern Botswana, is a regional variety of the Bantu language Kgalagadi. Tjhauba exhibits a number of striking linguistic differences with respect to other, previously described Kgalagadi varieties, some the result of language-internal changes, but mostly due to contact with different surrounding Khoisan and Bantu languages. Making use of newly collected field data, this paper shows that Tjhauba has an extensive inventory of click phonemes, contrasting different click accompaniments and, in the speech of elderly speakers, also different click types. Tracing the sources of Tjhauba click words shows that these originate in different Khoisan languages, but also in the Bantu click language Yeyi. Semantically, click words, but also loanwords that do not contain clicks, cluster in the domain of flora and fauna, particularly species found in or close to water. These linguistic findings also shed light on the history of Tjhauba speakers. The adoption of a large number of click phonemes suggests intensive language contact, as still evidenced by ongoing Tjhauba/Khwe bilingualism. A number of the likely source languages for Tjhauba click words are no longer spoken in the area, suggesting contact situations that are no longer ongoing. Furthermore, clicks occur in loanwords, but unlike in neighbouring Bantu click languages, there is no evidence that clicks were also extended to inherited Tjhauba words. This suggests that the sound symbolic or identity marking functions of clicks as posited for other Bantu click languages do not play a role in Tjhauba.

Using a deep neural network to classify echolocation clicks and identify biogeographic patterns of Pacific white-sided dolphins

Pacific white-sided dolphins are endemic to the Pacific Ocean; and two genetically distinct populations overlap along the west coast of North America. However, they are visually indistinguishable and the degree of spatial overlap remains unknown. Here, we use a deep neural network to show that the populations are acoustically distinguishable. Previous studies described two distinct echolocation click types associated with pacific white-sided dolphins and hypothesized that they were population-specific. Our neural network was trained to classify the click types based on spectral and temporal properties described previously. The neural network enabled us to analyze passive acoustic recordings from sites between the Gulf of California and the Gulf of Alaska over multiple years to investigate possible population-specific trends. The latitudinal occurrence pattern of the two click types supports the population-specific hypothesis: type A clicks continue to associate with the northern population distribution, and type B clicks with the southern population distribution. At long-term monitoring sites in Southern California, type B clicks were dominant during periods of warm water anomalies. This pattern may be an early indicator of future biogeographic shifts in the distribution of pacific white-sided dolphins and demonstrates the utility of long-term passive acoustic monitoring.

Examining distribution patterns of foraging and non-foraging sperm whales in Hawaiian waters using visual and passive acoustic data

Following the end of over a century of intensive commercial whaling in 1986, the monitoring and assessment of sperm whale populations is essential for guiding management and conservation decisions for their recovery. Species distribution models (SDMs) are a useful tool for examining and predicting cetacean distribution patterns and typically incorporate visual, ship-based observations. However, understanding sperm whale distribution and habitat use based solely on surface visual observations is challenging due to the significant amount of time sperm whales spend foraging at depth. For the endangered sperm whale population occurring in Hawaiian waters, we used visual and passive acoustic data collected during four annual NOAA marine mammal line-transect surveys and a suite of biologically relevant environmental variables to develop SDMs within a generalized additive modeling framework to study the distribution of sperm whale groups throughout the island chain. Additionally, the passive acoustic data allowed us to differentiate sperm whale groups as foraging or non-foraging based on their click types to account for differences in distribution and behavior within the archipelago. Foraging groups were predicted primarily in the northwestern region of the archipelago between Laysan Island and Pearl and Hermes Reef as well as north of Maui and Hawaiʻi in the main Hawaiian Islands. Non-foraging groups were predicted to be more uniformly distributed throughout the archipelago. Foraging whale models selected temperature at 584 m depth, surface chlorophyll, and location, while the only significant variables for non-foraging whale models included the standard deviation of sea surface height and location. Each variable provides insight into the oceanographic processes influencing prey abundance and, thus, sperm whale foraging behavior. This study furthers our understanding of the distribution patterns for the sperm whale population in Hawaiʻi and contributes methods for building SDMs with visual and passive acoustic data that may be applied to other cetacean species.

Identification of western North Atlantic odontocete echolocation click types using machine learning and spatiotemporal correlates.

A combination of machine learning and expert analyst review was used to detect odontocete echolocation clicks, identify dominant click types, and classify clicks in 32 years of acoustic data collected at 11 autonomous monitoring sites in the western North Atlantic between 2016 and 2019. Previously-described click types for eight known odontocete species or genera were identified in this data set: Blainville’s beaked whales (Mesoplodon densirostris), Cuvier’s beaked whales (Ziphius cavirostris), Gervais’ beaked whales (Mesoplodon europaeus), Sowerby’s beaked whales (Mesoplodon bidens), and True’s beaked whales (Mesoplodon mirus), Kogia spp., Risso’s dolphin (Grampus griseus), and sperm whales (Physeter macrocephalus). Six novel delphinid echolocation click types were identified and named according to their median peak frequencies. Consideration of the spatiotemporal distribution of these unidentified click types, and comparison to historical sighting data, enabled assignment of the probable species identity to three of the six types, and group identity to a fourth type. UD36, UD26, and UD28 were attributed to Risso’s dolphin (G. griseus), short-finned pilot whale (G. macrorhynchus), and short-beaked common dolphin (D. delphis), respectively, based on similar regional distributions and seasonal presence patterns. UD19 was attributed to one or more species in the subfamily Globicephalinae based on spectral content and signal timing. UD47 and UD38 represent distinct types for which no clear spatiotemporal match was apparent. This approach leveraged the power of big acoustic and big visual data to add to the catalog of known species-specific acoustic signals and yield new inferences about odontocete spatiotemporal distribution patterns. The tools and call types described here can be used for efficient analysis of other existing and future passive acoustic data sets from this region.

An intervention framework for developing interactive video lectures based on video clickstream behavior: a quasi-experimental evaluation

ABSTRACT The purpose of this study is to develop an intervention framework based on video clickstream interactions for delivering superior user experience for video lectures. Apart from existing studies on data-driven interventions, this study focuses on video clickstream interactions to identify timely interventions for creating interactive video lectures. First, a framework was developed through an exploratory experiment, in which 29 students’ clickstream behaviors were tracked on an online platform and then individual interviews were held with 17 of the students and a subject-matter expert. The framework shows how click types are transformed into interactive elements with five question types (where, why, which, how, what). It includes click types, click reasons, interventions, actions, and interactive elements. Then, a quasi-experimental study was performed with 18 students to investigate the effect of the proposed framework on the students’ satisfaction and engagement. The results showed that students’ satisfaction significantly increased for interactive videos created using the proposed framework when motivation was controlled. In addition, students’ frequency to go back to important points decreased significantly in interactive videos, whilst students’ frequency to skip unimportant points increased significantly in interactive videos. In conclusion, the proposed framework can be used to transform linear videos to interactive videos.

A machine learning pipeline for classification of cetacean echolocation clicks in large underwater acoustic datasets.

Machine learning algorithms, including recent advances in deep learning, are promising for tools for detection and classification of broadband high frequency signals in passive acoustic recordings. However, these methods are generally data-hungry and progress has been limited by challenges related to the lack of labeled datasets adequate for training and testing. Large quantities of known and as yet unidentified broadband signal types mingle in marine recordings, with variability introduced by acoustic propagation, source depths and orientations, and interacting signals. Manual classification of these datasets is unmanageable without an in-depth knowledge of the acoustic context of each recording location. A signal classification pipeline is presented which combines unsupervised and supervised learning phases with opportunities for expert oversight to label signals of interest. The method is illustrated with a case study using unsupervised clustering to identify five toothed whale echolocation click types and two anthropogenic signal categories. These categories are used to train a deep network to classify detected signals in either averaged time bins or as individual detections, in two independent datasets. Bin-level classification achieved higher overall precision (>99%) than click-level classification. However, click-level classification had the advantage of providing a label for every signal, and achieved higher overall recall, with overall precision from 92 to 94%. The results suggest that unsupervised learning is a viable solution for efficiently generating the large, representative training sets needed for applications of deep learning in passive acoustics.

Effect of Low-Level Laser Therapy on Sternocleidomastoid and Trapezius Muscles’ Spasm for Treatment and Alleviation of Temporomandibular Disorders

Objectives: The aim was to evaluate the therapeutic effect of low-level laser therapy LLLT on pain intensity of different cervicofacial muscles symptoms to alleviation of temporomandibular disorders TMDs. 
 Methods: A randomized clinical trial was conducted on 60 patients. Patients were divided into three equal groups. The LLLT were received in Group 1: on masticatory muscles and temporomandibular joints TMJs. Group 2: on neck muscles. Group 3: on both masticatory and neck muscles with TMJ. Pain intensity was assessed before and after treatment by Visual Analogue Scale, and TMJ clicks recorded by normal listening, feeling, and palpation. Inter-incisor distance measured by digital Vernia calliper and LLLT therapy provided by BIOLASE-EPIC device. (Germany). 
 Results: Demographic features were not different among the groups. The highest pain score per case for TMJ and muscle, TMJ alone (arthrogenic), Muscle alone (myogenic) before and after LLLT was improved significantly p-value of <0.001.The majority of click types 38.4% improved, the frequency and types of clicks that were decreased significantly in all groups. The majority of clicks were either disappeared 40% or changed from painful to painless 31.7% clicks significantly p-value = 0.003. The maximum opening capacity MOC was significantly increased p<0.001 after LLLT. 
 Conclusions: The result recorded significant association between TMJ pain and neck muscles symptom. Further, there was significant improvement in TMD pain relief, TMJ click, and MOC after the application of LLLT to the neck muscles, Therefore LLLT could be suggested as a TMDs treatment option and added to the treatment protocol of TMDs.

Characterization and comparison of echolocation clicks of white-beaked dolphins (Lagenorhynchus albirostris) off the Northumberland coast, UK.

Odontocetes produce ultrasonic clicks for navigation and foraging. These are commonly categorized as regular or buzz clicks based on the inter-click interval. Buzz clicks are linked to foraging behaviors and may be subdivided into slow buzz clicks for prey chase, and regular buzz clicks for prey capture. This study recorded these three click types produced by white-beaked dolphins (Lagenorhynchus albirostris) off the Northumberland coast, UK. Acoustic parameters (including duration, centroid frequency, and root-mean-squared bandwidth) were calculated and compared across the three click types. The results showed that the regular clicks had shorter durations and higher frequencies than both the buzz click types. The regular buzz clicks had longer durations, lower frequencies, and narrower bandwidths than the slow buzz clicks. Additionally, regardless of click type, about 30% of the clicks had high-frequency (200-250 kHz) secondary peaks and >90% of the clicks displayed spectral peak and notch patterns between 20 and 80 kHz. These findings are useful for future quantitative assessment of the echolocation performance of white-beaked dolphins in the wild. The patterns of spectral peaks and notches identified may facilitate for acoustic identification of this species.

Cetaceans and seismic surveys in the Southern Adriatic Sea

The first long-term, broadband, passive acoustic monitoring effort was conducted in the south Adriatic Sea from October 2018 to December 2019 using a High-frequency Acoustic Recording Package (HARP). The recording effort coincided with seismic surveys that took place off Montenegro in the fall and winter 2018–2019. These surveys occurred at distances 70 to 125 km from the recording site, at water depths between 70 and 800 m. Recordings were manually reviewed with start and end times of air-gun encounters and ship passages marked. A generic energy detector and unsupervised clustering procedure were used to extract odontocete echolocation clicks. During the surveys, sound pressure levels (SPL) in the 15 to 200 Hz band increased by up to 20 dB during fall and up to 10 dB during winter survey, with an average increase of 7.5 dB. Cuvier's beaked whale echolocation clicks were very commonly detected in the recordings, but sperm whale clicks were only sporadically present. At least three additional echolocation click types were also detected and their attribution to particular species is underway. Although mortality related to seismic surveys has not been recorded, high levels of noise have the potential to cause long term disruption to cetaceans in this region.

Developing a click type “library” for Hawaiian odontocetes using machine learning methods

Though odontocete echolocation clicks are highly useful in passive acoustic monitoring, their acoustic features vary depending on behavior, orientation, and location relative to the receiver. Using unsupervised machine learning, regional sets of echolocation click types can be identified that allow for automated labeling of bioacoustic data while accounting for within-type signal variability. This methodology derives labels for clusters of click types based on parameters including spectral shape, inter-click interval, and click duration. Here we present classification results of echolocation clicks produced by Hawaiian odontocetes. Unsupervised methods were used to establish a set of click types for 10 years of recordings (200 kHz) collected from a bottom-mounted hydrophone off the coast of Kona, HI. These automated labels were compared with manual labels for species with established and recognizable clicks, which allowed for ground-truthing of some click types identified by the unsupervised machine learning method. Towed array data (500 kHz) was used to assign click types to species with visual verification, expanding the set of known species-specific click types for the region. The click types and method established here will be useful in future analyses of Hawaiian odontocete density estimation, abundance, and distribution patterns using acoustic data without requiring visual confirmation.

Identification of species-specific delphinid echolocation click types

Delphinid echolocation clicks are often recorded in large, high-quality datasets collected by moored autonomous passive acoustic sensors, but the particular species present during these acoustic encounters is generally unknown. We combined two acoustic data types with visual observations to obtain species identity for some of these clicks. We assigned species identity to acoustic encounters in eight years of recordings at five western North Atlantic mooring sites using sighting data from contemporaneous shipboard and aerial visual surveys. We also assigned species identity to acoustic encounters in towed acoustic array data collected concurrent with visual surveys during four ship-based cetacean surveys in the western North Atlantic and the Gulf of Mexico. 114 mooring encounters with seven species and 181 towed array encounters with nine species were labeled this way. We identified recurring click types during all encounters using an unsupervised clustering algorithm to groups clicks based on pairwise spectral distances. Labeled click types thus identified in the moored data were compared to labeled click types identified in the towed array data to determine their consistency and utility for delphinid species discrimination in moored passive acoustic data. Results for bottlenose dolphin, Atlantic spotted dolphin, Risso’s dolphin, and pilot whales will be presented.

Species-level classification of beaked whale echolocation signals detected in the northern Gulf of Mexico.

This study presents and evaluates several methods for automated species-level classification of echolocation clicks from three beaked whale species recorded in the northern Gulf of Mexico. The species included are Cuvier's and Gervais' beaked whales, as well as an unknown species denoted Beaked Whale Gulf. An optimal feature set for discriminating the three click types while also separating detected clicks from unidentified delphinids was determined using supervised step-wise discriminant analysis. Linear and quadratic discriminant analyses both achieved error rates below 1% with three features, determined by tenfold cross validation. The waveform fractal dimension was found to be a highly ranked feature among standard spectral and temporal parameters. The top-ranking features were Higuchi's fractal dimension, spectral centroid, Katz's fractal dimension, and -10 dB duration. Six clustering routines, including four popular network-based algorithms, were also evaluated as unsupervised classification methods using the selected feature set. False positive rates of 0.001 and 0.024 were achieved by Chinese Whispers and spectral clustering, respectively, across 200 randomized trials. However, Chinese Whispers clustering yielded larger false negative rates. Spectral clustering was further tested on clicks from encounters of beaked, sperm, and pilot whales in the Tongue of the Ocean, Bahamas.

Automated classification of dolphin echolocation click types from the Gulf of Mexico.

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.

Palatal click allophony in Mangetti Dune !Xung: Implications for sound change

Click consonants are well known for lacking allophonic variation. This lack of variation has been attributed to the existence of articulatory constraints on the coronal constrictions that are imposed by the existence of a second dorsal constriction. The current study investigates temporal acoustic differences among the four contrastive coronal click types in the /i/ and /u/ contexts in Mangetti Dune !Xung. Clicks have been described as being either non-affricated or affricated. However, when vowel context is taken into consideration, the typology is more complex. The alveolar click is non-affricated in both vowel contexts. The dental and lateral clicks are fricated in both contexts. The palatal click in the /i/ context has two clear anterior and posterior transients, followed by palatal frication, while in the /u/ context it is non-affricated. Results are consistent with an analysis of the palatal click in the /i/ context as involving allophonic secondary palatalization. There are trading relations between the duration of the click burst, frication noise and aspiration noise phases. Results have implications for understanding the synchronic and diachronic phonology of click consonants.

Investigating phonetic bases of sound patterns in Mangetti Dune !Xung: Contributions of high frame rate ultrasound to the description of endangered languages

High frame rate lingual ultrasound methods (Miller and Finch 2011) allow investigations of consonant and vowel kinematics outside of the laboratory. I summarize the results of a number of studies that investigate place and manner of articulation in consonants produced with the pulmonic and lingual airstream mechanisms in the endangered Namibian language Mangetti Dune !Xung. Results show that clicks, like dorsal stops, exhibit different postures of the posterior part of the tongue when they precede [i] and [ɑ]. The tongue dorsum and root are retracted in the production of all four coronal clicks when they precede [ɑ], but differ in their postures when they precede [i]. Further, tongue dorsum and root postures are less variable within click types before [ɑ], than they are preceding [i]. Clicks also differ in the timing of the anterior and posterior releases, resulting in different constrictions being adjacent to following vowels, thus leading to different co-articulation patterns. Timing patterns of the two...

Posterior lingual gestures and tongue shape in Mangetti Dune !Xung clicks

Clicks differ from pulmonic stops in that, in addition to containing lingual gestures that shape the filtering mechanism of the vocal tract, they also contain lingual “rarefaction gestures” that form the source of the lingual ingressive airstream. The current study uses mid-sagittal lingual ultrasound imaging to investigate (1) overall tongue shape, (2) tongue dorsum and root positions, and (3) dynamic rarefaction gestures involving the tongue dorsum and root, in the four coronal click types recognized by the IPA. The study provides quantitative evidence that the four click types differ in overall tongue shape. Additionally, results show that the palatal click has a farther back dorsal constriction than the three pre-palatal clicks, and the tongue root is raised and bunched in the upper pharynx in one variant of the palatal click, but involves retraction of the tongue root proper in the lower pharynx in the alveolar click. A second variant of the palatal click involves posterior gestures more similar to those found in the alveolar click. Results provide evidence that the kinematics of the posterior part of the tongue are important in describing click production, and shed light on synchronic and diachronic sound patterns involving the palatal click in Kx’a languages.

A method for estimating lingual cavity volume in click consonants

A method for estimating lingual cavity volume in click consonants is presented. The upper edge of the lingual cavity is estimated from stone palate casts, which were scanned using a Polhemus 3D Digitizer. The floor of the midline of the lingual cavity was estimated using 114 fps mid-sagittal ultrasound traces of the tongue collected with the CHAUSA method (Miller and Finch 2011). The width of the cavity from front to back was estimated by measurements of the cavity width at six different locations in linguograms. Changes in lingual cavity volume for the four coronal click types in Mangetti Dune !Xung are estimated by changing the mid-sagitttal ultrasound trace that corresponds to one of five stages in click production. 3-D images of the changes in cavity volume for each of the four coronal click types recognized by the IPA (2006) are presented.

Contrastive apical post-alveolar and laminal alveolar click types in Ekoka !Xung

Ekoka !Xung has four contrastive click types—dental, alveolar, lateral, and “retroflex.” We provide acoustic and ultrasound results of five speakers’ productions of the typical alveolar click and the contrastive “retroflex” click. Ultrasound results show that the “alveolar” click is apical post-alveolar and the “retroflex” click is laminal alveolar. The burst duration of the post-alveolar click averages 12 ms which is “abrupt,” while the burst duration of the alveolar click averages 30 ms, which is “noisy.” Mixed effects logistic regression models tested the effects of rise time and burst duration. Burst duration differed significantly among the two clicks (p < 0.001), while the effect of rise time was not significant. The ratio of energy in the click noisebursts below 20 ERB to the energy above 20 ERB is between 1.0 and 1.5 for the post-alveolar click, but between 0.5 and 1.0 for the alveolar click. The ratio was a significant predictor of click type (p = 0.014). The highest concentration of energy for the post-alveolar click is between 12 and 18 ERB, while the highest concentration of energy in the alveolar click is between 25 and 30 ERB. We attribute the frequency difference to a larger lingual cavity volume in the post-alveolar click, and a smaller volume in the alveolar click.